From 8dc1f744fc1a34e78ff44e746ff121c08bc7352b Mon Sep 17 00:00:00 2001 From: lyhue1991 Date: Wed, 2 Aug 2023 20:58:24 +0800 Subject: [PATCH] update torchkeras --- .gitignore | 12 +- ...347\250\213\350\214\203\344\276\213.ipynb" | 3311 +- ...347\250\213\350\214\203\344\276\213.ipynb" | 3536 +- ...347\250\213\350\214\203\344\276\213.ipynb" | 2865 +- ...347\250\213\350\214\203\344\276\213.ipynb" | 4391 ++- ...346\215\256\347\273\223\346\236\204.ipynb" | 573 +- ...345\210\206\346\234\272\345\210\266.ipynb" | 198 +- ...350\256\241\347\256\227\345\233\276.ipynb" | 206 +- ...\230\266API\347\244\272\350\214\203.ipynb" | 31682 ++++++++-------- ...\230\266API\347\244\272\350\214\203.ipynb" | 18399 ++++++++- ...\230\266API\347\244\272\350\214\203.ipynb" | 20847 +++++++++- ...346\236\204\346\223\215\344\275\234.ipynb" | 221 +- ...345\255\246\350\277\220\347\256\227.ipynb" | 273 +- ...,nn.functional\345\222\214nn.Module.ipynb" | 115 +- "5-1,Dataset\345\222\214DataLoader.ipynb" | 186 +- ...346\250\241\345\236\213\345\261\202.ipynb" | 28 +- ...345\244\261\345\207\275\346\225\260.ipynb" | 24040 +++++++++++- ...345\217\257\350\247\206\345\214\226.ipynb" | 15879 +++++--- ...347\247\215\346\226\271\346\263\225.ipynb" | 553 +- ...347\247\215\346\226\271\346\263\225.ipynb" | 3126 +- ...347\273\203\346\250\241\345\236\213.ipynb" | 1236 +- ...346\263\225\344\270\232\345\212\241.ipynb" | 17 + ...346\263\225\344\270\232\345\212\241.ipynb" | 4 +- "7-3,FM\346\250\241\345\236\213.ipynb" | 636 +- "7-4,DeepFM\346\250\241\345\236\213.ipynb" | 572 +- "7-5,FiBiNET\346\250\241\345\236\213.ipynb" | 485 +- "7-6,DeepCross\346\250\241\345\236\213.ipynb" | 491 +- "7-7,DIN\347\275\221\347\273\234.ipynb" | 579 +- "7-8,DIEN\347\275\221\347\273\234.ipynb" | 631 +- ...347\224\250\346\224\273\347\225\245.ipynb" | 4 +- ...344\271\240\345\272\224\347\224\250.ipynb" | 4 +- ...\207\345\212\240\351\200\237pytorch.ipynb" | 380 +- README.md | 106 +- push-to-github.ipynb | 427 +- ...346\250\241\346\265\201\347\250\213.ipynb" | 17 + ...345\221\212\346\216\250\350\215\220.ipynb" | 4 +- ...346\254\241\347\273\223\346\236\204.ipynb" | 4 +- ...345\277\203\346\246\202\345\277\265.ipynb" | 17 + ...\232\204\344\270\255\351\230\266API.ipynb" | 4 +- ...\232\204\351\253\230\351\230\266API.ipynb" | 19 +- ...\232\204\344\275\216\351\230\266API.ipynb" | 4 +- 41 files changed, 106879 insertions(+), 29203 deletions(-) diff --git a/.gitignore b/.gitignore index e3c442cba..339295408 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,12 @@ .DS_store .ipynb_checkpoints .ipynb_checkpoints/* - __pycache__/* - eat_pytorch_datasets - *.pt +__pycache__/* +eat_pytorch_datasets +*.pt +data/*.pt +data/*.zip +data/*.pkl +data/tensorboard +data/mnist +mnist diff --git "a/1-1,\347\273\223\346\236\204\345\214\226\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" "b/1-1,\347\273\223\346\236\204\345\214\226\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" index 35a7a7854..5fdfb11c9 100644 --- "a/1-1,\347\273\223\346\236\204\345\214\226\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" +++ "b/1-1,\347\273\223\346\236\204\345\214\226\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" @@ -10,18 +10,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "e707554e", "metadata": {}, "outputs": [], "source": [ "import os\n", - "import datetime\n", - "\n", - "#打印时间\n", - "def printbar():\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", "\n", "#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量\n", "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n" @@ -29,23 +23,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "f74d1bbf", "metadata": { "lines_to_next_cell": 2 }, "outputs": [], "source": [ - "!pip install torch==1.10.0\n", - "!pip install torchkeras==3.2.3" + "!pip install torch==2.0.0\n", + "!pip install -U torchkeras" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "83c3b159", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__ = 2.0.1\n", + "torchkeras.__version__ = 3.9.3\n" + ] + } + ], "source": [ "import torch \n", "import torchkeras \n", @@ -53,17 +56,6 @@ "print(\"torchkeras.__version__ = \", torchkeras.__version__) " ] }, - { - "cell_type": "markdown", - "id": "c6afb823", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__ = 1.10.0\n", - "torchkeras.__version__ = 3.2.3\n", - "```" - ] - }, { "cell_type": "markdown", "id": "29bd80bf", @@ -98,10 +90,243 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "af13fa86", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
049301Molson, Mr. Harry Marklandmale55.00011378730.5000C30S
15311Harper, Mrs. Henry Sleeper (Myna Haxtun)female49.010PC 1757276.7292D33C
238812Buss, Miss. Katefemale36.0002784913.0000NaNS
319202Carbines, Mr. Williammale19.0002842413.0000NaNS
468703Panula, Mr. Jaako Arnoldmale14.041310129539.6875NaNS
51612Hewlett, Mrs. (Mary D Kingcome)female55.00024870616.0000NaNS
622803Lovell, Mr. John Hall (\"Henry\")male20.500A/5 211737.2500NaNS
788402Banfield, Mr. Frederick Jamesmale28.000C.A./SOTON 3406810.5000NaNS
816803Skoog, Mrs. William (Anna Bernhardina Karlsson)female45.01434708827.9000NaNS
975213Moor, Master. Meiermale6.00139209612.4750E121S
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 493 0 1 \n", + "1 53 1 1 \n", + "2 388 1 2 \n", + "3 192 0 2 \n", + "4 687 0 3 \n", + "5 16 1 2 \n", + "6 228 0 3 \n", + "7 884 0 2 \n", + "8 168 0 3 \n", + "9 752 1 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Molson, Mr. Harry Markland male 55.0 0 \n", + "1 Harper, Mrs. Henry Sleeper (Myna Haxtun) female 49.0 1 \n", + "2 Buss, Miss. Kate female 36.0 0 \n", + "3 Carbines, Mr. William male 19.0 0 \n", + "4 Panula, Mr. Jaako Arnold male 14.0 4 \n", + "5 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 \n", + "6 Lovell, Mr. John Hall (\"Henry\") male 20.5 0 \n", + "7 Banfield, Mr. Frederick James male 28.0 0 \n", + "8 Skoog, Mrs. William (Anna Bernhardina Karlsson) female 45.0 1 \n", + "9 Moor, Master. Meier male 6.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 113787 30.5000 C30 S \n", + "1 0 PC 17572 76.7292 D33 C \n", + "2 0 27849 13.0000 NaN S \n", + "3 0 28424 13.0000 NaN S \n", + "4 1 3101295 39.6875 NaN S \n", + "5 0 248706 16.0000 NaN S \n", + "6 0 A/5 21173 7.2500 NaN S \n", + "7 0 C.A./SOTON 34068 10.5000 NaN S \n", + "8 4 347088 27.9000 NaN S \n", + "9 1 392096 12.4750 E121 S " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import numpy as np \n", "import pandas as pd \n", @@ -115,14 +340,6 @@ "dftrain_raw.head(10)\n" ] }, - { - "cell_type": "markdown", - "id": "04d4eadb", - "metadata": {}, - "source": [ - "![](./data/1-1-数据集展示.jpg)" - ] - }, { "cell_type": "markdown", "id": "a7edf254", @@ -155,10 +372,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "78e13165", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'png'\n", @@ -169,14 +397,6 @@ "plt.show()\n" ] }, - { - "cell_type": "markdown", - "id": "20641f47", - "metadata": {}, - "source": [ - "![](./data/1-1-Label分布.jpg)" - ] - }, { "cell_type": "markdown", "id": "2f6fa85c", @@ -187,10 +407,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "2284f17f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+539m1KhRGTRoUKudAwAAALSEdrsyf+6552b+/Pm55JJLGgT5JBk6dOhaP0+YMCGrVq3KSSedVB/kk2S77bbL17/+9axcuTITJkxo8boBAACgpbXLMF9bW5trr702Xbt2zdixY9drmzX3xY8ePbpB35q2O+64o/mKBAAAgDbSLi+z/+Mf/5glS5bkIx/5SKqqqvKrX/0qkydPzvLly7Pddtvl6KOPzpZbblk/fuHChZkzZ06SZPfdd2+wv379+mXTTTfN7Nmzs3jx4myyySatdi4AAADQ3NplmH/qqaeSJJtttlkOP/zw3HbbbWv1n3XWWbnyyitz7LHHJkl9kO/Zs2e6du3a6D779u2b+fPnZ/bs2dl5551bsHoAAABoWe3yMvsFCxYkSW6//fbcc889+f73v5/XX389L774Yk4//fTU1tbmhBNOyOOPP54kWbp0aZKkS5cu69znmpC/ZMmSdY6pq6vL4sWL1/oAAABAe9Muw/zq1auTJCtXrsz555+fL33pS+nTp08GDBiQSy65JEcddVRWrFiRSy65pFmPe/HFF6dHjx71n379+jXr/gEAAKA5tMsw361bt/r/3dgD8Na0TZkyZa3xy5YtW+c+a2pqkiTdu3df55gzzzwzixYtqv/MnTv33RcPAAAALaxd3jM/YMCAJH+7bL5Pnz4N+rfeeuskyeuvv54k6d+/f5K/XZ5fU1PT6H3zL7300lr7bkxlZWUqKyubVDsAAAC0tHa5Mr/mifS1tbWpq6tr0P/mm28m+ceKfHV1dX2g/9Of/tRg/Ny5czN//vwMGDDAk+wBAAAovXYZ5vv3759dd901RVHUX0r/z9a0/fNr6D7+8Y8nSSZNmtRg/Jq2Qw45pCXKBQAAgFbVLsN8knz1q19Nkpx++ul59dVX69sff/zxjB8/Pkly0kkn1befcsop6dChQ370ox/lD3/4Q337rFmzctFFF6Vjx4455ZRTWql6AAAAaDnt8p75JDnuuOPy61//OldffXV23HHH7L333qmtrc1DDz2Uurq6fO5zn8tRRx1VP3777bfPJZdcktNOOy3Dhg3L/vvvn06dOuXXv/51amtr853vfCeDBg1qwzMCAACA5tFuw3ySTJw4Mfvss0+uuOKK/O53v0tFRUWGDBmSL3zhCznhhBMajP/P//zPDBo0KJdcckkeeOCBJMkHP/jBfPWrX83BBx/c2uUDAABAi6goiqJo6yLaq8WLF6dHjx5ZtGiRB+dBE42rGNfWJcDbOrc4t61LAABY7xzabu+ZBwAAABonzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMh3bugAAaA/GVYxr6xLW27nFuW1dAgDQxqzMAwAAQMkI8wAAAFAywjwAAACUjDAPAAAAJSPMAwAAQMkI8wAAAFAy7TbMDx8+PBUVFev83HPPPY1ud9VVV2Xo0KHp1q1bevXqlYMOOigPPfRQK1cPAAAALafdv2f+E5/4RLp169agfauttmrQduqpp2bChAmpqqrKAQcckOXLl2fy5Mn59a9/nUmTJuXwww9vhYoBAACgZbX7MP/tb387W2+99TuOu/feezNhwoT07t07U6dOzeDBg5MkU6dOzfDhwzN27NgMHz481dXVLVswAAAAtLB2e5n9u3XppZcmSc4+++z6IJ8ke+21V0466aQsXLgwV155ZVuVBwAAAM1mgwjztbW1ue+++5Iko0ePbtC/pu2OO+5o1boAAACgJbT7y+yvvPLKvPHGG9loo42y3Xbb5fDDD0///v3XGjNz5szU1dWlT58+6du3b4N9DBkyJEkyffr0VqkZAAAAWlK7D/MXXnjhWj+ffvrpOeecc3LOOefUt82ZMydJGg3ySdK1a9dUV1dnwYIFWbJkSbp3797ouLq6utTV1dX/vHjx4qaWDwAAAM2u3V5mv+++++aaa67Jc889l2XLlmXmzJm56KKL0rFjx3zjG9/IhAkT6scuXbo0SdKlS5d17q9r165JkiVLlqxzzMUXX5wePXrUf/r169dMZwMAAADNp92G+fPPPz/HH398tt1221RVVWW77bbLWWedlVtvvTVJct5556W2trZZj3nmmWdm0aJF9Z+5c+c26/4BAACgObTbML8uBxxwQD74wQ9m4cKFefjhh5Ok/j30y5YtW+d2NTU1SbLOS+yTpLKyMptssslaHwAAAGhvShfmk9S/eu7VV19NkvoH4r300kuNjq+pqcnChQvTs2fPtw3zAAAAUAalDPMLFixI8o/74LfffvtUVlZm3rx5efnllxuMnzZtWpJkl112ab0iAQAAoIWULszPmzcvDzzwQJJ/vHKuqqoqI0aMSJLceOONDbaZNGlSkuSQQw5ppSoBAACg5bTLMP/QQw/l1ltvzapVq9Zqf/HFF3PEEUekpqYmhx566FqvojvttNOS/O1VdrNmzapvnzp1aq644opUV1fnxBNPbJ0TAAAAgBbULt8z/8wzz2Ts2LHZfPPNM2TIkFRXV2f27Nl57LHHsnz58uy000758Y9/vNY2I0eOzCmnnJIJEyZkt912y/7775+33norkydPTlEUmThxYqqrq9vmhAAAAKAZtcswv+eee+aLX/xiHn744Tz66KNZsGBBunbtmt122y1HHXVUvvjFL6aqqqrBdpdffnl22223fO9738vkyZPTqVOnjBw5Muecc0723nvvNjgTAAAAaH4VRVEUbV1Ee7V48eL06NEjixYt8po6aKJxFePaugTYYJxbnNvWJQAALWR9c2i7vGceAAAAWDdhHgAAAEpGmAcAAICSEeYBAACgZJoU5gcOHJhvfvObee2115qrHgAAAOAdNCnMv/jii/n617+e/v3758gjj8w999wTD8cHAACAltWkMD979ux84xvfyBZbbJFbb701H//4x7P11lvn/PPPz0svvdRcNQIAAAD/pElhvm/fvjn33HPz4osv5u67787hhx+e1157Leedd1622WabHHLIIbn99tuzevXq5qoXAAAA3vOa5QF4FRUVGTVqVG666abMnTs33/zmN7PNNtvkrrvuyhFHHJF+/frlnHPOyYsvvtgchwMAAID3tGZ/mv1mm22Wr371q5kxY0b+8z//M0VR5NVXX81FF12UQYMG5bDDDsvjjz/e3IcFAACA94xmD/Nr7qPfeuutc/nllydJ9t5775x99tkZNGhQ7rjjjgwdOjS33HJLcx8aAAAA3hOaJcyvXLkyN910U0aNGpWBAwfmwgsvzJIlS/LFL34x06dPz+9///ucf/75efrpp3PDDTekQ4cO+cY3vtEchwYAAID3nI5N2XjWrFn5yU9+kquvvjrz5s1LURQZMmRITjrppBx33HHp0qVLg22OOuqo/PKXv8ztt9/elEMDAADAe1aTwvz222+fioqKVFVVZezYsTnppJPywQ9+8B2369GjR1asWNGUQwMAAMB7VpMus99xxx0zYcKEvPLKK/nJT36yXkE+SX7yk594XR0AAAD8i5q0Mv/kk082Vx0AAADAemrSyvzq1auzePHit71kfsWKFVm8eLGVeAAAAGgmTQrzl112WXr27JkpU6asc8yUKVPSs2fPfPe7323KoQAAAIC/a1KYv+WWW9KvX7+MHDlynWNGjhyZvn375qabbmrKoQAAAIC/a1KYnzVrVnbaaad3HPeBD3wgs2bNasqhAAAAgL9rUphftGhRevTo8Y7jevTokQULFjTlUAAAAMDfNSnMb7HFFpk+ffo7jps+fXo222yzphwKAAAA+LsmhfkRI0ZkxowZueGGG9Y55pe//GWeeuqpfPSjH23KoQAAAIC/a1KYP+OMM9KpU6d8+tOfzsknn5zp06enpqYmNTU1mT59ek4++eR86lOfSqdOnXLGGWc0V80AAADwntaxKRu///3vz89+9rOccMIJ+eEPf5gf/vCHa/UXRZHOnTtn4sSJ+cAHPtCkQgEAAIC/adLKfJIcddRRmT59er7whS9k0KBBqaysTGVlZQYNGpQvfvGLeeKJJ3LMMcc0R60AAABAmrgyv8agQYPygx/8oDl2BQAAALyDJq/MAwAAAK2rWVbmk2TlypV54403UldXt84x/fv3b67DAQAAwHtWk8P8vffemwsvvDB/+MMfsmLFinWOq6ioyMqVK5t6OAAAAHjPa1KYv/POO3PEEUdk1apV6dmzZ7bZZpt07969uWoDAAAAGtGkMD9u3LisXr06l112WU4++eR06NChueoCAAAA1qFJYf4vf/lL9tprr5xyyinNVQ8AAADwDpr0NPtu3bp5qB0AAAC0siaF+ZEjR+aPf/xjc9UCAAAArIcmhfn/+Z//yeLFi/Pf//3fnlQPAAAAraRJ98xPnDgxBx54YL797W/npptuyvDhw9O3b99stFHD3xFUVFTknHPOacrhAAAAgCQVRVEU/+rGG220USoqKrI+u6ioqMiqVav+1UO1icWLF6dHjx5ZtGhRNtlkk7YuB0ptXMW4ti4BNhjnFue2dQkAQAtZ3xza5JV5AAAAoHU1KcyfcMIJzVUHAAAAsJ6a9AA8AAAAoPU1aWV+jZUrV+auu+7KI488kvnz52fPPffMZz7zmSTJK6+8kvnz52fHHXdMx47NcjgAAAB4T2tyuv7973+f448/PnPnzk1RFKmoqMiKFSvqw/zUqVNz9NFH58Ybb8yRRx7Z5IIBAADgva5Jl9k/9dRTGTVqVF599dV85StfyS9/+csGT7Y/5JBD0qVLl9x0001NKhQAAAD4myatzF9wwQVZvnx57r777hxwwAGNjunUqVOGDBmSP/3pT005FAAAAPB3TVqZ/+1vf5uhQ4euM8ivsdVWW+WVV15pyqEAAACAv2tSmF+4cGH69ev3juNqamqyYsWKphwKAAAA+LsmhfnNNtsszz777DuOmzFjxnqFfgAAAOCdNSnMjxgxIo8//nh++9vfrnPMLbfckmeffTb7779/Uw4FAAAA/F2TwvzXvva1dOrUKYcffnh++MMf5rXXXqvvW7BgQX7605/mxBNPTNeuXXPaaac1uVgAAACgiWH+/e9/f66//vqsXr06J598crbaaqtUVFTk6quvzqabbprPfe5zqaury3XXXZdtttmmuWoGAACA97QmhfkkOfzww/Pkk0/mK1/5St7//venc+fO6dSpU7bddtt84QtfyPTp03PooYc2R60AAABAmvie+TUGDBiQyy+/vDl2BQAAALyDJq/MAwAAAK1LmAcAAICSadJl9h06dFjvsRUVFVm5cmVTDgcAAACkiWG+X79+qaioaNC+evXqvPbaa1mxYkWSv91TDwAAADSPJl1m/+KLL+aFF15o8Jk9e3Zqa2vz61//OjvssEP23HPPPP/8800q9I033shmm22WioqKDBo06G3HXnXVVRk6dGi6deuWXr165aCDDspDDz3UpOMDAABAe9Fi98xvtNFGGTlyZO68887cdddd+Z//+Z8m7e+//uu/Mn/+/Hccd+qpp2bs2LF58sknM3LkyAwdOjSTJ0/Ovvvum1tvvbVJNQAAAEB70OIPwNtmm23y4Q9/OD/96U//5X385je/ydVXX53Pfe5zbzvu3nvvzYQJE9K7d+888cQTufXWW3PPPffk/vvvT4cOHTJ27NgsXLjwX64DAAAA2oNWeZp9ly5dMnfu3H9p29ra2nzhC1/IjjvumNNPP/1tx1566aVJkrPPPjuDBw+ub99rr71y0kknZeHChbnyyiv/pToAAACgvWjxMD979uzcf//9ed/73vcvbT9u3Lg8//zz+dGPfpSNN954neNqa2tz3333JUlGjx7doH9N2x133PEv1QEAAADtRZOeZv+zn/1snX1Lly7NM888k2uvvTaLFy/Ol770pXe9/+nTp2f8+PEZO3Zshg0blhdffHGdY2fOnJm6urr06dMnffv2bdA/ZMiQ+n0CAABAmTUpzI8ZM6bRV9OtURRFkuTTn/50xo0b9672vXr16nz2s59NdXV1vvWtb73j+Dlz5iRJo0E+Sbp27Zrq6uosWLAgS5YsSffu3d9VPQAAANBeNCnMf+Mb31hnmO/UqVO22GKL7Lvvvtl2223f9b6/+93v5tFHH83EiRPTu3fvdxy/dOnSJH+7P39dunbtmoULF64zzNfV1aWurq7+58WLF7/rugEAAKClNSnMn3feec1UxtrmzJmTs88+O/vtt1/GjBnTIsdozMUXX/yuryAAAACA1tYqT7N/t7785S/nrbfeyo9+9KP13qZbt25JkmXLlq1zTE1NTZKs8xL7M888M4sWLar//KtP4AcAAICW1KSV+ZZy5513prq6OieddNJa7cuXL0+SvPzyyxk+fHiS5Be/+EU233zz9O/fP0ny0ksvNbrPmpqaLFy4MD179lxnmK+srExlZWUznQUAAAC0jCaF+X/lXvg1Kioq8txzz62zf+HChZkyZUqjfcuXL6/vWxPwt99++1RWVmbevHl5+eWXs9VWW621zbRp05Iku+yyy79cMwAAALQHTQrzb/equKZY8xT8xo63zTbbZODAgXn22WfX6quqqsqIESPyq1/9KjfeeGNOPfXUtfonTZqUJDnkkENapGYAAABoLU26Z3716tU5+eST061bt/z3f/93Hn/88SxcuDALFy7ME088ka997Wvp3r17Tj755KxevbrBp7mddtppSZILL7wws2bNqm+fOnVqrrjiilRXV+fEE09s9uMCAABAa2rSyvx3vvOdXHHFFXnwwQfzwQ9+cK2+nXfeOTvvvHOOPPLI7L333tl2220brJY3t5EjR+aUU07JhAkTsttuu2X//ffPW2+9lcmTJ6coikycODHV1dUtWgMAAAC0tCatzF9xxRUZPnx4gyD/zz74wQ9mxIgR+fGPf9yUQ623yy+/PBMnTswOO+yQyZMnZ+rUqRk5cmTuv//+HH744a1SAwAAALSkJq3MP//88+v1QLmePXvm/vvvb8qhkiRbb731Ou+n/2djxoxp1ffTAwAAQGtq0sp8r169cv/999c/Ub4xy5cvz/3335+ePXs25VAAAADA3zUpzB9xxBF59dVXM3r06EafbP/iiy/mqKOOymuvvZYjjjiiKYcCAAAA/q5Jl9lfcMEFue+++3L33Xdn8ODB+eAHP5gBAwYkSWbPnp3HHnssK1euzPvf//5ccMEFzVIwAAAAvNc1Kcz37NkzDz30UM4888z87Gc/y8MPP5yHH364vr+qqiqf+cxncvHFF7vMHgAAAJpJk8J8klRXV+eHP/xhxo8fn8ceeyyvvPJKkmSLLbbIHnvska5duza5SAAAAOAfmhzm1+jSpUuGDRvWXLsDAAAA1qHZwvybb76Zxx57LPPnz8+AAQOy9957N9euAQAAgH/SpKfZJ8m8efNy3HHHZfPNN8+oUaNy/PHH5yc/+Ul9/09+8pP06tUrv//975t6KAAAACBNDPNvvvlm9t577/ziF7/IBz7wgXzpS19KURRrjTnyyCOzZMmSTJo0qUmFAgAAAH/TpDB/0UUX5bnnnss3vvGNTJs2Ld/97ncbjOnVq1d22WWXTJkypSmHAgAAAP6uSWH+1ltvzXbbbZfzzjvvbccNHDgwL7/8clMOBQAAAPxdk8L8yy+/nF133fUdx1VUVGTx4sVNORQAAADwd00K85tsskleffXVdxz33HPPpU+fPk05FAAAAPB3TQrzH/rQh/Loo4/mhRdeWOeYJ554Io8//nj22WefphwKAAAA+LsmhfmvfOUrqauryxFHHJEZM2Y06H/22WfzqU99KkVR5OSTT27KoQAAAIC/a1KYHzVqVL761a9m+vTp+cAHPpD3v//9qaioyP/93/9l1113zQ477JAnn3wyZ511Vj7ykY80V80AAADwntakMJ8k3/zmN3PDDTdk5513zjPPPJOiKPLqq6/mz3/+cwYPHpzrrrsuF1xwQXPUCgAAACTp2JSNFy9enIqKihx11FE56qijMm/evLz44otZvXp1+vbtm6222qq56gQAAAD+rklhvrq6OnvuuWemTp2aJOnTp4+n1gMAAEALa9Jl9j169Mi2227bXLUAAAAA66FJK/O77757nnvuueaqBQBYD+MqxrV1Cevl3OLcti4BADZYTVqZ/+///u88+uijmTRpUnPVAwAAALyDJq3MV1VV5bOf/WyOOeaYHHzwwTnkkEPSv3//dO7cudHx++67b1MOBwAAAKSJYX748OGpqKhIURS54447cuedd77t+FWrVjXlcAAAAECaGOY//elPp6KiorlqAQAAANZDk8L8VVdd1UxlAAAAAOvrXT0Ab8SIEfnWt77VaN+cOXPy5ptvNktRAAAAwLq9qzD/u9/9Lk8//XSjfdtss03OOOOMZikKAAAAWLcmvZrunxVFkaIommt3AAAAwDo0W5gHAAAAWocwDwAAACUjzAMAAEDJCPMAAABQMu86zF999dXp0KFDg09FRcU6+zp06JCOHZv0SnsAAADg7951wv5Xn1jvSfcAAADQPN5VmF+9enVL1QEAAACsJ/fMAwAAQMkI8wAAAFAywjwAAACUjDAPAAAAJSPMAwAAQMkI8wAAAFAywjwAAACUjDAPAAAAJSPMAwAAQMkI8wAAAFAywjwAAACUjDAPAAAAJSPMAwAAQMkI8wAAAFAyHdu6AACAtjSuYlxbl7Bezi3ObesSAGhHrMwDAABAyQjzAAAAUDLCPAAAAJSMMA8AAAAlI8wDAABAyQjzAAAAUDLtNsxfeumlOfLIIzN48OD06NEjlZWVGTBgQD796U/nz3/+8zq3u+qqqzJ06NB069YtvXr1ykEHHZSHHnqoFSsHAACAltVuw/z/9//9f/nVr36VXr165d/+7d/y8Y9/PJ07d84111yTPfbYI3feeWeDbU499dSMHTs2Tz75ZEaOHJmhQ4dm8uTJ2XfffXPrrbe2/kkAAABAC+jY1gWsy2233ZY99tgjnTt3Xqv9Bz/4Qb785S/ns5/9bF566aV07Pi3U7j33nszYcKE9O7dO1OnTs3gwYOTJFOnTs3w4cMzduzYDB8+PNXV1a19KgAAANCs2u3K/D777NMgyCfJl770pQwcODB//etf89RTT9W3X3rppUmSs88+uz7IJ8lee+2Vk046KQsXLsyVV17Z8oUDAABAC2u3Yf7tbLzxxkmSTp06JUlqa2tz3333JUlGjx7dYPyatjvuuKOVKgQAAICWU7owf80112TmzJkZPHhw/Qr8zJkzU1dXlz59+qRv374NthkyZEiSZPr06a1aKwAAALSEdnvP/BqXXHJJ/vKXv6SmpiYzZszIX/7yl2y55Za5/vrr06FDhyTJnDlzkqTRIJ8kXbt2TXV1dRYsWJAlS5ake/furVY/AAAANLd2H+b/7//+L7/5zW/qfx4wYEB+9rOfZY899qhvW7p0aZKkS5cu69xP165ds3DhwrcN83V1damrq6v/efHixU0tHwAAAJpduw/z9957b5Jk4cKF+fOf/5zzzz8/++23Xy688MJ8/etfb9ZjXXzxxRk3blyz7rO1jKsoR93nFue2dQkAAAClV5p75qurqzNs2LDcfffd2WOPPXLOOefk0UcfTZJ069YtSbJs2bJ1bl9TU5Mkb3uJ/ZlnnplFixbVf+bOnduMZwAAAADNozRhfo2NN944xxxzTIqiqH86ff/+/ZMkL730UqPb1NTUZOHChenZs+fbhvnKyspssskma30AAACgvSldmE+STTfdNEkyb968JMn222+fysrKzJs3Ly+//HKD8dOmTUuS7LLLLq1XJAAAALSQUob5KVOmJEkGDhyYJKmqqsqIESOSJDfeeGOD8ZMmTUqSHHLIIa1UIQAAALScdhnmH3zwwdxzzz1ZvXr1Wu0rVqzId7/73VxzzTWpqqrKMcccU9932mmnJUkuvPDCzJo1q7596tSpueKKK1JdXZ0TTzyxdU4AAAAAWlC7fJr9rFmzMnbs2Gy66abZY4890rt378yfPz9//vOf8+qrr6Zz58656qqr0q9fv/ptRo4cmVNOOSUTJkzIbrvtlv333z9vvfVWJk+enKIoMnHixFRXV7fdSQEAAEAzaZdhfr/99stZZ52VKVOmZPr06Zk/f346deqUrbfeOqNHj85//Md/ZNCgQQ22u/zyy7Pbbrvle9/7XiZPnpxOnTpl5MiROeecc7L33nu3wZlAyyvLawkBAIDm0y7D/DbbbJOLLrroX9p2zJgxGTNmTPMWBAAAAO1Iu7xnHgAAAFg3YR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACiZjm1dAACwYRpXMa6tSwCADZaVeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGQ6tnUBAAC8s3EV49q6hPV2bnFuW5cAsMGzMg8AAAAlI8wDAABAyQjzAAAAUDLCPAAAAJSMMA8AAAAlI8wDAABAybTLML9s2bLceuutOfHEE7P99tunc+fO6dq1a3bdddecf/75Wbp06Tq3veqqqzJ06NB069YtvXr1ykEHHZSHHnqoFasHAACAltUuw/zPf/7zHHHEEfnpT3+aDh065NBDD82wYcPywgsv5Nxzz82HPvShvP766w22O/XUUzN27Ng8+eSTGTlyZIYOHZrJkydn3333za233tr6JwIAAAAtoF2G+Y033jif//zn89RTT+Wpp57KL3/5y9xzzz2ZOXNmdt999zz99NM59dRT19rm3nvvzYQJE9K7d+888cQTufXWW3PPPffk/vvvT4cOHTJ27NgsXLiwTc4HAAAAmlO7DPMnnHBCrrjiiuywww5rtW+xxRb5/ve/nyS5+eab89Zbb9X3XXrppUmSs88+O4MHD65v32uvvXLSSSdl4cKFufLKK1uhegAAAGhZ7TLMv51dd901SVJXV5c33ngjSVJbW5v77rsvSTJ69OgG26xpu+OOO1qpSgAAAGg5pQvzzz//fJK/XYrfq1evJMnMmTNTV1eXPn36pG/fvg22GTJkSJJk+vTprVcoAAAAtJDShfkJEyYkSUaNGpXKysokyZw5c5Kk0SCfJF27dk11dXUWLFiQJUuWtE6hAAAA0EI6tnUB78bdd9+dK6+8MhtvvHEuuOCC+vY1r6rr0qXLOrft2rVrFi5cmCVLlqR79+6Njqmrq0tdXV39z4sXL26mygEAAKD5lGZl/umnn87xxx+foihyySWX1N8735wuvvji9OjRo/7Tr1+/Zj8GAAAANFUpwvzLL7+cUaNGZcGCBTnttNNyyimnrNXfrVu3JMmyZcvWuY+ampokWeeqfJKceeaZWbRoUf1n7ty5zVA9AAAANK92f5n9m2++mQMOOCCzZ8/O2LFj8+1vf7vBmP79+ydJXnrppUb3UVNTk4ULF6Znz55vG+YrKyvr78MHAACA9qpdr8wvXbo0Bx54YJ566qkceeSR+fGPf5yKiooG47bffvtUVlZm3rx5efnllxv0T5s2LUmyyy67tHjNAAAA0NLabZivq6vLYYcdlkceeSQf+9jHcv3116dDhw6Njq2qqsqIESOSJDfeeGOD/kmTJiVJDjnkkJYrGAAAAFpJuwzzq1atyrHHHpv77rsvw4YNy80335xOnTq97TannXZakuTCCy/MrFmz6tunTp2aK664ItXV1TnxxBNbtG4AAABoDe3ynvnvfe97ueWWW5Ikm266ab70pS81Ou7b3/52Nt100yTJyJEjc8opp2TChAnZbbfdsv/+++ett97K5MmTUxRFJk6cmOrq6tY6BQAAAGgx7TLML1iwoP5/rwn1jTnvvPPqw3ySXH755dltt93yve99L5MnT06nTp0ycuTInHPOOdl7771btGYAAABoLe0yzJ933nk577zz/qVtx4wZkzFjxjRrPQAAANCetMt75gEAAIB1E+YBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKJmObV0AAAAblnEV49q6hPVybnFuW5cA8C+zMg8AAAAlI8wDAABAyQjzAAAAUDLCPAAAAJSMMA8AAAAlI8wDAABAyQjzAAAAUDLeM0+rKst7ZxPvngUAANovK/MAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlEy7fTXdY489lsmTJ+eRRx7JI488kpdffjlJUhTF22531VVX5Qc/+EGeeuqpdOrUKR/+8Idz9tlnZ++9926NsgEAKImyvDLX63KBxrTbMH/BBRfktttue1fbnHrqqZkwYUKqqqpywAEHZPny5Zk8eXJ+/etfZ9KkSTn88MNbplgAAABoRe02zO+1117ZZZdd8qEPfSgf+tCHsvXWW6eurm6d4++9995MmDAhvXv3ztSpUzN48OAkydSpUzN8+PCMHTs2w4cPT3V1dSudAQAAALSMdhvm//u///tdjb/00kuTJGeffXZ9kE/+9kuBk046Kd/5zndy5ZVX5r/+67+atU4AAABobRvEA/Bqa2tz3333JUlGjx7doH9N2x133NGqdQEAAEBL2CDC/MyZM1NXV5c+ffqkb9++DfqHDBmSJJk+fXprlwYAAADNrt1eZv9uzJkzJ0kaDfJJ0rVr11RXV2fBggVZsmRJunfv3ui4urq6te7LX7x4cfMXCwAAAE20QYT5pUuXJkm6dOmyzjFdu3bNwoUL3zbMX3zxxRk3rhyvKAEA4L2hLK/QS7xGD1rTBnGZfXM588wzs2jRovrP3Llz27okAAAAaGCDWJnv1q1bkmTZsmXrHFNTU5Mk61yVT5LKyspUVlY2b3EAAADQzDaIlfn+/fsnSV566aVG+2tqarJw4cL07NnzbcM8AAAAlMEGEea33377VFZWZt68eXn55Zcb9E+bNi1Jsssuu7R2aQAAANDsNogwX1VVlREjRiRJbrzxxgb9kyZNSpIccsghrVoXAAAAtIQNIswnyWmnnZYkufDCCzNr1qz69qlTp+aKK65IdXV1TjzxxLYqDwAAAJpNu30A3l133ZULLrig/ue33norSfLhD3+4vu2cc87Jxz/+8STJyJEjc8opp2TChAnZbbfdsv/+++ett97K5MmTUxRFJk6cmOrq6lY9BwAAAGgJ7TbMz5s3Lw8//HCD9n9umzdv3lp9l19+eXbbbbd873vfy+TJk9OpU6eMHDky55xzTvbee+8WrxkAAABaQ7sN82PGjMmYMWNabTsAAAAoiw3mnnkAAAB4rxDmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkmm3T7OHtjauYlxblwAAANAoK/MAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMsI8AAAAlIwwDwAAACUjzAMAAEDJCPMAAABQMh3bugAAAIDWNK5iXFuXsF7OLc5t6xJox6zMAwAAQMkI8wAAAFAywjwAAACUjDAPAAAAJSPMAwAAQMkI8wAAAFAyXk0HAAA0i7K88g02BFbmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACiZjm1dQHOrra3NxRdfnF/84heZM2dOevXqlVGjRuWCCy7IVltt1dblAQAAbFDGVYxr6xLW27nFuW1dQrPZoFbmly9fnhEjRuSCCy7I0qVLc9hhh6Vfv36ZOHFidt999zz//PNtXSIAAAA02QYV5i+88ML84Q9/yF577ZVnnnkmN9xwQx5++OGMHz8+8+bNy2c+85m2LhEAAACabIMJ82+99Va+973vJUm+//3vp1u3bvV9p512WnbZZZdMmTIljz32WFuVCAAAAM1igwnzDz74YBYtWpSBAwdm9913b9A/evToJMkdd9zR2qUBAABAs9pgwvwTTzyRJBkyZEij/Wvap0+f3mo1AQAAQEvYYML8nDlzkiR9+/ZttH9N++zZs1utJgAAAGgJG8yr6ZYuXZok6dKlS6P9Xbt2TZIsWbJknfuoq6tLXV1d/c+LFi1KkixevLi5ymwxy7O8rUsAAACaURlySFKuLFKGP9M1NRZF8bbjNpgw3xwuvvjijBvX8B2J/fr1a4NqAACA97Jv9vhmW5ewwSnTn+mSJUvSo0ePdfZvMGF+zdPrly1b1mh/TU1NkqR79+7r3MeZZ56Z0047rf7n1atX580330zv3r1TUVHRjNU2zeLFi9OvX7/MnTs3m2yySVuXwzqYp3IwT+VgnsrDXJWDeSoH81QO5qk8yjJXRVFkyZIl2XLLLd923AYT5vv3758keemllxrtX9M+YMCAde6jsrIylZWVa7VVV1c3T4EtYJNNNmnX/yfkb8xTOZincjBP5WGuysE8lYN5KgfzVB5lmKu3W5FfY4N5AN6uu+6aJJk2bVqj/Wvad9lll1arCQAAAFrCBhPm99lnn/To0SPPPfdcHn/88Qb9kyZNSpIccsghrVwZAAAANK8NJsx36tQpJ598cpLky1/+cv098kly6aWXZvr06dlvv/2yxx57tFWJzaaysjLnnntug1sCaF/MUzmYp3IwT+VhrsrBPJWDeSoH81QeG9pcVRTv9Lz7Elm+fHmGDx+ehx9+OFtssUWGDRuW2bNn5+GHH06fPn3yhz/8Idtuu21blwkAAABNskGF+SSpra3NxRdfnJ///OeZO3duevXqlVGjRuWCCy5I375927o8AAAAaLINLswDAADAhm6DuWceAAAA3iuE+ZKora3NN77xjWy33Xbp3Llzttxyy3zmM5/Jyy+/3Nalvec89thj+eY3v5kjjzwyffv2TUVFRSoqKt5xu6uuuipDhw5Nt27d0qtXrxx00EF56KGHWqHi955ly5bl1ltvzYknnpjtt98+nTt3TteuXbPrrrvm/PPPz9KlS9e5rXlqfZdeemmOPPLIDB48OD169EhlZWUGDBiQT3/60/nzn/+8zu3MVdt54403stlmm6WioiKDBg1627HmqXUNHz68/r9LjX3uueeeRrczT21j3rx5Of3007P99tunqqoqvXr1ypAhQ3LGGWc0Ov6OO+7IfvvtV/+O7OHDh+euu+5q5arfO373u9+97fdpzef8889vsK3vVOt79NFHc/TRR2fLLbfMxhtvnOrq6gwbNiwTJ05MYxejr1q1Kpdddll23nnnVFVVpU+fPjn66KMzY8aMNqj+X1TQ7tXW1hYf/vCHiyTFFltsURx99NHF0KFDiyRFnz59iueee66tS3xPOeyww4okDT5v55RTTimSFFVVVcVhhx1WfOxjHys6duxYdOjQobjllltap/D3kB//+Mf187LDDjsURx11VPGxj32s6N69e5GkeP/731/89a9/bbCdeWobvXv3Ljp37lwMHTq0OOKII4ojjjii2G677YokxcYbb1zccccdDbYxV23rhBNOKCoqKookxcCBA9c5zjy1vv32269IUnziE58oTjjhhAaf6dOnN9jGPLWNP/7xj0Xv3r2LJMVOO+1UHHPMMcWBBx5YDBgwoOjQoUOD8ZdddlmRpOjYsWMxatSo4rDDDiuqqqqKJMV3v/vdNjiDDd+MGTMa/R6dcMIJxfHHH1//b4377rtvre18p1rfpEmTig4dOhRJiiFDhhRHH3108dGPfrTo2LFjkaQ47rjj1hq/atWq4ogjjiiSFNXV1cUnPvGJYr/99isqKiqKLl26FA8//HAbncm7I8yXwNe//vUiSbHXXnsVS5YsqW8fP358kaTYb7/92q6496BvfvObxTnnnFPcfvvtxauvvlpUVla+bZifPHlykaTo3bt38cwzz9S3P/TQQ0WnTp2K6urqYsGCBa1Q+XvHVVddVXz+858vnnrqqbXaX3nllWL33XcvkhTHHnvsWn3mqe38/ve/L2praxu0f//73y+SFO973/uKFStW1Lebq7Z17733FkmKz3/+828b5s1T21gT5l944YX1Gm+e2sbrr79ebLrppkWXLl2K2267rUH//xsknn766aJDhw5FZWVl8dBDD9W3z5w5s+jdu3fRsWPHYtasWS1eN/9w9913F0mKfv36FatXr65v951qfStWrCg222yzIklx3XXXrdX31FNPFb169WrwS5c1Cz+DBw8uXnvttfr2SZMmFUmKQYMGrfVvj/ZKmG/n6urqih49ehRJimnTpjXo32WXXYokxR//+Mc2qI6iKN4xzB944IFFkuKyyy5r0Pcf//EfRZLi29/+dgtWyD976KGHiiRFZWVlUVdXV99untqngQMHFkmKJ554or7NXLWdZcuWFQMHDix23HHH4plnnnnbMG+e2sa7DfPmqW188YtfLJIU3//+99/V+FNOOaVB36WXXlokKU4++eRmrpK3c9xxxxVJiq997WtrtftOtb4///nPRZJi++23b7R/zZ/7//zP/9S37bDDDkWSRq+UOPTQQ4skxaRJk1qq5Gbjnvl27sEHH8yiRYsycODA7L777g36R48eneRv91DR/tTW1ua+++5L8o+5+mfmr/XtuuuuSZK6urq88cYbScxTe7bxxhsnSTp16pTEXLW1cePG5fnnn8+PfvSj+rlpjHkqB/PUNmpra3Pttdema9euGTt27Hpts+a+ePPUPtTU1OS2225LknzqU5+qb/edahuVlZXrNa53795JkhdeeCEzZsxIVVVVPv7xjzcYV6Z56tjWBfD2nnjiiSTJkCFDGu1f0z59+vRWq4n1N3PmzNTV1aVPnz7p27dvg37z1/qef/75JH8Lib169Upintqra665JjNnzszgwYMzePDgJOaqLU2fPj3jx4/P2LFjM2zYsLz44ovrHGue2t6VV16ZN954IxtttFG22267HH744enfv/9aY8xT2/jjH/+YJUuW5CMf+Uiqqqryq1/9KpMnT87y5cuz3Xbb1T/Aa42FCxdmzpw5SdLowk6/fv2y6aabZvbs2Vm8eHE22WSTVjuX96qbb745NTU12X333bPjjjvWt/tOtY1tt902AwcOzMyZM/Pzn/88xx13XH3fjBkzcu2116Znz5454ogjkvwjX33gAx9o9BfTZZonYb6dW/OXd2N/Ifxz++zZs1utJtbfO81f165dU11dnQULFmTJkiXp3r17a5b3njRhwoQkyahRo+p/k2ue2odLLrkkf/nLX1JTU5MZM2bkL3/5S7bccstcf/316dChQxJz1VZWr16dz372s6murs63vvWtdxxvntrehRdeuNbPp59+es4555ycc8459W3mqW089dRTSZLNNtsshx9+eP0K7xpnnXVWrrzyyhx77LFJ/jFPPXv2TNeuXRvdZ9++fTN//vzMnj07O++8cwtWT5Jce+21SdZelU98p9pKhw4dcvXVV+fggw/OJz/5yYwfPz6DBw/O66+/ngceeCA77rhjrrrqqvpFnA0pX7nMvp1b8wqtLl26NNq/5i/1JUuWtFpNrL93mr/EHLamu+++O1deeWU23njjXHDBBfXt5ql9+L//+79cffXVmTRpUv7yl79kwIABuf7667PHHnvUjzFXbeO73/1uHn300VxyySX1lym+HfPUdvbdd99cc801ee6557Js2bLMnDkzF110UTp27JhvfOMb9b/QTMxTW1mwYEGS5Pbbb88999yT73//+3n99dfz4osv5vTTT09tbW1OOOGEPP7440nMU3vz6quv5je/+U06dOhQ/wuXNcxV29lnn30yZcqUbLvttpk2bVpuuOGG/Pa3v81GG22U/fffP9tuu2392A0pXwnzwHvC008/neOPPz5FUeSSSy6pv3ee9uPee+9NURRZsGBB7r///gwePDj77bdfLrroorYu7T1tzpw5Ofvss7PffvtlzJgxbV0O7+D888/P8ccfn2233TZVVVXZbrvtctZZZ+XWW29Nkpx33nmpra1t2yLf41avXp0kWblyZc4///x86UtfSp8+fTJgwIBccsklOeqoo7JixYpccsklbVwpjbn++uuzatWq7L///tl8883buhz+7vrrr8/QoUPTr1+/PPzww1m6dGmeeeaZjBkzJuPHj8+IESNSV1fX1mU2O2G+nevWrVuSZNmyZY3219TUJInLdNqpd5q/xBy2hpdffjmjRo3KggULctppp+WUU05Zq988tS/V1dUZNmxY7r777uyxxx4555xz8uijjyYxV23hy1/+ct5666386Ec/Wu9tzFP7c8ABB+SDH/xgFi5cmIcffjiJeWora/7ckzT6ALw1bVOmTFlrvHlqH9Z1iX1irtrKrFmzcsIJJ2TTTTfNnXfemaFDh6Zr164ZPHhwrrjiihx88MGZNm1afvrTnybZsPKVMN/OrXlYzUsvvdRo/5r2AQMGtFpNrL93mr+amposXLgwPXv2LMVfGGX05ptv5oADDsjs2bMzduzYfPvb324wxjy1TxtvvHGOOeaYFEVR/0RZc9X67rzzznTp0iUnnXRShg8fXv/593//9yR/+2XZmrbXXnstiXlqr9Y8SPLVV19NYp7aypp/s3Xp0iV9+vRp0L/11lsnSV5//fUk/5inBQsW1IeM/5d/D7aOGTNm5E9/+lO6deuWww8/vEG/71Tb+MUvfpEVK1Zk1KhRa/2ybI2jjz46SXL//fcn2bDylQfgtXNrLgWeNm1ao/1r2nfZZZdWq4n1t/3226eysjLz5s3Lyy+/nK222mqtfvPXspYuXZoDDzwwTz31VI488sj8+Mc/TkVFRYNx5qn92nTTTZMk8+bNS2Ku2srChQvrVwn/X8uXL6/vW758eRLz1F6tuVd7zf2g5qltrHkifW1tberq6hq8VuvNN99M8o/Vw+rq6vTv3z9z5szJn/70p3zkIx9Za/zcuXMzf/78DBgwwJPsW9g111yTJDnyyCMbvd/ad6ptrAnfPXr0aLR/TfuavwPX5Ksnn3wyK1asaPBE+zLNk5X5dm6fffZJjx498txzz9U/COWfTZo0KUlyyCGHtHJlrI+qqqqMGDEiSXLjjTc26Dd/Laeuri6HHXZYHnnkkXzsYx9b64no/y/z1H6tCYkDBw5MYq7aQlEUjX5eeOGFJH+bmzVta1YUzVP7M2/evDzwwANJ/vHaJfPUNvr3759dd901RVE0+kuyNW3//Bq6Ne/CXjMn/8w8tY6iKPLzn/88SeOX2Ce+U21lzbML/vjHPzbav+ZWvTX/jdpmm22yww47pLa2NnfddVeD8aWap4J27+tf/3qRpNh7772LpUuX1rePHz++SFLst99+bVccRWVlZfF2X6XJkycXSYrevXsXzzzzTH37Qw89VFRWVhbV1dXFggULWqHS946VK1cWRxxxRJGkGDZsWFFTU/OO25intvH73/+++NWvflWsWrVqrfa33nqr+M53vlNstNFGRVVVVTFnzpz6PnPVPrzwwgtFkmLgwIGN9pun1vfggw8Wt9xyS7Fy5cq12l944YVin332KZIUhx566Fp95qltXHfddUWSYueddy5eeeWV+vY//elPRa9evYokxS9/+cv69qeffrro0KFDUVlZWUydOrW+/Zlnnil69+5ddOzYsZg1a1arnsN7zZQpU4okxVZbbdXgv1n/zHeq9T322GNFkiJJ8YMf/GCtvqlTpxZdu3YtkhSTJ0+ub//xj39cJCkGDx5c/PWvf61vv+mmm4okxaBBg4oVK1a02jn8q4T5EqitrS323HPPIkmxxRZbFEcffXT9z3369Cmee+65ti7xPeXOO+8s9txzz/pPRUVFkWSttjvvvHOtbU455ZQiSdGlS5fisMMOKw488MCiY8eORYcOHYpbbrmlbU5kA3b55ZfX/6V+xBFHFCeccEKjn3nz5q21nXlqfRMnTiySFJtuumnxsY99rDjuuOOKAw44oNhiiy2KJEXnzp2LG264ocF25qrtvVOYLwrz1NrWfJ8233zz4qCDDiqOO+64Yp999ik6d+5cJCl22mmntf7RuoZ5ahsnnHBCkaSorq4uDjrooOKjH/1o/QLB5z73uQbjL7300iJJ0bFjx+LAAw8sDjvssKKqqqpIUnznO99pgzN4b/nc5z5XJCnOOOOMdxzrO9X6Tj/99Pp/++20007FUUcdVeyzzz7FRhttVCQpPv/5z681ftWqVfULPz179ixGjx5dDB8+vKioqCiqqqqKP/zhD210Ju+OMF8Sy5YtK84555xi4MCBRadOnYrNN9+8GDNmTDF37ty2Lu09Z80/lt7uM3HixEa322OPPYouXboU1dXVxahRo4oHH3yw9U/gPeDcc899xzlKUrzwwgsNtjVPrev5558vzjrrrGKfffYptthii2LjjTcuunbtWuy0007FV77ylbddaTJXbWt9wnxRmKfW9NRTTxVf/OIXiyFDhhR9+vQpOnbsWPTo0aP48Ic/XIwfP75YtmzZOrc1T61v9erVxf/+7//W/7l37dq12GuvvYqrrrpqndvcfvvtxbBhw4pu3boV3bp1K4YNG1bccccdrVj1e9Py5cuLnj17FkmKJ554Yr228Z1qfTfffHNxwAEH1F+t0rNnz+KjH/1o8fOf/7zR8StXrizGjx9f7LTTTkXnzp2L3r17F6NHjy7+8pe/tHLl/7qKoiiKpl+sDwAAALQWD8ADAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHAACAkhHmAQAAoGSEeQAAACgZYR4AAABKRpgHANbpkUceSUVFRSoqKnL++ee3dTkAwN8J8wDAOl1zzTX1//u6665rw0oAgH8mzAMAjVqxYkV+8YtfJEk233zzPPPMM3n44YfbuCoAIBHmAYB1uOeeezJ//vzss88++dKXvpRk7ZV6AKDtCPMAQKOuvfbaJMnxxx+f448/Pklyww03ZMWKFY2Onz59eg455JBUV1ene/fu2XfffTN58uT87ne/S0VFRcaMGdNgm6Iocv3112fEiBHp2bNnOnfunB122CHnnXdeli1b1mLnBgBlJ8wDAA0sWrQot99+ezp16pSjjz4622yzTfbee+/Mnz8/99xzT4PxU6dOzV577ZU777wzAwYMyMEHH5zly5dn1KhRufnmmxs9xurVq/PJT34yxx13XB599NHstttuOeigg1JTU5Nx48blox/9aGpra1v6VAGglIR5AKCBSZMmZfny5TnwwAPTq1evJKlfnf9/L7VfvXp1xowZk2XLluWiiy7KE088keuvvz6PPPJI/vd//zff/e53Gz3G+PHjc/3112f48OGZNWtWfvvb3+bmm2/Os88+mxNPPDGPPPJIxo0b17InCgAlVVEURdHWRQAA7cvw4cMzZcqU3HjjjRk9enSS5I033sgWW2yRDh065LXXXkuPHj2SJPfee2/233//DB48OE8//XQ22mjttYKPfOQjefDBB3PCCSfkqquuSpKsXLkyW2yxRWpra/Pcc8/lfe9731rb1NbWZtttt01dXV3mz5/fYJ8A8F7nv4wAwFrmzJmT+++/P9XV1TnkkEPq23v37p2DDjooy5cvz4033ljf/uCDDyZJPvGJTzQauo855pgGbdOmTcv8+fOz9957NwjySVJVVZU99tgjCxYsyKxZs5rjtABggyLMAwBrue6661IURUaPHp3Kysq1+tZcar/m4XhJ8uqrryZJ+vXr1+j++vfv36DtxRdfTJJMnjw5FRUVjX7uuuuuJMn8+fObfE4AsKHp2NYFAADty5p74n/3u9/lIx/5yFp9b731VpLk/vvvz+zZszNgwIB/6RirV69OkgwaNCj77LPP247t3bv3v3QMANiQCfMAQL3HHnssM2bMSJI8++yzefbZZxsdVxRFrrvuupx11lnZYostkiRz585tdGxj7X379k2SvP/976+/jx4AWH8uswcA6q25fP70009PURSNfn73u9+tNXbNyvott9ySxp6r+8tf/rJB24c+9KH06NEjU6ZMyZtvvtlCZwMAGy5hHgBIkqxatSrXX399kuTYY49d57hhw4Zlq622yowZM/LYY49lxIgRGTx4cGbOnJlvfetba4296qqr8sADDzTYR2VlZb761a9myZIlOfLII/P88883GPPyyy83eA0eAPA3wjwAkCT59a9/nb/+9a/ZbrvtMmTIkHWO22ijjeqfUH/NNddko402ytVXX50uXbrka1/7Wnbbbbccd9xx2XPPPfOZz3wmX/7yl5MknTp1Wms/X/va1/KpT30qU6ZMyQ477JAPf/jDOfbYY/OJT3wiH/jAB9KvX7+MHz++5U4YAEpMmAcAkvzjwXdvtyq/xpox119/fVauXJm99torDz30UA4++OC88MILuf3227Pxxhvn7rvvzl577ZWk4YPsNtpoo/zsZz/Lbbfdlv333z8vvPBCbrrppvz+979P586dc8YZZ+SnP/1pM58lAGwYKorGbm4DAGgmJ510Uq644or84he/aPSd8wDAuyfMAwBN9uabb2bx4sXZeuut12q/4YYb8slPfjLdu3fPSy+9lK5du7ZNgQCwgfFqOgCgyZ555pnstdde2WWXXbLtttsmSWbMmJGZM2emQ4cOueKKKwR5AGhGVuYBgCZ7/fXXc/755+e+++7LK6+8kpqammy66abZe++9c/rpp9ffNw8ANA9hHgAAAErG0+wBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZIR5AAAAKBlhHgAAAEpGmAcAAICSEeYBAACgZP5/2RPsOioTNwAAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'png'\n", @@ -202,14 +433,6 @@ "plt.show()\n" ] }, - { - "cell_type": "markdown", - "id": "58763f84", - "metadata": {}, - "source": [ - "![](./data/1-1-年龄分布.jpg)" - ] - }, { "cell_type": "markdown", "id": "25ab6193", @@ -220,10 +443,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "e8a0979c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'png'\n", @@ -237,14 +471,6 @@ "plt.show()" ] }, - { - "cell_type": "markdown", - "id": "23dab310", - "metadata": {}, - "source": [ - "![](./data/1-1-年龄相关性.jpg)" - ] - }, { "cell_type": "markdown", "id": "d4ba0563", @@ -255,10 +481,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "a28dcead", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x_train.shape = (712, 15)\n", + "x_test.shape = (179, 15)\n", + "y_train.shape = (712, 1)\n", + "y_test.shape = (179, 1)\n" + ] + } + ], "source": [ "def preprocessing(dfdata):\n", "\n", @@ -305,19 +542,6 @@ "print(\"y_test.shape =\", y_test.shape )\n" ] }, - { - "cell_type": "markdown", - "id": "e96d2734", - "metadata": {}, - "source": [ - "```\n", - "x_train.shape = (712, 15)\n", - "x_test.shape = (179, 15)\n", - "y_train.shape = (712, 1)\n", - "y_test.shape = (179, 1)\n", - "```" - ] - }, { "cell_type": "markdown", "id": "95d6c6d9", @@ -328,7 +552,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "1d744935", "metadata": {}, "outputs": [], @@ -341,10 +565,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "5ec2fc6b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[ 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 21.0000, 0.0000, 1.0000,\n", + " 0.0000, 9.8250, 1.0000, 0.0000, 0.0000, 1.0000, 0.0000],\n", + " [ 1.0000, 0.0000, 0.0000, 1.0000, 0.0000, 0.0000, 1.0000, 1.0000,\n", + " 0.0000, 89.1042, 0.0000, 1.0000, 0.0000, 0.0000, 0.0000],\n", + " [ 1.0000, 0.0000, 0.0000, 0.0000, 1.0000, 65.0000, 0.0000, 0.0000,\n", + " 1.0000, 61.9792, 0.0000, 1.0000, 0.0000, 0.0000, 0.0000],\n", + " [ 1.0000, 0.0000, 0.0000, 1.0000, 0.0000, 36.0000, 0.0000, 0.0000,\n", + " 2.0000, 71.0000, 0.0000, 0.0000, 0.0000, 1.0000, 0.0000],\n", + " [ 0.0000, 1.0000, 0.0000, 1.0000, 0.0000, 32.5000, 0.0000, 0.0000,\n", + " 0.0000, 13.0000, 0.0000, 0.0000, 0.0000, 1.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 45.0000, 0.0000, 0.0000,\n", + " 0.0000, 7.7500, 1.0000, 0.0000, 0.0000, 1.0000, 0.0000],\n", + " [ 0.0000, 0.0000, 1.0000, 1.0000, 0.0000, 0.0000, 1.0000, 2.0000,\n", + " 0.0000, 23.2500, 1.0000, 0.0000, 1.0000, 0.0000, 0.0000],\n", + " [ 1.0000, 0.0000, 0.0000, 0.0000, 1.0000, 80.0000, 0.0000, 0.0000,\n", + " 0.0000, 30.0000, 0.0000, 0.0000, 0.0000, 1.0000, 0.0000]]) tensor([[0.],\n", + " [1.],\n", + " [0.],\n", + " [1.],\n", + " [1.],\n", + " [0.],\n", + " [1.],\n", + " [1.]])\n" + ] + } + ], "source": [ "# 测试数据管道\n", "for features,labels in dl_train:\n", @@ -352,46 +606,6 @@ " break" ] }, - { - "cell_type": "markdown", - "id": "8cbd018d", - "metadata": {}, - "source": [ - "```\n", - "tensor([[ 0.0000, 0.0000, 1.0000, 0.0000, 1.0000, 0.0000, 1.0000,\n", - " 0.0000, 0.0000, 7.8958, 1.0000, 0.0000, 0.0000, 1.0000,\n", - " 0.0000],\n", - " [ 1.0000, 0.0000, 0.0000, 0.0000, 1.0000, 0.0000, 1.0000,\n", - " 0.0000, 0.0000, 30.5000, 0.0000, 0.0000, 0.0000, 1.0000,\n", - " 0.0000],\n", - " [ 1.0000, 0.0000, 0.0000, 1.0000, 0.0000, 31.0000, 0.0000,\n", - " 1.0000, 0.0000, 113.2750, 0.0000, 1.0000, 0.0000, 0.0000,\n", - " 0.0000],\n", - " [ 1.0000, 0.0000, 0.0000, 0.0000, 1.0000, 60.0000, 0.0000,\n", - " 0.0000, 0.0000, 26.5500, 1.0000, 0.0000, 0.0000, 1.0000,\n", - " 0.0000],\n", - " [ 0.0000, 0.0000, 1.0000, 0.0000, 1.0000, 28.0000, 0.0000,\n", - " 0.0000, 0.0000, 22.5250, 1.0000, 0.0000, 0.0000, 1.0000,\n", - " 0.0000],\n", - " [ 0.0000, 0.0000, 1.0000, 0.0000, 1.0000, 32.0000, 0.0000,\n", - " 0.0000, 0.0000, 8.3625, 1.0000, 0.0000, 0.0000, 1.0000,\n", - " 0.0000],\n", - " [ 0.0000, 1.0000, 0.0000, 1.0000, 0.0000, 28.0000, 0.0000,\n", - " 0.0000, 0.0000, 13.0000, 1.0000, 0.0000, 0.0000, 1.0000,\n", - " 0.0000],\n", - " [ 1.0000, 0.0000, 0.0000, 0.0000, 1.0000, 36.0000, 0.0000,\n", - " 0.0000, 1.0000, 512.3292, 0.0000, 1.0000, 0.0000, 0.0000,\n", - " 0.0000]]) tensor([[0.],\n", - " [1.],\n", - " [1.],\n", - " [0.],\n", - " [0.],\n", - " [0.],\n", - " [1.],\n", - " [1.]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -420,10 +634,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "617186ef", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (linear1): Linear(in_features=15, out_features=20, bias=True)\n", + " (relu1): ReLU()\n", + " (linear2): Linear(in_features=20, out_features=15, bias=True)\n", + " (relu2): ReLU()\n", + " (linear3): Linear(in_features=15, out_features=1, bias=True)\n", + ")\n" + ] + } + ], "source": [ "def create_net():\n", " net = nn.Sequential()\n", @@ -472,10 +700,181 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "87c5039d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 11:48:14\n", + "Epoch 1 / 20\n", + "\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 661.90it/s, train_acc=0.654, train_loss=0.65]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1108.37it/s, val_acc=0.698, val_loss=0.584]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.6983240246772766 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "================================================================================2023-08-02 11:48:14\n", + "Epoch 2 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 761.63it/s, train_acc=0.718, train_loss=0.574]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 918.43it/s, val_acc=0.749, val_loss=0.482]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.748603343963623 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 11:48:14\n", + "Epoch 3 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 816.67it/s, train_acc=0.788, train_loss=0.513]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1031.02it/s, val_acc=0.765, val_loss=0.478]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.7653631567955017 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 11:48:14\n", + "Epoch 4 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 783.66it/s, train_acc=0.795, train_loss=0.508]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1012.42it/s, val_acc=0.777, val_loss=0.416]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.7765362858772278 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 11:48:14\n", + "Epoch 5 / 20\n", + "\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 792.31it/s, train_acc=0.778, train_loss=0.5]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 849.80it/s, val_acc=0.793, val_loss=0.454]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.7932960987091064 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 11:48:14\n", + "Epoch 6 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 816.62it/s, train_acc=0.792, train_loss=0.466]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1071.58it/s, val_acc=0.793, val_loss=0.48]\n", + "\n", + "================================================================================2023-08-02 11:48:15\n", + "Epoch 7 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 799.33it/s, train_acc=0.791, train_loss=0.486]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1063.58it/s, val_acc=0.782, val_loss=0.441]\n", + "\n", + "================================================================================2023-08-02 11:48:15\n", + "Epoch 8 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 724.34it/s, train_acc=0.789, train_loss=0.466]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1211.66it/s, val_acc=0.81, val_loss=0.433]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.8100558519363403 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 11:48:15\n", + "Epoch 9 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 742.96it/s, train_acc=0.787, train_loss=0.473]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 891.92it/s, val_acc=0.782, val_loss=0.438]\n", + "\n", + "================================================================================2023-08-02 11:48:15\n", + "Epoch 10 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 780.30it/s, train_acc=0.812, train_loss=0.463]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1006.84it/s, val_acc=0.782, val_loss=0.418]\n", + "\n", + "================================================================================2023-08-02 11:48:15\n", + "Epoch 11 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 823.80it/s, train_acc=0.788, train_loss=0.466]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1130.61it/s, val_acc=0.782, val_loss=0.477]\n", + "\n", + "================================================================================2023-08-02 11:48:15\n", + "Epoch 12 / 20\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 803.21it/s, train_acc=0.791, train_loss=0.463]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1183.49it/s, val_acc=0.777, val_loss=0.468]\n", + "\n", + "================================================================================2023-08-02 11:48:15\n", + "Epoch 13 / 20\n", + "\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 89/89 [00:00<00:00, 817.11it/s, train_acc=0.795, train_loss=0.46]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 23/23 [00:00<00:00, 1159.69it/s, val_acc=0.788, val_loss=0.469]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< val_acc without improvement in 5 epoch, early stopping >>>>>>\n" + ] + } + ], "source": [ "import os,sys,time\n", "import numpy as np\n", @@ -517,7 +916,7 @@ " \n", " total_loss,step = 0,0\n", " \n", - " loop = tqdm(enumerate(dl_train), total =len(dl_train))\n", + " loop = tqdm(enumerate(dl_train), total =len(dl_train),file = sys.stdout)\n", " train_metrics_dict = deepcopy(metrics_dict) \n", " \n", " for i, batch in loop: \n", @@ -561,7 +960,7 @@ " net.eval()\n", " \n", " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dl_val), total =len(dl_val))\n", + " loop = tqdm(enumerate(dl_val), total =len(dl_val),file = sys.stdout)\n", " \n", " val_metrics_dict = deepcopy(metrics_dict) \n", " \n", @@ -614,81 +1013,6 @@ "dfhistory = pd.DataFrame(history)\n" ] }, - { - "cell_type": "markdown", - "id": "7b038d69", - "metadata": {}, - "source": [ - "```\n", - "================================================================================2022-07-10 21:55:18\n", - "Epoch 1 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 192.16it/s, train_acc=0.664, train_loss=0.646]\n", - "100%|██████████| 23/23 [00:00<00:00, 252.37it/s, val_acc=0.721, val_loss=0.571]\n", - "<<<<<< reach best val_acc : 0.7206704020500183 >>>>>>\n", - "\n", - "================================================================================2022-07-10 21:55:19\n", - "Epoch 2 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 212.44it/s, train_acc=0.725, train_loss=0.576]\n", - "100%|██████████| 23/23 [00:00<00:00, 183.68it/s, val_acc=0.726, val_loss=0.503]\n", - "<<<<<< reach best val_acc : 0.7262569665908813 >>>>>>\n", - "\n", - "================================================================================2022-07-10 21:55:19\n", - "Epoch 3 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 128.57it/s, train_acc=0.772, train_loss=0.517]\n", - "100%|██████████| 23/23 [00:00<00:00, 195.21it/s, val_acc=0.782, val_loss=0.445]\n", - "<<<<<< reach best val_acc : 0.7821229100227356 >>>>>>\n", - "\n", - "================================================================================2022-07-10 21:55:20\n", - "Epoch 4 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 139.91it/s, train_acc=0.784, train_loss=0.495]\n", - "100%|██████████| 23/23 [00:00<00:00, 281.71it/s, val_acc=0.793, val_loss=0.435]\n", - "<<<<<< reach best val_acc : 0.7932960987091064 >>>>>>\n", - "\n", - "================================================================================2022-07-10 21:55:21\n", - "Epoch 5 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 216.33it/s, train_acc=0.788, train_loss=0.493]\n", - "100%|██████████| 23/23 [00:00<00:00, 246.54it/s, val_acc=0.81, val_loss=0.409]\n", - "<<<<<< reach best val_acc : 0.8100558519363403 >>>>>>\n", - "\n", - "================================================================================2022-07-10 21:55:21\n", - "Epoch 6 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 191.69it/s, train_acc=0.765, train_loss=0.481]\n", - "100%|██████████| 23/23 [00:00<00:00, 251.35it/s, val_acc=0.777, val_loss=0.436]\n", - "\n", - "================================================================================2022-07-10 21:55:22\n", - "Epoch 7 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 192.42it/s, train_acc=0.781, train_loss=0.493]\n", - "100%|██████████| 23/23 [00:00<00:00, 241.61it/s, val_acc=0.771, val_loss=0.462]\n", - "\n", - "================================================================================2022-07-10 21:55:22\n", - "Epoch 8 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 211.52it/s, train_acc=0.801, train_loss=0.475]\n", - "100%|██████████| 23/23 [00:00<00:00, 263.07it/s, val_acc=0.793, val_loss=0.406]\n", - "\n", - "================================================================================2022-07-10 21:55:23\n", - "Epoch 9 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 199.20it/s, train_acc=0.798, train_loss=0.444]\n", - "100%|██████████| 23/23 [00:00<00:00, 265.92it/s, val_acc=0.782, val_loss=0.43]\n", - "\n", - "================================================================================2022-07-10 21:55:23\n", - "Epoch 10 / 20\n", - "\n", - "100%|██████████| 89/89 [00:00<00:00, 193.12it/s, train_acc=0.81, train_loss=0.445] \n", - "100%|██████████| 23/23 [00:00<00:00, 259.94it/s, val_acc=0.771, val_loss=0.506]\n", - "<<<<<< val_acc without improvement in 5 epoch, early stopping >>>>>>\n", - "\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -723,25 +1047,221 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 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train_losstrain_accval_lossval_accepoch
00.6531460.6629210.5896800.6815641
10.5952000.7008430.5237220.7597772
20.5316010.7584270.4932270.7653633
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_metric(dfhistory,\"acc\")" ] }, - { - "cell_type": "markdown", - "id": "d9263b7b", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h426dvo2upj20fy0a9t92.jpg)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -826,10 +3459,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "da16f9ab", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.0487],\n", + " [0.5014],\n", + " [0.2651],\n", + " [0.9025],\n", + " [0.4703],\n", + " [0.9044],\n", + " [0.0710],\n", + " [0.9568],\n", + " [0.4578],\n", + " [0.1043]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#预测概率\n", "\n", @@ -837,31 +3490,32 @@ "y_pred_probs" ] }, - { - "cell_type": "markdown", - "id": "bde97226", - "metadata": {}, - "source": [ - "```\n", - "tensor([[0.1146],\n", - " [0.6517],\n", - " [0.4307],\n", - " [0.8692],\n", - " [0.5542],\n", - " [0.7894],\n", - " [0.1096],\n", - " [0.7125],\n", - " [0.6027],\n", - " [0.1139]])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "a31cb281", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.],\n", + " [1.],\n", + " [0.],\n", + " [1.],\n", + " [0.],\n", + " [1.],\n", + " [0.],\n", + " [1.],\n", + " [0.],\n", + " [0.]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#预测类别\n", "y_pred = torch.where(y_pred_probs>0.5,\n", @@ -869,25 +3523,6 @@ "y_pred" ] }, - { - "cell_type": "markdown", - "id": "574bdfb5", - "metadata": {}, - "source": [ - "```\n", - "tensor([[0.],\n", - " [1.],\n", - " [0.],\n", - " [1.],\n", - " [1.],\n", - " [1.],\n", - " [0.],\n", - " [1.],\n", - " [1.],\n", - " [0.]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -928,30 +3563,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "e6098000", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "odict_keys(['linear1.weight', 'linear1.bias', 'linear2.weight', 'linear2.bias', 'linear3.weight', 'linear3.bias'])\n" + ] + } + ], "source": [ - "print(net.state_dict().keys())" - ] - }, - { - "cell_type": "markdown", - "id": "bb2c5f22", - "metadata": {}, - "source": [ - "```\n", - "odict_keys(['linear1.weight', 'linear1.bias', 'linear2.weight', 'linear2.bias', 'linear3.weight', 'linear3.bias'])\n", - "```" + "print(net.state_dict().keys())\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "4cfa68ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.0487],\n", + " [0.5014],\n", + " [0.2651],\n", + " [0.9025],\n", + " [0.4703],\n", + " [0.9044],\n", + " [0.0710],\n", + " [0.9568],\n", + " [0.4578],\n", + " [0.1043]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 保存模型参数\n", "\n", @@ -963,25 +3616,6 @@ "torch.sigmoid(net_clone.forward(torch.tensor(x_test[0:10]).float())).data\n" ] }, - { - "cell_type": "markdown", - "id": "47260e15", - "metadata": {}, - "source": [ - "```\n", - "tensor([[0.1146],\n", - " [0.6517],\n", - " [0.4307],\n", - " [0.8692],\n", - " [0.5542],\n", - " [0.7894],\n", - " [0.1096],\n", - " [0.7125],\n", - " [0.6027],\n", - " [0.1139]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1000,44 +3634,36 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "4c969c33", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.0487],\n", + " [0.5014],\n", + " [0.2651],\n", + " [0.9025],\n", + " [0.4703],\n", + " [0.9044],\n", + " [0.0710],\n", + " [0.9568],\n", + " [0.4578],\n", + " [0.1043]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "\n", "torch.save(net, './data/net_model.pt')\n", "net_loaded = torch.load('./data/net_model.pt')\n", "torch.sigmoid(net_loaded(torch.tensor(x_test[0:10]).float())).data\n" ] }, - { - "cell_type": "markdown", - "id": "dbb6ab5e", - "metadata": {}, - "source": [ - "```\n", - "tensor([[0.1146],\n", - " [0.6517],\n", - " [0.4307],\n", - " [0.8692],\n", - " [0.5542],\n", - " [0.7894],\n", - " [0.1096],\n", - " [0.7125],\n", - " [0.6027],\n", - " [0.1139]])\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2f7a2e7d", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "52eacb75", @@ -1064,11 +3690,10 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,md", - "main_language": "python" + "formats": "ipynb,md" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1082,7 +3707,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/1-2,\345\233\276\347\211\207\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" "b/1-2,\345\233\276\347\211\207\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" index 0c30e8aea..9cc94295f 100644 --- "a/1-2,\345\233\276\347\211\207\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" +++ "b/1-2,\345\233\276\347\211\207\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" @@ -10,18 +10,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "d66df564", "metadata": {}, "outputs": [], "source": [ "import os\n", - "import datetime\n", - "\n", - "#打印时间\n", - "def printbar():\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", "\n", "#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量\n", "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n" @@ -34,37 +28,37 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install torchvison==0.11.2\n", - "!pip install torchkeras==3.2.3" + "!pip install torchvison==0.15.2\n", + "!pip install torchmetrics " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "0ecd187d", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__ = 2.0.1\n", + "torchvision.__version__ = 0.15.2\n", + "torchkeras.__version__ = 3.9.3\n", + "torchmetrics.__version__ = 0.11.0\n" + ] + } + ], "source": [ "import torch \n", "import torchvision \n", "import torchkeras \n", "print(\"torch.__version__ = \", torch.__version__)\n", "print(\"torchvision.__version__ = \", torchvision.__version__) \n", - "print(\"torchkeras.__version__ = \", torchkeras.__version__) " - ] - }, - { - "cell_type": "markdown", - "id": "75f8960e", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__ = 1.10.0\n", - "torchvision.__version__ = 0.11.2\n", - "torchkeras.__version__ = 3.2.3\n", - "```" + "print(\"torchkeras.__version__ = \", torchkeras.__version__) \n", + "print(\"torchmetrics.__version__ = \", torchmetrics.__version__) " ] }, { @@ -105,14 +99,6 @@ "![](./data/cifar2.jpg)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "e5d38f2e", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "16ecef43", @@ -131,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "61535b2e", "metadata": {}, "outputs": [], @@ -145,7 +131,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "a242a652", "metadata": {}, "outputs": [], @@ -159,10 +145,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "53f01ce9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'0_airplane': 0, '1_automobile': 1}\n" + ] + } + ], "source": [ "ds_train = datasets.ImageFolder(\"./eat_pytorch_datasets/cifar2/train/\",\n", " transform = transform_img,target_transform = transform_label)\n", @@ -171,19 +165,9 @@ "print(ds_train.class_to_idx)\n" ] }, - { - "cell_type": "markdown", - "id": "70f47187", - "metadata": {}, - "source": [ - "```\n", - "{'0_airplane': 0, '1_automobile': 1}\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "0aec4c82", "metadata": {}, "outputs": [], @@ -194,10 +178,661 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "de412f15", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T11:49:32.917393\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", @@ -227,10 +862,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "367ccf3b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([50, 3, 32, 32]) torch.Size([50, 1])\n" + ] + } + ], "source": [ "# Pytorch的图片默认顺序是 Batch,Channel,Width,Height\n", "for features,labels in dl_train:\n", @@ -239,16 +882,6 @@ " " ] }, - { - "cell_type": "markdown", - "id": "0dfed26b", - "metadata": {}, - "source": [ - "```\n", - "torch.Size([50, 3, 32, 32]) torch.Size([50, 1])\n", - "```" - ] - }, { "cell_type": "markdown", "id": "8c614279", @@ -269,10 +902,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "dde64c23", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "torch.Size([10, 8, 1, 1])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#测试AdaptiveMaxPool2d的效果\n", "pool = nn.AdaptiveMaxPool2d((1,1))\n", @@ -280,22 +924,30 @@ "pool(t).shape " ] }, - { - "cell_type": "markdown", - "id": "87369fff", - "metadata": {}, - "source": [ - "```\n", - "torch.Size([10, 8, 1, 1])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "39d23d45", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (dropout): Dropout2d(p=0.1, inplace=False)\n", + " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (flatten): Flatten(start_dim=1, end_dim=-1)\n", + " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", + " (relu): ReLU()\n", + " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], "source": [ "class Net(nn.Module):\n", " \n", @@ -328,70 +980,47 @@ "print(net)\n" ] }, - { - "cell_type": "markdown", - "id": "b0dcc7ce", - "metadata": {}, - "source": [ - "```\n", - "Net(\n", - " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (dropout): Dropout2d(p=0.1, inplace=False)\n", - " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (flatten): Flatten(start_dim=1, end_dim=-1)\n", - " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", - " (relu): ReLU()\n", - " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", - ")\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "98f6d94d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Conv2d-1 [-1, 32, 30, 30] 896\n", + "MaxPool2d-2 [-1, 32, 15, 15] 0\n", + "Conv2d-3 [-1, 64, 11, 11] 51,264\n", + "MaxPool2d-4 [-1, 64, 5, 5] 0\n", + "Dropout2d-5 [-1, 64, 5, 5] 0\n", + "AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0\n", + "Flatten-7 [-1, 64] 0\n", + "Linear-8 [-1, 32] 2,080\n", + "ReLU-9 [-1, 32] 0\n", + "Linear-10 [-1, 1] 33\n", + "==========================================================================\n", + "Total params: 54,273\n", + "Trainable params: 54,273\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000069\n", + "Forward/backward pass size (MB): 0.359627\n", + "Params size (MB): 0.207035\n", + "Estimated Total Size (MB): 0.566730\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "import torchkeras\n", "torchkeras.summary(net,input_data = features);" ] }, - { - "cell_type": "markdown", - "id": "8420cd5c", - "metadata": {}, - "source": [ - "```\n", - "--------------------------------------------------------------------------\n", - "Layer (type) Output Shape Param #\n", - "==========================================================================\n", - "Conv2d-1 [-1, 32, 30, 30] 896\n", - "MaxPool2d-2 [-1, 32, 15, 15] 0\n", - "Conv2d-3 [-1, 64, 11, 11] 51,264\n", - "MaxPool2d-4 [-1, 64, 5, 5] 0\n", - "Dropout2d-5 [-1, 64, 5, 5] 0\n", - "AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0\n", - "Flatten-7 [-1, 64] 0\n", - "Linear-8 [-1, 32] 2,080\n", - "ReLU-9 [-1, 32] 0\n", - "Linear-10 [-1, 1] 33\n", - "Net-11 [-1, 1] 54,273\n", - "==========================================================================\n", - "Total params: 108,546\n", - "Trainable params: 108,546\n", - "Non-trainable params: 0\n", - "--------------------------------------------------------------------------\n", - "Input size (MB): 0.000069\n", - "Forward/backward pass size (MB): 0.359634\n", - "Params size (MB): 0.414070\n", - "Estimated Total Size (MB): 0.773773\n", - "--------------------------------------------------------------------------\n", - "```" - ] - }, { "cell_type": "markdown", "id": "a141ab6f", @@ -418,7 +1047,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "5beafb9e", "metadata": {}, "outputs": [], @@ -484,7 +1113,7 @@ " \n", " def __call__(self,dataloader):\n", " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dataloader), total =len(dataloader))\n", + " loop = tqdm(enumerate(dataloader),total =len(dataloader),file = sys.stdout)\n", " for i, batch in loop: \n", " loss, step_metrics = self.steprunner(*batch)\n", " step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n", @@ -553,18 +1182,166 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "62192136", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "76a95080", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 11:52:19\n", + "Epoch 1 / 10\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:07<00:00, 27.67it/s, train_acc=0.739, train_loss=0.53]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 56.70it/s, val_acc=0.777, val_loss=0.442]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.7774999737739563 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "================================================================================2023-08-02 11:52:27\n", + "Epoch 2 / 10\n", + "\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:06<00:00, 31.16it/s, train_acc=0.838, train_loss=0.371]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 71.84it/s, val_acc=0.878, val_loss=0.302]\n", + "\n", + "================================================================================2023-08-02 11:52:34\n", + "Epoch 3 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.8784999847412109 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:06<00:00, 32.75it/s, train_acc=0.882, train_loss=0.29]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 63.77it/s, val_acc=0.905, val_loss=0.23]\n", + "\n", + "================================================================================2023-08-02 11:52:41\n", + "Epoch 4 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.9045000076293945 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:05<00:00, 33.38it/s, train_acc=0.9, train_loss=0.245]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 74.00it/s, val_acc=0.91, val_loss=0.23]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.9100000262260437 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "================================================================================2023-08-02 11:52:48\n", + "Epoch 5 / 10\n", + "\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:05<00:00, 33.56it/s, train_acc=0.908, train_loss=0.229]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 74.86it/s, val_acc=0.898, val_loss=0.247]\n", + "\n", + "================================================================================2023-08-02 11:52:54\n", + "Epoch 6 / 10\n", + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:06<00:00, 32.83it/s, train_acc=0.909, train_loss=0.22]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 74.57it/s, val_acc=0.888, val_loss=0.269]\n", + "\n", + "================================================================================2023-08-02 11:53:01\n", + "Epoch 7 / 10\n", + "\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:06<00:00, 32.27it/s, train_acc=0.915, train_loss=0.213]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 70.35it/s, val_acc=0.916, val_loss=0.204]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.9160000085830688 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "================================================================================2023-08-02 11:53:08\n", + "Epoch 8 / 10\n", + "\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:06<00:00, 31.96it/s, train_acc=0.911, train_loss=0.217]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 69.81it/s, val_acc=0.918, val_loss=0.213]" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.9179999828338623 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "================================================================================2023-08-02 11:53:14\n", + "Epoch 9 / 10\n", + "\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:06<00:00, 32.45it/s, train_acc=0.927, train_loss=0.185]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 72.63it/s, val_acc=0.916, val_loss=0.211]\n", + "\n", + "================================================================================2023-08-02 11:53:21\n", + "Epoch 10 / 10\n", + "\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 200/200 [00:06<00:00, 32.46it/s, train_acc=0.926, train_loss=0.187]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 40/40 [00:00<00:00, 70.51it/s, val_acc=0.925, val_loss=0.207]\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "<<<<<< reach best val_acc : 0.9254999756813049 >>>>>>\n" + ] + } + ], "source": [ "import torchmetrics \n", "\n", @@ -581,7 +1358,7 @@ " \n", "loss_fn = nn.BCEWithLogitsLoss()\n", "optimizer= torch.optim.Adam(net.parameters(),lr = 0.01) \n", - "metrics_dict = {\"acc\":Accuracy()}\n", + "metrics_dict = {\"acc\":Accuracy(task='binary')}\n", "\n", "dfhistory = train_model(net,\n", " optimizer,\n", @@ -595,80 +1372,6 @@ " mode=\"max\")\n" ] }, - { - "cell_type": "markdown", - "id": "bac6747f", - "metadata": {}, - "source": [ - "```\n", - "================================================================================2022-07-10 20:06:16\n", - "Epoch 1 / 10\n", - "\n", - "100%|██████████| 200/200 [00:17<00:00, 11.74it/s, train_acc=0.735, train_loss=0.53]\n", - "100%|██████████| 40/40 [00:01<00:00, 20.07it/s, val_acc=0.827, val_loss=0.383]\n", - "<<<<<< reach best val_acc : 0.8274999856948853 >>>>>>\n", - "\n", - "================================================================================2022-07-10 20:06:35\n", - "Epoch 2 / 10\n", - "\n", - "100%|██████████| 200/200 [00:16<00:00, 11.96it/s, train_acc=0.832, train_loss=0.391]\n", - "100%|██████████| 40/40 [00:02<00:00, 18.13it/s, val_acc=0.854, val_loss=0.317]\n", - "<<<<<< reach best val_acc : 0.8544999957084656 >>>>>>\n", - "\n", - "================================================================================2022-07-10 20:06:54\n", - "Epoch 3 / 10\n", - "\n", - "100%|██████████| 200/200 [00:17<00:00, 11.71it/s, train_acc=0.87, train_loss=0.313]\n", - "100%|██████████| 40/40 [00:02<00:00, 19.96it/s, val_acc=0.902, val_loss=0.239]\n", - "<<<<<< reach best val_acc : 0.9024999737739563 >>>>>>\n", - "\n", - "================================================================================2022-07-10 20:07:13\n", - "Epoch 4 / 10\n", - "\n", - "100%|██████████| 200/200 [00:16<00:00, 11.88it/s, train_acc=0.889, train_loss=0.265]\n", - "100%|██████████| 40/40 [00:02<00:00, 18.46it/s, val_acc=0.91, val_loss=0.216]\n", - "<<<<<< reach best val_acc : 0.9100000262260437 >>>>>>\n", - "\n", - "================================================================================2022-07-10 20:07:32\n", - "Epoch 5 / 10\n", - "\n", - "100%|██████████| 200/200 [00:17<00:00, 11.71it/s, train_acc=0.902, train_loss=0.239]\n", - "100%|██████████| 40/40 [00:02<00:00, 19.68it/s, val_acc=0.891, val_loss=0.279]\n", - "\n", - "================================================================================2022-07-10 20:07:51\n", - "Epoch 6 / 10\n", - "\n", - "100%|██████████| 200/200 [00:17<00:00, 11.75it/s, train_acc=0.915, train_loss=0.212]\n", - "100%|██████████| 40/40 [00:02<00:00, 19.52it/s, val_acc=0.908, val_loss=0.222]\n", - "\n", - "================================================================================2022-07-10 20:08:10\n", - "Epoch 7 / 10\n", - "\n", - "100%|██████████| 200/200 [00:16<00:00, 11.79it/s, train_acc=0.921, train_loss=0.196]\n", - "100%|██████████| 40/40 [00:02<00:00, 19.26it/s, val_acc=0.929, val_loss=0.187]\n", - "<<<<<< reach best val_acc : 0.9294999837875366 >>>>>>\n", - "\n", - "================================================================================2022-07-10 20:08:29\n", - "Epoch 8 / 10\n", - "\n", - "100%|██████████| 200/200 [00:17<00:00, 11.59it/s, train_acc=0.931, train_loss=0.175]\n", - "100%|██████████| 40/40 [00:02<00:00, 19.91it/s, val_acc=0.938, val_loss=0.187]\n", - "<<<<<< reach best val_acc : 0.9375 >>>>>>\n", - "\n", - "================================================================================2022-07-10 20:08:49\n", - "Epoch 9 / 10\n", - "\n", - "100%|██████████| 200/200 [00:17<00:00, 11.68it/s, train_acc=0.929, train_loss=0.178]\n", - "100%|██████████| 40/40 [00:02<00:00, 19.90it/s, val_acc=0.937, val_loss=0.181]\n", - "\n", - "================================================================================2022-07-10 20:09:08\n", - "Epoch 10 / 10\n", - "\n", - "100%|██████████| 200/200 [00:16<00:00, 11.84it/s, train_acc=0.937, train_loss=0.16] \n", - "100%|██████████| 40/40 [00:02<00:00, 19.91it/s, val_acc=0.937, val_loss=0.167]\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -687,34 +1390,146 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "1ead5148", "metadata": {}, 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train_losstrain_accval_lossval_accepoch
00.5297000.73950.4415780.77751
10.3714280.83770.3017060.87852
20.2895710.88160.2300520.90453
30.2449450.90020.2296610.91004
40.2286570.90790.2467430.89805
50.2201910.90940.2693750.88806
60.2129870.91480.2035960.91607
70.2171820.91070.2125650.91808
80.1848430.92730.2107200.91609
90.1870240.92610.2070670.925510
\n", + "
" + ], + "text/plain": [ + " train_loss train_acc val_loss val_acc epoch\n", + "0 0.529700 0.7395 0.441578 0.7775 1\n", + "1 0.371428 0.8377 0.301706 0.8785 2\n", + "2 0.289571 0.8816 0.230052 0.9045 3\n", + "3 0.244945 0.9002 0.229661 0.9100 4\n", + "4 0.228657 0.9079 0.246743 0.8980 5\n", + "5 0.220191 0.9094 0.269375 0.8880 6\n", + "6 0.212987 0.9148 0.203596 0.9160 7\n", + "7 0.217182 0.9107 0.212565 0.9180 8\n", + "8 0.184843 0.9273 0.210720 0.9160 9\n", + "9 0.187024 0.9261 0.207067 0.9255 10" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dfhistory " ] }, - { - "cell_type": "markdown", - "id": "6b0e1fde", - "metadata": {}, - "source": [ - "```\n", - "train_loss\ttrain_acc\tval_loss\tval_acc\tepoch\n", - "0\t0.761911\t0.6896\t0.503468\t0.765\t1\n", - "1\t0.500893\t0.7627\t0.403210\t0.830\t2\n", - "2\t0.417750\t0.8128\t0.328020\t0.870\t3\n", - "3\t0.366155\t0.8444\t0.370906\t0.814\t4\n", - "4\t0.364717\t0.8428\t0.290701\t0.876\t5\n", - "5\t0.610342\t0.6406\t0.693153\t0.500\t6\n", - "6\t0.693610\t0.4976\t0.693386\t0.500\t7\n", - "7\t0.693578\t0.5046\t0.693815\t0.500\t8\n", - "8\t0.693735\t0.4988\t0.693718\t0.500\t9\n", - "9\t0.693681\t0.4960\t0.694350\t0.500\t10\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -725,7 +1540,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "d17bb6de", "metadata": {}, "outputs": [], @@ -750,40 +1565,2106 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "9350e7e2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T11:53:41.059397\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_metric(dfhistory,\"loss\")" ] }, - { - "cell_type": "markdown", - "id": "eb3f085a", - "metadata": {}, - "source": [ - "![](./data/1-2-loss曲线.png)" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "id": "d790db14", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T11:53:46.464195\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_metric(dfhistory,\"acc\")" ] }, - { - "cell_type": "markdown", - "id": "022747c2", - "metadata": {}, - "source": [ - "![](./data/1-2-auc曲线.png)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -802,7 +3683,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "id": "49191373", "metadata": {}, "outputs": [], @@ -816,38 +3697,56 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "a2df2bf7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.2046],\n", + " [0.0154],\n", + " [0.0424],\n", + " ...,\n", + " [0.9893],\n", + " [0.9854],\n", + " [0.4706]])" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#预测概率\n", "y_pred_probs = predict(net,dl_val)\n", "y_pred_probs" ] }, - { - "cell_type": "markdown", - "id": "11793bc5", - "metadata": {}, - "source": [ - "```\n", - "tensor([[3.6409e-03],\n", - " [3.1401e-05],\n", - " [1.4732e-02],\n", - " ...,\n", - " [9.6308e-01],\n", - " [9.9835e-01],\n", - " [7.8825e-01]])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "60b72dac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.],\n", + " [0.],\n", + " [0.],\n", + " ...,\n", + " [1.],\n", + " [1.],\n", + " [0.]])" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#预测类别\n", "y_pred = torch.where(y_pred_probs>0.5,\n", @@ -855,22 +3754,6 @@ "y_pred" ] }, - { - "cell_type": "markdown", - "id": "94c98285", - "metadata": {}, - "source": [ - "```\n", - "tensor([[0.],\n", - " [0.],\n", - " [0.],\n", - " ...,\n", - " [1.],\n", - " [1.],\n", - " [1.]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -897,30 +3780,45 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "878b235b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "odict_keys(['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias', 'linear1.weight', 'linear1.bias', 'linear2.weight', 'linear2.bias'])\n" + ] + } + ], "source": [ "print(net.state_dict().keys())" ] }, - { - "cell_type": "markdown", - "id": "62f87cae", - "metadata": {}, - "source": [ - "```\n", - "odict_keys(['conv1.weight', 'conv1.bias', 'conv2.weight', 'conv2.bias', 'linear1.weight', 'linear1.bias', 'linear2.weight', 'linear2.bias'])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "id": "d128841f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.2046],\n", + " [0.0154],\n", + " [0.0424],\n", + " ...,\n", + " [0.9893],\n", + " [0.9854],\n", + " [0.4706]])" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 保存模型参数\n", "\n", @@ -932,22 +3830,6 @@ "predict(net_clone,dl_val)" ] }, - { - "cell_type": "markdown", - "id": "912e01c2", - "metadata": {}, - "source": [ - "```\n", - "tensor([[3.6409e-03],\n", - " [3.1401e-05],\n", - " [1.4732e-02],\n", - " ...,\n", - " [9.6308e-01],\n", - " [9.9835e-01],\n", - " [7.8825e-01]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, diff --git "a/1-3,\346\226\207\346\234\254\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" "b/1-3,\346\226\207\346\234\254\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" index ce35f975e..fd63b2cde 100644 --- "a/1-3,\346\226\207\346\234\254\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" +++ "b/1-3,\346\226\207\346\234\254\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" @@ -16,12 +16,6 @@ "outputs": [], "source": [ "import os\n", - "import datetime\n", - "\n", - "#打印时间\n", - "def printbar():\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", "\n", "#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量\n", "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n" @@ -34,37 +28,35 @@ "metadata": {}, "outputs": [], "source": [ - "!pip install torchtext==0.11.0\n", - "!pip install torchkeras==3.2.3" + "!pip install gensim \n", + "!pip install torchkeras" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "95288a6b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__ = 2.0.1\n", + "gensim.__version__ = 4.3.1\n", + "torchkeras.__version__ = 3.9.3\n" + ] + } + ], "source": [ "import torch \n", - "import torchtext \n", + "import gensim\n", "import torchkeras \n", "print(\"torch.__version__ = \", torch.__version__)\n", - "print(\"torchtext.__version__ = \", torchtext.__version__) \n", + "print(\"gensim.__version__ = \", gensim.__version__) \n", "print(\"torchkeras.__version__ = \", torchkeras.__version__) \n" ] }, - { - "cell_type": "markdown", - "id": "f27c47e0", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__ = 1.10.0\n", - "torchtext.__version__ = 0.11.0\n", - "torchkeras.__version__ = 3.2.3\n", - "```" - ] - }, { "cell_type": "markdown", "id": "47c4202e", @@ -114,7 +106,7 @@ "id": "758a8aa6", "metadata": {}, "source": [ - "在torch中预处理文本数据可以借助torchtext中的词典工具并自定义Dataset。\n", + "此处使用gensim中的词典工具并自定义Dataset。\n", "\n", "下面进行演示。\n" ] @@ -129,56 +121,55 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "1eef3f86", + "execution_count": 92, + "id": "87d5fd57-44e7-4f64-9743-673d6b090f7a", "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd \n", "import torch \n", - "from torchtext.data.utils import get_tokenizer\n", - "from torchtext.vocab import build_vocab_from_iterator\n", "\n", - "MIN_FREQ = 30 #仅考虑词频超过30的词\n", - "MAX_LEN = 200 #每个样本保留200个词的长度\n", + "MAX_LEN = 200 #每个样本保留200个词的长度\n", "BATCH_SIZE = 20 \n", "\n", "\n", "dftrain = pd.read_csv(\"./eat_pytorch_datasets/imdb/train.tsv\",sep=\"\\t\",header = None,names = [\"label\",\"text\"])\n", - "dfval = pd.read_csv(\"./eat_pytorch_datasets/imdb/test.tsv\",sep=\"\\t\",header = None,names = [\"label\",\"text\"])\n", - "\n", + "dfval = pd.read_csv(\"./eat_pytorch_datasets/imdb/test.tsv\",sep=\"\\t\",header = None,names = [\"label\",\"text\"])" + ] + }, + { + "cell_type": "code", + "execution_count": 106, + "id": "2bca1d76-7644-4d1d-b609-08523f3d8a99", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "vocab_size = 29924\n", + "[145, 77, 569, 55, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]\n" + ] + } + ], + "source": [ + "from gensim import corpora\n", + "import string\n", "\n", "#1，文本切词\n", - "tokenizer = get_tokenizer('basic_english')\n", - "\n", - "\n", - "#2，构建词典 \n", - "PAD_IDX,UNK_IDX = 0,1\n", - "special_symbols = ['','']\n", - "\n", - "def yield_tokens(dfdata):\n", - " for text in dfdata[\"text\"]:\n", - " yield tokenizer(text)\n", - " \n", - "\n", + "def textsplit(text):\n", + " translator = str.maketrans('', '', string.punctuation)\n", + " words = text.translate(translator).split(' ')\n", + " return words\n", " \n", - "vocab = build_vocab_from_iterator(\n", - " yield_tokens(dftrain),\n", - " min_freq = MIN_FREQ,\n", - " specials=special_symbols,\n", - " special_first=True)\n", - "\n", - "vocab.set_default_index(UNK_IDX)\n", - "vocab_size = len(vocab)\n", - "print(\"vocab_size =\"+str(vocab_size)) \n", - "\n", - "#查看词典前20个词\n", - "#itos: index to string\n", - "#stoi: string to index\n", - "print(\"vocab.get_itos():\\n\",vocab.get_itos()[:20])\n", - "print(\"vocab.get_stoi()['']:\\n\",vocab.get_stoi()[''])\n", - "\n", + "#2，构建词典\n", + "vocab = corpora.Dictionary((textsplit(text) for text in dftrain['text']))\n", + "vocab.filter_extremes(no_below=5,no_above=5000)\n", + "special_tokens = {'': 0, '': 1}\n", + "vocab.patch_with_special_tokens(special_tokens)\n", + "vocab_size = len(vocab.token2id) \n", + "print('vocab_size = ',vocab_size)\n", "\n", "#3，序列填充\n", "def pad(seq,max_length,pad_value=0):\n", @@ -189,9 +180,9 @@ "\n", "#4，编码转换\n", "def text_pipeline(text):\n", - " words = tokenizer(text)\n", - " tokens = vocab(words)\n", - " result = pad(tokens,MAX_LEN,PAD_IDX)\n", + " tokens = vocab.doc2idx(textsplit(text))\n", + " tokens = [x if x>0 else special_tokens[''] for x in tokens ]\n", + " result = pad(tokens,MAX_LEN,special_tokens[''])\n", " return result \n", "\n", "print(text_pipeline(\"this is an example!\")) \n" @@ -207,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 107, "id": "f8338e8d", "metadata": {}, "outputs": [], @@ -232,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 108, "id": "698296a2", "metadata": {}, "outputs": [], @@ -243,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 109, "id": "6b0e7824", "metadata": {}, "outputs": [], @@ -255,7 +246,7 @@ { "cell_type": "code", "execution_count": null, - "id": "8fe5ae7f", + "id": "551a7b66-c7bc-46fb-8bd7-e0a16de4e421", "metadata": {}, "outputs": [], "source": [] @@ -280,10 +271,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 110, "id": "15b42dc4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 110, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import torch\n", "from torch import nn \n", @@ -292,10 +294,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 111, "id": "37193036", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (embedding): Embedding(29924, 3, padding_idx=0)\n", + " (conv): Sequential(\n", + " (conv_1): Conv1d(3, 16, kernel_size=(5,), stride=(1,))\n", + " (pool_1): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (relu_1): ReLU()\n", + " (conv_2): Conv1d(16, 128, kernel_size=(2,), stride=(1,))\n", + " (pool_2): MaxPool1d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (relu_2): ReLU()\n", + " )\n", + " (dense): Sequential(\n", + " (flatten): Flatten(start_dim=1, end_dim=-1)\n", + " (linear): Linear(in_features=6144, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], "source": [ "class Net(nn.Module):\n", " \n", @@ -362,12 +386,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 112, "id": "ab57016b", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Embedding-1 [-1, 200, 3] 89,772\n", + "Conv1d-2 [-1, 16, 196] 256\n", + "MaxPool1d-3 [-1, 16, 98] 0\n", + "ReLU-4 [-1, 16, 98] 0\n", + "Conv1d-5 [-1, 128, 97] 4,224\n", + "MaxPool1d-6 [-1, 128, 48] 0\n", + "ReLU-7 [-1, 128, 48] 0\n", + "Flatten-8 [-1, 6144] 0\n", + "Linear-9 [-1, 1] 6,145\n", + "==========================================================================\n", + "Total params: 100,397\n", + "Trainable params: 100,397\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000069\n", + "Forward/backward pass size (MB): 0.287788\n", + "Params size (MB): 0.382984\n", + "Estimated Total Size (MB): 0.670841\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "from torchkeras import summary \n", "summary(net,input_data=features);\n" @@ -400,7 +453,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 113, "id": "7436987a", "metadata": {}, "outputs": [], @@ -560,42 +613,296 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 115, "id": "2900cc1e", "metadata": {}, "outputs": [], "source": [ - "import torchmetrics \n", + "from torchmetrics import Accuracy\n", "\n", - "class Accuracy(torchmetrics.Accuracy):\n", - " def __init__(self, dist_sync_on_step=False):\n", - " super().__init__(dist_sync_on_step=dist_sync_on_step)\n", - " \n", - " def update(self, preds: torch.Tensor, targets: torch.Tensor):\n", - " super().update(torch.sigmoid(preds),targets.long())\n", - " \n", - " def compute(self):\n", - " return super().compute()\n", - " \n", "net = Net() \n", "model = KerasModel(net,\n", " loss_fn = nn.BCEWithLogitsLoss(),\n", " optimizer= torch.optim.Adam(net.parameters(),lr = 0.01), \n", - " metrics_dict = {\"acc\":Accuracy()}\n", + " metrics_dict = {\"acc\":Accuracy(task='binary')}\n", " )\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 116, "id": "e8a04882", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 14:20:21\n", + "Epoch 1 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 400/400 [00:10<00:00, 39.28it/s, train_acc=0.496, train_loss=0.701]\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 51.21it/s, val_acc=0.518, val_loss=0.693]\n", + "<<<<<< reach best val_acc : 0.5180000066757202 >>>>>>\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 14:20:33\n", + "Epoch 2 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + 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train_losstrain_accval_lossval_accepoch
00.7010640.495800.6930450.51801
10.6930600.503350.6886560.58042
20.5798670.690100.4695740.78083
30.3856250.829900.4076330.81944
40.2616530.892600.3949010.83585
50.1759210.932100.4556040.82846
60.1191780.956100.5584300.82867
70.0754090.973300.6701720.82328
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" + ], + "text/plain": [ + " train_loss train_acc val_loss val_acc epoch\n", + "0 0.701064 0.49580 0.693045 0.5180 1\n", + "1 0.693060 0.50335 0.688656 0.5804 2\n", + "2 0.579867 0.69010 0.469574 0.7808 3\n", + "3 0.385625 0.82990 0.407633 0.8194 4\n", + "4 0.261653 0.89260 0.394901 0.8358 5\n", + "5 0.175921 0.93210 0.455604 0.8284 6\n", + "6 0.119178 0.95610 0.558430 0.8286 7\n", + "7 0.075409 0.97330 0.670172 0.8232 8" + ] + }, + "execution_count": 116, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.fit(dl_train,\n", " val_data=dl_val,\n", " epochs=10,\n", - " ckpt_path='checkpoint.pt',\n", + " ckpt_path='checkpoint',\n", " patience=3,\n", " monitor='val_acc',\n", " mode='max')\n" @@ -627,10 +934,124 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 117, "id": "ff9d1407", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "plot_metric(dfhistory,\"acc\")" ] }, - { - "cell_type": "markdown", - "id": "f2960e2b", - "metadata": {}, - "source": [ - "![](./data/1-3-accuracy曲线.png)" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 121, "id": "9a148942", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 100/100 [00:01<00:00, 50.26it/s, val_acc=0.836, val_loss=0.395]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.39490113019943235, 'val_acc': 0.8357999920845032}" + ] + }, + "execution_count": 121, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 评估\n", "model.evaluate(dl_val)\n" ] }, - { - "cell_type": "markdown", - "id": "b2796b76", - "metadata": {}, - "source": [ - "```\n", - "{'val_loss': 0.36953783154487607, 'val_acc': 0.848800003528595}\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -740,7 +3277,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 122, "id": "e6a67857", "metadata": {}, "outputs": [], @@ -754,31 +3291,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 123, "id": "9f916311", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.9372],\n", + " [1.0000],\n", + " [0.8672],\n", + " ...,\n", + " [0.5141],\n", + " [0.4756],\n", + " [0.9998]])" + ] + }, + "execution_count": 123, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "y_pred_probs = predict(net,dl_val)\n", "y_pred_probs" ] }, - { - "cell_type": "markdown", - "id": "9976d04f", - "metadata": {}, - "source": [ - "```\n", - "tensor([[0.5638],\n", - " [0.9990],\n", - " [0.9573],\n", - " ...,\n", - " [0.9188],\n", - " [0.8004],\n", - " [0.9998]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -797,15 +3335,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 124, "id": "fee13dc3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 124, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#模型权重已经被保存在了ckpt_path='checkpoint.pt'\n", + "#模型权重已经被保存在了ckpt_path='checkpoint.'\n", "net_clone = Net()\n", - "net_clone.load_state_dict(torch.load('checkpoint.pt'))\n", - "\n" + "net_clone.load_state_dict(torch.load('checkpoint'))\n" ] }, { @@ -828,6 +3376,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/1-4,\346\227\266\351\227\264\345\272\217\345\210\227\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" "b/1-4,\346\227\266\351\227\264\345\272\217\345\210\227\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" index 19b8d1c92..7ce6021e9 100644 --- "a/1-4,\346\227\266\351\227\264\345\272\217\345\210\227\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" +++ "b/1-4,\346\227\266\351\227\264\345\272\217\345\210\227\346\225\260\346\215\256\345\273\272\346\250\241\346\265\201\347\250\213\350\214\203\344\276\213.ipynb" @@ -30,37 +30,22 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "e5054d82", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install torch==1.10.0\n", - "!pip install pytorch_lightning==1.6.5 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "306f3ae5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__ = 2.0.1\n" + ] + } + ], "source": [ "import torch \n", - "import pytorch_lightning\n", "print(\"torch.__version__ = \", torch.__version__)\n", - "print(\"pytorch_lightning.__version__ = \", pytorch_lightning.__version__) \n" - ] - }, - { - "cell_type": "markdown", - "id": "88732a82", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__ = 1.10.0\n", - "pytorch_lightning.__version__ = 1.6.5\n", - "```" + "\n" ] }, { @@ -85,61 +70,11 @@ "outputs": [], "source": [ "import os\n", - "import datetime\n", - "import torchkeras\n", - "\n", - "#打印时间\n", - "def printbar():\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", "\n", "#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量\n", "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "603a13af", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "24772c05", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install torch==1.10.0\n", - "!pip install pytorch_lightning==1.6.5 \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1b79bb0", - "metadata": {}, - "outputs": [], - "source": [ - "import torch \n", - "import pytorch_lightning\n", - "print(\"torch.__version__ = \", torch.__version__)\n", - "print(\"pytorch_lightning.__version__ = \", pytorch_lightning.__version__) \n" - ] - }, - { - "cell_type": "markdown", - "id": "1c91d093", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__ = 1.10.0\n", - "pytorch_lightning.__version__ = 1.6.5\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -172,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "738b171a", "metadata": {}, "outputs": [], @@ -184,10 +119,1112 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "903d0408", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T14:35:25.712652\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
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" + ], + "text/plain": [ + " confirmed_num cured_num dead_num\n", + "0 457.0 4.0 16.0\n", + "1 688.0 11.0 15.0\n", + "2 769.0 2.0 24.0\n", + "3 1771.0 9.0 26.0\n", + "4 1459.0 43.0 26.0" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dfdiff.head()" ] }, - { - "cell_type": "markdown", - "id": "77134e8f", - "metadata": {}, - "source": [ - "![](./data/1-4-dfdiff.png)" - ] - }, { "cell_type": "markdown", "id": "15dc30a0", @@ -264,7 +2418,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "d654bf5d", "metadata": {}, "outputs": [], @@ -322,10 +2476,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "42466f8b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (lstm): LSTM(3, 3, num_layers=5, batch_first=True)\n", + " (linear): Linear(in_features=3, out_features=3, bias=True)\n", + " (block): Block()\n", + ")\n" + ] + } + ], "source": [ "import torch\n", "from torch import nn \n", @@ -377,41 +2543,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "e02fe265", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "LSTM-1 [-1, 8, 3] 480\n", + "Linear-2 [-1, 3] 12\n", + "Block-3 [-1, 3] 0\n", + "==========================================================================\n", + "Total params: 492\n", + "Trainable params: 492\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000069\n", + "Forward/backward pass size (MB): 0.000229\n", + "Params size (MB): 0.001877\n", + "Estimated Total Size (MB): 0.002174\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "from torchkeras import summary\n", "summary(net,input_data=features);" ] }, - { - "cell_type": "markdown", - "id": "730073d3", - "metadata": {}, - "source": [ - "```\n", - "--------------------------------------------------------------------------\n", - "Layer (type) Output Shape Param #\n", - "==========================================================================\n", - "LSTM-1 [-1, 8, 3] 480\n", - "Linear-2 [-1, 3] 12\n", - "Block-3 [-1, 3] 0\n", - "==========================================================================\n", - "Total params: 492\n", - "Trainable params: 492\n", - "Non-trainable params: 0\n", - "--------------------------------------------------------------------------\n", - "Input size (MB): 0.000069\n", - "Forward/backward pass size (MB): 0.000229\n", - "Params size (MB): 0.001877\n", - "Estimated Total Size (MB): 0.002174\n", - "--------------------------------------------------------------------------\n", - "\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -437,165 +2600,18 @@ "\n", "有3类典型的训练循环代码风格：脚本形式训练循环，函数形式训练循环，类形式训练循环。\n", "\n", - "此处介绍一种引进pytorch_lightning库实现的类形式的训练循环。\n", - "\n", - "该训练循环的代码也是torchkeras库中LightModel类的核心代码。\n", + "此处我们通过引入torchkeras库中的KerasModel工具来训练模型，无需编写自定义循环。\n", "\n", "torchkeras详情: https://github.com/lyhue1991/torchkeras \n", "\n", - "注：循环神经网络调试较为困难，需要设置多个不同的学习率多次尝试，以取得较好的效果。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93701678", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f4d1cb4", - "metadata": {}, - "outputs": [], - "source": [ - "import torch \n", - "from torch import nn \n", - "import pytorch_lightning as pl\n", - "import datetime\n", - "import sys \n", - "import numpy as np\n", - "import pandas as pd \n", - "from copy import deepcopy\n", - "\n", - "class LightModel(pl.LightningModule):\n", - " def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler=None):\n", - " super().__init__()\n", - " self.net = net\n", - " self.history = {}\n", - " \n", - " self.train_metrics = nn.ModuleDict(metrics_dict)\n", - " self.val_metrics = deepcopy(self.train_metrics)\n", - " self.test_metrics = deepcopy(self.train_metrics)\n", - " \n", - " self.loss_fn = loss_fn\n", - " self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(self.parameters(), lr=1e-2)\n", - " self.lr_scheduler = lr_scheduler \n", - " \n", - " for p in [\"net\",\"loss_fn\",\"metrics_dict\",\"optimizer\",\"lr_scheduler\"]:\n", - " self.save_hyperparameters(p)\n", - " \n", - " def forward(self,x):\n", - " if self.net:\n", - " return self.net.forward(x)\n", - " else:\n", - " raise NotImplementedError\n", - " \n", - " def shared_step(self,batch,batch_idx):\n", - " x, y = batch\n", - " preds = self(x)\n", - " loss = self.loss_fn(preds,y)\n", - " return {'loss': loss, 'preds': preds.detach(), 'y': y.detach()}\n", - " \n", - " def configure_optimizers(self):\n", - " if self.lr_scheduler is None:\n", - " return self.optimizer\n", - " return {\"optimizer\":self.optimizer,\"lr_scheduler\":self.lr_scheduler}\n", - " \n", - " def training_step(self, batch, batch_idx):\n", - " return self.shared_step(batch,batch_idx)\n", - " \n", - " def validation_step(self, batch, batch_idx):\n", - " return self.shared_step(batch,batch_idx)\n", - " \n", - " def test_step(self, batch, batch_idx):\n", - " return self.shared_step(batch,batch_idx)\n", - " \n", - " def predict_step(self, batch, batch_idx):\n", - " if isinstance(batch,list) and len(batch)==2:\n", - " return self(batch[0])\n", - " else:\n", - " return self(batch)\n", - " \n", - " def shared_step_end(self,outputs,stage):\n", - " metrics = self.train_metrics if stage==\"train\" else (\n", - " self.val_metrics if stage==\"val\" else self.test_metrics)\n", - " for name in metrics:\n", - " step_metric = metrics[name](outputs['preds'], outputs['y']).item()\n", - " if stage==\"train\":\n", - " self.log(name,step_metric,prog_bar=True)\n", - " return outputs[\"loss\"].mean()\n", - " \n", - " def training_step_end(self, outputs):\n", - " return {'loss':self.shared_step_end(outputs,\"train\")}\n", - " \n", - " def validation_step_end(self, outputs):\n", - " return {'val_loss':self.shared_step_end(outputs,\"val\")}\n", - " \n", - " def test_step_end(self, outputs):\n", - " return {'test_loss':self.shared_step_end(outputs,\"test\")}\n", - " \n", - " def shared_epoch_end(self,outputs,stage=\"train\"):\n", - " metrics = self.train_metrics if stage==\"train\" else (\n", - " self.val_metrics if stage==\"val\" else self.test_metrics)\n", - " \n", - " epoch = self.trainer.current_epoch\n", - " stage_loss = torch.mean(torch.tensor([t[(stage+\"_loss\").replace('train_','')] for t in outputs])).item()\n", - " dic = {\"epoch\":epoch,stage+\"_loss\":stage_loss}\n", - " \n", - " for name in metrics:\n", - " epoch_metric = metrics[name].compute().item() \n", - " metrics[name].reset()\n", - " dic[stage+\"_\"+name] = epoch_metric \n", - " if stage!='test':\n", - " self.history[epoch] = dict(self.history.get(epoch,{}),**dic) \n", - " return dic \n", - " \n", - " def training_epoch_end(self, outputs):\n", - " dic = self.shared_epoch_end(outputs,stage=\"train\")\n", - " self.print(dic)\n", - " dic.pop(\"epoch\",None)\n", - " self.log_dict(dic, logger=True)\n", - "\n", - " def validation_epoch_end(self, outputs):\n", - " dic = self.shared_epoch_end(outputs,stage=\"val\")\n", - " self.print_bar()\n", - " self.print(dic)\n", - " dic.pop(\"epoch\",None)\n", - " self.log_dict(dic, logger=True)\n", - " \n", - " #log when reach best score\n", - " ckpt_cb = self.trainer.checkpoint_callback\n", - " monitor = ckpt_cb.monitor \n", - " mode = ckpt_cb.mode \n", - " arr_scores = self.get_history()[monitor]\n", - " best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n", - " if best_score_idx==len(arr_scores)-1: \n", - " self.print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", - " \n", - " \n", - " def test_epoch_end(self, outputs):\n", - " dic = self.shared_epoch_end(outputs,stage=\"test\")\n", - " dic.pop(\"epoch\",None)\n", - " self.print(dic)\n", - " self.log_dict(dic, logger=True)\n", - " \n", - " def get_history(self):\n", - " return pd.DataFrame(self.history.values()) \n", - " \n", - " def print_bar(self): \n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " self.print(\"\\n\"+\"=\"*80 + \"%s\"%nowtime)\n", - " " + "注：循环神经网络调试较为困难，需要设置多个不同的学习率多次尝试，以取得较好的效果。\n", + "\n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "2bf9b113", + "execution_count": 13, + "id": "fd846cf3-8a65-4cbf-8bc9-ba28c6eed841", "metadata": {}, "outputs": [], "source": [ @@ -605,87 +2621,1305 @@ " err_percent = (y_true - y_pred)**2/(torch.max(y_true**2,torch.tensor(1e-7)))\n", " return torch.mean(err_percent)\n", "\n", - "\n", "net = Net() \n", "loss_fn = mspe\n", "metric_dict = {\"mape\":MeanAbsolutePercentageError()}\n", "\n", "optimizer = torch.optim.Adam(net.parameters(), lr=0.03)\n", - "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.0001)\n", - "\n", - "model = LightModel(net,\n", - " loss_fn = loss_fn,\n", - " metrics_dict= metric_dict,\n", - " optimizer = optimizer,\n", - " lr_scheduler = lr_scheduler) \n" + "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.0001)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "d8185438", + "execution_count": 14, + "id": "9bfa7af9-ab5f-4fa5-bc61-3e1cd77cc087", "metadata": {}, "outputs": [], "source": [ - "import pytorch_lightning as pl \n", - "\n", - "#1，设置回调函数\n", - "\n", - "model_ckpt = pl.callbacks.ModelCheckpoint(\n", - " monitor='val_mape',\n", - " save_top_k=1,\n", - " mode='min'\n", - ")\n", - "\n", - "early_stopping = pl.callbacks.EarlyStopping(monitor = 'val_mape',\n", - " patience=3,\n", - " mode = 'min'\n", - " )\n", - "\n", - "#2，设置训练参数\n", - "# gpus=0 则使用cpu训练，gpus=1则使用1个gpu训练，gpus=2则使用2个gpu训练，gpus=-1则使用所有gpu训练，\n", - "# gpus=[0,1]则指定使用0号和1号gpu训练， gpus=\"0,1,2,3\"则使用0,1,2,3号gpu训练\n", - "# tpus=1 则使用1个tpu训练\n", - "trainer = pl.Trainer(logger=True,\n", - " min_epochs=3,max_epochs=30,\n", - " gpus=0,\n", - " callbacks = [model_ckpt,early_stopping],\n", - " enable_progress_bar = True) \n", - "\n", - "\n", - "##3，启动训练循环\n", - "trainer.fit(model,dl_train,dl_val)\n" + "from torchkeras import KerasModel \n", + "model = KerasModel(net,\n", + " loss_fn = loss_fn,\n", + " metrics_dict= metric_dict,\n", + " optimizer = optimizer,\n", + " lr_scheduler = lr_scheduler) \n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "9cc46c53", - "metadata": {}, - "outputs": [], - "source": [ - "dfhistory = model.get_history() \n", - "dfhistory " - ] - }, 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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< val_loss without improvement in 10 epoch,early stopping >>>>>> \n", + "\u001b[0m\n" + ] + } + ], "source": [ - "```\n", - "epoch\tval_loss\tval_mape\ttrain_loss\ttrain_mape\n", - "0\t0\t5.974455\t0.661542\t6.936645\t0.737031\n", - "1\t1\t5.086240\t0.590996\t5.974455\t0.661542\n", - "2\t2\t4.237600\t0.524000\t5.086240\t0.590996\n", - "3\t3\t3.408828\t0.463179\t4.237600\t0.524000\n", - "4\t4\t2.614143\t0.422679\t3.408828\t0.463179\n", - "5\t5\t1.896354\t0.413116\t2.614143\t0.422679\n", - "6\t6\t1.304007\t0.437700\t1.896354\t0.413116\n", - "7\t7\t0.866170\t0.474878\t1.304007\t0.437700\n", - "8\t8\t0.585183\t0.517995\t0.866170\t0.474878\n", - "\t\n", - "```" + "dfhistory = model.fit(train_data=dl_train,\n", + " val_data=dl_val,\n", + " epochs=100,\n", + " ckpt_path='checkpoint',\n", + " patience=10,\n", + " monitor='val_loss',\n", + " mode='min',\n", + " callbacks=None,\n", + " plot=True,\n", + " cpu=True\n", + " )\n" ] }, { @@ -714,84 +3948,30 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "bb5f075f", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "46963c00", "metadata": {}, - "outputs": [], - "source": [ - "#使用最佳保存点进行评估\n", - "trainer.test(ckpt_path='best', dataloaders=dl_val,verbose = False)\n" - ] - }, - { - "cell_type": "markdown", - "id": "4c6a30ce", - "metadata": {}, - "source": [ - "```\n", - "{'test_loss': 1.8963541984558105, 'test_mape': 0.4131162464618683}\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4f1a46fd", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"loss\")" - ] - }, - { - "cell_type": "markdown", - "id": "770aa7ec", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h48rhp9jpqj20ej0acwer.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9fce9cf8", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"mape\")" - ] - }, - { - "cell_type": "markdown", - "id": "c6f575f1", - "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████| 1/1 [00:00<00:00, 63.91it/s, val_loss=0.384, val_mape=0.505]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.38373321294784546, 'val_mape': 0.5048269033432007}" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h48rj15ctvj20f70ag3yv.jpg)" + "model.evaluate(dl_val)\n" ] }, { @@ -820,27 +4000,94 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "4164d6eb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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confirmed_numcured_numdead_num
41143.01681.030.0
4299.01678.028.0
4344.01661.027.0
4440.01535.022.0
4519.01297.017.0
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confirmed_numcured_numdead_num
500.0999.00.0
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confirmed_numcured_numdead_num
1370.00.00.0
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1390.00.00.0
1400.00.00.0
1410.00.00.0
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" + ], + "text/plain": [ + " confirmed_num cured_num dead_num\n", + "137 0.0 0.0 0.0\n", + "138 0.0 0.0 0.0\n", + "139 0.0 0.0 0.0\n", + "140 0.0 0.0 0.0\n", + "141 0.0 0.0 0.0" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dfresult.query(\"cured_num==0\").head()\n", - "\n", - "# 第132天开始新增治愈降为0，第45天对应3月10日，也就是大概3个月后，即6月10日左右全部治愈。\n", + "# 第137天开始新增治愈降为0，第45天对应3月10日，也就是大概3个月后，即6月12日左右全部治愈。\n", "# 注: 该预测偏悲观，并且存在问题，如果将每天新增治愈人数加起来，将超过累计确诊人数。" ] }, - { - "cell_type": "markdown", - "id": "3f3c999a", - "metadata": {}, - "source": [ - "![](./data/1-4-torch预测治愈.png)" - ] - }, { "cell_type": "code", "execution_count": null, @@ -926,86 +4306,35 @@ "id": "9a78086b", "metadata": {}, "source": [ - "模型保存在了trainer.checkpoint_callback.best_model_path路径。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "239f6463", - "metadata": {}, - "outputs": [], - "source": [ - "print(trainer.checkpoint_callback.best_model_path)\n", - "print(trainer.checkpoint_callback.best_model_score)" + "模型权重保存在了model.ckpt_path路径。" ] }, { "cell_type": "code", - "execution_count": null, - "id": "6f5bef85", + "execution_count": 23, + "id": "859daed0-91c7-4ad6-9789-2c238ebb9990", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "checkpoint\n" + ] + } + ], "source": [ - "#pytorch_lightning不仅保留了模型参数，还保存了模型结构，可以用LightModel重新加载\n", - "model_loaded = LightModel.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)" + "print(model.ckpt_path)" ] }, { "cell_type": "code", - "execution_count": null, - "id": "748f2b69", + "execution_count": 24, + "id": "b3ad034a-63a5-42df-b65e-1cd4d7c543bf", "metadata": {}, "outputs": [], "source": [ - "trainer.predict(model_loaded,dataloaders=dl_val)" - ] - }, - { - "cell_type": "markdown", - "id": "06790009", - "metadata": {}, - "source": [ - "```\n", - "[tensor([[1.4974e+03, 8.5825e+01, 2.3000e+01],\n", - " [1.8469e+03, 6.7295e+01, 4.6768e+01],\n", - " [2.0153e+03, 1.4337e+02, 4.3701e+01],\n", - " [2.3063e+03, 1.5312e+02, 4.9068e+01],\n", - " [2.7721e+03, 2.5358e+02, 4.9834e+01],\n", - " [2.6352e+03, 2.5455e+02, 5.5968e+01],\n", - " [2.2421e+03, 3.7744e+02, 5.5968e+01],\n", - " [2.4147e+03, 4.9740e+02, 6.5935e+01],\n", - " [1.8918e+03, 5.8420e+02, 6.8235e+01],\n", - " [2.1208e+03, 6.1638e+02, 7.4368e+01],\n", - " [1.7599e+03, 6.9733e+02, 8.2802e+01],\n", - " [1.4374e+03, 7.2561e+02, 7.4368e+01],\n", - " [1.0808e+04, 1.1421e+03, 1.9474e+02],\n", - " [2.8870e+03, 7.9193e+02, 9.9669e+00],\n", - " [1.8840e+03, 1.3391e+03, 1.0964e+02],\n", - " [1.4324e+03, 1.2903e+03, 1.0887e+02],\n", - " [1.4610e+03, 1.3898e+03, 8.0502e+01],\n", - " [1.3468e+03, 1.6658e+03, 7.5135e+01],\n", - " [1.2477e+03, 1.7789e+03, 1.0427e+02],\n", - " [2.7895e+02, 1.7353e+03, 8.7424e+01],\n", - " [6.3425e+02, 2.0573e+03, 9.0511e+01],\n", - " [5.8716e+02, 2.3365e+03, 8.3624e+01],\n", - " [4.6230e+02, 2.1747e+03, 7.4431e+01],\n", - " [1.5267e+02, 1.8011e+03, 1.1512e+02],\n", - " [3.6241e+02, 2.5261e+03, 5.4495e+01],\n", - " [2.8963e+02, 2.3633e+03, 3.9916e+01],\n", - " [3.0889e+02, 2.6834e+03, 2.2262e+01],\n", - " [2.3327e+02, 3.5342e+03, 3.3777e+01],\n", - " [3.0461e+02, 2.8151e+03, 3.6080e+01],\n", - " [4.0876e+02, 2.5594e+03, 2.6868e+01],\n", - " [1.4410e+02, 2.7683e+03, 3.2242e+01],\n", - " [8.9172e+01, 2.6756e+03, 2.3798e+01],\n", - " [8.4892e+01, 2.5877e+03, 2.9171e+01],\n", - " [9.9159e+01, 2.1360e+03, 2.3798e+01],\n", - " [1.0201e+02, 1.6403e+03, 2.3030e+01],\n", - " [7.0624e+01, 1.6373e+03, 2.1495e+01],\n", - " [3.1389e+01, 1.6208e+03, 2.0727e+01],\n", - " [2.8535e+01, 1.4978e+03, 1.6889e+01]])]\n", - "```" + "model.load_ckpt('checkpoint') #可以加载权重" ] }, { @@ -1030,7 +4359,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1044,7 +4373,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/2-1,\345\274\240\351\207\217\346\225\260\346\215\256\347\273\223\346\236\204.ipynb" "b/2-1,\345\274\240\351\207\217\346\225\260\346\215\256\347\273\223\346\236\204.ipynb" index 92cdc7d86..f80ba3443 100644 --- "a/2-1,\345\274\240\351\207\217\346\225\260\346\215\256\347\273\223\346\236\204.ipynb" +++ "b/2-1,\345\274\240\351\207\217\346\225\260\346\215\256\347\273\223\346\236\204.ipynb" @@ -14,25 +14,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "30005fa6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__=2.0.1\n" + ] + } + ], "source": [ "import torch \n", "print(\"torch.__version__=\"+torch.__version__) \n" ] }, - { - "cell_type": "markdown", - "id": "52bb4347", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__=1.10.0\n", - "```" - ] - }, { "cell_type": "markdown", "id": "41f40abd", @@ -73,10 +71,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "08ee241e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1) torch.int64\n", + "tensor(2.) torch.float32\n", + "tensor(True) torch.bool\n" + ] + } + ], "source": [ "import numpy as np\n", "import torch \n", @@ -88,24 +96,21 @@ "b = torch.tensor(True);print(b,b.dtype)" ] }, - { - "cell_type": "markdown", - "id": "0e05cbeb", - "metadata": {}, - "source": [ - "```\n", - "tensor(1) torch.int64\n", - "tensor(2.) torch.float32\n", - "tensor(True) torch.bool\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "639539f3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1, dtype=torch.int32) torch.int32\n", + "tensor(2., dtype=torch.float64) torch.float64\n" + ] + } + ], "source": [ "# 指定数据类型\n", "\n", @@ -113,23 +118,22 @@ "x = torch.tensor(2.0,dtype = torch.double);print(x,x.dtype)\n" ] }, - { - "cell_type": "markdown", - "id": "c904605d", - "metadata": {}, - "source": [ - "```\n", - "tensor(1, dtype=torch.int32) torch.int32\n", - "tensor(2., dtype=torch.float64) torch.float64\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "b44d6e5d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([0], dtype=torch.int32) torch.int32\n", + "tensor(2.) torch.float32\n", + "tensor([ True, False, True, False]) torch.bool\n" + ] + } + ], "source": [ "# 使用特定类型构造函数\n", "\n", @@ -139,24 +143,23 @@ "\n" ] }, - { - "cell_type": "markdown", - "id": "f5b4572e", - "metadata": {}, - "source": [ - "```\n", - "tensor([5], dtype=torch.int32) torch.int32\n", - "tensor(2.) torch.float32\n", - "tensor([ True, False, True, False]) torch.bool\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "58181469", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(1) torch.int64\n", + "tensor(1.) torch.float32\n", + "tensor(1.) torch.float32\n", + "tensor(1.) torch.float32\n" + ] + } + ], "source": [ "# 不同类型进行转换\n", "\n", @@ -166,19 +169,6 @@ "z = i.type_as(x);print(z,z.dtype) #使用type_as方法转换成某个Tensor相同类型\n" ] }, - { - "cell_type": "markdown", - "id": "fa089f2b", - "metadata": {}, - "source": [ - "```\n", - "tensor(1) torch.int64\n", - "tensor(1.) torch.float32\n", - "tensor(1.) torch.float32\n", - "tensor(1.) torch.float32\n", - "```" - ] - }, { "cell_type": "markdown", "id": "15bcb7cb", @@ -205,107 +195,119 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "c7185aec", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(True)\n", + "0\n" + ] + } + ], "source": [ "scalar = torch.tensor(True)\n", "print(scalar)\n", "print(scalar.dim()) # 标量，0维张量\n" ] }, - { - "cell_type": "markdown", - "id": "ce9531a2", - "metadata": {}, - "source": [ - "```\n", - "tensor(True)\n", - "0\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "6d9668ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([1., 2., 3., 4.])\n", + "1\n" + ] + } + ], "source": [ "vector = torch.tensor([1.0,2.0,3.0,4.0]) #向量，1维张量\n", "print(vector)\n", "print(vector.dim())\n" ] }, - { - "cell_type": "markdown", - "id": "a325ff49", - "metadata": {}, - "source": [ - "```\n", - "tensor([1., 2., 3., 4.])\n", - "1\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "2f1419cc", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[1., 2.],\n", + " [3., 4.]])\n", + "2\n" + ] + } + ], "source": [ "matrix = torch.tensor([[1.0,2.0],[3.0,4.0]]) #矩阵, 2维张量\n", "print(matrix)\n", "print(matrix.dim())" ] }, - { - "cell_type": "markdown", - "id": "6d1e50bf", - "metadata": {}, - "source": [ - "```\n", - "matrix = torch.tensor([[1.0,2.0],[3.0,4.0]]) #矩阵, 2维张量\n", - "print(matrix)\n", - "print(matrix.dim())\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "912a056d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[[1., 2.],\n", + " [3., 4.]],\n", + "\n", + " [[5., 6.],\n", + " [7., 8.]]])\n", + "3\n" + ] + } + ], "source": [ "tensor3 = torch.tensor([[[1.0,2.0],[3.0,4.0]],[[5.0,6.0],[7.0,8.0]]]) # 3维张量\n", "print(tensor3)\n", "print(tensor3.dim())" ] }, - { - "cell_type": "markdown", - "id": "2ee9cf7d", - "metadata": {}, - "source": [ - "```\n", - "tensor([[[1., 2.],\n", - " [3., 4.]],\n", - "\n", - " [[5., 6.],\n", - " [7., 8.]]])\n", - "3\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "657029ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[[[1., 1.],\n", + " [2., 2.]],\n", + "\n", + " [[3., 3.],\n", + " [4., 4.]]],\n", + "\n", + "\n", + " [[[5., 5.],\n", + " [6., 6.]],\n", + "\n", + " [[7., 7.],\n", + " [8., 8.]]]])\n", + "4\n" + ] + } + ], "source": [ "tensor4 = torch.tensor([[[[1.0,1.0],[2.0,2.0]],[[3.0,3.0],[4.0,4.0]]],\n", " [[[5.0,5.0],[6.0,6.0]],[[7.0,7.0],[8.0,8.0]]]]) # 4维张量\n", @@ -313,28 +315,6 @@ "print(tensor4.dim())" ] }, - { - "cell_type": "markdown", - "id": "cb5bce68", - "metadata": {}, - "source": [ - "```\n", - "tensor([[[[1., 1.],\n", - " [2., 2.]],\n", - "\n", - " [[3., 3.],\n", - " [4., 4.]]],\n", - "\n", - "\n", - " [[[5., 5.],\n", - " [6., 6.]],\n", - "\n", - " [[7., 7.],\n", - " [8., 8.]]]])\n", - "4\n", - "```" - ] - }, { "cell_type": "markdown", "id": "2b47b791", @@ -357,77 +337,89 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "3f2499fb", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([])\n", + "torch.Size([])\n" + ] + } + ], "source": [ "scalar = torch.tensor(True)\n", "print(scalar.size())\n", "print(scalar.shape)" ] }, - { - "cell_type": "markdown", - "id": "9ecde758", - "metadata": {}, - "source": [ - "```\n", - "torch.Size([])\n", - "torch.Size([4])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "af6cc014", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([4])\n", + "torch.Size([4])\n" + ] + } + ], "source": [ "vector = torch.tensor([1.0,2.0,3.0,4.0])\n", "print(vector.size())\n", "print(vector.shape)" ] }, - { - "cell_type": "markdown", - "id": "52995a18", - "metadata": {}, - "source": [ - "```\n", - "torch.Size([4])\n", - "torch.Size([4])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "c1426615", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 2])\n" + ] + } + ], "source": [ "matrix = torch.tensor([[1.0,2.0],[3.0,4.0]])\n", "print(matrix.size())" ] }, - { - "cell_type": "markdown", - "id": "e2eb650e", - "metadata": {}, - "source": [ - "```\n", - "torch.Size([2, 2])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "6216e5d1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n", + "torch.Size([12])\n", + "tensor([[ 0, 1, 2, 3],\n", + " [ 4, 5, 6, 7],\n", + " [ 8, 9, 10, 11]])\n", + "torch.Size([3, 4])\n", + "tensor([[ 0, 1, 2],\n", + " [ 3, 4, 5],\n", + " [ 6, 7, 8],\n", + " [ 9, 10, 11]])\n", + "torch.Size([4, 3])\n" + ] + } + ], "source": [ "# 使用view可以改变张量尺寸\n", "\n", @@ -444,32 +436,26 @@ "print(matrix43.shape)\n" ] }, - { - "cell_type": "markdown", - "id": "19a9fe82", - "metadata": {}, - "source": [ - "```\n", - "tensor([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11])\n", - "torch.Size([12])\n", - "tensor([[ 0, 1, 2, 3],\n", - " [ 4, 5, 6, 7],\n", - " [ 8, 9, 10, 11]])\n", - "torch.Size([3, 4])\n", - "tensor([[ 0, 1, 2],\n", - " [ 3, 4, 5],\n", - " [ 6, 7, 8],\n", - " [ 9, 10, 11]])\n", - "torch.Size([4, 3])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "9c5e90e3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[ 0, 1, 2, 3, 4, 5],\n", + " [ 6, 7, 8, 9, 10, 11]])\n", + "torch.Size([2, 6])\n", + "False\n", + "tensor([[ 0, 6, 1, 7],\n", + " [ 2, 8, 3, 9],\n", + " [ 4, 10, 5, 11]])\n" + ] + } + ], "source": [ "# 有些操作会让张量存储结构扭曲，直接使用view会失败，可以用reshape方法\n", "\n", @@ -488,22 +474,6 @@ "print(matrix34)\n" ] }, - { - "cell_type": "markdown", - "id": "b78afb57", - "metadata": {}, - "source": [ - "```\n", - "tensor([[ 0, 1, 2, 3, 4, 5],\n", - " [ 6, 7, 8, 9, 10, 11]])\n", - "torch.Size([2, 6])\n", - "False\n", - "tensor([[ 0, 6, 1, 7],\n", - " [ 2, 8, 3, 9],\n", - " [ 4, 10, 5, 11]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -540,7 +510,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "8cea696f", "metadata": {}, "outputs": [], @@ -551,10 +521,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "2c27e5ce", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before add 1:\n", + "[0. 0. 0.]\n", + "tensor([0., 0., 0.], dtype=torch.float64)\n", + "\n", + "after add 1:\n", + "[1. 1. 1.]\n", + "tensor([1., 1., 1.], dtype=torch.float64)\n" + ] + } + ], "source": [ "#torch.from_numpy函数从numpy数组得到Tensor\n", "\n", @@ -570,28 +554,26 @@ "print(tensor)\n" ] }, - { - "cell_type": "markdown", - "id": "0f309518", - "metadata": {}, - "source": [ - "```\n", - "before add 1:\n", - "[0. 0. 0.]\n", - "tensor([0., 0., 0.], dtype=torch.float64)\n", - "\n", - "after add 1:\n", - "[1. 1. 1.]\n", - "tensor([1., 1., 1.], dtype=torch.float64)\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "88fdbf45", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before add 1:\n", + "tensor([0., 0., 0.])\n", + "[0. 0. 0.]\n", + "\n", + "after add 1:\n", + "tensor([1., 1., 1.])\n", + "[1. 1. 1.]\n" + ] + } + ], "source": [ "# numpy方法从Tensor得到numpy数组\n", "\n", @@ -610,28 +592,26 @@ "print(arr)\n" ] }, - { - "cell_type": "markdown", - "id": "c00aebed", - "metadata": {}, - "source": [ - "```\n", - "before add 1:\n", - "tensor([0., 0., 0.])\n", - "[0. 0. 0.]\n", - "\n", - "after add 1:\n", - "tensor([1., 1., 1.])\n", - "[1. 1. 1.]\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "13fb0e0e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before add 1:\n", + "tensor([0., 0., 0.])\n", + "[0. 0. 0.]\n", + "\n", + "after add 1:\n", + "tensor([1., 1., 1.])\n", + "[0. 0. 0.]\n" + ] + } + ], "source": [ "# 可以用clone() 方法拷贝张量，中断这种关联\n", "\n", @@ -651,28 +631,23 @@ "print(arr)" ] }, - { - "cell_type": "markdown", - "id": "b4c609e9", - "metadata": {}, - "source": [ - "```\n", - "before add 1:\n", - "tensor([0., 0., 0.])\n", - "[0. 0. 0.]\n", - "\n", - "after add 1:\n", - "tensor([1., 1., 1.])\n", - "[0. 0. 0.]\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "72f9b26a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.0\n", + "\n", + "[[0.4581589698791504, 0.46063995361328125], [0.5779597759246826, 0.40021681785583496]]\n", + "\n" + ] + } + ], "source": [ "# item方法和tolist方法可以将张量转换成Python数值和数值列表\n", "scalar = torch.tensor(1.0)\n", @@ -686,19 +661,6 @@ "print(type(t))\n" ] }, - { - "cell_type": "markdown", - "id": "8a21d33d", - "metadata": {}, - "source": [ - "```\n", - "1.0\n", - "\n", - "[[0.8211846351623535, 0.20020723342895508], [0.011571824550628662, 0.2906131148338318]]\n", - "\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -728,6 +690,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/2-2,\350\207\252\345\212\250\345\276\256\345\210\206\346\234\272\345\210\266.ipynb" "b/2-2,\350\207\252\345\212\250\345\276\256\345\210\206\346\234\272\345\210\266.ipynb" index f60624228..10742790e 100644 --- "a/2-2,\350\207\252\345\212\250\345\276\256\345\210\206\346\234\272\345\210\266.ipynb" +++ "b/2-2,\350\207\252\345\212\250\345\276\256\345\210\206\346\234\272\345\210\266.ipynb" @@ -26,25 +26,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "1ca9b970", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__=2.0.1\n" + ] + } + ], "source": [ "import torch \n", "print(\"torch.__version__=\"+torch.__version__) " ] }, - { - "cell_type": "markdown", - "id": "191847a6", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__=1.10.0\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -83,10 +81,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "f007e5ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(-2.)\n" + ] + } + ], "source": [ "import numpy as np \n", "import torch \n", @@ -104,16 +110,6 @@ "print(dy_dx)\n" ] }, - { - "cell_type": "markdown", - "id": "234280b7", - "metadata": {}, - "source": [ - "```\n", - "tensor(-2.)\n", - "```" - ] - }, { "cell_type": "markdown", "id": "171c6ddf", @@ -124,10 +120,26 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "830a8071", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x:\n", + " tensor([[0., 0.],\n", + " [1., 2.]], requires_grad=True)\n", + "y:\n", + " tensor([[1., 1.],\n", + " [0., 1.]], grad_fn=)\n", + "x_grad:\n", + " tensor([[-2., -2.],\n", + " [ 0., 2.]])\n" + ] + } + ], "source": [ "import numpy as np \n", "import torch \n", @@ -149,24 +161,6 @@ "print(\"x_grad:\\n\",x_grad)" ] }, - { - "cell_type": "markdown", - "id": "ae429fd4", - "metadata": {}, - "source": [ - "```\n", - "x:\n", - " tensor([[0., 0.],\n", - " [1., 2.]], requires_grad=True)\n", - "y:\n", - " tensor([[1., 1.],\n", - " [0., 1.]], grad_fn=)\n", - "x_grad:\n", - " tensor([[-2., -2.],\n", - " [ 0., 2.]])\n", - "```" - ] - }, { "cell_type": "markdown", "id": "a962975d", @@ -177,10 +171,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "61e9640d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "x: tensor([[0., 0.],\n", + " [1., 2.]], requires_grad=True)\n", + "y: tensor([[1., 1.],\n", + " [0., 1.]], grad_fn=)\n", + "x_grad:\n", + " tensor([[-2., -2.],\n", + " [ 0., 2.]])\n" + ] + } + ], "source": [ "import numpy as np \n", "import torch \n", @@ -203,22 +211,6 @@ "print(\"x_grad:\\n\",x_grad)\n" ] }, - { - "cell_type": "markdown", - "id": "d81bdd26", - "metadata": {}, - "source": [ - "```\n", - "x: tensor([[0., 0.],\n", - " [1., 2.]], requires_grad=True)\n", - "y: tensor([[1., 1.],\n", - " [0., 1.]], grad_fn=)\n", - "x_grad:\n", - " tensor([[-2., -2.],\n", - " [ 0., 2.]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -237,10 +229,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "474468ab", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(-2.)\n", + "tensor(2.)\n" + ] + } + ], "source": [ "import numpy as np \n", "import torch \n", @@ -265,23 +266,21 @@ "\n" ] }, - { - "cell_type": "markdown", - "id": "005fad86", - "metadata": {}, - "source": [ - "```\n", - "tensor(-2.)\n", - "tensor(2.)\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "41abfc48", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(2.) tensor(1.)\n", + "tensor(3.) tensor(2.)\n" + ] + } + ], "source": [ "import numpy as np \n", "import torch \n", @@ -303,17 +302,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "id": "d30abba9", - "metadata": {}, - "source": [ - "```\n", - "tensor(2.) tensor(1.)\n", - "tensor(3.) tensor(2.)\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -332,10 +320,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "fc700c05", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y= tensor(0.) ; x= tensor(1.0000)\n" + ] + } + ], "source": [ "import numpy as np \n", "import torch \n", @@ -364,24 +360,6 @@ "print(\"y=\",f(x).data,\";\",\"x=\",x.data)\n" ] }, - { - "cell_type": "markdown", - "id": "72e8fd55", - "metadata": {}, - "source": [ - "```\n", - "y= tensor(0.) ; x= tensor(1.0000)\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3009819c", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "b0602053", @@ -404,7 +382,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -418,7 +396,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/2-3,\345\212\250\346\200\201\350\256\241\347\256\227\345\233\276.ipynb" "b/2-3,\345\212\250\346\200\201\350\256\241\347\256\227\345\233\276.ipynb" index 8c7a8172f..fa06c1255 100644 --- "a/2-3,\345\212\250\346\200\201\350\256\241\347\256\227\345\233\276.ipynb" +++ "b/2-3,\345\212\250\346\200\201\350\256\241\347\256\227\345\233\276.ipynb" @@ -69,10 +69,28 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "b4663b84", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(12.2329)\n", + "tensor([[ 2.6299],\n", + " [ 0.9402],\n", + " [-0.2065],\n", + " [ 7.1444],\n", + " [-0.7712],\n", + " [ 3.4682],\n", + " [ 4.5334],\n", + " [-0.0911],\n", + " [ 3.8250],\n", + " [ 0.1377]])\n" + ] + } + ], "source": [ "import torch \n", "w = torch.tensor([[3.0,1.0]],requires_grad=True)\n", @@ -86,26 +104,6 @@ "print(Y_hat.data)" ] }, - { - "cell_type": "markdown", - "id": "d1907e4f", - "metadata": {}, - "source": [ - "```\n", - "tensor(17.8969)\n", - "tensor([[3.2613],\n", - " [4.7322],\n", - " [4.5037],\n", - " [7.5899],\n", - " [7.0973],\n", - " [1.3287],\n", - " [6.1473],\n", - " [1.3492],\n", - " [1.3911],\n", - " [1.2150]])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -124,7 +122,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "567ce1d0", "metadata": {}, "outputs": [], @@ -173,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "eb1b41f0", "metadata": {}, "outputs": [], @@ -198,10 +196,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "090a5095", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[4.5000, 4.5000]])\n", + "tensor([[4.5000]])\n" + ] + } + ], "source": [ "import torch \n", "w = torch.tensor([[3.0,1.0]],requires_grad=True)\n", @@ -219,39 +226,26 @@ "print(b.grad)" ] }, - { - "cell_type": "markdown", - "id": "af3e5a75", - "metadata": {}, - "source": [ - "```\n", - "tensor([[4.5000, 4.5000]])\n", - "tensor([[4.5000]])\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "9f0bf02b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], "source": [ "# Y_hat的梯度函数即是我们自己所定义的 MyReLU.backward\n", "\n", "print(Y_hat.grad_fn)" ] }, - { - "cell_type": "markdown", - "id": "d3caa97a", - "metadata": {}, - "source": [ - "```\n", - "\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -278,7 +272,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "1dab19a6", "metadata": {}, "outputs": [], @@ -356,10 +350,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "f5edd627", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss.grad: None\n", + "y1.grad: None\n", + "y2.grad: None\n", + "tensor(4.)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/var/folders/s4/ttc61tl56lvcn5gbyxqvwst40000gp/T/ipykernel_3633/3580456012.py:9: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/build/aten/src/ATen/core/TensorBody.h:491.)\n", + " print(\"loss.grad:\", loss.grad)\n", + "/var/folders/s4/ttc61tl56lvcn5gbyxqvwst40000gp/T/ipykernel_3633/3580456012.py:10: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/build/aten/src/ATen/core/TensorBody.h:491.)\n", + " print(\"y1.grad:\", y1.grad)\n", + "/var/folders/s4/ttc61tl56lvcn5gbyxqvwst40000gp/T/ipykernel_3633/3580456012.py:11: UserWarning: The .grad attribute of a Tensor that is not a leaf Tensor is being accessed. Its .grad attribute won't be populated during autograd.backward(). If you indeed want the .grad field to be populated for a non-leaf Tensor, use .retain_grad() on the non-leaf Tensor. If you access the non-leaf Tensor by mistake, make sure you access the leaf Tensor instead. See github.com/pytorch/pytorch/pull/30531 for more informations. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/build/aten/src/ATen/core/TensorBody.h:491.)\n", + " print(\"y2.grad:\", y2.grad)\n" + ] + } + ], "source": [ "import torch \n", "\n", @@ -375,25 +392,23 @@ "print(x.grad)" ] }, - { - "cell_type": "markdown", - "id": "0c69ad53", - "metadata": {}, - "source": [ - "```\n", - "loss.grad: None\n", - "y1.grad: None\n", - "y2.grad: None\n", - "tensor(4.)\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "395f5d8b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "True\n", + "False\n", + "False\n", + "False\n" + ] + } + ], "source": [ "print(x.is_leaf)\n", "print(y1.is_leaf)\n", @@ -401,19 +416,6 @@ "print(loss.is_leaf)" ] }, - { - "cell_type": "markdown", - "id": "a13c7f76", - "metadata": {}, - "source": [ - "```\n", - "True\n", - "False\n", - "False\n", - "False\n", - "```" - ] - }, { "cell_type": "markdown", "id": "e3ca6499", @@ -424,10 +426,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "e3c9243c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "y2 grad: tensor(4.)\n", + "y1 grad: tensor(-4.)\n", + "loss.grad: tensor(1.)\n", + "x.grad: tensor(4.)\n" + ] + } + ], "source": [ "import torch \n", "\n", @@ -448,19 +461,6 @@ "print(\"x.grad:\", x.grad)" ] }, - { - "cell_type": "markdown", - "id": "d6907ea0", - "metadata": {}, - "source": [ - "```\n", - "y2 grad: tensor(4.)\n", - "y1 grad: tensor(-4.)\n", - "loss.grad: tensor(1.)\n", - "x.grad: tensor(4.)\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -487,7 +487,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "d39ce53a", "metadata": {}, "outputs": [], @@ -508,7 +508,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "f8bf8db1", "metadata": {}, "outputs": [], @@ -560,14 +560,6 @@ "![](./data/2-3-计算图可视化.png)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "03cce56c", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "36351f46", @@ -590,7 +582,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -604,7 +596,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/3-1,\344\275\216\351\230\266API\347\244\272\350\214\203.ipynb" "b/3-1,\344\275\216\351\230\266API\347\244\272\350\214\203.ipynb" index 83572b164..a863df476 100644 --- "a/3-1,\344\275\216\351\230\266API\347\244\272\350\214\203.ipynb" +++ "b/3-1,\344\275\216\351\230\266API\347\244\272\350\214\203.ipynb" @@ -14,8 +14,8 @@ }, { "cell_type": "code", - "execution_count": 1, - "id": "4c5c8a62", + "execution_count": 12, + "id": "be06effc-3a85-4c31-b5cb-94ea23034be9", "metadata": {}, "outputs": [], "source": [ @@ -28,8 +28,7 @@ " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", "\n", "#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量\n", - "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n", - "\n" + "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n" ] }, { @@ -42,35 +41,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.__version__=1.10.0\n" + "torch.__version__=2.0.1\n" ] } ], "source": [ "import torch \n", - "print(\"torch.__version__=\"+torch.__version__) \n", - "\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "589c5a1d", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__=1.10.0\n", - "```" + "print(\"torch.__version__=\"+torch.__version__) " ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "7fd55a31", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "361ef742", @@ -89,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "3e278470", "metadata": {}, "outputs": [], @@ -115,7 +94,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "5addb8be", "metadata": {}, "outputs": [ @@ -125,46 +104,45 @@ "\n", "\n", - 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" \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + "\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n", " \n", " \n", - " \n", + "\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "L 711.864062 7.2 \n", + "\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n", " \n", " \n", " \n", @@ -1714,45 +1701,43 @@ "L 629.296875 27.878125 \n", "Q 629.296875 29.878125 631.296875 29.878125 \n", "z\n", - "\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\n", + "\" style=\"fill: #ffffff; opacity: 0.8; stroke: #cccccc; stroke-linejoin: miter\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1778,14 +1763,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "id": "f3a94d4e", - "metadata": {}, - "source": [ - "![](./data/3-1-回归数据可视化.png)" - ] - }, { "cell_type": "code", "execution_count": 7, @@ -1796,22 +1773,22 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[-2.3965, 2.0888],\n", - " [ 3.1974, -3.6487],\n", - " [-1.9557, -3.2792],\n", - " [ 0.0663, -4.4026],\n", - " [-1.7914, 3.5813],\n", - " [ 2.8500, -4.8544],\n", - " [ 0.2580, 4.8496],\n", - " [ 3.9957, -1.0908]])\n", - "tensor([[-0.9882],\n", - " [29.7027],\n", - " [15.1208],\n", - " [22.2092],\n", - " [-6.6428],\n", - " [32.1106],\n", - " [-6.1506],\n", - " [23.1054]])\n" + "tensor([[-0.3932, -1.2790],\n", + " [-0.4021, -2.1115],\n", + " [-1.7178, 0.9134],\n", + " [-0.6046, -2.1865],\n", + " [-2.2676, -1.0583],\n", + " [-3.7235, -2.7356],\n", + " [ 0.4728, -1.0100],\n", + " [ 3.9323, 3.2088]])\n", + "tensor([[12.8721],\n", + " [14.5131],\n", + " [ 5.9134],\n", + " [10.7377],\n", + " [11.9112],\n", + " [ 9.5725],\n", + " [13.4364],\n", + " [ 6.4052]])\n" ] } ], @@ -1833,31 +1810,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "id": "2e43310a", - "metadata": {}, - "source": [ - "```\n", - "tensor([[-4.3880, 1.3655],\n", - " [-0.1082, 3.9533],\n", - " [-2.6286, 2.7058],\n", - " [ 1.0604, -1.8646],\n", - " [-1.5805, 1.5406],\n", - " [-2.6217, -3.2342],\n", - " [ 2.3748, -0.6449],\n", - " [-1.2478, -2.0509]])\n", - "tensor([[-0.2069],\n", - " [-3.2494],\n", - " [-6.9620],\n", - " [17.0528],\n", - " [ 1.1076],\n", - " [17.2117],\n", - " [16.1081],\n", - " [14.7092]])\n", - "```" - ] - }, { "cell_type": "markdown", "id": "f599a732", @@ -1868,7 +1820,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 14, "id": "67a27738", "metadata": {}, "outputs": [], @@ -1888,10 +1840,7 @@ " def loss_fn(self,y_pred,y_true): \n", " return torch.mean((y_pred - y_true)**2/2)\n", "\n", - "model = LinearRegression()\n", - "\n", - "\n", - "\n" + "model = LinearRegression()\n" ] }, { @@ -1912,7 +1861,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 13, "id": "6fece800", "metadata": {}, "outputs": [], @@ -1942,17 +1891,17 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 15, "id": "642a2a96", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor(90.5805, grad_fn=)" + "tensor(68.1951, grad_fn=)" ] }, - "execution_count": 11, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } @@ -1964,19 +1913,9 @@ "train_step(model,features,labels)\n" ] }, - { - "cell_type": "markdown", - "id": "088e8941", - "metadata": {}, - "source": [ - "```\n", - "tensor(92.8199, grad_fn=)\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 16, "id": "273af6fd", "metadata": {}, "outputs": [ @@ -1985,65 +1924,65 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-08-24 16:50:01\n", - "epoch = 20 loss = 8.18395709991455\n", - "model.w = tensor([[ 2.0945],\n", - " [-2.9754]])\n", - "model.b = tensor([[5.4696]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 20 loss = 14.40532398223877\n", + "model.w = tensor([[ 1.9802],\n", + " [-2.9087]])\n", + "model.b = tensor([[5.4715]])\n", "\n", - "================================================================================2022-08-24 16:50:02\n", - "epoch = 40 loss = 4.5071187019348145\n", - "model.w = tensor([[ 2.0553],\n", - " [-3.0089]])\n", - "model.b = tensor([[7.9237]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 40 loss = 4.50400972366333\n", + "model.w = tensor([[ 1.9678],\n", + " [-2.9619]])\n", + "model.b = tensor([[7.9420]])\n", "\n", - "================================================================================2022-08-24 16:50:03\n", - "epoch = 60 loss = 0.9626429677009583\n", - "model.w = tensor([[ 2.0341],\n", - " [-3.0199]])\n", - "model.b = tensor([[9.0282]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 60 loss = 1.308245062828064\n", + "model.w = tensor([[ 1.9609],\n", + " [-2.9882]])\n", + "model.b = tensor([[9.0544]])\n", "\n", - "================================================================================2022-08-24 16:50:04\n", - "epoch = 80 loss = 2.035726308822632\n", - "model.w = tensor([[ 2.0226],\n", - " [-3.0225]])\n", - "model.b = tensor([[9.5253]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 80 loss = 2.0600852966308594\n", + "model.w = tensor([[ 1.9560],\n", + " [-2.9965]])\n", + "model.b = tensor([[9.5551]])\n", "\n", - "================================================================================2022-08-24 16:50:04\n", - "epoch = 100 loss = 2.53568959236145\n", - "model.w = tensor([[ 2.0212],\n", - " [-3.0246]])\n", - "model.b = tensor([[9.7486]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 100 loss = 3.130641460418701\n", + "model.w = tensor([[ 1.9550],\n", + " [-3.0019]])\n", + "model.b = tensor([[9.7809]])\n", "\n", - "================================================================================2022-08-24 16:50:06\n", - "epoch = 120 loss = 1.0717757940292358\n", - "model.w = tensor([[ 2.0202],\n", - " [-3.0258]])\n", - "model.b = tensor([[9.8493]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 120 loss = 3.818127393722534\n", + "model.w = tensor([[ 1.9550],\n", + " [-3.0040]])\n", + "model.b = tensor([[9.8821]])\n", "\n", - "================================================================================2022-08-24 16:50:06\n", - "epoch = 140 loss = 1.17708420753479\n", - "model.w = tensor([[ 2.0161],\n", - " [-3.0264]])\n", - "model.b = tensor([[9.8946]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 140 loss = 2.4734010696411133\n", + "model.w = tensor([[ 1.9548],\n", + " [-3.0032]])\n", + "model.b = tensor([[9.9277]])\n", "\n", - "================================================================================2022-08-24 16:50:07\n", - "epoch = 160 loss = 1.2027546167373657\n", - "model.w = tensor([[ 2.0184],\n", - " [-3.0269]])\n", - "model.b = tensor([[9.9151]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 160 loss = 3.3532516956329346\n", + "model.w = tensor([[ 1.9555],\n", + " [-3.0047]])\n", + "model.b = tensor([[9.9481]])\n", "\n", - "================================================================================2022-08-24 16:50:08\n", - "epoch = 180 loss = 1.7261393070220947\n", - "model.w = tensor([[ 2.0155],\n", - " [-3.0272]])\n", - "model.b = tensor([[9.9242]])\n", + "================================================================================2023-08-02 14:54:50\n", + "epoch = 180 loss = 1.9561536312103271\n", + "model.w = tensor([[ 1.9546],\n", + " [-3.0075]])\n", + "model.b = tensor([[9.9573]])\n", "\n", - "================================================================================2022-08-24 16:50:10\n", - "epoch = 200 loss = 0.7087655067443848\n", - "model.w = tensor([[ 2.0168],\n", - " [-3.0274]])\n", - 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20, "id": "ff32331c", "metadata": {}, "outputs": [], @@ -8633,13 +8700,12 @@ " acc = torch.mean(1-torch.abs(y_true-y_pred))\n", " return acc\n", " \n", - "model = DNNModel()\n", - "\n" + "model = DNNModel()\n" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 21, "id": "960a2668", "metadata": {}, "outputs": [ @@ -8647,8 +8713,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "init loss: 14.60618782043457\n", - "init metric: 0.08381413668394089\n" + "init loss: 11.617703437805176\n", + "init metric: 0.2786138653755188\n" ] } ], @@ -8668,7 +8734,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 22, "id": "05475551", "metadata": {}, "outputs": [ @@ -8678,7 +8744,7 @@ "6" ] }, - "execution_count": 24, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } @@ -8687,16 +8753,6 @@ "len(list(model.parameters()))" ] }, - { - "cell_type": "markdown", - "id": "124d3815", - "metadata": {}, - "source": [ - "```\n", - "6\n", - "```" - ] - }, { "cell_type": "markdown", "id": "0dc0c58c", @@ -8707,7 +8763,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 23, "id": "ad7a2698", "metadata": {}, "outputs": [ @@ -8716,35 +8772,35 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-08-24 16:55:23\n", - "epoch = 10 loss = 0.49705509379506113 metric = 0.7482500015199185\n", + "================================================================================2023-08-02 14:55:17\n", + "epoch = 10 loss = 0.4736742579936981 metric = 0.7737499994039535\n", "\n", - "================================================================================2022-08-24 16:55:30\n", - "epoch = 20 loss = 0.43582154996693134 metric = 0.7815000024437905\n", + "================================================================================2023-08-02 14:55:18\n", + "epoch = 20 loss = 0.3228449109941721 metric = 0.8784999975562096\n", "\n", - "================================================================================2022-08-24 16:55:37\n", - "epoch = 30 loss = 0.39972304441034795 metric = 0.8022500032186508\n", + "================================================================================2023-08-02 14:55:18\n", + "epoch = 30 loss = 0.22225805193185807 metric = 0.9099999928474426\n", "\n", - "================================================================================2022-08-24 16:55:42\n", - "epoch = 40 loss = 0.3834446675330401 metric = 0.8140000003576279\n", + "================================================================================2023-08-02 14:55:19\n", + "epoch = 40 loss = 0.2106029569543898 metric = 0.9162499943375587\n", "\n", - "================================================================================2022-08-24 16:55:48\n", - "epoch = 50 loss = 0.3687734051793814 metric = 0.8185000002384186\n", + "================================================================================2023-08-02 14:55:19\n", + "epoch = 50 loss = 0.20301875596866012 metric = 0.9174999958276748\n", "\n", - "================================================================================2022-08-24 16:55:52\n", - "epoch = 60 loss = 0.3550195214524865 metric = 0.8302500009536743\n", + "================================================================================2023-08-02 14:55:20\n", + "epoch = 60 loss = 0.20313117776066064 metric = 0.9179999929666519\n", "\n", - "================================================================================2022-08-24 16:55:57\n", - "epoch = 70 loss = 0.34594474658370017 metric = 0.8330000004172325\n", + "================================================================================2023-08-02 14:55:20\n", + "epoch = 70 loss = 0.19921660327352583 metric = 0.914999993443489\n", "\n", - "================================================================================2022-08-24 16:56:05\n", - "epoch = 80 loss = 0.268934119194746 metric = 0.8899999967217446\n", + "================================================================================2023-08-02 14:55:20\n", + "epoch = 80 loss = 0.195224122479558 metric = 0.919999991953373\n", "\n", - "================================================================================2022-08-24 16:56:11\n", - "epoch = 90 loss = 0.23292805092409252 metric = 0.9069999951124191\n", + "================================================================================2023-08-02 14:55:21\n", + "epoch = 90 loss = 0.19632970970124006 metric = 0.9224999934434891\n", "\n", - "================================================================================2022-08-24 16:56:17\n", - "epoch = 100 loss = 0.2204695837199688 metric = 0.9144999963045121\n" + "================================================================================2023-08-02 14:55:21\n", + "epoch = 100 loss = 0.19567020332440735 metric = 0.9209999939799309\n" ] } ], @@ -8789,7 +8845,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 24, "id": "5d1e757b", "metadata": {}, "outputs": [ @@ -8799,46 +8855,45 @@ "\n", "\n", - "\n", - "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", - " 2022-08-24T16:56:22.392606\n", + " 2023-08-02T14:55:24.458826\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.3.4, https://matplotlib.org/\n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", " \n", - " \n", + "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", " \n", - " \n", + "\" style=\"stroke: #ff0000\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -17801,7 +18055,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -17815,7 +18069,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/3-2,\344\270\255\351\230\266API\347\244\272\350\214\203.ipynb" "b/3-2,\344\270\255\351\230\266API\347\244\272\350\214\203.ipynb" index 0e44b554c..82d2d62b2 100644 --- "a/3-2,\344\270\255\351\230\266API\347\244\272\350\214\203.ipynb" +++ "b/3-2,\344\270\255\351\230\266API\347\244\272\350\214\203.ipynb" @@ -14,7 +14,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 1, "id": "f2e6ae11", "metadata": {}, "outputs": [], @@ -33,7 +33,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 2, "id": "95872f92", "metadata": {}, "outputs": [ @@ -41,7 +41,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.__version__=1.10.0\n" + "torch.__version__=2.0.1\n" ] } ], @@ -76,7 +76,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "id": "a19c99d1", "metadata": {}, "outputs": [], @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "id": "a1c16fc3", "metadata": {}, "outputs": [ @@ -111,46 +111,45 @@ "\n", "\n", - "\n", - "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", - " 2022-08-25T19:44:43.059968\n", + " 2023-08-02T14:55:43.143232\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.3.4, https://matplotlib.org/\n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", " \n", - " \n", + "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", " \n", - " \n", + "\" style=\"stroke: #0000ff\"/>\n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - 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" \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1594,99 +1602,99 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", + "\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n", " \n", " \n", - " \n", + "\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", + "\" style=\"fill: none; stroke: #000000; stroke-width: 0.8; stroke-linejoin: miter; stroke-linecap: square\"/>\n", " \n", " \n", " \n", @@ -1700,45 +1708,43 @@ "L 629.296875 27.878125 \n", "Q 629.296875 29.878125 631.296875 29.878125 \n", "z\n", - "\" style=\"fill:#ffffff;opacity:0.8;stroke:#cccccc;stroke-linejoin:miter;\"/>\n", + "\" style=\"fill: #ffffff; opacity: 0.8; stroke: #cccccc; stroke-linejoin: miter\"/>\n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", "\n" ], "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -1763,17 +1769,9 @@ "plt.show()\n" ] }, - { - "cell_type": "markdown", - "id": "0cd9bb40", - "metadata": {}, - "source": [ - "![](./data/3-2-线性回归数据可视化.png)" - ] - }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 5, "id": "d479e06e", "metadata": {}, "outputs": [], @@ -1781,8 +1779,7 @@ "#构建输入数据管道\n", "ds = TensorDataset(X,Y)\n", "dl = DataLoader(ds,batch_size = 10,shuffle=True,\n", - " num_workers=2)\n", - "\n" + " num_workers=2)\n" ] }, { @@ -1803,7 +1800,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 6, "id": "5917cd7d", "metadata": {}, "outputs": [], @@ -1811,9 +1808,7 @@ "model = nn.Linear(2,1) #线性层\n", "\n", "model.loss_fn = nn.MSELoss()\n", - "model.optimizer = torch.optim.SGD(model.parameters(),lr = 0.01)\n", - "\n", - "\n" + "model.optimizer = torch.optim.SGD(model.parameters(),lr = 0.01)\n" ] }, { @@ -1834,17 +1829,17 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 7, "id": "155b5c6f", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "112.17118072509766" + "240.01583862304688" ] }, - "execution_count": 39, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1865,22 +1860,44 @@ "\n" ] }, - { - "cell_type": "markdown", - "id": "55a15196", - "metadata": {}, - "source": [ - "```\n", - "269.98016357421875\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "299926c5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 14:56:08\n", + "epoch = 10 loss = 5.838783264160156\n", + "w = tensor([[ 2.0288, -2.8935]])\n", + "b = tensor([9.8821])\n", + "\n", + "================================================================================2023-08-02 14:56:18\n", + "epoch = 20 loss = 5.669178485870361\n", + "w = tensor([[ 2.0938, -3.0517]])\n", + "b = tensor([9.8500])\n", + "\n", + "================================================================================2023-08-02 14:56:28\n", + "epoch = 30 loss = 7.185830116271973\n", + "w = tensor([[ 2.0145, -2.9217]])\n", + "b = tensor([9.8800])\n", + "\n", + "================================================================================2023-08-02 14:56:39\n", + "epoch = 40 loss = 1.520167589187622\n", + "w = tensor([[ 2.1401, -3.0707]])\n", + "b = tensor([9.8327])\n", + "\n", + "================================================================================2023-08-02 14:56:49\n", + "epoch = 50 loss = 4.159793853759766\n", + "w = tensor([[ 2.0205, -2.9621]])\n", + "b = tensor([9.8832])\n" + ] + } + ], "source": [ "def train_model(model,epochs):\n", " for epoch in range(1,epochs+1):\n", @@ -1906,226 +1923,6597 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "b8384a3b", "metadata": {}, - "outputs": [], - "source": [ - "# 结果可视化\n", - "\n", - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "w,b = model.state_dict()[\"weight\"],model.state_dict()[\"bias\"]\n", - "\n", - "plt.figure(figsize = (12,5))\n", - "ax1 = plt.subplot(121)\n", - "ax1.scatter(X[:,0],Y[:,0], c = \"b\",label = \"samples\")\n", - "ax1.plot(X[:,0],w[0,0]*X[:,0]+b[0],\"-r\",linewidth = 5.0,label = \"model\")\n", - "ax1.legend()\n", - "plt.xlabel(\"x1\")\n", - "plt.ylabel(\"y\",rotation = 0)\n", - "\n", - "\n", - "\n", - "ax2 = plt.subplot(122)\n", - "ax2.scatter(X[:,1],Y[:,0], c = \"g\",label = \"samples\")\n", - "ax2.plot(X[:,1],w[0,1]*X[:,1]+b[0],\"-r\",linewidth = 5.0,label = \"model\")\n", - "ax2.legend()\n", - "plt.xlabel(\"x2\")\n", - "plt.ylabel(\"y\",rotation = 0)\n", - "\n", - "plt.show()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3ba62788", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "6be03bd7", - "metadata": {}, - "source": [ - "### 二， DNN二分类模型" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "93105316", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "7728a6a5", - "metadata": {}, - "source": [ - "**1，准备数据**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a212c295", - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np \n", - "import pandas as pd \n", - "from matplotlib import pyplot as plt\n", - "import torch\n", - "from torch import nn\n", - "import torch.nn.functional as F\n", - "from torch.utils.data import Dataset,DataLoader,TensorDataset\n", - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "#正负样本数量\n", - "n_positive,n_negative = 2000,2000\n", - "\n", - "#生成正样本, 小圆环分布\n", - "r_p = 5.0 + torch.normal(0.0,1.0,size = [n_positive,1]) \n", - "theta_p = 2*np.pi*torch.rand([n_positive,1])\n", - "Xp = torch.cat([r_p*torch.cos(theta_p),r_p*torch.sin(theta_p)],axis = 1)\n", - "Yp = torch.ones_like(r_p)\n", - "\n", - "#生成负样本, 大圆环分布\n", - "r_n = 8.0 + torch.normal(0.0,1.0,size = [n_negative,1]) \n", - "theta_n = 2*np.pi*torch.rand([n_negative,1])\n", - "Xn = torch.cat([r_n*torch.cos(theta_n),r_n*torch.sin(theta_n)],axis = 1)\n", - "Yn = torch.zeros_like(r_n)\n", - "\n", - "#汇总样本\n", - "X = torch.cat([Xp,Xn],axis = 0)\n", - "Y = torch.cat([Yp,Yn],axis = 0)\n", - "\n", - "\n", - "#可视化\n", - "plt.figure(figsize = (6,6))\n", - "plt.scatter(Xp[:,0],Xp[:,1],c = \"r\")\n", - "plt.scatter(Xn[:,0],Xn[:,1],c = \"g\")\n", - "plt.legend([\"positive\",\"negative\"]);\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "98e6f048", - "metadata": {}, - "outputs": [], - "source": [ - "#构建输入数据管道\n", - "ds = TensorDataset(X,Y)\n", - "dl = DataLoader(ds,batch_size = 10,shuffle=True,num_workers=2)\n", - "\n" - ] - }, - { - "cell_type": "markdown", - "id": "93c70cd9", - "metadata": {}, - "source": [ - "**2, 定义模型**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b5f8942c", - "metadata": {}, - "outputs": [], - "source": [ - "class DNNModel(nn.Module):\n", - " def __init__(self):\n", - " super(DNNModel, self).__init__()\n", - " self.fc1 = nn.Linear(2,4)\n", - " self.fc2 = nn.Linear(4,8) \n", - " self.fc3 = nn.Linear(8,1)\n", - "\n", - " # 正向传播\n", - " def forward(self,x):\n", - " x = F.relu(self.fc1(x))\n", - " x = F.relu(self.fc2(x))\n", - " y = nn.Sigmoid()(self.fc3(x))\n", - " return y\n", - " \n", - " # 损失函数\n", - " def loss_fn(self,y_pred,y_true):\n", - " return nn.BCELoss()(y_pred,y_true)\n", - " \n", - " # 评估函数(准确率)\n", - " def metric_fn(self,y_pred,y_true):\n", - " y_pred = torch.where(y_pred>0.5,torch.ones_like(y_pred,dtype = torch.float32),\n", - " torch.zeros_like(y_pred,dtype = torch.float32))\n", - " acc = torch.mean(1-torch.abs(y_true-y_pred))\n", - " return acc\n", - " \n", - " # 优化器\n", - " @property\n", - " def optimizer(self):\n", - " return torch.optim.Adam(self.parameters(),lr = 0.001)\n", - " \n", - "model = DNNModel()\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a9bf0327", - "metadata": {}, - "outputs": [], - "source": [ - "# 测试模型结构\n", - "(features,labels) = next(iter(dl))\n", - "predictions = model(features)\n", - "\n", - "loss = model.loss_fn(predictions,labels)\n", - "metric = model.metric_fn(predictions,labels)\n", - "\n", - "print(\"init loss:\",loss.item())\n", - "print(\"init metric:\",metric.item())\n" - ] - }, - { - "cell_type": "markdown", - "id": "8d0c96de", - "metadata": {}, - "source": [ - "```\n", - "init loss: 0.7065666913986206\n", - "init metric: 0.6000000238418579\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9d2c094c", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "45c684f7", - "metadata": {}, - "source": [ - "**3，训练模型**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6c69fa16", + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T14:57:12.976450\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 结果可视化\n", + "\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'svg'\n", + "\n", + "w,b = model.state_dict()[\"weight\"],model.state_dict()[\"bias\"]\n", + "\n", + "plt.figure(figsize = (12,5))\n", + "ax1 = plt.subplot(121)\n", + "ax1.scatter(X[:,0],Y[:,0], c = \"b\",label = \"samples\")\n", + "ax1.plot(X[:,0],w[0,0]*X[:,0]+b[0],\"-r\",linewidth = 5.0,label = \"model\")\n", + "ax1.legend()\n", + "plt.xlabel(\"x1\")\n", + "plt.ylabel(\"y\",rotation = 0)\n", + "\n", + "\n", + "\n", + "ax2 = plt.subplot(122)\n", + "ax2.scatter(X[:,1],Y[:,0], c = \"g\",label = \"samples\")\n", + "ax2.plot(X[:,1],w[0,1]*X[:,1]+b[0],\"-r\",linewidth = 5.0,label = \"model\")\n", + "ax2.legend()\n", + "plt.xlabel(\"x2\")\n", + "plt.ylabel(\"y\",rotation = 0)\n", + "\n", + "plt.show()\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ba62788", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "6be03bd7", + "metadata": {}, + "source": [ + "### 二， DNN二分类模型" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93105316", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7728a6a5", + "metadata": {}, + "source": [ + "**1，准备数据**" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "a212c295", + "metadata": {}, + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T14:57:18.801625\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import numpy as np \n", + "import pandas as pd \n", + "from matplotlib import pyplot as plt\n", + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F\n", + "from torch.utils.data import Dataset,DataLoader,TensorDataset\n", + "%matplotlib inline\n", + "%config InlineBackend.figure_format = 'svg'\n", + "\n", + "#正负样本数量\n", + "n_positive,n_negative = 2000,2000\n", + "\n", + "#生成正样本, 小圆环分布\n", + "r_p = 5.0 + torch.normal(0.0,1.0,size = [n_positive,1]) \n", + "theta_p = 2*np.pi*torch.rand([n_positive,1])\n", + "Xp = torch.cat([r_p*torch.cos(theta_p),r_p*torch.sin(theta_p)],axis = 1)\n", + "Yp = torch.ones_like(r_p)\n", + "\n", + "#生成负样本, 大圆环分布\n", + "r_n = 8.0 + torch.normal(0.0,1.0,size = [n_negative,1]) \n", + "theta_n = 2*np.pi*torch.rand([n_negative,1])\n", + "Xn = torch.cat([r_n*torch.cos(theta_n),r_n*torch.sin(theta_n)],axis = 1)\n", + "Yn = torch.zeros_like(r_n)\n", + "\n", + "#汇总样本\n", + "X = torch.cat([Xp,Xn],axis = 0)\n", + "Y = torch.cat([Yp,Yn],axis = 0)\n", + "\n", + "\n", + "#可视化\n", + "plt.figure(figsize = (6,6))\n", + "plt.scatter(Xp[:,0],Xp[:,1],c = \"r\")\n", + "plt.scatter(Xn[:,0],Xn[:,1],c = \"g\")\n", + "plt.legend([\"positive\",\"negative\"]);\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "98e6f048", + "metadata": {}, + "outputs": [], + "source": [ + "#构建输入数据管道\n", + "ds = TensorDataset(X,Y)\n", + "dl = DataLoader(ds,batch_size = 10,shuffle=True,num_workers=2)\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "93c70cd9", + "metadata": {}, + "source": [ + "**2, 定义模型**" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b5f8942c", + "metadata": {}, + "outputs": [], + "source": [ + "class DNNModel(nn.Module):\n", + " def __init__(self):\n", + " super(DNNModel, self).__init__()\n", + " self.fc1 = nn.Linear(2,4)\n", + " self.fc2 = nn.Linear(4,8) \n", + " self.fc3 = nn.Linear(8,1)\n", + "\n", + " # 正向传播\n", + " def forward(self,x):\n", + " x = F.relu(self.fc1(x))\n", + " x = F.relu(self.fc2(x))\n", + " y = nn.Sigmoid()(self.fc3(x))\n", + " return y\n", + " \n", + " # 损失函数\n", + " def loss_fn(self,y_pred,y_true):\n", + " return nn.BCELoss()(y_pred,y_true)\n", + " \n", + " # 评估函数(准确率)\n", + " def metric_fn(self,y_pred,y_true):\n", + " y_pred = torch.where(y_pred>0.5,torch.ones_like(y_pred,dtype = torch.float32),\n", + " torch.zeros_like(y_pred,dtype = torch.float32))\n", + " acc = torch.mean(1-torch.abs(y_true-y_pred))\n", + " return acc\n", + " \n", + " # 优化器\n", + " @property\n", + " def optimizer(self):\n", + " return torch.optim.Adam(self.parameters(),lr = 0.001)\n", + " \n", + "model = DNNModel()\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a9bf0327", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "init loss: 0.6275655627250671\n", + "init metric: 0.699999988079071\n" + ] + } + ], + "source": [ + "# 测试模型结构\n", + "(features,labels) = next(iter(dl))\n", + "predictions = model(features)\n", + "\n", + "loss = model.loss_fn(predictions,labels)\n", + "metric = model.metric_fn(predictions,labels)\n", + "\n", + "print(\"init loss:\",loss.item())\n", + "print(\"init metric:\",metric.item())\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d2c094c", "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "45c684f7", + "metadata": {}, + "source": [ + "**3，训练模型**" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "6c69fa16", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(0.6963499784469604, 0.4000000059604645)" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "def train_step(model, features, labels):\n", " \n", @@ -2148,22 +8536,34 @@ "train_step(model,features,labels)\n" ] }, - { - "cell_type": "markdown", - "id": "8f4680af", - "metadata": {}, - "source": [ - "```\n", - "(0.6048880815505981, 0.699999988079071)\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "9d2aac2d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 14:57:51\n", + "epoch = 10 loss = 0.24713441669009625 metric = 0.9112499916553497\n", + "\n", + "================================================================================2023-08-02 14:58:04\n", + "epoch = 20 loss = 0.216802254387876 metric = 0.9144999922811985\n", + "\n", + "================================================================================2023-08-02 14:58:16\n", + "epoch = 30 loss = 0.2144702924368903 metric = 0.9199999921023846\n", + "\n", + "================================================================================2023-08-02 14:58:28\n", + "epoch = 40 loss = 0.22559154781927646 metric = 0.9204999931156636\n", + "\n", + "================================================================================2023-08-02 14:58:41\n", + "epoch = 50 loss = 0.23063559879607054 metric = 0.9207499933242798\n" + ] + } + ], "source": [ "def train_model(model,epochs):\n", " for epoch in range(1,epochs+1):\n", @@ -2192,10 +8592,8983 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "c7a7878a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T15:00:20.745275\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# 结果可视化\n", "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))\n", @@ -2242,7 +17615,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -2256,7 +17629,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/3-3,\351\253\230\351\230\266API\347\244\272\350\214\203.ipynb" "b/3-3,\351\253\230\351\230\266API\347\244\272\350\214\203.ipynb" index 351a64ae7..9b2167c2a 100644 --- "a/3-3,\351\253\230\351\230\266API\347\244\272\350\214\203.ipynb" +++ "b/3-3,\351\253\230\351\230\266API\347\244\272\350\214\203.ipynb" @@ -11,20 +11,13 @@ "\n", "作者通过仿照keras的功能对Pytorch的nn.Module进行了封装，设计了torchkeras.KerasModel类，\n", "\n", - "实现了 fit, evaluate，predict等方法，相当于用户自定义高阶API。\n", + "实现了 fit, evaluate等方法，相当于用户自定义高阶API。\n", "\n", - "并示范了用它实现线性回归模型。\n", + "并示范了用它实现线性回归模型和DNN二分类模型。\n", "\n", - "此外，作者还通过借用pytorch_lightning的功能，封装了类Keras接口的另外一种实现，即torchkeras.LightModel类。\n", - "\n", - "并示范了用它实现DNN二分类模型。\n", - "\n", - "\n", - "torchkeras.KerasModel类和torchkeras.LightModel类看起来非常强大，但实际上它们的源码非常简单，不足200行。\n", + "torchkeras.KerasModel类看起来非常强大，但实际上它们的源码非常简单，不足200行。\n", "我们在第一章中`一、Pytorch的建模流程`用到的训练代码其实就是torchkeras库的核心源码。\n", - "\n", - "在实际应用中，由于有些模型的输入输出以及Loss结构和torchkeras的假设结构有所不同，直接调用torchkeras可能不能满足需求，不要害怕，copy出来\n", - "torchkeras.KerasModel或者torchkeras.LightModel的源码，在输入输出和Loss上简单改动一下就可以。\n" + "\n" ] }, { @@ -37,39 +30,19 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "a469f8d4", - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import datetime\n", - "\n", - "#打印时间\n", - "def printbar():\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - "\n", - "#mac系统上pytorch和matplotlib在jupyter中同时跑需要更改环境变量\n", - "os.environ[\"KMP_DUPLICATE_LIB_OK\"]=\"TRUE\" \n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "db03c304", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install torchkeras==3.2.2 -i https://pypi.python.org/simple" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "932c81f4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__=2.0.1\n", + "torchkeras.__version__=3.9.3\n" + ] + } + ], "source": [ "import torch \n", "import torchkeras \n", @@ -79,17 +52,6 @@ "print(\"torchkeras.__version__=\"+torchkeras.__version__) " ] }, - { - "cell_type": "markdown", - "id": "9ad9ac52", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__=1.10.0\n", - "torchkeras.__version__=3.2.2\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -111,7 +73,7 @@ "id": "cf19d513", "metadata": {}, "source": [ - "此范例我们通过继承torchkeras.Model模型接口，实现线性回归模型。" + "此范例我们通过使用torchkeras.KerasModel模型接口，实现线性回归模型。" ] }, { @@ -124,7 +86,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "36470b05", "metadata": {}, "outputs": [], @@ -149,10 +111,1683 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "abf78477", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T15:27:22.791350\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# 数据可视化\n", "\n", @@ -174,17 +1809,9 @@ "plt.show()\n" ] }, - { - "cell_type": "markdown", - "id": "51121814", - "metadata": {}, - "source": [ - "![](./data/3-3-回归数据可视化.png)" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "ccb5ef52", "metadata": {}, "outputs": [], @@ -192,8 +1819,8 @@ "#构建输入数据管道\n", "ds = TensorDataset(X,Y)\n", "ds_train,ds_val = torch.utils.data.random_split(ds,[int(400*0.7),400-int(400*0.7)])\n", - "dl_train = DataLoader(ds_train,batch_size = 10,shuffle=True,num_workers=2)\n", - "dl_val = DataLoader(ds_val,batch_size = 10,num_workers=2)\n", + "dl_train = DataLoader(ds_train,batch_size = 16,shuffle=True,num_workers=2)\n", + "dl_val = DataLoader(ds_val,batch_size = 16,num_workers=2)\n", "\n", "features,labels = next(iter(dl_train))\n" ] @@ -216,7 +1843,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "55605afc", "metadata": {}, "outputs": [], @@ -234,40 +1861,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "c3e2474e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Linear-1 [-1, 1] 3\n", + "==========================================================================\n", + "Total params: 3\n", + "Trainable params: 3\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000069\n", + "Forward/backward pass size (MB): 0.000008\n", + "Params size (MB): 0.000011\n", + "Estimated Total Size (MB): 0.000088\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ - "\n", "from torchkeras import summary \n", "\n", "summary(net,input_data=features);" ] }, - { - "cell_type": "markdown", - "id": "d95bac43", - "metadata": {}, - "source": [ - "```\n", - "--------------------------------------------------------------------------\n", - "Layer (type) Output Shape Param #\n", - "==========================================================================\n", - "Linear-1 [-1, 1] 3\n", - "==========================================================================\n", - "Total params: 3\n", - "Trainable params: 3\n", - "Non-trainable params: 0\n", - "--------------------------------------------------------------------------\n", - "Input size (MB): 0.000069\n", - "Forward/backward pass size (MB): 0.000008\n", - "Params size (MB): 0.000011\n", - "Estimated Total Size (MB): 0.000088\n", - "--------------------------------------------------------------------------\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -286,10 +1910,1495 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "261f8aaa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< 🚀 mps is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T15:35:12.198578\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# 结果可视化\n", "\n", @@ -353,120 +5230,136 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "5380c5e1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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epochtrain_losstrain_maelrval_lossval_mae
84853.8869481.6023240.013.9271541.568657
85863.9735111.5968340.013.9074081.565720
86873.9847561.5995510.013.9338451.571336
87883.9728101.6053670.013.9836061.578675
88893.9345181.6057680.013.9875941.580409
\n", + "
" + ], + "text/plain": [ + " epoch train_loss train_mae lr val_loss val_mae\n", + "84 85 3.886948 1.602324 0.01 3.927154 1.568657\n", + "85 86 3.973511 1.596834 0.01 3.907408 1.565720\n", + "86 87 3.984756 1.599551 0.01 3.933845 1.571336\n", + "87 88 3.972810 1.605367 0.01 3.983606 1.578675\n", + "88 89 3.934518 1.605768 0.01 3.987594 1.580409" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "dfhistory.tail()" ] }, - { - "cell_type": "markdown", - "id": "b69d7227", - "metadata": {}, - "source": [ - "```\n", - "\ttrain_loss\ttrain_mae\tval_loss\tval_mae\tepoch\n", - "15\t4.339620\t1.635648\t3.119237\t1.384351\t16\n", - "16\t4.313104\t1.631849\t2.999482\t1.352427\t17\n", - "17\t4.319547\t1.628811\t3.022779\t1.355054\t18\n", - "18\t4.315403\t1.636815\t3.087339\t1.369488\t19\n", - "19\t4.266822\t1.627701\t2.937751\t1.330670\t20\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f42e3a0", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "18806439", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()\n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31b33440", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"loss\")" - ] - }, - { - "cell_type": "markdown", - "id": "b1b4b3c3", - "metadata": {}, - "source": [ - "![](./data/3-3-loss曲线.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "31a7c0af", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"mae\")" - ] - }, - { - "cell_type": "markdown", - "id": "9fa665e8", - "metadata": {}, - "source": [ - "![](./data/3-3-mape曲线.png)" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "1578280e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████| 8/8 [00:01<00:00, 7.51it/s, val_loss=3.89, val_mae=1.56]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 3.8944740295410156, 'val_mae': 1.5583606958389282}" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 评估\n", "model.evaluate(dl_val)" ] }, - { - "cell_type": "markdown", - "id": "b2ff4283", - "metadata": {}, - "source": [ - "```\n", - "{'val_loss': 2.9377514322598777, 'val_mae': 1.3306695222854614}\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -485,69 +5378,37 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "3cf065a5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[24.30810547 -0.18447018 10.48405933 10.95958519 20.05254555 25.67943192\n", + " 25.41451073 22.11546135 9.20176315 19.23609543]\n" + ] + } + ], "source": [ "# 预测\n", "dl = DataLoader(TensorDataset(X))\n", - "model.predict(dl)[0:10]" - ] - }, - { - "cell_type": "markdown", - "id": "89997a39", - "metadata": {}, - "source": [ - "```\n", - "tensor([[ 3.9212],\n", - " [ 8.6336],\n", - " [ 6.1982],\n", - " [ 6.1212],\n", - " [-5.0974],\n", - " [-6.3183],\n", - " [ 4.6588],\n", - " [ 5.5349],\n", - " [11.9106],\n", - " [24.6937]])\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "4e517835", - "metadata": {}, - "outputs": [], - "source": [ - "# 预测\n", - "model.predict(dl_val)[0:10]" - ] - }, - { - "cell_type": "markdown", - "id": "df8afc8b", - "metadata": {}, - "source": [ - "```\n", - "tensor([[-11.0324],\n", - " [ 26.2708],\n", - " [ 24.8866],\n", - " [ 12.2698],\n", - " [-12.0984],\n", - " [ 6.7254],\n", - " [ 12.8081],\n", - " [ 20.6977],\n", - " [ 5.4715],\n", - " [ 2.0188]])\n", - "```" + "\n", + "result = []\n", + "with torch.no_grad():\n", + " for batch in dl:\n", + " features = batch[0].to(model.accelerator.device)\n", + " res = net(features)\n", + " result.extend(res.tolist())\n", + "result = np.array(result).flatten() \n", + "print(result[:10])" ] }, { "cell_type": "code", "execution_count": null, - "id": "21f0f0f5", + "id": "1d4c846a-95ef-4dcb-8cd0-3af31899902b", "metadata": {}, "outputs": [], "source": [] @@ -560,14 +5421,6 @@ "### 二，DNN二分类模型" ] }, - { - "cell_type": "markdown", - "id": "010aac54", - "metadata": {}, - "source": [ - "此范例我们通过继承torchkeras.LightModel模型接口，实现DNN二分类模型。\n" - ] - }, { "cell_type": "markdown", "id": "23026251", @@ -578,10 +5431,4634 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "e02230f7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T15:36:56.513941\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "import numpy as np \n", "import pandas as pd \n", @@ -622,17 +10099,9 @@ "plt.legend([\"positive\",\"negative\"]);\n" ] }, - { - "cell_type": "markdown", - "id": "a290d8f7", - "metadata": {}, - "source": [ - "![](./data/3-3-分类数据可视化.png)" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "75547978", "metadata": {}, "outputs": [], @@ -641,7 +10110,10 @@ "\n", "ds_train,ds_val = torch.utils.data.random_split(ds,[int(len(ds)*0.7),len(ds)-int(len(ds)*0.7)])\n", "dl_train = DataLoader(ds_train,batch_size = 100,shuffle=True,num_workers=2)\n", - "dl_val = DataLoader(ds_val,batch_size = 100,num_workers=2)\n" + "dl_val = DataLoader(ds_val,batch_size = 100,num_workers=2)\n", + "\n", + "for features,labels in dl_train:\n", + " break " ] }, { @@ -662,7 +10134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "881fd06d", "metadata": {}, "outputs": [], @@ -684,30 +10156,51 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "id": "0df9c08b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Linear-1 [-1, 4] 12\n", + "Linear-2 [-1, 8] 40\n", + "Linear-3 [-1, 1] 9\n", + "==========================================================================\n", + "Total params: 61\n", + "Trainable params: 61\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000069\n", + "Forward/backward pass size (MB): 0.000099\n", + "Params size (MB): 0.000233\n", + "Estimated Total Size (MB): 0.000401\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ - "import torchkeras \n", - "from torchkeras.metrics import Accuracy \n", + "from torchkeras import KerasModel \n", + "from torchkeras.metrics import Accuracy\n", "\n", "net = Net()\n", "loss_fn = nn.BCEWithLogitsLoss()\n", "metric_dict = {\"acc\":Accuracy()}\n", "\n", - "optimizer = torch.optim.Adam(net.parameters(), lr=0.05)\n", - "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.0001)\n", + "optimizer = torch.optim.Adam(net.parameters(), lr=0.001)\n", "\n", - "model = torchkeras.LightModel(net,\n", + "model = KerasModel(net,\n", " loss_fn = loss_fn,\n", " metrics_dict= metric_dict,\n", - " optimizer = optimizer,\n", - " lr_scheduler = lr_scheduler,\n", + " optimizer = optimizer\n", " ) \n", "\n", "from torchkeras import summary\n", - "summary(model,input_data=features);\n" + "summary(net,input_data=features);\n" ] }, { @@ -728,78 +10221,10520 @@ }, { 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\n", - "\n", - "\n", - "##4，启动训练循环\n", - "trainer.fit(model,dl_train,dl_val)\n", - "\n" + "dfhistory = model.fit(\n", + " train_data=dl_train,\n", + " val_data=dl_val,\n", + " epochs=100,\n", + " ckpt_path='checkpoint',\n", + " patience=10,\n", + " monitor='val_acc',\n", + " mode='max'\n", + ")\n" ] }, { - "cell_type": "markdown", - "id": "fd876583", + "cell_type": "code", + "execution_count": null, + "id": "a5b09244-f0cd-4732-b19a-1b40217fdfb4", "metadata": {}, - "source": [ - "```\n", - "================================================================================2022-07-16 20:25:49\n", - "{'epoch': 0, 'val_loss': 0.3484574854373932, 'val_acc': 0.8766666650772095}\n", - "{'epoch': 0, 'train_loss': 0.5639581680297852, 'train_acc': 0.708214282989502}\n", - "<<<<<< reach best val_acc : 0.8766666650772095 >>>>>>\n", - "\n", - "================================================================================2022-07-16 20:25:54\n", - "{'epoch': 1, 'val_loss': 0.18654096126556396, 'val_acc': 0.925000011920929}\n", - "{'epoch': 1, 'train_loss': 0.2512527406215668, 'train_acc': 0.9117857217788696}\n", - "<<<<<< reach best val_acc : 0.925000011920929 >>>>>>\n", - "\n", - "================================================================================2022-07-16 20:25:59\n", - "{'epoch': 2, 'val_loss': 0.19609291851520538, 'val_acc': 0.9191666841506958}\n", - "{'epoch': 2, 'train_loss': 0.19115397334098816, 'train_acc': 0.9257143139839172}\n", - "\n", - "================================================================================2022-07-16 20:26:04\n", - "{'epoch': 3, 'val_loss': 0.18749761581420898, 'val_acc': 0.925000011920929}\n", - "{'epoch': 3, 'train_loss': 0.19545568525791168, 'train_acc': 0.9235714077949524}\n", - "\n", - "================================================================================2022-07-16 20:26:09\n", - "{'epoch': 4, 'val_loss': 0.21518440544605255, 'val_acc': 0.9083333611488342}\n", - "{'epoch': 4, 'train_loss': 0.1998639553785324, 'train_acc': 0.9192857146263123}\n", - "```\n" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "08ca32a0", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T15:46:18.997499\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# 结果可视化\n", "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))\n", @@ -817,14 +20752,6 @@ "ax2.set_title(\"y_pred\");\n" ] }, - { - "cell_type": "markdown", - "id": "5580f23e", - "metadata": {}, - "source": [ - "![](./data/3-3-分类结果可视化.png)" - ] - }, { "cell_type": "markdown", "id": "e5bb9c8d", @@ -835,93 +20762,30 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "d817e473", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "abdb3627", - "metadata": {}, - "outputs": [], - "source": [ - "dfhistory = model.get_history() \n", - "plot_metric(dfhistory,\"loss\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "1a0095c1", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h491k7wtl0j20f70a6wes.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "709dc9ee", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a74c677b", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"acc\")" - ] - }, - { - "cell_type": "markdown", - "id": "b91fe873", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h491k8if3hj20ev0aaglw.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "0517f63a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████| 12/12 [00:01<00:00, 10.94it/s, val_acc=0.924, val_loss=0.202]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.20166969237228236, 'val_acc': 0.9241666793823242}" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "#使用最佳保存点进行评估\n", - "trainer.test(ckpt_path='best', dataloaders=dl_val,verbose = False)" - ] - }, - { - "cell_type": "markdown", - "id": "0174ba79", - "metadata": {}, - "source": [ - "```\n", - "{'test_loss': 0.18654096126556396, 'test_acc': 0.925000011920929}\n", - "```" + "model.evaluate(dl_val)" ] }, { @@ -942,92 +20806,51 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "eda36b16", - "metadata": {}, - "outputs": [], - "source": [ - "predictions = F.sigmoid(torch.cat(trainer.predict(model, dl_val, ckpt_path='best'))) \n", - "predictions " - ] - }, - { - "cell_type": "markdown", - "id": "de9c34ba", - "metadata": {}, - "source": [ - "```\n", - "tensor([[0.3873],\n", - " [0.0028],\n", - " [0.8772],\n", - " ...,\n", - " [0.9886],\n", - " [0.4970],\n", - " [0.8094]])\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 37, "id": "aa29f557", "metadata": {}, "outputs": [], "source": [ - "def predict(model,dl):\n", - " model.eval()\n", - " result = torch.cat([model.forward(t[0]) for t in dl])\n", + "device = model.accelerator.device \n", + "@torch.no_grad()\n", + "def predict(net,dl):\n", + " net.eval()\n", + " result = torch.cat([net.forward(t[0].to(device)) for t in dl])\n", " return(result.data)\n", "\n", - "print(model.device)\n", - "predictions = F.sigmoid(predict(model,dl_val)[:10]) " + "predictions = F.sigmoid(predict(net,dl_val)[:10]) " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "0116222c", "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "357d80cf", - "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[0.3352],\n", + " [0.9824],\n", + " [0.0443],\n", + " [0.9682],\n", + " [0.0016],\n", + " [0.0012],\n", + " [0.9986],\n", + " [0.0016],\n", + " [0.0079],\n", + " [0.0654]], device='mps:0')" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "**6，保存模型**" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6f0435d4", - "metadata": {}, - "outputs": [], - "source": [ - "print(trainer.checkpoint_callback.best_model_path)\n", - "print(trainer.checkpoint_callback.best_model_score)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1570f270", - "metadata": {}, - "outputs": [], - "source": [ - "model_loaded = torchkeras.LightModel.load_from_checkpoint(trainer.checkpoint_callback.best_model_path)" + "predictions " ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "08737fd8", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -1057,7 +20880,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1071,7 +20894,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/4-1,\345\274\240\351\207\217\347\232\204\347\273\223\346\236\204\346\223\215\344\275\234.ipynb" "b/4-1,\345\274\240\351\207\217\347\232\204\347\273\223\346\236\204\346\223\215\344\275\234.ipynb" index a210415e9..9f0bdaa25 100644 --- "a/4-1,\345\274\240\351\207\217\347\232\204\347\273\223\346\236\204\346\223\215\344\275\234.ipynb" +++ "b/4-1,\345\274\240\351\207\217\347\232\204\347\273\223\346\236\204\346\223\215\344\275\234.ipynb" @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 1, "id": "2d5140da", "metadata": {}, "outputs": [ @@ -24,7 +24,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.__version__=1.10.0\n" + "torch.__version__=2.0.1\n" ] } ], @@ -33,14 +33,6 @@ "print(\"torch.__version__=\"+torch.__version__) " ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "fad8f216", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -67,7 +59,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 2, "id": "67963200", "metadata": {}, "outputs": [], @@ -78,15 +70,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "ca122ddc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 27, + "execution_count": 3, "id": "38784279", "metadata": {}, "outputs": [ @@ -105,15 +89,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "8fba8aab", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 28, + "execution_count": 4, "id": "089f2763", "metadata": {}, "outputs": [ @@ -140,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 5, "id": "8a56defe", "metadata": {}, "outputs": [ @@ -168,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 6, "id": "79ef680d", "metadata": {}, "outputs": [ @@ -197,7 +173,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 7, "id": "c6e294e2", "metadata": {}, "outputs": [ @@ -231,7 +207,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 8, "id": "827a9fb4", "metadata": {}, "outputs": [ @@ -260,7 +236,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 9, "id": "a2b191f2", "metadata": {}, "outputs": [ @@ -280,14 +256,6 @@ "print(a)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "fb95296a", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -298,7 +266,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 10, "id": "3a0e6228", "metadata": {}, "outputs": [ @@ -328,7 +296,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 11, "id": "897c8f4d", "metadata": {}, "outputs": [ @@ -359,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 12, "id": "fceecfdd", "metadata": {}, "outputs": [ @@ -388,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 13, "id": "d231f907", "metadata": {}, "outputs": [ @@ -453,15 +421,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "d6a66369", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 38, + "execution_count": 14, "id": "2d562ecc", "metadata": {}, "outputs": [ @@ -495,7 +455,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 15, "id": "cd695542", "metadata": {}, "outputs": [ @@ -520,19 +480,9 @@ "outputs": [], "source": [] }, - { - "cell_type": "markdown", - "id": "2d808227", - "metadata": {}, - "source": [ - "```\n", - "tensor([4, 7, 0, 1, 3], dtype=torch.int32)\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 16, "id": "7e2d299a", "metadata": {}, "outputs": [ @@ -557,19 +507,9 @@ "outputs": [], "source": [] }, - { - "cell_type": "markdown", - "id": "3fb550a3", - "metadata": {}, - "source": [ - "```\n", - "tensor([6, 9, 3, 8, 4], dtype=torch.int32)\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 17, "id": "83e9c09d", "metadata": {}, "outputs": [ @@ -596,20 +536,9 @@ "outputs": [], "source": [] }, - { - "cell_type": "markdown", - "id": "16409447", - "metadata": {}, - "source": [ - "```\n", - "tensor(4, dtype=torch.int32)\n", - "tensor(4, dtype=torch.int32)\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 18, "id": "c3bec0e0", "metadata": {}, "outputs": [ @@ -630,7 +559,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 19, "id": "b056c6aa", "metadata": {}, "outputs": [ @@ -650,20 +579,16 @@ ] }, { - "cell_type": "markdown", - "id": "2293e75e", + "cell_type": "code", + "execution_count": null, + "id": "eeda6705-5298-43d0-a42e-13962516200f", "metadata": {}, - "source": [ - "```\n", - "tensor([[6, 8],\n", - " [3, 0],\n", - " [5, 8]], dtype=torch.int32)\n", - "```" - ] + "outputs": [], + "source": [] }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 20, "id": "f8989377", "metadata": {}, "outputs": [ @@ -674,7 +599,7 @@ " [0., 0.]])" ] }, - "execution_count": 44, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } @@ -688,7 +613,15 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, + "id": "37cd24a5-1285-4710-ab1e-2ec3a54609c0", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 21, "id": "33abd721", "metadata": {}, "outputs": [ @@ -717,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 22, "id": "7ddeb6d6", "metadata": {}, "outputs": [ @@ -736,6 +669,14 @@ "print(a[...,1])" ] }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba75917f-f20c-4956-991c-d7d5e25abf80", + "metadata": {}, + "outputs": [], + "source": [] + }, { "cell_type": "markdown", "id": "047c830a", @@ -748,7 +689,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 23, "id": "1c8edbc7", "metadata": {}, "outputs": [ @@ -791,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 24, "id": "3cc30352", "metadata": {}, "outputs": [ @@ -815,7 +756,7 @@ " [39, 29, 40, 40, 5, 6, 42]]], dtype=torch.int32)" ] }, - "execution_count": 48, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -827,7 +768,7 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 25, "id": "e82bc37a", "metadata": {}, "outputs": [ @@ -862,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 26, "id": "19230d02", "metadata": {}, "outputs": [ @@ -872,7 +813,7 @@ "tensor([55, 52, 42], dtype=torch.int32)" ] }, - "execution_count": 50, + "execution_count": 26, "metadata": {}, "output_type": "execute_result" } @@ -881,12 +822,12 @@ "#抽取第0个班级第0个学生的第0门课程，第2个班级的第3个学生的第1门课程，第3个班级的第4个学生第6门课程成绩\n", "#take将输入看成一维数组，输出和index同形状\n", "s = torch.take(scores,torch.tensor([0*5*7+0,2*5*7+3*7+1,3*5*7+4*7+6]))\n", - "s\n" + "s" ] }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 27, "id": "d753e409", "metadata": {}, "outputs": [ @@ -906,14 +847,6 @@ "print(g)\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "0aaa4799", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -940,7 +873,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 28, "id": "59ebf9da", "metadata": {}, "outputs": [ @@ -990,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 53, + "execution_count": 29, "id": "8daaca71", "metadata": {}, "outputs": [ @@ -1022,7 +955,7 @@ " [100, 100, 100, 100, 100, 100, 100]]], dtype=torch.int32)" ] }, - "execution_count": 53, + "execution_count": 29, "metadata": {}, "output_type": "execute_result" } @@ -1035,7 +968,7 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": 30, "id": "1cdbf318", "metadata": {}, "outputs": [ @@ -1067,7 +1000,7 @@ " [60, 60, 60, 60, 60, 60, 60]]], dtype=torch.int32)" ] }, - "execution_count": 54, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -1113,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": 31, "id": "6f2eb3fd", "metadata": {}, "outputs": [ @@ -1148,7 +1081,7 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 32, "id": "99a96211", "metadata": {}, "outputs": [ @@ -1173,7 +1106,7 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 33, "id": "af828000", "metadata": {}, "outputs": [ @@ -1221,7 +1154,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 34, "id": "c5fcad41", "metadata": {}, "outputs": [ @@ -1247,7 +1180,7 @@ }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 35, "id": "250cab70", "metadata": {}, "outputs": [ @@ -1293,7 +1226,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 36, "id": "f0214621", "metadata": {}, "outputs": [ @@ -1311,7 +1244,7 @@ "torch.Size([100, 4, 256, 256])" ] }, - "execution_count": 66, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1343,7 +1276,7 @@ }, { "cell_type": "code", - "execution_count": 67, + "execution_count": 37, "id": "217205e5", "metadata": {}, "outputs": [ @@ -1394,7 +1327,7 @@ }, { "cell_type": "code", - "execution_count": 68, + "execution_count": 38, "id": "a4d8c45d", "metadata": {}, "outputs": [ @@ -1424,7 +1357,7 @@ }, { "cell_type": "code", - "execution_count": 69, + "execution_count": 39, "id": "c5f1b5ee", "metadata": {}, "outputs": [ @@ -1452,7 +1385,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 40, "id": "ae893622", "metadata": {}, "outputs": [ @@ -1463,7 +1396,7 @@ " [ 3., 4., 7., 8., 11., 12.]])" ] }, - "execution_count": 70, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -1474,7 +1407,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 41, "id": "550ec0d4", "metadata": {}, "outputs": [ @@ -1490,13 +1423,13 @@ " [11., 12.]]])" ] }, - "execution_count": 71, + "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "torch.stack([a,b,c],axis = 1)\n" + "torch.stack([a,b,c],axis = 1)" ] }, { @@ -1509,7 +1442,7 @@ }, { "cell_type": "code", - "execution_count": 72, + "execution_count": 42, "id": "50e02ce3", "metadata": {}, "outputs": [ @@ -1542,7 +1475,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 43, "id": "fae5c5f1", "metadata": {}, "outputs": [ @@ -1602,7 +1535,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1616,7 +1549,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/4-2,\345\274\240\351\207\217\347\232\204\346\225\260\345\255\246\350\277\220\347\256\227.ipynb" "b/4-2,\345\274\240\351\207\217\347\232\204\346\225\260\345\255\246\350\277\220\347\256\227.ipynb" index d0141c645..6ef6c5267 100644 --- "a/4-2,\345\274\240\351\207\217\347\232\204\346\225\260\345\255\246\350\277\220\347\256\227.ipynb" +++ "b/4-2,\345\274\240\351\207\217\347\232\204\346\225\260\345\255\246\350\277\220\347\256\227.ipynb" @@ -28,10 +28,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "a6c5e1c2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__=2.0.1\n" + ] + } + ], "source": [ "import torch \n", "print(\"torch.__version__=\"+torch.__version__) \n" @@ -103,7 +111,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "id": "8bff7b5f", "metadata": {}, "outputs": [ @@ -114,7 +122,7 @@ " [ 4., 12.]])" ] }, - "execution_count": 5, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -127,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "id": "5b2a982e", "metadata": {}, "outputs": [ @@ -138,7 +146,7 @@ " [-10., -4.]])" ] }, - "execution_count": 6, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -149,7 +157,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "id": "7b7edda7", "metadata": {}, "outputs": [ @@ -160,7 +168,7 @@ " [-21., 32.]])" ] }, - "execution_count": 7, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -171,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "id": "80742f80", "metadata": {}, "outputs": [ @@ -182,7 +190,7 @@ " [-0.4286, 0.5000]])" ] }, - "execution_count": 8, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -193,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "id": "f8aa2e1c", "metadata": {}, "outputs": [ @@ -204,7 +212,7 @@ " [ 9., 16.]])" ] }, - "execution_count": 9, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -215,7 +223,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "id": "d944cfe6", "metadata": {}, "outputs": [ @@ -226,7 +234,7 @@ " [ nan, 2.0000]])" ] }, - "execution_count": 10, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -521,7 +529,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 24, "id": "a75c0a7f", "metadata": {}, "outputs": [ @@ -531,7 +539,7 @@ "tensor(5.)" ] }, - "execution_count": 25, + "execution_count": 24, "metadata": {}, "output_type": "execute_result" } @@ -541,14 +549,6 @@ "relu(torch.tensor(5.0))\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "01f31fad", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -575,15 +575,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "02ce64d0", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 26, + "execution_count": 25, "id": "56781e21", "metadata": {}, "outputs": [ @@ -620,7 +612,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 26, "id": "e5d1cce1", "metadata": {}, "outputs": [ @@ -650,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 27, "id": "b15460d1", "metadata": {}, "outputs": [ @@ -681,7 +673,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 28, "id": "0075688b", "metadata": {}, "outputs": [ @@ -752,7 +744,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 29, "id": "a8336d39", "metadata": {}, "outputs": [ @@ -774,7 +766,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 30, "id": "a0b62312", "metadata": {}, "outputs": [ @@ -784,7 +776,7 @@ "torch.Size([5, 5, 4])" ] }, - "execution_count": 35, + "execution_count": 30, "metadata": {}, "output_type": "execute_result" } @@ -798,15 +790,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "1115b864", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 36, + "execution_count": 31, "id": "3b114762", "metadata": {}, "outputs": [ @@ -827,7 +811,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 32, "id": "d43f10f5", "metadata": {}, "outputs": [ @@ -848,7 +832,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 33, "id": "0cfa03e4", "metadata": {}, "outputs": [ @@ -868,7 +852,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 34, "id": "ccf1a509", "metadata": {}, "outputs": [ @@ -888,7 +872,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 35, "id": "55f60815", "metadata": {}, "outputs": [ @@ -896,7 +880,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor(-2.0000)\n" + "tensor(-2.)\n" ] } ], @@ -908,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 36, "id": "6b7b5646", "metadata": {}, "outputs": [ @@ -933,7 +917,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 37, "id": "a30bcd31", "metadata": {}, "outputs": [ @@ -974,7 +958,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 38, "id": "72fee65c", "metadata": {}, "outputs": [ @@ -1127,10 +1111,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "42f733fa", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([[19., 22.],\n", + " [43., 50.]])\n", + "tensor([[19., 22.],\n", + " [43., 50.]])\n" + ] + } + ], "source": [ "import torch \n", "\n", @@ -1182,10 +1177,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "db60fd1f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before: torch.Size([3, 4, 5])\n", + "after: torch.Size([3, 5, 4])\n" + ] + } + ], "source": [ "#例1，张量转置\n", "A = torch.randn(3,4,5)\n", @@ -1199,10 +1203,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "6c89972a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before: torch.Size([5, 5])\n", + "after: torch.Size([5])\n" + ] + } + ], "source": [ "#例2，取对角元\n", "A = torch.randn(5,5)\n", @@ -1214,10 +1227,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "86ef3f2f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before: torch.Size([4, 5])\n", + "after: torch.Size([4])\n" + ] + } + ], "source": [ "#例3，求和降维\n", "A = torch.randn(4,5)\n", @@ -1237,10 +1259,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "68f0cd2d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before: torch.Size([5, 5]) torch.Size([5, 5])\n", + "after: torch.Size([5, 5])\n" + ] + } + ], "source": [ "#例4，哈达玛积\n", "A = torch.randn(5,5)\n", @@ -1261,10 +1292,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "873afb6d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before: torch.Size([10]) torch.Size([10])\n", + "after: torch.Size([])\n" + ] + } + ], "source": [ "#例5，向量内积\n", "A = torch.randn(10)\n", @@ -1285,10 +1325,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "7f74e25a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before: torch.Size([10]) torch.Size([5])\n", + "after: torch.Size([10, 5])\n" + ] + } + ], "source": [ "#例6，向量外积(类似笛卡尔积)\n", "A = torch.randn(10)\n", @@ -1299,14 +1348,6 @@ "print(\"after:\",C.shape)" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "2d17b1e2", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -1317,10 +1358,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "2e659e48", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before: torch.Size([5, 4]) torch.Size([4, 6])\n", + "after: torch.Size([5, 6])\n" + ] + } + ], "source": [ "#例7，矩阵乘法\n", "A = torch.randn(5,4)\n", @@ -1341,10 +1391,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "4db4479d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "before: torch.Size([3, 4, 5]) torch.Size([4, 3, 6])\n", + "after: torch.Size([5, 6])\n" + ] + } + ], "source": [ "#例8，张量缩并\n", "A = torch.randn(3,4,5)\n", @@ -1400,10 +1459,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "63935d22", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a.shape: torch.Size([5])\n", + "A.shape: torch.Size([8, 5])\n" + ] + } + ], "source": [ "#例9，bilinear注意力机制\n", "\n", @@ -1456,10 +1524,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "6bc68830", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "a.shape= torch.Size([6])\n", + "A.shape= torch.Size([8, 6])\n" + ] + } + ], "source": [ "#例10，scaled-dot-product注意力机制\n", "\n", @@ -1492,7 +1569,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": 50, "id": "a6c0433f", "metadata": {}, "outputs": [], @@ -1508,7 +1585,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 51, "id": "f3510af4", "metadata": {}, "outputs": [ @@ -1516,7 +1593,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "19.5 ms ± 2.07 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "1.83 ms ± 78.1 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" ] } ], @@ -1527,7 +1604,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 52, "id": "9b994c02", "metadata": {}, "outputs": [ @@ -1535,13 +1612,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "8.82 ms ± 1.41 ms per loop (mean ± std. dev. of 7 runs, 100 loops each)\n" + "636 µs ± 27.5 µs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n" ] } ], "source": [ "%%timeit \n", - "A = torch.einsum('bq,oqk,bk->bo',Q,W,K) + b\n" + "A = torch.einsum('bq,oqk,bk->bo',Q,W,K) + b" ] }, { @@ -1582,7 +1659,7 @@ }, { "cell_type": "code", - "execution_count": 66, + "execution_count": 53, "id": "24e8a6f5", "metadata": {}, "outputs": [ @@ -1604,7 +1681,7 @@ }, { "cell_type": "code", - "execution_count": 70, + "execution_count": 54, "id": "16d4b93c", "metadata": {}, "outputs": [ @@ -1616,7 +1693,7 @@ " [3, 4, 5]])" ] }, - "execution_count": 70, + "execution_count": 54, "metadata": {}, "output_type": "execute_result" } @@ -1627,7 +1704,7 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": 55, "id": "f76bb5df", "metadata": {}, "outputs": [ @@ -1684,7 +1761,7 @@ "cell_metadata_filter": "-all" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1698,7 +1775,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/4-3,nn.functional\345\222\214nn.Module.ipynb" "b/4-3,nn.functional\345\222\214nn.Module.ipynb" index 06e678d7c..3ceaa99fb 100644 --- "a/4-3,nn.functional\345\222\214nn.Module.ipynb" +++ "b/4-3,nn.functional\345\222\214nn.Module.ipynb" @@ -10,15 +10,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "a7a6e4e6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__=2.0.1\n", + "torchkeras.__version__=3.9.3\n" + ] + } + ], "source": [ "import torch \n", "import torchkeras\n", "print(\"torch.__version__=\"+torch.__version__) \n", - "print(\"torchkeras.__version__=\"+torchkeras.__version__) \n" + "print(\"torchkeras.__version__=\"+torchkeras.__version__) " ] }, { @@ -104,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 3, "id": "a4ff03a2", "metadata": {}, "outputs": [], @@ -114,7 +123,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 4, "id": "00572afc", "metadata": {}, "outputs": [ @@ -124,7 +133,7 @@ "tensor(0.)" ] }, - "execution_count": 30, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -135,7 +144,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 5, "id": "1eb413ec", "metadata": {}, "outputs": [], @@ -145,7 +154,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 6, "id": "7eafc178", "metadata": {}, "outputs": [ @@ -155,7 +164,7 @@ "tensor(0.)" ] }, - "execution_count": 32, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -202,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 7, "id": "45884d27", "metadata": {}, "outputs": [], @@ -214,18 +223,18 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 8, "id": "7526ff94", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "tensor([[ 1.3588, 0.2338],\n", - " [-2.0469, 0.9945]], requires_grad=True)" + "tensor([[0.1829, 0.0693],\n", + " [0.0767, 1.2441]], requires_grad=True)" ] }, - "execution_count": 34, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -236,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 9, "id": "7cc7d2a8", "metadata": {}, "outputs": [ @@ -245,8 +254,8 @@ "output_type": "stream", "text": [ "Parameter containing:\n", - "tensor([[-0.5157, 0.3468],\n", - " [ 0.0230, 0.3132]], requires_grad=True)\n", + "tensor([[-0.8092, -0.8830],\n", + " [ 1.6357, -0.1740]], requires_grad=True)\n", "True\n" ] } @@ -260,7 +269,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 10, "id": "a65226f4", "metadata": {}, "outputs": [ @@ -269,8 +278,8 @@ "output_type": "stream", "text": [ "ParameterList(\n", - " (0): Parameter containing: [torch.FloatTensor of size 8x1]\n", - " (1): Parameter containing: [torch.FloatTensor of size 8x2]\n", + " (0): Parameter containing: [torch.float32 of size 8x1]\n", + " (1): Parameter containing: [torch.float32 of size 8x2]\n", ")\n", "True\n" ] @@ -285,7 +294,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 11, "id": "d4c83e34", "metadata": {}, "outputs": [ @@ -311,7 +320,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 12, "id": "13194251", "metadata": {}, "outputs": [ @@ -320,32 +329,32 @@ "output_type": "stream", "text": [ "('w', Parameter containing:\n", - "tensor([[-0.8443, 0.7039],\n", - " [ 0.0111, 1.6415]], requires_grad=True)) \n", + "tensor([[-1.2390, 0.3316],\n", + " [-0.4232, -0.0090]], requires_grad=True)) \n", "\n", "('params_list.0', Parameter containing:\n", - "tensor([[0.1142],\n", - " [0.5918],\n", - " [0.7504],\n", - " [0.6933],\n", - " [0.4971],\n", - " [0.3506],\n", - " [0.0842],\n", - " [0.4484]], requires_grad=True)) \n", + "tensor([[0.8785],\n", + " [0.6456],\n", + " [0.4697],\n", + " [0.8962],\n", + " [0.1122],\n", + " [0.4837],\n", + " [0.8089],\n", + " [0.0515]], requires_grad=True)) \n", "\n", "('params_list.1', Parameter containing:\n", - "tensor([[0.0165, 0.0690],\n", - " [0.0338, 0.1076],\n", - " [0.9422, 0.4481],\n", - " [0.5050, 0.9336],\n", - " [0.7795, 0.4915],\n", - " [0.3318, 0.2124],\n", - " [0.6288, 0.4301],\n", - " [0.7679, 0.1147]], requires_grad=True)) \n", + "tensor([[0.7440, 0.5626],\n", + " [0.2430, 0.0113],\n", + " [0.5884, 0.0815],\n", + " [0.7125, 0.4120],\n", + " [0.7275, 0.1608],\n", + " [0.4658, 0.0085],\n", + " [0.8578, 0.7290],\n", + " [0.0327, 0.2239]], requires_grad=True)) \n", "\n", "('params_dict.a', Parameter containing:\n", - "tensor([[0.1251, 0.9954],\n", - " [0.3776, 0.9712]], requires_grad=True)) \n", + "tensor([[0.6698, 0.5646],\n", + " [0.2482, 0.8258]], requires_grad=True)) \n", "\n", "('params_dict.b', Parameter containing:\n", "tensor([0., 0.], requires_grad=True)) \n", @@ -373,7 +382,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 13, "id": "49edd89d", "metadata": {}, "outputs": [], @@ -443,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 14, "id": "46fcd26b", "metadata": {}, "outputs": [], @@ -477,7 +486,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 15, "id": "9c1845cb", "metadata": {}, "outputs": [ @@ -515,7 +524,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 16, "id": "b1084d51", "metadata": {}, "outputs": [ @@ -553,7 +562,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": 17, "id": "030281d9", "metadata": {}, "outputs": [ @@ -627,7 +636,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": 18, "id": "653ba472", "metadata": {}, "outputs": [ @@ -654,7 +663,7 @@ "Embedding(10000, 3, padding_idx=1)" ] }, - "execution_count": 45, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -669,7 +678,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": 23, "id": "3ccedfd7", "metadata": {}, "outputs": [ @@ -692,7 +701,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 24, "id": "0ca0a92f", "metadata": {}, "outputs": [ @@ -760,7 +769,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -774,7 +783,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/5-1,Dataset\345\222\214DataLoader.ipynb" "b/5-1,Dataset\345\222\214DataLoader.ipynb" index a1a5bf49d..a421c72a1 100644 --- "a/5-1,Dataset\345\222\214DataLoader.ipynb" +++ "b/5-1,Dataset\345\222\214DataLoader.ipynb" @@ -22,7 +22,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "f1565cef", "metadata": {}, "outputs": [ @@ -30,8 +30,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.__version__=1.10.0\n", - "torchvision.__version__=0.11.2\n" + "torch.__version__=2.0.1\n", + "torchvision.__version__=0.15.2\n" ] } ], @@ -43,17 +43,6 @@ "print(\"torchvision.__version__=\"+torchvision.__version__) \n" ] }, - { - "cell_type": "markdown", - "id": "45557a5b", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__=1.10.0\n", - "torchvision.__version__=0.11.2\n", - "```" - ] - }, { "cell_type": "markdown", "id": "6cb7a37f", @@ -158,7 +147,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "id": "0ca3be77", "metadata": {}, "outputs": [ @@ -166,11 +155,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "features = tensor([[-0.3979, 0.4728, -0.9796],\n", - " [-1.0995, 0.7045, 0.7593],\n", - " [-0.9703, -0.6259, -0.2886],\n", - " [-1.1529, -0.7042, -0.8151]])\n", - "labels = tensor([1., 0., 0., 0.])\n" + "features = tensor([[ 0.4871, -0.4812, -0.0125],\n", + " [-1.0566, -1.1058, 0.1595],\n", + " [ 0.8301, 1.2801, -1.9947],\n", + " [-0.1087, 0.1810, -1.0611]])\n", + "labels = tensor([0., 1., 1., 0.])\n" ] } ], @@ -213,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "id": "c8d0e25a", "metadata": {}, "outputs": [ @@ -222,13 +211,13 @@ "output_type": "stream", "text": [ "n = 1000\n", - "indices = [776, 144, 127, 140]\n", - "batch = [(tensor([-0.1744, -1.1102, 0.3292]), tensor(0.)), (tensor([1.0112, 1.3905, 1.7684]), tensor(0.)), (tensor([ 0.6682, 0.6509, -1.1334]), tensor(0.)), (tensor([-0.2228, 0.7622, 0.0318]), tensor(1.))]\n", - "features = tensor([[-0.1744, -1.1102, 0.3292],\n", - " [ 1.0112, 1.3905, 1.7684],\n", - " [ 0.6682, 0.6509, -1.1334],\n", - " [-0.2228, 0.7622, 0.0318]])\n", - "labels = tensor([0., 0., 0., 1.])\n" + "indices = [63, 672, 994, 283]\n", + "batch = [(tensor([-0.0396, -0.2129, 0.9823]), tensor(1.)), (tensor([-1.5184, 0.9135, 0.2675]), tensor(1.)), (tensor([-1.4275, 1.7845, -1.4629]), tensor(0.)), (tensor([-1.2925, 1.2267, 1.0238]), tensor(1.))]\n", + "features = tensor([[-0.0396, -0.2129, 0.9823],\n", + " [-1.5184, 0.9135, 0.2675],\n", + " [-1.4275, 1.7845, -1.4629],\n", + " [-1.2925, 1.2267, 1.0238]])\n", + "labels = tensor([1., 1., 0., 1.])\n" ] } ], @@ -262,31 +251,6 @@ "print(\"labels = \",labels)\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "3d6896fc", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "f39a7c66", - "metadata": {}, - "source": [ - "```\n", - "n = 1000\n", - "indices = [776, 144, 127, 140]\n", - "batch = [(tensor([-0.1744, -1.1102, 0.3292]), tensor(0.)), (tensor([1.0112, 1.3905, 1.7684]), tensor(0.)), (tensor([ 0.6682, 0.6509, -1.1334]), tensor(0.)), (tensor([-0.2228, 0.7622, 0.0318]), tensor(1.))]\n", - "features = tensor([[-0.1744, -1.1102, 0.3292],\n", - " [ 1.0112, 1.3905, 1.7684],\n", - " [ 0.6682, 0.6509, -1.1334],\n", - " [-0.2228, 0.7622, 0.0318]])\n", - "labels = tensor([0., 0., 0., 1.])\n", - "```" - ] - }, { "cell_type": "code", "execution_count": 62, @@ -313,7 +277,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "fd20d9a2", "metadata": {}, "outputs": [], @@ -363,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "b8d8eafc", "metadata": {}, "outputs": [ @@ -371,11 +335,11 @@ "name": "stdout", "output_type": "stream", "text": [ - "features = tensor([[ 0.6718, -0.5819, 0.9362],\n", - " [-0.4208, -0.1517, 0.3838],\n", - " [ 2.1848, -1.2617, 0.7580],\n", - " [ 0.1418, -1.6424, 0.3673]])\n", - "labels = tensor([0., 1., 1., 0.])\n" + "features = tensor([[-0.8581, -1.1772, 0.1349],\n", + " [ 0.7672, -0.1178, -0.1553],\n", + " [ 1.4551, 1.9753, 1.4102],\n", + " [-0.1069, -0.6730, -0.2066]])\n", + "labels = tensor([0., 0., 0., 1.])\n" ] } ], @@ -467,7 +431,7 @@ }, { "cell_type": "code", - "execution_count": 73, + "execution_count": 6, "id": "73763881", "metadata": {}, "outputs": [], @@ -479,7 +443,7 @@ }, { "cell_type": "code", - "execution_count": 74, + "execution_count": 7, "id": "2d4aca1b", "metadata": {}, "outputs": [ @@ -510,7 +474,7 @@ }, { "cell_type": "code", - "execution_count": 75, + "execution_count": 8, "id": "6ed9283b", "metadata": {}, "outputs": [ @@ -518,14 +482,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "tensor([[5.1000, 3.8000, 1.9000, 0.4000],\n", - " [5.9000, 3.0000, 4.2000, 1.5000],\n", - " [4.6000, 3.1000, 1.5000, 0.2000],\n", - " [6.3000, 2.9000, 5.6000, 1.8000],\n", - " [5.7000, 2.8000, 4.5000, 1.3000],\n", - " [6.3000, 3.4000, 5.6000, 2.4000],\n", - " [4.9000, 3.6000, 1.4000, 0.1000],\n", - " [5.0000, 3.6000, 1.4000, 0.2000]], dtype=torch.float64) tensor([0, 1, 0, 2, 1, 2, 0, 0])\n" + "tensor([[5.6000, 3.0000, 4.1000, 1.3000],\n", + " [5.1000, 3.8000, 1.6000, 0.2000],\n", + " [4.8000, 3.0000, 1.4000, 0.3000],\n", + " [4.8000, 3.0000, 1.4000, 0.1000],\n", + " [6.4000, 3.2000, 5.3000, 2.3000],\n", + " [4.4000, 3.2000, 1.3000, 0.2000],\n", + " [5.6000, 2.9000, 3.6000, 1.3000],\n", + " [6.3000, 2.9000, 5.6000, 1.8000]], dtype=torch.float64) tensor([1, 0, 0, 0, 2, 0, 1, 2])\n" ] } ], @@ -540,7 +504,7 @@ }, { "cell_type": "code", - "execution_count": 76, + "execution_count": 9, "id": "4855ab27", "metadata": {}, "outputs": [ @@ -585,7 +549,7 @@ }, { "cell_type": "code", - "execution_count": 77, + "execution_count": 10, "id": "c50c3e99", "metadata": {}, "outputs": [], @@ -608,18 +572,18 @@ }, { "cell_type": "code", - "execution_count": 79, + "execution_count": 11, "id": "13c5bb0f", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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6K9XeCaLYoyIzRm4sAEACCEiEntfVEs9veJ72Uk+qgLi3t6c+TfP5XL2Fw6jG7fj4OMuya9euPXnypNPptFqtn//853t7e4eHh3/5l3957dq1qqpGo1G73T46Ojo8PHTOHR8fe+93d3fVAarVah0eHk6n00ePHj158mRra6vf7ys0KoAFjYWubV0zk8lke3v78uXLu7u7x8fHn3766XQ67ff7RHT9+vUnT548efJkf38/z/PFYrF/eAAAAlAUhXIDauBQn+eYG8NGeK3Yx/siBg9o4hGCGEdElhq1c+RcqX+vjDYLCoCAK6sQGSHqfK+2XhZmZucBQHTtqdBMCnuiHdJ/AoDm3EknFhfmK/7U/q+dDyJyzFsEkXf5OvEDImeU4GQqIjpf3Lg0Q2M4y5IlYxoCNGJuxiCgMHA9Yi8A+EVbaUS1jSRgAHsQEXVXcc6rKKxLUIIrUAOc1Dgy0IYqL+z/0MLeWFv68cEa8Oj/R4gYb0sEqC2Om7xzbHVeg1jVc9KqO0ncz5iHiPsWy/TQbNE40iAGYOdc6V1wNlHyyhEVU5qWphaBEDFEm8Sb/JzuyXJs18YTGw1Y2jRjTJ7mRGQTE2QaJQ5BEqClhTsI0Iy4YgZGxMY6UfNEzXNRIDoWFkFmzwynp6ez2WwyGg2Hw/F4PJ1OF7N56Sr2UnrnG28voNpJJ00SZnaqSJRznHBCZzbxdbNtXqaPm8/nL7/88unpaa/XGw6HW1tbpavkvPbxxx+/9tpr3/zmN//mb/7m5s2bH3zwwaNHj/7hP/yHH3zwwWefffav/tW/+vf//t9//vnnv/M7v/P+++8fHR2ladrpdKqqGg6H3vurV6/u7u4S0Z/+6Z/u7u6q6/VgMFA9xGw26/f7ipdlWWIjBxtjLl26tL+/T0SfffbZ0dERM2dZNpvN2u32p59+miRJp9M5Ozt76aWXrl69enZ2lrbysizVrW+xWOhuBYBYxS2NfsV7j2Zp+gkggY0EHLoRu+9Gy3XJa8bDG69DVfmU84USEFafLBGvgjgzIureFK9UJXbyh7hX580rg+e1J+o3qtEJ/Wy+5YY+LZVqIZ4wPC5eIcFYFpRMepAkSbh/oAOqqwi3jcO0OPJm4MY8DC8A+Le2BdiAhtB675mdsUo3a+9aZlb8WCxcVVVl4aqqinlf2ND6QgPAARXCbtflGxbx2kYNBCIIu+GCWEsGItjYhGLtsT43iVRJcTt/A2+YmZ+FwbCk+BR3Pm7eewXXwCkzQzFbaLCsShsh9Ejl4FoCBmh0U6kxife+qsqqqjx7aFxSSHXltW6Xw5g49swc4Dx+U2NMmqZ5nud5nmWZ2vDyJEdEMhhJNhgknhWVPiIAqHeotUhEhDawGiosIhiQ2ijArJ50KAzAXFXVfD4fDoeDwWA6nQ5Oz+bz+Wg0mkwmtdbdMwB4QCRr0AAAC0jFACBoiqKqx5wQAIGBkADBw0regk26HxaMTiU7H7NiYdUyc7fbPTo6yvPcGDOdTtM0LctyXixC5G5RFNhEnrzzzjtPnjwpimJvb+/P//zPt7a23nrrrZ/+9Kcff/zxv/7X//o//af/9OMf//gP//APFW6vXbt2fHw8GAz29/fb7fbNmzdff/31zz//XB2mFCB1gnQk9/f3F4vFeDwGgL29PWaeTCb7+/u3bt2aTCZVVZ2cnBwdHZVlqbplaMKo5vP5zs7OxYsXu92uOpEVRTGfz+ezmXfOGkOIuuTmRRFzitIo+auy1PO6PGKgDZsiZjRJgFnEeWFWfwRdh1mWhZF3jbeX6sCdq12u1ERikQwiqcljieYiCDVDr3G9tQDcOHWAj/G1fheBqqoAlyBNjT+gAAMCgCAC1QEdAGDyPJVI2xRved070viB6lfqFxZfueYuSqsq+jxJA8ure6oebawHyjXKqhcA/KIBxJIus/e+rCoFYNTUCiJqwtRtXJZVAGBQPZOgZ4h5Ov2M4+ihWanGGOeWkLmJYaHFQB6vft0sEehydH49OcNXaYgY+2585Z+sjF7gIRRTedmgKAqQGrMr9kFEhsbSJuotXKsElQtZWtdiogOrI1xTHFinSrWqsE6skSkAqx4sYtVjwho7NEUOZciizlbYPEsfCAB1uBqo4KrXe8/ei9dBaCJNh8PhcDiczWbHR0/VIUjdbrWfjCBu3bmG1yaQRShm8szaLMuqwHTulMmqf5+IKDb0ej1mHg6H4b1CvpfGKF6v7dPT0z/+4z8ej8dVVbVarU8//fRb3/rWvXv3Xn/99R/96EcicuHChdu3b/d6vX6/P51O1V25KIobN24kSfIXf/EXWZaNRiP1yQqTKCJq0KmqSqdmMBggYr/fPzg4yPP88ePHX3zxxXA47PV629vbGq6W5/lgMOh0Oru7uzs7OyoTq2qhcJUaiWQZKYDGmDgsDQJTK0ICQEu1cwDgNE3XFqHeKuaS47EtyxIAlMnW11Hh26kIrtJtrZxeemiu6K/q7UywMblho8VTqSQECRCXccChKXBCREPCnzHcxn+uba76cbxifgr9MU30c4jIqn0sWq0wkhiZ0gLLHlQILwD4t72FpazQW1WV99V0NtboTyIyxiKiqqBrVVJZKgDXdwACIAVgbNRT2EhOAX0DCwkARCsGV9ggmhGg1paVNb9EoqXFZbXVyR++3hhsoG/Mn262uM8cO5h4r4RPxbtG3IdyXgjXZMvJioekblREdI2KzHvvXNVsWgZgAFZ+SFVnslSRCQCvsQ6IGLRerVYrAHBI3wMApGlMGgmYiIiWL7XSoHYBIyRCS1jHTugUgcSpBrBJjsblvJjPCkWa4XAYRN7B2VkIfdEnq2s1QMm4fI3lQcxVCIAIIAgC0UoY0hqbci4Ax+gbLlY+YH9/3zl3dnamGkVETJM06Gw1m5hKPFmWvfzyy++//74i6GuvvabS882bN3/4wx9ev3794sWL77333pUrV9rt9nA4ZOb9/f3t7e1er3d8fPzo0aOdnR1jjGovAUDD9hBxPp+Px+O9vb3ZbOa97/f7ly9f3tvbM8Y8fPjw4cOHRKRe0FVVaRYtVUHv7u5euHAhSRKN45pMJs45IVTliupFoAHFzfEJm0Voac4P/kEhdHipeQIAgLCG6y0nIk22LL0+5LlTbTNXTkSAFX2bHDUN1yhRZwAAkYAQwmTBOegLABg8VaROrAG1vkaaT4wSaxgiDIzF+gg8Q+kVHkereXXCD0NYUWj6iDS1ej8Wp5SzHqLayFKL4zoU8AKAf8tb2D+Nd64qx4L5MDHGKKldI3mrf9YngxcuRB6SMY8Jq4AaLgiXxa0B7PWGuHTCCrbJcP7ryb+guxnWOvm8yzdIeYDbkN9ACZACcDFbKACLiERyJ8NSQJFIJ19VPqYIARpVFbxG+CRSzuvA5nmumY80DDTkz8OG0woAvDaevk73EaxlAo3ga6JGRCpwlIWDhg1S+rtYLIqiGhwPp9Pp2dnZ2dnZaDTSDC3OOWks2YHqiYhnXpd3m3YulKq7juDKZZsAHFaURGFpen1YlgpOaos9PT1NkkSBVnGOiPI8D3Od5/k3v/lNTX11dHTU6XT++T//5//m3/ybP/qjP9rf3x8MBh999NHBwcGFCxfUIfn09HR7e3s2m7388ssPHz48Pj4+PDwcDof9fl99c6RJqKQmw2BT7PV6t27dOjg4GAwGjx49UpZO03qcnZ0tFgvNaZUkyaVLl/Qnx8fHx8fHqr5O0zTGWt/ktPLeB6eksL/0mFeXZfhKf4tRTii9j2mQI1wWjClB9l3qeACgqiVRAgRaZnjlDZ8MRARAwaUzVzynHOWkjOiDGEMbNwGI6EZYt6bJMhbv5bgPoYUexvs9fi4iKpsbwopCXK/wqpP5hsS8tn9fAPBvaQsUSrdoI91qPo1gZK3dmkREuWNrVeZryJmg97UTFjSbFhoHpTWg0jWtQkBAzXONqWGlwoaku9b/cP3y8+uOw6oQeT7dP68FpX3wqAoSsBIgBeCqqoTDHl5ShMrXAAYAtBqtAaukIQBGICi4qtqCJnI0y7KQ+1eJQvC9CjdX+xqZmPevJXiIbOq1XLyurCZEUpN2M0HEzJpSeDgcz2az48cn8+lCZV/1v9VVYRKr0bx6U6kHUOrsphsycJ2Ei1ccCxjlq7FJKxMamBiIVqMmtWi329rPfr8vItbayWxaFEUYNI1W2t3dvXnz5l/+5V8+evRoMpm8/vrri8UiSZLDw8Ozs7PDw8Of/exneZ6/8cYbR0dHg8HAWvvyyy+rcvv4+HixWLRaLefcaDTSOCWdGiIKtNt7f/ny5atXr164cGEwGNy5c6coip2dnW63e3Z2Zq3tdrutVms+n1trr169aq1VtfNgMJjNZroMdPmt8L4AiGQMqio14lYRNcAaNLXrEns4SvNkmvi0IA2z53hLx/nGAwBzxGyhOuYtpc96CmNU25gyH89XfLJpiEjB/FSrZFZ9UFTlE943pidrz41hcoWSNBwGNNaiWNusnFOstJeGmEKjuYcI1wmQms30AoBfNICItdSdU5ZlVZXMHMQglXWoFhBrr0hmxqUmaXmrGNE3T4aHBgAmWhGstcVYu7lV1jovIrUJdFWM/lqDcO4vngPDgZMI5i5F3JDnL+jfltbcRsoL0ti5bMeSaDYUKjyFQx6rxowUOhkUhqpt1mBTVYiZ1aIRFBIyRwAcpqshOpFertZALJmhuucsAGCtVb5NNainp6cnJ2ez8XRwOiwWVQg8DVMcv/Ly5LPnqqG/y2mSiCTjKu8FG4sk0NCg7YznTtEiz3PvfdF4J2nTOVWdgfJVnU7n5ZdfVunz/v37BwcHly9f/nf/7t/9vb/395xzP/rRjy5fvnx2dpZlmff+8ePHeZ7/7u/+bqfTGY/HP/vZz3Z2dnq93uPHjzXRh4rXOnGLxWI0GunMXr9+XaN4v/jii8FgwMxbW1vKBDjn5vM5Il64cOHy5cuqwX706JFmUmPmdrvtnFPfcv1JaAGMg4gWAEPH31rLeP4wBmZRRDQMXUR8VXHkRxkcCXV1BVdKvY9RuJV1NI2XQQAqEXWcXzesbE50xEMsz8TnAUBHKf723NUSOoYR8xGgWkSc89RksNLcNSGiN9yh2ewsIimhNCIEnCc/rOH9CwD+rW4xnAT1kUiQxtQKojtEgp8tBNj2IoJaPEBWazAE4kurgUnnxvyEJqu6aDjP4wkavmGN5taVGr7mAGgqyrWnfJURC4ZzlQAUhoM00FwGiEublqo31Tpl0yTcbW1PQiRex05bQTSRSBGtcKupB9Xb2TY1nShyal3SFKeTslLEQhqHnUiqqF1hmp8TQK2+U8tWWToNNh0Oh2dnw9PT08FgsJjOy7nnikvnRJDINvycAGCjKUHNqMVACCjgpZk13BjqYCqDZnSYGWhdTMFVJg+iRRUbleOmKTU0Vkf1z0GXEFgTVatub2/fvHnzpz/96dHRUbvdfumllwaDgXo4P3jw4Pj4uNfraTbKBw8epGl65cqV119//ZNPPtEUzZpkO1DwsN1ERIsi5Hm+vb2tIcKPHz8ejUZZlu3s7BDRfD4HgH6/r2KlMWZvb68oivv37z9+/FhfWfEg3JYi36jAvYX3Uq2p+lEzs6/tIMthjMcTGxEwmKi8964OR3TxFmjsF+fUBNO4NF4tyiKrnDeDRHgoHGdXJQxHYWrV8oREBkAiR5NYi26aaIg1MhIejZEYyk3sdUx/9G5ZJ41j+WJ3ijUWp3llddcAosDRAoAALBWH8VC/AODf9raGKIGVq90d6nBfQgRmDmUGm40nAuSFPXsAMJhAs7bUK3IDZSlG2M3OrJ2MCevaQQNpEfssIE2Sy6/Vvjr6wiodjzFS5YBQEqq5Ei0kgEHTsLQ/2TQJN1GvDKp9SDC+eYgvMlGJi3hUFYBbebvdaWVpbhNDaADFmgQJSH2GgZAABAHBo1uCUzSkjcQQj78AgFUtCKBWlGNmYRCRyXimKtDT09PBYDQajcbjcVGUyBaYRYAIREiNd8wuTVvMjhmbxIFMgB55hWMSCjKvRog2wm6dgFoYNL9lPGubmAGrGHDu3KlueTabVVWVJEmNKCAmTaiyYMiDFK5ywp2t3qWrV/7Df/x/v/d3733nO985vHzp/fff/4O/90ef3/liPp9rGaWdnZ3ZbGaM+e53v9vr9cbj8WQyGY/H169fV1H16tWrx8fHRNTtdifzmTiPTTHEK1euvPbaa8fHx2dnZ5PJRC0Ik8kEABSJJ5NJmqY7Ozt5nt+9e1edrcIyU05X/8zzfDQamSa42zQ1fGpa39gsAwBX7FV9awDBUPhkBK6cF+HKOWGuXOEqrlxY7XGsnY52SAW/tqEYahuVsvPhc2Nq9JfswcNSx6HMMQUGTs8rzhIRIDBXCFh7BhACISr/hECwpDyhV8orkMaW60M1poNF1BpDSIBojSqNt7a2LK5WGPTMjckm4G4dUE9SFYtYJQ6wjBCRSPWFIUf0Jol50f5Xti+l+GuE5lnXUxN3G1+pWiKMrT6RBBBWpPeV95UagNkDgOpxUBjVjw8RvC+DN6/33PhoCIAkiEld1QMbdg9SqxpmY4gMIin1FFk05WmNMcF06pviAWY1wSwRIQuwB2ZgFu+5fiiIeNIwVjKIgsAGDRH5DdvS2jaI9wYSEDGgBP9JajhuzbAj0d0QEYSqqlSn36pyRVEuFsVioSporiqv3ld1z5tafFXpEDHJcnBuNpsRUafTEQ/qJaqZCYCBwACIA2YQL+w1/TYCGiLCoiyNMSmhNYldloEz7aydJEmetfMkS5PcGAQgALaUiHhhjccAIiCyiDXQiogwSO1tp7HTsvSN8l6lbYOymM87nU6S2KqqQDAxdlbMBoPBdDo/PT199OjJ8fHxbDar04Z48L5EREKLVPssWyBjrUFAAvHM4pzzZMBQYowtvSMwgDWNrtk0IsQmkQJArRcEBGQRjyxICCxB2gPQnP9LziyQcLImrHNmr2HTInLh8KC71Ts9PWWQRVloeqnhZNxqt2dl0d7qbe/v3Xv08OrVq9/4zrf/23//i+F0knXat95+6+zsbFos+nu7Ns8+/L//x8nx8cULB9qHg4OD7e3tsiw/+uij+Xyu+c4U8B4+fKiROUme2SQZT6ejp6OXXnrpu9/9bqfTefz48enp6d7eXrfdefTo0Xw6297eZuaH9x9cODzo9XoKnM65zz777OHDh4r3gW8OQAiRhjMuWKTG5jBWQY+VkAEDXli8F+8FCZFYQBASa8RVrqpKV7HzzjstmRCi2ENRB4nMIrDK+jCiMSLsWZjVGkCAAgF8N6mZOv8jGgDRFas3Y1Zp0oSVTGQA2CSt8NDIs5Ccc8qpqZOlMaYOEWZBRGUyGrMGCiCyAGFiLFqT2cSkSWosEXXzbMkriA/2GtSccWZpXQbx4peq79Bi+hOvWH39FwD8G9LWoaI5ptX8zIEFUwkVGj9G1YsaYyT2MQXFQkDUfFhLUwcswZYBoCnMrn5btfFDRNZic0MfVFhcVvmOMvDFryAiHgQJnHPKthPVcQHWRqnjNszP57Y1phuW1GqFfj3rhyICQk7TX0dywNqYxE0YPNSvFkskQS2/HBx1CRYpqpJhpaBKcCQxxsR6MCJCNE36DkOkso4qAIxaECLdbz0dz1LSN0xPMP8jEVmDaE1CRpwvZvPFoiiKYjgcnpycnJ4MRqPRycmZCmQNh2fIJqCKZYZa2EVBQBbHzIAMAkjSXMPIKCqNLPtFIgiC0JgbABohGAhpaSMMIu8m/GAjEKtWNvIjq9fJ7u6u5t8PlpeyLCtX8WLOIJeuXGbmvN16/Y1b82Lx4c8/evTk8Ztvvtnpdf/2Jz++cHiwf3DhJ3/603lZ9Pv9Vqulz9ra2kLEs7Ozu3fvagHguFciwgjdbvdsMCBj3njzzUsXL45Go4cPH85mM1eUs9ms2+688sorx8fHd+/ezbLs+vXrJrEXL14cDAYff/yxWjc0zKnT6UijJokxOKiaY/SN9+DmvKMARYZhz947X3EZ9DpB8A3uCGHxh/V53rYCgJq7BS2a0CQ7x0YS3dxxIuo1rZRk+dkgsTTuV7UTlrEWTLNBoCEawiLimwfGi8QACovjFfE9QROi5+uqi83oGcBzdowIiZpSNElq9M2z3RrObS8A+DeqBQECIp4rNtUEjjWojIIeFRGttQArppqwPbxbcSJdPq558lpPGmeQJZSqwKdbV/P1aA2+EPAQbwlsbJbM7B06cojYgLTKsgkEC+VqGYZnjUz8Z0S7VTQ/xxwL0daVZVCHiGCwisUuV9I4jsYb3jnHLDEAbw5jYEq8xlM2DEGgpEpGzWp2WQXg1KSa6TnQwZjjDmeeQ4JDH6Cx+9aDb1Try5pJ+OzsbDgcjcfjk5OT09PTwdloPp9Pp/OyLKnx5g13W+NIdBxgldo2x6oJj2eQRQCJQARYcbQxE+PyspirO/dd6oGFlZ9g43vf6/VERFlA1fCDSo1J0u12Vdmb5/n169c19Mha+3u/93t37tyZz+fvvPPOF198cefOHXZ+Z3fv7Oys2+3eunWr2+1+8cUX9+/fV3dljFxnUUO0hReLhTHm8PDwxo0bWZo+ePBAE0xeuXjp3r17ZyenFy9e3Nraevnll3Vr5O1WVVUaleS912DioL6KR1ibJoKg1eh5iKTezdHDxjNLp0n9GJQpiQLqOFCP2LQcfhtmfHUfsebV0J6EtQHn8UzNmq8LftVO9mHpNgeBH0VEVidEpNB/ZhbP7D1F060GFF0JRtSU5gK/osokVVSEoN4m5B1gGZe//vkraS8A+Dek4YYfSviTmdVLKMSE6Lcx6xpjXgCbhrOuXW9guf7qn8ASeNfLCIbjwJ57x8ys3VAHTuWvQwcggooYMyQyUeuuBgDnaoEYAEykWodnwMwm/Egt+/KGg049PoEn8E2woyrqtAVmYg2AtcV3C/QCAIwxMf2CtXAmlWYQ9Mq4oLqSiTjpHSIiGmQkWlavgyWUrrSGAp5X0w1Ay1HoJYHCusJVAsViLs5PJpPT09PhcDSZTNTcO58vVCAzKwm2tHLlinih571flodbzoWIWuua1RgXeUREVLeVcKvGXW69NkY8a/GwA4Aqt8N6xiZABQAmk4m6akcla+y8Kvf2987OzsqyvHXrFiLevn27qqpXX3212+3+xV/8xfe///3Dw8N/+2//bafTMTmdnZ0ZwK2tra2trfF4fO/evdlsdunSpZOTkxXDYR0JasaTydWrV69cuYKIJycnmlerLMuHDx/u7e1x3x8fHw+Hw2vXrnW73el0OhgNHzx4cHZ2pgih20erO4RhCQoVAEjTNAaqMESxtBorYHQDh0UYYtl1DYSYoiBqhw0bBGttsbgf7Ths/KVoTRW3dvGSKG3QATrPnVibRRRfPzjgukac02rVsvqhrLqWeksGjnYNgMOUoaZBb6jB8hEbNuxfuL0A4N+QFqACVm3AANxkkSw1W400RXIwKjoWdpfI0grYbHISEVkJGVkeN89acgDSePyqDjXAlXfsvZ/N5soNqPIZIn+EtV1XbwNRFTeyF8Qg1bGvM914RARHYIAQGeDrejU0hKPOFhu0nqI5bxv0VWHdOccMIOfkwRAGkKCtQhAQXiohcLVUlEqNsOGaoWeQMJCG4Ngc4g6DRIWIAMSVGkhFBUdu4ihMVIClGVQAIGAGEIElI6WobIiIQJGycuW8iSOajEfVohiNRmdnZ7PZPFR05yb7h/aZaz9tQGNjIhv62cRxYkOX6/4YoxkfdcYTCPWMdZANxuFLjCiwTvs2iXi8DsmaoM9XdiHo8NXHWM8AwHw+Tw1Np9NXX7vx4MGDy5cv/+AHP/jwww9/8pOfAMCNGzfee++9yWRy48aN09PTu3fv/uAHP3BlNTw9+8bv/M7FixfvPrj/8OFDtKa33T8ZnCV5piPvvK/KipvQu9dff31/fx8RHz16NBwMgqQ1m81arVZibKvV0mBu59xwODw9O1XZV3Njaa5pDXkKw2tWU8XFwBOPDDYcdnyxNClUq6bFgeyBUwyr1LlS16HeJNoHDiBmo/XYqAlenxUmwkd5ImMwCz+M3ytexjG50GMvIp69X6aktbqOlUbVhZoitgzQENkog1XIaRXUBgi1EUhEvrZL59dsLwD4N6T5Jo4oxmAAAFjWoA1mxbjJqlFWGnQJGy/sjvCTFQpYW3VWCnkCQKO8WgqvrvLOudmicFGqWGwCBpRCrYkygbLXFiNc0nQFbyJCrFn7sMHOHZ9zSRIIMINIqJ2w3KlB2xYH+DrHwuv3DMdxzwMTo1uMV0NEAg0KtLJ2W7XWCKOhEPYQ4g7zPKcm8D+WdxlJuxTRvnUAXpvw1T8bwQU8M3rvi6IYj4fD4XAymZRluZhMtKbCZDIpyyrWZBpjAChGghCsG5PUZhWt6DyxEcptYvWWjYHc1OZGF6I7TFgVgKyy8fJlVlWCYfCXr9csP11sIRyo0+loxuayLFWpOx6PW8J7e3vdbhcAdnZ2rLUffPDBkydP3nnnnYODgz/90z/9/d//fRH58Y9/fPHixdFoxM6/+eab6hKl5Z7SNNUKSFmWxbrZJEkUWb/1rW89fPjw3r17g8GAvVdfLcXXx48fZ0n6xhtv7O/vP3r06Pbt2+Px2KaJznuzPSVNU031DA0aBdfFNdYknoUVqTcat7IqVSMVWKvYqhLMQxBtybV1xatOIStMAKrNKObIOWbd4i41nV5ykDGvQBtuE/qZEHHEYdjmel85fWiIaSIiA0iIys6qbinormPL0Uo/zUqhpM1B/iXbCwD+DWkq42oBMmnUy4hItLR5xOvYNdliuTHyNcXLAFbVmOFW5z4XG7MhRHQQABrf5mWgTlU651zhVhw6sDFQcVMSNYYxIhLnEdkYb61lNmyETL0/m21CROentji3t/FlMdDGTUSqJuHAmq1XvSxiMrSqfW0E4kZrp9fqQ02Tji4eqxgsAVEIyS6drYJCLET3mtWstmRXbOdB3dfY3jAgbsPRx/PYcBPIOlNluRiPx4PB4OzsbDqdlGXJZTWfzzWjZJ1Pk4iI0tSKSJhfaEQrLysa0TXiFU+EqXPnpgGikiRBNFVVNY7uygVK5IxFGsqFGxi81paMYFmG5aSjF8zqsX+W7ghEvHTpUlmWly9fbrVaH3300dOnT7e2tt54443JZEJEN2/e/Pzzz2/fvq21//q9ratXr96/d+/09BQRW63WYDDw3vd6vclkElt/e73ehQsXdnZ2nj59evfu3YcPHyJimiSqWVGHiUuXLu3v7vV6vbt37/7d3/3dZDLZ3d1V90PNMTmdTjXXim+iBtYAaW1ZxhPRqCiWCdf0TOGKEMse0wTZVMwgBuCUJtA/XBB7AIQpQNSRX48AjklK/AoAIE3UBiEZWpHsIULr+npNGBOzGgLiWQB8VXePBFTAVeY1acpjx/kjIaqDtKa/WZNYwtM3JZlfrL0A4N+Qpjihql1p4nwQUb2FeSNAXhWqspKHUgMMlgJN2MOwbkONyN+z+6MArAdVVSkAO16Gz8bUOUjAyztr8qZKk+wshRtiUgxmpma3LDn0Z40PPsNGHjj9oILTYVF1vW8cPgOy4tIvZJlMCiK8iQFYBJnXi5dp49XcyLV/FmKSZzEAB8Y86JZjGiQixqAINPG1SKQwr2+qcT0CQMxOx9BQUKot0RcAVPuoob1nZ2eDwWA2m7qqMoDKSyklDG+hJZ4a3+1lI2uwybd33oDEvAIZY2xC3tU6gMY6mzSMUe1ar9GqImIAOMqtsTaVYX7jlRCAKsZaRCyKIs7VpcZ1dS0+Ojr69re/3el0fv7zn6vk2uv13nvvvU6nMxqNbt++7b1vt9vW2usvvfzkyRMtQxR4LMXI4BanCzvP8263u7W19d/+7M+0XEeSJKqLVxPD1atXt7e3Z5Pphx9+qGUeEPHx48c7e7saYaxlg/V1er1eqOUQWBldKn7D61snQn3NAje8dGmG+kw8OxLF02Ok8hWRwNAHBlq/jb0Lw0Ed5B61cMOwZWhVc+6qlfJ/4S0gAr/wCQICsiRTLJ699158LXKQgDQ3SZJEWVqKEtQ8i0SEToqr1i6DdWL4S7UXAPxrbrSuIfyStklr9EBzwy4WiwDAmpuQCGMloTRSWkjfGm9Lza0RQy9upHhce3QTU1+DRFA7N2akpXLbVZ6ZhcwaoeSm+Aw0aXe0MzV/ykJ1bRZgBtU0swcHLt7Sqr3E1WCkNawKm4oj/6nZbB5GIDihKEciGw1qeqGB0oRoAAiAADQnNiEaRPDeec/e18UVlDKqHlv3/xr0BnMUGbO1s42GaMOLNRBciUQTETGW1IVK76fGYACx1jI7ZifiEU2wEJeuyrOMDGkqK+aaPZpMRqenp84556vHjx4+efJEJ2U+mRZF0e12O53OZDKdzWYq2LXyzmw2E8FWq9VqtTSdRa/XG4wmIUGuJglRDbZzVZom0CBinmfdbjdN0/F4PB5PQINo+z1mnk7nCBbEL+Zlr9MhopOTM0Rst1uzxRwFkzQhIp3BwJ0URdFut6fTaVmWnU5H57HT6bRaLdUMa+JJRNR6FePxWOsI6czOZjNEfPr06dbuztbW1s7OTlEUH3zwwWw2e+eddx48ePDpp5/+0R/9kXNOr1wsFoeHh3me/9nf/td+vz+eTHq9HgC4srxwcFBV1d+9//7v/d7vTadTz3z9lVdeffVVRPzzP//zPM/v379fFMW1a9cQ8fT0tN1uX7lypdfrPXny5N6du/fv3x8OhyKiQD4ej4lIVcSq1g6IvkYZgs0ohNTbphR82FOBzw7rv/RLDUG8zkWWwBafrCoHyEhgSVd+3TxXK72pM0DhmjAdkDXEJa8AKoDGhsWsgz4dI/k75gmqqgq/Zs/svDCTgAEkYxJrgybJWpuQabVa0ZBJcISghJb9juDVnYe1EqnQY2qzeeWXthcA/P+XLSzNNa5Ndx1znFBwJSpJIofG+CBsj9gJIt4bm5Bf70kQEQE5Zw+vXR/+DC1W44RjJRm0tOmu/FAVxogoEouVrjGDYWIzxBVhMWyMMFaxOWqNHgU52DeZbjYa4qrUGwZ57d2xKWKBiHpnnQhlNYI6WqdMjVLG2iRJgNYJEER0KmYpAMC5ElG9NTF4RAFgU1pYlfyq/xBNjRzxW0Vj3i60nh2ZZRYUZaE053Ochl6Fxaqqut0uM2gdeI3qOT097fT6KtLpamyi0aTT6ehJhQeF7SRJptPp1lZPuRAdnCyrKwDmrbo0b6uVaVLPLMvK6TTwLtSo/bWHs9ksyzLlI9M01UAgFtHhVRdi7VK73VYGK15aiJgkttPp3HrzjV6v99lnn3nvr1y50mq1Tk9Pe73eaDQ6PT2dz+e7u7tbW1tlWQ5Gw8PDQ2UWRURfSu3ov/u7v/v06dN+v/+d73zn0qVLn3zyybvvvluW5fHx8UsvvZQkycOHD9n7mzdv3rp1q9fr/ef/z//XWiuedUl471XqxYqC1T+AYgyNa5srHHDjsRwDcFjnoQWv5hhln73rmTWYe6M9E37CnjlP4by2qgE0Z9v6ZWv7F6OErAHdUepiMAYQCdM0tY2qOU3T1Ng4BP9/n/YCgH/NbW0LfWlbI8qy6kMYADjeS+HbeB8GFWtsEMI6jxvGTKi2GFri23r2iHW+9QbUlsi62Y0Y/OJ7BjE9xkgAMJQAoAhq+guROkchIpJfipLqqmMMBu/TWHyMexIPgve+KKrYyuubgGlpMsjHGlaILFsAEhTwRMh1MDGKoKaVbMZz6eOGkVyi4SKqGdNMzlmW2SSxSSIRDxQeGmvt4gXA3iPpIBAgIBGSJsD3omkQmkllFgIS5LIsva/KspzP59PpdDabVFU1m83KqshNboxJU9tu57XiveK4nyrpZlk2GWsFnroOhHoGVVVFBsiATXTkRcCTgTSzZcl6kowloiQ1ZEDzie3u7uZ5XpXee9dq5UTonBPxSZJ4L1VVtTt5Wbiqqmxiw+QGhlLnNEmSyWSS57m1VgFYrcteOKB7iDjqdDqnp6fx2lNWI2+3mfnSpUuDweDHP/5xVVWXLl1i5idPnmxvbxdF8fjx49ls9sorr+zv7z958uTk5CTP88lk0u/3FclUUWyMUaflq1evlmX5+eefHx0dnZ2dXb58WcXxJEl2d3f39/Zu3bpVluWPfvQj7/14PJ5NpiLS6K7Ie6+Mi0KyqlUjXf05+wsxpKvzsX6Lo3zOsconAPDm1l47IyK/AABjqP+xqkmOfxjDLQJrDsg1ABZpUpCCanP0D2Tn6wqGXLPjaeO0mJoogTMtwz3O7efXTSL/dduzxucFAP+a27MA+FnnNxEl/jOYgrgp+aI2G7/q06sq1rAz9Q4N5q0Q+tDihVt7ENcqaA6nVoFV4lcIZ5r8i8tLYdWpIVjOOCq3EFiEcE9jUEQgKp+nAqc+NraBxdgZNO0BgIPt3K1G9J47vPoGsbAbvdeKKTSMYTB1YyPvagthRWnTgksIRpxPzDfgquyrk4YGEJf66sA2xdeHA0JxwPP5fD6fVlU1n89Ho9FsNmHmJK0jjAEgTVOtXOu9f/r4qXY7yzLnfBCCAWA0GmVZa3d3V5Mql2WpvxIRhbqAE9w4gYefq8jonJtOpxcuXMjzvCxHZSmqXFXhtdVqTSazsixVezybVWSN3iGsqDCwalWdz+d5nuur9fv90WiklRu48bxVkUg12LFOSCF8a2srz3MRuXv37tHR0UsvvbSzs3N6eqoivm4cNQkbY2az2cnJSZakXLnDw0Mtaygi0+l0f3//3Xff/b3f+70bN27cvn374cOHzHzx4kVjzNtvv33v3j3n3K1bt268+upkMnn//fc///zzy4cXtVKhCuitVouZi6JQlkI7rMsphqtNchEk4wDDIWfkmjdDYL7PJS9rPHRzwAL+62FVFHQbq21XlmVcL0SWDijxapdVR/o1smAABWpnqzzP0zRtpdkyrRUtjVPmGbni1xiR/2XtBQD/mtvXBeAYGOJrfJQbXSLtrqoiA7S4Jr3cWmKs8CtjlujS3KQO8YyeuMQAlYCxKSGw2bFnIXF8PaymLD8X/6SWfUFzdREZZgYHhOzr5BYGkZ1z6lMcYDJAY+A/dARUnlCipBk26uTLdRIefXQt6Tab/3z5QKLk7PHLYqM2DGH+7XZboVcJa/DG1Ps75yixcbGBNZoFkS1fH6TJaAPjFX0LKqkzcyhvBV5c6SfDwWg0EpGyWsyn47IojDHd1pb33hgsyzIhk/e21CNpPpkjYqfT6XQ6zDKdTrn27gYtDq9GNUTUP/NOW2lZq9XqdruIuFgs5vM5MwdxRGOrnHOLxQLZG5CE0IB4duIrAIPiE0OWkIQtQmqIE1tYMg2rEVBEeQ5jzGQy6XQ6WpsoTdPRaHTlypWzs7M8a+lqt9a2Wq1+v58kSaggqWOou6PVam1vb9+4cWM6nT558mRvb+/1118notlsdnBw0G63Hz16BACvvfZav99/+PDhcDjs9XqT0VhEPIgTLr1DVxWuKr176ZXrve3+J59/dnx8PJyMx+Nxu90uquqnP/1pp9N56623vvnNb7qqeu+99x48eICIn3zyidZEUm5D+YlWq0XWBGsFrVbwlCVTuLKVwrKP1c5rDHf4yZpKNtw8rOf4WWHVw1dutNqC4B4D8MpxJBLH/YkZjvjYYu2OoexdnuedvJVlWWrrooHGGKyLiDAzmw1vbW3M54j1v8L2QgL+37R9XQB+1sbYNPBILTbVwBOETtckcY356MAvK/yEzSlNTT1NxNHs+c1+no+7z0HitRYktrVXk0jZHu4U38177xw2djIpy9LamqZQk4YzZj60NSl+sKqWMVExyK1tyOdw3+H6+KtwfTCIQoNY6n6iVskgJkqUrQmg5h42R2mDMGFi1SmMAiXlKHVRLNY758T5xax8enQ0Ho/zPDe2dj/O81x9iBBlOp2q5rPf73vvT08HaZpub2/v7+8bY6fTaVVVKqXt7u62210Fs729PSKqqmp7e1sTXGxtbek9gwuCSifqA6USpF6vjmntdjssUiIyJlGhWQcqz3PvxQlDFWwly6AsHb0gfIcoI2ttp9NZLBbK8eR5rtG96qvlongb5Q/6/f7Vq1f/7oP3Hz9+fHBwoED79OnTV155hZkfPnzY6/Vef/31oigePHiwWCwuXrzIzldVNRqNnHNqyNen/8mf/MloNPrhD3+opYKdc0R0cnJSFMX3v//9t99++/T09KMPP3z8+LGOjxY70vlV/bO2RVlwE0EUJMV4c8WqF6hrq/hY/xwoA0dxvdhYr86VCOMFvLYA4Rlx9s+yrar0uWbPgmch68Zqhw3n5LXPROPjmwCzNE3zNEvTNLVJeC6scirn9vPX1V4A8P+m7VkLJXaaiC9bc7JwTepdRHFRgg5dgtjEngYxcdMYLE2ZTN0gCCZ6bgSHCLDK0p77IvEWehYzK6v27OWVck4oVCORewAgg0pxEKWqKomuDyCkBFfDtEIIlggyg3cSCBMgNtZfqt9UqxpI86fUji1hMLkp07vWST1QdaVeEABY5cX4HetJAQBsKog2xCJeCSvDsjHOAXe5cfOOk/qWZcmVGw0mTx8/KcvywoULeauT57kIdzqdxlO0JpTdbrfbaxeLqtvtEtHOzs6FCxfSNNMBTJKE0PZ6PWvTxWIhInqHqqqSPFMdb6/X0ySLsfE4VDXXDldVdXBwoOqBfr8vtYOYdDqdLGtNJpNWO0dE75iIOh0YjCdVVQHWpJabAh4K1bPZbGtrSwsQ7ezsHB8fdzodZQgQsdVqdTqdra0tZg7pLwK2AUCapr1ej4ju3r17dnZ2cHBQFMXp6akC+dOnT733h4eHWZbdvXvXOdfr9YbDoXIVs9lMY8bSND08PEzT9P79+xocrAZ+VbZba//Fv/gXvV7v9u3b/+N//I/Hjx4dHBzs7u6WZfnSlavD4XAyGqvaWYN9lZUJ/QxCcGDXwqSH4xBuFC4Lsn7gZbWpQzWaFVtyuCZ28oo3pkSuD3F7lmrXGgurjCNtOBiuPAjq8gbIIQWQ1lg4Xz7utNshsNsixTa4AL3h/puM9Zf2n59lM/6a7RkMzQsA/nW3ZwHts86vSVrx+eBVoQfQLPfwFaxG3a0pabEx18Eq76mrnjB2u9eNqsi8wsCqJZYAUSD8W3sX3QqAy0r1AcBk9R8IMRAiaBZjrZhLYBiZGRjZCDKKcVxZto6ByDglVaK+0N5zVXnnSg361DCtAMAaROQZxLMgGCSgOm87Adb1QRFEM7lrDxmBBIQYOIgR0EgAGL2RvqwCWLAyajyMArBv9KgAgESGCBEr9nWtUvEiy09r02YMWJPgqrVexAGETJBL5xrvvS+rqiz1fevCU1V1dnJydnYGAPv7+0mSICKAtFqtoiioqalgTEJE3om6DSdJ0mq1t7b6KgEXRZGmaZ61tSKe2oC99wpg49lUtevdbldFUpX4RSSztbuZ2lkXdmaR2r2e2jXV22g2mznH1tp2u+1cyZwi4nQ8AUSwhquyqgpjrco8yml57wHYWhqPx5cuXbp//761W7v7Fz764MPXXr/ZbbeMQUsma+WtVivP0/l8XlWF9xWv+iqqnZuZx+MxAbbbbWBJkkQTaLz//vvb29tXrlwZjUZf3L7T6271+/0f//jHl69c2u/ve2HlPwThyrWru7u7P/zhDz/55JM0TbNWPp3PxuPxTn/7X/7Lfzk4Of3000/vfPFFmiQ3btwwxujUTCYTY8z29rZqTZvMaw6o9p0MEj9HvpObALxYLIJ+XpsShBhgwt5XtQk3PHRMVVYpQOAv8VkFtZ4FMMENPr4ydAA3vDt1m63dbY2A1HdARMR2u61LlIiQ61upOi+8QpCXlWYJAsr6pyE69zz8Avi7eRcUgPPuji/KEf6vas8C1PiCeDuFgva68SCKssVIQgrXaxIrEXSOnavlueBpYkxCVOdE1F8hUgOCuse0ECF5LzGL6r1nrnRLx8htjCG0jcgoWkzOIBGhIfHeGwHxzM4Di0ECY0GwYo9eBNB7ZiSh+kVYBI0VEUYEJA9okIQMm8bB2iMaBAZBEc9eHDCahNAYQlN5lEoMQWIASoeUOi+Vq8sMz+fz+WJaRXVMva+a6SAEw0gGUUjLAoMxhAiJsU5YnAcQ0vAd5733CCCMAprYfSkZJKnSBVF+PKTR2O5thUhftIaI9MHOOSFEMjXbj6icDjkPqHp/RiAt1QeE4l0jkyPoNwCALIRplojIfD73ziEiV34+mw0GAwSYTqeDwcAVZZqmloxzrp23Xrp2pdfr7e7uIuJ0Ok2SZHtre7FYtFvtJEls1/Q6fUQoigKYLNlLh5f29w4Sk+5u53naGgwGmuapvbc3nU4PD/bLsnz8+PGF/Ze3d7YXVanUEAAsUq/dsUiLxSKzCQGmaZpnuTW2qqosSfu9LTRg0sYP3CY7Ozs6nvPZLE1MnrWzLBtaQ0SLxeLBg3uPHtz91jvf0Wjadrt948YNL3zv3r2rL188PU0X5eS1W9d7vd6iKt94+zURBqquXTtczOYmsYv5vNfdm05GhCLg9/f3np6eDYfDVt5GYw8uXvr7f//v//gnPxkPBxcP9l++dvVH/+NHp8PB7//+H7z71+8Kwx//H39y9+7dn3z206JyJ2cDQfq9P/jDTz7/ZOGqwWR8//79Gzdu/OCdH8wXi//63/7bcDgkY3Z2d4fD4d1797797W//yR//8dlw8PFHH43HY1eUBtAAZjZpZzl0uqqjNsYI1qUcyRpLqMkyVZJWmVhEWq0WIqpeZzVRObuyiolDaMACjY5XkycDgCBw5NVc65cj7EOsz0kdZ7gE4AjXABrvMNhQ0njniChpQsOXfVvVnAcLsa+cEIn6NTRdERFfOWOtalBUZWKJADjLMmMaeR1BGh+uFchvoBcAmH3Nxq5+Cntlrdc+0caav7WRAYBYISAATIgADEgALKCp7AlEiyyLHmtcvl7zAoB/zS1WxXCUYsY1hQqUhcSmXoLmaoZI5ch1kvSVFlmM1rMAaltjeENnAusZzgd9FKy6VKjTjygsMBKRkSYNBCABGiRLwkQWQESMAUaoREA8YJ3WGRppGnQpi4AIAajjEKr8LcIIyCwYXlxLYVs0lsggEaLxAuQFkJUW+cbDeT6fzudzz3XiEe+9Jn/W52NdjB5RTakqWiI6Ye89S6MbEGAEISQORCHuO8Qyipo51Ryo9jw9D6tpDer7YPOJgMBIIsuKuYyg/m1QxxqRiICgB0EBByI2qSlXnufC7JybjWaDweDoyRMickU5n05RIEtSawwhJmnGzGoQVSkkROloh50jZba8Z2ttnreTJIPGSG+M0YJ3Ktlfu3ZNYeDVV1/13s9ms9TY4CYrjbFDPVGZGQHUD94QtdTkacCkjWraJsHD2RBNJhNLxhAkiRZcxN2d/na/lxpKDRmDrlgwu26ns7XVffz48WI+tdZmeZLlCSCLtyI+tYjivK9EPAizeGspyxJgKcp5aqjVaiU22dvbu3Dhwng8Ho8GeZoeHBw8evCgLMtXX75ezhdZlr388stnZ2dFUZWlS5PkpZdeGo+mn332WW+rd/vOF0VRfOe777z11lvD4fCjDz48PT2dTqff/OY3792799FHH33/u9/7wz/8w9Ozs3t37z558sQVpcJt8EdDRK3PEWozBCOuxiurSTscqJs3M4ciK7FP2doCgw2BcnlemJ+hUj73V3EWslgyxshpea2ZmlaY4KYQswVLDVDzrCBsMLOCqS5vVajUcb02lP/6Sp5TEn3q9v3qn79oqyPvla7DcuhW4vLha9eOedF+1S1mIWMfXQlF0ZvEGo0paMXXJiijQhXPkNNVN0CSNKW1NrQ6a0u/4VvrjRH3MJZ9aTVJk2ddZbh5PUQ704ARQA9CJNhksGtk8fX8zMFfDBGJRABktT/6Kc0/BnDM4pwHj06KqhLg4Gy1WMwWi8VKBsMQyChsGIisRIG22KjFtA+w4Vy2OXfQBICpRVDT7ud5miRJnqWBiPhax8YCjWpemnLe3AjBAKGuOEQH9bNYEEHYK+/CKDYhYQdoEAAFfFmNR4OnR48f3X+YpikKKNymadpudQAgyVI1x2pvw0Rr2aUV+xmAWjpV4NAXV/uuviAAXLhw4cGDB9baw8PDhw8fOufSNK146WqLTd2t1CZlWbqq0rG11mY2abfbgkyNM5ptjOUAwJVLjEUW8GwAEzImxV6vt7fdNwR5lmxvdefTmSXotLLUGnZVYqjTbnXbrTxNfFUaBCSbZRkBGxTPzhgj3llDmrTLsVcXbu/lyqWLly8e3r9//86dO9125+rlK++//741+MqrL9+9cz9J7OXLlz/44AMRUXTY3d4ZnJ7du3fv8rXL5aI4PDj41je+2e/3f/rjn3zwwQeq0p9Op/PJ9HD/wve///2Dg4P//t//+507d8hzUJmqPtk5h4g6sLSaXkNEdLIAoIoqAy4Wi8lkEiz9sRchwQqDDl/WYgk4bqEnMX1o3COWSrj4ICYRQYuWJkn8Z4BeH2WijQ+abaHsJgVP5m6nS6Ea2FJFVzvk/y9oa/TzGTzNJstyjr9LfPACgH/NjZpsPirFhlSIFCUcCGTRe00ruESpsPHiSiaBF0ZEohX+FCKMWeuJno+dO+LtZKLMTSE1DyJKpbwqwJK3XXoIL/ckCLLecBlQET8amjvEbwcAiUlladdZuqJwlOgnCOhIAsBE4NmFsqZluSiKwlpbp+8g0oorIgJC3guij19Kb7Vpu6q5ch9HHjc2dRL17E1Tq0gWypwpR7/2giiqjQIBYCVqhCKCwKb+8hxzchiZaNLEoXiPXLn5vCjLcjabHR8fazWFdrudp5mKWZ1OZ6u7BQA2TdSyq0FB2CQrDjwTNHoXAMjzXIneWlkIEVEfqOl0qt6/6vc0n8+L0onHyAcQlYC2spyICgC1RyoAgyFBDqxfABhg0RIFiEJEmlq5Ksp2lu/v7aVk2ll+sLM3QNNOMoNUzuaHe/vOubzd6ne6iFgApjZJUtPKEiLKEitYJz9B9uzdwf7e6WAIAEVB7Kq9vb1+b+vnn3x09/YX12+8utXpIuJufzsxtiyKdpZPR+PBYNBKs3arNTgbDQaDPM1cWTx5+PCb3/n2W2+9hYjvvvvuJ5984r3XiKaPP/74xvVXfv/3f7/T6bz33nvHx8dVVW23uwkZXV0hWgERO51OeH1uKieG7e+9V+90Y4xmTZlOp7Ka1a4Z8PWE6msHq01X+DlfbXLnm7zyGgN6LoOu3MMmzQnLOFCb5ot6zVhr8zp5c0JEabJkZANB+IoS8C/TYhYEnom757dnYXZ8/gUA//pbYAnj+IGAuIE+NtuM4p9wlGRjYyvWTlUUhQ8GOhuEvBjeuMkRHaDONtoe21QADQd6H/KgYhwzN4YiCA9SiBIRlGWmrQDAYRPiakHvwFvAKiGIFVZqGFNOhUKtb/A1AHtfFIXzS7VzgzHBhU0/vUjdpTVCEJxW1uiOCAdfTgUSMkBEeZ4bg01cb62fBwDnS/Xnqse/vpEaEkQPULPSArAAIWETZ4Ubwnegzs2YcLEogWQxn5+eDDRGaDFdVIvCuxIkq2sJdHq9Tq/VaolIp9ctigIRVUmu/lOIqI7BRKQCmVI9ZlbcVYKIdbgUi3CSGKLk9PR0a6ub5/lkMup0Ws65onQGECIYIEAkAhHTVF21SJaMMQYMoTFa0YGjDIn6mFaWJMYGi/J0NO52WtcuXVyUZWYNtnJxncwaYo/eXTy4oJjdzlLH3MrSTivv9XrWWhafddoM2Ol0Ks8g4suqv9sbjUaLxcK7spVnezvbnXbuK2eQLl88mM8m/V6nnbdOj486edbtbx0/PULms9OTvNUSXz09eny4fyHPsoPDw29/45v7+/t/+Zd/+bd//TfzyXSr00WWV156+enTp9/61rdee+21H//4x59++ikAaHoNoJrP0zUZhle99FXSVabHWqu+IIvFYjwea4SVqvqrRpcQ1sPaPvrqxOdZKuhN7FELK8DKZ/h2yW1HbU3M5SgoP35W2GL61lkd0ZUEnbyJnEB56TrHaE1jp66V6era8kzZ/8uVAs8ch8233miofizPunjz5AsA/jW3GEql8caMz1dVtYqaK/AZWsxQQ7QfYlwJLKTewTeBpxylqdMNDxssrV0thxf6r731tcYbAAAQlIIzMwmoBzAQsgB7oEiEpagoEES7VCIJWFEBm7CowGQESX0NgBGFqNbdsQTHkDCkgLji5k1kY7YdNnT7uNZkmcA5SRKb1D+0dllWIVQjICJfOUaMs0vqg0WEoGEHlhEXAgQCgCyibiAg6gwCnp2wfiILIxhAIZmMBoywmM7OBiez8UxEvKtTM2oFnk6n0+v1NPzJNVmLMYpDC+swvLieVGYLo/SZEIWh68gzs2bbOD4+7na74/GQ1XAfSfwsrAxTWN5qDWBmFG+MZQEtX+Oc4xCb63xNi22iuSkKO+/kre3t7dlsxsxzQNOF3CYEuNff3mp3F7QAgNymgpCSyfN8Z2db04BYaxmk3+uNRqOE0BpkXyWGJmVhQa5evri3u50Q9rrt1268stPrf/zxx60k67TaR8dPt/u7aZoNqkE7zY4ePhKRTqvNzImxu/3tH3z/+4T4/t/93c8/+qgqy62tLWtM5dzFixd/93d/t9Vq/e3f/u3t27fTNBWRqizN6uCEVa1YpRisK1M90ufz+WKxGI1GWgopXHYuMUHElYLVq3Tm3NPnXgwR3xl2StgL5zphxas7Ph/ImkRt7SfxgPQ63ZAeLqGlti9eqBGA/VJ22q/VwvsGYvXVf7J2Jj54AcC/5rYGoopn1lqN52vyEseWmKVkFotEAU3XADjssYCmel7V1OER6s2hRDBQZ7PaAgBjJI/WAQDivfcUGZ7a7TYzewHH7L03rkIy6KpyXq4iEeJ5NmBsePnSVfpQL1z5pnu8DMaAprCBiDA7MlqalB170LAoFEFgQA0aBgGtuhteZI0EBJqoZ8KL659Wmkry1hobFAPBBmysrZUESCrwcojg0AcjIgKaJqG0ukd6BSdA8LViTY1hDRkT1iJTdU1iAURGBJHpZIREVVkakFaWishCSmHXabV7ne52v9/r9bZ6/U6no0Mqng0SIgILOw8iqvRLjLVkREQjx6hxRNfKEGuLVteP1udRYPDeDwYDAHAulJPTYrB13l6Vs9UFnPWDPRqCsub/nC513/AklQMyjsvCsXYMWLIk7bRavVarqqpZmlbOqYfEwc5ukmdGBABaWQaEBWJqbUom7/fYV4hYOpcYQ4DtVtbvdgBkp9edj0eY5TdeeWWr2x4NBllibt54ZT6bPH3y6MKFC1li8jQxBEdPHqFAak2W2PlkfHBw0VrLrnr95mvbnd4nP//4Jz/5ycnJSb/f7/V6rSzv9beuXrly5fLlL7744qMPP5zNZt1uV+tZZVmmyZv0NYOr4NbW1tp21kU4Go0mk8lkMtEB9E0wYUC1GA++Cip89XYeBqtteF07vfnDwF5js5KlidzXqy2ZNWW1Pktd/JoMVvWdY81zaDW7/Ct84S9ra+zCuZeszcDm9Wuo/AKAf80tFmQxklC1qqCKpBHvqRGuS3kxFlzC8SqTCLDKvhGtKLF9VAKoqqqgZQpyXtA5r20VvbOmBRYDAECNBhoRNaTSCxg1XRsSQCHERRXWsazqdmDVZKVNyRas5OqCmHhBZAZm9iKE6FXyr2/FQbwDxeBglzUmYVeDPTbagqDSx8bmjU3JVUTMqK6pQgYadkQQMU1TYzCoaqGxciW4RC8RQQZAEAKqo30BRYCF6lhBLfTkQQiQgRGQQQjAs/MsTjwwVygEJAKGBKqiNIZQpJXnpkUsKHw28qx+Up1OR4XgPM8V52JbPkb1mlwDZmGK6zmKVqm+RcDj+Xy+tbV1cnKi+DGdTnd2dh4+ehK4tGaBShjP4FYmIoBC4FVeD25EdRIGRINkkKqq4splaUpEhqiTZVK6fr+PbZzlLe/9YrGYzWaddjtNUyMAAFmWe+8dF25eTM3o8uWLE0veeykdsUeQVp5tddre+163OxqeJWl++eJBSnR2cuyLYv/w8P179+bjMe/spjbZ6fen0+n923cuXbpEiHu7u48ePUoNbW31z87O3vzWrfuPHt7+9LP7X9zxzJ28BSyHh4dvv/325YuX7t69e+/ePVUFPXnyxCZJK8+ZmRFCTmy/LP6xXLHQBEQg4mQyGQ6Hi8VCBWJufNE5SpizRk/iLf+lDbFO/LLWVpn+NT/N9ZMQcQArkq6IIRP7NCyBp9GoBXVRTAqk1sREzp6VWz7la9pif+0tTNMauYMXAPwLt2etgNiWGROy+ILlAm3S3BCRerUESVRtQhKZME2TdS9+XMw1S6S5DT1sFKQ2/NwtcyC7WPaVWv6u6+8mUdMz0GyMeO+F12RmXzkRSUyqCjdjjDU2Ub8bZ8lY6920cK55ZWNMCJoKryORrYiI0jTnJstELB9AXUO+AqjJGTM7V1prEzbW2ig+yJdlRUZB1DQYnGg2ad3ksGpgpsZqbpsW6iVYWOYfDugLAOptjoiKqJ6FtKyFxbVlICIAUhWFhuOrOCo1t4/CHsADsxeHjE4cMnrw4sSDR0b9loVBvKB02y0iMMYAWQKoPPc73WuXrwyH492d/cPDS71ejwGrqhLBPG/XaTpFfMPZ6Fun1oIIqQQJoJZeAECszd6Ngb+eoMViToTj8UhXVlkWSWInk3GeJczsXekBCJCoHgbxLKzyrSCiMAqR1o0kEatjjhieYQi9qwyhAZyNx/XCFkmSpCzmWZZ1srSqKpQkMR2FpSzpEZEGvuztbuuA99qtUSufz+c7lw5FWKqyLMsLe7vW2tt37h5e2H/11dcswu3PPh0PB8aYp08eP7h3t9dpd9utu1/cvnDhwtnTpyAegTNDdz//rNvriatOT55Op9PU0OMHD0HktVde/fizT9Mk+ZN/8H9cv3794NLF//Jf/suDBw+Ojo6UGdVsJKlNSCCxibVWnQS3t7f7/f5wONSVPJvNtELw9vZ2VVWTyeTp06eIeP369SdPnpydne3s7EwmExFpt9vraLfa1s48g14hyFKwi28VvCyxkQqIqImfWUlRrlNjzsskRepSwBJiqII5I1CV2KqFiJQsIUkiU7HGBNcAtnxBEQAf4gVqfdJ6NPNal849z19mO9eRCcMY3ycaaiWhKxQ4vkBWlQrwAoD/J7WwlAPLE5igsMTjz7XfMnMonS2R+CuNx0Q4E7ZHuH8AD6pdPJaSa+hV0N8GmzE0yyXL0mDgjM2Za6+27G7DvAdWWv9cClKxOVko5nY331oi/y9etQFzE3QREFoBGJtEylx7iaMmymoeQZo8UhiaZJpL27aIYGRTDwOIjfrdNrH/6thsjMmWZYdIPcwBQMP7EBFpqWbQOdFvAXRbGhGPQvX1wEgGgCmkBED2rGphxvgTRNgj1MfAjAgIDMCtLA3zC0BJIuJFDbGtdoaIzjlXU8AVi+/awblrWOEXzhOzogW5fp/lwl5V7Idltly9LPUQBWW71P8IcBkYLgJ1lUcE74BBnEUDJGAJ0Vghj/o3kJCgWh6AEMUQdPMsQcgSKyJbnXZhjU1MmiY7vR4z56kV54l9O03yTvfzTz79+Oc/v3bt2s1XX5lNF1/cvTMejvb6/U6WHz19evHCwbwsHj969O1vf3s8Hv/1X/7ob37019defilL0m+89fatW7defumlTrv9k3f/9tH9B/PFXBHIiK3N2Iviws6uLyvV3IRFrqk6T05OnHN5niv/ba3VnJeIOBwOdVFNp9PNWXvOJH5p06k418oQH6x+rphCEdeNFGEXg4h3LiSoCrMfK5+DvuQZLMLz2i8XqvtV29pQwAY3gxsbBFZHbPNWob0A4F99k0gVLKve/JsNI9DixhlKA+01sihml2IAhiivpIioeioEzwQyF7L7ho5pc423izQuOdrVTqcbdoVCjm4Vv1Ggvu6PBwBAWa9WW6NCg7XGGPJsYKlx4tUQ2zAC8dCpEjtIqDEAhyJrAOCFAwzb1LAgoBEAJGEQBvTCCEhioFZhEaABQmTQyqMQgYQOV/DG1LCiUC4wt6rCjdVxtYAeoS8gCpHy4gwgmg0FGBAZRQC4KRghUKfIEUABEOdZwCMLgACzCCMLIAA7zckBILU3qgCIGBQEBq2YgQaBLBkG2tvZ1cQa3nvvPDMTWkOoLxotp5V1+6z1DA2ViWd/beL0z1qRwJqoC0TTRYqIjzKp1Si7lFxAVHoRZPX9jlC8SeGEAAgszL5iB2gYAcgSWDJChGBUXS/YZBqq0w5Kp91KE4tAItLf6pZl6p1You2tLggZxNl0XCxmiTX9bufo8aNWmtx89ZVuKy9m8+lkZAiEHYq08sx1WoPBaZqmlw8PHgt/+umnVy9fzNNsPp9/57vv3Lp1azGdkUBZFMPBQHlYVfYoAJeLotVqTSsXB+srk3d2djYYDDqdTqvVmk6niklHR0cabz2ZTLrdbqvV0oN4j/wy6Au1IYbCFgua7RhTI55SRCQEMqwtic3GAN45A/XPg2u30haMvEwCU+6e4UT262pfir6rJ5Um4POvj796AcC/YHvWuo99KAKchG9XxaP6OBgdNSvsfD4vimI2mwVH3CAChjvEN6EmrWsA/vhZdd3c1SI5sbo1FnARsdvtwnlFxDjyZlxRegsTEcFSAaXnAwDXySWEjWFGCAGyMdWGDWc0JVtEBLWAtIxN0vuHvAQYqRYAQEWH2pMcQkp6IaoLDcESa1EQrLExHaEm2F9zSYacVrWvGWJmlNWIRUAUETX1BjpVDz4KCAMgIKvjlIiegVqX1hR8JAHQYqvsEEA8A4h4lXcVjAVAhDU1GADXSmuqtdYkIIBIBhDJInTbLUpSQuNlqRBDRJGltj8+iD+X14sw1Eb9Z6EvRFaVc/dF/BOsFSHNSc2sLUICzE0Q25K3YxHxqhFptNkNp1UBGCLRROGIJARNMQsrANjkSfVllSc2M1SUDgCTpFUldjZbGEPbWz1Ew4JFNUOWdjvf6nUOL1zodDrvfPNbw/H4s08+vX/n7u72TqvdJoCrl6/87GfvpUny9htvuqoCzy9fvlqyAJn08PCVl6/v7ewen548fXJUzhe7u7siMl8sAMCQAQCtN6zaV03TgYg7OztEVBTF06dPdb2JiArEWomh1+sx82w20xCm6XTa6XSiaiLnC15fvRGRMaS+8YHJ3gTgoPJZA+Aw46E/MaEAAGOMAYz3VEhC0hAoE9ND575mGNUzlc2/yvbV0BdiAA7n41FaOwkvJOBfeQvypW/qIoR9EtZxzPQpFGlCV/Uo0drmZVkG9NU763008akeByEMm/QIENG7WD4LeyOEKgVNdfDKoSajYbxEwic1IUMSidHMzELGmBpPGgLKTR4uagBY39qDBAD25xXQDTfHJu4IzQrkx7SemmiZwEMo8dLMTczsPagLUDSAtXuUKqtFxGY23lTGGBV2YwCOS/Yie80h14xtg9xLYF6qH2p1RS2NATaXQ/N7XE5Zs0UVbhX46k/9V+fMBwnFYQBR0jQRUf9fQDBorKEEmtkkIhQUA9TYoqGp64xBtK17Up/UhlBHJYs0nmONjU19auoDZAEPyKLpvaBWHCtSGgBEYBENjiRCo2HsCIjIrEoK0NzXWrxYmJEFAHxVBX1L3SMhZLGpUc0AMAMaQmo4ikDECRvNCyIJO5umREa1vlmeOWvE+8RmYCwiLorKl5kxptvtdvL8jddvnpyd7vS3Tk+Pjx4/PHr86Nqli2+//dZgMDKJbWXpze997+rlyz/7u7/b6nYvXTz46JPPX3v9je9973smsUmSXLty9cc//cmdO3deu/X606dPy5DHqijzdmt3dxc9a56sxWKh/S6K4tGjR8Ph8NatW2maDofDdrs9Go1OTk729vYUd7XmIzfxweqYGfND8swCgl/SqLHL6p9h5waVGERiQ5PVZ8XsFWhRuOHyVwAZWYNUp5BUL4qG/44uq//9YrI8B6twg8fPGojzzb/PbZvQuzbOq4B6DgBDw7+ee6sXAPwLtk3eU5sinIsqwQVBDZuQygZKjZqINPvVbDabNy1E2SvkBKdHZlWvAW14Yz3LvBcH+wbZN8b10Cvtjyb+jXlYaFTKehx6UvMWoAZXBgBskuFBbOpb7l4ioQDAYfduDmzMXiByyDESOhCPZ2hYO0MlwQU3vCARGYMrrrkhGR4h4dIi1WTSsEEFrUjc8PsrYZYxEURUAI5V0/XTAWTt+nCssqBqWRs4XCK0UhNucLG2v6mAUj+FU2M01MfXNV0QiRDJe2+AvPGIqDHDuhgQVpbH2lxLpG1GRIVVWY0WDVfGipDV960XZLwSlMQTLAfHGIMA6J2m4QwMlpoMNWllfU8BqT3VhSyG8Olaj9JslMDOItScGbAQCAkgokUAIGvQYOpb3jlvERDJGux0Oj0ia1OtHHCwf8EYc/L0eHg2uHXz5ltvvXV44cLO1s7Hn336xuu3vvPdd+7fvz8dj1+9fv1sOPpH/+gfZa3OtWvXzs7ORpMxM8+ns4P9C1udrpYyJAG0xhiTZVmv15uNxsJL1rksy+l0+uTJEy26rBZfIhqNRqPR6Nq1ayFR5WAw0EqFABDow9pUwgY8rM3aZqMmhao0IeCBNIVH4FJy0E0UtCnLFiYaYp8PxNwklkyQfYPgEchIvN+lKYHwP6kt/UC+ZtvEzjVADQB87sUxTVgF7BcS8K+6rQGwOlIpiFJj+AwcJTOr2nk6nc5mMy2Wp7AdJE5o8E+XqcI3N/mtwnPXJjh8FVTiMfqGywLqKOQYYxBNTGFhAzbC/WtySUZEtGgB1T6u60F7KxzfhlcXwDporfTf+9K7OLu10n1jjKlrMhmyFhullgdEQBB2wiwMCKgqtiQxxgAyMwMCIwCCpmBS8A0u3wGAg8fmGqCGvzTtn0Ad6RuwthklXnudsEdFRJwHAJ0Q4OUIk1nZ4fFnOBk1YlYBFAmQQZhZ2CESGQ1/JkTkupgxAXBi17MNb455eKiyA/VndM0a+i6REhtnbu2c6uVZABANAQA1T6t5CAERBhBxXjRJtq9vWxXLeHECBBC1h6MBQF6bFG4S99drUhrrDIAldhUgoucKwXDliGxibFVUDkphJIB+r5O28sW8HAwGWZqkaToaDgeDARF8/3vfvfHKq6PJuLfV6Xbbr732unhXFeWtm69f2NtPk+yf/pP/5yeffn706DEYms/nt2/fXiwWBxcPT09PNZIQAPI839raanc7aZJOmMej0Ww2a7fbyn9PJpOqqm7cuKH7VJNvzOfzoJRCxG63e+fOnaqqLl68qOxmzKPDL9Gw4WIldpxsWJk1ekWkqSWXHGS8EsJlgaE3RCkaSyZGX/2t+qxAVO0YAOR/GvT+Mu1cQN0cdkQM1HfzJ+f+CS8A+Cu3zQJW53/GbT1PeqQyDUyfVjhfLBaKvnFMDqwGNcF5ItRaL2MyDQ2hlEhzu4aLgbdtTKdJfWdGQY9gWByCEXVjIk1u4UAr3guBAIpG03hgdMIkwOjVvMqNu1AjwanUAmQASQC5/rccY128tVcSIAiI45qhkcgWXifeIlLLqjIUyqh7XylrHhxNsSnIo+PvoDR6qaAHZGahWhVvV5um11CbIyLVI2cAlxuMQBiEMFbB1dpgUE0yko4U1GeXanwPAAq9sa6Vmi0ZNjk1ryeN01JQ2SGAQQNIOmgExEC1hKiUUUoBqOsdIgKAxUz12IKiM6T/ASyP6/8aNbvIcl2Fz/PRNzogIowk4Fql6VcM0mSANdYZm16DDxwjohBZIhAwWhfZg3debRpWgildAFBIkIFJiJHVS7xWIlhYaHkrz4iCWFoL1ITZeGJDydZWt9PpDGB0euZu3njlg48+Onp01G7lFw8PelvdslxYwqoqXrp6+dq1K//5P//nTqf3zjvfHo1Gh5cu3rt3h8WdDU76OztpajUVzM8//CjNs8o7QwQMnbx9uH+Q5Fkxm/uyOjs7KYpqa6ubZS3nSudcliWXL18eDs+8F2tpPp9XXLVarbJcqLNSmqZVVXjvd3b6zrnJJCnLxS+kUl1vqpMwSIwSMrQo08MIVMeh12yQXqmRA15rWwWTE4AAqB4psdZYW5cg9NLYj1ectoJNB9a8BFYZtV97Oxd9N69BDEsdzlVBP+tWLwB4pcXURIfJOXWcAUAGrQoHgITCmt4XBVDLtbP36hPsvQ8VOqHBAF51+tUAXG7S2cflEwKHC1HKiMCc1gpJRNNkadblq5jUaCYhEnkRAIkQgBFNkKoDMjW5I4wIeu8JbVNbgYwxJKRF+Ywx4tQ8qdEdiZBXMi91UA0Zo6ZDZnGlLwmNFTRgGNSOyABSVnNESVNbllhVXsQzMjNPFxMGMKouJQMN9+3Zla6wiVVmhYiSNPEVJ5mtvBcv4CGhBAA07/F8Oi5dQURZKyVANXkjogHTbXeJaL6YAkvebnElk3KUmtQYCiCRpqly6kliNK00NPIpEQkx135TAspRICMASW3NpWVOSUFGQAFmxbAGX2s8ZPEQcFqz1woAgLiqXoia8SrMF6KABHRv/iFXnogMEQMxs7AwekBkccDs9WFAXC9pIxUDgAHDyCT1f6yMI7IBw8AGDAsbqA0fIMCyhFvtT9WkLIVVYA57R8TrOhItWQkeEMSIqOIdhAHFQ2KpXqq1oC4M3vnSi8OavIOAIAogEQCzN1p/EsV7V4+NqEwsKGJQlHtRAPYePPiEDBiLRI7BlaWIqFmHAITd4OxkOhmlafrytUso0u+1kLevv3L5nW//zmg6qarp9s7OoqwuHOxOZ6O33r41W8zBuAsXdxzLrBg9OT65dPUAET/4+XvT2XBne6/dbk8n88q73e3dbq9vjJlP55OzKRG8/97fAbpet3/p4gF7+PSzj22S/eD3fnc2nbY7+V/+1d90u20im2T2m9/45mQysgSurPb29ibjl9M0v3Tx4Gc/+9npydMsy7qdTpIk0+lUKyMpNbh0+arq0qTJUqdGLhFZLBZE1Ol0QhQyoly6dClvpePhaDKbtrIcABazeSvLy0Xh2Pd7WwwyGgxbrdbli5e8OBSYFbPpeDJbzH3lBMEgAaE1lqxJbZJkaZakJrEK52RrjI8jKoko5JwPdK9eTJqWdalSWlfpIdY8exDWdfWGb2HVZLbWeDWNwdq3a0i5diY8ETbiiZstoPWAz1EvwXl2ehF5AcArDVet5aADLZqFAAEBQUe4ngUAFW8EhIK7UxA6Y+lTvX6CLZYav/9YORz6ALDCJWGcRZKWoesNpVuRSwIkx2+0+WphJUFEQBHVjAsi4T61i5C+TdPZ5bGtRwkRpSa4IgC+8s4iINVurjoOzmvtJC/AweVCHZdsmuqfBkBwqf7yCw5W3lrznCQMYNNUqgoITWKB0At7YSRNUKDj6ZESLV3OzOLEWmuQvMnAQEKpt5LYzFprbFAALIN8YVm5kwBZxXEAAGC1UZGArPqNUFNaUE82YKmCPgAIsshyWwbRfwW9AKLpU1cpABExgCIQElMhNc4eQigEQCTAQgBs6mJP4EldmKHhFQHQK8/NulbV9xpZqZh+xmeev1NWu32Otg0jo/IzbsO1EkL91Wuvdd+sCkQUTWqJtZc5IJF6nzVjWM+UiK+5ZAYALdfolfkARgdgELlRl0jjfhj64VzlvQOA7e2d7X4vS2273TY27c97HqTVas0XpbVUVc4mtJV06jqG8+Lgwk7eyYvZ/Gw0ylJjDFXlYm//IDWWgZIkTbLUVzxnWUxmi2L26isvt9t5r9d7/8MPrr/08h//8R//xV/8xenp6Xw6S/Ps//FHf3D3/v3JZPLmy28ag2+8+frtTz4RS0W5SLPEEM7n0yQxeZ6enp61Wq0UU9Wftdvtbreru6bT6QDA2dmZ9357e9sYox7XusKDPkkrZva3uoqFWoHKkul0OlmSEuDR0ZElc3h4yM6Ph6OzLL948eDJkyeVK6uqQgE11uiu0XKKS9LULGPbpM7dxJ54qazQqA303Vxj9fIDwI2Tz11v538VE8NnXfmce/4y7QUAr7e1gTYGJXh/Kv1c/wEo7GodObVthBBVXq1rFKAoCK/q7RzYw2DZpag8LcXNLDPIxJiqAB9W0qa5d/PVzsVgiViBIPEEa42PaiHUOByZhDUWp5aQqgoAyCSB/yhcVUZG8fC+Gmm0dDNG5AiEKu+NiLGWiKzWkdWgIGsqX+uldcz1+larZQ1qUntC226323nOzImxWZapQACMGiDb6+aJQWMoSZLE2NRQQmiRDBLVIiYTAApSbQlFBCDNB9RMPgmgQChkiKKqdFh+BCVbJEGuDTs0+kQf2d2fs0rX5qiBunU6ElYINp4zayth7WCTyiwXxnn9ia9f4yA3+xBWI0Zf1cuJ2a/6c6EhxKV/HKLKtAgEIQj7Wb0NBwyisAtogDn+lfN1YqawfZzz3vvpNCHCVquVJIlNrbHtYlEVRbHV26qcc2XVTpIsa+XtVlVVrmJfuSwxw/l0Nh7tbPVEJE/ym7dufvHFXc9QFMV0NuPKJ5a2t7pVlbz51ndZ3H/4D//BlcXhhb2zk+P93R00lBnzxltvisiFC3vT6dSD7O/sHh8fP378+PpL165cubK/u3P//sM7X9wenA7Y+dSa/d2dTqd3dno8Gp6libl4eOPg4GAwGDx69Gg8GvW6bWvteDQQkZ3tLU2wZa1V+TjP8/7Wzs7OTpIkmupSSVae55oJxBjT6/WISM3Ve3t7RHT//n2tccLMwWEiKMkhkhFjnv7cdQWri39zBT6Hai0P9Flflpv63PYcgH8+6v9q2wsAfmbjJiWTkjhmJ6IJ/SlivY26qaqNSmSJghhZYYPCOQBG2PnKfvqNKkYc0QuK8sXYJMMoZTTE/lCrVGmFqWzUJjEqr5EwXi1PtHZ/iPA4psUeVDhbiccVBFF/TlNncFV/NA2vCglGoPaVFURstZLloOGynz4myohJkmgSPi+s7uK1WNxkC3HOtVuddrs9m83YQzvP2+02IvrKKQOT2qR5L8qT1CYhRUCUEAAZcZlmEgDqKoTCiByk3lrGFSBQUwRAg6PhGqmF3hUA1mMAwA3MWAG88yDtXCBcO9mMZEOYIgB+Fplb+/nanddoZXyNrGqMwuoKl61R2Hj5wWpcGUTLUufCNMwnqisWsqA0tnncvO36GKIqo1FCYHozAoox0nj8Bv+M2WymZ6qqYpDaT2o0Sk3qvUfPCRIKcOnAc27NeDhYVKV31fZWR6C71eu1W92D/Z3Tp8dENC+zLJ2BUKfTIbJlubj/xe3eVner2/t7f/iHe3v7/9f/9Z+zVqvf33r99VsXLh7c/vyLvd0dQDp6+oSd/+Tjn9945fqFCxe0h/P59OHDh8aYLMtff/11a+1wONB8lpcuXTo42N/f3x8MBrrLgu6HiPI8t9a2221jjJYy1Dzh29tbp6entbd2oxb23g+Hw36/r+Wz9P67u7tHR0d37969fOWSptMJ6Etx3NFakgPRiPZ1ETMmKfEkfm04/Dqy7/Pv/Px9EZ9/7iO+NkK/AOC6xXREIgUyEWkyAGbHDAGAGwJt9FgBGAAUZgLYhDJ/i8VC74mrnsAUOTPHdgXlK6FxVA5kwmw4L2wC8HOW4yZBjOkXM0vwICWiJsVHbKpZ7i7tuTAHj6Da5gd6q8o5gaKqKiesgc5lVZVlVbkqlvi1LlCetcKdYwCuvNciSJ7ZC5M1ebtFTfD0eDyWJscsM8/nc/JC3U4nb2U2KcsytSa1xloLSaKDmRgDAM457wRTI8Ja0FenBFVfK0SAjfOSZpdVxYKo6SAMq4q/seYZJDpGFmGAOpxXoUYzWDUZGGvZdxNWw7zUk6ieUKtovVy6a1O8NvvNhCkOw9chWLLxRNww05zbahYgWjbL49V3DBcAEYqAIQBJMAmL0DTZyvTHiBIAOF7ta2wiQO2eXpvBo/Ka0KSBDM59QRqu0CWpFcGqqjQzNgFW5WI0PAuufFIs5rOptbaVZyQyHJ7t7uzs7++fnJyVZZm3O8PTp+08aXc6WdYSEV0gk8lkMChOT46LxfRf/LN/Nhye/fyTj6+/dBXR3Ljxyis3bn5++9PLBwef3f50Pi/a7fy9Dz882N/7p//4n3z8yUfvvvvucDiczWa9dqvb7WZZ643Xb/71X//1hx9+uLOz+4M/+oMrV66MRqNPf/7Ro0ePtjptA3L89CjP86tXrxLR8fFxJ89aaYKIeWJ9lrazNLPGIo0HQ3Zlp91ut9uLxUIx25WL2QSJqNfr7e3tIeInP/9wNBrt7ey00gwbr0YNuIdVrktTywUYdlzBBrytrah4MWyC4vNORj/5ikt687LnP3GNNXzOnX+x9gKA15tECSuY2fsqxuMYLHFpmnWba0tbiEoKOtswkWvyxNoEh9B4arylwhMDoQnu1jFHGa9+WGUs4ufKaorptXUWIF8ahbnGI8boq8y1Gn7DHghm0cp5ZK4qz8wV+9ohnLmsXOWdj2osGloGPjXDUd+QAZIstYWNlQdxmFCWZWVZIqIWli8WleqWgaWdt/I0A4DE2FbeyrKMXUVksiwFhqJYeM/WmulsjIgWCQwkhEBkAAVYY0hJRABJQJCRAUnU2So2/SIAifIuEL5qvmcvPph7VzAs8nWPX39tIS3/9OvXx5Mrka5vbSEpnaJl8syVxXAuTYnvf67A/RxiJKvqk3jJrR0jItSubSvF9RAAEUmWSzFED0vjbbDWh+XyW83EJCAe6myp3ETu6VM08lVEQuhtEHylLtPpMQOVHTt5a+qnCdk8ydI0LeaLeeUtUCfLLbgEud/JD/e2pSpnhbHWnh5P9nf7Guhcel9VviiK2WRcFbN/9v/6P+/fv5tbc7SY+bJ46aWrOzt7NjXT4SAhdOWims+ePHiUZcm1SxffeOP10fDs7OwktUm32+202p3OvJgvEGU0PCvLcn937+1v/M63vvENBnn04OHDR/edc7AA51yapnmeK4HKsiwgZbvdbrVaKh/P53Ob0P7+fr/fb7VaWjt5Npv1+/3T09Pf+Z3fuXz58ng8vnPnjibFvHr1qvcOEUOu+EAiYu3auQt4bb2tXRx/+3yMXLlstcrQ5pXPapuL//ko/qy32Lzxcut/tfYCgOsWrwyOIogWi5n6hqxZVZciaaMcRkStJBQ28xrWhn1uVx2YzyWdCsDYCMEBVjfRN6Y4MTnWxs/YEjErEF4cANQlOCh14xePZYu1u+mtQvZ9wdq/rHA+ZCPRu3hA513TZ02VVb/OsjNLAgpERNZYSIgNeW/ThKxBQ8aY/f39TqdzcnLiK9fKcmNMYc3+7q6vysVsniVpp90CgDxJ2+1WWZZVuSC0iUVkdOXCezGY5EmKGjdqgADRIAEAIQGIAAGqPxoIa/mDuAjpMqoVNF1w6HtQBoivM2cBhIouEUJThL5Bbj13TcJqsZr16T5/PWsG6oC8dWKszbmLf7g2v+dOdzg4lwMIF3wVCZiI2BpEQEbi5iZEoH5SzbZoxgfJ1tVmag0+AhLqShKRcLauwizA7EL3REiEAAwier8SD01E6kRRVGVZoa8cMxD71BIAsXdbva4rHYi3Bm0rA/VCLAqTYifPymI+GQ/zLBF2VVn1O/lWpzWZz+bzOqEsCvd7rb2d3uVLFxbzcTGfvfrStauHl6aLSVlU0/k427GH+3uffXY7TczO9pYx+K1vfqPbbf/1u38zGY9evnZlNJkMz868r4Ynk3a7PR6cvX7zxsHe/s7+XrmY3b575/joSWptnmbHx8fe+/29vU6nUyxmALDd76kKSkS6nXa321V7zXQy2tnZ2dnZ0XxbWpDNOXdwcLC/v3/jxg3v/dOnT5n55ZdfVhRvtXJsEjsrqHNUPFtW88uuUZjN9tUx+Dkr9lzAPpdx3Pztl94tZpqfdYdfpr0A4JUWaFwjBFdFUYSUwvGkKvcXlMbNsfdeQ198nJEjBBrVoBiBTQg0WgPg2EUTI6FWY4SeBYqyqhs893iNZ4x/KE2sbeAtuMnJLBtNf0XhtmHJosa7culdUZTq38HMqKl70QSmAWuVIgBAmqTLEaD63b1IUZXUFEPUNAVEhALOue3t7VaWz6ezGc+yLOt1OlWe7e3snjw9KhcL6XbzJDXGJMa28pRQ0DsiaqUJiLjKePJ5ljjnAJgAgdTKCOpliwDqPgzAWFcWAECOwnzrVp+pUzFqVGpggwVY1Kk4DNEm07PZakIT/cn+HJ9keYYqeI2WrWukN66BVZr1HPq1+aCwnL7iT1b6r17NgvUKjzMYRHJwc3UtFMfDH/N/4czaCIeOBY9IIirLEqJyJmFLKgiJZ1V0MzOwB4DUJrPJlCtvALMkFe+K+XyxmO1cvrC91RuPx08eP9rf2fVVOR4N292Oq+boy05mO73tPM8ZITVpnucf//zTwcnJH/3hH/74Jz+Zz+cHly7+5N2//Sf/5z9dLBY//+Tj/d3+3Xu3L+z03/nd7z24e++0mL31xhuLn0zLxeLO7c8no3Gvv9XKsssXD1pZfu2l6wcXLgyGww8++fj2p5/OJnOypqoqRVPdYp1OR6MikyRR4T7Lsu3tbWZW36v9/f39/X0AGI/H0Di+9Pv9733ve1988cXf/u3fLhaL1157Lc/z09PTsix3d3dipoqjDPMiK2FF2kxUD/tLV/7a3D0f8xCb8mu/hBPWs04+/9tfYcPAvPyGtWcNVsyz6zKFVfxj5rIsNTFkWS5iAIYIC9X+EYuzen/2pHkly7JUANYMG+G3cVtbbbjUaePaUlheiSaguzR+TDEZWrstNPxE/IIxl7o2YnnWMU0+ZIwq/WmSkMBMBNu2MUsXDBFw7Gs3K4b5fD4aTYajUVmWRJTneZJnWgBQkd6QNcao03De7tYDyOwFXFMoeDAasvrTIGZJqtqz1CZbW1vGGNtY8rz3lkxi0C1Kg2IQQyGjLDE6X1jHifk49SMzA4omwIs+uf5cutvV0JtYApBlOQHQTE/AfH4GPQa/VJmuFPlhPRNWozYkG1hAjCVLzTIW36EZdmqK4IqWeEL0CADoapmR0CZEmqZMEpOfuy9CN+LOAEAs3MQHm8R0jaRKSLSpf0ZLLkqzwKLVn6L30lzQz7qeDHKTvGVta4QdvZr37Zxk7PHuw4i7RUQRv9Rsgan3IaM4j4gGLQa5XMATsAU2y40HaEB9BEKZS2XlQLRimDACELB4YRFhZdFAHj58SIlNkgSJvHPT2Ww2nS6KYjKfjaejwelwtpinNtnZ2710eHGrv3P/3j3nudNqf/zpJ4PTs1anffL0FA1duXr9888/HwwG29vbeZ5Pp1Nm3traeu+99/b39/M8Pzw8fPPNN5Mkefr0aenczs7OcDg8OzurqirP8263m+e5MeaHP/xhnue9Xi/LMu99WZbGGFVTJ0miAQV6XunAfD7vdDppmqr7i7W20+lkWTYajWTD1Q4ANEdCIHTxwbnrk86L6xWso+bidRvWxiaaIqIJWquvBthrvfoyDNboyvN55c27wQsJWJtEKlD1WA55JMuyPBeApfH9WbrOAoCQCKopJVRTUBqqpiZuCtHDxuLDVdtt3DeIqBuvVjTCVStIvFCeI12d+62eUTYZG9U3NMs6DhMK9Mt7XxSLwPZyHc7hSufm82KxWEymU/VV1ls5YZOk4XHCIFJH7XCU86s2piIiYp7nQEhEibFpmrayPE3TLEkIAD2LQGJtJ81rKwAwthywGITGS9NYMoBMWJtvpfb18YBqehStBKiezAQ1gURoziCHb6Eh/tIojZewcf7K0vQjuDkvuDnTzbdrBKue5WX+j3MUd8vjFUHwmRL2uW0TfcP9N1fa5q+ef3L1eHkIq0ppAJ0doUgDFAEwMp6j697sQBjd+LRELea8RYJX1zLDq4gAscZKLxNoIyMYRo1WBwAGBBIUrDOIKpMXOuwRERiAUATAN3fygKC+h4Sanhi3t7s1/QGpAPLMIGRpSlluWu2k3+kuyjIxptXp9HudLEuuXDx8+OTIV+Wrr1yf7e8/OX66t9O/cHh4994jAM7TzLlyMRPnSl/x6enxO9/+zng6+vY3v/PW77w5OB0W1eLWzdcHo+FwPD4+Pn7y5Mn29vYrr7yyvb395MmTzz///NKlS4HPBgAiStNUSyWqS6k03L+6ZVRVlWWZRijoT4IXC3yZgmR9Df8SbXOVPh87f8nH/fLttxqAsfFpkqYUFwBoCKmGtxdFMZ/PZFW1ons1SRxFzsnRCiMtKairEKMgSG6yZGCIsjAm/nONlKyxdTXIyVIqhcaJOkA4PHdJBXITX7NGK1Vwl9oevGKoDlxCYD70J8xcOg3wZe99UVXe++l0Pi8Wk/FsNptV7I0xTjhxVa+fYNRCr3T8m47VDyKATqdDREmSZEmaZVkry5IksWS898hikLRgUZIkICKuShBYvKbT08pRqFSYPdYCiSKZVxJaU8xlQlEmAVa/KliXg0FU0m3KQoSZAkE6d9jrPJy1pBvq4yJq/itVppLUhVw8rKQOjdGjpukbRGxlMDH4hZ4/+7ABVHHbBOAYhmMMho1VunlnWUYnP6813QZlg5pHYG0v37ycPUZxL+EgXj/x6gKAULQqvN2mnTLwvgYRQUIJMbXYi2rLEes5w5BapX5BEiYBBFRuCxq2krhm2XBplGiSpSPqgV65u9N37FV8BEZMrbEokjHzjggKaUrI2lBCRrLk5atXJuOxALTTJE2tpcSxf5qf9LYuW0oEOTFpmicEpnTFlUtXr1y73M47i3J+6eKB4+ruF/e++Pzzew/u7x8efP8H31Mv6Dt3b49GoyQ1VVUYY7Z7W71eT9XXVVUx+263GwRfREzTtNfrqV5KReeQ0H4+n3vvtRjUuYtkE3d/Jej7VYD2V/K4X0n7jQXgLyU0AUuC9UK/UqYvAPBiUYYwJE0IqAFIWdZCFGMSY7ApAF7zzqEKITSytTTVFCDa8Jp9SSPwgidXuJ6jnFZxgyidpDZcbfAMGSW+fu18THO9q3tLTbRGuIZXmyr61EFa91tZVhp65b1flJUmuK6qykM9LBKFfBARAqn6VAlooIPh04hgo+dvZVmWZSoBJ2SstShgiDJbe2MCi7cmJfTeOeeABYCFnWcGdsYQBxU0AABoHTsSAaBa9j1XDo7kYwDRbM5faykqSY//DAxNmKHwLXsvjY1/VXwDCG5cqybeJWOETWoCBGaB1bWxhqlrLf42Xg/Pgu34zLl3bm7wJRiMS6YBIfaoVy3IxuVEBLh0oVg7iDlFfWlmBFjhGzZddjFSTWvAN+gWJNBUXNjEokGdJsyoxkOiHtZIrBlrl/1V7o0j1kkABNEaEjDGGAQ0SEKE5EXEeAbv0VgQNgBgjKZVtlpFzVWswY0+td3ulnclACXWAkCv1zs+Pv7Ot76RZK06FgAgJMoAgNdfuzkYDE7uPN3e3kaB4fBsOht/61vfYgR1tlLDmVqLsixTUUSBFhubDoJRdy1V6YXiJVpHfLFYjMfj2WwWqIQC8Pnz/gxF8S/Q8Dz1zLmLBJ/97a+l/cYC8HNa0D6pk0VwlQIAaVJGhMZN7VFm3d71v6ryRNDUBYCQkllvooAaRFXvvWaKkSaWRgsQ1TXaIq/pmAJqb3FVe7MGtLFBK/5qrT3r2zWCKyIiGGMtNAs0PMiv1jcsqnJRFqp1nxdlY7pl59l774SFsK6mDqD22uBfDSDee8VhFg6Qj41NXUcStWixAAEawIRMapNup5OQsequpXBFmJlMSV6NviLMUherxVDiQd9I8/VrZmdGgLrObhPjWwNArbSuv5V68SyHZdnOs1HVxt/G0FtXDFYmvZacVvz+aofw5k+MbMAiKzQrXgAKv/qp4lQQhjcFjjpf6DNaDL2bgBqf3ATgzZMrAPwMFbRQJOnSEnq9LHW50UCrZLnUtIcDadTZ4R8SCSwZ35htlY2ghnpga19rL8JCCEKE1KwaEERBkNpJL3CxmnYREYlR1hZFY6xuAFjI2JpDAgJjkAjQIIAIMQLYhASs5kAHJ8ycJcYaGyJuHXmD4gx22/lkPOOqvHTpinPu8/EI2V+6dKm/vaeJ6LXM2mI28UmiEuqP3/3rw8PD1169/vnnn49Go1evv3Tt2rXjk7PTwdnJyUlRFOrhqNVRL168qMa4siynk0lVVWmadjqdJMmstVmeK31jZnZu1kQ6lGXpqypvEnSwJk9Y3SwxZQsHm3j8dduzfhjufO7Fv170hd9gAH4Opx/nXlZ7hjKVOhnB4ahR85IIi2i6K2V5BQC1YqAxHsAQcXimOnEobAQzLTb1UAGghpPmWQFp1logHLEhFgCkAbPw7SrL/7z1FF8gjUZxBQBEEG3oW1DLK6sbhPLa0FuWzrmTk5OyLOfzeVEUlef4bjGjEyIW5vO58iLGGK2tS1pa0KbhSrOsv1urwQnQWpsamxibGGuNIQECtETi2XkGFq1gVFWFiCbmZkQxxhAKI5k6Oz8DKM1vqABIjYwAANzgLoMiQajkVH/ymofF8wdcUzkvicuqMVWnNB78ujjShjwadQHCkK7hMa6roL+eBBw/LsbgeMF89YPwduHks4ZJ+bC1wfzFyCJGqgWl/oiEdc2Fc/RA8Ts234qAR67LROlMU12ABRHAa0ZtrBOeoSzNvStThrWuYqOLbE2KiE1tDAHARiXDRJQkaExOiasqoyQotWnsVqkFAY0xRVG02tmFCxfUy+zg4CBNU2aYjsezxUJTaqgsqzYa732321VGmZlVXZzmyZ//xf+d5nWU8GAwYOZer3fhwoWzszM1+qq/1XQ6FZGqqra3d4PRR32v1N8l+Jm22219qHpfj6fTwOVsrquYIv1iky4iYaTXYP7cO28ef8kW/prqrq/bfmMB+FlNhTC1dCr6TqfT+XwOjSSqwFk19cDLsmQPwa0fmkWTJAkRCKMwmsZZUtSHt4HGYKaNa28FeFPXJIpqJcVLQTVO2uK16/l84SPmIiEiB2uLK+7GBrlU4K9/HnTy4ZqqqrXKoVVVdXp6qhp75xxDw90DOLd0RtUXZJAY0RFXascGuhkcy5XPaOctRFSdc2qTJEnSJLGouFu6RaGQmZABz4IyG09YvEZ/GhVVxIOwAw8AKIykGZ25EUx1QGJYbY6X6TbWQLcm1voSzRALxCmyAKCpJLg27MACEpI0RbwXM6/aWTcnDiMPuE0MjgG47ttXJh+rTnDn8AFrB/Eygw3ofc5JWJOARRBFoDad6vqRJh4pHk5dLLJScXmFzkKEwdAot5FIK1URYKOWBwBBBEL0nhEBUJBA6+0BC9YFr1AYEJHRExAAacY3QABkQZWPwYtYQA8S7LoNcK9U2whvbFIjIqY+zyx1nhD9MSOSUGosERiDzIlFiwJaY0wHwRIJ4nA239vb6/e2Pvvss8WivHjxYlEUZ4NR5REQlXr0er3d3d0kSRaLxXA4vHnz5p07d95///3d3d12u/3gwYPZYnH16tWz4UDZ6FartbOzo+7TKkNrlkpEzPN8b29vZ2en0+kFfbiqrHUWFI99VNxXSRxEi39tOX0pQMbt+Yzjc+6w/hRZOf+Lof6vsP3GA/BmvV71c/ZluShLN5tNRqPJdDomstZSkmTWEqLx3qt31HxRikAkNNfFMpUCAoAWtdWTguC9sBYlFNYAAzVzqoJRrwFmBiFmAMiyLFaOBRxV2wlGQipEuIiagVoJGDMDWGOkLlrXCOkAEOQPgfM58mhvNGBfu5Z4YahFARCEclqWrlrM5rPFPHyWrprOF5Ury6Ly3nNDNxmBhEq3kvFKEdkmSZJmaZZrkDEAoEm0mkIA4NAAQFn1zCaKvobIaDXIyrmyclVFAqlNJDHCpqr8ZDxgdgIeEVNjiVDzWgJ7RGzMh9xAJ1mbNqQyrBMdmvM3PCLGwmUYQ2lUynE4k96GcDn2IURYNgFYZ7meEQLwIHU9VqRmbqlJF2IAwTRlHxCRpKkTxA2PRzVtr3uia8OJBwCtm7v6iaz/gUdG/UvrvkrzRshSvx2sBG5pjcw66Ah5ufBq3ub5TMDSpL4On7X2P/KCRhTWDGXktWwiMgkKCaLx4FFISMAjo0c0ACjEKFSXM2tWuwFsHozKpBqog4sAgGtnDnXaEwTa9OxRTz0AIGEBIbEATGIkThXeUPuYfTNNLXDduSJ1jlL1FtT0L6hVn4xlZAIjol4oonteZetetzMaDfu9fpqmVVGORoPB6Vmnt9Xr9bJWZ7FYDAYDAkiMmU0md+/f6/e3b9++nef5lStXPv30UzSm1+kURXE2HGV5fuvWTUXlsixPTk5OT493dnaY2Rij9mB1dU7TtNXqiEhVVVoGcTgcTqfTsiyzLFPnUxEJOQ+eO+8rbXXSWes7x5+qklg7DwDnxqRtcrHPQuWv0FjL4GmOP01t0xyf9wnnn4f42/iawLPEPDVEHPH/4iZRlelmkBp9IMBK/XYAEOJGhbjWaq9IMIDsKp7NJ6PhZDafzGeFgHcVL4rZbLqYzsZl4TxXaZLnrbTb2ep0W4nNPFdl4UpXHZ0OvDRlWVk0PN8gGGPyLMmyLDF12SJLhMaUnoWUPFJMpLSAtZ63SGAoIYOIPoqHw1Wb7rnjozmldUOiABqyZIDQkgmTqjAfK8HqPkStdJXu6zDR2gER9BrnSmiRPAhXzgmL8/OyKHQEZ/PpYl7OF0XliqosvatKHy8YBmnnrdlirhtS3TeMMQx49aVXyCSaS1YVWYtFOR6P8zzXBPFqZjbGdDqdTqfDTlpp1s4za62w82XF3iP7YjYnEBQA79j7qip8xZ6LeTGtJZF6QBudZC2JrjjpAIBdypGAGNKKnL9FGdmLg9XdXo9w8FZrmCNdsakqvxsWJ4TGBtEBIu4HgBwH5W3NSmmvwicRGiAwaIBUmV473FKQwQyQ1vjTPkf9D8peIQYfPgXZVyzIoAAsxODrBCTNu3yVz/ohq0JwXFx9TQYC9X9r2iZDo8chFWXNj2L9X60pbv5j4LVPQWEpmZ33HrQnLOK9eNYQsnrREgaej9mHbdjEOJia21619mh/mkQTEVRsvE44YACbJmHGAcCDKFsDERGIB0rLQyOLXrm83rMHQUYP0hyLFwCgxaKcLxZeXbkAvPeld0+PTtI863a7Qnh6enp8cpKYpL+zXRSLLE9UR31yfHZ8fFyWVZqmFy4cVlWFYPr9/s7OHhFpuPBkOhuPx+PxWF2xELEoitlsNhqNACBN0+3t7e3tbWvtfD6fTMfe+xCKHezuKjGft78AEDdYQ6orVTfH+p8eiwg2K2xd2D2vGT6/TvAzfsKCNdOpbLB+KiVfzRzQMKPi42/D9Wu/CuctrqYXfla/f33tmXwACihndP63YETEeVebIsZTZdam0ylo5ExZzufz+XyhgeQ+FRGxZmGt5QQAVOoVx+BFNJ0+KfvCLIhEwiLAXghJR77WPBuhRgNIytIDAnBUR5gRDKJGE37dRqoCQ9R8BU0FHjzneFV6WMPgmDrEs4/WkurbmQupk3k55zSwaj6fq3N4URRFVVbOeUEWFITY+VNEzoYDFWpbrZbWQauqylWOBff2LiDi8fFxUfnLly+neXtRlmpz2tra2t3dRcTaBX023+3vJklirQERrlxVlq5YCHt2noCNMDOLq9g7dpWwR3CoMSP6TxCh1mnW62K1Mcdacd5E1rXLkVaI7GZTGTf4cCmt10HBZXkAUSUerKt80bFv1LHaK1r2rqbOKISEpG+EZAFACIMLdOOHhfq4APnqi97wSR4AWH0Gofb6Aq0DDSDiYXmmqYS8+tmkeoTVTyYBFYKlsQAv/SPOO2A5h24GHGoGvXadk9pTrontWlrp1fcYBQHrSs26UQAEfd2jOrCpVoN4hpD0AwBQhKQ2GyMiUvSvGXmpb4nNMkIAlqA+AYEV9JWmrLzUf4IgsFOZYYnBvHTmCDaEpkyWiEEQRgEwgIJgBAShqQADgmJEhAAFhAAFkyQxdSpP0dKlDOi9v3jx4nw+H09ms2KRZdnh4WGSpFmWFmWSptZ7f3T09PYXd4bD4XZ/d3d3p6oKZhCW09PT09OBUtGyLKezeZ1Zlnk2m0kTRSIiW1v/P+b+7OmVJMkPxXyJzAQ+fMvZqrq7epm5nCHNZNIDpf9fdt/mgTKZZJRpqEtqKM6d6eqqOnWWbwGQmRHurgePiAwkgFNVPc0ho8vQOPklconw8N1/fu8dDD9+/DhNEzP3Q8eMZtnop9O2Lhf2Di7YGl/4NDIENF/tK6gsf5FREkDUCMHRaDF7eLS4+lafmcP7imP5hIJuC95PfjmyAA5XgviLv8YvGnhZ4qoVuqzDMFf+GBYwL2usZ8YU0xSjR3k/f/788ePH5+fn43H0t/NqNs8kMsvVuGGakLlLCQCSN+lOSQHBI7vZfvqihtV2Ky2b0MyoQLIhIAGSp2/88mk+18FXf119uTZyJQYAlARRAQOzDnKemueA1GSr5+dnL0vwEsAKiSVGbYAcCiH1fe81DFZywodh6IZN3/cvLy/b7fbdu3ci8vz8vNtu//oPf7Df/94LfJ8fn1w+3e1uN5sNe0mol4fN8zRNMs8ugBlNVMzMUkwpaYpqGfj3fBJa5dJOGlEsQrfl+9c0YqKQ2xmVmGX5S2PJ1UnO0YaTByiK2kVN/IIDbfV4KwKg0p6pIbmTUQW8P8c1ZKv65VQhsGsQI1c8ZO7WXRvBq/Nbha/107Qk3YLVtOt1jbBbtr4sIqHpMldZAPuMNQhZbSDgPKvx4q3tNDPjfFx+TkJDbAVwnQovFmopsLyUd/XLNJT/AyBc/rUcNqw70cxEowGpoar+8Y/vn56ePn3+nEzfvHn31VdfDZuNWvrj/+cff/zxh8fH55ubm3/37/7dr3/9a8IwjuN//a//P6KgAsfjcb/PWDohhPc//uB1R66Ue6XvMAxfff3OvVz7/f7T508xxt1uN2x6Im5ftmaWXJw3PKnX+xnzCVB3288UwL9UtCEiAhdVCldfLpx/ao7X29Fp8Xo9HmqXGyjz8ksf8b/DOInafuE8QzW87FIwMxcYx+Nxv98/Pz9//vz58fHJqwAJg4FIclIOQEqYy+zGcYyzuJs6qoiIIUGmG6SAmHM7iCh46wUCyi3SiJAyUB1Uj9npXm2n+kuvdn1jUwHfWB1ffc9HLl3HsPQgah7GR22hWHFw/Huty2o7O3l0XHTBxcRSyux1hC6tHb7Os6uenp7E8N27d998802M8cOHD0PXudvqcDg8fX58eXkBgPv7+20/PNzdP376bO6NiHOMUTWBKZhoms0U3QmhKpJMVU2tKQVpJXFdhZMZxmwgtly+LtCl6Ve2S6DMl5ap3mV1pTpFcMaFAbwU7cKCXpMH5bOhq+bdrfH76ZIwcDI5rTi8JIMvc4MrELZfEsCrI/n7eZJa86V+1rdeKS7tPK+mC4q5vvzWnVAuy9o9Uq7vnuf2JxdHS0vXQnUXt6epmci5AAYHkT9d3/pTaAi4XkrPcAIAwBS9SNj3Xc58TCam/+k//ScAMACP6e52O2LeH6Z3797d3e1U4eHh4d27r6dp+n/9x//3f/yP//H3v/8DIprhPOXCQig4d1SagVYsjpubm1/96lefPn16//79fr8nooeHB+/FtJRine5Bh6K8OG8/KX5W83NOPD/7tz99NgBAgzX98wX8OSVf/FM4HA5ellod0X/Og/4lhxZ/UvlsxjlTtOLjAgAoSScAkJJOScY4j3GeYppiOk7z/ngEoAX0AlCBgAnRkgGIQUwC3h5NVEFUzWMunp6Zd+kqV4goy0VA5pxMeTZ7rcisW/fPmORSX4Hn6wqXll/PFBR3uhGxoqEJIihaMhUxVZ3myev/XNBWF7QaGKAaiFoSzf+llMSSnuBi+vfHx0cA6MsAgHmeLaan/fjVr3/DAX94/50ludlub2+2h5fn8bAnoptN//rhN24NSJrf//AdI1kSEUkyS0wmyVRNVSQiKKqBqYmqJgAFUAIFyy5oQsrewtw0opgRUDCorLgk8za2Ast0eVhTRF59xMustrvUcieidrSr84Uci6I4VeHhl28v1jh9a85tMcGt/LwVulbcyu4lOpeFVxQCuPaY1yxg37Pn17+KKU1cmTI2GpILpDIDC5G3Wmwrli4qTAanDM2Wq1Ut1syMltwLOGN9q2drHwCaEMZqtIrC8tuiD1tr0ToTE6gPBs0DUFlNP4SL58DyC+JCHEbQdQEANhYA1EUwEpDB73//27u7u+3uJoTQDdthGGKMjPSHP/zhV7/6ChH//u///n/9X/+v33//njC8fft2HEcRSclh48hMD4dxnuebmxsoqz8MQ9/3t7e3Nzc3XmHo0NMPDw+vXr0CAO+/VN+3VTgusr4vWMDtr9rPusQr1ndFgYazTZnHtfMBAPHEeIUzH1V7mS8+/oURHh8fPf+lAqbAldn5VxmaGUr7eToMlixfR2u9eKEM5SxiOSuUzFAVYozMygTEHlwRMEMySZYCpgipZyb1gLuCiYonmBCRgYGxp8MQcdmzgUCzAMbgoTi4skgt98n77YsutfPRlgW31HxNDK8uU/+VseALoJXbuKq63x9cAFfHcrV3W3nsFfqzJFP0S9Vnrj/0HBaPHgHAzc3N5manGFJKHz9+7LouIAHA7c3OcyzJS7aIOw4AMEmcx2nT9ZLmGGNK0UQ0RRWxFFVi6dAkBdAfy8Su5/DMqqhDARFAAbjxuPCZDyZ/IqjIZSV3JRj8i8carGEK9ScnAsCsfkfyOOzaAj6xGs8sMGktV2zii413Q7L+cTIzrQBuuWTz/RfRp6la3baru5zftKXhdmBTmNfqmvW3q3leiedFKmP2Lhc/0IU9Au1tzkTvSkE5H9cE8Plz5u9q2ixQ/VKR4M6f7eL1V6PciAI6g0JmTl0n5vwP7+9f7e7vdrudgs1TiioA8PBw97x/+vz58/Pz8z/8wz98+PBht9v+7nd/ePvmq//yX/6/0zSZxZSSikzTtN8fj8fjb377jW9wx9iqSFvffvvtPM+3t7duEBORh/aueVyuCeAviLCLChlc8aBcv8jPmc722me69Rd/YHoitlq9rdXkKjGHx8dHByeqhR//E4y6CNj8I6shJ1lnQADSsAgCANfxY5IkKmqGhByQggGJ0nE8MnMIhjmIolBzTQWYbU7EnBCz4pMLBsHMjLl25bv42MvmOd/JWXVtf4xoABR+WSXYiumcE8QXaLE+tSFISmYWVZIkj4XPMbqwrGhWlRe3Arj9oqqmqHaSWOE/2W63XmhUi/Q9RwPD8P2PH44v+3fv3m1vNqCmKQ1d96uvv94/vzw/Pz8exr7vh2FgwCF0cR41xRSjpmhmpkkkqczmoB+a3LQDUHL4DY/Qmi+fG8BotubRzdJI8ZJZY2g6dNGFT280W7Tv5UudAcQll0brWiyOxOZJihHj/0Hj711J9NVjtyTo5aFtc8pW6J5Yw/5dm9+uHPKXLeBfpNTnF7h4i1YLaZlR+/38S2W4/n0lmK9tt/I0lovQigEKC2xDO00nkvv09TNu5cpBWIeXup6PNpZc67adTqoAbm/UCpX2+7WMv5Lh5edXk1rBAA0Y0QIjAmP2MSSxlOb9XoFwntM4jmLWdV2K8fOnDy8vh/u7u//Lv/8/D8Pw4cOn/+f/4//+m9/8VrY34zg/PT09HV+mcSS03W47TUevTXLADTMrzd/GYRg2mztHHIrRc2vkbLv9hAXcgJyt/nSmyiAioifBnTO9azz6lw/KWDrlweubXHrKXKp6/pe22KT9kl3QznC/UP3yP8MoBYa0cERPvs3vSwDuYTMAdPBIsIBgCB0im5GqpejAVoJNKA4Ru44BMalKstzFBEARNjcDiKeKqKJn8XGVpA6PlWsFzcCTTk+nsN1m8BeijHaZLn7/svQFB6wGW1m0be5V65M890/WnZBs6fjUnlOx60TEsyXHcXx5eRE43tzcgOh+v98Om9///ve3NzefPn0yUU2CiLfbm91uhwD7/f7l5YkRJM4iAiaISO5kBhARAQEVA28v6GD1Smie8G8gCAFQwZP/Mdexum8jWyKoli0YMYNSRJ2PXPxcMc36RZtOGy2b0FNLqH7WKV1NLEDAU3u3Tmz950poWYNeaSXZKl+/8Wb770VOLJIVQ2ylQvnTn88QVm+xEqt+Qt2A51N68ZrX5rmdpeUtvAT41IqvknUlgGtIqL0Unnoazl/tSiwcal17BVj1IwpZAMPpuq8A7y7O4bVpWc5Bo5I1b5GMjACAEInfvn21P4xjnE10nsfPnz/vj0ckY8ZhGF5eXr777jsV+P3vf/83f/M3/+bf/Jv/7X/7L2ETEHG/Zy962O129/f33//ww83tznsweKLly8vL4XB4/fp1CytUKfMiMZ+/1y8d57rX6ss1+rkm377wPBdvce18F8DnJ5yTt38Jqur4ZN7NEQBa8IR//SExIaL3N/U6HWcoSbPy7iKvVr/VZaa8bSRFTSkdxlkEokAUOE7pMMY5gRhHtVmi6oSItZUvAko8qe+sC3z89BgCbfqhHzpF95JSCOSOFzMTEcyFSO7lVqBllhGx5nP6wepmuLiEVszNVctCaBhHDbi2j9oy0BMORaRqNUnK4ahUNYp4Ad9hPLYOZ6BgQMgdM9QXNHdKAyF3oSfkjlJCjpTScfyMxXXmqR9e4OtZb13X3d3dORCYl/ZS6IlLUf9mYyBm8nB32zGFod/0fSA2TWmOGueeOMURQRFUJcaUJEbNzRUYVc0ETNzv4Yi7hK6BKpqZJTAzx4/kAF7RpwJgpgJAYF4VmKEGq+16LgYqZ3SNO69dnXkzUMmbu1zC6vdyavkFAEDxptQrW7GoseBfLqYaeHZ9vpAhgoG4Ze/8P5cNuQAuLk1/sUVUeKHO5bbFa9FbvyNe9op9QUCuKkPan5wLeCkQsKvz2x10Udauzl9lmebbgc3zrKisgK58qKGomUGS8oJYV8E7o9R7tfkW9bFXnzFGf9/Vy3qbBVNUQ9QGYuFUUaifwGwAWjJNFl/0qVeysowrSUykksCz4olCyZA3sE+fPiKzmhwOh8PxGALttkOM8bB/eXl6jHN6++rBDJ8+f/zhu+/mKX38+NkfhJl/95tfZ/x2wj99993t7e3f/u3fisjf//3fE9Ef/vCHb775xmPAtbrd1Xo45WAnL3LNi3i5OuDq+fX46oRr13Hw+PP5v5LLCwAZH7y+ByICWGA6v7UZMoeLkdNrI3z48MEDwHd3d+67d9b/L9RQ/txBzJ2XdCz1G0hiBkAKoF547n5FQAM7HvZASEDISsZimmaJok+PezHVhElknlSUEAJxv93cRklpjoYARmoEYmLicBaMntGM7sk3tNxAG1ytIedHiOzYcxkXK0elwQGLFlvzzMV3/v18WHG1VZna7v/zHIJrrMGHFoluZgqmql5e5ViSL4e9Z1uAMyxCaioZ/FKy9DZfrCUs2r2na5mZFycMQ8aV9S5m2FSZe2j54faeOPeiYGZ2SaWmqqBqogjkaog/q6VoIJDERECV0RDNCEwSoJpJKUnNkdrCuNUMHHMXssXsrkL3bWT0XSRYNlfD4tvXPJ9qAHAQCFAvLyuAFUs55vJ5MZa5kuutzFNLF4FlWoWg/XSDtmqiqmpFjTOz1gJ2wwuvgN9ds8h/IUNUbFxwq3NWFz+n1TqqBnnxCu1p9bO1UOtxQzNQo6zouKStzqvmkRbN9drt2lhm8+TWKhyr314U5NAsfXsylV4sq11/LVv4yvwLIJVqzIzGlltqcvBbJM+IjHGaTCRyoCTgyFbjOKsqUfA01a4b+n5gZknZz3ycp3//7//93cO9iOz3+2+++WYYhjdv3rx79+77779HxLZS0V2qFx/+LzLO1+u/x8AlpafeFgAMkTEnYvpKeekvWK63/7kjOKaJN4Ocpskx9+G0nfK/5iCimkbi4Sc36c0gJokxJVOvxAYgBTscZ0MlYCA0gTnKPM5zkufPLwoGSmomUWNCwK7rbySZzBOgZ/UyIKmJqO/CDLoDGYcfapFo5YkXOXV7glmbetnsk9YPc/rL9lJ147UbuBWHNUd3tQNbBto+qmZ86hzETSmN85xSenl5OU7jfr+fpknAOxkEMlKPo7YCOE8QKqA6bC4hBSTAQIyInu9mpUmwQ0O/efMGSgjNj7ts7rqOQ9eF0HXdEDpvwEJEJmJIyOaQhppEYtKUJCaDBKJgCU1NhQCA3AJQ9JIkMyRDRxW2vHqIFYEDASBJhqVdF3oCYI7VFvgORHCc34LeYYuFWopYKs9WqJ5rE0NEk4zn6d+TXu5T26LfNAzdefkFhr7SDCrV5f7QjVxbWcCVKpwx0BVVP59/4ZF+EQ9VbJKwTv5wmuRV7wXln3jqnbPmeKu7LFnop1NRKbaVyobGwVS19O9d6mjblwVY1/N8YX6uCeDVe7XSt6W6q3k2lxgFLIrjzxyERKUHok9H+YOrumLM1PcdEaqkCYyZvbfgOJGZqBozdl339u3bw+Hw+fOn4/GISLvd7t27d9vb3buvv7q9u3t6erq9vfnmm2/2+/379+8Ph5d5HlOaRaJqIoJh6MwCIo7jfPFBL8pOxKuulZW4tcbP90vm54Li8uUrEBGQgnm4ys0yAlQHF6/ArM3xXzYyrLaDHB2PR7dmAMD7O/4PGEZ1nyCSOgswnObpMM6HwzjFudiBaKYGYmYqbmClcRz3L9M0Tdm5CAEAVGgckyghdKKkCqIEqIAdERox2pzE4XeJCQ0DgLooYK/zzVW/hEgI3suIcp86zVy56tbLq1y3dM85SPurVguu5+R/2oWtDqcxLVjYEAOCiCRJtc/xYRxjjIfxWEFIHO3HJ98cROfUAQunrAdLP2NVff36dd/38zx7JrMV26t2s1DVEIJ3UPETTi5C3HHomDUJQ9Z9TM0LnjRFNDFLpqoSVTXDG1u2fdFjwODOVgGwlS+p8m6vd6+5x5kbgietX8ieu7YtsU7FUl8LUCxFR7FyY8e/mxQMmS9ev5KKZlP+KsGc/lNFwTXR8lCLBZxnZbmyq+uxxatZre9qoeF69u8V2j4RwO05qzKk+nkOxAGnMbbVvJ1PQt0d7Q+rAEY1x4v2e0NJh7zGdOuDnSppq5hxnSJTS3VLrqaulb5VAJ8/eX1+a1xfhVDt2vxfGcoUXOq6DC7YtGCiyTTOkiRBs9zuNM6hIu7mefYb9n3v4SQz225vvv76629+87uHN68/PX5OKTlo3c3NzePj4/fff//f/tt/c0RoEXE+4MzhFz78l8YX+OR/x4FK7DhABmBAYOaqnCKZf/f4E4LB2l/zs0ZwP2HFTKgWyV/+ZX7eEBHMmfQg6k3xUlL49On5+bB/eno5HEctAJRiNmw6M01JJNk8p8PhcNhP0zSFsAEAMBfAbqVJjDEZiCFwAFAjlpwFS1q3BhAgITpkjjFbWAp+qe4lJ+B2w2nuKvsThfnrvXem1kGz/6ERIZkRpMv1lNnJfCqGAYg4xKSeY3WcJ/c8z/NsXqrKXcddu+JIVH1HrS3iAtVKrkplLq9fv97tdh60dkJqWx75T7quu7293W63mQ0RlIiYGahoEkEmwlwAaWgKpmDqGW3edNRzr2vvNlDxtumAgOZWLDqwRg6BlSJgLOFAV5rqf36cMPuoqyl1afWs5DnlBsPeINpaX30N2pUAMCJK1pfyfMDavDknDypOn58YjYD0j4X7FzBpg2y2F7EKAECqcvEhWjHQysgvJBldPMyAdpattrpOy6j86zWLtuWw5xpMe85KZ61fRMTI1BB9cusLNpJyJT/ba7bPc2mompwmoS+XRURVj+YyFCBpzVWRWD/9DqoeC8//lQhUVrMuLsGlQdkWyE+9/FAkRknjNE9xLk+SAHQ6HszMf7DdbPqum6cUY4zz+PrVq3/z13/z6tWr7TaXFUVJX3311T//8Y+epPmP//iPjuw7TZMj5TlMhzcxtOw//wXYy1+wgNvRrrJ3b/v5w8xabJCfOBsR0UrpcP30/xRzwYUiklnlIb9MdAaA3KHPC9G8tKt2qvnXH1WtMYMY0/4wHg6Hwxzfv//w+en50+PnwzghMAYmCgDa96E4VzUlnafo7X3B9gBkDviqoOL9FLQfgLkjqso+qIoZVsek02Kxd7kLGgLWBnlFJXZ0U29olKe+br8v6Lntxs4C9ZTb2ilzXF2qMjVrpG/V0Gux0CKGgRBjTOp5jGPMbbpTSqHvsHQmrhmYqldSdMpf2/CwDxe9wzC4JueMo+s6TyxQ1WmaEHG73YYQksowDBS4Cx0zk7cpjCkpDNstAebofxJVRW8MJ2r5vWIVwEhAuWS8bicsQKFW8Xtbb3PtLtUeBwDPUb34vu07WlVukmpNdzqbjZXapN5a6tLKngsPKFT0ZQG8EmzuglZcTLQKtAQNdVmx1FUXH2w7Kg3AJTF8Pq4JYADvjmR2SqvX+EnNaVhNxbX7tpWyLQNdJWFl5Q9NLSl6MSJ6MwZyEHVp1y7LoFZ2rq5ftclTTVeTzABfckHXG7QZwpUOVxdfHTezEt/4mcOyL9TnpJHAqgWTXE1AnZt1Xbfb7dw4AYAQOiLSLaSUmLvb29vXr98+3L8eho1v5CnOEHIzif1+/8MPP3Rd9/r16xDCmzdvvCXDOI5uBGMOTn2pTvrPHs3Pf5EA/qU3LXAU5nA9GdDZnO2gggGSmTpuhPkq/yIZnN2GOSOmNGH9F87Ov3C4V0oEjsfpw4cPHz58+vyy//HHD4/P+0+Pn6c59f1muNn23YCMCOrZPSKWoqSk85xS9GRAMkPT3AeUKCAaUhcCdV1ARI+JBkbg1k4hBCLkwIEDdCwhYDM9VAUeYg4tQd5CYl6GtGy/RdGlgkTbDkWwNqu2GS3bogZ7sj3TTgEZtMHKKAmxKKJzzEDqjqypYFCa/koTUfMUrQ6plStWkrCgsJjqIvMTaszCA731Ye7v77fbLSKO4wgAXjL4cth3XecCOITQITEzI7ezmlQlJY3JK309B9tSBFEQpdIZxxZSwYyvfTqqr8IftcbeLqlHF5agnYFG+UC77GlYJ1v50iguSXnn1z+Tvl9iJddcuO7gPokBQ/NUawF8Vb6v3re5y2Vuck0AYyOAV2L44guaXnAdX3yw1T9X81YTCds/GZrorKhaBDAakJqZkSyzBJ6oYFpTUOtYPU+VvlUAT/N0LWaPxZNc01obtY+qJnFeYdz+M0dlLl3//GB2DOa+TAC56hkBgBg4MPKGGZMaIrIZM3sfJO/ebY4UBgwAu90dIiLyOB1ch/aM6H/85/9dLSWZp3l6eHXXdZ2BEMPudscBRSOShRBubjYAQCOldLzwnL9cxJxzvy/Nw/Xr+wZYqVZfuKuHOAsfd3dN7igI2d9pSGBmgIoZEfYX6ATh5ubGzDw+h4geKTSz3W7386/yU+NyWOvSIKLckDCpHI/7x8fH797/8PHT48fPj08v+6fHl6S2uUn3SEjMwJrE+9FmJ3PKWdDexROAShIMMgEHHOdpQ33HPTMDecMiQAIHizAzT68mRg4UOmAGYqTARFQsLOcXaiYK7P0WzIzIC4sRQIkClEw5xNy8xD8RrX5ywTnAswCCuq+KqHg50L5oKy/8t7GD1fA4xilFD/eqqj+8S0oRiaXe198LABDZTqszXai3d4FG9m82m5qoWU92lxQAhBBEpEJiuf+YkQJSR9yF0HHoOQTv2WdmIKJiyVSTWjKzlGY1caxnz2pGBCIoeBIK0ErfxfytHNC/rHL78xfvrA55Cc5n1aOGzX9aQPx9Z2JNvjAjJLOcjsH+aQC2tLtdMdZ1mNOX/Qz/vODPLEl2dCL4TZfWRbqgcOQ7tmVIznogXJTAraRsv2SBcd52rfYGPul/bN4g1E7Hxdf/8pysHqwOVQUwzK1lssCtj39+PGpEtASNBQwAaqC5FRIAkJIGZHP75ZR7nkaI1gIYNM0RyNZtGQkZyQjRfEVRABgJEBUEER1NANSQLxj0DWHk9zpfr8vJXIaZhLBluQaAnv84AGTUBzBV4IAOQkwYUkrTFGOMAEgYmGm73e12u64bxnH8+OHzDz/86fH5+f2nj/f39x8/fNjv97/5zW+I6P3792b24f2PKcXjcRRJm81Wuiiih/2+lLGd48rB2ZF8/EuiYk0hDr/jToKTzzJvq+NXQ4SX72YAOSxkuKxSjhHkyW30YQDkXyqAd7ut14Sophin3W7LzO6o9MxYRLTcD9eyRLg0rmkcIpEDeu5luyGJPErvltPyxGKwPx4fHx8fn15eXvY/fPjw7bff/tM/fysGL4fjeJy7YXPf9zc3t0wcZ9kfRudKSD0HizaLyZySiCIyEmExsBRiFJVIimDEXdcRgRGCqYE6nXRMfd8PQ+fNAwCU2BCNmIGImIBIEZIKE4AImClYII8koqFWRxPlrnDExESBiKHp7VomjEBi0YtPt5+BqUoSIwohEHPW9A1FTRXMMmysm7qALKpzlNJGweGs5OnlCP5uyEjk6dBmtigctbWaAQDM84zFNd3ymv1+X/V3a3qQvXvz1tv6zvP88vIipjebzWazef/9jz/+8KHrOgfNkaheHzztDy9JwHTo+tvb2/vdLXSsSNQPjruNpiDg9cEiEdmRfTowrj2pNBc6lm5xBb4qS1VQQiI0QmPymGw2RHEJNLj0Jqh1gZpJ0zvFen0qAxJiTvUTNVUiUDUFNRNA8sZRIjGEHlQRGc0QFUAR3X1xOXYArZFU60SRFeQEUi2f60YSASAYYZELhpBSdKeyB0O06AyrXVm+qJl8weXefpanTADL/BpkSLDA/qYZrY8KwtQSL2v/u3S/8o3qv66wkJadqXtZzdwxuOSgSrIlE7X9VAFEASsBRgQ0IoppgqyAIZixIiKhgZQQaab22syxEPxplEdAk5rWbq8uehWM0XtyKyJSkSvoQRZno5a7ORcfThUSubOkucPMTtxmyxo1cqRKbkRMqlgy9fNxBARmRFUhoqEPQx+SiIh1htyHEAIRzzMCauig7zbDsP38+fHp6fPj46e+38QoP/744/v3759fDh8+fX55eu77fre9+fzpIxHd3e42m83L45NIjLeimlTBTMjgbrudVRVMBdRMRQy827gQBXDWi2aqgGpqCOrdk740TgxWNhVHFKxGDkDeshcFPCOV0BXkTwO4ptAAaa4vyojbldnkLphlA2AJRdsSlMSVXnVxhDmOBqLae5g9NXXxK5n65QtdGZ7OY56X0eaK5D5CDfRWSppSmqbjOI77w2G/f345jPM8G0DXdeP+gMBdN4Dhy8sB4ONmc+M1Sw40oapRLEaJDpQIwGRe0uYSUSSpqoj57um6jjPWJAJqyD2OmBn9P2IzUyBCb7bqz0plX2aDSYyqCpbnyXKToSLYCmepxlmdTjNDo2J85jl3/W1Jsm1qocx195JeqKVFibODRvCWdkaqbafeHE1tQrl2lrTSLn0Jrqdq42LTq9zfxVFcQgg3Nzfv3r0jIi8ZvLu785/P8+xJBu5lcf5AnuYmGmPsEITy+5KIAXjelec5+02N3K+GBUoQC1cCXJ4bAYApz3ObelqnvRKcy2BCBPKW84VpN5Zf6YRYW8uiIap4UNqBTZb5jHECAIDY3C4aIWGoZuhKvNUzq6vcMIFRbc/umTvuV3TPClhObs3LhCCqVlPj15bn2vQs/ucvef9WD8kZ5aT50ymhnh0/AU1bkdml8YtciFWke1HV8mlnR/yTSkJS1QQMSl8KKDtTxYAMkhnFpCuaaadRT4eZGJl7wxBd5pOiImJ2kRChqBIQgFIJRSEWmYB5OQs5l4la7n7eGgSbgPGXJg1VK4gKqjdlUHBkTc9MNAJgxJgmSWZmfR+IerCgmnx3zlOqedEOLvvb3/5WASFDzOZm6uP+sBm6lJAwpgQinlCk6s0gAZAyUqwZWQYOlKXMNLt2f8r8vUwPXo2riAg5E6q2qFibxQAIpNdI7tLIiwnVG1Q3b5lxbHg9FBrDlQC4bqCG42GKQbpOAGCz2czzTBSKxl13jldY+iV+2RT5z7PCqCeAMlC0GVWYpumwH6f5OE3HeZ6fXw7Pz8/P+3EcRzPzbPiu67qOYpJxHEVMEnr+rdt6KaU5y54koi4pvQUeAJmlKJOmhFY5ZiQGIgzExBC6QEQUOPQZYRzQSQUdoKLdlng6oeeb4Zz7rHZ1e3zFEKtw1QbxKnsgAaRE2KBwBE+gcwHsruYKJ5lO0RWqd3rFnU+erbRzr8q+X83dyN44y4cnzKPBfr/3zGqHh91ut7e3t1TaMHhtW5sd3XVdYHLnij8SF/u+Eq7LeDX2qLqqUkUzAAMAtJpyhYTVNyuuyVIz8NLIUw1mmH1Y7Xoti+jzUzUXL6du2i9a06yivsLCu92/grC6bLs7TmpUCMHIijad3XdeUmz+Pf8lX8pzr5oEwOqp1tOlrze8JnwvSWuDSwKgpdLzW3iWXDuZVaG5eJ1rx6/FmN0C/tnn5wpfLBXi6K6qWk+sVjcgZuCLErI6FcDtDNcBoBigZOYXra5R8lY/R0QPJ5JZcdlbXmC/HWaiBkQrqYTns4RncKTXphHAawQyghsgqWYJRIiAxMzjOI7HmYiGYQMA8zyP46gKFfLdiyV2u90wDL//w1/5Ca7iT9O03++Px2NgxMZnBgDJvEvaYl60D7nKrm935b9k1ItcLeT5hTfxNT9/thUbr0tQj6/+eVUAj+OIiC7hSuc48pys1W9aevrZI4MTlZgilZ2QXTq+zPM8H4/H/ctxHMdpPorE4zgfj1Pp6I5E3HWDAQGwAUvKaUEhhDG3z0vzPMecgiRiysBV3gBYlkkifSBnmgBABiEwEFcTxPP3hmEIIRioKiqYC+ATZl45i7N+f6lLmukXpguxQPM3i6RNdk/LRil3ETcjbs+oQtd3SwsqWcxcrHKu/skKuPxKGCAih5pVnq+JiKEMbybqOc8+HUPXv3792vdkSmm/3+92u7s7z+BAEXET2UW4mYXQ9X2/GXoiYljs6fy0TieNjUhEDqbhjMDM04qJHLXL2ZWVQI2VVLuzJKya89IyVucZUlfTJ00VzIFOQd0DKVqlrGSMruxCaGjs0hIrzClerTkt6XVWVTHnVpLfPX/a4kQp/3cigE+6LzSjpUCrP7xCjyuBCpcE8DkbWp0MACKpPWLNUl6bgS8/z+qw2WXkqesCeKk58e3mbhX3uLgArp6Di0Tin+4EqjtLqwt6ycMv0rPhyFp6OVQ65MY9Q2BERErOX/wSDdEiABAvj3FxDlcC4NrIDAFcPyivabnPhG/AaZpEZJriPKXtdhcCExFTx9yllI7H4zRFb90SYySi3W7nxQ4hhMfPH2st6+KBE0lAlf7POdtKSv15YyXz6kJcO/va/HzhFueisF0RWzQ8WJVFnW+Q1QjznAAgJQWgEA5EwQw9bnfp4f+cYmNTdFeEm5GqoCoe8J9KfYzXp8YY5zia2RyzLKn3JaJxiinN5nW6wKrgKURJYc7mr1jtgSpgYDpFIpFGL0OPByKIafXjExEwYWAXM9wFDgE8ZmFa8q6WAYXBtdRf90D90/mX89MQESDrOubNhdTM8qRln4FlHGxvdavFo+Bi0suK3NN7mgtKuVfFJdZcn6H9J5asq6L6GBF5ONwdyDc3N/7F7VFVlZi22+3d3d08z8/Pz94TdJqm+/t77/t9d3fnGC++oFXFYWbHUXSO49nU5IpR4wBg7sxILS1EbIqIZHgigCGnVxBRFcAtJ4VmW9blUDMzETBUsxycUGuUAHOMTFHxbslmamqquReTsxIRK/4JLBXAPhRBChBHfYb2S2XQZauAh8KcxnKVv0cxTgrEigVvIHJBAJ/sviIBzR3QxQ29Gq3F3H6pmkXLK121Wp1pRbFubn2i3p3fFADMLiM9XROoqvKLBDA05gu2AtgR770+Asy1QyIiCu3D1/lsXQvtPGuStQD2zh8lRmOo5nkh/s8GmoNUKeTTNGTPjamiy2x1EsrPXz1A7eOdCwA091gClKJybRwwqACm2pQMjOPYdd1wO6SUxuMkoj2H7d12u9m5MQMAhMGs7wj3PM3zPI2HcRyJyHRQ1fG4Px5eHp+eqtzVUo4RVQBD5Z51Pq1RF+qf/jwxjI0D48++yJ89Wmbe8pbzcY0+gxmmNKfktSWhqifb7XbFnestL17o57w5IqqqA2Icj9M4jvv9/nA4zFN2UapqjNFA5uhrT+ypstQB0DTFw36i0HE3ENnxeKyNferPq3BKJqCCjhLXrHEr1BWxbqeLz49YQu4NHz9jcD/LAvbNow3C8/nMLDy9BoCLxu0nKJgCimWDzAWwW8DzPNcza5GDgalgFiyN+QuNF0uLxzuboQK+kRyY3jOnHPim7/vtdrvZbBymNHuoOl/No5sIXm7kk+AWs9cEH49HN5Fjyhp3CCG3igMws77rVNU7aniKGiOocUqzmYEGZPE4KBoBGnuBBRkilubkCMCEkANCp2S5Fr1lKsSSC2BVNVnsSX84UBMRKILZzDSlZFpd0NUCXmn0hfmCXHLB1S/nFrBUAbyYqy6A2wBQdaGDiFQXdLu+rVxfiM3In/qc1K9ZwFCopX21lnrrlK6usxrX+Ma1ceU6ZvZLXdDLcWryYM1V1RzOsSJ9Pa/xZP+22+RMAGt2lJRdv0QTWj3stPZ3EcBEbJaDZWZG2WKubvP6k/wuZaxmFX+GBYxXrLE0x67rtsNGBwsY5r7vuO83AyjOUabjNM4TQEQKXQhD8GiXeM2JV/0+Pj6+vLx4M9PUtDFV1WRArAWDZO1Fv/j9zxjn2+ovOi5b6uczX2ZVSl7hRRfUegTCYBYl6UwJYTLzIhl++/ZCQfGf8YbtT1QhRqmidxzHl+fD4XBwY8tzg92POs6ziFDI+P7DEDfDzQuPZqPlWhdTGdWg60OM0eWvZDsRFcB1N6Cq1SIyoanqEuMxyA0N65aKInNKXfLkBVM7Ueetfi7oQgvd25k2dPLbM561+kl7ppX4a+XsANnZmNSS5sQozfHvlErvay11w9llDRYFU41glo0NReTX0CwiusR1bu5FCy5xb25u+r5/eHjwALDjtEBhRh2Hp6enw+EwjqMbym7dui/B7xtCuL29vbm5UdX3P37ykuqu67x4nU1dWmuBAWG3DhHUSDXXGasVbx4AUha6njKZBXAuBDlhxO0+qUeq1DSzpHGZdgdnKP8ALc5nXwU1BUtF9VkJ4Hr9lksaOYD22ny00oC95ciICISxaWfpFZl+Scd0OxfA7hQ5F8Crbd9I02tAAZVr1LnCTHSXXPfXZHB7/HTv/0UE89XmB9euT425L9lRkT0cZobabmunjfXjZWo4xYKuAtg7gvjJ2MSAVzJ4GWrgrl8yYbdKS+KFejdNYwArZkMr71dXhksiwWkDm0hFbZC6yGBRwBxm3vQDAUpMyDT0PTObQJrjdJzFdB7jOI6u6k7H8Xl/iB73Hafj8egZHq5bE3crfpUMVLXrvc4/x4POJhDOnv+XDTy1gOuX61eziwroF65/UcBXgllpP2YGsCSIfPFJAAACIoGRmUqyiBEA+i5Ow/TzH/HnvAAWh5u7TOuy+Sp66nWJEENKMk2TCvTU913XBev7tNvh/jDNkxqQEacoCmpmc8pqVy2wcWyglBIQcSFTcuwrYpWME7PeF1ngeSp4FsAG0hbRQ2PvrhjQRYm7WqTzyTn/Yf3tyrTy2RPTKBrlxNWTSi9nO9PTRSEBCiwdjdoXlzLMFp9t6LsaC/dIhFcZeaesupFqFnSc5pubG0/liDG6KK19LQ+HAxF5boGHjbvuxWVz13VuZaIkM3MXurugc28ESWpS2k8ZOl/C4pAlAnBsGndBe47JRffqyfK1E6uqos1ucXZsRgZxjlqcz1UAmxkQJhFTzfa4ZXcEeTEELv4+QnSZrQ29QdnGheZPZLAhzGrVAvYWyJCzu51OGs6aqya9KGcNBt4KpOUFs0P7QsSazuAgwHUaxZXAw0v0v/pycQl+0fFrl3Hs5V9w/QaKpIQqsi5iRd66blftVDgN0PhMxtgoaot3Wjisaa4Ky/MnRETFRlRUk1dzui1g3sX1nLq1V3O+EgbnbASa3UCW95Q/3PIAaje3O0/gYOYQOlN9eTl4ZyRnfSIGACmlz58/f/r4+TAeRSGlNCWvd1QAGIYhSZNKBiBgXlvFXXCPVyuAW531J5nkLxoX56H5s/4i6Qtnis6169upKdVQyE+8UUDkV69eh9CLeAMpjDH++P7j69c/vnnzpu9v3Dzt+x5AU4qt+txe/VoyRYyuQbM7n4/HnDX39PgiIqpeGuvoSznNISVF4K4LgXsA9BKX/XHebDbjmMYpmoLnBXz69DjcDB7TDSGogJh6zTJlUOtAgTEjT6KaqoiaMVhAYG/RxQSECjn51MxmSYrATJjtQhflefJV1VSYCCp0nPuUimhzgqvY0fVgnbR23mKUQrXe3lU9mcx1CHcFV5mRVBSotjYqYtWnPyCaBxEKLwYAm+fZ0ynrNvaredpdHdhkLbnc3e12m81ms9nUPoOtMrikUqsRkf81xvjx48ePHz8eDodXr17tdrvXr1/f3t56YdLt7W0Fji6qks7zjJJChSB3ddLrjFU9/7mQt+8Eh0rIdXdEhOSA/gyggMa5UGe9c6qnvc4PVM3MvMbXTFVEUE3MJCUQVWcTamjuh1YGJkTveJBiipJMVEy9KSFBhnaB0qYwGtTE8hP+rhkCqirkiCgIEDpZ1fw7R8vJTUthUjmeU7ibbQ/1s5xWSQxU4WIzBncMtVcAAEDjAk1RZ/JcwLejpe2qDq6Or36x+uGXzye+ygGvHV/kaI74enBVoURJockJWgcFqqenyxbe6VSApYpPDlACWwBgSeoiLporUdSE5RALM7MwIaIlqKot8CKxoMhgV4srJm5bL9pONRfEniX1yeeNglZgPkAwyLmNMTEgcaeqcZxUdei4f7g7dKP7mTP7Ynq43fV9H1N63u8/f/48xxE0oZrXW2xudmYs5uYgV2/+OM9Q9l2dOiJyDxCcibRrdHVtmHskm39+WQabXbaAf8avTr5cuyOWPuD+r6orpyttJcPQb7suI1ASmYsRkbR/Od7cjJtN39wmf1586GsbwAGBvRnAOM4e9PWUnJyx3BhnAFCAfjBXrgMBGCIzd8wdU4coXhtEFIgMkR0E2B/IN5eAoWQqrDqss8dGiKzm98RCUvV9pOGSYvGF1VoJ3bqLvjysFBTVeLaUAtxWAItpFcB1+M99ge00RqWe7UzrZXLFQkuZk2sMbqEO280wDB7rHYbBjdcv9KasC4eIDrtxe3vruvN33333ww8//NVf/dXf/M3fbLfbz58//+f//J/fvP26PqdjR2XPWOHyrYJi6kf8kxAVgBHNxW1u2pGjAArAgIIN0cMpWbZqaR3uvfdYrwtg8LbtTdG0F1DXPPO6WFUHqgvRilhENHQklwuuyBUtZWmKYES6ltYuURsdPKdvm2HuB7zSu+FUXuKStHkV5aAyvhP5h1rp9/Q6V0m6Cq26mj+HtX2Bwf3M8ZMCuKU0K4GGAqqwWMBmi8+pqDj5SHUX+V9V1UwIFJpmDK3Qbb8vSPK+uI2ODsVDK1RgKUtyMmImnQbMwAEV1iKkTjU2vN9yJkGB6gNcQb3XvbaaPUTcbDa73c7MPFyoKQ3DcHt7O8aZiNI8O1dX1V4spUShQ0TLOHoQU/KoMJzyT/sLWbr/M4xlqpsvVxXHKwZqSEmLuzIiIhGo6jxPwzD0Q9huh77vPTe2aOQnt6xjRbWrP01T3O8PLy8v+/1+/3KcpinGJVkuy8fMXFxDX9g6IoOJ15461YIhIjNZCA4eV4IorpsDoSpzTn5RdVBHzNC8VMB7zJIpqwZ38uT27uopTohopojGiEtY8CddHAC+VVatk9oTznmNX7tmVHlSVVFQUrU1VTWpGLIaViOyir8kCwDCIoMRkEuPoUZSQslWzW7nUt3LzJubrYd+XfpWpfsLmqkVG9w91Yi43W6nabq7u2Pmw+HwH/7Df2DmX//619988804ZUVBRBCXGTgh4uayiG7degTuJORzYSmyobxI8VacQwucUZOwYvIpExFzfCDJBSquBKk6FKZoTDUQWEm36jHVNmoFsCIIMxQPRP3E4hFZiWQllJQaC9jrns+BsZaXT2IXkz6YW6D/+sVa7WS1iJekoHmE9OKKf2G0ovonT74ofa9tNM/h+MJFzs5vqvYcHsljAr6UJQu6ocB1omK71jVeA5kMUvBWf2fY4O36VgFMpxlYVGSwEXovE+QsmIHViIxQC5R0JRK/RdWJW6G7GthGf4sTHtFLK8s20xKbMEMDKuFjDpRlRt+ZDGgao0xzetkfD8eXaT5qSkTIHIAVEaeYmNmNdI8Oiog2zpaLq/w/z/hJxv6Fn/yLBLDHNkQkpZmIiCHGOE1HZr7Zbe7v771Tu1+dma/x4Wsv4AGGp6eX5+dnT7yaxow42p7WsMvqOkHI7eMUkbswhDAzB2/Hi4hESBaYOiCDjMPsOqyaGXLua1sFcN4S5Hqw2zc5DoqYTX9VTCk5bCEzIZoxm/0Comn3WMtbW01zpXj6w7iToApgb67p9pkVp58oGJnaUlpd2UH9Z8sIijy+IIqqouDS10t7mZm7LIwrxWgTkbo2CtNnRxQfhuHTp08Vh/LVq1fDMNzc3LQGhE+7XbGQmvlp38k5CRZYGNfyyXJHMO8HvFxE9QIzXclgf6Tc69D9MV7MFlP1QqjbunMsmtkSSK4CuN6llcEOeqqMLcv1EypXbelEAZNZI4AdKLGh3vxmC88VQWja/7V02P6zsAa66H8+PeeEjzgBVRo+ZzpfHn+G9P3yT9QuM6CrCmKTlOsFv1nEFCAOM8uATYiVe7TzUN1RrdMIMl0KUY5OXKMELOp43XGLSFYVkRNbWUhz5qIys9FSWOE2ermvVT7jb1kefrWbEKvrpLRwNctZLCu+AStnjIiXBXsvUUT87rvv3r9/P85xnNM8zxyoCwMyUUoA0+enPVGgkCiwo7+FvuMueGloXdn/4dIXS1LFX+I66zgXXN8X1148bDZb5z8hECISezqGeNxumibVG8QFqKj6oy5edHXczD5/fhrH0aXvNObUIUQkcqPKczJliSmatzBaYs1+ywqWZO5ZxZDvQm4huGZH3tVDREPfuej11hWI1ZsNZhkcBJMRYWQ2MhIAgFRa2gGioVGhmfx/P4/ptGtwTnwrGelQ+iISo8xTirPEWVKSFFXEQ5CFFyi2v27vdU0XI8RZRPHkNN+6LmI9Yar6mZlZwdzq9b1dnatXsFIXZl05ha/Ub3/722maVHW73X799de3t7cOFo3UISKA56Mb8zqmt7yvr2lmiJkzQk541mo9q3rqqJWIywU/sxXHVxWcVQCfRB08o80FcEqSkjigZwOvTQXVpLWAW77cymAlxNA5zEIGZdPiWtTiMCTz6KCzBQFtBLBLWt8J7mteOEhhoL9ow9c8rPN1pHxCPRMAAM2ST2dd6y+L3pUc/VliFdWqZxgA8EvnXzv+ZQGc11qL0QeAjQBGa8MHSzJjK4ClZL9D42Y3U8OM1KYNxpw1mB6tJkREuRaZy/6FDEWpMRnhapdZaZBCBSsDlnVf6oNX81P/rQXr2Lye3KB6ChFz7h4U9yOWKF6mPVMHyQ2EjBAIQ0YL0c2mv7vbdUPP3M1RnvZ7LUB18zgqGAKHvuu6nkLwF11txi+s43//oRe7S10Vyl+A0bk4Ll4cAC6mDno3JMwYbBEAiEA0bjb98XiE0myuEhwAIC4olT9nfPr0ydOex3FMcV0Jg014zMzAqEHBRDBCZICEyAAGRuqBUiWiBOBVxdFZGABIEx21krfTKonV9s10YEgedwEKHuHO0WXP7inOqYWGflrxv0ZqFycHAOZpqSZy89edz5WzlyfPVg+W11jdlEoW64lEREDLTbKKxyuDY282G4/7VlzJHGcC46bzMfyUBewvomedcd++fbvdbolov99//Pjx6enp7u7u9evXj097IgJTEXGATy6YHlCCCH5rfyNdgB2odEMyAAbT0gD4RAH1x2kn34r/wE5ErWjDmvORYuygZmzq1lz2n6SyLm203koMvpW++UG3W2esXme1ys6jJsGViFxftMXWrNpGqSDP22fxOTu/PdfAVsRQl+vcn9yOs/O9K9cJAViJOFy8QisI66XqKpwNq5/tyXCdQV8TtNfOb4ViK4Cp2dWWPWcnqfL1srXcoDIWahJ6U0pqaaWKWVMyVJfYsT5K567lCbGpIU7+7kumtEOm1zxNqHszpVSvnDOe/GrNAi/90wwAWh6ecQvqfK50R0QMYdhutwDgMIUxxvv7+9vb2z/+6btu6EO/QcRxjuP8lKZpmqaHh4cpxeMhTnFOKhbBkAJAaHqN/+Ti/isMxMvgqlcf6Qt649lmWTGjn3P9ELgPHRFRSo69AAah77NbsuXmSWZTHIYtNGy30pwDQSy8rBCil/l6rZFntLvV4RaMtVhURkaW0fibXOsqnivXS+oEh2IyHiYKyNwRkRapY5jzdwowWMZxRMiGVPaFIqIgCyOZCJkZkqESizfsI6u5Gj+bYla7d8W56qgS63A4aKkm8rTDVFoE1nNaAdyKFmh2VEsQ9Z8K1nWdIlBJoayxXkez8iNtyqUVab2wrUwol/Ow5AzT1b98++23u93u9vZ2u90+PDx4ndKPP/7YDzdEBAbSlD8BgIq2BO2qGQCwg8OUXkWI6Il2BQkLiMiPWPYo6LmfoJ38VqbaSiS7BSwCouZpVtkFnfy4FD+kiMSm7ZQz4paLQTXizTypNVdelSxWh8KuWlF+NkMI3E4jrGTqWSZUK3rb73Z6ZpkKL7y5aAGfuJ3LFy2m05qevywIz/nslfOz6nx+62vX/6XHsd1urQBerNh1V4mqDvr36oK+JIAlxlmtTWfJe7bGGqrzI4d7OSAiK1sjPrEWEAMDQCgJ/T7zekrG+TpnFJ73b5sSfP79VFf1PwAAIGBukJI71zESE6v77TUR2nbTh244TnPoO2Cap7Q/jh7B7PseKZAEpqFPURQQ0SF+Qdfa8FVfxf/QcfWprljA1wTtNVPlagyYA4auC8zIyEmAjDCo9V3Xdf2A6KlMJGaSLKXoAhhOZbCZTdNUpW+rDLqZ4P8AADE1sVq2gVYKAZwFYGlkUhHU0L8XM8U0mYqKIqFSQpvmCRSHAF53VOUHoHmVqBeEKOSie226lzu6kYggmWoHoCqkrGbop2bQVANYgxcsaWL+qOSiq9nDUGorq1oNRTy3knicpyqA27Rn9waXRkZedUMKmUtrQdwsVFLzwE+e0hADc7XAqre52mGVL1SNvl5P1UEOhSgQ6TBs3YcFTXsvNMdqR8q7exl3d3dd1zlIFhG5NezZH0RUCzcQEZEJTLzrgiGjmc85MoAwkYKCkqKgMWBuOuZY0JRbZXgbMgTAmDIuxYrQ2zn3drAO9FwFcC75TWJJVDV5EtwcU0qWpApjX8EUVURSVC9EUkumCCiYM7EZyRDYEGSckMlCANEM+yDqlScXzE1g9G6D2Uyi3MfltPYPvRApp2X5opwI/vq+cOY3tkagns5P/WvzpaTNtmTc0vOl69j6Ij9tAcPq/C9c/9q4dn7ZzGaWrX8vv/F5VcduMYTczG5J06uPvRLAUCbB/ziPo8FJOp4PV7Aw582o72gioj6vOzM7pfru47KyiOipBh5bU8Tiic6IUu4Iqp9WrAQ/x9ln6bjbzAm5gHFUgxNIy/LdAKpvEkTkOI0Sk4Jx6HvEaYrH5+dXr15x1yvAHvfDYbzZbId+C0x/+tN3DLTZ9AP2WPqfAsDj45PvuKWhiZm/9eV0BANvfGaoqy7L5fOk+/JPj9qYEqAWIJ1fudhnZ8evkOFF89fMrjUvuSqAj9PxMI+BCBh6DsSBCZnp64d3QNQHnqIhRAxIELyhLAAAIRiK6FyavZuZG3BVAmV7TmQWjUmnJKjOJr2YiYEQjQwB1eHlVM3mGKOKAvTM/Wag0CvMKEoBh22/vb05xnkcx1kiKsbcasYrN0DBBMzrSBAd85mQ3N8CiIjm3WNUQEQFDfMPDQNS13WBEBQ1mZIBIzBKVERQSAkACJmZyUUEKIKpkKoxYcDATEjIaJbzpHzeSxRz8Q1oU3E0xinmxkVqhoYmaIrmWc2ADmiV6zcNIVnuCOPBp4W1NbW80MgbJOYuo2pU6UulcrfSzWJrm/spDZEYEIjAENTm40yMgZEYfcUkiZr0HAAVDRhRFQQtUFBVoNz0wkyOx+Ph8MJdCKEfj9GQ0IDAOuYY00whMDESopI75CBXj4EhmgIYGVhGyEIEQ3LQc0MEA1XIGxtMAVVN2lITf7Mu0DzPKc6qimoEJhJTSkxBkqQ5aVI0YGYR9+hMEpN3V8727hxFJDd0VxAx8bbu4HXCWLJcACB3lfAdTwEc19skUlLlQIHTlEII3HUhgJARKQWG3M80EHsHaw9RIhEmmAEAXXK7wQIAACYJbPF2ou+sRSBRFYIAAJAVpubkkxBJDcRirl3OLmiAE2MKrlsAudFdlt/LF8KLDMhvttQ8LEztrHauUmh7sH5pGVyrbCGhSFJQQPBic7ScIK7JUViMAQHJ1x0BRE4iFP59mmaXsuVJ3ScnlDMHUi1YyNsK2O17B3gHzychm3AOIQRCwqXFGRHFcWRmTqycqAtmNhAhI6pvLVORpKaqpEZdYGJAQ8clchedWgGmAVUXyIvXNwPVnGTz5KCvuyA9g0JNARgM5nkWMKytG4n7Teg32zkaU8cAm83NV18N9w+vn56ePj893u9uMfCP7z8q2N/+7d/ePtx/9913nz89McCcUpznJOJoCqqgahiw0e8qvptDzCICuYvcGW1+gZxhmX9U6IAATgqssIEByPSKRelEc76xKJylJSLW6/sfs11I3DQlar9gcdxa9a8gIlGMEU7LUJ3ba4w1E7Ml1DCliYjUiIEjIidQAlL6/PTklxiG4WaX8Qi5ZxUAzGZuG7PUpi8eADjPmuc5ec0MFgPXzF2Ek0QicoJwnmJMbMh9FyKoYdd11IWuY8PBECh0k6bt8WY7jclUpimqqIoRE+YyJG0UFg/nApnXJeEy2WV7Z3XTE3g4qmBCz1dVUlXw4Jej5xatmBG9GbzrnkCGgoqKyevKGSAV6MTTZoJYkKeqwzOlFFWSitc+qanX/zmxFBIgzJX1CGBqoGCam0M7w4LC4rzvfG5QQIX+Qp8dzt4CwZOtKh1U86iq9k0BP7af6s2IyBCYiNAQg6ohqeTdCzlk7peqxqJru1XtUBF/qUDMiCbqAEVoBoaEgK57Anh9twF6eD5A3hzueUbPRUJ1e9fDo0COvHyhGMBfk/3yzvvUTDRKTClJzCC3bvsmJ905pqa9o8RZxJA6y2VgrtJn86O4sNafUYRENEmi6EUmKYQQAjKJSDBTVfdDACGSkREaOT/xhV3tWPRIuJmhFIwMPRW6V/KkTuyAlolVJ5Q1/2z/9K80zlftJw9e/GvzJ628tD2ezBix9sk2M9Ns6WLZ7C6/q2vKU4JbEauqZhoITJYYsF8QEd03W7mwh2yJqNegp6AfWLYJlnBMvS8AUGACMEZSM8puPFQzNCqfCormm6UE+fHa5KgteXyufkHx05vmsvsEhZcqgqmZk1uOUxsATdP0+dPnp/0LAHAIr+4fuq43s5QSMw9D9+nH99/+0/8+jvOwvQPo3DxIoiklVRBV5g7AmowxMEB352CRlQBavhdPUD7SHl8Aji6RfbMfUcFxSHPOZpajdZZW38Fl/yVOAgUdpV1xX1+DzIHbg27zVLa/ZBwDhCoysXQ4MXMQwRc/PgzDfbwDAPdhahLRWGtmPBLmU7+6VLaAlWpcpHWOFYo8Qa5AxNSBgqEs9TwheHQWttvtzc00TVNSd7tNrqu3HKpeZ3UEALJvDxCM0cFlKqSfaoxuGCERMQHnBnmkaokAhZVUM7+jXNNuBqZIufwp5bjyAiZXy+erkGsFcIwxqqSUpIL7l5xcMyvbwxYKMFTMG6wlCyzZGa3aVZOtKHBtQFRhrZhZmm42laQyUV8aognQ2J36jIhkRmas81TC+WYNYIU3cvZrqjO1qKqzKiAQM2PXCfSADKwmiMSU/SO++bJMNcr4fIWECKmqHx4nzqwES+I0AKLlrWPFDtamD4eL3lps7YTqlnFKySl7HKcY4zyO8zxbifuqGJG6uLXWfX1dEnhsOEqOEBMRd4ELVnZIqeu64AEfzNGSuu1dq18VkleCQC3ZrQDLhJRzLj7Ml8fKq4YlC+y/6ygK5+rI+vvPOXjOf60MKHzAyrJl2sjJFrkOUCSHp/y3fnCaptIiV+W0DjgLYDvJhM+jKZeq/gZE3Gpfs7qoScfz3WpFErp5IyI9DvWOlYfUd6mvSbnhEgDqyjFbn7Zq2+2MtXs/X/ZsLRZxhZ4FBpDRHSYAuAlhu93e3N7u9/vHl2cReXp8fP/+/cvL4fb2PokAYAjBzCT5rjE8jbnWbYnmXLo+QqMRXnG4lMU9P3L5B+7ROSehn9wyqxO0qQ1pxW2g4PWQiIigueqBcJqOvqYuKCujXjJBah6sT4jXkLguFjp2XCQiANEks3cRrM5nK0lY2Li2/ClL6GQN09EKY9cmELHW4wGY95yJSc1ADZOJOwT6bthsSpUS4piWFME6TRdjA/mOXgzg4ipDLJmZpWiIwiqcUuB8UCx33QEAU7ScpGomYEhmaoDsJjHmtCklAskLLAW2RktSVdV9qgUsoqk2eG92fpmfZvkNFUGbwpNW26ikUJm7FxpR4JoB1ObfelJVnf9lZ14hdPFWDYW4swGtMrmR4Z2Js/CaU0r7/V6bzhAK4O5cwp6RMQQBBApgTqOagwEGDpdL4FCyhOTO9zIVWJmRrZBdEb1u1i1jqDMJ5R1FxMkRNPdRdrPGJbGWMPA8Ta5Z+gkugAtnhJQWtt6K3osbXotr1ACWAjxJOQYfQpdS3/edivNQJgaU8hbg2XNmRmoABoSg5sCb1lCFf4WFR6y/Vxr6gkCtvPiC8nrN2/wXGHj6tF+SuPmIwbUz2y+r79gwnKoBmxl5qqYtTTUIF+3Kpa/j1TtWeWW75WoqJmYXWnHU/dU+DyJOM7i9zKHRw0KAIl9VtURgcpUwBYaSBXkug82WXoqIqKit974dreoAjfer5Z/NpF2QSWZCmB9mt9slU28jezweQ+gUzJIcx6P7urbD5uHh4ds/foehW6wp40CETJLcSS4ebPCI9amk/UKuVolxn5q/rTC+QP/uXD5t5nH+jqupcGbTTl0rbtuLuOJUGpMblDZ9AOB4RNCgFXlX9eC2US1H4dx4QMzMsU+t+A+nadrv9ynNHXEVwJWntOpY9XT7IzrEdNawiqVSCbSC+LisyiAbwEZiZjFGNVQ1DyG7SliJBpCBGMgFkhnkVFktW1qtdF9r3Gl5fjkgertdMANVRQNJJiQSVBTEu8xkDzAokme+ePckREymZIaIAt6vVwm8M/ziwsKz2tO6FV1iJRUDSvl2UGI2qAbVOrdsDS9CxcocVrUayv6sulRdUG84WvXrynq8X0IrRTL5Us6oWg0zIQMHDEPNTYfU3KeUXIZVtcx1Pc0NfTmEYAiaRJMGNiRlswDAAAEsgLECEoAKesoJEZWe5IrGHtOqbiEzN4lXdiGYmBkhojf1s5xll4cDa8TcLq26cPx7muaa3jxPUwPBbWYgBmaAngIHYEBalJb8SHV5zgZzV7oVYW4jnMxUoxiLqUJS6zOxcDBiRSZzMF3HYWjfceEynjWSVZaTnOc/22xdcWf8V7OArbpDbQkfWJsd3WJ4Xa5msTMLuOpJyz8bjVNSMjM+h04D0ALDPk1ThUOoHmZ3wuYcKyJNArT4YIoWixi4PoRf1lWZZCpiIuLV4TkwlJNXSj2SeodCFrYYY3AUSUBkUBABRAPxdSfK6YTMQIZMhliczOey5ETHaqVvESeNdXSiwSxXAIDa3ttMjsf9+/cfPj8+htD96je/JgxsHCDshp1McnjZp5TYEJkJIBACEiEjk8jcBjkawCw6Fb3VcoNy4lr0+l/P1Yur9HZG1Suls/206l1rtBZoOs5VinKBoyV2gIgxRncE1uzXm5ubu7u729tbR9rvui64T7I2qwEAF5nb7dZprsYwjsfjPM83Q59krtUyWEzpvu/9yU6A04xaNx1C61j2tgGLuPLV8GRmNDXDmHSOx5gspTRFO4zH42E8TOM8x6kE5+q0Fm9n3nWiSkVhBDxRlCrD8rNVDdVycbpCSkk4VF9xjq4BAJAAooISoCoaKQKpedd7VhMQQDU2VdUkCoYesk0ipmmOYtrC9KuI5q4+CyVBcYjV4kvMWRIIAIZAakjrrqIA4FB2VFCdK5iGp+2sZqny69UONFHGy6LErWcoqgPkGJgcj0eRbCxO01RDEvkuZiLiOOMdc0ddoMBEQ+iHLmwCB8KAxC59s24DaEiIBG4Im4DDbmQWCqil0lcRS3/1suwIpJXJlqGa095KrNdqtrmbwrFokyKSSi7hQpZAAGKGyYyJPTdKTE1Us9aFmuRiFiWejrzECI6xIqYsS+wmiPZiHKzrOuywRpPMDHJo3EqIvJQLexStZZNrrX/hPX9BC7gVfn+RsZKpK+HafPmS4Vvfoh5EXKBksRG0PrKraUnXy4bvOI5Oz/5llWCVr4YIAF3XXWyPWEfdxT5UEzh0mSxzrqq+YS1JBdw1MzZOlKCwTWgs4Pq9fSP0zBa6gJeyML2fawHbaq78+BSPT0/Pj4+P4zgepvHz588fPnx6fn7+9W++YaQo836/9/h3jJHEuhDaEBJ7apUa55zFwv38IeGkf/MVI5jOj7cMrR5pLlxvcvI6F0j99AvAEmOGU0qjJoexvp2qTtMYJZlZCKGaOre3t69evdpsNnd3dw8PD16cmaGQfLFPIX8VAPq+r4kAlqGaoogweLaCmiICM7Ez+r7bnKQOJktR02mHACr8yMOUeil4JiIpC+YkmmKM0yzzPI+zHqdx/zLux2NKGpOKmJQ9VZar7KJKaoQMSMUvbX4g93AzMzBTs+S1CqjqkekYY9d1MSUK2Clne0szbiwlQAKR6J4eBgMAhwsiU+hJvIGdGXh0SCSJpBizJUsU2PFhUA2JcOnY7o0oFExzfZaLoTIYyJipIiu1zgZXyUPxa9WQ8EJPgARIgIxESKA5AQENMBvcqIh81X+FRIBo3qDXRZequl/EuZWWCmaoSrSamsQkoe+IuB+6gfpAHELf92HouGfqCBlMQREs50AbISghUk7LtOLRcDsUWnumMFU3UDKz9f/XgvBsqiZqOQdEUc2LgshAY5I5zo2PUZI5fl5KWvoygCp4plzCOYlKTFGSq1YugCWmcwHsg5m9yWOlSQQUUVFD0YQyJ0lqYtAnMcOgOVR8zhnrm0OxVqwYxK0APv/Vzxkr7oz/KhYwnPK1n/nXaz9ppa8VS6Uar9iAUTguiqmoapu74PlW3rGtRuyqnNMGIYuIAI2GriqsrdwSWzYCIhJmM1ksGy0AYABREkyQUsr9s0OKMdaGngAAUnuyUU3kgVNDDRYe4VBKrsaCl36AWSUjf38sK42IpxNoAKXi/tQDV2Xb835/nI+zzGMcD4fDPM/e5eztm3dd6D4fHg/7fZrzNO7ubnsOqeS4IDDmPuXKwRVHXIULNV3G+j4dbUb9CeiCs4WfQ//t5jqbxkYG29Kmc2ltCaai6A7Iwjw9ZgYAKhEAmMLQD9397Zs3b7766qt37965Beww+xVWIdS7tv5M8PqZU3hh/2c1DqA4PKsIT037vJo9CI0AxgsA9CdKtyGmmJKY4+/GKOM0j+N8nONxnA/jfDyOc5QaAGbmVMyU1vPsRwSMG/WkJTSsniJEAzJLpiqISZVFhDgnvqagCp48mtCCmhkQIiUzMPc5KwHlsLABAIsrKC6osMipFJN6rSGZIDIRGJCicrPe9WErB6mP6U5ZQ0UOVLCFazkvFrzZqki116xXqA7qGs39AlGuRpX0VqyElFKSeRwPiztXpD5JBnRU1SRihgbAXYd003eMFEIITB1TYGI0JjQ11Iy07GoHZze8QS68SgDord/8KaD2d7UcoyWPRJwSrRUHTD3iPvaFoxUIFMm40FBp2JPhzbKvWETnOItIjBLjlJKaidUqwqZCun6aQNfxYDddx8wdIoARoKl5gikClDZ5FMyw65RIXDEicOrMepIiYK6eX3hB3jinFo+dGUD/046fKXS/fNoXLl4Ddaq1HR8AQNd1KSVJS3KGD/fleFzTeZ1vikWySoO8jRZCzi2vXK5GXqDVncs+FREMzE0Fi9OeGzxQKqCWn5zCBrSjvhdVTA8DsDVYNp66T1eE4T9sN37G/TglJyhEdTi+AOBut2NmEUPEu7uHr7766v0PP3rs6W532/ebHz982L+8MHd934OoJRGwwMxIiEjeoRwdsENrW8gvrOm1EM9FUv9J+j8RsVcEcFVa8Mz/jIjVz+w/zC5Jhq5n0WBmLm7v7+9/97vf/f73v//DH/5QoXiooPyaWdjtdlhihPXpVRf8whrCLISIiMQLZikhMlGYJk8oTYUdRxEDoMPhefVizvg8UOE9d6tEV8NpTCnpOHlAcT6O4/E4TUle9scpyjTNMakZIgVEVAQlUCiZjWYVNYa6QL4ZECjnpLnF6ncEQHd3RPeElOw/FDMFS2pzEqAZR2PGvu8NCRjYIKmCCQMCKqgAZoQd9F4rAgZuF6mKx6XRkKIkpNxsxMTQJTiiiBUzC9UjwQgUODkylALliqmsu/R9j5SRrVpoyYqsVBUpX1Cy1utABAiipgaciYsQmRgo6ypDH/b7/TRNfd/f398Pw+AGwc12OwyDt/X94YcfAOCH77+f5/Hl5QnQuJCpiM4e9ug6ImKiEBgA+i5sh347DF7yywRMSAiM5kDIZJr7WgGQKahFE1NTSAaeHyCgmJvJm1npJ5pD/I7jAUZMWlu2iYKUAIfzLFEQlRIFVJFpPI7j0c0dn4EUNcZ4c3OrKjHmsoGUUpzTnOLhMCq4VJamYyaoubvC2T1VDmliQApjNDNkLC4E2e5uzSQZuFqmCsdxHqcoYrvdruu65Ek3Rl3XEbEWbczfHjLAr+VWDXjBUeb/apkLZKrIUqfdldW2O2VJGEKoAqZlYXKKgPYzRealnyxqYqt61qiqn0xL2fNJ54nVXc5E1IK7RETejMF5xWGeRcSblnrilXubX44H/yIq1AUXmSmlWVJlvgKG6oQlU5raxOH27u1+rCcMm44tIGLHRMxk5MAv0zQRUQqp67ptMeZijH3qrRcyYPB4JCISgM5xxNrUwcAAAUkRmQM3/JaKzSMOMVjYul9ISxZDNpCwMIVG4WhkPZjZV1995XFxtfDmzSsviP/8+XOcpj/96U9g9P79B0R+eP16t9kNXR9TCsTdEDw1zDxTCexmOySNkoyYmTojdKea0IlneHkEzRJnJVkzxZ4S7eqE/IUIwFQv2IGtZF3dYrcdDodDitFjpnGewd3DcbLGIPHdZIQ3t9tvvvnmm2+++frrr+/u7ryva9d17969a03cGngNrSLm+9CNG4Bsxaqqd6NvhfSK1KS0zqjcqvpt6ivVe59OzdLlQ0REwYswUkrjPO33h5fjeDiM4xSPU0xJqvTlXAiGqtp6nv3KGZrKamntFfXZzBTBrWfDglq3wK+L8BwlKCKKXzdhRuWK7nBGBRU0ry/L2O6glYnkqJVlDEVHUXDfS/68yKWs0UnbvYQFbMXlbg0XIWLXdVAUMWxsX2p+3ornujPrmX78+fn59vb23bt3KaWXl5fHx8fNZnN/f+9JADHGf/qnf5rn+enpaRzHcTx8+vQJCRgarzig36Pz8AQgIvZ9f9MNQ+gCEhEExEDAaOg9cz0rzgzNQ7yQoZjRgMVQ6lxWyJCyposDSlURLM1RLWUDvcBomJlJJuxq3bqeOB3HeZxqcpaquu9jHMd59nysKEnFNMYYxea0BNEBDDFjXzQo+g5/FpwJ90PeNXEW4LKshL7dDAHRdVzQOFphiK5OaenEzKo5qQey3K2fWMqU7F9s9WIrqk+/rEgFfh4WNFwSkOc3tQu+0PUofzUwcoWsvVq9wvlFRMRqr4Vi7KaUPDvBopgZiDri8ViatdWofGYsTf1CvaOqiqauYyBjpKzDe6Y6AiMpmKP+eS6IHxdLXdf5hboQ6h6XgrrvPnOPhUHx0zgzane3lHailVcgIpshnvSfrlu7nZnFaMacu1D/ilVLo0XgtQGOrguIG2YehmGe0jiOx+Pk5b+3u/t5Tnd3d2YYiG5v7rrQexoK5ewiEVNm7tFUUmAetuxFzR4aZ8auY98EF1dzRUKtTLlItO2Zjbf1cuQbTjeRf3c3O5as4SrXhmEoSUJQ3YoA+r/81V8/vH71q1/96quvvnr16pUnPPuKV7Jp+W2O+7bh25Q82TiLIDNjoMUULil29Vd+Ia+Ta0Vy1QtgcaIur+2fHgnWUvcWk46zHcd5v98/71/2L4fnw/FwGKc5iaGqiQIiO0EIgKmK5QziZW+UKGbG/VDV1hekjvXgCHT+ypm1ixGZmgImRBaeZwBQpcSshqIaWAEUTNBdAqBkYCBYQido0MUTTODlZbN9l1EkILOz3CLNY9CFRHJ0BKH6jStcsxGRt92sDQSduD1o1O46XwgqD7ISwNgouS0p+9J7fiWXkI2nOz49PQHAH//4zyGEP/7xj0T08vIyHl4QMwkOXb/ZbLq+DyF0xH3ohmEYQue6wnaz6fseTRmNgNwCJjQCd9lhiepa0qTqRdVKaAbJ2a4pWmm9h4itYl4o2/0J2aWcAyIpM1kp6M2m6pWd8zw7VrlXhJk5gEya5zSOnn/gJcKF4ZqjnC7xdWqyYy7yiKHkUsQYdU5mlkNHwI4RjZTb6fiOeXl5kTn6jrjZ7TabDZsSUYdDjjBh1ix9hM6xsk+k758hia8wsgvy8su3oAbbeSWu4EyK10u1AuDL8tjMVigT/qUK/paqLXuMlRqu6mvBiCklKVCj0zQdDodxHKcUkxa2BmZmUVJSibJgQUMusEwiabPtkYGRKHAgBgDy5AVwPDfPgtCasjcn6FLn1xy6nktdrZdX+C0yRgKiijCREKUYA+d0Sp/B7AknglKtkQuoaBHAzrRb+lwJHneqVAHswrdYkydRqlaWdx33fW+2m6f08vKCiiAgRu/evv3w46eHu/uUNCV9dfeQTBiycEoq8zyiWAgh9N3T01PoKAQysHlKKU2O48UcCsGY2YJ00aIoNSnQCtQ+M5bXumrRrv55kSBbrWU87lNKXdepBjOb5zHGWJKlgJk3m81ut9vtdjc3NyGE/8P/6f+43Q739/cPDw+73c4h9xExC8TGLvUFCn2fC8PbUQgXihaGUAoZJav8UG1lf9x5nvPcFEOtvuEiAHDxxpSNmq/jqe1zlMMo4xT3x8PxMB6mOcaYxMyMKBgqIwNlh5uqiuVqPj2VIuhNp21RjavgUdRWec+oBzkgZxmOzt3jCGIWJHDwtKNOKJu8BiJzdBHv/tKKMTQjcSPt2nDs+RoDuNW3ohVERDcjmwh7cAHMPYeOW0yr1Txjw2sAAERXf83zcFrDWtYI7u/vx/Hw/PzsOXu3t7f7/f79+/ePj4/H41FV9/u9iDw9Pc3z/Pz8uNtu2hkOIWw2m03XE+AwDLvNdrvd9iUrOxCaREQkQCSr4bniVc3BBANTEDBUSKA5LFCirReYuxUvkJlYTA3QZ6oCGItjQ0vQd5qm6ThKjFZ2hUPMzLNDvCURT5oW3y+GzITD5gavaJPnNA8Afdf5vczML2opmRl1gYzyvPESxZ/GgyVJpXwKADawscDsnBRxga4tsuYL/OWXjnP5Wrnb6viKO6+u06qAX/jh+a3P/3rx/JX0hVOqaD99QxWpuSCxx5S8/CylpDE3IptSjDFqMZVqnnxTlrYkYfl9iZkYajlv3e9VD1BVQTEBBTDVZA6pJimlOXTDMPSeHeA/hOzSTCkh5EhTaxNXumpnuNI/ugJdBHBl9NCwF6wIXFn6YiOACxcAgJaYGw/EPM9dx10XXKtGRDLqumE6TkTh86enrhsszdM8bd9uZ4mdGfdMRPM8e3ZSCGHow3bTIwOYRElJ5pSiGYBA6Ehhkf31+T2prSUGM4PsbINzF3T5fhqCqe9zfZssM2MGAG68qqpjAxwOBxHp+/5wOHhN0du3b9+9e/fVV1+9ffv27u7u5u6GiPriu6oFI57WXsVrvUtw13b7NEWpb5pUg6qqM5GOsttEGgih9rcheNEXlHDF1c2jqimp5zvs9/vD4TDNaYwwJ5mnmJHYSsFStryLh8TNXyumgF9QHBW33EjLk7USIqIQWEfuGKSlUVIp1hQ1oFzd1DlfNgZLAhaQvPYVbBHAAEoGLoBz4kxBw2CGAMRMCGgGCKgKRNgkg6iBGZKiGmEF9UVEB+lk7ure9hBu34fQce2rA9Ub1qCrtAJAPKpisPwHJbfJS6IaxzWiDcPGUpQ5pmk+4l5Vn56eHh8ff/zxRzN7enpCxI8fP4LZ/unZNKdEubkcOuoC9UwhUBfCzdDvbjbb7WboshpIAAKejAaA5mJEEMHVEBMAyBnMqIDo8wMmXtBUYqsnTLaStWfOY3Ywi4h45jPqaZVJhe6dc7mR+d2z7TvHmHHKTMHFopflhtAzM3EwWgzf9hkWPoWIGWEfTT2m3w09ABJxtsFTEgAxjFQqxziEjml2xJzj+OIRvhBCCH3gbJM1Bndp6LoYhdAwrGty7tq4LHqbcNqKj0uDigzX2RkUjkaneX8Lh/VkBN/F1qBp11yiRoobuGd4rYHBqeu7vVEIwesqRcSVMH8eF8DRoYRSjrVl6sJsOmtBrGsDar5ekPedbDYDNAK4KMoVVfCkHC6oqqUkklTjNDoSgOqm77ph2wHkVCM3lVI2NzsiciPMvdCqSx71ep4XPDsERNXF1+oBYMvZtSWzDAGImjzkpZinujbbxbXiB1LNZkwI4ebmpu83YzeqQh+CxhnUCLAPXQicTNnrW1FFO0QMjMz85s2bKY7jOFKyjoj7IMmSqZlU8HzvWeWcUMXX1M22LFxdSFUp2wrgRv2qVGrghl8pG/uCDD6hH+cW0+Ra0Xazub29vb+7e/369a9+9au3b9/e39+/fv363bt3d/f3z4dnn7eWJzNzNVArSTjPCq1fBfIG9q2yqHKq2W+TUhLu/GC1MHzBVhHisnFsNRFV+KvGlNI4zofDIUvfaYpJBTqv92HmAGiGolGB4jybojeb8FknIlNAkDpb7QNktnh6d9clzYxhCYIWYYUG4D15RJYJAUI2BTXVIN4eWJNqkpjIK2cQyYNMXjhkQIBe5OBxWSvNeuu2acxWUHGs8DzzDVgVu/O5wloxM6L1fSDG6nmGhgFRSYZs54EWYb/MUjtasjARmSdHafn06dM//MM/fP78GRH7vn98fDSz9+/fD8Pw4cOH+/t7RPQeR56ktt1sNv2Qmz0g7jZbBxkYhmHoegAwEdWUgRRASiJvmWlN9R8uiR0e2n9Sjru75UQM1xCGF9aSSuvIsbJZnQPmqt8Y/bMKYMnQ5TGl5Jpn4A57cqi4KoCJeS4FIVXO+UgFEq51MSVEjckdKiEEZFLVqGJmHrJJBQPLobqhC4QBQcxM5jiO43A81v4ZwESZgxR2Yw7C8BMB1J8/zsXwimZWfzo/jqehjdX4wnNe1BtWUnapA7liAV8cleVB9gKKltxjJwMonaC1JHJWkVkJqb5pxZgDAAXtOjayVUbk+c5aPIvOOHOeTfb/mVnPAREdCheJnbCogEhXC/g85NFeHxApiTYx4Mrl6mK175IToxaL0FbzWV4kL40iVBzKGKOqqQkR9X1g5MNhvL97hfAcqNvtdsMwUOBZou8h504pzUQUCHe3N3QwTckgBSOAzsyi2DSL6smC1hcxsyKbKgVW2mupqCXdtSP65OxT8rtISx6oMjPvz7vb7e7v79+8efPrX//67u7u1atXzgOd1acYbzYbKT4MV5icKlrfiTag0MHBE0ri1fLQVLrZOH7BgryheQFqhPh8Z1ZOVHMZ2s3gP5/naZ7n/T7bvhnlCihpUkBE7LoOAxCJAQEJEKlA8vwmdHToAJhQlUq4IhsEWDTJRt4DABCaKYgxB0WtW6QQoyPXiIChqRmQ59mWQoCkQmgg6gLYRAEzGJbDh3u+MZgQUe6vAKZgwTEkPZ8CjAsJICKgxzbyhBO63dzVnRZCv8BaUUcMiLJyK7Tzv+JiZsankB3V2K0aehU/MUbRyGAcMMb4/v37b7/99vHxkYi22+3T01NKaY7jNB854BzHrmci2mw2fR9uttvdbrfb3gzD0HNgIneSd27Z+d5T0yQKWmp5W8RQxWz7eBqbMwJFUwQ1yLLZ3R0ATnXcklmlRo+jeWZ5FgMuIGOMJe6b5gXJ3CRJdCiOKc1JwBC7LnSBiQJ33WCYs0KQmJlRkvdUqBpncV9LjfW6MFZTVFARAgQv3SZmZgL36GhKSWatjF9U40wDBSamwM52vSSGvM9mYDYLDbJB1ULg1KuG12OoXxjn0rfy63NiW+uvDRHapXHNQq23aBlue0L7mrlB3CXpe+V9LcYokjz2BgWzLWUhmMkACjD4LMm1oup2hqYAD0q6TQWPNDRmNLJ6zvKojQDj0sEXABSY+65PvYiY5g6VnvCIiIE4hAChc4YODmdB5MXBrdpX5+rEfFJlBr0UA24dY816oZlVAexewOxludy9yldBSo2lqSpjR8QERDZ+/dVX281GBYZhk2YJPU9xMjJD7ziCMXYCBqDH/SFJAgAC9CNE2HMwYFKQnP+7rHKZQKf8SiGVi7dG8HqKymdekZ+zMerPVdXDah7T3W63b9++ffv27ddffx1Om6m7trQdNqSSTEXSNM8A0HWdmyJ5H4ioCKiiKSMEx3lJBUKByNOHjSirOc6nPJEAvGix7KjqDKnMqO7Vape0u6tNm4njNI3j8Xj03IckgsiGOMXZiAMwcAhAgCZIxtLbTZSUks7eT8MMRM2UGCr6eO4KW7yspl7tY2iEaKCIRCaKheoKHXpnFENEIwTxKiFjcLSjPFvBqUGTlw2ACSIiKHrrLLKKLB1M1dAQkAJQMixxcVkoKU9XUekZyAwDExF1xMAUkICp50Ah9Bw4hJ4ZmFI6SQFd7aiMXG3FcWdGHNqYdG2VNU0TNqGE4zROxzHGeLPb/PjtDz/88MO4PyBiF4bD4fDxw2czm+O42+2O+8OrV6+enp76vh/H49dfvRmG4W53e3t7e7u96fs+MJNB13WBctkfFpBbEUGSGl6yjPbl/gMHlwYBRQUFQTM1BYtL9xJdWFs191tqNJ9iVSlWgU8CAKRS7FsFcJaUkpLoFOMUU5KEyF2g0A2AFELP3YDIqqpivo7bbocBOuq5J4YA7Il0ogJJ5nlK03ycJxetrjTkDS9WNEQiIN1sNp4IDQCxwnIR8ZbQQcOYwSilNM8phDmEwACKOYUhx+1y/sIFAflLx0XpC4Be93UuaC8KYB/tolwUwHVYU1tVb2pN6W17phXn8MUkrJVQL18pTqMn7pGBlHKPeXbFR6KkOUVNUsnDEHJCqOMOMbu9e3G7KRgGIjSigOhlPLm2OyX1I0R+3POTFakLCNiBmVkSiUlT0iTP4zEQhRCGThxqBykQoiQTNkkmyYSMSAgDoqZUkj0LpgcAELPisi52yTo6mX+HuSkC2DsdIQREZBRgIkNDJGQ1QzAyiClBacSNCgzuUiZRMZNXr+5CIOZut9v98Kf33SbgZIpgJq6yhhCSzFHkw8cfiVzUypwmMwshUKC+D6jGEc0MLYmhmZgREYFW67Zaet44HBAYLIOaef19qZLhzF7yHCGQIiAZCBgZKEL9FCeSkmfv9ffe1/zVq1e/+tWvXAa/efPm7du3FR+NmR1bAwBUdZyOiAXNEJCZhmFzc7M1A1VJKQPcqioiIVoY9y/tVtGSYKICKmrJUBEVTdQEDOQwL222QuN1yQcBQU0kQ94ToSUBgIDECGiu2oiqztO0f3n58OPHj5+fDuNRwBwjFMJgQIwc+iFwr0ii4EWxQIE76wOLiKZoEAEllOC2a0Z5os0QHUu3LJgYqiJa13tHbLSkBmCebQt0HPc+A0wQQgDiaDDHOYCwoaEaeRWniIlKzPWe7oJGBPFm8aaq0aAjUANQUzVNLvJl0/dMwdDBPrHjgMzBg4UYOISh77u+77uOmDfDAA7Y4ZsYfGaV2H3wgL5R8uYBdRSLE1WXyCDNycGXgcxQMYfnYRxHEZlLvvBhGqfDcZqm//Z/+8e+7/f7/fvvf5jn6e7ubjt0MUYCTCIfj58Y6fnx6e729vZmO3z99W9+/XUIGdJ205Veh4DDMBAioKZ5jJO5tABUAgGsvpalvl5iTs5ighwQTkktlRVdnL3ZktC58BTzILQZqCITRpWoyVRc+YqSYoxJZJrnw348Ho8pRlXVqGY4uumTUhIzoBD60A+h64lC6Dd9v2EKXh85TdNhPA4h7Ha7gOH58GyWtrdbQ3x6+qyAKc3znOY5TjGlFL1uBb17YCNRUNT5EVJAUiQhzO7OCIBEQWSLuO06IJqj8GEkwD4MKekWuB86YBinSUSoCwDQdV3gvpUQepoS7LuViYl5nmfEEwjSlcxeCTbwpnR48p9zOkREcFzUot4BqKV6tUYxyhqnNiMvpWo2vAoiJOb6Zqm65PKfefnNklDtzwCQQVvwNC5gubqAzMy7hWsyz9Edx3kcp3mKMYqZJbXJc6JLxBcRh2Fbik/M01mLscGOQkTetZa8LbmBGhKAASCYM3ZzQQLEBEaW+51yx4GITCUngnmqKUBSgJgMInVhCP2w2WgSVYhREGcxEAtqHIwQNYTQdUxdIGIjh6wHTUsybPPALsagnTcAMHShhojoWUJmuaFnnCMyGS5Ytq7L3m93eeHMQfVybgqF/mb7lSne3W/MUER+9duHlGSXtt+//+FwOOzu7u5fv358evr+h09PT0/mWBwgUoC9BMQ0BuY0jRI1BBaEaRrNbOi33ioRTBFzaBgA0QhNnAh9kzoinhkwsoFo9mFAcT+rqQCoa0ZESACqokn2h4O7tpFo6Pub3e7+7m673b569cp9zvf399vttjZWv7+/11NMUCfvQJ1INIGhG25vBmYEoDhF8i7pioECI3pqQYxzqMHhlnwBIENBeSvylDKAeNnP7fa2JmgPRYetaTCUARkJEb3ewtHDXUOhHGkm74eAYGmeFQwhBFHuFJBTMhERAwBxmFNFJQIKGJSg2Xgn3v28Z5toAeQMh5yhkDnCkpeRVWkgb7rg1e1aUhsEkbINJlq5k0v48t0AkAmIgBCIFUGBMtS8d6J0/SY3byZmDuCxXnZPRc2uch8UrnpuoLq+tuKVAMBNWhDlztIGBpt+YMxZB2Iqc3Tr8OXlxQ3f4/G4H4+Hw+FwOEzjvD9OSSlFbxqGZrn5PAca+i2CqiY0I4Dbm93r169f3z8wY5e95EtWtmsBXqaVHxbUTCi3PD4J0i90RZQBuj38ZmomNQbcvrI2SSKL7MmdMJJ5w6radSrG1BQBe4RGk8Yk85SiRFEwBOKOuOPQhdAbct9vhn6jCil5zEbB6PPnzy8vL/M8v7y8iEi3GYhoirktmKp6H4gscREpdP6YiIVKTQEoLFrSSWHYLCqoQS2oIeQ20r4XyUqMX1r/zYkEPROoeHrQQghtfmUbI7w4zNY3am9xHtrAhibrGrWE2ly5ksAayBdPfbmNPuFM5oK60L5Ce8fghY5mUjKqWtzyKcUkyczE3YawZI207wWlMnt1EDxFBHy/MWBhOEYOSw4IYASYzTMAIO6BidgR7xmJOwrSy26zTSnJPDkhTXNiOopIzz2AMltKChQRETECdN6kVcBANQIQGACwLJ6JqqoWhfWkb1g+BwF4YS9lCACqCgEpEUEWX34dJvDqIJfHYOIYgLP4syEzA5CBEiuKbjbbX3319X6/P06Hw/MLmNztbgHs5XgQiV5/4+vpW0wITdVAVNQMAqECGUi77gudAICVtuH5NQgbkze3tCkNpgC164Kn8qlILPmYbsgSIjP3fV9zm/2Lx31vbm6cOfvnOI4r2s5zl0NhVpKoHBDNU3aCmXjvoBD6rkOAm+BVxi0j8MvV/pdVEvukMy0+nzq0dM6qwrgKYN9i7rim3JvhBMnSAyomLtUsxiimpokocSfEnSomkSiJiDyWZpjTvohIil/Xjfq6jVVOtmJ9L3/OekRLUsY5a8ASMEf/HyKgQxuCly0tbCLfCMwg9AynXLWwOVt0UiRE7ELHzAPnblRe1+v+LizIVu0jeZKF2fIu7dpzU9Rfa3/RIHAwM8r0nWKMU5xF5OXlZRzH5/1LHc+H/TzFrhvMEESYGaGrSSVENAxDF8gkgtqm7x8eHr7++uuHux0iBuJiAuXpw9w+AZDMc72LZ1wqzVjjeoHTGHaWlJYkzrUvRbv9KnPPf/KYgoG4veU4z2Yppdy5a8p9EitWzDznYiSvv6xqEPcdcSAKm82mHzbjOI/74zRNWjsokwABAABJREFUHnk6HqfD9Ljf7yvofAiBAkfH+i6NRqjLRSmiAuy+f2or0ozQLCthwuxo5ODJXAAxxq7rnIgiEjN7FNCfnDkLv7ptq4+UyNtYVF/9iZhEcmhGPd/yF2UkALSZtO2o67WSwbVc00ojtVaatty/xM5PClXrb6sI0VIv5z69+qYtF7r2cygIKVXrqtJ3Kk0nF82p4Z2+MM6enKxD1y3vjjmD38xqYKR9JCgCbzXP5n4oACNk5kC9mVEvqtohzvPsXihLUVUd2ggHNDNAVDPNGeNO/D0iNqks+fgQOt8kkP2xCAZI5AULdQs51zJCRlQ0dA+S5YbkgKgi9Qg1bX9ADUydMwKQC0BTjfNERIE7JCYChyIRVJV4c7MB0JfD8/F47Dbd7e1t6MNhGiWzaEMXHGIqeozPTB05Mon3+EESKVgLxPmVADKOvmFVRr1hmSfcuPabmSBwYdSiKpbTzPPwDeI+PM+r8rKid+/e3d7eOhrgZrMJpb9kpXY7tUWdnqsFEkqnwervqdNYW8apavAgPzXheicdT/1qswF9AdqS9iyfTvewNYmpSZMLpqQJEV2FVzErXercohcziDGpKBgzSiowvECkYEZqpuogUARwgh9Vp4DIM6IqOWashnqkvl2Vr5CTY72tU6vI1I3t30vXlBKM9NVebXussbGFzTbWQ3EHeTksEfWhCyFswuDzUIE1lus081znttx5uWMrkKqAX7BnYy6ujTFO83Q8Ho/T6Ekf4zg+vzy79D0cDuM0xjkBUNdJQByGASwMwxAIcwiHoA8UNjuXvnd3dyGEzXCDZLQYJWqqhfspIHgwArwYCCQlL+pdC+C6IpWuVNVA0jzn9PJT7t+O5Xw7afvY8twqgBfiTGlOMWkt3wqBewwBwR2MgUPH3JlN0zQ9H/YMDEyKMKcoIqHvAGCcJ5M0dIGYPZIEvIQ+FExUisqa+8hSrl1kIgNmVQ25MYCi89nyeCEwVijEeYYSyQbIsXWoYIRXBXDWNasAxoVfnQhgvRSj9Y3VTn5VXqnBaWot4Azx5qTQWMAte3Fu489ci8HPR8teihguXbFO476Vu7XCHmt4RsCaLLm2Rrwyt5L1440AsVbf+xcqaa5njwcao8BCw3XopewnAxRF7jssWfEBCYyCGpgGDdr3iGiJwRNESuexbHMXTFAR6ftcwOKTWXdQxWlYMZD62CdMwwAItbQfbtfRp6UWXNVL1cIBAKDcdmlxneopZBgRHQ/TPM/TnJhg6AORqx8Gkoho6IICi4hvQ0Q4Ho/bTS49ZcBh24PROEfXHwjQEdExe6ININszSFnQEpFq8uB4lXmZvNGBhpbndJsnhPD69ev7+/tf/epXtbLITd4W7KiSUMuLKr21c+gE08I++69EZL/f//jjjz/88MOHDx8Oh0OofLDuk5Z/1XFO9FXFO3c0Vf3CXYhGpt4DxMt0DZ0ZdV232Wy2DohOBHFOKoOjhtrlZPQTHg1Of6E9rTn5RBGuZGcNAEX7jp505j8pOhSaCSKroGEOm3sgoSHlfKt6fTzLsssLArmAoe/7QBhC6EPXdd3AuR2k127jFUS99mo5hRux4jwDgDbYvFSEPSJqMgGNko7TuN/vX15e9sdDjPF5v3f0HweYBICOmDokA1RBr7XA0HUdIxAMgT0HCO/u7r56+/bh4aEPFMeJF3eCerGhs1RNc7F3sxfEbYY4TRmx89SCSaVvR8vCAEBSolOXqRuaIQT3/Jl6pN1RZsRyixuTYvK7ANZY6uiKiHZXvBcaZX2F2Z8kqRlQSgkxjnM6TPNxmkEQmY7HcZoTUtjd7DQJHvYiQl0PrG7CGoABiSGpl814lim79DNEJEY0BTPwyqJAYORFsOgNncoOwuzaEZGMFZxSjBHRuO+IyNr0urL0tfjeebKvDjMTkpe34Lr4e61KrrZSJexWYNcj1wRwYYVZ0NKZoM1L32B4rsj+fBeYWe5MsVBI9qwQ1etYUb4VQFRVTJOKo1m15QmOIOUQVPUgMVMpv3bMOT+CoVhRDZqgqcV5VrwQ224tYKyBJMT9PPfDVgUQmDeIHKjEJTA3KWHUQR2RMC76ImLOTMZSBYqIQQjQ2/LkSwhKywScD6iu3QyZaZQOoO7lMKK603zhvPl3y3in475eH1jJrQmDIXB+7xQd1RfVyGy73by8vKikh4d7RHx6eX58fDzOx77vMSAAzCkej3tNUVIC8C5tirZ0+iEMqjprMgQEC/lRPQuBiJCwbQ7oLXSDiOMLOARtLHFGAK/CKALIwTRubm7+7b/9t+5wfvPmze3trRuv0EiK1hCt26elZB9VPNdfeZKNO8yen5/fv3//z//8z//0T//03XffPT09BdWTfI3WXq7/bNcMmir4ls5ahau1gM1EUV30MnjkO7eIz5ZfGpJqUk0qYho4iKkkQARkRmLwOkfX7Gh5Z29zczGrvArddgu3jOOc3dQXx6IyuwAmIsjNW3PBLmU+gM4rAU5a06g7cEqhet1+jFArevuQgRv7vq8u6OpzttOYTX1syn7s4llB9GpmPyGVk63BEACANIsDjT0/Pz89PT0/P78c9vM8H8YxpwR7bJOo73tVtWwDIAckoEAYCI36zdCpps0w3N/feXRE03zYj5DRqQAwp0gU57PLuOQR2Tx1pknmVpOrn1WzbgeogKYWSbRduzpdjQUjBiIi3gpTiv95miZosKClVMchImGpKeIOkdRQVS2ayoR8DEH2x8MU5znGNIuYPj09RZUhdNAxICiCI1uq5qznvDmVYjarA+mSsw0AmpPeBQAIA/DC000kNABMCYyZBUlV53l2KAZ3QQfs2W3uwmQbvpDrYaoALhKSXMvEsy7F16QvAJQg1loA1++tDAYAB5Opy4rF1VwBf+rw56NSNLIs+iW1G4ubvQFVOQmEnb+Cb3/vbVVtX+fFFcBgxS6YOXDOwGgreltWq43nRsGmKJ4Dr5YkmWj0diwqAKgIjGSEAckQ2BB0SnOxmUG3m81m4I4YO+pSmRQEoCQiUcGSKRYU6Iz247kC8ww5veiktLe+CxbvSBalp7um/lOTw/Hg6hPUkCkQS2BGqk02XVkvDLzOcyBawot5Y4IB2NvXD4A6T2l7M5ji8bg3SZbim9cPSXO2uZYVAZDtdmBmJAvGBKAxGbrstAx77q8GWNWOQoq+ml58lcxEC4LVOI7FkYApzZtt72FdR8/4+uuv7+/v//qv/7rGAav/g4hyvX7hG1f2yCJZKp24Q8XBs8Zx/Pbbb5+enr7//vvvvvvuhx9++Pjxo8Pph9R0DKx7BjIm8Amtt8Rav1uxu6V0p7FaPZxS0gSgApJt34w1ujwxN62IsXERo08vEYcAwFIxqP2H2e7EesfVY65osZ0pwkXx1yaOKLVtDuQYCQCYuX8STYkZzFUnQMd/xlzbhPnRDICUNbfzqFfz67s3Kxf1Bu66buiHvu+33aYaEO27aOPbr44UpzgriacBF8uDMVRRNEmsYma/38/z7Lbv09PTfr+v5OjOdzNjIuLitwQOITCBGbJb7UxEdHd7w2jbYXN/e7fth00foA99t9HiqfCVB1QGA/Sgv6omNQFJzjsU0Wzx2Gnjf5ZTIIuFEWtaORj8i8qiaVaFT1VFYkrJuxillMZxjOMUxwkAcrZzKdL1VDuCQMjIwUFovYApik0pRQOiME4xJk0GU4zHaTpOMxCCpXQ4AkD03i4pIZYW0wBqip4Kp4bEBoaq6DpZ7ieBZkAMjIgWiI1DEFUr2Qy+56OpmTGSiMQINXrddVwI21riwSwjqarF1QKuAhhyeOzCuMhcoOyyc4sZL1nAbQy4bOQcLavre2Ip4oWcgHYjrHicltyL9pqVa1kj+K2Ify1wkhlvsgSA29thzh4CLmryYlEgAKFDIRZni7ak6yaV5IaVpu6aU59nb3GtbkcCoaFKnOHgWadGRMO268IwMCFiTOIEpQjeN1xiAgBQUgEuGE5m5riEvpTBQxvOJQoxUAnrGlHr568LnUUWmiIwoLdO909FAFFgUmLqQkAyKqU71dWvDGpoCiqOF2RmoFBFvaiAQZqnm6HvOcwyj+OMZPcPtzey7bru8/Pnx+P+Zf8cC9K1qu62N7kmlsFMjvsZAJCZMFh2IbmXLYfiFMCzxkrkZemiAXllgQgdDLfrOHS39/f3X3/9tfdL+Prrr7/66qu7u7uqb1USTSm5XttqbJX+V2KxnV73V7nZ8/j4+PHjx+fn5//6X//r58+ff/jhh8fHxxreVdXQCtq6r/wqa7F25qdabYPKDWtwRVQQTUG9aMFoucuyuU9fKYmnqhoukPcEiOCIvm1WJ2C9ddl7bVF/dSNfvuOK9VQugIiqVTV2RQTLq3rUwZWAAnNJ6pFmAq9c7aCpUK03rR6Vql756PseF3m//KTO58lDAhuIN1hwllrpJhBXd0ec56l0mP/+++9dC/MxjqOjh97c3LQMtPpbmIMXP6kKO/B66ALjzabv+3633W5vBkD1K3R33f7p6LSuKi6ABRKAJ+uJmqgmsJTTQBUIzcjTN0XVRKMpqqVaTVu/g9eiS1otX0uNRIu1d8pqU63sdMMXSzC19XUjooO8+0wYkAcbFWSaYjJCSLNoMjXAWeU4jd3QA5OZOaCV64hJJXSdM2WBrMO58EmmQUEVVVWycUCGigampAwEXssh1WFbHzUAI6LmNI6TCFGliZY8ViTtk3R63OXol8qQLrGUy1vGv6xlcEPA1ljAWFibK+tUIpeujWmTq3W+F1aLbqf2LjZMYHURx21YHHKNGHZLrvCNhZZaQKtT3nJipufLAiAHVEXUGpKEpiG0FYd/wVA0IpJivzqOIzL1fU+IGKOCiQhhCbIAzPOMitW8QUQPlnoReQjEXulymhPUPryPM0dm1Wm8DwQCoeaiDgRCiQmZkJTNAAkI1cAQKATFkxWxU/Q3oo6WXvLy/sfv7+7uCHH/9Pj8vA9DePPqNXf0/PysKb08PR/2LyGEfrtxst/dbvcvxxgjIcSk43RQgb7vNxtCYwSwendUdKS5DDnS9J4H8b8SEWLv6+mdAX/7u994Ue+7d+8eHh5ub29vb2/DMOyfnrigEEIJ987z3Pd9FZbYOIFSMRhW+8Shfvb7/fPz84cPH/70pz99++23nz59+uMf/+g+SBHxrC5/iyBnfT1TaTVcl7w9QWVR/apQWRxQZ+eLiKJicZZSyRmpjrEcAY0xzNMU53mek4okYEZaZG0uAzBc8pYNXR1bKFsboAbM2sACAJn3fzjxLLXviMX0lyaW7rzbiIkVMoK0d+p17zei5oQYMwGyGCMQGGfWacUIqC/bdV3fhWEYNsOm73usMG+N4qKqvv3a3yKiASTxGmcKHPoQIGs8KjHn9B6Px3F/cJN3mqaoySUuEB2n6ccPH/oQ3rx5czgc3NliqkTkWPBRBBHNCwAC9aEbhm672YRAr149PDw8vH542A5dICYC7/3n7dfUlCinBqmZqlAG5iQjxkDep0gsEaElNVNU9doJMQERU/OF9Vp0zwvxo1horiUwZrYSjW+1ljhOMcZ5ynI3Npmu8zwf59kXlEvq+TwnNSBFwpDUpmmaoiCzmU3TlBRi0pRknJMYdtubcRy9tB2ZmAPmXCQe52gnhO+xSd0MXQJVASnJx8RMHMDEAERBTFQ9JhgISVMMIRCiq1NYagqI0IWHg2R5eohJqnKuyo+MnZs9KAtMj7tDrZhN1LglV6NKVihOoHoEr4/8Q8iaXN2JVIJZZuYmRXWYm1mi2BK/luyYNn0Uil9NVU3BjU8iYjoRk5nbmAXuqKMY4zgdFWFOaX887g+H4/F4nKYpxskLwb0aTRVqlREzMhuimJH/0y9bokI1hce9iyICxN7yVNXNz4Bk3DS1XUS4+ROaeI0cStEOVUQMIEaJMSZJnBuuIxCHoWdAQEwqoOoYcgoQEgEAuk3e9dWJOOvcMo1U8A2vrfV2uwFQBfBn8j3lT4xGQmIOXQReRWsx5LJJ6joiJHAjGECSQ2ipmseM+sA8DLc3gMDH49FUNkMX+jCOh+cPz4j49PQYCL/+6l3gjgKr6n6/f/r0ues60PT8vN9ud//LX/2h64aX/fGf//mPjvUTuk7EhZQx0dPxSAQhhNAREc1xPhwOx+PhzZs3WhK/b242b9+9/s1vfvPmzZs//OEP2+3WMa1cErm1WpCq8m6qZd8QQigGaxu52G63UMwkL1J4fn5+eXnZbDaPj49//OMf//jHP/7pT3/6/vvv379///T09Pnz53oylQTGkP0GV8bqT/ZFU9JOvUBnu3ORcHVbehSht17M+hi7rutSFxKYgOk6qJPpuLl1uccC8u5ZeXkSizV8LmLbf7bso71yvalVE19RQYm8yLaQKDRP5UfMYEmiXk/UhSNn01/PvLg0fd/XeuLKCFJKh5d9jNE7W4z7g1PDOI5G4KBLHz9+HMfx9evXoPr4+Og1Lda46VyHStMMTBy47/vbm+3t7e3uZtP34fXDw8Pd3f3tDTOj5Qi3qsZpBlAAtQzyrJaLd73yWXN/X8vGDizJp0s6on+vLNjfVFVNFbwRuvqKLvEqicmP5LZXmDGISqFnRjKqcd/KwV0yFc8B1wKNpJZSmqLMKYGYIYlCTDLGOE/JUdhEFZAVxczz8UjBvOrRO2q4dQtGgOoliWpIsBCD1tibZQRTy9bSsvrtuld6bnNAfNIwJVFZkVb91Uouls+1TXmRL5/+dl1Qh01YZHUXPM26WF2zfqcGtJxOy4rat14dbJ9h9YJ1k16btzbzuc5hdSOvnq0iJJuZ9/H1FfWsPTEFNW9ZmNSz56R95vax61tYDv+bOgoEMzID4ZzSYTyqKiPEaZqnCJqM2My8Bw4AaGlY0b6X65Sef8Ql7933MtDaM391oc3iNDu6gDuZ/bPm8xfuikbu/QfyKlDf0aKCEdTEY8bEDhxEwMBO3aqqw9DteItkng01z/M0jr/+9a8PhwMjTXHePz9HEWaOEoe+Q6Ku6xz1QlOak8V5VEnTNBG9hNCLyBw9Iqs3tzvRmFKMR4fCEDPtOn58/HR7e/vmzZt3X73xQO/XX39dWwQOw1AzXuGSoxSqwVZsXz+/TSKpy+pxDYc2+ru/+7tPnz59++2333333adPn2rU79WrVy3zgRI4W5KwViKq/X4uCeomXIyzxpNTtxZBBiihJTSQX4zZKyaxRzDEOaVhHpJKFLQIpgoLpOdCTFgkfb62O/SW0XiNSoUcNgliF2VhZSX1BDhjZ+rtFoygIK2WbDBygQs5p9/Knjlxi7UXX63xisVQE61pJ99KcDqEUNvTx5S8kfg8z4+fPnvU4XA4HF+8TnU/TdNwszmMx3mej+OxD912u53neX556boupYSWYb80CbI36ZXA5LDjd7e7+/v7u9ubTR/evHp9c7Pd9MHMTBIAgAmCEiiAt1hQ9bXSZJpACdVhaQQNzEQ0qarE8VyWVAFZhWXmj6IQBSCrPIQIBR3MPE5gVnVzP8cdjONxcgG8yGCwOUc3FJk85RURmDowUwBL3hRY4ywKGvpNEhljPBwn71RthuaZxABgHtwjQw9jghZPowFkHF0kA4gqAcmKZFYwATDzLk8IqIAADiGKnk+wXn3N+XTWipCUEiCKLW6x8x3aikwi8qIlVXU7tpqzcDZOZS2ejy9T9cVRNy+cOkjdGva/Vke0E0MVrtB6UEvO2sV9Xa9QrzOnOMV5ivPoDrYUPRfa+/uamYGRQ1IQAiGyC2A0d+343cAMMKlUr0xSEZWk3mn4pNDZ39DX0RqRaWaKkJJS4JD5JE5TfLKnA3Eg1jRrEga0brH7AREKkA7ZQg+zg6mBEmAwBFFjNlrYY+XMPkXnjMUfUFMC0Db9qlVzjZiErSRhqaPOARv6/gFJopYMALoOKRgxMgEqIQMhGIzHAwF2XbfZ9MPQpay76263ffP21Waz2R8PP/74oyf5E2OaZkUlCvd3t0O/ocCqcDNsXt/foUNkI3qWgySZY+TI3lZARNyZutvtQqCbm5s3b1/97ne/++1vf/vu3bu7u52XFbnza1VvUplwJaR6XEUQ0ZOzPLirpX+Gk+XhcPj8+fOPP/748ePH/X7/d3/3d8/Pzw7X46EWdzj3pS+4lpQuX8rQRhewkcF1wVbS12yRVXWNW520/RUauoebSp/AVoNzX8Xihe77KKlPoGAqScRBMBRRtcR0/cG8nhgZENEhyqoAXoJJp6/Tfta5hlPusrz1KTPxVVLV04lZ2lr5ObWXJzhm77rH1Fqon699y1Cs0V61FnoBuHmWVzFG7yU1TdPz49M0Tbmv1OHoMeBZUgfD5w8fUkoPDw+3Nzun1IeHB/XSUrUa/TWzgBS2W1c/d7vtbre72Q6bPnRdNwy988oYJ4kJEb34wiAaqIG406JmPqfk+IpRVcHETRGV5M1Qq6VbBTCUItfWFalJWE9anxBRKqwWVtoJAAC4H76GwFXVddaoUqWXe0ogRy4YAMRUBZIZUkA2FUtis8g4xXGe5ikJON4vAqEZmyd8m+Wu00iGHvkD9ZhI6dAhakDaNZUzIu7ghATGpSNUXnTEtpWN6QJxQ4SpZpC5SEasjRErqbR7ExHdfs37FAjQY8wLmMBqU7Tk6mNl6V7cTSf//CkLuBWQAABcazdVVd3z7EecNfm8+T8BADBHyus1W6HebhkfTgk1Gb4SWCqNzFevnHNtq6FfnjWTK5hlqVyAyc8aNLVPtRr1fNcwnETjBIjYIZtZAAsheLm2vxER1rfLSw8CAJ5fwWARKSBVpUQawJ/zJVsNApvm6ZRglnUnImVmYanCGxUwkYSMtJxlAZkZEzFAhgoiAIAEGAAYSTRa9H1HQzf0D2F7M4jC7c1uO9w8yMPd7nacp3men5+fP8YfTawLfHd314V+nmdVCR3tdjszc0lCwMKCiGDy/rvvFIGZvX/569cPDw8PN7uNlxW9e/fu1at7D7hWP5+cplO1X6rK8v9n7c+aJMmRNEGQmQE59LLL3fyKyKuqpma7l2popmmpH3f3nw/tU7/0U1NVVldnRkZGhruHn3brIQIw8z4wAIGqmnlm9YyQk7qYHiIQgME3f1zklOFkuNz82PhnCME5t9vtbm9vP378+PPPP//4448///zz1dVVYrzjqKptPojo9va2KNCFGCYLGPdtL6xcQwfkpdmBfLAnSzZQTc2m0xvwo+YUKk3yYxRQFWdBdXvCJjZNo1F4RLakHtGQ3FrYlCtkerL99qQL/dFD9z1OZaQuQ6ESUW47aodZPFC5lhFTIoBLmm6CM5+CPWX/F+5Q7j6pw+aRqFoYFwoogvzgJwqw20nUBKoXdoMJ4N1ut91sdrvder3ebrc8BlVFdN7DbreLKqb0mYJvtci7zQYgdYpunFdVVHDOKYJzrmv8rOsX8/l8Pp/1rUWLwQLkIQozESiASARQAFZlBU78iYOKjIamwiHPRJTILAEkSAy1D6dYwxwj56ZbySyOjEikFZMtjDJrMDVxCkIIMYYQx8Ahms8wGtQ+R2ZmlZKEbzB+uQoTDUy7aRpFgsDb3Tha25wgDAhE5Dx6i+oXnFO0VTdXexoMAlgGDgKAmmnOoGRJ9FarqZIzlmzPWBHqIyxScg1o03ipAqUxRiTCXPxak9B+me+BADb6P+TRx7ujfJQTp/dY1cFN4Qn+fnwc/wRT570kg8uyWvy7FjzpV1lUQxZLxRq2R4O8eVVRWMeckFgXoZUr1E9aRiUISGnWCnWN45ji+gmzGgVT65eMd3vgLzTCyu2tAQCUEBwJAEQRGIMyO+cYHRFFGItXj6OLqG5iDDmhAJRE2VKbQdABc8rXS/dCtKLkYwv4UYWAQMfdzuGh0YyI4BwYWKY6yNcRBBnZcSAAAkFlIlJDTWh89kehiIAwqohwJfyYOYqqc65pXOebXdgNQ/BE8/m8bdvb+7txHFfL5RiHxnedd6qy3W026y0RDUOIrFl3JREZh2EMY4yxX/Tn5+eXl5eW2Hx+fj6bd//wD//gPZmrmYhURdVyilxZ9zQ/9micojn2J1auF+NRqjoMgzVHNzvn69evP//887t37z59+vT58+cvX77c398/f/5cVS01SlWNJ0t+ft7vr4WIyQKud52Nr1gYh0fl5DnedeU6NghN3ClfUJM7l4hCiOlvR2htQLpOQLdDgrQUkShi6AWiaqDfUNJ2psFhNbjaqwCFRSNW38kGZf10kF1hacb3LVTVlNFeEXG2DAruaNom+Wy/xqYwEd2HNxGRAiJfeNwkbPJ4pDRwtrIizsmc29ROysIqFocYxxElV6cQ3j88LE9XnW9M918ul66jYRgs+5oyBpslhTokAXXO5c6Xy9VqtZh1TdMQgCeQaHs16VvMykEAI2ByztlzMnOitxgKv+MQWYYGteaAZUIOZmyPoo7eLJSmh+sCqfwtXVSm+F/COyMqECUibDq1YowSRBAcevLqRhl2IYwhRLaKIgQiJURC4dL3w+ohUNXCD5PoTQRvFnFFEraTMxK4gYqYwMyCRy35n2v6LDu0uNPtcN573MvhyBs7CeCyo9OrtUoRsVu7jJV2MJlwaA/taZBPCVp9jL8/dexdpJKyB2tKKalHtFKaCQ6Ts8qzS4UIVOjKKt2Lklc+qm5eeREoseAikPaH+cihssccvjEPmb2j5Mi0Mnnv0UMDYB5eRnTF5zGxBCzcIKbe5OJBo4IjcM7FlAYKiMgi4B6xgMsj1weBSoyam/WWmSwTXl8BAHLgB41dSKqsIwCIqV2xLa6kbDuJ93Hw3jtqnHPgKCV4k+v62abdjNtxs1mv19vIvH54WK/vz8/OEBXJiUgIOdhbQJsFEB351CW91/7y1bPV6emrV6/evHnz8uXL84vT1WrVtu35+WnFZbnsevKNhffNtw/mx6qsHajYvqo2TWNVJA8PD7e3t1+/fr2+vn54ePjXf/3Xjx8//uUvf7m6ujLvt3Pu7OysIEtirmWwvkmWTQ0VALNRl6/1x3ob8H4dcKF4wkM6K3LiQIRMywnJeCwSqjDfFGIyFAhQdHRzt7GPYowxaZgWXTVEXVRVNiWXLA27qYZZJ2G5wrSlii3JPkBP2TY1lIHoJEHz7pmMg7JpoXBYo0pwgIgoxctUOIXuS5daAMOE4HPoLdeseU2BTJHb29tdSDF/E8AGZRXDVPvrIP2WS/d47yQGZQ4hgPOq2rWt915RLEQhIiDinFsu+vl8fnp6amCTy+Vy1jXOuTiO+xBg6QEBxSofQUVBRQ2IilMJEoMwqICwxijM4vweOWlF7uXiFeOzImuAfZ3mgI+Ud1RVomjq+TUJ4HEcGZLKaPkvmjQbiCqqGEIcYnDUOGpEsDjDbdERDPFW9zZFqmwHRERHlgY7DSkLY6cAmtp7GNkHVQBonLMNQUSpKywo1i2Sq51lijOXMrMQaBx9k4yONJxJ1h6CR2IVA7Y5rQzcR4ykPZm0v+iPTvvfchx8GY8keuEbmhVirJzVZf86dLgvp3UqgHkEnKhsijInsG/oHwgqu9DBLY4fZLq+7N2u0CdV4SQoJZEorCl3AQBEUWn/+koiYCqXRZfJu+L3y9xDES0grUUAEyKomgBGRqv9pSNH/cEhFj/R5M1Bzf8QyzmBlYEiACiiYUmKxBgnzA1EdDvLjm6y2uQAGgA3jiN78l3fYkuYMDjbtt1ut9775XIZhO/v18zctu352VnXdW1rkAYsEp3DrvE2o957USSirpv1fd91HTX+/PJ8eXJi5bxn5yeLxaLve+tUoak2MhEPS0BwRM6KZYgIiUBVMp88MACKHLy/v//48eP79+8tsfnLly8PDw9//OMf7+/vr6+vh2Hw3i8Wi8Vi4b0fhgGqnQsACRO+ynohmir1/TEHtKPmNfXIGm9mKOXYqILVvyoSoYilIoNIup/Js4SnjyiqBJp65QBCaqtgUqijDKaTbhrt2xbRDVER2QmoiDVORyDpW5cyYEHLMI1cy0RM0jfTsKoh1eFkR2O163SS0CLJbwggVXDYDCCxXgNKgCCGAQSE8PiONX8ZgjkxBVURGASVKnZZc6iiN0w6IPPaMqq2w2bzsN0Ou91m3I4hDBaiIvLOEaEXjTFIFJ6vlrvtEMY4n89R9eFh7YkMxpmIVCH3MUIJ6lAvnz/vZu3JycnJajGbda0nAFVm5cBgcdQRxNr/qcSQ8OyQQYVUQSVanQUqoACKtWGdMCn3zMJHDkR0gOzQWYYSC6DlVLOBIBzTas0oLXVAdCoKNOWZiJTAoEs8kQqqRGANGq3tbgiBHTQOA1tHEBZQJbU6C1WNwiCARAwKmB4DLcSicGxfmOkoQISgQKJISKxCiqrs9xoVqKqaFM4ChABAgUCtUZMKAKtYHHjk6LNcUZV6Nmr1pRYq+VUB5EBCH4jAWstM5ScZ/uFwzrE63zt0/x9kXrEXi6mGZLc2bm6Fs2aTpd7H1sDM9iNlz24u1k+1M3aFFA2wtHxNsPvlXlTltaSFsw1LBpFhPY0mj3TN/dIPBff+aQbo2NezAcCBtQ2wTsCJ3QmgiIUirD+hQ0RAsmasisqAqgKCKuykyameaCCKCmQSH8XEsGTQj6kvXuSCbb7ngj7Wn+zCVNEJHDGiAxICdORSrTCzWumt5WgTDuZ2LVXUxkKXy6XmmAKMHBuPSN77m69Xs+Xi+cWzvu0kyDAMrm0MHkuVxzE+PDzshoSON1jSiSMHgM671nXzbrlcdrP+zZs38+Xy/Px8tVp1XedcCgve399jAnpyTdMWJT6EUDaZq3D7i0V0oELd3Nx8/fr1p59++uMf//jDDz/85S9/+fTp0/39vf2k9Iq2GAcinp+fq6rxkxLxxQrfAxE1cvTOAE+sqhqshT0iIKESAqIqWvCsDhqDRIcNESgikBI6QkVCJOCooiyCGlkVVTlXo3sHyACIkHHnAAgDBxZB1M65rvVt26pgFLn6ekfYxDGOY4xhMDgSIo/AqJRxw0FFAUFJoo6WOh9VsrfKEZG1fwBCyGaFAhBSjIHNoAFAdEqKiKQU8kwZnQERqJKycwiKwqmdWZK2iKyW+muIpIhJTzDDyBGgAowxjOPIXQ9KzjcIDgRBkMA5cKSkWuphcvOgygJgkTFDGa/X67u7u81m8/XmdrvbPTxsdruNCBCBp6adzWOQEIchsFNsvEdyEXRkgW3wvgF0683IcZQQEWSz2f7quzf/+I//eHt9tdttX796cfni/MvHT8O49R7OTlbPnp/Pu957cqQIkUNkHlUih4ggCCpRmINwUN5ZExIQ4VxiZIRe/D8srKDk0XmvzEBJ41K29RIiLyE65zx6ZlYWMOgzAhBWUAEBsfYsCcS3wqLIMBcsIhI4QfQxCscxhFGEHVEIAclHjhyVnUFAkyPcjqMCRRB1oChB2JIDm6aR0XCuUpGogLIoeoc58T6yQs5hdFX9KwAAoQMEFFHlXC2pKs6h89S6FpULm3Np7zGgI1FANJAzJlYOzBzHcQyBiMYYNrvdClYAEMMAPCNEEEUAby0ZRGIMiQWb29nmGQAIicCTQ1LzJaXOmYSYk9oOio4AgDXUjLhmTzW5pqdGoZQSbORs+7UEQQ+EOJjVj4gIzlnHXGAVtE663jsnPkM8AkZy6uIYBERArCUamkxwKCLkqcFGQCxTZgjDZrcGAGaN0SwE7z0MIUZryEakpERI6IA8Oo9V5leZBEIPCMIhOydNfUewZncOhFODYkjFgcDMtS/DJg4RFMw2RQSHBOB8auItqsqI4JE9OrvQAMAspA5VAMxORUTHKqisMTrXKCTvKTmnKuMYfNMqIYOKCsqk9Dg3pQuU1SRQh4CIlEH6Cq6DdZivv4yIAuBbFyRlUJpDRYFEJIhGZafgQR0ogwZhN2CM0VPTNI1vvPMOiCTysNmu5gtQ3D3cE+jry+fJpefcu08f7tcPm/VORKJwjOKa9ny5UoQgjIiz+fLZs2evXr26vLxcrJars1Oy/KHc/CCyRo7k0rMAyjBGJPXeN86rIiGBqWLMkvlV27bbYVyv14i4XC4B4N3bd3/+85/v7+9//PHHf/mXf3n37t1ut1NVBzjv+hgj5PSFouEJwjiOmuOYVHWRMsAQMwCcpRgAOkI/0VnWmIr/+hGFyUhTQDCKeOagSujEsFrK/rIBCTKANRDaU35NcSbnANGcuAb5TQiKOJ8vI8N2tthuBw6CMZqBQdQgokOMajilxYNk2xAcq6SnFVVlsY2dbe1sakjqacTWo6/iBYfOrsQ4TNPf65XEAF6BExQXAIJ1SlPA4i5KX061g8xATpwFwm0LOkoRnAarixctbLfbGaqG9UtIQJK74e5+nTGc2WQP4wgBiKifLfzKxxgN8QpSvKEhcoiEKNR0TdebutM0zYcPH1rvLi8vQxhU+Ve//u7k5AQ9rU6Wi75rGo9mScTIHEFZVQAVVAAUMFq2M0g007aIW3P7FEMErV1nTmAhJdPrVc13kibcpWURRhISYhKS5LwlJGIiyL7EPWghVWVMyKtFSIiISJQJN1BUEFQUlVTRMpgJoBhk5jE2OCBAAQYAmfwmLmtm5e6PHJP0BUArXAaKFjZ3ifBJVRgistNptJiMJEAAIg9gfhcCYAESZMFkdh8356335sHJwUf2dSJEmgAsDy519I4gIFJyUdpDYlo7xRSXMUFann1qkGB6f/nmE5OGAIAkVsUKyaeEVgproGHkVIHUsYiSy1efYhOAhBKsz7dMfhfIufQZBkurWnCb3zTvhKkF+JGVqDkoaF83U9vM6cTIlBQlZWGWJQDSKrnSytIUTGQ7VQGEFHwiBwCChGB+QtswIIqiyuSSqLNm4sKSwmOpy0cx2dLd6ZGlx+xsP15fhwaeo41z5H3rPXnfOGeVygLgCsQXZtc/kHkjIIcJIDPPA4IEAEFQhtQ1h1M3G8iN0e37Hom8I0BPLgr72e++Xl19/fp1GIbAMYRgIMT9rH+2Wp5fXFxcXKxOTxaLxXw+d21jPTNKQkMZz2T9pyozQnAKZF465Yk2EmV6bzqKQSnc3d394Q9/MKv369evnz9/3u12pVl7nRxwTDA1alA5DFiJUoiDktqp4FE0uWEUSMEBOtPGNb0DU5UbWCiXRSC3UKWcuZdV4MlfYd4izkLOZqeMLANB7L1JRKvVCpDGcdztNiEEAVZRhWQpApK5qkQE0CFi4wlRDetSRhSWqKlgABFJPBEBlT7eCixIkLYb5g2mSpqsqsxiQMzI1uJGO3SgaW2F5cNwOgCLGEjJbxGjlViwCqsKpLrDOglORM3rICL39/ebzcbaJ1hR78PDw3YcdkMwR3rioQBSYNK869tZ23e+bdpdZypRDBEAHEHTNJ1v2tb3XeO9j+Ow2+3OX744OTm5uf56dnb2D//wD+fn59d3N4vFbDGbIyLHsTzANOrEZlVEWKLGCDmHqE4xrUlwSvFAdWj1sekiWnDsLGTPUjaGWP0Sk7Irb9r1yqsY9iSmSDlk1z3nkuKUuiHC1qvc8nSy6+K43qy87r1pO+oxH145pEpanLaAamvQws475xwCojokctCSt62FiE1GiCRECYYF9qRwpQl+zu+TXvnaoSjVqTYUXGrLdJj1euC4LvKYkFL3zyLu8nNCtnMha1hGF1CyxI3INTugHzt0Kuc+cnhWW8z+JKIUdM0fQda36imy40D6FgGcCGn/eWv2/cggHyudIAu5VTOfX4GICg5B/TiQw1MHk3xwr/q8CGDV0k2drbdnYS+Sgn2FST5yFE/b/rpD1zSI6smRd43z5J0nZ3XAljefsucJUYEIGdSp1UdRDdhZLwFUG4E5IKqIExHLnUVTYnKSNyJ6a07TdYrQRTYoR1X10MxmM6sOePXm9cXl81evX19cXLT9LN3GkYhgBchYkwrtA1Skuc0Q2ZJha0u2x/X19YcPH75+/Xp7e/vp06c//vGPP/3004cPH6xazGp5TY4WHNPjw3gfHLEC7w0PY29RwKAop81W5SXB4/shyxWtYl5U0Vm1wLnEor7j5LZyCf0/3UJEbDkXi5kC7na7zWYzbscYYxAhFUIFJET0QILADkEJHTRNIoKAyFEBQCMzqIigI8rtYqy4D8DiFnvltk+y3Un/3WMgmo8yOYXuNIe1ys/Lhufpd3uHocLaDGQg5zHGeHNzs16vb29vHx4ettvtbrfb7XZDDApU0FDLjrIbbTYbETF008ViYRVKRmeE2nVd37dt26rE7XbLYby8fHZ2dhZ59N4/e/ZssVioyunpqm1b77wCcwTmwDGyBBABZVIwh7OIMAcJUUWKAD4+YJ83AaonKgIYdWIxYHCTJJKLQbMARmUqQt2YCSICpD59IQTQwMyWRkeJTU0yOC0Bo6oBWKWolQlgzOBNJmJt1GW5Nf/91H47oJn6h/bUxhqScw/BQA8I1TakCWBCdUjOISGOcYTCLMq2yn8mVkNNxn/1B1YbJkVzoo36PKWv5zLTg/yjw8UCyLY5wNNyouZxj26l498eTBvs+5+wstjqd9BkagUri/sTVe5VGHHW2Pb23sGXD+YhqxlVpXL61R7nnH4okx5Xfi8WtT6eT1B4bDYQ9xJH6tEaciomXdU6ewpKSjct2UNGaWadagZaOVCwypsFPBgR2rZFUo+E3jXk0DtrvWCNGQwbq5wLgp+iFemo92a9NIkYYMo5TfMGgojk0ixpVh2sQfIou77vT09PZ7OZ976fz7rZvGmaF69ezufzxcmq7TpEl+wromh9HKsntTks1nk52KLWkYsKKyLmXNxsNn/6058+fvz4448/vnv37vr62poo3N7eWrqMqhYkrBpI/PiQXEQzrWxWZBGRKkhmG60veLM1adaqBFYWajpXjJWhozUppw7WzjlLZwDEuiitpDWlDbYPo6EK2jZN1/Ks77q+Na5lKi+iZWwBIqB57QiIqCFn7QoBlF0cyTzwLCKoCKRmyaOisV2x5iVKoJT6aaiUvQEwbRUt4asjO8mOQoI0MYJHdMBCkZJwzw2rgscYBcB5NNFbO5xDCLe3t0Yc1rzIjFAFcI2P1lw3Z9EDIqIjwsh8v1kPMWzHwXBbiOjkZGmtCcZxjG1DRBzHYRgaR2cnp6DKMf7qzXfnp6urq6vFYn5xcQbKkVP1W4xRmIuPUUQhAVzFlEacFZQDHndsEdoKOnQmgAtXSZSg8YDNEZEBTlmLhGrbJwFc2JOh5CYWUMDIMn0mGz4CZdA+hOzKxuQe3F/Zwxq8bAEjpoSfvfwizO7ryVWS33cAzEyAiqwKDAogiiiongAAcgqMAoFG63ZlgIUswKltLaSfGVoTee+ahlxDbs8CrgUVVHKo/OmcKwIYDuTJvlSomSmUlrGpyUTWVExQJWVTy/ui03makOzdgceOSTjtq2tlADXzwQrGr/7CwddKHpBmR5H3vlAIxUBEnG9XSyZELGs48cMsgKthpB+Sauk5VA0e8AjRrH5ehYl11DKjnn9N202kav2S8EByTF2riBUieu/Jkrlokr7F8C17ivbsRXDOpQoTk8rmX0X0zikUXKyk8vsjI60IjrIcRemxN82ZIxJFgKgRESIRBeWgqpaO65yoOsQGkebzvumb8/PTKOxduzo9WZ2dzudzK0yKAsyKyM45QAeALsci0+Tn+1Itv3JzBWaOw+gyEtZ2u/3y5cvHjx+vr69///vff/ny5f3791++fNlut1ZabenNxpytorc451zVx7o+TAAfkDEAZPzEg/lHb3VLuu/EP/iz2iQW3kh4WqngsiJl4tTnR1URHBEpTexgEm8mxhHNDoaiqxKCEyJqmqZr2sb71lkHJXKgQM6SjJFS8h4RulQfDADgPXtyTFHEaodBU3KpKGEuqBCRPX2/pqejPcCHbHV/h+RZTm9OS15p7qaQ2rRgToezBY7CrboxhN12e3d/f393d3t3t91shnG013EYIjNhLg9PbkrMGiUWQ8HELQBY/8u+78/Ozp6dnRPo+uFhvV6P4+7hQftZS4AE2rfNbrfjOF5cnL16/cKy7U9PT0Qk8igiACKR47hTFiKElMYsKqw8eYlVOCWjHZVa1RNSTsp0GT1qsZmSm3qSDcbYFRJ0RqFSc71ayVmaYUmbMMaII5hUPlgvZlFVIEbEFBYxW9HRwTeNn+4JhvRurV89ckxCq+w9BYfkM1QvwKTMJYZovrI8gVGEOSe2Zp2jUJRh/WcF1xO5uoatDMDUylrRhrI9CYgolSRUj/zUU6TKApxc4oXsD07KRQ5OcN8H+NisTcMoGwcqFnYwPPMBalbyCrGVLyS+lpsTuJxelGY4Rh+Dc46rljP7noDHAxD7rryp7Ng5At3r+HTwOPZX/bDHU4GIWPHGenpFJrihp9gUJEeuB0LvGs113pWlW9PAngC26CMBoiOHZK9A6MgpAgFqdkFbBq13rkx4WYWDwdSjihaIsNokyD0fncuBiVTqnYYHru36vpsjIjOTaxaLxWp14vsujjEim9rnGk9EiiQy1XzWe7aeWMjs0TASPJK5V+/v7798+fL27du3b9+a6C3Gj2Tw567r1ut1jNEyckxWWsJXTXKPHlglNIKFJnHqPl7WxZt9Q4ha6VYIQIiSeVR91YydNyH3lpgzETnxZeoTiJqb8rwQEwyYJISNaXx5RZnAWX6m99Q03gDAlBygdU+1UARpKXwEJOtS6Um8G72LkVSVoyAgCpsXzfRvWz9K+RTF5aiTvKzU3kmzzakvNZ3V0gWx7Lf0a9pPpARHvmnIOfROCVlFY2AVJ+7ubjeEcX3/cHt/d397d79+2G22YwxsXVJCBMLGuu5Y7gVz0Xgg506bGuSs21L254vqEEOL2LatAx2GIYaweQidb3zjmqbZbDZt47qm2a43Xdc+v7iYzzrROI67BDYpGmMEkQzQBVZaCyKQQsJW/zDZi0UlL0l8ZfNXU7hHplmsUhZRFZ5oJYArC9hCOxWzhtQgiJlZoyk61U2x/NySlhFc4ng4ibAkY6eFndb6b3FB5/HvaRuE2JBzRLaPrKbZGc4SmDqMZCKxWOsaoY6CA2TsnuxLRO+oIfSErr7XdKJw/PiZ+SayqR/w4Ap70hSFQYCm5yoctv55vSOOxWz9w2/MW82+6/HUD2KrjI+Nv2y3WgAXLaQW27QfGqyP46koAyN6ZDxoFrk+oprsP2/hKkCUfQn796qPch2sRPjBN4vVMfFe58i7ru1rAVx4PVQCuFjGQKAISuoAwZE5nz2S5MYnBRdaCTPwJOI+srRU+I71YaOKGhPIuSqpiJBqKyLoJD0Gknmz7FliGJy39unoW+c8hhCGGIQVHZFviQjQqVo93V7U42C/l11QBPB2u90+rK+urqxbkbUqMmANzVX+9vPkWhO5vr6mfcQITLnuf4MArhQ7w4igjOBU6MQfTNmjF6o+IocaK+9HLYBVFQhdRnM9klJ7O1xSDhfiQRifmFQ8knfOk/OOEDygFTIRIgg5Ycg9cQFArO7IqToET+SJxLKuFKwpnqpOkJFVq5Bjun9kP+QGwPVUHP8Wi/Ku0zv1MhS3GABEFVLQqCLy4dPHcRwNacWAJK2IwpY/uRG8Q0RWFrQ2FWB6fXHHqWrXdXavvu+99+M4fvny5eH+/nfff2+Ikk3TPNzfbrdb5diqV12B8unqhJnfvXv7j//4j7/61a8224eUQBwjOUBR5oggIM45x7m2UoVFonX/LW04ofL6HkzvIcd8QldFRMjJfekKqqp0YJTYNzXrYYiIkFJCRGS9fSjaZU11SZjV1J4N7uND96MJiTa+bf8+YQFb5MmlKnNFUiXnHFofG++dqipYMHuQyI6oWMBHE0i5SbaJFofojhN3C+Ed/FlNxlOPfPinIauAdXfCqeuUdQcqHaisCt/gaCy/4ilm8u1J2xc5jx8+t+AslLZXJ5nl67Hgcbm5+sEs1HRS799yzXo49W/tV4Qksu8vOVJTil+n0NXhTY+OiUqfXjKs7PX0yM5575WmrsaF7dQekfJOEcDpWfKucYjMjGDe5ylXHBWEBae7Q5lY609QDixo7TzVChvKHkB0LlVbw1EgIK0yEXk/ny9mizmC243Ddhi6riMPIhLHKKCOmqZpQEBQjqexbEP705JFhmH453/+5w8fPvzwww9v37617rzFQ6DZb2cJN+bH3e12VjPSNI1WCN5Gh99YmoMp9ZSSsOwLZXf7QgeFado7RtZGx/XMegQA8KLaAKFzFMcYSiqvKjCLtbJ1lBrOl2fDjExbP0Ah1rSdQgwcx3FA1H7WRumtY6tXFURAhwje7+n+iKoSFYQceEKHQCCeMDAzS9s6zG2kFAGtBDfddNIkjhlWPVP1htFs4eG+e805p1bTqeknTdP0fW+gLeAoqsgw4DiCISaGYF0kLbpgiJLJ9FHhyLZVlDAIl4ny3lu4Maowa2YuzcePH1++fNn03d36ARHPz8+/P/112O5uPn+GGNrzc2t/dHt7e3568vLyzedPn16+vLTkoMVsxsxXXz651iVgDYm73aAszmPrG0Tc7XZqfSKtZxAHUEGdFq54TWsBfMxc7KuJtHBKqKHK3Vd2QoqB5qvVMqlpunJx73C6L4r3PhhOkBsBYLsbx3EsQcEQAkf13jd9Z5lQdnHnnACyiCp673e71DbYUJrzfkJNXMMi8BNhJAsAtNg3KW6mooYSo1YFCuIAFSMqIqoB+LnkQkSXOgELpiKCksB1cXExm80Mm6zr5k3feNcS4ThGRDQU9XqezQ1QuEDmMhpjPIgBl/N6P06PICqWcwCQwGtUxaoYJAHuYK7qk6ziHKw4VElVB3fZR3ffs7+xEpw2QhHhmLoFl2FTxjBC3NOtbeqs+auhCVrLnSGM2+3WIBQApuwB02hjGI6vg4ils7iWcIahmMGUBZn9FpNozBOb55lQM+b38YTXm6VsKCPR5KCxAj8VB+icR0TvnDXqKYvunAOXzN+6rrf0F6+lgqKiszaaEx8uHO9gOdRgaViwyPtKvzGvbEH9lAwL2jkzjncsLtcLRUT0bY+IrvEASkRskHqRG9+FMACIx3a7XQeO3rVA2DRNjDKMG4aE9GAp0E3v+6ZV1XEcEaBrW+ccM/vZTIYElX99ff327dsffvjhw4cP796922w2ltw6DINmY6B2qpu4BQBmtuwBS5Wt/fmqex7cmg1i1nKayg2Dok3TuJyGbFQdY/RZNE5QHfVQyv5Mfyo4VMl5qukLhJPx7siIoHaAFPKqJVbWWw+dNkNgEY1h0Mik4oi8Q1EUUYcoCA4dAGi6rNj6AQAqCrLzaJoLh5h7r05aZ61O1ju2fl591A5+7NDpmDaPJwcZacjivkUTF5GQ0CLiEIO1jr+5uQkZL172UXvK0k53tGQfBLNFkoqKCITPnj1DR5vNZrPbgqhvm4vm/ORs5SWG3fD58+eub168ePG73/x6fX/3yy+/nKwW3hMi9n13fn5+dnbmvR+2W3XMHFWFnK2ZZQ5CjCOooLBolJzRcMBqaz7+1Dua7U4TV9U07gng8hbuX62mH5yOxKYhN8oWBO/93d0DM7dtEJFhZxnRQkRUEhpD7OYLFmBJCS+swowsfCAJ9simNnMhwyzY8CqbGhEdoCdyUxGdOoeN886hAiOis13tijsHJLKrVDpD6jZB0vd9184a3xGRMAQJzLQ46QsEQZrtKmJyTMllQWr2ARXDPVwpUSRNuDeSemEjgkRWUGXRScOYKLP0lK1fGdTOrUjacuFFJ3Zfj6fsygMaK18z2Vw2S7GMSzWavVkXbtpEBY7jOHJ1ZQv7GVxa4/eQkKsBPOaCBtCqAiAP+0m79t912DDMRPNujw8bl7bnqjmtqyxge/A6Fl7PcDrBaaofW/3DN4+PAyqCSoSnLzAUp7fzXkTIquB8izh1aUijIvLRCwATuciKLDFEtZ+3pSgWMcHHEOKw3TEnbaNtW/Q+7HYPDw+3b9+aTvDly5c//vGPf/jDH96/f//w8GBy16ydg+ethajux7kLyU26y2MGBu77+bES7Q7MJaCqanapUayXyESkgFbvL8QMCBVsSkayyJtE2Tnni9Eg4sRPKo8j4xpt27osiR9dQstC0py/UCyYYQhq9fIcQNkjiHMEwKyA5FL2b0kytIySHH/1xOy09UgaYm7RpYwAlGv3LZBqINygqf5ep6oqrGayNrIrPrXvJStbLg2ICGnSCp1z1mE0cDRg7mEYwjBuh924G0IID7f3JswgaVguDSKrpaoAE0cga8UDxSVpiKaI6Gi92Yh1qUO9vbtDxMadn5+f7x7uP3z4cHtz1bvmu9evTldLVf2Hv//dct6vFsuTk9WLZ88Xy9l2u72/vwWMgKn9D4CmNoKROQZVBhbRKJGZg+6j5D/K8ev9mQVuomlmTmX5pvOZABat6QFUEhRpdUF7eM6Qe67K8wSAuZ93Xdf0Xdd13rcAIIpN03C8t6wF51zjARFFNQN1pDvk7beXQWZWTib+BMFapASmXPTEzEwAl6kgQDQgeNuVDp0zSwsNKMYBOOecN58hEpEzcGBN+2g2m/X9vG3b1WrlnGuarvgP7SdNi03ji9YLAAZjWfPZelG+IYALT5H9tBplSXGYLIDNNSEmgEGTC1oN71oVEzychTxRwaEleqcJhHwuoMV/e7DdCvsrJzXLK3yQcgjZ1MHSrtF2X9/3qqnTQN/3lk0DhEQ0hMAZWztjcwgRedcd0LOKJV3XjaeyzxZRoog+EnY5eKIyn3+LXK6vxcmUJCIyaS/MKBojeqKiorXVUQRwoYoigOFgh1ohwnHid5Vnvn/sCdpK59h7BypnlSCocmocZwkYEIlINCIgMNbXA0TfOESBkAoOFdFytFHQUi0BHWLuNK+6WCyYQxhDhKAsiGgJVr+8e399ff3+/fv3799/+vTp6urq7u5uu92a9VBwewrZmzUM+5IYAMyDW4it7JrjTE+bnbZtMWtCriom8khN07S+aZpmsVicnZ0ZdrQ/sLoOPKtlq0+Sn1NZUclr8NlyLgLY0g4pG3+VW7LY2BKCtaxJSmv5TgjZ3SpMCgjiQBnQIRlmDoIikKACkKKYVwzRAwARtd4DAyIOtGPHIgmHj8USX58k/+IGqAXGoejdN5RrvWGf30EdgjLlerfbhRC2223pVmtgoWMMliFW5rncrsyXvUOWUp5HkGoD8uvDej0MQ9/352dnMcarq6vPX75IDBf97Hy1fPPmzcePH66uv7571333+s3/8g9/9/zibLFYnFjR8HJmJlTTNJFjniVREFAGEbbWkBJBDYGSRSQJ4P2OOiVgUei7nkOj6mL0cBZ7kplYgeZIjkEQxD2zuKyhPuaoQETvPCKaHZwi777dbDZhFERk3Znv3znnu9Y3Lk1hmnBQhazP5bJjSDV8pnilL013TP9SKVoeX3lqE72Nc+ic81QEcEseERvLm2td26aiu/msMwFsy9F1XdfNSjtIkaS5OvKE5mycyjqTKwj2Kk9wbzxay4964Wqqg4qrGnBWkq/pCcFgy1BB1Iq5IedYoMrUVhEBQYEgockZoRhEmN2DMGEp11uskAo8JgbKmItktfdN9NoMmDprEpcyorvxGYspeO+HEErr6MDpt5T7re3xgcptU3ZiGiRAaVZxcFCVpvQ/cdTCvIzEHsHqgDk1qWxms9lsNpvP57PZrO27ru1MABuLdlUmWj2xtaTU/f46ZeahWpE0ADUc4BTizTQ5MfBaUKmqQXwgJoBocpNCkFYWE6oSM6sCEcYYLW3WcWQORCQQRJwIIxG6zvvEKk25896LRMuxurq6enh4sPrdn3766Zdffvnpp5++fv1q8elUiilTZ47CnyWXpdj4i04PAF3X1TNWJocq4QqTzqTee8hGsM8HEXW+WSwWZyenp6enz58/f/78uVUY+8IoC40W6VKGWHz9iAjgEIAIihVK+e5p7vPCo3MlmJKTn1MFDjOPo7l9phaniQJEM60IpuZWqsq59SkCAuRNiwoqig5ArFYHxGHTOmBoGseMDGZPoZiJ6Z3ShOBZnlSrd2BfypY430SIFZOqDyICgyCvPEVm9TLzw8NDCGE3DKFqeSvMJaMdEYFIpnh5LjmlZLBIFjwGcpfDjaqghNjNZ+Co6/um73RE61wrIp8/f+69e/Xi8mS5uPrydfOw3qzXF+en9/f3zrnlfB5jfHh4ICJUWS3n6y2LRomBRZVFDHI+i3kFAOu/liEGy8zQfq4ATOrIFCMpcd78gFPsA58SwARw2IBhTzHSdMlSkmR9R7lpmrOzs7ZtfdPd3d2p0Gaz8Xf39/f3McRhGNC7vu1CMr9tkLYbJ70HEQlJ0YooFBVQnhDAhFo5zE1iE2DXNYaERd45R85h0zS+obb1zrmm8V3XtXMzYnzrfNf66vFTLM85R1ZiP6FfESKSU+cml1ditejLzq2X4NtHTdJlyciQlmFy+ZQTZi6bsyw3AEQRgy0sr4b46BBZrfQeLUuCCFmVCsrrvgyoFbiDoR7oDXZilkoRwDzhQ5GhqZdBWiPOXrUIYMtisatZB+sykvKIOKm+2b1BpKmipqZwsPz8YixWdPs3HYXQ7dX5lIXDzNYtmxBMjrVtu1gsVqvVajFfLBazWd/2nZUh4ZHhNF285l1YXhLplhOUehTpPwZrDpLYna1RsalsWorX09QtD2gCmIjIo3NJneXUFCgiqkgRZiQiFIWJmJmYiRiBkNBwlZJw8d5RA0CC8ssvv4Qw7Ha7+/v7T58+/fzzz+/fvzcBXHQOS6pCS7PIs0Gpa1MSQEXoFn2lKD3HUwcZW/tgYg11xNicDdUgM9u2vbx4dnZ29vLyxeXl5eXl5cXFRd/3ALDXDameSs3qzAE/hewNKbcXqL5Q8QJzzDELc+mDraUgehwDM8eY5HG5S+sbG70xFpeaMIAjEgQ1HHxEl3CcJXVVyHPXgLf1HrvOrq8xdacDsOYfe7Rezmtd+2Cn2ZuPyOZKBtt3zNGPWekus0pE9/f3McYQY+1yQCIrJpGqdMeWINVS7/vldH/YWoVbXr9+bRBapuhZ8tfJcuH72TAMV1dXq9Xq+fMLZp7P+7Ztu8VstVr1fW+J/qraetf1DSImT5dEUVYVBIPnVTfZK5UIPIqa474CdzRXMNEYVATGljO5F48AZVRAnQQwIua2ORXQeWVFscYYI4M2TdP3nfce0DVN03eL7Xa7uL27urq6v1ubwRRjDCyiaAhZrMDCMUrgWNgoEWX8rFSlhBVUVirUQwDYjw2bzWfgc1ZYbLajAce3brGYee+7rp3NZt2iMwFMRJ2joqRmrw2qqve+7/vlYtX3PaIz9ylzQIpNi3UYuJQnIWIlgrGavWn8ZXUOdkT6fmpLAggp/9kEpoA6hIT/aoZwgWzA6rx6JYPuTwX5SgioQAQCqHk8BzxOKzO0nBzvTXs13koZx14KUC6A5hzdYqU1TYO5jN4558JY1GLrjW3kcXDrmhskdRPAe8R96NAk0WTyogHsTfJfPbTKBreTGHkcRxkHROz6bt71s649Ozt79uzZ2dnZata3bdt3rW8bQ8IqQz12IezPsCiootQfHZ9My6EAVYj0WEphpfkBAIAgx7LH0663nyCJCCUxlvI5RCTG0TlnOhQxI0YCQgHftI7Im80aNciOWYOEn378883d9adPnz5+/Pjx40eD0dDcDKYspZmaMcama2syK0ehLmPChxvhSDbvP2ZixWSuaaLkZz49tfSa2Wz2d7/57enp6eWz5+fn56enp/P5HNE6VVivAdG9f6lgBw0a2qPV4SIhiQPM1liatX0LuAxRVAFUmEt3VRPAxlws8hICFwFsT+IdkubcFOe891HUoThCRDLUc1POUBWAxLD6rZwJrQu0CGjf9+UuJFFAUr6SGhhRQtkT6wOhYJjYZsqgqL3WpFyOY+m7x86sUwsBiApqcSqMaQamuKmgVXKAKHIOQ6YNDBQ5+wlp2tugexUXtXaGiKvVyvKcVXU2mzVNM47jajbb3t/ZmyfLxcnJyfPnz589e3b57BwRPZEnUPIxjqo6DEMYd9bGoN5UkLCQDg5JvTSOpC/mZmQHfDNJ1UlvmD611GiVAwFsoFZcz/CBgAdIToo0JoNHyMkpiDifz51zy4UOw7A8OT07O7u/Wz88PKx322EM6T4Z1apwClVLxknxfC2eZ0lsKD24VYiqCZHc3xrASAhwjzd57xrvu67r+ubk5MRUhPl83s7bpmlMBTcBnEhFjPwcIo5jNIaCGYrEucY5bLvOebXYcKKELHSrCStkrNOD7B+PchxUcCJYyuusY4BpIvYWAgImlAEEQCTfHKZfmQUMKAiIKghEwKAEwAQOJgZSTH94DIqy3nrHB1UrXgQwVodxHkS0QkRkLgUaBvpv+us4bAFK248pyFPft0yRArStY5Wc0WK7EjRJtr9eVXVwzXo5MFNjjDGGYRgGiMG6d5+enJ4sF8/Ozy8vn5+envbeIaIjJOcQiLPZWkyp41XOJ7rXPrXmQSnSk3xWZV1clc1+oChUZHZg9wtYd3YzMyy5zKIs2kAWwJCxdFTZeDogARAqEikgiUBkZNVx5N1ut97shjj8y+//+cOnXww/crvdikjf97PZrOs6i/uGEMwaYWZr1jt5mPJND+bn4Lmgyn8uWt3xkhGRgs5ms67vT05Onj179vLFixcvXpyfn8/n81+9+W4+ny9mcytqgiyzp3aEUv2zHBxFVEJwZK/gSBEtCbnJyVAm01TVkgWslaFgLgcEjMyRJbDEyCLAbG5nYRUWjSpBWHJPUwcoDILo0CkCkYUzBCXxWiFTN7VQCwohoGG4pD5xSAzaNe3YjK3zglHBVhgJNRqHOtoUNWnuSZRciZdVHQBrSID1Dy0WlTznCaUv92OYLpWP9ENL/kFIjXKVEY28nSrHKAYJl914VnEJWNohaIL6MpHz5cuXF69e9n2/2+0sc3Icx69fv259s5r1F6dnZjkhYtv2Jp6H7TbEsWmavm2aphmGYdhthmEAiMmoQwOeFvOGa9LuSxj0kDfZuUVDTTemxMZTYA0kTVwK7FZzmCXudIgIoJpHJzMky7BVRRCOQOihUQOzwqQrgYD33gCjbGAWQBXGruu62fz09HS7GW5ubj5++XxzczNytJYWgI5URZCcOAHm4MxqRYcaFQGEWRVBrA2PIoBC4l/2aiPMWhtkV1VRJZum7bv5YrGYzbuLi+dN42azWd93Xddhgw7QAooIUmxf87oRUYxJNQkhOCez2WyxWPWzZrO7d14dNVic+SkVzGwLOy8kLAJsal/tC0JEyK3rMKH2QBI9OvlS7RK2bsW4BU1tiYywHTWKgkoCjECESRATOLSdDYxAAGx50A6tTZWIkYckuMUD7jYpWFaCLGiAstacBVCsYB6U1CuCU8fW1du71kYlGschAgBsnQLP0FNDne/QIwBZfDDG8aeffuKoCAEUDLDWkAZKniYialX14ByCOBAVJ8qiZKWyiMBAAKLWlFWTn3riG+XI79m3AQEEweUnjSogph/sQMSoZT7vz87OTk9XJycnq+XcI7FEVMDUDxJMaB685lvtvdLUmgzqE3zMzFBVR4/o3AfvVF9nbw1hTWdTYFUHyJCQ+yhBOkeDhQYgYAfgzJFF6JgYgVQx8g6ARLfjGB8227u7u9u7h92w+a//9b/e3t9st9u2bU9PT8sjfPjwgYhOT0/NI7gddg6p7btx3EkKOWfHODnM3ukDLo2IpvIePCZWuED1UjqF09PT+WLx/NmzN2/evHnz5tWrV1ZAeLpcee+tWadBSdtFPKsQksWYmi5VHO8pBeZrooTzjBEASDWSWvs/y9UB9A1nxyIgSIbBj2MATriVpUpMU74iiiigwwIzg4hNB4CRjZEgOO8N7iFGRCQQJAeAImIO9+126LrO+cbI2OIi88Wibfurq6t37355+8v7m2FAlrZtyXlUEoCuaRRxGAbnHHm/2WyS9ppjq6JCSCDaOsLECm3qSZXQGaAlgqBEHSV6T8wYCVoP9ugHIv4Yv9sjgSdhJgQgU5xZWC2/ykqdiYwVqqpxUkVWU32AEHL8TED/9Ocf73eb/9f/8Z9ef/fml19+IaKuaT+9fT9c35wsFgB0c317dnryq199Zy2j27btmsZZVDeGYdyOYTcMO+WIluJWBSYAQEIUiRxZFQAbU7tYosccNMoeadMPfNNOu1GUgByCEFg9cUpBrtSdWlu3TG90lDgHAlHCAUhpaAhAqgQCrCqgpGKOV0y5gZFjZDbXsNj10XvfdD0RwQW9fPnyt7/97S6MP/70p5v7u6urq4eHDTMjYOfIEXUNIVJgtTJidH6xWCwWi3E3MLOyldygAigDq0hUdNS41vkmJQwKCiiztG17cnKyWi1Wq9VqtVytVv2snc/n3pPlXnnvrURfVEUjALpmSnU2pdvlsivCnNsIcTsE33lAUUzJCqREmuYczFqFJE0BOOkykLMfwHLtwWwNE9YOJpgPRB+lKEqauLNZqJbCjtXSVxwck/1vopwU0FNjIhkhAUkjkOk0IKLMwAxiCQdAiBIZzaxD1IzKB6roGsz6n5UnGx9qHAkDIhB6JGUBh9A1/RjirF84akMYvYve+/kshDCaZ8GhI0+IBDADQFU5OTkzkMLr6+vNZsPMiM57Kn5XSMpV6mzoG0eeOtdSgFGYx+Csc4FDVkUCVkUUxNTaD5zB9KJH37a+aRpLU4ohEHlERUtBUAFQB3h/fesQGucsZfLF8+evX726OD35+9/9HYE4AkQgh4SkzIEjkFMEQErrDObzxRA4u6zSK1ruDO+tYDkxp/3emmJWISpUMDmS3FAuhNkYnviAsEqQIJBQ3L330UeWttW2gcaBI2gACBmEMAArsO9cimciDbvd7e3d9d3t1debv7x9+/79O+bw4vKZdUx3BCJye3e72WwElDml2rCyoCIoiJKzrA1iZlFWsTnEEAZTduuURkRczhfmHUlLD6iqNmuWc24P2XXdycnJYrX8/vvvV2enL59fXlxcrFYriwHXtdppukwjceTJAArAXLOJzUO9Hmjl+MnGNYcRAKScIAABjSpOMKoIS6pPUA3MIBIjq7nTAAVJUBlUAcTiijRlldowREAtpzSlpKbDTeDXE0QDgjbOOUQAaH3Ttl3f97PFvO/7GGTW9aenp+v1OuwGE7G5jRfHVDvn2rZFR6XBRU2IBoKKuRCLKr/TAdMBsK4SKqLjGOExfKVDAt2nVct1M5Zob1LKpCi6mKU4EZl2jFbCIZapDAD/z//tn758+fLP//r7Z8+eLfrZarXyzi+Xy8vFcri72+12y+X89evXl5cv5/N5Q4bMogSiyrk9kSCAJ5OhydyRSgsWw0Tai8U6FYUcQ7LLZP9X5VEQVREQQVCqsCpqh8807bUHAglYbC0AUj5acr00NcTpBLsqYQp6JT3SUqjCFPMza8k556L7f/yHf9ztNpvNbrvdDoGHMY5jGGMYIyAiCwzD+LDZjeMoiogYdmtCYTRCMpQ7ZRYEByxRAQBc0826vm177/3r794sFrPT0/PVatF1XdN4qzsyV1ZJlihmv2/2ABPqPJryZuEOKaUhb1LIq6gAmBYlaUfJaDPvMQCoslm6ycY1Jcl8YYDme1AGAFEu2X8Hrwe7YDpX1GTwTK9RrNlGXbEjAJAwTUVQlEQN4y7VSxIAiyTnnl3Y6g4t/ITmZbD9YQnPmVODIw8enTrnrVX7GEITY2zb1uKCSaEvM5mPruvt32p1Yp1ADZxOKw95KfRAxPVmBxGMKWsy7wCVQxyrJwUAYERBWMwX00qJ7HY7YygxDCLCHFS1dX6xWJycrhazvnMvW+8Ws24xm58sF6vV6vxktehnu2GTEAO9A08OiRCdczbLquYSl+qV8yiSoyW/s8eIvsGmvn088UNNmTqVNl9OMCVwRRESIRMvzGzhDUQWcIKsToWkn/U2/yEMhtM3m/Wr1cJt1Xk3jiNlJHxLep0vF5YnoaoMmur9HIXBPItI5FKVAwAi+BTWSXZwKVUatjvKwBpQNXUwyjFsnJOTk8vLy9evX5+fn58/u5jP56vVajablVx0S+h5dN4STHnWVSaKKUHmOtLOANZmG7O3lEVUkmJooZQoKU/O5ouDGIhoTYvHC49FvO3v8DIGOy/rh9k1P5t1AOCcMx1ktVp1s5lzLozcti2rPDzcXV9fqypYZhp6DmIzaAIYABxSyMK14ukT46vDmQeitzYTEc2CeeQxD361dxAioHN7WcSpvAdzrREAqFrD5qwmWUwlwZGvVqubm5v7m1uPNHvRhmF0DVxcXMwBb2IkoouLi+fPny+Xy/l8vug755yqYC3yoSg60zDKM+5bAGWxLBu2hKKrh9oXwGnhko53uF1VD12O07QRlevXcvpALJXViZpyX4uwwlSGKgCAqT6vIaKo0kuPGVFORHZj3O7G7XY3hDFKSiiKkdfb4eHh4WGzHoZh1rc5j0GFbUMqs9g1m6abzWYnJ2fn5+dnp+ez2ezs4rzr2vl80baNc04hhRuKBEqZ3woASKq+aQBSUk+tjEvOCq4zQcy216q5wqM8VHUyedOdkkADS+22d0TVMh9AQUvf+idclNUK7W2Kp+i8BBFr0uLUeF5Vk9YOqZMSaqWmSWnfm3os7d3Oxh8keZicc+gIreRBtQGIMXrvjZ8WV5ylxRbGUviVbxtzGIbnlwaZZI1Br6+vx3EcZAgxMIsSI6JWyLuU6/7rrNIyXYnBEoqI8955VFWJAAopSRhbVUXxzrnZvD8/P39+8ex0tThbnsz7drWYz+fzvm289x7BAd7f3aBwVHCO2FNybyIECXVkp5xMIznACdEp1q66N7ePHv9eCS0ixwLYptqoWnIeMiKqIuCoLtkDhICkGEnJMtGidSUyfahr/Xw+D3FQSVVn5v1aLBYnJyeKqSmLqmVtJmYQEc3nDACWdG0D884TkXMN5ZzzlLekUPK67dkNQ6PrurOzs9evX79+/frVq1f2enp6KqDmRcYMv/jteUvCWVP6wHQcMDi7RGRuXauolDVu4WhsYBjHIQYrquOqMt0S3FyFllC4SSKI/RPCaUscHGXQtRHQtD2zonPz+fzk5OTk5MS3raouF61VfVkhV4zRfHeuaaOkUqvJkZK5G+zlcRMmH9jePNSjLfOGue6tcY+T75OEW0Ga1TRa7lX/UFNbslSfhUSEyQL+y1/+0vf9yevlrOsRcbfe0ByeX57E2/tnz86994vl3DkHyvOuPb84HbZbUQU2DVSySICD+5bdUifI1JMQYjywgPOcFkM58dhkrh/ZUgcbvmbomiug4IhOjtWg6XZZObOSCEIPlDJagJxzrvENEXlCVd0OG99Q0zSEvp/DIkZLUEADZECHiFF0GMJ6u9ntdsN2V5i4CMQglsnftp2jpm37+Xy+Wp2enJwsl8uu6zbbbcZU4cihMAJmScJGhag42VxKFQSzj315RWxKVnEp81HSwEwZeQ2KJEc80ALTwllE0rz4CprczqqaUWoVQDRGLgEj1Sn6Xmp4p3g84HG288F37FUiFxxpy6NOV/OkmJLNAIAIzSuGgAb7AaJICFZ8h3tYzZlj7IXuilgtG8psF8oIuHXZSZkce9P0ANPmicj4ydnZ2Xa7/fz58263M8B2kwHGoNt+rketRyxUX5Nl2uCAu+Ghafuub1I+fNPMZl3f94v5vGncvOv7vu/6dmbJPK1fzRZd4yxLwyFI5HEc1NCThKNwjIGD9dRCRYgaFSdXs3k6AMT79iA33aBFLK7z6E78v+XQ7FR9lJOXd4y9IBqQmQWcUImJCIkphjAOIbIZUZ1vAKDv+1nfii7GcYwqZk0RERL1fX97e4s5UktS9CFB1MLbAaaExwMS0lJlLonDGyEtl8vnz5+fnp5aNuuvf/3r77777uLiYrlcJhD+GGo4hyJGt9vto/Pj0bvEZQEANAiDkHBqouecAwQk2yJKFQI+VNaqiAzDsAtjKqrTSdttMEWUXUEAr/ZGvR61AD7g/uVrWPUXs2rjrp8zsxKmUvS2ReeYuWmd891yWJ6eni6X85ubqzGE9Xo9XzZWTaGqKezkXNM0JpUJ0FVCuGZktQA+oKFygqhsvd6PjrpUpj5SFnq2Q0sIgPeSkCdeI1W1OyGCI2PZNzc3Z6uTWdcDgANcLBdd13GI3vsXF+fLxSLG0DXtfD63rOA0tzEKRw6RQxLDoAqlh3b2tzy6eQBAQb9tAafBZwFcaj4PZHD9pPVRtKJjoVvvmXqWLA3QcgZL+T8aaBGoKW/gyPqIqmrf95ThtJzzs/m8aVryDl3HzKLonPNtR0SW7KoMk3YshuktzNq2nXPOkYVEXCHdpkURFInpOQgQFRE5JYQqgGUYIhGSQwUQ0hw/BfRJLjtyJe3IHLGgQASkh0kxUDFTPerCa07hFFoQRQAV67KAoCCiahkbGSgmjHyQ1Wz5zOX8uHn7MQiloEE6AZqTWYFBUVRQmq5Rpx4JHBGRFStIyWtXaySsaebM9wwFqQ4LAdSIRfWElM1irGNalxyTs3eKyzHxASIiMoaeqglWK4MwNHhhO8Zx3I6DY0z4ZcapBVWsT48W4kXLY0C3PDvxbWNBwcaTmQ2LxeL5+Vnf98vFou9TQjuKgrInh5oKSVBFWVQisHStV7sds4EjoVr3Kq4FcIn1IqKdVwqceTp99ksnELdiKVd8bDqpzJZ9unrCBV3vZT2ys3Hf8yoiZAjjxKKoHIiAvGOmzWZj9a+zeeec60IIYei6run8bre1VQVQERaOouwbZ53ymjYVII3jGMJhK45CIUYVMe5jwKkOu52lVZtj+fXr17/5zW9evHhxdnZ2enpqJ13XAYCtUdM2RjwAUEpJvymAq3mxKYi5VtXtY5glYs3fUwQRGWLYjcMYwm63s/MYI6uUiXYVFGWRwXZxrQtO9tfy4KOyi0z0FoBc51w/W6iqEtZtGlV1GAarg37x4sX19fV6vf7w+cvt7a2SY4G2T2ZxjLFv2/l8vtvtamx9m0QHeFD5czzO/fc1Rv53uaANiu9AtNdCvTw+IrIVuphjWgDc1A/29evXYRg/3312gP3r18/OLzrf3F9f/frVq4vzk77tQhgW8/mzZ+dd32y3mxAHjSGGIcaRQwxxjByYGcSaXemBAMajSVAtLuUy2moiKmc16fRcZcbKa70JYX9nglmCJSJcfcQZP7ZQLyKqTJ1ekgpVupG0jWreDtn6MQFcVGCiHLNpuqgCRcwzO+fapu26TrnGeUARiUFExPvWfg5KE+ZoGNABOWybhMqbqW5sGlS1VkiAgEiKpIqK3uW+m8mGT950a6YCADlZGlBKksQBWR6T2fRmmXa1fBK1ekG2ExYxVjUkC2+3G1OUopr98o55wsprsbDKd8orqFoef8nKRQABFu2IQJ0j78D6dap1iTWFycZslpogUak5RoN8zQGywqYOdhDtQwlB9vPVVJfSRQ2e0FGJ8xVflHNutVqZsExZtdut4Qk/bDeaE7K0atJakJWg8tgB4snZc/KODOfAQdd18/l83rVd13nv28anoYqCA0KCGGKMYdiBCFkbNEfe+RhHtZuCYm5qqcbGU86dGbgWYnGqJfo7vRapXO8v+L8WDH6M8B4xeQ/+lATiTQxsxROM6BQNFJoxbLcb71vz2Zhgc84RQRhThfdut0OfOhRwiMXnKt5rLgsu+JG5zGwqKIrRaCDRhsE1W/uEs7OzV69evXr16uXLl69fv37z5s2zZ88Wi4UB1fnc5T15qrNDpX40fdq7MAWHC3VqVuWstN+ua98RhNH4TTaQktUbwm63GzmGmHFk8v4R3KP1ybWb2zkUkWn3jTxV1EFVpGXi1kSvHQniq58759DnCLkEA4oxL1PTuNPT1YsXL25ubm7u79abzW63c9576lVlGLZE0Pdt0zokU1cVUR2oAyVQRFPAp/kBeESWQB1E2Se4v3okEOT9uxjp2NVVpw7NLpcGgqrF5Is+upjN7+7uSGGxXPJu3K03/ekpAV6cn877vm3b5XI+63uySjsWHkOMYwxjjCPHEOOoMSoH5kkAa9WYtqFDBmcCuBaQtQs6xinFQ3N50X7t1p4MfmLDK1JhJZk7AAKAr/TCibgRLMBT+F2ST5TUPkn15d5m3tijiBV9ESkCILOyDAwqIizADFElxNHSFfdrpAHAmiOD6oAJAcMA1KRtvW8gyqgo6FyqqQezgWEK/SCldn+29M4BTE4OiwJnqTdNXTnH1OjwW8G5A5lkhi+qlggsAMgYJtt3GMMwpiSU3QiYWwHAJIAn4A3zxGQxDEmlOBTAlvR+LICVmTx6713bqPeUqxnJuZTNbc0mVdE5JTsFQgCFVKAAWNR6qYAkE01WdatFGMs+6k7xE1roTchFjEKOmYOCIKu5Jzxhk7JeTccaY+C8WEVfNeI46D5kjBQQfTsHAEBxWasxXOMQAiJo5JFH0YiaO/2FMY6Bw6iqTS50BmObqRTeeYd2desQCvv9bzKVSj2esjFSceNfC+Ef0NLf/L65VjMudA3znt/B3FglXSQBnIiigAdlIIPisFwtRRFWAOdx1jXz+Xy9ebAYRAjBiTTkgvcAQIAxlxtp7vtuD1gULNVk1NkCleWzUhqDy/jVd99fXFx89913r169evbs2enpqeEX5V05PbstzW4c6jkpVFEE2cHhy7CK3C2fYUZoqhdm5MjMGtlOhnEchiHGaH/W2iUAgKjkbsz1ClEFJnLwUR1NqWmlhL6tu5/9ae+3bUuNZ2bDW6bk5U/Z3UQ0m82Wy+VyudzstiyxdZ33PoQQx7BDHMfR/FeQ61br42B+6smt+dr/FcKtZXl53noVynlqlA0gIlGFs6tYVd/uhrAb/u53v3v14uXVp8+//PzWA876vnWeCGazbr7olWXzcKddtzpZ3N+OMdq/IByYg0gUiZwVoCSE8/WhUgvKyEWfFMD/rkNzVmR55MxDqVLV92bD7UPBVdOVhYLLyYM5z0gJCSqFUs3qTf5n7303mzddC0CRuWkbgBQD5hRABgCgVJFSU4jhj/q82cx7RE1DQD4KRh7DyGMYvbTWd4FcI2Jt3Qgr4CpBUBXEJCfqwywqeMTC25uZSvYckms6E02dHyUVBYAqKHCIyhxDGHdDGEbOyFAaGXFyTh68AiKItVxI7iPJpYiHrykLD2pyZ2BQIEk10+YpMUUIc1mzptR5q7I2eZuq8zFnv+sT++Ux8nhkI9d/Jqht51zVSoj2Oy/ZY7Zta1ju1HgT/2b7YtV6qKxjyWaXBHGk5TocR2Ym8+EjAICyRLsOEsQgIZpZ4hCFOXIMqo0nASJESsWKKJBqhIxHFDZS751jAcy85+CoCQn+7zi+IYBrNisljUYJAJgUIwoEIlKKEmm1WCiiVcMDAKVOBLLdbYhoGMf1ep3GzLKLwXvPIY4hlQ+xTrFezTlWUHEDg8pyTpxzi8Xi4uLi5cuXp6en/9//9/9ntVpdXFycnp4afIIt5TiOmHObCrOEystrh2b/ypMuaMsVFBGuNDhV7bpOQNEROjJQWvtBGJnZ0p2Tp8WiYNYxEbMqqroHuFMM38LVKQNh2xembZkLaO1STdMYlqY5n036Wo6ZJof5OAxbBlVVZhURcoE5LJcniKiATdMsl/MXL56PPC5PVl+uboZheLi/9W2zXM2ZeRi2XdfM2mYXRue89xRjcATOO0Ptr0dYE1ZNRoWmWfm41v4bR11UVw6FHOZK1Y8qqsxRVeMYmLlpmvly4ZxLKJJt+/njp5PF8ubq+ubz10XXv7x8IWNkgOVi3rW+8V4iozIRsoSHh7vdbiMcYhiZg0aOPHKIzIE8lQSc4q4UEQkRoK4iUVUFFP/I8AFgsvDsyFs/GW2FSIqAP+CVk6cEAWASUQcnxwfndFqnaMVqpnkyqHfeu9Y13mK0wIyIHIXQ+aYpPNS5xnufCxxVk0cUEm6BK8pyNHqzByEqDbophV9FVAVI0FPrza6l3EXNZTma7gHZ1cEsZq8DoZXgqwpzynU3UoP8S9UCnJ4Gi1lpFutZW+l2aGJTk7sldQNkCSFIiDyMcQzWqgtEDPIXFRqXLDl4gqcDQG4shqDgHpN5ABBFEy6YqkHQqCoCA5IAMKDhbBM7NUz1yDYP5B0BgiiHqBHQE3rnyCFlOBezPwwLOts6IsIqKuLJ1dSSYw1TI+pCn4VjQpbT5X3zt8G+y9QofAhDeb/JjWdg3/JOPBiADaikbApSRPRIDTkHoCwCsWs9tYaUxCAyPETfNK3VhSMQkUdARI6J36JD713CkUABiEVdrFlWPQnl0UAT0RVSfOIon2GZIqiEdH2jvRO0vu/THi87vWgqZT4nEgVB1AgAKMjknENl5jBGEZGm7bquU0FVEVnhB1RllbiY933fX9/eXN98Hcfxu1/9ijkMd5vtsLPEF4nh+v5us9tijmZ635ZmCQh0dnb2/PkL8zOXrObLZ8+LO7p4m1XVGm1NnDCLXsNZ0yzjC4EV9Kua6gDAF0Is0QvOzX01e4zLPlfVGGTkKCEGYY0chCOnZOtpEQ6WoRK09U6oqaQ+is5YN7Rv29Zc0KnHX1aarOlhLj5RVSXnym7hHMzu+95y0zfrXdd1NjvbYQcA3jmrDpQQ2bkiG9IcPS1NaxlQCEv/nS7o+gr1uSkZdmiVitzN+nEcIePtpU7RgK9fvIwhjJudivTkJbIyk7j5fO6dNo3znhx6kRjDsFuPoKwqAKLKCpxOclw86fLVSlkh8n5rouxZzElY9XPr/oauKaq88+gGhsoNgFUlNFRi4Pj65Sj6EBFhsjycUu5oRHs8SGtLttJkEVLNcaqhTFBFCMCik+ehDAFTdM2WIk4pMMksgeq1+GX3nqjQDzmPtGccHHyzDPXRSXhqWtJHmkBnzYmuLBqZx8AhiiXiRQYREHWpQlgBVREICEhRLenLGdaVohCQoqBiOQcBRam/Y584BQF2iqzpFQUFVCJbaTjngZnygUzgiseZwBA5AMApsAigt6YnNnsIHIJAcmHp3+aUqme1Ng9q8iif1i5cyCSqqifd6gDL2s5LFLA2+5xq03iBbKKgAfsigHZNw8yizrIXVZVZAHGxWIC5ZDWh5zpQAPAODdDGeWocFQEsArnX1COW/fEEWDzhSYI5nC5VsLTqQw5/cF5OTHU9UOAgY3dj5Qc1fi6VOc6IFEd2jiIFHACwcQ0hKIsiOIK+b9+8fHF9d4uIOyvn3+1MUuw2m2EYiGjez5xz9/f3Hz9+/PDhw3e/+t6u3zRN389L6q4jf3p6+uqVAVi9efnypQV6Wz/BRlr5kz2atVKoVxxySOjRqXMZKfYg8OoLO+MKtLn8aR+VgioRGaNEYYnMKiBqpn2OaQPo4RpoJYDrYRWCrlYXEZFcEr3mW7ZmW23bmtu9OAwBUj42GQa7emZGZAAwyC5DNgFyvnFmQNtD/Yf/8B/ef/zw888/bzab1Wp1dnGuqjfX16aLNeQaSsFpEFUWKIbNXzv+50SvlN0Le9tGYjA1zREROSpadtPAlphZEMwToaoEeLpczbveLVYSooxxWG+ai+cvL5/3XQMQG4dt4z25yMjDrlCSqlomTi5GlRBi0d0wP5eIkPMAqf04TCLTkKoeEcBYC7nqeY8FcJF89aYthPFU7IRz33XYpyKL1GKWvs45cI6sRYHLTBZVQBlUEbzhzaL5usn8ydkyAKuiM2BIw0cHEWsQW6ux+YnT0j3xWr5TO8/NgnQZMEOgEgnloQ4UBUyekSPLQw8P2A+5EaCKIAszp0rfEJPD2cRwjMriAF0BukJzCyIQZiGMioRqrwklPFs3IMrZbWAWOWASHgkj8+BVYxRBYGHHwkzRUUagRUdCROKdcyhJJ1OfAP0zATijmBCC5mA/VG75Y1Y4TVdehTpRNI4BABI0poJDco4AUlFpWrUy+QizWf/oLQx2eG8tAAR0sVjujUE0JbaKsGVBslh1IDNK5JYciCZ49gRrwZB6sSeRbM6SPKqC7TxtIgBw9Dgjq2OOB+9jfswc2YdjUX3A7Y9OjkX44/t6ikApe6GIouAsEkFutKc2WwvQbAbwnpzv37x545zjMcza7vrutu/7xWLxsFlvNpv729vr21sAMBPuxYsXL1++fP/hF3NpzGaz8/OL58+fX1xcLBaL3/z6t6vV6tmzS6sySlLZOQKsqh6kKPflGQ896vuCr56oA2Kwn/hCfFql8EnOhTYKniSxSOleXX5lfz66ipr1mlrHP3i/uMvtz8Y3Fus1q9eCviaPywaoX1kiEQFg7cm0KWuapu0b55z3Q4zx7u7u88dP9/fr0/Nn//RP/3R9fX19fb1db9q+WywWXdcN42jmtY54MNHHR1FcnvrC33JI7iNbpqWwDFPfzNtcaq4Q0cLVhvtj7cQxMLBs1usXzy9fPb/UMX758BECL+bz3/32tyJCKcgQozJzUGVywNF6/UZVVmVRNmZQwhBmAWNlFwJAUc/LgP8Wf7tO3qfDCTw2U/al2pQTcDh1Vf9mOPJO7x0VqJjuWycqgFWFuhZNEQxgGowF5DAJml9Q5XCQ1VNkiYupYyNUybwHBxbjICPyADlAqes1M3b9VEBiiUT50WUvNpxXauILsieAVQFDNGhYtWLfMcRx5BDjGFJBiwKQA5cp00poFBXVgdnAaEljgqmiFxUE7G/RyEqK1soivwKpA4TD2uDErKz5FqhKsYARlRgcOcMFE0V1HkkJNbKImL4IqmQxY0IRUUwqwt6E73kppvOaadafmo/HsmZqvaeuG66ptBbk9Wspi6rdOYjYzWbTdVKaI4vIOGxFnD2dobUws0SGyJAIMy0rWm8Sh6qCyQsjeTuwSqxKj5xRkdUBH1QGJ/Tpv8bByi77tqx9SgBbDLgQZDlK4tXB/JMAU8JTB41YynBSaSt2iNB4T84cD1+/3t7f3zdNs1qtbh/uref654+fLJvd4pWqent7O4SxEr3nL1++fPPm+zdv3jx//nyxWPzut3/Xdd1isZrNZhYPtm6VnlztFS4VsCYfa1GYaEkmuVALWlPIjg9P3puCJwCsank4yUwGUMQoUsc/WME8cIV29bFSolrWHt+1jDj74qdGzTM/M597ifuWbuQHLM9uMeyGpmkEIIRRxNpzioiopLxERBRlqwlDxB9//PG3qm/evHn14vlyOU8Q7DE0TWc1Z4jYsAWfSMQ/oVoc0mUem6qqHLd6eOrQlM+SZix5EcwghtQuSoRFWtXGwgxE3uCDvW9jVFWh0JBrmmbYbG++XvW+XS2W864/OzltyKGwaRHDMHCMIQzA0YJ2NafWhFc1ZWDBPs9K7xwtKOIEgljLUq32G+7rFlBB7R9PYzmxJR4qaMl6SM45TUU4EzIUAGShhoioqRXAVOjFxua13M4uZuZvZbQDJKPO+AApACgDolMhzWisgOXKxysugAQoAlOmOiApkt3KcE4Nf8xIE0oKBSkoAakagm3ql+KseYEk5ASxpCWpsyhkOiC97unHTgGZdYwxhBgjh5TzbBlYyoKiACjekpsIQJFSbzQ0bSSfqwKCgCIoq6ACq6CiCLOKADhQVXCqouAAzGUoALj/CmDZQ8kIVBCxHixKhJJqhxwRiqIDVFOeLFtHRIREnHNKWBC7VKRQoqq6jFJS62o1LdXSESxsIFJqNApplTxN1WmmVa2cSzBV/mi+DHQ+hZCo7i+rqTVBJviUBIcgDTkBVG/y0gOASASRMERl0YgRiYWY2fp0OQIREBQg6xmRy3rJ1wK4vBL5LHoBgFQhnz+u4GJlYFSzhzmZ/VDGPCWAmdkwL3X/qF369fyb/MII1tMJGWI0Pz3FuB1DANB52zjvhWHkKBpFI0vYDZu7m6vr6+vb+/sPHz7085lZKUowxBDiQAj9rP2n/+1/X61WL168+O67716//u7Zs2er1arrOhUoId7avDRnYWITFShkOYpspkrFPxaI9fs1l/NGauW69cfMPI4jV82KFUEgDbHmevWEHq3ktKI16Zt9Wep6S0PT3vUl/zABKTxhidpjxxidc5piokBEKhpCaJs+xjjGtapa04WTkxNmjqJXV1efPn06OTl58/3333//fYzx9vb2fv0QY4QqYZuIvKdxfEICV3p02dtPGjtPH1KtU80gDMDLjhijqoYQLCvNVsTUFhFRH1vnz8/OPr//8P4vP58tT/7x7/7+V99/v5zNP336dHb+G3KqyuM47rZrHgdCdY5EolV9qqqYESzRhOXBlkiPuW9JlJ1JT2RBMx/Om2YL+IADFlOjfK2cqCpX3UjqUdUxpOJBActD2ZPHCBXdZ8u3aAxUj6pEAUQVDHzbLGBJHbhUBYqz75urWr0+fmSS3ruU2mjMILYpUSs9I0iS2FAtzGLkJG5KalzpY6iqkVV1QpRMzwWpeV6MYRglWcBBYkQFsLZkRJq6MQmAeocKgmbeooIAooBMQaUDNc48FmKue2RE1GQs59hq5b1XVbTEqfzW5EN2zuK+QqTkIOf+aGQgFBRlVCfomJ1TQu998lIn6VLm+VDVq2nvmNRLYXH5WqHStLT5eZk5lSaXErKKxRWjZW/VcfIVqqrKhEuKmZrLeEQQVVtqVVWjwSLa4jIAqMSEYQcJ3Ngqyggfh5ZjrqWvbQETpfQNQi3bB79pAT96rqpFXYFqD0L2th7MLSISWgCDAMBZah5ipIiIDiCwWFZXG6P3XgQk8uuXr5xzHz9+3O12wzBst1vrPxhCsBbafd+f9d3p6el333334tXLZxeXs8X84uLi4uLi9PTc4KsQUQUQJxQBzIhPYRjz3hIRGcfR0m7ati3lTDV56BMC2CpjC5rmMAwmW6cYcCEsOzCn+WSUc3LOASGQMxV0uln2yZWlSqX46c4THZeJhlzXW1A1SnaVV197pKk6ahKfNrCl0iVPBTuHimT+gRDCaGXsgQ2h2x7QrOoQwk8//XR1dfXmzZvf/OY3f/7LT4hocNaSwV0lxqcyFGqKnKYeH5n6bx/MnAvnJ3YAAI1vy2PaisQYRGQ2zCw32wRw0zTet33TAsB8Pocxtt5bc3siUhbnnGjgGIWZjJUKi3BO1nv8+MZjlvcxRyIfPY43JOZy9bqsqL5dvT/rn0DFLotkLRTiKsBkQERSLZqBuQ0RIecGc5IcSVq7vF55PNZqIvELREwKUq7JBrBmi49GH465WE7LOpwazIwvf6pUys0hQZkWVm76weRDzp5mTby4dvHVApj3BPC0sALCzCHhDqSQE7MvmC6qJolZQZRdSwBic6iVw6N4OGpHnJZysvwPzZ8MoNWenVgNEKKzPJKYDEoD4cS2bcHaQzhKDmFAdISqkcVWgpnJO2S274hFjiVVoBkIl+I0/weSaW9Nqjr+KSbxpOtFlUiIGIScm3IgjpTLcvHySqm3tGiGxvNoWd6CAOZYdJggtBTANx5F1ZFkJFejK3NZJyUA8+BZCA7vm84tqK0KgIWKDKPuMf/NIyE2RARAVTmg/+Od/hQDrOfE7eM7gZmVIJryy9TWAgCQIgCwAIOiU5YwjoPxA1FumsXzi2fDMNzf33/33Xdt217f3j579mwcxzdv3rz5/rvFYoHetW27XC77+ezv/+5/cU0SzNYuxSTLYr40TmCcRLMqifs5sOVBSpyutjxNC3p0/pnZBnl1dfX169fr6+v7+/vdbrdXK1JvJKj4oElfVSXvEMmUwMIHa66NKRXjUNk8OOBIABdkKwh7gZkihuvCdi0aqKqjxrtWCZ1jAPK+BYBIEkMIIZirbdiND3f3t1fXX79+JQJm8d73izkihjC8/+Xtzd3ty5cvgXC325VJQMQhBO/bR7FtMTV60RRJse2Set/9rYcCCHNC9nF7nEKtdYT3iJiUiXE0FSyGIM6FpnFN61U7385n3cdfPnz/6vXF7343rDdxN9xcfX12cnL54hmIMo8hBBRuG+ddE4PGOBKoKKMCZP00vwqgKDBk2ZdHCgApJppdrgqpyjZ9yvtGcCGMiiXsKWE1jdVUJHUpeUUDB+kCWPn3igAml4pWEZEsamjmgWjMrlkAg9FGRYioDhIcqJZFKfFJ68QsiBbXLCFhTdVGpmqqGdMl7ze/AgAATQzOvLkISQPIPvDy4ABAoppLQk1hQlFBNCjHAuvIau5iYwqT8Cuvml8l+6JVVcByz+wdy8NiEFEVUicpyi1qfFZA1BBRDy08Y0DTTffzQgTBISmhZYEbRrGhQFvaZnkViIiORZiVJagZy+CA1JMjEHUOJAJ7cZGIQFVKuqAqkVNWVAcggYEIRTwpOfXoEJGESrPM6d/xBi3S114ZIHXhtjaIqsnti2gtBQsed6pQBkg9nJQ0tfEVjmpdiu0VSQnNsSymKmUmk6jVuovacG1DiiqIMEY0VzUVui4Vw5QriNCgTVCVQetEjYPNNR3AoKQqT+sk04EHWjhK9bxsbnsT5ynzXxHQNArdt8amC9J+sUmiJRUEiEAALAgEgEQYIyJGVu+986SqcRhR1HuPznWtNyjmy8vLYbt7+/7du3fvgGi32bx8/frs7CwKM/Pp6emLF68Wq9X3v/qVIiA68yY2TePbFpzj7WDzUnZQqrANoQhazGYx5TYSib/V0ipBzOTu4Pl12O7W283116tfPvzy7ue37z/8cvXl68Nm7QOL+dFZQQAVSVGBEsydoTMCoiIJoLKiKWmACKgMBjtgrKPwxHqua60BEYu9W/zPyfDNhy1M2cx2bl2RD65v31wsViNHDtH7dta6GON6vX7Y7MIwjOO42WwMNG673eoQZl3Pw1YbJ6CbzdpY4hCGh/X61asXz56dM/Pnq683d3fXV1fe++XJKvDIoMASVVAUveub1jXNuN0poTf0JBVJjFZzS7rDoxY8k2oCkLpRTSLONo0G4fK8lpU2Xy090rBZnyzmTdOA6LhZcxgDNeOaFl27vr3pFV6/evXi2cXpycnZyenpatl23seuAYggEoNyQB4djyBCIqJCiVQck6LobvdQ9m0JqJau3WmTGEsCUNXWO60FarbmPTaF/ydA8xwywBwty1RB5ckh4wppDmrUIrYYuweiF3Pb5ARnBharVFXr/w2qGpJLHJE8Wr9eoghIgAyColjpCRlbIrFhpwT23AgRWLMKYilIIqrGYpORSAAIOTu3mGBpigTStxzZuiNiTotGyDhACCnfzRzfpkQk+1cUNGNoIBDRyDKOQUTMW81BYoxhN2hJVgLQyCEEDaNHdCrAEOPIQUIcJDBz8B6RlMhhgrlI8FObYQOgHsml6j7UfcU/M61cm8UKhICECW/Ren1hiGMSw8IS2Vg4EnIchVkFEJjQIYioRo73MSoKOO9b1/fzrmt825N34Bv1ROgteqAoiA6c1T4gNW1DPqtoZPvoYN+RTuyoFHwVmje8Q9bUDhWsgI2SkiXWWCIjZiCJd86MwjofVsWmpeSDSerFDBqnvuCWNgXRfIiQaoxEhBNzF1X1zoH180JCBWuCzKpoyB7O4KCFRZFBAffLjSbdGtHsbhEtwlIBxRpH1Qrcnqw9dBtwik4ks8NANhTU2qKWcwQVU16La6dWCOBIJyhzF8mw5YRYmgbQO1JBYQRgZi9MoI7AOzQU/Lvrm/v7e+fcYtbPu3Y+73/7698Ejg49Odd1XTdPKEyz2aJpW2hc4lM2jKghTulRhADCktRKJkj2Z5G4RRip6mq1ijF+/fpVRC4uLpqmWa/Xfd+jAbk0HkTv1w9fPn2+vr355d37D58+/vlPP/7w45/+8uefrm9v+rY7OTv1Y0ZdL6VENTker8GBBXO0QvvmC0ABiis8tMR3DwSwsdc6J6C+jlYZiZAdQaIK4EkSlPw4xt1ud3f3sF6v7+/vx3EctjtD5TXffeA4IX3YKqgRk/7pzz8CQDvrnz179p//83/ebrdv37/7+PHjEAdMkCApDzlyHLcjAKAiW+sX678OCvsJmAd0PMnd+iS58aG81o+sORgPAAwqmEprWt+YtiuEHqlznoOlbivmfgSY+mUkpEQAQQUEsWZfkVkkirCKmGPWWKhzToEBinTEauST29qsXpTDgJNtsjL6+lnSe5VHumjWB3pJcrfQlIdMuaaoJOvVF6l5qKZedimtCVKTvQwggB4s0Iio6ChnwB0f0+DFLJCMDAqJM5e11iRYbfUVje1KWdziasP6pCjO+ycCedxYjSGdZ4+EZPxAUAByALlIgVkij+PIYwjjKCISWURQ1BzOGsPcN1G5vGMBYGHehAdUSADRLFYPIxqpIRPAtltdbmKRlkwBslUHbCnXkIqCAdi85Tk5hSNb3r7lNBSeYKQHAErKYMkIwjoCIjWCro1xVFKMQQECOmq8VaSQd8459OLQqYwi1m1dAcCDFyeKSNRMlqMqIjKUSEWZbICK76EZolrH3GFa8b3XjPw19X8DrVxK+zu75qi1XTgVhrKKRfQkRxtQsmwGJIPanOzFCmhFk3N4UoUL8aTnqiIRdh8QUIjZXP7bBPAexGw+uHq62qf9SGgZK2P6WNCAOYkSrxDgCCHpB41PbmEJUSgKEimwU8IGWJQIzKNgiwto5lzTNH3Tdr5rqEnRK/3bU2T3BnkwM6q62+2cc+fn52ZMi0jTNJY1PYxDeHh4eHj49OnTzz///Pnz53fv3v2P//E//vCHP4zjuFwuX798NY7j5mHtN5uNwYtb3LTcspQnJVWxsjzqkZXlyYSYFrK6TnIdF+lbdrKBWTdNU7sWC+JPfZd6Oor1Y/MRYwonhBCGcby9v7+9vX14ePjy5YvlkZcSLhGJIuBNi6yKLAEAoO97a/8ZYwxR5vP58+fPu6779PVTcUTYAOxS9XY9JvqnFvL4pHQ9OjySoEs+gBgjBYqAs8aDU7KCV/VK1DrfOT8Imz+/6DeJ4FgO9hVOsQO0hWNWwwCOHBxilaM0NUUuieiTKDXlIDxa4L/3ELUw5sc6UUKl2B2uuKOaeMpzFVz1g0slEY9a0AgyRSUgQMSUu2w3E1FzOGPNHTCxX/s3PY4o4OOAm2WzHJw8kUKQfnIsg4t4ri8L+zNfro/m8TJI58gSeRiGYbMdh2EYBoksISGaiYhGVolbUVJJJUYWdRUFVWGxQoGcqJP2xWazURUHaG6Ytm29c5ShMTHrWkkAqwLtNVkpO2VCv9luzYYoC6qW24ZoGgwzR+EojESO0Buo0I5DCLswBiVri9G2bdO1TdP4rvXeKwARGSKegIoIOlLEpplgTfZL/gAg5bofkOLhCgLA0/WyB0GEcvLU9/ONDoloEt/7C23JSpYIgCCUBXC6vk4HHpFNPX5TelKQQqc9Gznq0XE84DI/pV/QwUGP5cnCYzviYKoLJT9F6gAkIt615goOIUzoreCAHFEC8YZK0Bh4WZdBnEqS77cSIx87XO73XGSf3Xq5XFpqQtd1mGFAYoxuQSZ9Pn369OHDh7dv3759+/bLly//+q//alm0q9VquVwaoyYiv16vJfepLjLmgA8WmVe/XxamvHm8eOWbWBkxtQAuvYsPFgaOFr4cRVQDgAIwT8XKZvje3d2t1+uHhweLm5Y0cURUFD26oB0xxvlqOVO9ubl5/8vHpmkuLy8vLi76Rb9er29vb+/u7gzP09ay3m9Uwc49dRzI7PL9pzYqZHuxaDYAgAoaHIg6xMZ5QvVIjfNN0zidrRaL1WK56Gclow0AivKRNEQxt4FpLRKFAcBQ+DmEMY5dk9wCB6N1FSRn/bAS+eDZteZJmatAtdmemqiaosqfllRSVK5aDYIjIsGUVCUJTBjRMncREchpMmWn22WU9r2RazXaA+1KEUA4uR/3N0jNRGpu8nQlOcA+MezrEIds6IC35gGkulhlATNqxxDGcdjuNpsNjyGEYB1eTA8DjjwMHsE7Z6VsrfUhR3KNR1NLRVU4mvdQJOyGIBFFnXMhhNls1nedFUrqPjIaSjLQyyBrsBTn3DAM1sXPKhcsj7Rt2xS2MOhNSYXWkRlAVYhCiCohxs1msx12EZzv2pkBGAmrqkHZOO+NyJkZolGOU0Tvq9Xc64UKMHkxDhS4aWnKujwtgA/X6Fj41Qfu/9BOH/1+vVnyYETMfJjq8qu6FSP9KnhXkgMAwBB7UuSycjNyGGFvs2r5Sb25yms9DfUWqAXwsYit57N+ugMtB3KNvmQEQ3OQlLikqSNEMSviCfkRKjalQCW+WyKeCW/yCeb/jaPw9iIB7U+j3hjjdrsVEUuwWq/X11+vPn369Kc//enHH3800Xtzc7PZbExa27C99xcXF5eXl6enp3632+m+86HIy4NxHEjlMrM1n6oxZbKSkq5WC+C6xCiJxrKN4XDVv33YqgzDcHd39/Xr169fv97e3u52u8AcmKOImZgJzTf5ZDUlL+BUCffHH3/49a9//fLlq6ZtI/Pd3d27dz9//vzxH/7Xf5zNZrailremqoaPIaU0Ph9P7TrYj4XXJ9/4CQBYwE/z1kKAMUYPOJKDVr1D71OTqKbrzk5Ozs7OrA9865vGOdTkHmSDLeYSqGM0LCHZW3SPVcbQYw7e+qNH/9RsPu9t5lp4PPG8pSzhgPw0qykHE6hZQNa+GTPEADKIT0oltQdwkjzEAFbAqwyEIioKx49ZT0t5XxHyFfZI9Elu+zQBl+sfnBR2XDPfcn58TVVFBWfOYyTxPlhkhxwQB1UJ0fqOWAy4QWTUiBS9b8ix9w05AuyalgAii4Q4juM4DONuCDwC6RBHCRER+76PMQqzcTQAKJ5XqERLGVg9Lbe3t+v1+ubmxkDzSyu35ckKkk/YgSPJQmK9XoMjH5rtdsugYwjDMOw4ILUNRzQOqy0iGidJ0K1+Klw04kBMadu1iP32uuwRcz5/WlGW+rePnh/coUxP/lolMqtx2SAZAXItms1p6q6tYvECycDaAgoIBTRJVXPDaVDVpwSwVN8/dkEfcABENJdOPYH1Fjh+H/ZJ/WDzSgXTZO+zcNns9e4oBkyKWTAzM+VaDrtTXnQUkdoRuGc9fpvfPn2Ui9gs3d3dzWYzZr69vR2GQVW/fv36/v37Tx8+fv78+c9//vPPP//89evX7XZrKsXJyck4jkR0cXHx29/+drlcXl1d/fDDD74s2KMzrjXrhHKaFsmeTbN5XmfHlHHHMdRytyRb1cG8aQ9jqlI/WML6qNdGAUIYtkO4vb399OnTx48fr66udrudVcqySlmc1PUP5FGUDAG4vLwkopvbW2uOdHJycn9///nz524+m8/ny+Xy+fPn9mZKH/feXNOQPQTfXr962N/m2vWR6B6AVakmU2aJrIpAvnGucX4160+Wq/OT08V8Puv6vm2bpiFClSgWz4tsUGccYmSDcYkxRqskdWh55qoQDgZgRwjhGyN8hPXsi5DyqXsMzL18vcxk2ZlQXaS+YNkPRa8UESRljalk17KUERBQEFJ+qXmWwdoNqSJwbsd2LIDrN9Mi5u1bPtJKDT3m7H91iQ9Y0vGn5ZHlyHWPiADozW3sHBG1TdP5pmva2I/brh/HcbfebLfb3Wa7pa2BZZAqKIcQxu3O+DGxAsDpYklEjcH8ilSIeGEIQxxGG4P3vq8q1IsCBwCqk2fi+Nmtj72h9UKuprXKxpRa7x05Z5hWrHJ9d+sab17lEKOAEhE1voSuZrPZYrFYLBbdfNa2rTOFgFIjLOcsS4fMO20H7YuHenGr0e5plo/S6sEqfXuJH13T2jbVuuM41kgMFcEn1daQsJKwBACwdK9KcO6HArl+XycBXIGj7WNf1yRXz1IVeXzE9wlHDoNHJXQtUOuL1PNTDAb7tBjE5r81qSH5UGYmVgRwqcUkEZVSi/pqIkJ1OcbffEiVAm3kbelEtkGGYXj37p0Jnbdv3/7pT396//bdw8ODVRnZmE1b9d7/+te/fvPmTdM0d3d3P/3005cvX+7v7309R/Vr7crYn/FD55hNa53MXMcLt7Cp863KQRWqc2G+B2t2vD3sm5x7P4jqer1e77ZXN18/f/30+eun9XoNAE3TRA6sLMCQYkCgVoah5iyz+ksGAAECgMvLZwL68LC5uroaQuz7vpvPTr27urp6eHjYbrdnZ2eLxcLgqdfrdYkKF8fItxfyWKt46ifFgkyTYDBAiMCioA2hQwLRGCMIeHJGClZ1Puv7vu26pmkNLgshhKq8m3PxJ0cHCCwGf888oZByNCg723Vq/2rGejDsp577gEjKClIFfYqVGV0E3vF16quVMZQ/JRdtI6K1GgAQJaulNAFMgpBgdFWSACY0l6miS3m6uQ6pUDUYDgfsCWbRCQKzHvBBbsQ31rd+3qdO6tk7mEDY51/ee0J1RIjYOI+IGpljHIaBxzDuht1uN2x32+123O3CsLu/vuY4jtvdJm4ME0CGwMzDeuO9711j8OMJQktlt9ttx13YpUzR2WxWyF41QV2mIQFC7ud9fFiypyVklBVUhG0YrHLPlHT0ThHEOqiDgqMQYwjBt81yuVyeniyWZ+2sX65Wq9VqtpjPZrOm75qmAXNumXPCmig7UkREX0xOrC0N2Fu+ig4fWalvrOOBAP6rfCALyFrQVsIYJtAou7JkUEIAQE0wnJgdYwg5Lwump3uUfvYF3p6h/e0HPBaWj/LqYx4OR36y+p3jj+yYmNUUeR1VlaOa7lUEcIwRVAgbMm172o9JUjIzGvH46K3VNKXSgycftjqx81QxnyXOOI7r9doqa34BuLm5+emnn96/f391dfXx48fPnz+v1+txHIdhEJG2bVer1cnJyWw2+/Wvf316ehpj/OMf//j73/9+vV4/e/bs+++/nxC6D2Two5YxABSrv7xpRrBJWUuqKigwIhKGsTZ/a+fzFMp9zL12vJZQ0VNS6FTvN+vtdmuxJavi7ft+tpjf3NwAABhGnWUGi4JGxcddSf/6b//9+++/Pzs7B4APnz5vNpv5fL5YLGy6TXl/9erVxcXFbDY7PT199+5d8ergY9bP4dI+IYC/Tf3OBAOhF7Bwh8/9XowAI1IcQ3SBAB2gJ+eRrD93Q45QY4XbUOuDXduZgoiiwjyO4ziMYxwzlmxa2VpPenRd2taXZ5eciHigMMGR8DiQvvVH9XHwUf3l+rdaoGVRgBSxgoNQsOCkYVhZBrxC7lKNVtp5eDvNcX3cVxHgSBDCPj/6G6VvueBjMviRZzxgo5Wyop4cg6TEk6b13jsFEQm7gZk5RIOcHMdx3O3iOLz76acw7nbrjeUzi8jIoqrr7dYT7ZA8kopwiCGEwGPbN9txN253duvZbDaOo+nQqqq5CtEEMCIWBLSDGaiLESD3NhBQILSe9uQcNd41HokEgbyjxiNRVCF17aw/OT97fnl59uxF13Wz+Xw+nzdd6gVOlJKTOOUmQxHAVj97QIf6mLaXZ/vYJv6fOZ4igAMZaVsTcry8CGAAsPdrjwIqlzl3KVPbxPB05X0yPiTRFNEELU/HfPCTPXdxLXrtEHl85z41A8cSF58wlOtfafaHExFRgMT8lHLDx2zKqwEZAU9pvNkmSDHjkt4kIk4NxfzpZTs6irvbTrbbreUYvXv37ubm5sOHD7/88ssvv/xydXVlHYIt8ciIvO97K1A+Ozs7Pz//+eeff/jhh/v7+8vLy9/+9re3t7f//b//d18Hh6fU2Zz9VRf82RPGmC39BNaYhK5FvGezmf1Zhh7HkBUZKoEfu1eN40UZfKShpiz8gdgovDsV+z48bLbbT1+/bIedSV8lQQ+7sN1ebxBREMCBAb+I1XgRgRgTNnyBaaK7rluv10GYmbu+3W63m91mjOOsm/V9b+O/vb29v7+3Yfz93/+9Bd5vbm4eHh4snaTruiEMpalUWUIRWS6XNQeftFRCADj2i1dso3oHkIjG3aAudl0XOd5stxBl3nQauUg+51zftAQ4DLvpRpXCBILb9UYkBo7jOO5CHEOIkYXZGlybtoSI1iAIcmC+JhJEBJDiQIMJ5ccsxemA6jnqx6//PN7V8JjchX3OeCzwTKdIfyEAJDhjS79SmFIZOOVUJQo8uJctX3H81jRZKxyUj/IIB9qDJRyVVI7yBalSBw444KNHHccqsR4AbMg3mLGbFCRXXXddJyLapMZHqoqqqPL84vzh/vbLp89fPn6yxg4OMNC422x3ux2HSJrCBONuGMKO7yI4jEOCpLW2nl3XpQbjdQ5Byt9+JJpgLkTbsyJiwltEBHQbR2t8pqASIxD2bdvO+n4+W5yszs7PF6ul71qfAWvRd03TdIZk1HfOOWubsd3tOHewTivyGKmUqc6qAx6sQomfwP7+c7lzbU0ARnHw146a+HXfAi6fpvpdrB34SZmA5O0TEME8v8M4ICJqTnCxeLce9OHG6hZS1sIwaRJdwdSfWHK6k+5XstQMxHKSoZq4gzSlsiOg8iFrlR1W9oWd1O5iVd1ut5qtmnKpMgzOLfvMyUpVpkjCZXLON50lDDZN4yuRgdEhIstewHSihzwHzjl0HkiD6BjGWddtmDfb7fX19fv37y2varfb/fjjjx8+fLi/v5/NZn3fe6K7zWa9XrPq5eXl8+fPLWPxxYsXJycnwzD8l//yX0yErVYrVV2v1865yQLWff54MLmQmS8iFgxezDLVNsZsNjP5al2cKgfCFCSuGU3tZ6hXovxZD6BMoikX2+324eHh/v5+u9td3d6MMZUbGbESERuBI0CWsprSGR6P2JjKrJXa5b237C3Ta2quZ3/+9NNP9uwXFxer1cok8fX19en5qT2FDalpmpOTk+Vy+fBQAVxUauDfomaXwhcEaJxHJ8ZtISfgOKK2acz8JUAyzHqWEALHQdnQ9q24g61chJlLJxbzaNlQCD3mCp6yIeGJLDwALXA89VG+meXC9JTHseRy/frn5aOi3ZfV0Upmwz4HBdKgnCbVNi2hJQAAkqiZxAB/zV1Rrl/xnT1Lt36//jJWasRfvcWjN33UQ3Z8qfJOjURm02xVwqOwiSCT08aMAXA2m3WtPzk5efPmzfru/ub6+vr6enP/8HBzF4Zht9uFYdTIMYSt926g7bARVPNd9X1vEDoTW69Fy76VU09OWbiy6y1WBYTYete1s9nMzNmu72fLRTfrl6cn88VidXrSL+a+aSyGRY0PQZ1zJrNT7D9PiCkBe/OfyUWzXJ0m8LHJx8yvjye8lr5QEUAtVI6W8vDcpgAOBfC30rhqRdbgRVGr0PteyhgAIjhydXlwvosBYpRZKg+IFSIV5gyjb41/X6c5YO/HO+L4Cvad0k/XnMll8i25tYgPzcnP/qgKwx4vxkigZJnw9h2MAOB9U4M7QRbSrmqSUT/RnoIVo4E47Xa7xWJ1c3Pz7t27Dx8+vH///ocffvjxxx+/fPny8uXL1Wpl4cjb21tVffbs2cuXL4cQmHm5XP7mN79ZLpcmtsdx/PLlC+ZmShaBTc/1KPXAvvyDSgAT+aKY1LW8y+WytGqCquNbclUdpXFr7nxXk/JEFoeKiVq86uHh4e7uzhK+1+v1MI67MBqWrKpa7Md0wgRrliVuib8LVvamIgCYU5ocMjCZPeTRq4OAIsIZSefgWK/X5ou25pGFbj59+mTdnCxJBABE5P7+vjxmmWGb2wRBgADVuEjTeULtKYui4IkMr7TzDThS15irfLlcWt/GRHCiLBzHwGFkMQE8qrBKlBglQV7H4l1Ja4TYtda2DFKPHgWHk7ZISAnqIO0oLRr6Mf0c7FX7M+w3V6jn5HiS7dkzB5mOmsnWm19FokYghRQUcppQpJCcy7aNIlIBm1TQqZ1qnmh7v1jR5RM8YkwHLOnfe+iR5D6YtPK14zfLuZZLQQLnQlEVUUSXWxGoKLOgsiA0bTtfLM7Pz+NlWN/d397cbO4fPrz/ZbfZbh4eNg/r7XY7bLYhxijBQ6sqiuS9NxqjCocPpBoYmPTaW8qyZBYYK5DsANC2rW+a+WLW9N18Pu/ns3426xdzw+xt57O27+bzue9acg59yi/p+8ZYj8lvADBop0Qw1VIUsqnnp7zvHgP0LmzqcIb3j4OfPLqyIodiNb0m6n1cAB9f01ysZq+bAAYT4SIOEVUw1zczgFOoR49I1V0q90xaqYkjFVFXtKvy4OXx83Pt6b5lxuq8HzvqqTueKIvfaRbAMcOSc4WxjIjmPyairk12kYio5hCvd41Xp56qOxsIneE8141cbUV8M7W1LbOtqpYb6DLSZPnO73//+x9//PHf/u3fPn78eHd3d319/eXLF0uBRsTtdrvdbs0tFEK4urr61W9+8/r16+fPnzPz1dWVNf65urqywRe5WebfH3PDAynoXA1Ojk3TFn91EcAmckqQHCqG27Zt5c1PSofkuGlN08ZJSafvcE4aEpHb29vNZnNzc2P1DLvdbhzHyOzaJi0tIaJl4FhTF6xFb/Y2y4G3d0qMyz5wey4AUImqSniYXGMDds5ZG6VxHBeLRd/3r1+/fv369R9++IM1u2Bma7VheCC2YDXtptej/fs40AMAKpBCGEYR6Xwz72eNJ4/0/Nmzl88vL87OT5erxWzet23jHBGF0rHS8p+ZNUaWoGOMPIYQDKrf9rbBDqtg27aleqGOTUDFVcs8AJjJsbfB0hdqOXqkC5ePCq3Xt9jT1TjlItQ/0ewis6sVb7+iBA0mgJWQEnQyACKBQ0QFRkRwiIZYRAXS+5Cr1kykDEA1cUvMLuhacOK+EpnOnzCDyzzovq3wyLofyYPq1TqnYoHgN5tUESzEC4goyqn/QlCJpMosQwwOEAGo8auTk/l8zsy7zfbBQkgPD2vnlNC1DjYgwuCl67rlcmn6tG1JfVQA78dQy0auHwERm6bp+76b9fOzE9e38/l8Np/3i/l8Pp8vF03f+aZxjUfvFCCqoHWvBl3MZkSUujsficbETMzmRhAonp09KQhZaykWc71w9VDLMhUl9YAwvnEc0PzBtByc5Avu0YMVGEl5IhERSVXMIgLgCkA52uQDi1Re9ElVFald0DL5JHhK+6pHVRN2fUhF/8VfDXXZwv7z1t+p3y+esPL4KWAxTEp22d2ae8HlLydhj+wQvIC6vBqIeJASi/vOV+stVOe12JNaSNQM3zq2+H/+n/8/g9TYbDZ209VqtVgs3r9/f3FxYW2Xrq+v1+v18+fPf/e7352en5+eno7j+OOPP75//952ivd+tVoVk5UyqoFzztf8ro4zFSI7cL22bVdnUTXZO3RAuJQbCZS2TbX0hSzXDyaoLLPNxZCPGKN53s32tbYEeXVZVQq+mGYiI/KW1prs4Mf2S/4VSuaTAkyIRB4AXCOqjmAKM9QnhuwzjqPZ5avVyjDB/9N/+k9WEPb169ebmxtjNPP5/Bsc9qmDMjOrreHNZuORqO1ns9li3vdN+/z5c8MMWfQzE8AOUUU0RAcIOY+5sEKJMXIEFshdviGxxdLw0k0MCsj+1dI3b869HV6WHvfl5cFDudzwqw7wIKI5TgAOxfwYRthzwKTlsBiS1BkZqgLMyIqi6NByHu1SiIpcrm7/W33SQSuq47U+2BE1hzJbsPxWK61/emdq+vLIoUcWcDHDD5iIVNHo+hURNYs91Qlnz3ohIKLBSaMQqRMWRyRxHENQFkR0gNj6pvGXr18Nu93yZHV3s+hvbvu+7+ezcbeVz5E5ksJsNrOCeIsEJQvvOAaMySdp5lThpPP53H4lIrYjFotFP5/5ee/6djabzRcLE8D9fOa7dr5YoCP0DhCVUDNxGgNNBb5mAcORnDDFCC086/QxQXgw/zBJgsdjurUArn/4jfJD/KZe9Y0j3yW7kEETPqWKZgGsKSfG0vYICzozR5y0oj0BnOcmS3LrD1YJwv152LM3Ktf03ryV69c5OgekW89bObquq+e2lL2ttw9FPmEq8m6ICOGRqC1oSsJCcQe+MRPYPsMQTU4yFtO7ENNDiioBNvPF7uHh6vrrhw8f3r179/PPP7979+76+vrTpy82sNY34ziGcQSApmm+f/Pddrtd3z+0vnn98tVisbi8vHx2+XyM8V/+5V/ev3+f4pjMbdteXl6WRDDNto1xPE/7xwGfpaP8LO/bOoe5COOyTlRBgkHu21rPtWk0JXhQbmc/j2M0wWap3lYCZNZ9CGG32xnnTfY04i7sDDoVERlS8qqkBNhvSd9pSNmJWgsGu4VrFHmqW62oUK2q2ka+3W4tPb3ruhevXqxWq3/6p3/abDYfPnz49OnTZrPZbDanp6cHZP3t/fmoHYwAHpPe07btop/N+v50uTpdnSyXy941fdt2TesILQ1vbzWNZKsGsdMX0nqh5hKv8mlZVpdjMLUA0GQ7PuJ/ri3g+uPiUKH9fC63357sYGPXV8ZK+8bKySkiAhwhWt4YEYkykbXkAQFLckFwhBYZTQBMkxla3wj2ReC0Xrr35jFnORTDjyzj4TWfooSDBz94tcNaGEmuoBJQ8xKKqACmVuZE0PjOOVXPMSA4h2A9K0FUIquIa5sus8LUB4aZCBaLhQg7Cx53naoW3HiT9xO/tjrgPCtYQcZiNnlt81oV73w+7xYzv5i5tpnNZv1ivlgsZot503fO+34+S5jmBrpvKdMAYRfskazS1wRtTedHM7xnyD4qg+s/E8Tm07GwWpxjdrk/urj1yaN/1jIvnZTOkqoG6GYCeKJwFWVB1dS5sIZVJVIFZq5Yx6RMPGkBV56kg0eDIwpX1dJc4YDna6WS1hbno3ukjEezC7r0IygFPwe7z1pU5H2a2iGgOEctWGPK/Q5FFuqilP9IANZdGktigNFwKb+8ff/++vr63bt3f/nLX96+ffv+/ftPnz7d3d09f/7CLrvdbq2/gPk1T09PzfD9u7/7u//4H//jbDb78uXLX/7yl2EYbm9vi9S3WKRzzvBnVLUAYCR5SkdHYXAmpetQNlYx4FpIH6xWTb4uA+vXS65ZABcSL6bM5m7DzLvdrsaVNHlcJIfdkZk17fy8xiZKH9sPJmUzae5hQdvBGhF9ERgKxRychldOENE0rKKFMLON9pePv7x58+a3v/3t5eXlcrlcLBa//PLL3d0d7aeYFRIEBGsz99RhnmfIltHz589VdblYWkaMpV5bEJoSvgQhAooqTw1ia92wLA0igQgQZa0WVBXi1IWJsq8GAUmrlm6JkaEQ5e4ocHDxYmtCxfhUFY6iL7ivIx9wulohqK9fCKYQlYgIiCS/IwIAZnQOAGDDjkYCUERr3pbEhj6WQkK5kztWAWCAvW7vx8zlgHF849BK+u6/uTdjxzK4nCMiGGvORhtKKsxNvZIKkKcmzwRpIoWkiIiKioV5JDKwmKRs+66fz1gljG6+XaiwR7IMgzLniYHuW8Cqyhl8DXOr5jJa55wB5xaoVEQ8OTmhxndd181mFsyyZ9/tdkqIRGiNb8AaTwmJJ+tLKGI+G6gUMgdOrMswYob1r5ajDhCYIDnad4WeDzSeA7IsRym7Ol5f2Bdp+fopCQsm+mEoSTNQ5wxLARFK5M0pZmQJ7cariBJu95TVPY1i+sssZ6hUpZrAjkmxhOrLOO28YLDXb2pJgNoLPe197eB2ZiHovgA+4FSY/HaAKdhdAgHJVEB2je8NiKNk/pZXZjYubQLYIEut2bDdy/yXZtT9t//2366vr81ksnCniPR9PwyDYcgY+Ib91ryeL168ePny5cnJyWazefv27fX19XbY3d7eXjx//vr1a0OgtGnZbrfDMBSTAwCKMVnE6qH5a18tuBkllA2TZEpzWn54oPhA5Yur6dI2Z5FkhaxFRCM/bO5jjMN2d79+uL+7u72/W98/DGG05E7rm4uq0ZpwqcxPVoEjxMiZCgVBQJ/y/Jk3F45Cc2oN6qpHSPAMDYEk/LMknsu+YbaEeLNHjXoeNg9v37799OnTxcXFy5cvnz17Zt2h/+3f/k0rW+dRgQSw53NGeKQ86fLyMvLY+b7rm1Jd3XhPCkVTdrkOnTkws6bGR5myCUEInKqq9VBFEMPZAgX0ziOQNXcjTwQEDkCYFVDAeQABC6KaErOvzOBjcqJ+CKliRYUkTJ0q5FFL1im+u8/L8uY8BM/TpEekbQro7PqiEdUBRRBPBCpAtrBKCiACaI3tIDeWBOuWas3mqBjJXNCDAVJfc8h1WpPDGyCh/KvLDphSCoVQdZVO7ytYnfrfcOxPr2MJNmZVFkUQFgEUVkvdYQBN9VakRKh960CIOYpBOqgIEhIBCZG38BI1vm3b+XIRx2F9dwsqHl3bNs551WnbmvmbIKCxSiQUUWZATI3riIDQNb7pWiEws6BpGmo9Nr4/WapH8g22nh0GFYhBEJADIqJ3rknAPqwiDMt5W7YM5um2L9rqK6EkJHAg0KCVymjc3wBY9me1kJBhy8NjAhj2pU6mwEcUxOPLTtfPCFZVj2RRhBijIoAyi4Aqi9h30JH9xRxqAeySsWEDmpQ52gOESeoXpLgpE3mRSOhTCxL8/9P2Z9uyJMdhKGhmHpHzzj2cfYaaTlWhUKgCJ1CELrkk3cV1pXVX9+pH/eF96A/o+6AnvbR6tdgayJZIgkARQAE1nvnsMceIcLN+MHcLi4jcpwog23GQFTsy0sPd3NwmtyHqcdtwwE1jZ8mQdwSowKe9icQcAUGIHEKJqc5uohu6a5pGsx8hpIiJ9DkaTVq8ZYixjlGiNM1Vm73LcfIA2JpLPZ9iicgEkU0JTjIoRpZCuTtRJYQjZVOQfH30uHe1Wilz1Vjep0+fajIN5bUA8PTp86ZpzANxu91eXV29evXqX//rf/3o0aP5fK6/ury83Gw2m912eXLy8uVLRDw5OVEfaU3ihFkAVcuzBvJst9uWrUJWWH0WzeALBabtBABpbTIb1iPeEgABWo+tTPigpEBFVvgEVClTL0RAZJF906iUkWr3VvvderPZ72JV1xwxYBlG0sS2ILkAo1BAwrDZbRn0rI/ypgQCiI0I9k3QEQAgSJcBtE4NjdTQcBAiUvLFIIRav0AERM2VHDlyxIBFSCUZGm4aTmFt5+f31WH75ctXV1fXy+Xy5ORkNpv92Z/9VM/2Ly8vLy8vt9vtdDpdLo8vry9FvZ5EKOXwCYhYV1XQLPmQvByRRSSuN7cnJ8uCiqraLSbj2WwCwOv17XwyphCIMMYmak061X2bXWx21X5f1xVzI6xl6QVQIotmahARINEll4ZFS94BpLwGIIgyKgp1gUyOpahFCbhXHEhDXwBAcwJy8g9KZ6UA0MTaxDVwFM0kmx4D1uo6kuM27H735yhagFY02EiSIzmjuQnVzAiMIWIQEQqhzBQF1WM667oqnFEO2zAJMiEYizv0hXQMDq1/aWskSBuEMIogioAa+SEyx0YKImEMiBIlIAojZXNOi5mHbO+SLTH6J2HQ9wsDs0BkEWSRGDmRyPbYhBG4aRrSirBqTK5rriPHZhQKbprYNLGqm6aOIBFEBE9OzprdlhspiAhCZIYICBiQkiOACGBKqE1CEoVEUlElXTIQRNzUeyjDaDIPqu9OJpPJBEcFHU2kAAwllKUUBevxFmKuohYAMbJu61AU2OQ62ahlYjNYQgiCQIiiO5oZdWk4uzJZPoNM2EUkdnwgMn0Gx6ETlmT8tHNZg+mB8nwAuXjJkG2z7huJDJIZcBSEyHonRmGlPCDMINVmhygoBMhaJlLLb5ShAICgRzlarZkCk7CaXrPtSkkZAFRxL8LJF5qTvgGCRSg1LDijmZ0SmlSpVpkkZqkDlPoxIoQQKFBJAQKVSEJYAIj6zIEA6JErgG0LSCdWepdVAVaRQwXmQGWE7HSmrxUAMO90PVmMwCSMBNI0FQBELfwmUoEAAHE4Ozvb7veALCSxitMyRKHV9e3qZn18fExEL1++/Oqrryx31RdffLHZbBBxNC5fvlpfX1+r2nl9fTmZTEajoq73IeD9+/d+8pM/Pj8/X6/XFxev/+Ef/v7169dN08xms7Ozk3fmbz99/nI6n8UYV5u1csA6Nk+fPxORCU4CF7FmLZ2kud4Kwz/IXq8+X1VbBDTRAmZmS1iKErpEqkMmkiXqkJkaEc0jQ00Bmm1qW+1Xqxv90yokijuDVIZqn9J1UZY7fU7bxkC5+pw1UR5jw5O0B8Gqdnoda9iwe7akOjEAxBi1NiIR/cmf/In6Rb/11lunp6eXl5fX19evX78uRoUxFeXE2DAijstSRDBKFNY6n3r6+/LlS+ZmPp3NZjMNv9bDuTSSlIohOZDHGLmpm0bLUjBzW21Ui+TotheIBJSij7JlFrVmLmrKZERhQSCNgcDsgYMdVeJNhvQMIm9o8RylhXz32o4bPW8G7/nsLArsPGjIqqbbGJE1FQukyZrmrsJim0wDAPK5efo2daMDJgRUNQsgJ97JCAiYPNbSnYaZiPRTR6xDVKrGAAipbLpyVLrb8mwz9d8GV6Q5H+2lOG8RgWTSzGxGWCRVteNMaJXUMbPmTud07qiDhIDEVAg2GVykfSZPHxVT1FYqIhAxzR5Uw0oLhiKg0fYIRcCyoHEJk5JGRSSFIUeICAGyRUE4IiY3KkOVPICWXxphMQOPRyFGiBEE228xUyuL+7wLY31vwzu939r1wQ79zSgqrHifahaWJuVtboNwBFgkarRCOjqInM4XACreQ1Z3lAEHJCStIIRERBwLbA9WmDnrGqhaN4CKg5E5JZPGbFXKGe1aG77NoAgjg38IIVBoPTnS0ms68WzwAXGY034iojp7ai667PgZGEmZtNuJLWfxdMO1VJkRkpaS6AYRYAgistlt69gUxWi326HQs2fPXr16paG9V1dXV1dX6vOsGf6JaLvd6uwQ8b333tM6eLPZ7MMPP3znnXfKstzv919//bXat09OTnS9NpvN7XqFmMDv6ZvCVv0eiEgDd7VmQWF4bBxXGbDyf1/YjplFYlVVYuguSWlWMKA7SxMRpfSIIKR1O0Dxq+HYaK7aGDVxl6qMynQ3m1XVtFW7bQJ0hzup0jq7tos3s8y7mjhJGbo732+/4YvsYr1e69m5Rgbb8cZnn30mIrPZTDOTLZdLPd7+/Lef68Oj0SiRxX3dNA2xiEhIx8Taf0SU1XorIuvx6ujo6Hi+ODs51VUTNfxFRERIvLdWlxp/DGwOkOnERfpRznr0mbw6MLOS73euOQQmZPOTAc0KDPf6lEMNXEYqceqvLRN0aR8jF8V3VMUQUTXHhPGW9Ngi9sirxwHsWtR7q98bEmQBwm/I4cR9D5rqDxwG2qcfm72CnY9CR5LLZ3id5zmygLI1ZNHzXz2zjTnQpbdx9HCKsydUC5zuZwurDDHMkitHjmqsxiTfpyJx43EY9e2fThVT40un9g4ixnjAFHkQpADACA1b0clW0+09NkTvIR4O8S392b1vn24inc7TGrmzXj29zbJQDh1kZonKj5lj1F3cRND0tIhNVQOY6Q+JiAApwGg00qPxEAL3GXCmpdIO2BMHbBnwgdwgeqGqBTofEb3owRYcYsOh5n2AiOgguDzyW+or76t0kDjrHPf7PRAWAFoxcL/fExWbzebe6fk333zz3/7bf/vss89Wq5XKYZqo3Eh3VVV6M8Y4KsdnZ2cPHjx46623jo6Oqqp69uzZxcXF9fW1DV6Pn6uqqpp6tliS8wo06Kk31mw209yRugvqui5GoxSY3DM+69Gvz2yVg4kazU2h6EMuCZ90VRxVQAiTuUx70HNvq8utZ+CWJ7au64ZrS9Dh9eaDCyma2FPSakne+b8H76WcNi/hqHM0gwFFBkcTsSup7XZ7q0uBiArSGOOLFy+aptGahufn58fHx8vl8vz8/OTsZL1e64pW2x0RzcrxZDJpdnsiKrNFDlSlk6SxrFarqqpO1P95OltMZ2UgCBIIEBGayJzqbHsipZPLSxlFJJ8DKBoRkR58tmY6RNRI6N70v7P5LTGE210P97iIgV0OtWEPb2iICJLcMaRlG2J9HxxS71ruINbQRVF2aSa97mXnOOAwx+9SEUHuqHcw4MFGItO4c7IFDz2ATlk6u0BhjkwmQmh5SmFgrix9RxcUoSy4aVijG1xeccwwZGbKQozOIR0/Zo9oFm44ZZtB9esclWFUlhrpiylRmnQ9cjMDHuJPewTQo8LtQufGCJKLMfQeMwvKXQjZg3MvOqB9lA+oxQZDWzuPGwDJXSOvl3PCEoctSCJSliUzFiE0TSOBJdseVDZXJ2xXwlXjZVOsPKAjYn5IzjXHMLU3Wj8RjxLe46d10rxbXcE74rTsSXOvgbxH/Aa3+z56oueu1FsyZEbm3W6HgYAKhTkRxRi32+1/+rv/9PLly1evXpVleXx8XNf19fX11dXVycmJbU9Lx7Hdbh8+fPjo0aO33nqLiJ4/f67OVlVVHR0dWTweZu5ZjErM+qBJwDpg4612MZlMYozFbDYziPiwEK8TkyuHTkQApTAKxEynAyIxt3UaCANiso8EpBhjzTXHptrX2+1OI531UxmwWpubpmGJQBLV2ulkK3ESZXejdNbSX3wnRR4iRI8EUA72B7e1oIuORkPtvtYlVf0Sst+/iJyenqoyutlsvvrqq9FodHp6enp6+s5775yent6/f3+9Xq+ub66urtZXN1dXV4/O7zMz1GkVi6IYF2UISESjcVHt9rqoIlLV++12My4LYKEQiYhYWBoARgIiImn3oeQEZGDWZsQQdOk7CN3uSfl94Nnjai0BEsYuy/GbfKjJeS7lf+hJRvctAjDc8wjdmCARTRkFGWcPzO4Ag/SfXQdDA2+PywrEofrrp9yDObmMRdA1Thi4PG8Af8brzLDm1OZ/hcLCHCCVtkSX2yHGBjWc3k1WRLAIGAKqyRK1oAWDtKxYWawuLQAIJu9xNV4m2OY4GTOteadOcNTTdlOC4iBHCkCryGJXRgHHA7A9LDhgS4DMgHv4I2ITayN/9M+hN376bbZ59pbVPzbE1d66uP4dUiEBUIwxBESAsiwlctM0egZsnXfe7owoNhc/Bm1+p4vjoNrMQgaHGnabvZoOMdpezwcB6HeBON+OXieWqGeoAcsdTWvM6GNlWVZVs91uP/vsM9X0JpMJ55Sox8fH6mY1Ho9ns9loNNLr3W53enbv+vr617/5/PXr15vNRkRCWSwm42TK5ag8MYep436/T77rmngrWzcN4fXOYrHQgRWaI8KgYNMLg4WxByBH94qL9oO87RFRqIPuzKzKrpUtUnckvamafgJZ4qgdCdcI8V348M+iAduLPBVAF+c33FcGNI9DlDNqqb++wZOZ1fQvIprJ69WrV9vt9nZ9u1gs1GX6rQcPb29vr19drFYrqZv9fr+rttvtVpjLspyNJ6NRcX5+frSchxMajUaPzu8dHx+rDUN3capdllHQY6fNQrIjOgBgwuMDExRpPbQHRPD7tuEPO/RxYDhqN88gGgGdrDPsMF2kyBP/Uuq5BihdMgoFzj75nVNI+jL1T6egG5eMLkdub2ZwB5m2VkCbEUwcJ/aagecNHDtxnPaW2M1wpNeYeCenaKfkVRuZGZoGAAo9+csAZISiKGIRkKO6F6QBCIOwFcVLPFsAAJiVAiAAigbpEgWVp0flaDQqRilbqgAwSJE1Bl04G6qea8kBw2//DNj/CV1LgySP+APiGne98cWEb2z3iGfAQwUgDfWO6k9202MXIopLtJ77bz35EftiWaPlQRU0kZum4TpVooWsAaseRIBIolXAtQfGdqPRQMeFrmYpXVnTwbCDroaTfgvbfejux4Mb1gOws7myptGiq3uRcRnjvsYdeh2qElyWJWefbU3YouFDy+VytVqpBlhV1Xg8vn///r179168eBFCODk50ZzKyoY3m81f/83/9/b2VjMeqvVYRYGrqytEVM6KiClzVFNzBPXbV911MpnM53NNg6g/V5cd/SERFZar2RamZ2c3aKJT//Uin+619RJiw8JNg5r3igAgxlodvrWin37u9/vVaqUqvKVQQUQERMJ0dEEERAIQOUbuJOt3CN4i+l3L/D0bZlmhawdra8QOKeaQB/sfYtdKc3V1pes6nU5PTk60VEPTNJrd+smTJ6PR6HhxdO/evQ8++GCxWPzsf/7teDyehHI0Gu13u7quV6sVAIcQQoFH84UeKui5wnw+Vx8MpfjMHGMdY900VVXvtCRXj6UZ5YG+8qQkuKUd2ab4u7UhxPL+7BRdkBwR7imd51gG2x6P6R0a6WNaZRAAzJm5OygC0ftKuBL3hTuU7N7K6t/ichF4sPRA5EDdAUiPnA2BFrIG4NkDOumwda9TBlw3PYBLNkH3IAYAyoARJSVZi4161YoIxoiohafVMpnnRQSBIBCwehQn+Q7yP+kzYN0+mCqcFIECIWExHhWjdKSl2fiGc7foWAAQaQAApE1kmMHSyYpqrWeWzEwF6rrG0KFmQ8z0+NaaRA4tVg8VweLLB0JAu8F6HCIHXbjD1zbwhrkVmzSia7fbmROW5m7X9dKUTKgqkGPAAECUXMw0giANxkkqkpGqP3f3p/fiPnhh8/XQsFmbAHqXYOs79G8Poc1cBk6c8j375e5NwVped4kxMkhRFGqAHI1GWh2HmTXbRNM0FxcXIYT5fK6qsHLc/X5/dXX1i1/8Yj6fHx0daYJJPSoFl1CoBzckKAqazaaap0ErEmrlCcp5NUJAJc7MXJCrbYLZCj1kwOCYSnoToqRyVyTZdGAMDBHV1W2zWVkss3JfdbbSMGQlE5YcRBCwKAL00+jcsYgAgJp230bYu/idmsGBBydhMMAwe5Hd1wuf0UlVfy3OrEWpdrsdIqoYpcu52qxCCKPRCADW63WM8eb1ZQjh0cOHRERR6rq+vbm5uLi4fPV6vV4/f/4ckJuqZubTo8XJ8ng0Gi2OZlw3yl5EWCQyx8gpp5gmkfGHkZhMtWlekkJrdPcWknkSJLr/e8DyzvYG2tT7dMM70LwJ0T4ZIRz21euQe0MqkcSDD3Jf/2RnMN0xW1ceXdvenHVhOLs3/zn8irtNBqZm+zzohCUSg5qKlHWyxohHiax1OxkpdCMJjA4wptKZAMAgpH7O2rNjwCIp4wGJurMmNassy1CWYVQWRYFFUNML94HZjj/F13YGn3REuMME7VlgtmRC00SUPjWT4UlBu+sPsCVwG7wLT+FBXLsN5uBSakxjcmhOrbNSbnUjANd1HWOtVW25icycYg1ERCQkQ47NXXMd56PA4PTIQsO6UG0P1jwuoXPCspruvfF39KXBTMUJjgkmd1DiHoTtugdMY7S213oPDLvVz+12W45Ho1DGGOvYqGIdQvjggw9Wq9Vms2maRqnubrcTkbOzs5OTkwcPHuhR8dXVldZR+KM/+qO6rjViWNNBqwthURQaTaT8WDnrfDyqm2Y2my2XS9WhVU1SodMAbvbgqqoK6FIxT930JKBHMes6GqSIgtpMmJkwaEYdr8ows5pblQF3T3yZGRBDso2lPBipegliOixERArFKBQm0Ru3TsyeUUufrlarOjbT6bQYjzS+DJIu15F5a4dAfs1MsZCuMJGFDOghgScK/sI3zKk6IecmBQAr1wwAZVkWWCibFJEAGGPcNNw0zebmtiiKEmg2mz24f/+jjz66ubz69ttvnzz55uuvvn09ffXTn/703r17i8XC7NsSG4wNC4uwQFQhqztmyAtHMSYLOSJC8qTtlD0gIlWpqQ1IaHdFmjWmjELpW7ejeHBmlp8BQzboMpXeSmmzjDnilGMYONFgq7eRhvgQoZCG7wcklMSwgSgAFSEEDAQUYnNYyzER3nN60XjTQ/OyFIaYpVgLxPTUv0fvsCvLQzaNenVNH67r2tRfLyByHl4C+8CQYIBFRABpYlTPGxIASacVNgsWFhRCAUIqixKAq/1oPGaRzXpd1zUgQCCu66iis2ZbxsRYmBmwaGIEwhACEjEIIJT59MvU37Q1SgIiycFKFkeVFxbR5RXQpnmRPApp0+T+/YcRIgSUVE3ETp1xkFvbwNXUnbNPWxp26YM8eHtvtIWLgyIHCW+1fnZ2JVNiBrnsKUCb81yAAFhT3qoIkUqCswAABUJE84JGRBRAUtrV8UqxI7B2Io4r+qpEkDm0Fh3y+8u2PN1x6G57wUuEAFCMSr8u9vM7BESp70hsMmTABn+dBwAUibJFyxmw2WyOjo4KjpvNhhlubm5ePn/VNM2zZ8+apjk5Oanrejwef/rpp6ogffvtt7/97W9vb2/n8/np6enZ2VndxM1+1wiPYhNB9WnYVvtSmBFG08loOvHRQ7PFTKE0GpXj8Wg8HoeCBHhf7WzjiwhLpIBFGfoasF3oPgcng5hkZCRAmaL+WUMTY6zrVLzI+Jnmz/Ss14rVY/eUS9c3xhryOvWsDdqD+o/pMPb7/WQ0VS/z7XarR8gTwqBVqQGyjbGzo+5a3eG3xg4O7vl/libO2VuJbAmkc7y+vi4Ei6J4+vTpV199dXZ88od/+Ifz+fTDDz+8d3by8ccf3z87RZEQwnQ6jdVeUxECMLhw2NFopOEm2nlyduMmxoaIgmBRFOo2qQE8TcNvnibeYVaSfCBx+CddScU+DSk9A/Yb0j/pv/IkwC7SEZrb16B6leZixKAG8O+/jr1Nkfo99KQROOjSFPuTfTLezIBt98GA0pmCO9xrQ3T1UB2OwX3qngBIcbq5kgEIIJBABCncbmFNZaMBDYiAKWmJ8jZQPzZJJ7VpHVEYRBNwB0ryBwSiIpVgY4RAhIFMn+6N883LAa5eEN5ht2i7UurhZCBPvj2XsmakyZ70Pznwiu59QwPvhe6RmVNosz9R7p8u5/eSiIxGI2aKykQpxhiVAXMTEVOYkSYczaYWgXz2GfO4mNORghe4tXkBF5xPshVHgQH182C5Czh3NQO7/tBjctoRcACqQ2gPt7CBGgFEZDKZhLKoGhYRTSxR1/H29nYymex2u8lk8vLlyxjjj370o/fffx8Rv/3225cvX2632+VyGUJ49uzZ8+fPj5bLL77+2vJhaLoFE4gRUW9OJhNVc0MIeiCvRk2L4BWR9XrtaZ06CTVNU3h09NisGhvmiHWbZy73K02T0ljXlUaagp7p1nXd1G1K9/UunfVaqk9mTq6wkI7HRXUphOSQgJALuqoeg4A4Xxwdn52KyGq1ur6+btbNfD4/Pz9f3az1pUVRUBF0eCGEJNkdWsueyHbX5ucUZJV+Qs4L5p+RE4dcG1LlCRFRBvy3X351fHz88OwcEXWBV6vV3/zN3zx4cD6bzc7Pz5fLZVEUTVVxTqgAGflERCQqJ1bFwkf7GRAwS8QZARSzlVYRUYpnMx+Wu1JtH6REvdayCmgBLocYMLhF8Xd6f97NgJ1t1BgwE2L/jPCQs3R/Uogd+4do3OShxff0PbGoXA7IY4tX9/1pjo3KTsRNWkoCbl0bZDwJCwMbXw96/s8koOTihQfXK0FNpM2gSQiBMBBFEgBT+sxYm6wRIuISYlCulxCKVMtXKBVUoFwqLWKbN1Sy+RccOYLs8zwcJA5svEOCziwUWg4xpHJDoHlFsMd44BA2sktd6Z+39fKdi0iqV3WIARumpU8USOVciUMIIXBIDFhEKt4jptPtBEUWyWKWdhXBbRbnMOs1Eu/dTe6ceKjp+gs/WU9GrLX7/RC19Atx1x0PE3/R4/cyaMysNCwB14m8IvLVl19dXl4+fvz4L//yL9Ud+Fe/+pVm1dAAFj00JKK6rr/++uuqqkJReAagiqUaeEJRlKPRaDwuyhIQWUSrp4fcINtWNejIRqI9FF7MsQmg89wzdLSzYckGQI3fraqqzjFEQwYcY6zi3uxmBzHVwKqvJCrUlcaGYW08HqsTh4isVqsY47ba76r9fr9HxMViUY5HTZMMBSLZxQZgqAEPl9mm7F8dY9QS12TlH76foPf9m0FYsV5EgEEV1rquX758+fTp06PF4uHDh+OihBwIrzLdYjLWFPlNUxGwcERuRKJyX4Go7ti2rEVRhEJJTxJ1IB3DKWJEACjLouUhzsKUCS4gtl6ycIgkDUHUhTYOf9XbVDDgfL37veVzeNWXqCQlpxKkA74CNp5hO7jQStsOMmBt3tw33Efi9Hjquu96SsQ5j7w2Myb559tTgC64eiAdAApa8gxtJmr9M4IEgYgAtpoavaVBDYG0KIK6ZSETgh5mRtCDG0NgBYRKw2UoyrIoy6IoWKsHZk7A+ntmaTd6B5E8TIYL1yPNHk/st4wgMYIkq/5QCT4EohZufu3sW+ny72E4E3ZN0L1P6Kxb/7ozGATEVFwIREII2fTUmk8oRxQZ/KxDNTWLJYHJhndVKmwKPrynByU/niET7S3EQTCCuewN7vt9Otiznc8eYg/fbk2pGDIjwG63C2VRR1HFdDKZANByuXz8+HHIBRKur6+//fZbTXSlSo6W3WPmxWIxnU6rug6jUROjcWU9RhmNRho3rDW+1CKr23Y0Lu0x5ei6ecuytCNOHaryx+LglPwC2Kron2pI1oCizXqrPFiNzzHGukq81phuCvVnaEsgSBsY0Eq4AgAowFgWuXY6AGDklHj69cXV8xevNGbr3vmDk9N7L1++fP785bgotURo4iEx1hz1NCkJ74PW28Z+gf3+TEQz16UwwB1Gw9+3qWUY1ZoxGiEi7+uqqsqyvLi4oChlWV5dXr548eIPPvn0T//0T8syENF8OlMPArN11PstZF0CHL5qQq5MDjDvvQyHPFkiyv6lhIjJbJcC/A1wCWaYNM3OrhDpZEQSp6i55QaiYHzIc9MeWZRDOq7dv2s3uqaWUzrEXin5HCR69Tsz4IPv5nxGCM6MbF31foQ5/MMfD2szjusZsDdZD3k2HKJZdzJgEcRcDUKf1B96liPcWVlEDBRCgMACCJoZnghBhCRm8Q1DwDR3MaudMuAwKikEwM4qi0kkaKcPHcjLQAMeAPLwerXUHyHGvWCHjul1UXTcX2wVhs5HHp5wyPTaG1OPIfWlB8wr4ZYF7sA3SBaFbmoahbbLW5AixCJr0Ln6VGrPZFVfO1bfdmxlaPmux8aD8DQMh0MmB49y7fS7FiB73iwlPQY8rO7sQXp4V3Zhoww4Rs2wDbYli2I0mUxG5+O33377iy+++J//83+KyGg0Oj4+fvjwYYzx2bNnNzc3epR7dXU1Ho8fPXr05PlzfbXm/T0+Pj4+Pp7NZhrIq4FG0+lUGXDTNE2s1cei56t0e3trDDjPiBGlsFXpIYqtB2RWpB1ZOO/NzY1nwMwQY2xq9sK+ckHOGOPhNZSwREQAg3PS89C35Fn6qVA7v3fv4uWrelylWpK7HWT3BKUpklnNEJlgsM3A7S69GWPUvAJmGBwu/z+xFUXREtkmxhir9Xa73e7XmxDCZKSV0afL5XI+n19dXU2n46OjoxDCbrdrtvvxKMjJySgshBmkUTEBJAJym9sZQP28qmrXRI26jhqvSUHTvpCdBKvvuiWh7ADPHIbfqAHDgJp0dyb0fquNurUZYLAzhxvSOm9/ku8krDa6gCgAWtZIJOVvSkwaDrtNH3zFGxgwHlLNh814cy8NhWnGzS45W+XTevbct0clYUCbhih9cFL6nQ4lWsq62Ie17QUiwkBYBBSImpwtBMQUkWXEVAcJlLINFKMSi1CUZShLQISAZpSDBiFQDtO1oR7QYv2w79q8d0GbEWpm803wDNjvd8oeJ2o8PIhy+qTZNtCFenvg24WnbN85TlvB7jOih0cAKUUoZyVDui0lA2GxI6f83qTyegbMzN4EHbJ0ol2ZQe4uZPYjHO7f3pR7j/WWrwfn9GQ2W/bgfxBc1rkNnoiEGQHG4zEG0kQZkWPTNCLYNM3nv/rN+fl5VVX37t3TmODr6+ujo6OXL19qFg7tRANVmPmjjz6SfGiiLs2azoGIbHsqAa/rum6q/X5vWQgBwHRRq47jcYaZi168l+dA2kxrVoqw26UqTqvVShmwiuyounbTwksEmRkDRBBBQE3FnhVg6eqmKusKQmQWS15DLeEux6PRZKym7+cvX9yuV/fv3793egaRgUVDjXfVHtX10VwQDyFTT8Qbkht/XxPn2maD79pIv2uzPa8LqTt8PB6/8/BRWZbNdl9V1f379z/++OOj2fzm5ubq6oYZAtJsPilGE7VIb7fbcRlsJg5528P46FKjAYCkjFRpRytJgZxiJavGiWWmuWdKcnAikp2w7towGbZ5ZO5gxnfS++qgN6nckXlHACgVoW8nkRiw6rv5BaSJFfnOHOM2Hj+FNz/ZlTPa1gOCJ/o2WZvyfruTbIX2aIkDh+o8to7ZwN7F3ZxidlsTTVCXK/i90OsnnwQjEQkRUHbeDgFRIJ+AGmpJkRyelQdjEUJRUAiRWVEd1METBCVQQYaveQFtGOihOrzf+xwKxyKiqSi5u9MVgOY1Tc5f3f/cc1b7uX9GezON2beD40mAujtLEOY4hTQpFM1SaWPwvDwhjJ5NaEkMAU1LamYDho6ZGtrsfh0G3B5nOBLnXX8Mpd9AA4d37MmhCDLs0J7UXXto3fs6t++hpXfMqDy4u/son8seHx8/ffp0u90uFov9fn9zcwMARVFcX1+rkrPZbMbj8dnZGTO/fPXq8ePHmFNeS8sEd+qyoyFMigNN01T1PgQt1JiGrVUPLPGWqeO2/ZUBs2hcHwZE0U9VekUkxjpGUj+quq5jlGpf73fp9HVXpULKhAWzNFGDhYgRkKURHhUjgQgQMABigSTCCNQIIyAjBEAGIYEoQqyh/iSQCouRjaeqmrKkyWQ2Ho93u2q9vn3x4lVT7f/iz/98Np5MmYGwWUuMsa6q7XY7mU01nYRk81peM0VQrYeD2QNCBRN7I+lZlYigVTt3SAN3tu9QgIZtt9tRTtFXUhiPx/PRZDQalUjMvBhPT09PJ+Oxnq/fv3+/kWZclOPZ6PT09K2HD85OjqWu1qsbnQhDBNQK7Wkf6pKHEIhgMhlRADUfNk2jZchirCXZQ3S+mr+PUCvWAouWRVInWUAARkFK69ShMndBRgsHBSUK4mT5bry130X2bXqGhR2Vgx47FEQCYRAELoNuYlajOhJgEEKpo+7UKIggURgZAQMfOIRp5zKkArqptSym/wxaa0kA8mcABAxWd0nxGIgKIh2JlqeJzBJjE6PEKCLqPOh1yl7rwQo1Sb10Ri45ub+HmC6FLrT+kTRe7P4TCaQpQ9Nv/Nsll+cgCoAMFICQARlQgIBCCIEBMBAVyVINgYioFg6IEAhyRaCCUYQERFx1ssxiM+0e8GDOcQ29z2S67JzPI4uoPDbETH9269mqqYB+4uCOGHoL4RmwiTtyKHd04iIgkHzRbEXsmda6K8DCAqlKG4KSJGrfLll2jnXDzFqPAQW5qYWA9KTJCW3QOfFxUCoLgQjJBT4IRKXG2KHPAZCFk4B+F3eEwX7pbNJuM5XGc2URIUKRqMZzL8SY1d3z9fxqhJaHAYkgYtM0BAGYiWhEpFmoFovF7MNpCHh9fXt5efny5cuTk5Ozs7Nnz57NZrPNZjOdTt9///3FYqGM9vjk5OWrV6EoLHGVmqaM+2peB3XEAYAQwmw2I4IQSj2Qqev9druvql3TsBZeRBSttq7VxwvSmn6qogoIsEQR5LIYV3W139VVvWtq3lfb7Wa/3+/Xm7qu476u6rpumtgwNAxNw5ErAIhJmFXbM4gIN01iBlGZmW4wIQogIBIBBHOqWCRqIiczaECtOglCAjwqJ0jCESIzQphO59PJEZK8vrq+HW2n0+nRvXunDx/udrurq6tytXp9eTmZTKajiUq7ek4cRqTpzAEESRAQUk16KEKIsa5jRQIhlETAApFjwcLSiIYfF1jVUUt2ZxHGsC3915DGkE8vvMQN0HqMb/c7zFbiZlft93uu6hjjjz78aL/f/8GPPvm7v/t7FPgX/+JfIMvp+b3ryys6mofRXDCutjcU4qQI5TjUuwq4IY4g0YgIBaAyxAgxphKtKizWdUNElO7ojhJERIKQHGuVf7MA1xwJJBAAijCDRIkCwByFkTWMoadpYdYwIJ93hhCKEIio3u0xi/kqC+l1IMoqKqCAxGR2q/c7hS9no2mCZ2wQEQKFQKEoiYCoYMIIGDH7zYFFSAYmAAqAiBAAETLODQlHh9w7F7m04iktoOrP6rqvrsJUlsWoKKnQpGREQAjJgYlA68MjAAALYLvfUCQ2MTZ1U+npL5MAilC34Iwn4soAlR3GWEdhU/JYuIlNjLGkIJLSTHpBJytJ6XF1tSDCzW6XHlBpTEPGdQ4sARHKEljquhEgKkcSmWMdEaEoyulURIpQYFkQFqEIRTESQWYgIGao6siEwtA0EYiAEIkAkAQkB4kZnBFam5zNWgkuI2iQZ36gzZN1UPhjANCCmwMGqVZEPavWAp0NR9OTyIU86KtDCDFHZuoDSlUKCn4A/ic2I7sPIKFIam5XRWYAhKSa6q5IR79N5rOg80AQQmAgTcJAgYtSIqN1WJQqNufVRPWDQlWvAQAhZhMjgVAAtVUr6gMwICBI5BqAASKi0m3GZCsqvBTYW6PeTchWTA8fI3fkHBoMIEQgAFrcWm8rQCzBkUIXMsldrbdlOZpMJmFchNEoBZWo77FwWZYEPB6PG+HN+rYM9NuvvtzvtzHWR0fz8/OzyWSCiMvlYr9PLhdVVV28vlRhq+FIhCBcV/u62oPzqd7v94Q4n03T9BV5RiNmblh43+QYPW7qyIC7fYWU7HMsGSuBi3JkJjjFucDIIljVu6qqdvutJo7QQNvdvr693TUNV3Ws67rmGGNsosQYIejORgDglCCeAIA7Sb1bqbAovEqhLE2QUUQ4IgYRJiFJ8rqQCEOqMUkijdYpA6HNbrfd729WK7PRn5ydPXzrrfqzz3a73cXFBSLOZjOtA8UYb1Yr1oKmTowFgKzpclaO9ZOZebfbqPOXmvL3u9oSlnZV3sThelRATCJz1mbNgqaaesuigIgoUogxXl9fxxj/9m//dj6ff/j+B8fHxyWFL774YjTB45PFYjE/PjtezuajEiVyU+0ZhICjqjEEjKDWQiFENkbDIpB8OkAt0ikHlnJf1PwMiQqAHi4BarHsKFqilJnzYYaAbDcbPmRPtmTu6BhJIJqOxuBs5LYzTd+Nrkk2p+cd7GGLGeY6t6AnmUKpwKwAMpJyWhABUs04ZNaL6m7WOZR2lKLDtDKNQESOoL8iLUJjDmXjQrWQglxFE0A9nWHl3wipBjtAk1mCytTc5AMCad9ooLM/bTxpqApepBRdyi18uNtastixNiekaBNMJjDnwUQpYhRW0UvDfNMIAEADBAQpOUpgYLVxZpOnqvjKbQSRhUmQNWEoYxQGRk4DS70ioqcS4HiYMmAR7CvBbvdB7xohxtqndzbE0zBLg4+Ghejhn73X47O/9gPjbni3vcKLbh61UvWKTm/q7NmqxVqFUERSHJGeAhAGyW7MJFgUBCCRRQSJmZXoih7taQbPjlgzaAAqAutcJBm3EG0HQEvNjDYeEFX7HNcdxABAlI7m2q5Ql/saKLJRhFNY1aFjEd//cnlMRCnDmnJsBQOR1jWJOc2RiFCAk5OT09PTuq5vb29vblbr9Vq9qK6ubhQNdEO1b2EWh1mGRZbq389C86+ysz+ZxhxjREZG8RguIsVmtzWKw9wGjEKOddHqgep7td/V61XVsNRVZOZGZbQozIxFgEwBJB2kEwDAHZlNvKboCY1ghzWSyxNig+51Ze4q6nF+7969e/fuVVV1fX19cXGx3W6ZWQORhVoHMci2eIVUuCOHoQo7ZTkuikKDcTfrne0fn9nYBtxDGskIoQfYmlFBLSpqu7DpUzGazWZaCfjFt08/+eQTqZs//MM/fPftd8qy/OGHP3jy4ul//v/8P5Hg/OyeLtYuNvVuH/fb2XTK3BrgDKo+pssGo8O3nZNIpqqLAz8zfTodLrCYl7uIMMgmM+B4KEeEX9wQAiGOixIPMWC9UEz1h9acz0L0EUjGGrQPyEZUYBSyw2xfyQoBgAIZXIgCoiZ86tALB5x+OtJ2m7VWwM5P0KSooiAXQxlj8oLpwV9nZ8ECtumsZ6/+WoemCufhSQgB+gQdRC3b3ab3VSGxUYhIWg5vss5eJBQlxhhSWmLuwcQgkzYRIbmNrG9phJEhOu93tiBkNR078t3hDQMsQmXAHXN7emY4Kmtqi/Nrqg8rAVW+qyhnxFStyjYL6HIdD2RbCOme5UOmbx4T3PM9c5He9xnNEqhZk7oZBqZEbCgCSIEAhBID1r0pIt4DyKMud72RD7bvnC86L+i7mnSjG3pLY1/55h9j5jQTEaNRiupuq6JdJ0EqCXBotftGoxERaV0EfVjNyPUyak5GLa5ARE3TaOZnsE1HQXFDuLW4QCZQusSNywHu7Pw8Hk30tMxvFsnnxB699dtCw2rZOX3oCy4vL9XMvd/v1V662+32VVPtJUaJTSudKZg0OXiyoWTuCdBhwH4xPAPu2NmoH8XRWyFPUBhhHDpnMJvNRqsMnZ6eLpfL+/fvv379+uXLl1dXV3VdA+HieKHHkPZSzlE6d+FTjHG9Xl9dXakD+m5brdfr7LzWonUPab24bf2o2dlhFaxWKyIajUbz+fxkuVwul0fTWVmW9Ed/8sd//Mf79WY+n7949vxXv/rV1198+er1y+v11Wp1ttlsdpsNsQRibFwxuOwJBY5s9bZThpU4BEpsCxGhW4u7pcmKHrFTDMAb93oagDm2YOJ5iTDHGHOl1w5wDESeIfW+9fNQDpr1MfuCCFUV7k4ZtZw8CrRDgq6o7rFIuszMXyMEfcxvP899zYAJyfzeLoEXcP2FPYyodLWNQbB9Ia5GtT4fU9rUtl4IeFOES2zSnaCfZjTNCbjd/pLLwlOUgrkAzAkzElcAS/Kgyf8ISRAIgTCluCJkAAQBZhBhQIkgIAgSWakw9xjwkChDl2BBNs66KbWP3c2COziTH24Rz/ilwna73WqAn6YxsoXwe6GDKncwYBiwMf0zZgYMrT02MWZoaV0mHdmTQ+8TZtNrAFQ+rHnB1HscFSs643R9tndagFM2hNyRB8PQJl+gpdEdTtBDuL0TqNetIbNfa71IHDfLiH4X+8csqQ4ARG6Br+NMuozuxKJNPhqIyrKcTpvtdhtzXgpEVAdeyGWAQwiEaY8TSNMkAIFzaZYswynjCLnIpsZt20hsgibM2bANAYrIwCwxcl03am1Wp+qXry72+716cHGbGSDGBuoozMCWcZQxgqTUa1k2AQDN+ULetNWVdPRCB2cbgAqKIATCCAGBBCzjDiYNTaI63CAiADPXsWlio6dyq816tVmLyNtvv611oE7OToGQinBzc6OwVgpSlmUIJWLIe4Ag2YAo/wM7fthsNt98803TNGdnZ0dHR+v1+hB2JqONEXHT4URkt9vphbJ8jeyu63o8nYQQxuPx0dHR6cnpcrmcliMi4n395ZdfQhOJqN5Xs9ns9evXq9XqvcfvPXrw8ORoaarzZDouaBKrerhzvrNl1gi6G33zhMZmwVlDTZwShIh036R/2cmt0dzXmuiWCLI02zQNSr9ze2NPFwQAhmTrRTTdlxARQpF2YSDFFOWpoj9JQjJBFgcRgiSLhZZp0z4P2J/T3iPyY2vZLbaZqvxWt3qf6BQyEdHTCnHWZh9cZK8gS4OQD7CtWZ895qTNs3PL8C+S+BRmZdpexNwax5gb/ZWIoHOOE07mB4rJVkOAGmzK6fBKHYQAKHn6iIjyXWhdbsGGwSyIWn9ZoprHAjFomF8/g9hwmnaNiO1xN4BpRdBfSlMbIYSQDXIdJjRkk3pH9ylkWqmEVSNSxDUjoz1Mtj+pOy/bUBzZHs+o0snPZZiWpzEwvSqeM4OTtsFZArBrEhDH//zgte/YCGbpwnR+HAg0uUMW7sPc/ykO6/LNA1br3m89o2JhMKS1hHYi6cDIW+wQEXFxNAcAyNHMoLnqmPWwj6B1XBdKmt7Z2dlkMnn9+vWLF69ubm5YaxeyqzIQ855Kp3UJAl730MnqXraUk4i4Xq/tNAfz2bYnI9IVR4qbm1WMddPwfr/d7+vdblNVTdNU19e3VbXb7+sYawBSblI1jXoDqJyFiIKo4UYJoZ1Xi+Q0sz3sl+7ackrOIkpMiQJloc8vjz3m+0HEKImu2XIqjD7//PPJZHJ8fHx+fv7gwYMHDx7c3t7erFdffPGb2NQmufSQ9WAry3K3q66urjRqW8v/7bLTSm9SagIysmiGWbXk62mExnQfHR2VZfng0UNEVAouUZ49e7a+vlmv11cvX1dV9d5bbx8fH3/4/gc//vGPA+DRcvHs+Vfnp2fT6TQgSWQRBgzp1E1UMm73jAeX3wMKWkx+b+r4jb2tPuxBL7ykH4VJoDkEwCElVayt6r0Hmh9eD096lAtA7cpIGCQ56WG+kxhVROwnpKZkseJeWWD3xh4xtfEPZ+SvlTuWZanbz1RVv98on0eo/NozraMzNRuvDdAp4QADAtdZAmGR2HASibIRkmOMuWZOf7K+hzSYyEqqxDFgvUAWYU6JtLPrEHNMAT5qhAgEnJ1sk8wEDICY0k2nPIkxChDo8TICcwzMlMsiocvENMQchyEg2RHAVm24Ur4pA+5BABwD7tElX9RB8smCwd+WzPZslEa6NlVb+t4UJOua+bODV11yZ7X8DthjREQVD8zHBzn6v0+L/IXKEB4y+sK6ril0kNCPqtcPqCDrdo1HzuGf7YAtC6bjTP4nee+I+qCkiThbhS9DqYRA/0xnB+TOPtx2tlcDQCOsEUSqhmn24vF4rCbeyWSmfTZN09QaN5xOScTtGuPBthltt6Kx5Jxv1V7tl9i0YW1FHSVGqRve13G7r7e7ar+vm6ba7FQfbpQBK8bGyCxQsxgDBgDThhFTxtG0BgIAEKFNsg9d8mrLyywoTEQoXISOTAROfvfoZW9XW7zzikqVodTFyc4Uj46O5vP5eDYF4NVmrdowZK4AdzcRIaLxeKz9bDabo8Xx+fn5kydPjGLn0enhfC1O3fFn6pDD0Y6Ojs7Ozk5PT2ez2Xg60bP27Xa7X29vb29vL6/W6/XpYqkR3+v1+le/+tXr168n5ejBw/uhiIv5bLtcjgKNi5IImbmuWT1voUtWempWBnsCrf+0Xw1hcWDJury59wp90p9tg8/MV3eqoEBvew+IUeJhSYslIhIKyfZMqNVyIJAgAgZCVMctxACQ7c7JPwtFgFEQAJnAImqkf/pwFz7oOJuYnOmK3HSOZnm25c5/tolFG1eVWbJJ2TNvRCyw8HzI3ouO+9pbBKSua2PAuuOMNWNO099dJBHHwpUBM3PwB6VOja45iqCuoohEjswMwoTZq14VX9FgBogiCGqmlkIHjyia5IMZAFmEQVLJwpwi1FBF5WxwDEzBSDmAVSSd3MvgSMKvof0XUzWvziIOcdt2CnMb42CLxd00Z7pqer1tYg9hjC6DI1ZGImy0bk+1TKLHswFTjTJwTNvWj6CtRSZZMPHvPYg87A+nAZoYkcUb27UHnx/Cf2Iev29+7jaAdHEILL5P60REEIMaOsUx4AyxVgzyDne73a4oigCloQ04iV9j4LTmfV3ttbDg7e3tarVi5ul0Wpbl9fX1zc3NeDyWrNGqDZiINLWqdI1zMTvDi6SMb1qdELqxbT1eK5lVm51MvyrU29Z4wGazUdMoIirtiFHUa4uZm8gQCkAGSDF6DKL5V4ZOTGkBuIeCCZF6CUASupv13T15YFFN3QTZ7/dqI1LwqWwSQjg5OWHmqqpevXr14sWLVMDg9ORHn37y+vXrZ8+eXV1d+YN0eGPTxChEtNlsZtPFycnJkydPbBdl5BbIJ3PG+O3MIIQwn8+Xy+Xx8fF8PtdqTtvt9smzpwp5EZmNJvP5/P7pGRE1232j7uYxbrfbb7/9djaeXFy+fvjgZDYen5/dK4+P57PZdBwIULiOVS0pnGPob2Wbt2XAAG3YO7oUyn4veVDnHdIhEN7jewhJcRKAaVQ9+3PvFeiUP8ziF2QGTKr1qpkonUGo457iOjGi5Dw/YkZmRARgFk757W2HtjbSu9oQLE0TNcWE6b7gTI6SZS99mIjqujIGrJhg5MbvyXa+2CqCvbWALiEAAAGJMeq5PDMDJzcZhbiutf6QM0/VYz/rp10a7COM5DBcBhBJaZYEhEGYuQyBAdQJWSiFuFBal1xqSQQp2SQSHFkkuV2DqGeQZAU7A0QcmTbumzAQEDAB/Dv3rCEVuOXuQdWDwuvEPR9+EdFcg0roDD+JSBOneEGhp2h2oc2tg3Yra0K7Ebv7S1La1wEjzHiJA5HCOpeu0Ox9J8X8CQgC8zCjjU0HoP9qcb4jPR5s7/IXGPqY3Btbl4UjIbUasEu6QtSGIYEzZc3n8xAClYV5QZvOo1RC9xfnKrwislwul8vlbrdbrTa73a4sy9PT09FoxDlNlUGgKMu6rgT6W8P2TghBywNTDksTBtOATW6znnUk3rhVXFxe6XmkhRvtc0iUxx6jRFWzFwQEUrE3IPnoTHC1Y21HRdBdl2IGkAgA9k0bJ4p2+iUgIlFTKgiKO2n2q6joqutQFEWEpHcCAAQCxAiyrfYAAISj6UREIsizly+evXzx5ddfvfXWWz/84Y9CCC9evPjmm29ev3693+8fPnw4GpGIbDab29ubGKOWmqqq3Wg0mkxms9kMEXWZy7J85513vvnmm1evXmFWxCeT2cnJydXVRapLZeUFy3I0Gp2fny8WC80gulqtXrx4sVqtqqp69PZbGvmgD+/3e2IpimJclgAaYzmdTaYhBBIIhKcns5OjpTTx9uZmMh5PRsQiZVFIEyND0zQCEZU6E4qTvHQBmVlZb5EKRCvdyYEQAIXF+GdNKK1+E5k51pZZKylzRVFgjFUTuW5i9kqAbpWVTuPDDM/QQHr8OBSGAKm0jUroVACA3TEKVhBEOyKCkOoHIApJAAAhIEySvHpOd0mG7TEzRfbGqdFuloEdc+FOdHDTpscN7FwiseskTESmRhsPpmRQ7DgQ3NUiyG63U46oDNjGqQorD0SxJif6YLe44EyynbViRlK2h8zCKTqVBaRqGmCWyJqKDgAAgTkWlBxRIJVIYiENRgXO7FwtE8qQrFC5LbpeqA6AXYFMCFkit8zKJaZwzp5ErRWhaWqgjn1bAaI2M1NlbPXJnRpIDiPRRcecwdsEFwBYLBZ2vmBfiQtEOQhYeyMz5yDADuuFzID9zynZhxEAgYUgJfROFapZAKBposGNndOQihQ28ibVY5dpUWCjQGYvCNaVz1rckZLhEPe1H/b4MUrHEouHWHVawazht0Y58b4LXsR3Jmj1UhZWW7SqrXaaa+Y3439HR0ej0Uh9itWDuigKZXwKFgCwcshaLXiz32nqK+1fd72NH3POA25NCwnC6HwzbR0xH16o+F5cXl42TaPHk/7CdqZfA0ZosvMYqYmPSGsnGMHt/Yq59ZXtrWVvARJG5gz6/jG4Q+bVId2d3K0zHm2z2ez6+nq1Wk2n08lk8vbbb5+dne12u7/+679eLBbL5VJPjg2ao1Gx2Ww2m81yuUTE7XZbFqvFYnF8fHx9fb3b7XXuq1WqulgUhabqrKpK03k/fvz4wYMHIvLq1avPP//84uJCF16Z9MXFxWw2Ozo6ijHu11sAGI1GR0dH89Fkt9vFfaU8qSzL48XR0WL+9sPTcVkezRdvvfXWydHy5vp1U9X37500br59aHc1zjuhhP2fd2h0z/iZmyKuHXXbRvIEyL+XoLs/82fvpK2luV1BPu1VTNfD+TA478qWc6fwKumQACVkBN1usKuE9bCuLErPL8GRjx7QODnitTjveQl2dV97i9nqe6y3d59znKiS6SG2GzUDR3ZFJPLhdelMPI+HKbtOZb02pWbF3Hf+HiVFzAhoAhdBS60FwgLqDA1JB0ZEVELLzNI9ZlJocKYzhpOo7iaSstga3NIqOLMkZ3+RlOKjwyxarLtr+ragOgY1qik9tQcoxxDrsplp0bvIgjNi2/r03ugRzG8fz/D0oXbY2WRtDJhS1pTWdNz+Ng/VXtHZv8BNw5o60RBST1XeQCs82htIPYv18yqohC5VcSjaue4NWy/tZlF4C2snQszogO1AG4DkxJY6tel0qnVuLUMk5xMHPXxLvlRgRb1wt9tC9hYyLFW1yoBGbcULik17TkHmjE2k+KPPq0qmYVHF7Wbr0wXHGOvIkaWumx4NSkkaNFWw6KmbBhu05xOeVmkqQyUQPdBDN7vyAO4HzjD8de+HgtlbH9pBmOELuhdNzavVarvdTqfT+/fv37t37/Rkzsz/67/5y/1+rzmulY/OZrPJdLTbbTabTdOwQvPm5ubmetU0zYcffjidTqfTqVoOYozMUFXV7e3tbDb78MMPT05ORqNRXdevX7/+8ssvX7x4ISKKBMvlEgA2m812u1V+odKQqsLqeKn1KVNIUkZsEfnmm2+O53MC2G9OeDoPIdAIx+Pxbrs15EAXBjrEb+ju+d63RqyzaTNH96pdpxsjJCL1vpJ8cKhpOvR+tdvLoQLyFg/XU0rwUCOiJnIEISzIcupgQADEYFHPKZehgJ4SCxA4sitWsg1zIAcmnYGyi30PMlnV6MBKL/S8x45+jSKbwtpjwHowoc/Yca+3YZo9KjHapnNGbosYu1GCaY0yAxb337yyAJAihLtjag5Sww4zyFSbiNhisBE0Y5RuN8oFvDVUhvKes5SW0TaltLmdFNZqxkRBJoQYzU4jmeehY3XsyioLJQaMiUO3ZyJqEektGQBSqmrdOVaH9oSejQQbVO0Zz7fEmRA5O9kyMwGa6En55JgHinUeGPSaImb3mc5DpNWN0tBRpwTCIQSCpPUG8edBbXZum6Pk3M7SFeAEuKq3hlGYE36F0HpaGdAO0mq74G7OOH//rp97JERzCHee2ACZj3bzNHhPlbRVs+DCnJyodfrAqFy2KApN1iGMyun0SAiyULLbpcLqMSYZVRG6yR673nzC+cjV7tgO3O/3StL9Wa8Hl+73kFsqxePdhXrbstMFYihIEIQRsoVST3kTgKQdkyJMi/IDzPOkDbP43EPcTm/upn4yJk/rYfNMRRybuby8VJzY7/cvX77cbDaLxWIymXz88ce3t7evXr1SvyfFpxjjyclZjLGu49HRUQrcrnmz2Xz99dcqB93e3q7Xa0TUolQ/+tGP7LdaNur169ca6K06NwBoMWMi0vxcat+YTCaz0UQtXdvt9mR+NB6Pj6YzItquN7e3tzeXV2URzo9n7z56dHx8rELcfDqLdX19fW0z9RQ3ERTpAwQOUYcEpRxAZQw4CfWKHUlIi9zVzyTbtQyRqqoymg9uZ7LTbHqsCLtGQma1nbR0CxFNv412x2FFlIRyHnEz9+sQgHxRAPfDtzwRgS6tAQA977Hofj996akXjrhgtjt5HgwD+iXZ9O25oxxiwKllATfBmT3Og0hKaRDdOioDzq9gORTK0qOYKcsBK4EEIQRB0TScylaFNX+ocgJlvSHlQASAnOQp11sCt2o6Sc9ltfWuKXnJgjFgRDQGjKpu50aUvWcF7ewQBjRHnL5rq5bCKbvmH7YcSURa39Ns0WY9wW4zDi0dK3SH1+aLfuYKTzETBiefKUTExI8JCQAxacAAlgmgdWPWGZE7Rx+iKE2EuY2LA2d+P7gXesTZU2a/xw29zWHYYaYMxW4HEL/p0C9TF2hd3ayLTr2e095JTpdJu6V8NqyEdLPZGgVLNXRFBKEoguabwixgmf9d2h3O/teru0UDF0ujA2beK1jQ/xMgARKQcjQBp6lkfIcGo6QtxIikUQoiUihACdMwEIURELAIwTkZgmvtn9mGkJRpwmTKUtuXkxPzMuSwmTcan4erBQBN0yhQmHm73WqKzclkQkSTyURjllar1bNnz7799tvLq9dnZ2cx1sypakKMcbetNFr/vffeOzpa6lHfdDqdz48QcTQq1MlrtVqVZXl0dLRYLM7OzrTshvnB6+tms5mWawSAqqpIk7mOEDGJaZp6+/b65ubmRpo4Kovw3qPtehPrhpsIGftjjMFJ9JIrMUdhdY+5C1Id+GRTgRF6lcxSn00UkR4DVnQ0KmOGHXMUBLctzSaD2dilqBkGtWiMZglDWYzag14q1EdJKJmhddCKE20yxex2k5UpgpTdOhmoMyJRUhc6zpZpD5mJye9k3T+2efwx7VByNTueTdzbrrHLhIwCavyDAcExyyTg+zdyymWRX+23WGbA7GInRITFHxX17R/+Oo2QsjzkcmAgtgWhdeRBI0eRQrYFp3nlFSHK5gYnMHvA9tBSW0t5JEU9saSMydQ1QRsDRkSRmICvGRu7mdc8wwBH4mwhPPC1WQwPZcuzufyE5AzeFng2vzxxnNu2Q+RoFkOjZiIp5JXcsUsLIjUmYtaAEU097BhqW8WxM18bHmSuiU77HM1m9ry3g9pPAVC1Sv3XNLVfrN61Zzx5baQ3IxuG/cQYdsLAwTL56YhIzj6rhnfvnOVssZnbYXaDUjulxk2EECaTSdNwSrGckzTkIbX8zjKU2eJqnhbObh8AkA7UAQBxPp9DDjj2Hh4ePbwfX2Gqvdkr1AphEPE7RBD2dcUAiAEISZCIUAgsh61kBgygKecD6YEQGmajM03YnG0jMYIFgctAA7bnxUleB9tBsiIi0+lU8QwAtJQ9EVVV9ezZMy2CoVrpvXv3JpPJvnr78vKyKChG2Ww2yrw1JEkLLC+Xy8VioZ7kRTFCxF/96h+VhqqrMyKqk7OquYg4mUzOzs7KstQzYyqCqs5VVa1X681mA8vj4+PjJ0+ehBAWk+lsNlsul6PRqNlXyLy+uX3y5Ml8Pj87OxmPx7vdlpvm6Gix3WzACIcTciEzYCW4BuchfDyUjHyYfMdNStrAzpfSo03MWSZ0D9sO7LFYQz4zwpg4PMQ3QAihVNGViHRnQKD0Xfvp1h07E0mYOHhQsp4xjA62rQtu43lWIa7WjXQbZJri+ksxoz3F17aDQS8LTy3wZcDXe69j1NR9+bFO9Rg58APXlcEnCSvO9DrUiSXFO7RGZiLkmI3SWr9GRPXBfCosbbUlFs2IBIfakPvezZVVszdVrGUwtiKJ6iedD2OMIB0By6+jh5A+EHLFYr9NPHW2ddEHqBz5YaukRdT3pMtdiSbe9uNJNlRyK2IGGk08YtvThiQ5+5UzbmF3vaz5+fYQCXL5UQCDWCfmyi+ZPlbX+4OLSO5Ixe/rAEV3OyRAqUyD2dRhW1UETLaDrk9JDxd6f7Sr2X3ONN2oCcBR9IxPT/16YEE9I5O86wNtt5vWiOZS3W02G8NP04mpCKNyLCnFQuesV4+u9I534ypU6wUECgEAAkDIthebg31GLeaGYCmvELHHgPU3kBkw59QIXqKBwe6yryQD3eOKZ9UeuABDCtxvvh9oE/iRslKL39psNlVV3dzclGU5m83u3bv3ySefnJ4d//Vf//V+v725Wa1WK40eLsJoPB5/9NFH0+mUiLSu5FdffVXX8fj4WCEuIupbrjmzEPHx48fr9Xq9Xtd1vVqt1AkzxohNknn1+aZppG52u93q8rosSzg+WSwWJ8tjZt6tN/Vud76cPnrw8OH9ByfHx7PxZB3327rZbbfgqLknu8aA3wwfyRrw8L62FJnucy0BQE7EbX7R4BwW/Fa0Pr3iYphg6+4ZHhGJZjIii+jVm5p13KXMVKbLJCicDoFa6ilJWSZobSaEGjCjlXEdbGwAdt0bbU8r7TEVzKZ1m5p55HrlRrpOVWZUYGbvBZ03Yt96ZJ2wztKtd2f9pA368iPs3XGPm1Db6ZC7j4HTgEFrCwojJJuEOVFGEUp0oFUo265Qc3EjQfAb3K69AtCykPzpv7J16T0vIgjUNI3xfnHGTA8HT+LIhR33gOPJtPVA0FJhbfanrh36sALRUqEHUiOR2wW9dWk/Haayhg+50189CiQX/dKjokYfxAl2lRMlzeIlg3KWDoB9nLf5ojMAgG129mve2fIGbTdOEQGx84gMW4/aPfbR/3GXzmsQjaqhUU9qYlsCIOacWePxmHkvXWGLWQsxJWO1ES6FXlVVIYf+K28uy7IYlQgp7lxFMa0PNBqNZrOZ5huez+ca1KpDLda7re291hQDSWLVuekd3T9FMdIyS2l6WVAaFSVA2qyJMyIAghYxwk7lVC0QqxUnIWq1yXy/F5Q2xMi7bh5svT0GADc3N5rKSiNxNQd3COH09FQFE63icHl5+fXXX0+mo7/8y7988eLZ06fPQwjqrT4ajR48ePAHf/AHL1++3G43ZVmsVqvf/OY3u93u/fffN4VPF0nXqSzLV69ejcfj5XKpZ8MakRZCiLGp6wqRUEBLHVxcXr58+bLZV/vt7nOA0+Xx6enpuBw1TSN1/f7Dew/Pz5umuby8LAmPlotRUWy2KyIgAUCGJDOnvEiCQMnzVI0kHT6bwUIAEQShWwmj3XXalXr8swC31simrmOMsW4kMrAEpBAIQouCJmbGGNNZoQpplAp2MQumGsyASNksTaTVAzEwgtYQRERU/6lkj1GKDyKociEDQToR05EDImqlUBQBrXIigqj2VEoqXRdnetPHrvDHzAypPjGwaiIgCCGVtwQMpAEC6jpXFKXxCYOnp4A9E7FpIeI0Y7kjmgUAFNQobcC9LW3nz8FEpMv2ogCgBA39TUWMEqDzOROI6zlBmPvcIrNBSA7USrA0KXRbVCPZUJPhtbujpXvm5wkuYgobDoCCqOtHAkKIEhmBBAWAIIqueWSmAp1tltzRnR+2XRMRgKAAIhColIZIIFE0jwtjlEgRGwICkEoS4VaNx5wDMJ2apWVkzseEJF4OaSEp7RoZbSMBAAJuAIiERTDVUxfRUndaJi4KoUQQAlRbrlJetCg0z497DHhbV5ZAzfNC8+K25xVi43FpOI+55J4gBEiZYlMu0lyQU0SYmxzeSCJRtFyiECQKFQGCSNSdLsxAiJwMXQyiJVYA2N6rcQuEKAAc00FMC0oA0OquHIMAUVGWIZSjACC5ChYANU1KuKHmaIBU67eu68hsmbBqbgpsKzrYftT8ExbM1uobRSnZtDAej5XX6IXaMjUPhOYpEpGisSociaXqGQNIOoLt/kPUY14wApiDKhvdSo5/KrtN/heEAUJ7biHiL0CEMpsHJH1XzPEJnnh5ASrRkRQV4eVVv29bq45eTGdjgbje3HZMJSR1s6/qXfIDr+u6rq+uL5j57//27x49evTgwYPl4mj5yacq0ZRl+X/+n/+PV69ezefzn/70p48ffzD9X//Nb37zm6urq+Xx8Xa7QcSyLCaTMXOcz+cPHjzY7/ciUtdVVVVNUwNICIQITV2LRGBU227TNNJEYDl/cL/e7Xeb7a6unr54XlIAoCCR96v756cnp8vF0WQymYxGo91ms9/vp6OSmbmJYnkYWFAiBOCoMZxARECJQYfEiKlNncQsEqGJ5iKoVdEwAjMUVDDHJjYQEwPWJdiukukbGQoM7N0RRZpYR2xsvQiwwICICAEBSQgEUAhBCAOmjQyAQQAECAEVU5CCuldFrYgIKp2i1tHL2heoRpAPdo18MjME5ZaQjtAAICAAcN244RFRW8FQmXVybQEQVb0ajqlkOYiS/qCiTXLtVdITIFCBhIhlKPXIipnVrc2rtdnIr3+iCGy7xgwjiNZ6GgBHzf+ikkhLgWIaDgkIEAoLg0ThYL7kmITnKCwCDAjJZ7vz0gIQQQssCGMUZAGGqAgDIubVgiLIgA1HRClUqaNc9VigdkmC8hBFGCqswICfGwz4rn1SCEp8VQ1PgIxAiCCCSkgYQJgBgbBBwIhExBT92Rvc0ZAEUSgQUiiQGkZuJEqDAKQyiHBsQAABCwzAuTwWBRCImfYoWRQKUAABhlAkFrir2vXNpEz0ecq5nBT26QGWAEEUJ4GkiSQEVMTIEVKBdcnF1wGkGBVIkYSEUHc2xJy4GxEZ7DxbP0ejkTIeEWCRmLOqWT3vBJZAye0akKGt2BrzdYEERBgKLIoQCiwCIQkJS0QSQMUvFEBkQRKVLkRUwEMKkMqIhiLH1yBqlI3o/kIGCIAsIBpczpoQFUwiR9R6XEUWBSiEEqioo8Q6bfOiGNV1XVXNdDoHoNvb2+vr6/2+vr291Uih8Xg8Ho81M1VV1Y/eeXu7363X693qVkSU/k/Gk+1+t9/vt/tda95DKEblZrNRxXc0Gi0Wi9PT05OTk/l8fu/evaIo1O9nNpsp6S6KolBl1Gw7aP5QSkkxycL6D50Ma/QgUz6ArnWrAQEr6uilUIBA1Lg6wR2pOHPWuzaJl45/j2bJ8SWbIHRD7vd70wy8SXCz2Tx//jyE8Mknn3z88cdFUfzqV7/6h3/4h0ePHp2cnKxWq6+++qosy3fffXc0Kj7//PPLqxsR0SySIqJK8OXlpfWs3F2y4WKzWQHkjDas5zqi7uwFkZ7+TnW1wqhAfv/+2Scff3Rystxuty9ePjuulgS4XC6q7U4gJSMkYRCIucKxZBdRFVj1MC/nChaRjiMfuARe6sID2ZVaNY8GwM6YtQcdv9LEkJ0texpMe63nhEEwIhMTEKfACU5uS8kKm6PUhAjywURyBFK9ixhAkACTjVQQxAKMXFNlCDCfkkDOkCWMiI1DKmPDvZ/bhSDEGBk113FyxxVsk1eBWs4JA+U4vaT4dVReOeRtm4xmlrOza/ns4byNChGDJHezgSbXPqxTCyEQoED7iq6JWzkM2l5GgchCklVPwWT7EIjqLhpj07BCUjMlMBCoKMEcsqVasi9ub+QHd6g491r/2WJR91Ns+4gIREhzQI1ElgaBUqo+cRWlgos7d4PRc3QGVc9RAhIGJikaadSapCuPgoAMnGt8IGWfv1TSQFpFFhFDjJo/H8w7uq+Cu4MEZodyKY0qZOuvkgilgQFRAIv8NgHAhmMSobL2IsrXI+d+WslPRGLdqW5kzcIFe5+a4Ci6dVQTdk1EIHuRAkREgjCHQFqcRUQwihAQixASA2Di4/kTMQCyYEAAkVTJWytXJscCNZoqWAjbOwN7uKQnibI6KEAghEigZRxJioLVC8ccku/du7fZbNRTJ8aoHlsA8OzZM60aovNVzWSz2ah5z9gHAKi17/Fbb6vBeT6fL3Ibj8eLxSLktFnKHVSrbk/ID1JMvzZpMocKTWM+tDcq0BPbhxTZ78M+jR6I+cOB2QMyWIA3t96pg2TjnhFB2xs6sL/4i7/QcN7PPvvsv/yX/1KW5cOHDx8/fvzuu+8S0bfffvuzn/3syy+/ZObz8/OPP/7473/2c63hqHt+NpupiUMlTRWsOKdJijEmc63q6MlKjChwe3U1mUyO5kfj8Xg6nY7H43FRjoiOjo7u37//+PHjxWKxmE1EeHVzu6/Ws/GknVfnhMmDK0FMqa1N30ygzAxNbUdBiQFHZubCpUjEbFJWWBnfst3r1713ERNbFdE8EjnbDVLOkZhFtSHLSay+RRudknmBkpoNNdbed+Ivhihk47fWQ+MWWxBijC4Mpj3cNfXF63CIqHG92rxgZwKo/ZmEntg3NUu2cA6hOpyFPUDeK971gJnUe8bP3AaPdl4h0MRImEo86m9i0zBztd9zjLGqY4wgyWeTQAK3jjx+SJQDbTsb9pBvh58mfg93SwOj/5Xdb2JjibdssRBRY+6H1IaZLZTWr6wnGvYiEVG90C+Bnzh1PVuZueFOvLhIa7Xg1k3VrwJJ95RXJQ1yFj43X3VGy6QYwGRi2zkioilL0zloXenDlL2RTVSyeXk4e8XJNzt7VjTO3r9YlodzSt/VRASwwx3efGF4exfL6G15H4WBiFVVjUaj/X5vR/jigtFDCBFEM99pgWFm1qxnRKTOs/qVWZvP7p1PJhMtPaB0W3mtZVLzbjEi0ike7pthQ+++n38PBOD2zPB5cISs960H0/BiCO6DD3zPxi7ZOmQOJCIm+0jO8DmZTBR2FxcXT548ef78+W63Wy6XIqIpph8+fPjWW2+9++67r1+/1qPlBw8e/P3Pfq6Ank6ns9lMRLSWsKbWs22c7A9FcXNzI1kXEQCILIBa6F5ERsWoLEs1nI2LsigoBCrLcjKZLBaLo/m0rqvtesOueQYMeWslEuzMEH6ZdEicWUHLjFOwPjOzZNMlEcmgVAA43kOu+oJf9Eyw0pmoVRBDzKmXSMth+VZwzImFVQdAs0UiAiC5VPukXoT+EM4JjncIfEaRewzY+JYBR4UDZhZI4Q0HTZqSz4RS/Endynkm64iI5SE3xpy+csn9hxPx7Oou1mX3/cjzxAlYzcWdUbFzge6AS9r+dfR1XTdV3TRNU1UxxqgnJhJVoWSRIAVlCHj9oIdy7R2HJNqwlRFbIHxn6xF3J4KgSMsIbTzeS98Ign+V3ybiEll4rIA3shPPhg0gxoBbv/fkKOe75T6sMpgg+7d3t67ZoloN3qaQJV8n2HFrj/ESA7k4154Tlsei3hzbXh3LMMBqnS1P+g4umbSMQwT6SD583Ru4w0GeYtfRpaX0jHC1WqnV3eIPASAyI4IyV3W5retaD4mISPVaNTKbqTmy2POWrgcAzKJg81JwFfpFDyjQdftGJ4dS94SmR+AMxAbxnvxoLbhMIkMGfHDX+Yd/P+4LmQGbE1o6dc9WBfWI0/AkjVD6+c9/vl6vq6o6Ojp6++23AeDi4uKLL744Pj7+4osv3n///Y8++mg2mxGRGvr/5b/8l8+fP//mm28uLy/X6/V0OpVs+PKAMsK3226NlJehwAIJkATGoRiNRtPJeDwqp6PR0Wx2enJyNJm899470+lUs2menSyPjhZHR0fzxfj64tIYsAeNB1TWPPs74eBq9vpJhbjzCabeJHWUAq3EhPYPMZNW7JAqRijKkQbjJs4XWoKYcvaGkP4RZQ0JE8lRAQICIiAWoBWCCSGVZAAAKOlwbuHYPYO0ySrzP6i+e1B4kKHLIml0PHZTsRqI7mLA/nmvp8obGc93sl4bZu91eY5qSBhux8NcBBE1qUUUaThGddTXsk6xkSZq9m+SlO4r5Ol4gJhFoTdstMXt6qySnYbuorZ3NZtFS80HsqZ1q5qciVyZN7eJTD1IIeuy4EhnD9oef4aUynBM6Tt3zE66KB2FpycSSbb+2K7UMWASkY0BM5Bw7ir5wItAxv+Q7V4iIsqSCUVQBAEJJCWiIkJpahHN9s2WXUdEptPpkNsN0aYzckb1vRRQZ0kERI4pko0QOQICsuRz4SyLoFu+ITLk/dtZ+vYnh0aFiGp6VID49FMmYJFzbKYQ5sujUBbKCxJVRFT1dz6fn5ycnJ2dnZ+fn5+fn56eTiYTpIBF0Of9e7PJrrX66D5sGbBHX+jHXbXNDGEHQa9D7Ard/bg0/arH+Hu99aihuIood/3k+zfJQTUxh67GGNW1ShVTZlZf6LquuYnL5XI2mwHAzc3N7e0tAJydnS0WCwD45ptvfv3rXwPAvXv3fvjDH4YQfvSjH2mY729/+1urOuk9gTlnQld/PH2gKIrpaDyZTCaj8bgoiUhykp2CABFjU223W4xVVd1X2qGjBYDtdrvbr4yIe3qKiFqAPU3c02XD3IxVmBJ7B4N2TrKPiFg3jYggt3HG+o2OwdbL0M4n2beVYiCgYM5K7Srnw1R0RkIAEAQKqdBkBAFCJNTgc9ZjXUTBVEFIEMgd/NzVelzWvw7uMPnY/heQEAIWZklqI6zSBAe+tdy0VNuboA/wQJdtx48Tu6pAb2v0ujLe4BmwjYdZnTLaMO7eAvX+FFWmOO2UqslFFWMEkZgyXWHM1/cAAQAASURBVEojjJxYptUkMLpmyH8Q/iLqaNIBQm++HVr8xpUdLh9lG0mvW1V3zICR0QDGo5Fkv6pe574HD2ePPKZk24B9IyJjwGqUjjHGiMysCVLyanr9uzM1xPZYPb3SLF4igoLZhMt2hCQCuWwr5xBqkZQlrSiDH629ZTwec84ione8xwwM8JPdUZQP7pfkH9ZZX0NUdJIWkab2CkL99A+d6d+Nsb2bPfjrHU1kZtqnOlU1TTOdTjVhg5ZCEGWNZXl2dqYl5236atr8wQ9+MJ/P1dR8dHR0fHw8nU6pCMuTU30RZ68OzDbIHpyND7aVdP227FUpseYPvXt72FP/Hhs42JVvvZ3zhp8M98bv1DCfUhh0NLngdDoFAM5VqzRRc13XR/OFBu8eHR1pBV893/3mm2/u3bun8uB+v7+5ufntb3/7/Pnzo6Pjhw8ffvrJJ++8/faLFy8uLy9fv369Wa+vrq78AoQQihDKojheHKnaPVEGXI7KUQhIAalpmljXsa7qane9W12+eg0ST+ezB2enb7/18OTkZLk8irHRBNTT0dgrUtYssxIici7T0TQNZfdxMH0XEZxgFGMEDavViitJQmzVGr0oJ2VvBSVpDAV0DbyIyAAVS4/12nIrhkrK+J+CFYpQQjs8AkCkAhDVS1/z2qQkc4CIKPFNJsHetQ7BE1DI2374MACQ5tYpQsj55OyH2A1ht66apo339YZHa/pwywYOpSUBR+DAkRVwDLjHiaOLX7KBqUeLd8Iy6mCig587Isa6iRyb2DR1XTV1lOSmoFyNMVU5izkje1VVRoVDrgODzlzvSLMcDE+3gXmo3rWmHlDS1ZN0aUHAzNypf1AFg0GT+fkcqNmvzg9V+/QWFOokrGhrs3vuS+4klVzAhe4ve5iIENWXO42fnXFYp6KD13G09/1880mwZKgm9MM2zA7z5lJCgIiqIPcW3dN/HWRRFF4DNnTq4afhZM+0KyLGgMGRmiED1m4o+6EO1/3wSw/xjrytD4hQk8lEp6MeWOqWrPPVBzQPv+ZcGo3HDFLHxhJKz2YzTV38h3/8R2pnViTXXIqcizSw8zPXVR4yYIVG4SHlv+h5CVqLztDvuaz1y/m0I6F790jAtp95gfbA14P797n+nZrkpIkxRuV8elSuiTjUEU6lxel0ulgsJqPxfr+PMa5WK8hV0sqyfPDgQV3X6/V6Npvdv38fADSB5cnJmeqmWlwZAPSo+OrqSp2wlLVb/cf7Z/cQsaBQFAUJNE3DsSbA0WgksRbNAw5cNdV2u6+32/l0SkTr9bppmmq3WSzmx8fH986Pn3z9Tbso0MIw+rSu0M690Rjz9Firt5kpBgCSWYyTmMLMjA0isiNGBRbQZQO9dUR/vIrYNNHiBW3XctKEQD2qRJUqdacqUIlIijxMBBLVkSSbpRNKYFcH6iHnQbTx970M4Y9I7EkGCSFAX8Y/fGTl94gBx1rM9Vk5Zy9RyAenAffwtjdgm+/B1qvGk3/OKJAiwh3Qhn/aJ+ccZ7VGsiIAYcDQAKcslYQanQlZ4Ehr6l5tkooRAb/xh6TWbh786q6GTk+yiRDkBNTdZgKN8QNERIKmaXJ6/46eF12mrQ78s8cy55wb+oC580A3e7DRW0OYFGOaxSfMomSGv4NPlw0jIuZCkIgIIoQUpZdmK507YGfkjhfm7Hh+w2JXaaOcgFNELLOQbx55ujcPBBTYJztHgbxDUsWfg4t718XBx+5qOhE9eVRLpE5Q1Sp1p2IrJlgU5WxSxybGSESLxeL8/Pz+/fuq+GqArzjDVR2jj27wJNEPzMOhMInM/8Z67O7ehC62tZLnRdboPQgMpmqiNLpg/MD/pNf8rrMd5SV0f2EoYmikjYiixbPm1FcmpOiJOgDEGHe7nfpJeQ3SSIbanMGJwCKSDkQBEFGzauhgRqPR9cXl//s//b8ePnz48ccfQ+QvPv/NYrH4sz/7s2q7e/z48WQy+fbbb1+8eAEAdV1fX19//eXXAEAiRDQejSaTyXQ8Kstyu769f++8KArgZjaZHi/nwlAEmkxG8/lcw5MWi1lRhO16s7talWXZxHTIRJK4LLizKyLyeVlDIMnmoQ6cnfOFzREz01VzOrhjyxQMiiHl3CJSsXoym3m0iQIx6mETgYrqgEmJFQBCCkGo5dWWj5VFNFMboNqgVciTohgJgIZmZNrVAEABB6vidBiko3QALmfW8MKwKy09QlmWmgDS8GFIiTzCK+raSYd12y5KfkW6BuyNRFtvn/p9FF0CI8m6Lww2NQAAMAECdo5XjdQeeh7q2NRNva/2dV0DRzWJWoZnSIUZUqiwJiGxgXmPWU8lQpsouEUwD0Dp5qISZ+kV6ijHvdXsdYWoYWxdG6hCHtIpHGAqLg6IEqUhUn98HXBHX3c/11cURWG5oHsky+Zu6rLSorLLIEIICfB10pZ0De1FqmEnQGkdRqfQJ1khcWEQ5IaJs4CFOdYcAHRdRFFRWktM0Z2djZ+yhdzkRW29oyVr3snIbwTu+/elMC0iBEBm8MmyBDk2FYS2oK8Bv2f6bhGYOk6XOkcA0NgzvRMZ3P5Ki6KMySrD7nY7u6/utwAQmWuSs3zWq4mHZ7OZHvFq63liNk3UOE9BwJCIYRQej8YeT+yzCK5MlWmukjWAHuIqqfALAE6m65EMj47Dve0J4vBi2JXfCf6CXeUNdAx4u92agxUi2vmupp/UWe/3+9vb25ubm+12aymzLc268u8ydOLhusjUnyAAPH36dD6fr1arJ0+evPPOO//qX/2rX/ziF//3/+P/GM1m//k//+f333//z//8zz/88MOvv/766dOn9+7dW8wW0sSq2td1HZsGWHa7XbXbAEtV78blfHG8PL93b7lcLpfLR/fuEct8PqdcbXs0Kmez2WRaXL2+yOPsgM5bMjwD1uv+AotAts/EmHNpNZGZCwVyJ31EZGakUa551QFNHZv2vWZ4ASiKUlwuCMghfa25L7MjVHN0xFSNB1FJjf7pJ+lxGu6U6/oK8XApe63d5DYFQuZcg8ihMQ6CVUzTFW4d7np7xPf/5pFAd4P4PeK1ao+Kd+3HYbf+SXGild4022NaYUUg9dch4kAkKSuLurVj18JmfNdDpi3miMoBD0zfwDuEmAy4LziycBBo37N/gH7GMU9t/DDQnar0GIB/GJ1YkwCy3/e4tXTKY/RbssQoixWAzICVzVB2s0rvBSmKgrOAhf5QIqbgYDPtpFVuGo+x4jQ2Y8Dg4sVtn/oGrlsDTp5+35fIg8jjKrPaVFqBoLegft3hu3ZNj9VZY2ei8/v0+PhYZ6ecQhlEZJ5NR9P5/PT09Pj4+OjoSNkH5YzfPbMuAGCg0EVLv6y9JRY1QdtD/D08D2nAgAcyTvsm7Cb19pjnEd2Pku8wcdvZZG+E0np4dhiwr+xhxp8QAhFqRsndbqfsmYjm87nmhuwBTrqSl//k7tmb/Xl2djYej6+urp49exZj/OlPf/rDH/7w66+/Xq/Xcbv96quvAGC5XNZ1PR6Pz8/uvff2u01Vb7fb1Wq1Wa82m02938a6KsvyaD6djidHR4uj+WI2GR/NZ6enp7/91a+Pj5YgcTQaFQSkCdkw3sVR/Bk/YUtxkDvxqbbBQsZJOeRNjVrWxtAIscomx94hn8o9fkMqQ2UK3ikmATOrQoYkOVMaRBBATbcEiMp9NUxIqREAAGnsKR6QP3oICV1KjYcC+bvz7RIaZcAOT9p+BjXPE2I0HZbWroXTOzuvkP5oe1PoYalzpu1Qlt5PhtDwm8UeY+cplnBDJS3lk5qelgFEqAjcZPGOIRktQiAQi3KBrAdjtuISUdM0mg0/hEBFAOgEHQ3Xq9cOUhs/KXDU5q5lPQgZEUFMJmhdnZALBrS7JiMzWYmRnvqVlfUeTbOFburK7wj9Hg5RPG1JszzEgEWEJKUsFsnBTE4S1mqtKYY4Z4xSiwc44cDIlydr4twIPOmjnMZEgWCgu4sxx5xYxhq6k0pxtg1U4Q3aNOO9xbUV7Gy97rJiVxHoMQtwZTbsvo5cK9QpZ9VAGCISgPFyPlsszs7O1M3K9Jme86nfjzDYpOD4pnTFiMKXP/OT7KUisx/36IgMgiikq+l6QmzyAkCbcs+/Wrou/sN+hmDlbMQ3QNhP/G6R7Plcb/Y5zVilcNRIanRVq9BXbRTojVBf3bP121Af3HugVfl2u90XX3xRFMXjx4///b//9//xP/7H7XJZ1/XFxcWrV6+YeTwev375an11G0JQsev09PSth/fLUBDBfruez2cIEGONKPv99uXz3erq8vmT55PReFQGzW0GAPv9vom7FukHPMjwnrsLJ5nguhpkQtwS9JTEhoWZm1ghoqbjCHnvCVCz2wOhGQ96tqPebkTE9b7ydJFzulMdE6hhU9RPCAQpCkAEVP4KmM+IRZI2r2bGpF4DHDo+6oLCrj2xHrbh+Ht7e0gr7X6PARt6DHsevrHXVW+9eg0A/NolkHbdZLp4K5LknAMdGvdtwzPU1VljaVsjEwsRIUABDBBAo9Byy+vpJVSPAAYKRARGxuQKYFgqXQXXS/wiYuYT6KrafjXt+s03eyBKaydsDFhc8IVkQd+k+azEd1DC+Iq9pTfCuqmxPWlRHswAYLE3cAhLE5vxn9kihYhIICIIwEAV1xZmpgsKMWmuIgIte00DU1VPF4izEVSyD4GHsI2HqDURe0SCQetvnwwl7Ap/3J6SIiHBoEPsAnm4gv51it/SvW/fxlzXWYm80n+du/pSTadT9QoKISDReDmfzGZHR0daTcEMXV7rbScCLf86+NmbAgAU6ihkQ7RPu98TbdjWNTcTlPzLehe9zsExsN6nWTyk69gZu3HJ9nkXA8YsZ+lv7dSWpaFcp0LxbL1e39zczOdz3611GLOJRgbWuYPjf/LkSRmK5XJ57/Ts5ubms5//4ubq+sc//vFP/vhPfvaznz19+nS5XE7mk+vr681qjQJ/+7d/OyrLyWQyn88X0+l0Nh6Xo6Kg+/dOjxaLgkJV7Y5m88l0tFmtb29vj4+PNaO3hn4jQlPVEJ0sBh28NO0W9MAuC7YqboijuUn+2Kw55yJPlV60BkNkIhoXyT6TYquoGC0WnN2aWzOyA066VuAjcOwl8AcASAkuVDNmhNaQHAW1/JGKcQHygV1a4uRGlrUiROjGBXmk93+2e/Lg0+4ZGOx5fOMGky4DllyV3lMBGGyHdngDjmt92s3evvAu/eh0L39xEBS+Nx2wtxvpBBghiiZBwcQrgSAyqft60JwRUZcfkVAY8jGC14MtBSy7BowRALtog122ahvc/zlEMw/kFj1ED+PygkIbxUSAQ1sJ58Aq66q39OKYRw9P7Fe9kfiF8B26n/RncWCxkvjkEMR5SGmaWEIUZK5Fa6fEGFEEEaM6eKmINgx/Q1BnR0BCQhJSYUsIIUaJ0WFJmqxvNkJjoj06EIKdDZN9phUAUq8Q/QeAEpMziUeAHkB6O6h3P+NoSiU9fNh6tkhCdfM+OTmxHM6mQghAmI2LyRgDaSp1lZ2C4rYpDtDW5C6Kgv3CGWr1VjYPo7C6hr2x2qE0dnmwdDkudEn8EF620K1OwCwivTAn+6EnLuJUh54XInR3yEEGbAPwZrr5YmpP2pjVMkZEqgpDDtNmZo2F9VRD+zdbBHSJ6ermFhH3+/18PldicXl5+fOf//wnP/nJBx98gIivXr26ubkJIczn89l4cjJfgggz7/f77WpVN/tYN8DVD3/wUbXf3Ts9mc+nJ6fLo6P5fjFfLhdny9NHD98+Pj7WAA+NZBZp0yOCoyzSYwbY3kG3Fn4K/kIZsFYeLctRCEEZ8Din8WIKk3LEgNxtIqIWFE9W0jrmomqS9d20gV2xNdMqBEEgYBYpxCm4PUyzV9AdDBgG+7ndnYea/1LMrogt1vnmB+CxnZlRDhxhDKfguxoOWxwD9kgozk4oA/WxBxn9C9ykxPFyxfbooqTE8Qx9mpMrHyg9UmJHRDgI3KKudyu6sBzMDhZExCAccqzrACy+QzYnrDuOGHrzNVD0vrU75MKE7AIRMTKEvuyCTibgHB2bblKH1PiueuPRP0Mu22cQ6y7Q4ekcZMD6HvBMkWg8HisDZm5dp0Sk2Vcikj2xWi1CXJ6A3goa5G2EUURdZPzIbZwH0Y+cg46f9eH965BNBljhSdPdGzc/KZ07fgx2rTZOdXtW1yrNHNlqXwhMyWyjGTxERB+ILhLEkwLuvvQufLCLQlXDIS5KV66xXkwD7mEkdw1Ndh2zV22PcLAz/fsfyqHmRyJOJrVR+YX3v7IHKKfj0SqE5oBeFIXmDFuv1zZNHWoibbFjXW/hcIcX92w20yQe15eXarsgouvr6//6X//re++9p0UMX7x4oTbqZ8+evffOeyhUIISixKIAGJMAYLPf76+urkZFWMwm++1udXNd1/VisXj48OFyuUTEqqo2G2SOTdNQyMrJXeiYFrGVmiVXwSIirWmn8AnTqR0oppxWLCJSUiCiApMJLgk0FOp91WRpxgQsyPFaw8EEar2U1dqMWpoXkuKrw0XldgCClEIoyMoCt3mvcs0iXZc3peCwReyN6i6G3dswksUFZRvidkGPUkhX6OmdjNljfr+Ac4lA7OzBllC6jePh3HuGui7NB+Fgr/B9mgmE+4GegIjqj4eAKKJFHVFhL1rsCEHFNT2zyID1b9HJ2pCYua5rZAIIQwb8BvJqDHhIwXHAdG0ww5/0xgOe10IrT/jBeMIVc4gBho5oZSD1NLP70n5Cobx+HggdFTzfOMCAOYctAYBoQDGJppRHRAoBc4HeklpnW5+KsqkT/e9pPmZhtg2iZKGkAN3mQYSD5n1oehvh4EKLdDLpWjPG6btyEHAjOaRPWtP4Zltri+ZXd2hxBw1EFALtY23wsWNQcfwLurjUDKJ1Dv5p6FpEF3biJV/Nodjb3jrhHgM2qbkHdG161jsEXOPKwPkmA2531xz8nR4SKEA9oM1pYrfbqelfQa9z2e124/E45sRYnAOoy1Bsqy2otyFCAGRCLUJX7/Za4TiCIItei8i+qstQnJycIGKMUe3bzLxYLFar1ePHjz/++OMf/OAHv/zlL59+8215cnJ9dYWIATAEKkIoCgoFEZXvvfceBVR3lYury9/85jdVtXv87ns/+aOfFCVhoBCwKAqCIhCMAm02m6xsYWJFQgDRe7OTIyWs12l7pezHAFCzFJgizQImqQVZhCUyRE1KmM+WGpAdQ8RUCkk/daUDkd3pfBsoa736H0zXae1aJNEywCnjlfYopHV2gVDZQJ4aAECOyLyTdh9sqJw+1ze1T4B+xVOtZyh0QN2H7AXq8ZYERKidEiWHOcoJE/S+ZC2mQ3dEKzSnT4EojAIsrCoMsuid1uPjTnKWrF9ZXsxuZzZOyRYmv9ltHwXkCAxaOCMSFAgAhMCsBZ3bJ7VpSe+EBZpmUt1q9OxWADSttzCzsMQyYJav2nTN4qontcuUlCQRgIDIIkaSWXKN2QxPbrHdT8d32AeUjkOTc3gI+AvPgzOmt/QXuxwduwJBet6dYVtv6VfY1rrVX+hTB7N/KZXU5U9lvgQYYiMNUOsSZcOYTCaeyHPO9E4BgdubhgY9sgxZLdF6WEP6fJcXtAEIMrAA27J70r0Jiu4aZYAJVQiS5UkQENKnUhwBBGFBQk1ymc0RWp744AqGEJgbZsw7gokKIlgsFqNRMZnMikJt4zGl/enyJrbjuU5oOwGIos04IAOQECOTpP8xck4X1/9foXVqO3gAAAC3t7eapkvLhqjj0n6/r5tms9si4snJiWp76/W6auqbm5uzs7PF0QIRNdC2ruubm5v1en1zcXF8796jR480zcVisbDgYK8W652UGqm/QVJGpzRIyWe02SYGIpYdAv2nT2ApIiJlKBBRIjdcJ7c3CprHn3PGUgKMMVZVzcyF7katWMyshc+1GLvuaErOQaoTYFkGRGCIIAAExSgUTCLCTXW9uvnH9e1+u/7000//9E/+6N1HD7/99ttXL15fvb6YTCZvv/M2CnzzzVerGB88eHB5fUWAm83m9nY9Ho/nR8f3Jg/L6ez5q5dVvTtZHofJtKqqcVlOyhFw5LpJ9c2ZGQGLMjaxqhoAttSDKeAOWTFbQAQiaL1CAAyAQk3DARAhkDNqU0A9w1PFF2IEikJRAIPm7TPGktMyEAXbKoSJcQpCDMGbDkxDMRXQqSQgyJPJjCHVHA2IDECI7M9UMP8/pB0MXYOw9dcjKCLCCFodjRB6n4oPBBAFSK815iRCFGgaLe7LIpk+OpOUljpWuSJQUPcxPVzTkuOMUBRFIxwjK4NN95m5EUrVPxkARbKwAogQBARSyVv9StlhgSE6MKpmrEIkKKNnU3rAtoL0jv/tvmsxxgYBSsgu5k3dQCThoigQIBAAIInTijgT1t7+TbjntrU+V9dBK2On48gUYCNOaldKoYMvpzNGAAFGnWSCHkQWwiBon5pABqTOnXRUVd3W2GrJqC+lEDgxBRGJiKSr0FZ9RVFLjSQP5BRK19IZtZD5VL5J9hAAwNixaUPCCR0hg3OhTM8T9BiwulCwSIpB8sUBkbM6LGiHrsaMRUCEhIAZ00mxlKNiyH0h537v7yOWWLXOuX5z+Wt/cMk5xlqH4nefZJszmkCDWFAQQBZoIqMljSeE9kBMP1EEgDDQhFVoJpRcxF5flj+TDq5/x1iLaEYCAOCiKFOt11BmZTjtfl3IoiBBiU0FEgOVKIABWKAYFenYHBC1LqTWrUq8QO+TCKd6kIiEjESCjEiSr4vgCjCZ7OPhqCCYTCZFUUxns9evXx8dHWnirouLi91ud3R09OjRIxG5uLi4uLggorIsV6vV2dnZj370o7cePnr9+vV6vVbV8969e1YCGpzk2+KXkxB905AAoxT6Q8obuuXf+U/1sRq2oih7VNj2Q0cOsL1jFiBNOJ+NbCoHiG2U/HlXU+fqpmm+/PLLq4tLzajy4Ycf3js+eTKfXl5ePnv2jEBGo9GjR48++OCD337+q+l0Gqio6gYIF+NFMRrHGKumrqNR2xijmkRjAKREYvIeRhSEWDfKgJnZGLCKIEphmVWrEgBGkaIoSAbJmBC18m6goKkh0wAQtrv9MKyoN3e7IwBMKIdW2NSOzs3Obuh83hHwS4AM0nZllK43qt6fd71FTeJehUyZ87tI0h+2JAZidsVU0AnTtYjElICQQQkop2LHkrivZB7cNxfrt4gkmjdI2qOZQ2Px1IQB9OSh1b28BDx4kQBAIDJQk0BSzvCOXeokqu95n10GaXA5OtofujAYG1YEzSeS4NmBsEhU0wUigIQkjtjWgJ6NxITz9I3cab0c3pHslG/ExFOVN7dDmEnuM4EHLZnk8OcI0Ba2yrczV7ajBD50WIbKNQBiVsAgn9zrA6p3ee0oTbkskVs8MbnNO9WmJ0W4G+fNWcYCJw2DY8yI6ZQpJ4dPyd4x+4hwVoK1XxBkFBBiREuTZ50fgjojBl1mRENsBZSuHSOGfJLLIllSu6OpnKMX+YQgbQ1UA03+FJDeHf0sQtFJJehXKISgBqxANCrLWQhEpM5ZKRWIGg5Y9ttdjFEiL2bz+XweQqiqar/dXbx6vVwu79+/LyKbzebk5GSxWNze3q5WK8297AnHXUivra5r4BSWqIglRKyCs2t25OCpiZ+ddx/3j3E3AnL4ADja9IYl8a1l3gAxxslkUoZiv98/e/bs+vp6t9s9evTovbfeXi6X19fXNzc3sa6m0+nx8TEAfPTRR7vdrt5XKtDMZrN333330YP7u82K3XF4jBFzzny/dnbNKQF/Ju2QbFqHBBcrRtYJ/IAkqDovx0wlA0BtqSUHrx5CLz1wmAHfRdJ//4YDG6CXvXTzv+G3MKCkIpZ16zBNHN4USX5P9hP9eXSx12gugczMVva18yKvofrZHZrygTFYs9zU0q1P3GPA9lt/Rk6uZz/Z77Nyd62vSBtSoa+2jHV+O/sp+/n1EM8vtzib8PDbw8NLtdz7FHwIah2qT1v2+yGwoehdVIWZDzJgi4fpvrcdT69DM5v3/Gr9+noaiC4mGNze0eIoRlcxWdyS56e0VhYBUWUvHMRSs1FbONabWw9K+Vokh73hgPUeokXtkXlv7i1NGCy3f7XnF9h1LTy4fN/ZCs1m6Xe7x2AAiDHqllAPsU8++eT58+eXl5dFURwdHU0mk9vb21evXjVNc3Z2dnR0pD1MJpP1ev3NN988fPjw3XffPT4+vri40DK6+/3++PhYNcIe4Hp43xlocvptY8N1/jFr0j2g86GkIojYNJWHrAG6R4z8M/7hLsf6HZoGcRPSdDqdTCYAcHV1tb5djSi8/fbb7733XtM01W673+9fvnz52WefjcsQQnjvnXc//fTT8WT04sWLJ0+ebNertx7eR8RU71ptvAMJxiKR9EkB5K5nDUA+n8PW+KH3CUKPEBsaDCcuh5wO9OIu5ybyFXz//996ZLTX3sCArfU3nrRBO54cmFwPTrihQV5Jg6rXPvVXxoBxwIBl4HI1QNG+Bci/zlPMnlOk/tna/7siuMidDBh+xy1wN4VCcBnLIS9TzPlzwCcxUIGyq34Pe26RWfozuutX+qe4tMk9inRw/Klzx92/kxxLVyL8Ls59J2PuEaJ8IYZ7B1HF++gpVCfjsQ0guupwmhvZkNzYJEv7at+/WlLB4Woa1aEJ6q8O7k2TD3oxjR50HsgibSR2j2HDHQvhX+TXovfbdgpdhiguHE41ny4jP7hcd6JEYV5hvQ0gWUjXVanrWq1DH/3gBwhQV9V2u12vVjHGIoST42Otobvbbq+vrxHx+Ph4OpmMHjxgZlWCLy4uPv/888vLyxDC+fn5kydPhtDHnFlm2NKZhEpbRuZEjmZzT1DMRj1yKST9RdWLr8jNJzTorbpdeIJ418ZIr3NfEoAATCaTqqp2+x0RTccTs/D86le/0s1QFIWenavr1sWrF8vlkohubm7khrW68KsXzx+/+7bG4BZFURASIMaGuZOtxn8SkQhi0HkpoqisqggaEBGhky4OD8n7cZDRBpztpgdkRCRnOeyQsjs04Lva7yrpRBAAOWwiRez/e8N7D1EZcQzYbtp8hjQ9PcCOcGT7ssQozOJORlGERdS5qff2N42HLezQENXryi2jZYmS09FI1/4Mh3S4vB3Nanjnuv3TBCq0iViSBP2Cc5VWMKIEgCIQfVBysgO2CKsTUfzMCaBsRgde77cMdXSsHofo/VBEQNwqdwM07qIP1kuPDb+ZngybCWTtYAAA2FiBdJs5CbIr1IOIk6IwbqQkSMm+9/nw4pFP0OTHbGCUAWftTH+gYvYmTkRCCPoZSCi7aKW4f/0re3JlatJjpcNX57e0i2uuzl0C1VmUHiTBbUNjwOhi0wF+583QSUXZgxHmGpDMrPyvruuvv/46hPCDH/xgvV5/++23r169Go/Hp6enAHB7e6uF6wFAMzsul8urq6tf//rXl5eXZ2dnH374YVmWT548+eqrr3plEL2Qe7DpKZFBze77OGbIOp/9abTG5ngXApkrig2mhyLDZfidmmr8lnJrv9/rCGNdf/PNN69evULEs5PjDz744L333nv8+PFkVIQQXj5/8ctf/nJf7X7wgx98+umn8+nESkMDQAiBAEE4Zs8O6GrAAKD1YMCoPwCkHFJo9lQwEgwBEA8KrT7uuQUgQJM5gF8F6GKRYbkgNMNe3PMHX/FPaUNa4DfbG3RxuaOx+6bXbfs6HxOZewOHjZA1YKPd9u0buO/BsXE+13dPel05GhllaQ08XhW2MUhXZgIAy7SYZtodw++9EL1vbAycM/xwDkOQzDmSLVpPNO8QdA6OTaQNd/QTtN+6yaK5Yx1E5t4rejfl+2nAQ7DIHR7sCTh3/LDHD9rbybGpzwV7UWH2qy1iILIcdirZA8BisfDal4WHSOThAMCFC/u5JDfMO5ROG1t7Qcn78y7W2NvCkCMU/bdvgGeee2tA7b3Id9JO824NOEV+3Z17YDjNXiu8COxnaAHjtlGVB19eXJyfnr311lunb709G08m5ej29na7WusKzadTjcC5vr7WKrl1XX/x298+e/r0/ffff//99z/95JP5bPYP//APRUjh7mn9zKp8x6H3ydFSBbSyLEMu8S0iFxcXkEUS+wQAi5v2DboM2EOHXTSzh+ld1PAuAoRibrnQ6psAsW7KshyXI/Ums2pFi9lMY4IRMdYVEe33+8Vi8dbD+0VRnJ+fExFLPD8/f/DgwdF8tlndMHNd13Vdl4FCKFjEfFh6uw6zgChMKZ4TdHAa5oEADQAgKxVIPzN5U9U1VAmpW56yfVFd8/DmIU6cPv+JHPV3aXeNqt1vh/POAtydEMaCgHv7BbpTNnIsXa3IIxI4qt3ygDs8bmDAxe0CAATaY9QuA27NQpHbkq52cfBd2B7RCciBZ/4ZmxeOzR6utVp7w0NEQKTugr2Z1UnGXhxoZnDnZgE69BgeZPO59K4n/fAdGvCbtOphI6KDUqipsN0h9cfsR2tT8EiolGeYSrZUzhZCgUghhKYJmWSBy1Roy6f1lVOVbtc4tvW/zfKURoKIlIpoYS6ARljkRwmBQL25WxHQcUr9H7bBQu26dOVsBwE9mmwfOMiDh224mpJFRr/Zv8+C9lrhN4Aft7fFcz6cB5FJObq8vKzr+r333nv48OFkMtFC9PP5/OjoqGma58+fI6JWbqqqajoaa7Hi169f39zcfPLJJx999NHJycl//+//vcfJdALDPIXa9EyCXEVrxYAf//jHdV1rScHVarVer/f7vYpscEiGjYOUlpAZsMGht9mG13ftrje06XRaVdVqtQohTCaTxWKhnuTb9ZqI5vP5bDaLdfXVV1/94he/iDH+4IPHRVH84IMP/+iP/qgcFc+ePfv8888f3j8/PpobA+ZRKZQIVol9hwKAzGdNcNN8sYkRptNHogixpXSxvjPhyRACgh3vL4+C1M00lH8FgnQQdr8H7n6fNiSyvRfdxV6G3Dc1/J1X/yAuDTtBzPHRB5xN+oJ5b4SAHi37xmpvbR6SYL8Lem2YQa8zWru+Y7J3Pe8bd53LRP0KMwni7OpiMrHvH+/WOHtU5S7i2J8vZvAdGjMOeLB/3e+NwG/o9g3NQ8PBgSQ2yRTa5cHDOG9tCn8z/hs3AoA24yyivTHVBXe2ioPdGgdhYby7Ubd5N2brx1/4LYyIAITYqdtty32XVioi3hBtY/CveMNyuO3Tv/P7rWOBWbTsvSfmIg0AQIiUixEVSLPJtGmar774cnVze35+/oMPPjw+Wj579qza7cuyvH/vvKqq9e2qHo2WJ8dffPEF5SKLs/Hk8tXrarsry/L/9n/5vz59+vSbb765vb3VoSsHfeett80OjIha9H6z2ZRlaXYzTdqpTmH1vhqPx7PZ7Pzs3oPz+1oIoa7rb775pqoqjTxWh46yLKkIesiqHLpNZ6Mu37mGEubjkKIoql3lMUay3qB9enu4YmEY2rgEAIAAZ5MpTlMqqaaq6v1eIayB0fv9frdZA8Dx8bHKoRcXF3/w6Y/ffffdi8vXmih8u90+enB+cnJyNJ01Vb1arY7miyJncgEATRQXgfX8bDweCzcimlgj5oMyRkRgEVB4UhYJBRFraaBH3B2eQXdjC8JoNDKe5H9i5Qh9Y4CD4Ubg4oD77bsyVfVvE6aD7e5etf5F5XTJGTGxNa37yfLA6Um/7ZnypDUCY7vo4ntI91MPkkNard5U12mFqE3oYXo2OMuNHw+bp2HHS7PziPdbBKc5Uc4Nl9/bt+OJSHY6NrA4QB9erTsZ7cEjDACAvK9tsrqhNDWsrqDu+hijAGg5kLTvlIDmZ/wAbBDs1tTjsyeXYom6ugxYBty9xz+GEcDf2brg7ds8B4xHkaezB/XTqqZCRldmVvwSpxzbFmCXKMm/oijSKXs2k3BVV7YQphkXRUGBRkXR1LVRwqZptLKcWix6gNKLoii4k4qm3Q6q9Ybs0ZJ4MCNQ3r9EpjQrCDBH3uSVZZ/02b+XB5mqMAmUQtQiv98dem1TZuaGYwhBcq2RQIGcY6C4WAYrD7Xf782KYGD0YO9R1AL5Dpmuab1e/Wdk0cJMV1dXX3/99c3NjYa0Xl9fK2O7ubkhIq14v75dKbPUikPMfHFx8fTp07quy7I8PT09Ozt7+vTp559/fnV1dXx8/P7773MTleNuNpvb29umae7fv//jH//47//+7w1Hdcfu93siurq6Ut+l6XQ6m82UKxdF8e677zZNs91uN5vNarXabDb7/b7e7/i2obJQnu2BqBo2ZnG7qio1uc/KcQ98to3tGgeyjw1VRECghwrkNuEHH3yw3W6vr6+32+24LLTsc1mWk1Hxb//tv/3BBx/+7Gc/+6//7b+Mx+Mf/vCHs8m4qqq6rpuyEcuaBlIURV03nl5jPvWpG7NVAoGNlglQjYvKFWyEwzrQPerQQXTCfd2Iq49rF97S0DZUE/dBjDvcfm+L9UFS+4bHYLA3rJH3k8jUxzqHwQ7qQqPTbY8N/N6tD2f0ioh3E3O5naGDHtAl/T0lIH8yfg8v6H+K7aLDz9wdm6Z/Mk2UOpAcbr0hJ3gDg+w9r919f4Z6sJM3P/mdzxzsajjNHuVBRABlWge6Gl5o68WA9bYD57ybIkJEDSJJmyBKUwrqMaXlfvdHgQJAZcCh4JJH0lsdEQEqegz14BR6c/9O+PsHfM9v/tXvigbfv9nIC24OEUqAXg5kaySwoZWIXF9ebjabaruDyPXx8XK+qKrqel81+2o6nS6ms/V6/fT5s8XyaLvdvn79WlNuKTMej8d/9Vd/9eDBg7fffns+n//whz+8uLi4vr5+9uzZ02+ffPj4/Uk5enH9jGN8/O67RVH88hefxcrKeCGINE2laxzTlgEiUrY6nU5Ho9Hx8bFq3qPRaD6fKyfe19V2u26E1WotmYdhLlOlwou44w1FrP4W7Z7zizdzeV4lSYK16P7CKc0qyGkO6uVyeXp6Op9OptMpIlZV9eGHH1dV9R/+w3/4u7/7u3JUvPfeexcXF/FocXl5WRASS0EhINR1LSCNM1dAF9392jHntwKaApgyMosoe2Roz3JU9xERQOdq3902eAdDPWwCQj0j+k78dHj4OzwLNiq/3+SN1iE5pF743tqV1cew7TBhAmnCxUHPCeadkRzkGQd/2COF6NQdf5G808H7qEdoTY6xpYnS5uqCQ4W9h3/m47f8kzsYsL9/J5zvNk17owJ06aOfL2TKKwLmq+Hvw6HV9x32xtNfWem72/xO9HfY4cHmz279sg5/pQwVumhpF/700f0cQwjQxXYjj3eMp0//bWz6GV2hLUQsKQgRq8mEqCAqylLppGTnPitnKXfXCQZnhum9/eCT/qsOuKQPRoNJD7ByR8DSwYfzF+njDQvk72cqemcbEpmCq1p/1PsUTtGIkRkkxV4IABFdXFwo21gsFiLyzTfffPbZZ++8887R0dHZ2dl8Pl+v169evVqv1/Vu//XlJeZK7ep/tN1utf78119//Y//+I8//OEPP/74448++ujbb7/96osvH57f/+yzz5qm+eCDD87Ozi4vL588eaIL6c8nIO9MEqhirKqqaRoQoRBUA57NZqnG32KhZ6462obrbbXX0+LNZqOHqUbIPGZo41xUgLoH9bFbcN5WiFwniQG3ihNaz3b/9evXJycnWgxLYmNlkn/5y1+u1+u/+e9/fX19/ZM//RMNoT4+Wtzc3ExG5Ww0nk2mItI0TYzNfr8fh0IkY4xjKl10R/PMRSCRjpUYnYnSI0oPaby9lBFScs6BbeDghkd1o/ldGPBdlunv04Zk6MAzd0fQGui8dhil43YhoklJ0Q/UM1rPgO2r7znmNzxv/d+lAefMRO3Jn85UTdxD1PUmO9c4ZYZVOPwTGPCdRwwYjCf53xpZZJeRg9RXA5KZyt7oiTV0aeVBqt1jlp52G7gPQeNA0+etH3Ry0l0/gC52+TH3WE7u54CMyD70pZ0pUWYBPczxM+311iURZP1DlyakB0JbAbZ/gpv97UMIxoBzHkbErglaW4+qC6QnhkDuzfTgPu0tgU3ZL/RQIPAd+h8itgx4uC4O5oheHIfDRy0HKQwAFPvt7mAyem6iICj7BZYorJnEtIBuXdfT6VSY16vVzc3N7e3tZr1+++23P/jgg9OTk/1u9+rly91uN55MZrOZH4RwiqS8d3q22Wz2292vf/mrm6vrt99+++zs7M/+7M/q3b4siidPnqxub1e3t9fX16PR6OHDh69evQIRzvUoQHN7hrDb7VCkpFCU2U4YuYnVuon7zXa33mxuV5r7YjQaYRGm88nxdHLv3j0A0EPi1Wq12+3UYXu321VVBTECJnV4WowMNT26qIG6Xaq8SAFbjguZDYNWv8yP+Vqks9msrusnT56s12uJzXw+Pzs7m81mv/nNb7RixPn5+TvvvHN8fHx9ff38+fP33nlLje3LxdFsMh4VJVf7pqkIECCIwqfNly4hpDhvIkGTdpElMmLQc1/MYgMiWpnI3l71BLH1vAAQSbkAJf/T639icOg/pWU/Ph2zKvpyZxm7QyTJf98j3wf09zz9XidpQ3KSuxPFh5QCRVzRF38hdyvK2FWC/TNtRBlAL4Gl/VYHGqhzQG6zG75Of/fPxYDvbC6pjptCS6Ri7CjuLIhagolFSIQkiXWpGECSQNEg5hhA97UHKDUog7+Dl79xEu0G+Z1+YgPAwUt1HpIfhjsueoNEHDj0uGeGGI6hU50JTbHpFo3IVx1mrDTSWJqxOu9ewCl4N5O+vBzitOo05nzNoOW2EJ3FLGddTjcBUEQo1eA4DIr+4kILGE/JMQsZNoV2CRwD7q2RdAWX4SsOtuHy4Sf/8n/XSj69T64brfNjn1r5Z7PZjCbpAPX29vb29rYsy+PjY2Vgy+Xy/fffXywWl5eXT58+Xa1W987Pt9V+vV5vt1tmVmeisiybptGLqqp2u11Zlvfu3bt/dm88Gj1+/JiZ/8f/+B+//vWvR6PRYrHQE1/oEhRtddN4EUzyma5akhVMelZRliWVxWwxLcYjPTOeTCbm46eHxKoZq9+W9r+6vNZ+eiesmkqzNxhEHFEnXAe09DcAMLcMPCcfEJGqqe28eT6dzOdzEdnv90fzqYjMp7Mf/ehHH/7gA/VQu3d68gef/uj0eHn/9Gw+nY3LIiDF/W6/39bbHXMTuY4xNhJjjFVdxxhtRzGzMWBE5Kh6fy2SEpMrpvXqNHvUsX6MB0eQJjJj5wHIfPpAJwCI4XfSgBvXjSNMh5sggSC/MX1rr6nEOiRMPcrYMgCnMScEU3HHn6XFFkQQO8M2EHl08hcc28cMjHZhDqiWHqHWcnKHTNBpvO1UGQAKKm08vnFXg88XkbDtpJOK0sHw+zDgwz4BAMKNvLEBABElPyAirblBLp+i0nTF2yGhV4WkN+V2Fm59EVEQYowdztBFgP59UoZ9ADPvgoMF9vbIt1mMul1xbJqeBgzOMOBHKCIAXIDoQvd+Qtlr2m9SAIj5YAIHZxyH1yuyhaoP8ac/JEJ0FpehBjyYLwmmVLg9kFo4OLitBAAYD3tZQxcZspLNRVGUZRiPx8oR1EXJSjkRkUW6igiD1IhWUpSwcFPQntvqyGmcVB50whrSFv0sqv1ejRa9z6aupfusZkMJIUhkbiIVCCzAEpAmo3EzmV7vq1cvXhLge++9d3y0lMiviuLp06dhVI7HY61vqFWVbm9uAKBpmuVyaeV4f/vb3/7qs3/8gx//+Pb65p133vmL/+XPf/DBh3/3d3/3+vXr+Xye1ErL7cciIFpVRu/0HOL9n0qz6rqGQKvNbQRBRE2wvFwuNaGm8uOjo6Nedtzn3zyJMaqznwU49QiErXFva3l3iIQTAsDC0OqRN7c3ZVmqhXy5mIcQrq+vV6tVURzNZrOf/os/+/TTT6+uL6uq+slPfvLDH3x4c3WBiPv9npu4JUQBjE2MNbEwR5Y2NVqhYV3OO9TvecqhHZyTi2mLd2iKyW8YQShXAREpAAA5duNe9Hk1gQ46Ab4zsOWu9s+gSr+BoJh6etfD/d9ie99vpIMvFZGDmXreMB5we+7N/UNHDPdQagl6h8onBtymqfGjMuc7/4lICGKd+3eEO5yz3jy1YYvS1liVbsSX34Y2SAoEAC3qdky1eaaHFJQe3fcDbgl6dvv2P/w+Tb53JKh/oDew4UXvFb2L3leSDFJmh+pj3cEB+zu++bH5fmJs6UV+k4BI5BRRovG86mlMiBRapyrf7sATargdP3SXFfNBiQdFGCw9HFqOTKjB9+bnNRyPsVnpQsD698B5s9TSg3xnXu/9+F8dfE51suGPF0dHz1+9bPbV8dnpcr7Y1dVuvWmEm311/vDBfDK9ur3Zb7bL05PH77x7fHb6i3/8LDJbilEDUwhBc6woL9SgWASo99V2u10ul59++umjR4+ur68///zz58+f+zMP46kiUsdGT6kFUvGqIgQkauoasK1Ea4JFJbUQBkAIVFKAQAWSEJ4dn5ST8dFsPl3MZ+MJlYUKeqOiVKFhtVqtViutyRhj3G63QwaMiJPRyJugwbFhRAyD8R8fHxdFgaI5t/Ho6Gi5XI7H4/l0TESPHjxU//7/5c//5YP7D//mr//76fG8DFSQFt+SAFIAAvDpyRJUKQVAAskFtKv93ugasoAW3MtnFhzrnk1pv9/29qr+2QvzaO8LMwIyRojCCFobGZkjpMJE7lOPMOR30VDjwL70BhonSAw4LCYBhzaYtiKMQLOFidinJeuzOp+cSyIiIjuBA/L5t50BMzNwG95A0jeB6ki835y/4NjxmzMc62nAzs+FfRosEclVYhUznT1N991hTasVC7qfEYQPasBJaRgugUr9A4saRB5a2gCgqff2Q8+AdaaafQkR2zQR7kjIk1HN7Tq8j0WHkdin5zGemNaxkXw2UxDZtRXEtDv6GYUPVgO7C99IizZhsJ2omEVUGJb5b5u6ymWE2NWpTXd0FHpf7xBwCqfqzjkQ6YpqrFKKWAJoYi2EuiJq6YTIEaRAYoQAqNRSPxFRM/oRWIq0hCkxMiIQqU06fSIiEgkFEoBA1o/HASvHqZSjyXmSetvcymR5/i0iIQm4pG/HlJpD7FNjnTIfgKKk0agYj8fj8bgoilFRKl4pmhVFUYxK1YlFJIJE6GjADlWU5BeeAYtIWYzfoAEPtzw++MGfeay1ju5ymqB8zN/7JMQmxrIoQlHsttvb1Wo2nd5/8OBP//RPnz59qgFLRVGMx2NEtCA/ANBgMs4BWBcXF4vFYjqdlmV5dnb2zjvv3Lt3ryzLv/qrv3r+/LkeCc/n8zRUwqqqHjx6+Prlq4vLi9OT081uu99sH3/4wdXFZSpvjZBKuGs9WuSDZ94SGQgDUiiLcTkaTcaateqttx+qmUJNFoYWV1dXdV2rQ9lut6vrWhnqbr1R2UJzZM5ms1FRqv49JHAIwPt6Op0uF4vZbKaHvmoYuX///sOHD7fb7Ww2+8M//MPV6uZnP/t5U+82N5eL2fThowfvvvX2yfJ4OhufzI+OT442q1sRwWRQAoncxCo2UoTAsc3pqqf5Vh+J3GkxJAZQKwXvocsb2Z6WTWPPgBsGAJYO8yIAaOo2NaD/PGyyRogQD5qs5TBjpvhdYmiXUFLKAiaQjpO0kp1C6KBvBHYSR2Cu/hIwaWYiAtFUN+Qcp2jD7qGBD9sAgJ4J+i4GnPvnpt4DdNTEg/ITZCU45Lx9va96zne5Q4ZU/rbfVMM+nFTlEH1QF87efQCIVe0NRfZqn8nOz8WqqPWayqmJUGZpWBAgdOyfRugseVwPYiJRaYVSA7s2HPB3gDBiH+DahibZ9nUESpm0R92PgYoD2IbCTZ2EV4gqyKb9VTMgIwQkAWWhKuYCA4AxOWOuJYWDR41N0zCI4bxdG+XsUNEsiJO0+xqFdCSAHCAQgYkogoihYE1ZH0jhGZC0N3uX+9S4zTdZEfqrlgLttdf0CciEqlgVFJKqRVgAyWgclMKPx+PJaDzOeRXVd6woilCWVAQiSukNgqs6JR6F9DN47AIAhID5aNyb3D2++Vb4P+6S2vogOPQ5Ho+r29v1fq+Vg+ezGSKuV6sXz5/PptOPf/jDV69effvtt69fvdKKezc3N5gTXo7KMqGsiBbju76+Xl1ff/H550+ePPnxj3/8+PHjf/Nv/s3XX3/9m9/8RuspKS8fj8dfvn6tobGz6Wy5XM7n8wu8eP702WKxAE/vWFRWpRz5jN3PfVUhYiQKMTZVvd1uFXa/+e2vx+Ox5vlaZDYZQrh//76NX3L+PGaWJmo1xpubG9WS9/u9unqZXB9j1KKKErna7TQg/Pb2FgBCCGoMPzk5ub6+Pj4+Pj09/fLLL3/xi19cX1+/987bjx49Ao5EVIxHJ2en0+mY6+rFq1fz6dhsN7kwNFJQbPYmQgQQEBQWAIwiAGZKAtHC1jmxhr94Ez5IqtUpkh2AtHpnCnbyVBcA8A4sO/gOycz7ezbullP97udRQqJ2kiYM0FIH6OIJZN0OlLY6c3pHWLEpofhEteKtnQ45v2frMaS8NAme1v+bGLDWgHX37Vs7I+y/6I5sxGkig0VLpZx0hN/1iarZ5EB5L2qYD8dQsBjOcQhGAzVlei5dg+RBaqjATGn+3WeqDJvSuub7d1uePcftM+BUd1YQUyZUVdUkexd0PzPmq9kFra4zYNKDGAAhIWKEHEgVQVK9ZBFWSxVI9hTofKa3ap1dSlyWTOzTftJvFd8CpHUPpJInAEDSIAWFObliKygbBT4CcdpXWp1GlXSFq/tsuW+P0R5EAwBQnwBAPRczhkcpfl0PoQkBSVNbMiCAMKSUdlGEXEQDQ8qtIqjQbKmJ7esubTyMSLaP8I3ByohY+B/8U5qd+XlbcdM0T58+XS6XJycnb7/99unp6eXl5eXl5Wq1UlZUVZXqfIjIzHVdUwibzaaqqsl8PplMmPmXv/zlb37zm0ePHj1+/Pjf/bt/9/r165///OdffvnlarUajUYnJydVVS2Xy6IoLi8vAWA6nW42G3KFt4xU8d1hpRpRbke5pmdonpeqqtbrtZrK9Zkvv/xSHcSMMatLVwCcTaYhhLqu9aRKf7ter3trAwAIcP/kbFQURGQe/NobABwdHZ2fn282my+++OLx48ez2aze76YjXC7mDx48ePDw/mI6Y26EcDKZAGsijpYuJ5J6h49LD1fShaPOvytK9Pq3jT14rOVGwzH0HwY5zJf/udtwvgf3VU7z3U+A0Mp53SOrHkfs7Ubr9c7pu8XqXdsDiGiyiyf6frf7udzFgA9KBok9HFqDu9HjTQy7fxOAHAPWbnXn+oyzhtK9OR5kw74rcJPCwfUb4GA99MSmuzqxr3oCzYHRHvLY+j1aZ5qd+/0h3SVtDDv0QIiD8qN67RNlo/uPRIGc0l97EK1diCzSprLBXu2g4RS6aYsMjD4sqjudw7WE7efoS/hQx/h0kB62QzoEM89T3gxe6XJff9NP4Z+NAd/e3ipDYubtdltVlTo5q6f09fX1o0ePHj58+ODBg1evXr18+fLly5fqeKxPjsdj9XXcV1WMcTabnZ+fz2az29vbly9fbleri4uLq6ur999///79+3/xF3/x6aef/uIXv/jlL3/58OHDJ8+f6VRvLi7K6XS5XO52O8sF7VdREBo+UIwB8hmD2Q1MAF8cPRKnuSpbFRHNK6JwU9tFWZYlhXpfaUIuRFRrORHVda1+496aXRRFEcLZ0XGRuT4ilmWpD4QQrq6unjx5cnt7u16vP/nkk7Ozs7/7n/9jPJ4cHR0dHx9PJzNEBKCyHJdl2G+3IrVfRUQkLJo7OLBfdH+HLOvbQGM72HpMpT3DO8iApVPiotfPd6HY92qmoX7Ph23833O+AJ2fgO20bnk4z30P0soeqxu23gP+cLT3IkVz+3MISb/nEQ7UyYYuQffzSj/6/u13WUdU8tllwNr0jNlP9iDfPTjs7nU7pCGtPMgL38yDwa07ZKetYQ8dmHuAY+cBuINS/67NsMW/sQeTN/y8x7eG3bagu7sbcRKPPs8Agqy+4r0dYS6rvYEhtIKLP86nQd4Y/WSXitI3yAzYHz1Aqvfc95nqbVXEFH/UWxr/Z745SPmSTSPWcBDd5J9vE3j2sPwNq3WwNU0zHo81BFv3DyKqq4iIXFxc3NzcPHny5MGDB48ePXr//ff/5m/+5ubm5urqSn2aYoxWi2M2nRLRbrt99fLler2eTqfvvf/+er1++eLFq5cvT09PP/roow8//PDf/m//209/+tPPPvvsyZMnl69eTyaTcjQ+Oz4ZhWJ9czs+OwO1TOS4OMR0tnKwVmztnM7ItXqXyiCWFCblyHBiPB4rP9bT3yZWzb7aIe63u816rVkwRWQ2mylMlKdqCmsVOEajUUmhqeqQA6VUC1dmP5/Pf/azn41Go0ePHonI7f+PvT9rkuRKD0PB7zvH19gzcqvM2gEUuoFusFtkkyJFu9KDriS7c21o8w9mbJ71u2Smpzsy05seKLuUjKLYFHtldwOovSrXyIzIWH093zx87ieOb5GZhUIDzeZBmcPTw/3s59uX2Wxvb290dtZrPbi4GC+Xy+FwuL+70263kySez6e9TgdAaacORNTCZPMU6aIX3bzX0KG02zYBCBJA69DK/Cj/V3NcS/ttM1zIE67W/7auM78yvm9STJovF27qxtv8Ma5P3bWTU6mqugomKq0eeH3VqKj0OUMn4z4rpYGsb/L5Kf2Ut8Vjym+wSQINjTG9m0TWTTG9FdWmAGeeSQhBRbi0ATSVsSMAFFfWnNuamUG9jxoRZPUhP60Cz2r9m0vDFroGK5tHu/ocb0xT6lLCYVDchER5AmveJMZxy1pBJCiHoqg2vfG8FOikan+gOMNmVSUkWi1ZVDcSiHL9DyRmwENybkZkg7N8+6z7XCNYLiBaKM58aeqwbp9Y1el4B+wLAI7jAACznszGpWnKUmIiiuM4iiLOKs8W0Q8ePFgsFpPJZDKZcAoj5ilZdwt5Om7OpLRardI0tW2biI6Pj0ej0fHxMeuGOerk+fn51dUVo/zVatXpdIQR0mW9exCknYWBLJk7eI6rIIv7xSS5RIFCUKrYZEIIIVEAogAkgPl0JizpWLbX9di4gE1hrKEEAA7vrJRinTHjVDQCTbNw20KBBJYQHPubDbUYqT979kxKORwOPc9jV2k2Dp/P555rI9J0OnUsCxG7rXa3a1sSiUglqYKIDOaT6sp6SxnvVHfzTU5v6RMta8q1WNVS3l2bQcOtONr6ZH43/ParccDQALBKk0zF7NTVaS8V881aaIsGB9yEgE3si4q0FBSKh90cPuZEaxMH3AQlRAOibXqOxXy95ixV29qAgKnI7Rn3GYI017dUcxW4N1UORQgLpemqq7C0VQAaaaxbleo2MKsy1/G2lfP7TRxqHKdQ3BB8r62QwBBTC0SFCEYoC/2VUvWSGDJYxtLxKS1NPt7yk2qd60VBAKjYyefF5LtQigzdQsF/nYocbROFRA2sc3Wf1Iig3w0Bm5J9RGTD7rVpEhGHaAaA169fv3z58tNPP7Vte3d3l6N2cHhLtisGACGE53n9fp+IRqPR6ORk7/CQsTu7FF9eXv63//bfAOBf/qt/9eTJkwcPHpycnJyfn799+3axWGxvb0+nUzCgnh4jWus4zObVc10TJWuraek6PCMEQErFYazSNEnTdqsFgCpNw1WqOOpFkrAS1xYySZJgtbIsSxBYKECK3lbPnB99b0vLtW3LsoiykM6O4/i+PxgMnjx54vs+EzFMiPiuMz4/bbc8328j4nK5bLX8YX/Q73c5T3CyPueFKLLm+m66x0z8UoAy1zF5ettgfrMZAZcafbf99r6KiXGxwuhU3l4feMR13O8qgC7tOv2k6ttaBqP5fWnrlgCufoc97fTnpjuDOcD1iKiQR7Z0Uz4vCEJgLQJu2g9NnG4TApZWIbSCbr3k1r+en43ESmkVoA6y87Xan7wP63sobowqCufO8c5HFqfrCa92LqsK1hIb3Z6WPFTG1DTYKoGSd2zNb5Vu6itCqP5aGmbNPV9MPtiSUI18gCBQmIFKzImtbyXviD4v/FUTwgao17XXGxUaoSir8u0CAs4/T41Y2VXIic3JSasHvIp9wbSC/oqgcLlcsvUQpx4iIjboXa1WnBdPy1c5lsVPf/rT/f39w8NDtp+ybXs+n8dxfH5+btt2mqacnqHT6bTbbcdxptOp53nD4ZBx0sXFBcfS+v/9X//XP//TPz04ODg8PPzud7/7y1/+8m//9m/ZjKu0MNksA9RaQQdBIPL0W3rxSNEyCMy8mLZlpTmfp9I0SlPN2tqeJ4QIlytdAwuWLcsyA0ebOgkB6OV5FVkdDgC+73e73f39faXU8+fPfd9/9OjR2dnZ6enp44cPHMfheJme53l7+67rKaUmk6nv2EIkiBJIZEbJueHxtUC8upo3wbjVT9AAVaoxkXs9NK9vjiizsrx5NzYArQ1f3Xi8TIxV3y/Cu7JoAYz5b1oIs5SOehUTZ+efQd6N5XX8t/m89EIDAq4pTW6KTTCkFgELAhRlBojLWpRSRMC63doJrAViVazZhF2anlSxL98jsad4uRul5kpVQXG/XbsZaksTWDf33m3hudkN0+gJmpdVF5EnmCm8yRBBrDdYmULSL9bNmHnTFIfA3J/KiA5WnWfuJjYXU2G8YcBkcLebN+G1y4qIeOejH9X+1nTAmkpTY015QDkZked529vb+/v77XY7SZIgCM6OT9jeCgA4RhVHbJZSctICpRRLraWUtufO53N+8/Hjx0+ePOn1erPZbDQa/df/+l9hvUgiSzkp5Wx+xWHtIKeydfZHyPE041pmVhynEGxMczDsp6jl25qesqWlXaTs3MkM8lB5ugm92K5ta6qFDbVs297f3x+Px9qhmwUJcRyrJO61WocH+7u7u65rd1vtvf2dve1tx3HmVxPHtZAgCJZxEKIgW1pCiPl0poxAKKXTXoLsgGSx/0KRUKWKXGh9DEDq+pRx+Hn3aA+Q2hZL17rtRCBQh9a7FhykRCzUqZ5h/aEGBHyj0uvdlvSoFdPE2jtLrTsvIPNGU0qBEYoySdagx5zqkpWphgJpAroeXUxUVPiTlC2F0IEyDNqu6TzWGmFV38//VIhghja8tjSmN23ojyVkdbNRnl2n9FBvPH3uzIFoMKqvIFBKVFiYGf1adRIAoMHioNCEiUtU5pxTbLd5i1azD5nzU64fVaLWgVaguLFrH5b6f+2RqY0ismHIieFon3HyxONSkFs1Zl8REEJK9aFnS2uxbqXZDan2HOVyhzJc1VfNU7Fg2XVtkMK1bMdxPNtxHMe1bOYMHccRtgUc9cWxU6A0TXjys2Oo1hMipZWPQ5jtIki9B7jP3AGl6oFYwQ/46ygbEDNzw+PxOEkSzlkkpfyDP/iD6XR6cnJycnIynU4XiwW74RIRe9MyIuHeiyhme2Ol1OvXr8fj8XA43N3dHQ6H//7f//tf/vKX/+t//a+TkxNOGBwEwXK5fPzho+l0ymi71WpZlhWG4XQ6Zc8fkVsj65vUSP9gAkR+UpVdcPgOHWu0SfKmC8fQRkQ2GmfkDQDD4ZAzIgdBIKXs9Xo7OzvddsuzLEtiFEWsIHcdfzK+uppO9nd2gYQQIKWtZFLqp97fVKHO0KQWc2iyuc+FPVTyM65b/VpcWwKgTfuk1OhmnCGKXamtk95V0kNEpryOKM/jyHjRxBIFOXPB/GfzMPn96nM0AmUUJ7/wDhTBZW0x5dVgzGdJ52cgxa+Qjqo4hA3Pq7/q7Wo+Wc+8sYerNZRq2/xnqVS3xzXIqW4U77bBaoeAWJ9cAQprZE5Lje4cihytuUOoQWRdKlSRAQAUROlZzaWZ2QQb6kEBqXJPSjelE3TDqa4eimo92Tv5m0wbIAoFif5Jt5jvvYxcNqBofT+byteOgJsK5q4+nKl3PB6zBBsV3Tu8+/D+g5OTk5/85Cdv3ryxpdXf6XHOIkY8bb8lhAiC4Gp51R9uMZ5bLpej0ejNmzfD4XBraytJkp2dnX/zb/7NxcXFy5cvj46OgiCwLOtydIGIbb/Fc5REMRL4rpcRjorYyEoACiEzb+A8vYyJgC3LEig0gcMyaiFEy/O0VxJH5+FvdV5hXl6R50cCgDAMO50Om1Uz7mffLUbGHJmSq1osFv5gYEnHsgW7Gq9WK2nImZniS1lCroioJmdZaSHKG53Wb27eQzfc+iZigrp9zzeimBRWfytBZGecMpSTfZKnSSmgwzqVTInUMMHW5jHWzhWj4RzPrjusUsNNqMC2ljktszPVCSGqmaJaYAEACIikzHDTXxEBV+YEVTE274bJWf9UN6ObEXDpBQ0coCgVh5z3rZXPmQPP8HeloSq2rhYymK1qJ9eTX0RmBTaumdLa3HR1k7CKv0R5mPWXyBFlROAyKyzNWBXRNiG80ss1/WceOlMFsxU0P8rxcd2IzflRSgkdtkHVbwkt17kh2Gk6CIiSAImQCFUKpJAIGRKzRTTxMJCjVyrkIIrcZ8N+JV+RtWlt1qgoU9vm7q2WbwwBA4dWF4LDXLDr8Gw2s0H8ejrzfX84HP7pn/5pEARv3749Pj5GIx4s85pSSkvI6XgiHZuxHVtQL5fL5XL5s5/97Ic//OGPfvSjTz75ZGtrazAYsFnW8+dP+WyzsbGUstVqtVot9hrSfTMPvPlEgwbdB47OobO1eI5jyj30pinBF/2k3+9zMijLspbLJZtx8Wxwuohut8uhtRaLRbBcWIB3Dw52doZRFF1cjBeLxf3Dg4cPHoWrJaKETBCaISSzrRJoMPuz3tYZkVcGQI3v0/XyW6hIEcEAXrVn20SZ0AAHoQZ1EUusFRbAU7Xbug/UwHGWvjWPEN9lT4wRqVy8r5RCZSYVWE/7Bthh4jzzuZ4icxetQT8QqHWwHlE072xqaQMwRSyBmBtR8boIporqWr4J4uFuZ2RL0YgGoLymN1FCZxXeDO82dQmKG6+8CgLN56VT9g6lWH9mYG/WWZoNNCVYBulQujElHAWUYChoGw5I+dfaYor61ilzBCLV7wdVTKug/2SBS9ORN4+DOWNQ3ELVPVCcq7W2SPun6GQk69O9rjYPapQ7EBtwTNPiuQYz900zV+qbRMBNbWtgYeVxoHgu4jhm5+CdnZ0PPvhgOBw+ePCg2+1OJpMgCGaz2Xw+n06nIjeTRpnFkEICC4XI/Wi3B1uvnr84Pzm9c+fO/fv3P3r8gVJquVzePzw4OTl5/fr1MphLKT3bkYDBYmlbVgYvCSBV6yTzhvJf5IcB82iftlF4IEgcBg2RgNJMIZZVmwFNRAL+JxBZG80aryRJ2OAZET/77DPOADEajdiNuNVq9budXrvjeR6QSJIkDENEStNU660pz4lNBJZEKaVK4uuBsi5F7Nu0dsaOx0wBmi/2+iY/gzkQp/xfvQ6sWr++13+VDhIalKYBlQjzximXEq8/J8I8ZSzksQJqSxXcrFGuhmKGPbPW6ZoIWPdts17WfF6GjxsBKwIhKR17P09TtR5izdwaqXLMm/UE6lESAShOLl2tp8k467alFqyjjuNWoYnNyalOVAkWkzHAJgC9oZQqLH2eITYsWuFCJqRpArnYMG8m/YW6RVzHMaidqBKsrw5Wv2mG9iTDF85ybP05FgniWnzMOei4sAcwN5wlZyDgM4WIguPQNnDAtaOAHISYTZt90N3Q9yVYYVZbQr26HlIqJUDEFNMkSaxcW5wRAczJKwmYR+TEvPJClzJk3ASLoLhjm7bcN8YB84A5RLX290XE4+Nj3/fb7fZkMvnrv/7rTqfz8OHDu3fv9no9IgqCYDKZXF5e6mCWQmScMaeIUXna3cFgwDrj58+fj8fj3d3dwWDged69e/c4xwNn/ZvNZtPplHlQE/ZhTombynNT18uBNbIAWLatzaQpLcTW51JaAPMYz+fzKIrYy5kjZ3U6HTY3cxyHX0uSJA+hZQ+HQ3Yy5mQVw+HQ91vT6dR3HcgQwPrAlFii6+FOMwds/tmESBprLVOg19eZE9SZUXG1P1ihhUs1GCh5/aQGoDSPpXSuzOcZoqUyoqU16lq/abZYGrVZc7Uhc7DaKMG8Ik+UKqBnvej1o2qwbq1tl9aE1Fct184zlyoiKT3RqgooMsFNGAgNQfT1+79SShvPxL66P9XXABrdrmhjYPMSziDIc3NVOF1oeGjOT+3eg2aJVOlAmZVn9QikQvzzQtcLw9SIqzbMSh2hYLZevTH3T3UdzXWpTmNp7IqT3CMmkHCUpDV4JCIEKSUmCYJM05QDWJcqN+B5DUlUBUTfJAJuOnimRa6eAiGE67qs6yUiy7LiOH716tXbt28//vhjVohubW0x+ry6ulosFs9fvmBEyIWjcUkpT09P2XApDMPz8/OTkxNObDC+GG1vb9/Z279/9950Oj09Pb28vFytVuPxmJTKAwoCZv6P6Fi2HoJp7eY5WexMy7IsaTHXyzweIt8YO8ZYKATQHDAgMNfLcLzVatm2Hcex4zivX79mpLu3twcAHMmEZQDhaoWI7XY7w9NSpGmcJImUUopMDcwbiYqcEzZQuPp+gxFWFRSWHjaVEiTdjC9Lvc3I6zqiniq0Z/6wsYnSqDecilKv1lc0rnWArKm5zW3VTkVpsAURX4nqL2IFY/bqSgWj8I3mOIui3VuHNmlqt0liXJ2Z0myUfq3skPLOqf2kCRzfZCylz80nWAyRWG26WjYcl82dLB0fU2dv3mh6w9yW5mt6uioHp3woauADERkbwhxLVlX+BDmzODVOSHWM1TfNJ9VDUf2zdvULzbFvCwICJkliQS491eBRKUJQSqEUAKRUCkCs1q3uAf6r2mIVFGwAkpYgUKgEicp1w/OatFaCqOF5/TWNYmFbrmUpBErSMFwlIQohKFVSou+7OW6OgyBBxF/96pe2bfd6vb29vd3d3a2tB3EcB0Fw5/CA8WvmmGRnuf/29vYmk8mbN28sy+r3+51OJ4qi58+fX47Ofd/f3d09PDzc2dl5/Pjx7u7ubDb7m7/5G60PMKfYcV29lfknIda2V9pTCA2SbcMhrP7qu16r02anLLbDmkwmjE11Kxylst1uW6J3enqy1e/v7e0NBgMimk6n3Va702mFq4VtK4mWFJkVNBFHSBeI6xxBWEfh6ntEJJbBkEBeUU6CVr1mWX5B55up3WLV40GEOt0hQAGTmVOUTxRy+DIAg7Lh1wx5aUburCXE5WJS04X5JyFIqZvnJ1YpAKBKBeuQSLFrElCmHmIZfp5fijBL4IoCLKJUgFWIvwZrN/T1cyCdv0f/QyxNbz5XpJAUCBJEJFACJ4pThChR1ObfzUNF6nnQgJsnKruy7RWiSNK0YW1vV64l1EovN8HZKpBFRA5XhIiACjA3/hFsFC91Jl8A0PeUZ9g1rrLWhPianpT3jt7GDWNLodIu5wCu9kQIzo0JgJwcUxHlq8n5ejk5oSBIUWfVTRUiKkoBkNMLcu7gPB+zTtcnCNQ6MdEmenSN3TmPaVH4XHqnQBNnm7KmNE1v0xOuMAt10jC11a/Ka0dARr7FlJRSKlEKiQTf5Al70jSVAIoAUSGhEKZWp4yAzeZKVAtct/PxzsMf1CDZDVcDWORpoddXjh7FkaR0RsvavKq11w3dZV0pq11brdZgMBgOh51Ob7i1w5Jkjim9WCxGo9F4PA7yABo8HRqNzWYLTie8WCwcx3n06NFHH320s7NzenrK5mCz2ez09PTk5GQ2mymV/OCHn02n0yiKOJIXK1w9z9PZjZhPZTdfZus1/jDFU5zI2jTOMgsz0yVpNoumma33PK/b7XbbHVviarHc29vd399XSWJZouV68/ncQvA8z7UdFCQIhBAInBY+StIojtJUxYzncitupv4KaeFJKUqYHmTgpdFcZnyqn/CvRCTQ4vxjTQEjTTIcQCUqhboz1sThKZXUVquKsZGNndMQ8rAOuGw4GUqVnaEzv16IqWjBAWmjXS4AcPcFSBCEJACR012oJDs1CghYjauIUDFpQ+uMwvnqpGuqxYg3pAQkmOeWNTPXWkLqzLV8KjkATROQ1YEOSgcwiFZ6Ak3k1+TWIgzTdPMmTRv9X81K9H3JLUrfqDy5SNFJOtWks3bIFLkrMGKWkl0Iyfo+xDWFwVfME7kLey0UNJFEbScBwLIKVu76kw0cfy0CFsKqfU6U5eg181Wb+cv1+vLzJA75MHBmNJ0fLU0S0OM0rk3UZ5XjRET2MmC4UJoHsxiYSgIIICxNIBjZ80o1SGnrDmR9yPct5tl3TfipIIu0hUXli5lcR1ay83LlrEnkyPwtz7ccm62REJHhMKECACHAhNucrRFycZHJd3F/jDcBDcWlCfD1eC1AhQS3uGoaWhmzk1MWBLkJnMpI08w+PXOVuObKM1+7IcIoAAAhRBSHYRSsguXVdOI63tbWNpCwLIuNmXd2dnZ2dhaLxdnZGctsOfAyIiql2IuJpb69Xi+KIo6clabpv/pX/4odjofDISPC8XgcRcFP/v5ngKrb7dq2ze5A7FWslArDUINptl7m/IkaOpjRW9i0Sm8FvUJBECBmVnnsEGyeYSGE7/vc5yiKAhk4ndZkMmm3WycnJ0+/+GJ3d/fxg/vPnz//9DvfZYk4ACARIgogkABgCaU4wxthmkExI5iDCeAUEQHKooOfxsR50H/EPCIjgkwBgVnayrrV0VKC47JUAXTtgedPaveDbiT/17hzNpQmxr35AyUUq/HW/1QW77auJhJIKaIEUMgyfqA8heqaCS5eCSAFJECFSml6R6eGzVY2KwqBckM4BMjkFwKQKOUIFEiMWxAEV9U0Yl1/YeHW1qFFWkcH2KnF6FinzNs0r8a2N9/czEPkXyktXkFElkCgEMwN59F2ydzJxspT8dd6RrDK1uR/qkx1A0CG7RViQcxTqgoAclNE86pqr6TdGQyRCWv9GWqKXAPBEHgtmiZSSmk6WmkUyLXlPRGwKdZ6aVF0fm9zWszV0ehN3wBk2YdKv5YmudRQYRUQAMAy0juylSUP+bY2CtUdlW+dck8AAElQJj7U/wo0h3mTDeqWQOjWOmAilrBlHtxaH6YngnKhlr55XyWbfKIoiuI4XiwWAuXJyZltud1ud2tra2trq9VqMavK7j3skhQEAdtOL5fLwWD45s2beD6XrdbW1la73T44OBgOh3/3d3+Xpmm/33/y5Ml3vvOdzz77LAiCxWL2+eefB+GSsxAyxmWSKoqiKIocx+l2u+xBxL9yvOsSBYSIjFyVUiYHrN/URcO4MAwZo/f7/V6vx+SF73pX44vPPvvs5csXT58+PdjfPz4+/s0//PKf/bN/xjCRW1dJQkQCUQomorM4LJn2AxMDQKAGypqaUyBMy0WW7ebgWfO/xdWvFFWJ/MKl6l4COXw3YRzexufvGymlVWvoKnNyZJIRTTOmf9XuTWi8vGEqSkxkUz/BQIe1w9F9M1/j2OzVnnPgGqgCo/yVEg6+yVJW+3ktfYa5SFIPUwiBWKO0M8u1S1DtdqkD+Z9US9Nsrn9Do9e+YHajtNyYc34lUolLKSKh/lWIxuRhGyYQithXz1WZ6qq8CcXdZSwilJ6sgUBzJMt1G9dNmslp6IbM0vSttlovwSizksL9bRHwLfeKonXAwWZqomI7fvPS9IlmHMlMOksqDFcBRBzKw/d93/eZT3348OFgMNjf37dtO0mS6XR6cXExnU5//vNfSikH9+7t7+/7vn91dfX06dOf/t3feZ3O9vZ2r9cbjUZsAPXw4cPHjx/+63/9rxfL2Xw+n0wmHJD54uLi4uJiMBhw7gT2IGIJs+d5JsAyR6Q5iZKnvOahtfcwC096vR77KHMgDgBYLpdREM5ms1/+8pfL5UIIMRqNTk9PfcfWmyNrJU0BQAgUEtN0PW/Z1icmCBJYy3nWEhXRQJPVwtPqttbFFOFC4VwV1Gxa0liyaK1C3m9VqQKRhvcoVwbelJIgSPUOh8rJyl/aNCeaJig90fut6Suj+vW3nU4nx2rrYLmIyLHkyh1rQOQbED8Ye6OEgJv2gD4vOoAD72ETnJpV1Xasef7Wk1Da6g1vEeUJnWoRdvWDWz03f619x8QBlDOXJQQMxeHf5HBVsW92Y3xSWty62mhNqzd4QJirD8U9kz0ngJIx9W1AQnWwepdUJcNNG4OMUruRbrKjaotl2h3cajxQHFv14L1zn2qLFu3qLSWEQATf99OE0jRdLpeLxYKVqUKI4+PjbrfLUbGYLX706JEQ4gc/+Gfj8fjly5dv3ryJ47jX633wwQcs+p9MJi9evCAiDiQ5nU7/4R9+8cEHH3S6rf39/fv373/44Yez2ez8/JyvAOB53mAwSNOUo2YGQbC1taXXlXKv3DRNW61WacnNofG4GAezg5Pv+zze2Ww2Ho+ZjwdFwXLebXeiKFRKddttKWW322XG1zx1XHO+qSSHdGFhVx44aS1CXPNctEmkY0IZY//dDkGa9IcpJ9DoQfeE32yKJf7eSpMnMJvzZIUACFABK9QMaZXufyNiEybg4/uUPwbQki+Vt1IK9VxYx6wrFbmf+U4Jjd0EuOjPdf36IRHNF3MTWundW7L+rYWtla2yqelSz02Qgg3kjoF9oYCARcFvrTr2DYinhK6wgXUzXqf8Wu72hvpv/rz6Qmk+m7AyFF2SdK9MxCywOjc19WzeOaro2GZ+y5PTBCGIqFp/daFL9V6zjxsaKlWLRStaKWV1h2Sf0HqMXDRshwaZ063KbTngrHNNN7Ub990qLxVds7ZVBgAUIkkShMwNiRFeHMdpmp6dnTEyZmQ2GAz29vb6/f5wuPPgwYPPPvssjmMOtnxycnJ0dCSEuHfvnk4/zOkfPM/5zee/siyr2+3u7e2x4fS9e/dY5Xx8fPz27dvLy8vxeDyfz13X7ff70+lUx6HU0mYdV6/E/gIAh7ZO86xKbMgtpVwsFiyvZscqAPB935aWLfGP/+hHR0dvnz17dvfu3cFggCrd2dkBAB47EVGaIiKhUEoRFaK3EBGQIEo0A6GDXTPounaBqsCx9jUNoGtPl/mnCXPNnzIYcV0w7d9uKbCV5vAb+ymBo+vlKFXDgutpbTDBull/gfAtfG6isRKBr1+obbcJRe3v71d7CBXRtAEub02IVxFwtT8GyKt+zkoNk5gzMEfDuDb36lqEqrGLUgqgIOzZDPdui4DN1s13VJ6ErVqT+a05n2YlGTgiKqYQrOlPeUWo5h2zY4WDXETA5k+ljWHSE9fClvX7N5P4mgjYNMvSoRUZwFabIyKmudEQKkBOcF97pm5ScO/BJ7f6QFEKUJOdo9qb/OZ2AHTDRuRChbhfMo5TUuusFwDAmX9c12U/Jc7x53leu93WMmrP85h53d/fv3PnTq/Xm0wms9lstVrxknBCiIuLiycffzidTkej0dXVFRGxI3K73d7b22u1Wv1+3/O8q6urV69evXnzRid1qMIU27b5XrN9XJbzBRoBpRn7CiHa7bY2wvJ9n/vfarV81z47Ob13767neS+ePbt79+5Hjx99+eWXW72+7/uunRlhWZZlS4EC0jRNkoiJEiJie1EiAlQ6dAlRyrSLUooDopYAygaSi5qzZtWiW2hUOZUVNiZ63vCJfnMz4Guqhxo44AYraJWmKSdIKfWzsfUEAEBlTu+kSaIcyiARu3tlNYTRSrcoctkdANjS0s9x3WgKmLLKs8Sh8oclBLxhirRVeclAj837S8OkPF2g+YTr0eC8FjrXzH+RkzBxRrVyANAJ0smchDzLUFaJMAg7kalUzCaq1/UU1WkcdSl1kmce6hJMbR5vtTQRcBtmQ8MTMHEbrkl8s7fV0EAAQABK1RthsQFpdVyJKljLV4dTmDoSAAKNlL361zUMLxvBlOefdxQa+0TklRFCSusxVq2gS88RUdqWxrsc+4jlnZjHZGTFos6jQ0YqNmHEGNZx+zU856lICW9pBX2rgkR1CYpNamUz5fLOheM2mwGWuS3LsuIoDcMQc9txnsowDImI3WcZiHDmpYuLCyJqt9tbW1sXFxe//vWvbdvudrvb29uscAWAMAxt2/7ud7/b7/fni+mjR490NOblcrlardiiiqNmCiFs237y5MkPf/hD13Wn02kYhhyEi9MS84qykhgrCHh3e4fDTLbb7Xa7zUkMeaVXq9VqtdInwbZtR1qTy/Hl5aXrOp9++un3vvc9FqezkjhN0xgIACydSzUt4DMNvZm3RiyrabN0whtFUqUjt2Gt9b40P6kNiVetrVY3/K0ot8nNBwYXtRkPmcXEdiYEXP95g6qqB/7mHTALx6oDQ6qpO1k68tWNccMWzTEWwHfD6xpnm90oooprwp7cEEDVjtFslyrs2tcE/aCJnK3caIU0GDivBPfXva0TolRL04hMHNw0UVCcHMg3RqnbBqwoHJnS8SkPDYAaHCCb+lzCyqZpkflJLflYAlxFDYh+83arj7sPvnOrD9I03lRdhYwlapyI9/CckAiBBBaOX3ZDJq+c64ZtR2rRtEmq6FCU+n2moXZ2dizL8jzP9/1Wq8XkEvsFCSMZg44FrStk/pLyfMOr1Yodk0qi5jROUK1VxRohvX37ljExp0lmh2DHcXrdthAiTRNE3BkODw4OfMcOgqDt+WmaIinbtjutlud5SCqOY0VZjGIiStOY+8C6aci9Kg1/ShZQZ/PG0LaEI8srkHNOUHcgze3Of4ZxAMZW1oxXCfjq6wb/UWgGQzfdP8Acec0nZoXmFClaG6+V+l/fbopgaHaV4txaOe+b88SFF/Ji5taVmCW+RE7UkhWVQqIj9ZhI18RMJj3e6K9ciamZtdssKq+fz1vmA4Y6NlTfULEAQKqq8GetQc9rNAz6oKAHLbVSvSZ5ArFacKwryU8696oQvYdvSl4Auram5zeBe9VNXqqEx17CMRuGQwBpSrUIrLQZ1rBR1kg+qEiBraeLhFKQ+WKX/HfVOr5jsXtl/xGOLy2LPLH+JFU1ONVcKbNdEuj6no6cyAY3mudZ12/m1ca11REYah3m9EyAn40YJRibRPczcxCt7JPbuiEVeKbSgtXshm+uUIW74qJJHo2DqwfSLBcXF/orHfCSuV4wlkovg5NnQyo19P3vf5+IGCuzG1UQBEqp0zdHrLtlv6Y4jlksPJlMOOgHC7pbrRaHGTl6+/r+/fsffvjBYDCIguD09FSQ8n0fUgW50ztwpikQSqk0TvSE8A0iAkim3gRYhBmHnSGAtEzS4g1YGXPRMac/MEcDwkjXpYoIuwSAqtcbrvUN36wUdWuKtWJgfJPW0WSPmn23oDyT5Z8q0ApZ3VnatFDczO8wtBuOa3P/zbKZMLq2k3oCm0S1mxPd33AsRFQ1GSodBxMz8f8RC+9QhZfCIkdYqvwmC1SD25or1525dtTvsMSlT/TyUZ0woPpt7RQVa1vv22wgWZC59TvFmrFSw6ZSUpHogA26RV1/RgTLdaOlw1XbrmpwXaMKH89Pbm0FjYgNnxBoR3iAfF6+MQsazUnwrJmCeE2YmA91JB1dMorbKACARjANbqi0z3QEFmZYtX3d559/zoE+ut1uq9ViPI2Id+7c4R3AqmsdIpvvAcB13Varxdkm4ji+s7/Lf3LfwjBsuU6/3+cIVhLXNAGoVPNb7LqdR5LR9yqbIbmOyx2qhFBwXk8+BxwftbZwYJb1nxVwr+dwfa/WwUlqr1Dc300wovZ41/dyY2EwWvu8CYRVweuG1rUb4VouKggB00RBli2GXbOUxrAZJaSUbgcNvRfvV926QKE5YBMBv3PZPJyvWO1NXqjCdDBkJE02taZ7W4FyIfOdeuxltq4Ma2rzq1L38gWichuVtqpgvRZRNc1PiYCo4r/KStVsyA2Eyw2LiWCgeNKxyPHXfFdZAvPb2lNfLYVhFueg+vnmydT3jH05JherxswPGStLWeCnr+1nqRW9Fk0HyrrtcW2K8NIwjyqPRPNVS/0AmEJqLjxr2sSJpc4lMAdFk11zpxIR87tSSsbomONv057ZXBv+ifEov8OYNY5j27ZZlO15HuNmAdhttSF3AtaCESHEnTt3OMJlkiQc5YOl1g/u3724uOB8UJ1Wi62gJ5PJ3vaOZVnMAfOuSuNotVrlbjzlPQGZdqAgLubkK7lg7Xr5GCCopBCSUP/iOI7e4nmGRCKilNLSVtbLUdvWZkzQBJhuU5ql6/Xa7nqKu6kDJiIRYq39yuPJmXzqGvHwOwLXYJoRsNChFNf1F6SOUJlA888Ns1TCE+sP3zMirm9a35Q2hh6+0bHqVqyMujADZaRb/dMcNUc5MOWKTThDZCFWCmpXPYfVmmsbvXb/QN0mL6GK4qoVxEsmhjPfN6amEX5W2TV+Wloj8x2zM4hIZTEzlrqtbzaAmmw4Zv/BGG/FaG4TbGGT5hwj6mCFRKQZp1I/NQfcNJmlU2MuY/E5VQlKIro1dmyExd+yYp4Tti7OEgjaHIsXWL9pomHIEzVjrj8moigisyrIp5J1AFxKuhbtKCXy5MRCiN3dXcaLi8ViNptluBwwWCxljn3NWNC9Xo+DlAIA2zCz3Hu5mHF3lFKOZTmOM7+ajEajtuf7vi8RlFLEIalJseUaAOT+prwbBAARcRRoIqI8mmEGSjYc76ZSy0zoVWCSIovGFaelX0sQthZEXts6QBlQ3rAQUa1dVTExFKEgYpbUkAboUdeeTF2R8ROLK1mbS/neI3NfSWnpTVjSAa/3WwHZEFXiThROPv+57k5TNwH4KxNGNLy8obwbfGgCwdX7tEHUXGi3RKAAwMaNZHJvKidhTdhNhtV3sauZzM/EdiWIXCpU5ICv3bS6b9XTUd/KdZIh84BvQMClhtb3BZKxBokWxsXMDK1fLq3y5rXOatvMYnG1ueo4y9ZgnoX8NSACZpwUCrG2dIGc0cI66aZSa8ivBwjFAEpFj4n6OaxSRXx/aw64acGwiO2N51QriL4JYL3+fYLN3cfcnCpT3tqsuy17VZsnrTSK0qmjvLAOGNbCqOwFbTSkRcHMyC6XS22ExajUcRxbWi3XwyJtyxwzx9/QyRgQMY7jMAx3doa7u7u+743H4/PT016vtz3oP3jwgPMt+q4Tx7FjWUTku06VkG8ao/mwBBdK+Lg8w8XM7PrNOI71vJmK9kTFUDwhFRJyk8yt2lAt2n5fxRw71pEm+r5JxIcFySjXkCFgXb9aOwoX3Hu0lyOWRNB5VQpBIlAFFL7DSKuYYwMWeb+lCsRLQ7jJiJoQMEsdblgbUZZr0gS1GuDqdwybBoanBTh77bzpw1VqaMPLN9zkpaarJ7e8h2+8VfQnAkXpSenP4kZCKQVQzf7U3TCxcrVa0NNlxIJmoraAZTeic7MqRsCl4CFEFMcxHz2dv4eLDpMgKomWq/uWKFcaV9ZCqbKzOL47B1zviUG5iGJtlUCNNltfe1GVUIiatDFBDBT5MJNO4Z84yJT+SRTdY8zdo+dUI3XGtUwBeJ5XxehIYFn2GgEa7XY6nTAMOb4055MIwzAIgvv374ZhGASr1WpFaeo4jm3v7O3tffmbz7lLaZqu4th1Xde2zAgvREQKSK2FIYjI9rcAax0wIqaErJBUTDgiKqo3FBCgLMvCCmgooXNzolh7XQIotWT+teDmWiT9VUotcCw9ufbAAwBmVugmvBBEJC1BlHkAI66toM2taIYZEBUOGDELeoFF3F/qyc0np4p9EfO0Gzcu74z+S/dN9TQROmnasEW1qMHY4eYL1fmpbdq0XtYuKLlAoWaKzK2CzZzuZuoWmk+EWWcRo9cPSgv2KphyQ+OFsXAxpS/Vo1H3qdAcsPm0pDPevOhQBCNYx0zr+9pTqQeepimItQ227gYLC/Xi6g9Z9Aj5dNUOudDtoluwfqHpGFoAQKg47cONrqAIahKLZIJdJnvy57+lYkYNzG8UJQgoUBBwllAFqABEHIf5i2vfGERkLS8UTxoiZpnhFBClAEJKlNIWAuI44SRiQoAQFiIJYSEix9ICww2J62EKi8XLkNt2qSRNFCEix2VxbNuybUtKFCIMQ8u2Pc+LkyQKw1Spvuft7O4uFovpfGZL6+7dw729PQFwPro8Oj7e3dlp97ptz09JhasgUSoBtDGzAiIiEAiZAwwRkRAWj4hpBsxMgRiY3Nguj4SUEvO0LSb5UlKlm7oWExqa9/rlW6HhQndu74VpgNFNtZXgqfEtmC/UVIIAAJgnjOPUcry3+BtCgBRQCjaJAzAVn0ZFYm3vTCYMLQO3cqk5/Fku59oroiBQSJACIYJSiIJq0uQBYG36PIDGmmueF/u5YQi656K2RU40YnyQTT5wrCTzX2FWefVxbZfOV9QZeQEUosgNXxRkCZ4lUcrKRCwwfGVBwpqWquwf8+GGsVepTPOUVSvXBFMVDZQmmeg6+WHlE6jb/GRwJqVJAE5GQvU11N5Xu21OWvZ+fhY2VIuIpYNNhAAqVYBKEWMzkIC542UKiEqlQJIz2wPvUsYGiEiEDMuJKEsMyUyNUiasM7mREvqv3ScWoVKoBMANr5wCWgJm6Z113l8UKWUHl58LQCElKMxSvBfhqShO9PrmdlYfuYYg/0h/jKQECgFEaZJEIBEsgRIhp2OIAEghSCkQCChRmXGQJTPnV5Un9yUizsmMgEIgbyjXthicApIAYtMpKSUpyoLy5NGLAIBS5Vg2AICiNE9wK1FIO1s5CUgCFVGYxEmS8AwjkAQggQSgEISU0rGn4wkIy/VaYRy/PT6mVHU6ne3tnd7WcLZcXF5NiMi1nUSICMByHLBlFvwKQFgSSaASaZpiTighSpHnGVNAACSEFEIa2wiEkKbQRpOZgkhjX72OfFfyK9XvaD/O0n5oYkRuglDrSdHSRqlH5ESQcqqnBoBS7er6sJUEKvVFcGwyUECgSCAy8qIYCQgF5GETATlCFpU5Ni4sOZB5oMfsV1QgBdURTCXAm/WFgCAVWTI3JMqdBVSKKIEUEAKDH1J5rtz6KVWpqs2gJ1CxURjnIUWBnKc4iRVlTh8JghQS2Bwy69+6FcoPtAGCzbsskhpPjtI7DnNrVSJiLXv2Aud0xHVFHA+07F+OmfyZpy4jjZDBqUKk3IBOsSGwyqglyKMYapoKDA7NYNry+ySJ6haLzL1UexaaMJ95TBCxzk8aoBKYJecu1tmQmo5bqTOk1n9nQEAIAEjq/fUpTZNCmgyNN5kxYFQEwPmOkUCpFKkAZ3hl01wewxPBC4VZak7KsmwLAEEc5Z5AlP2bCQmFLSQBpkSgECQIEAoACQWiIkyUohhSFUslEDO1siIEAlIkJQmBQkgUgk+JUiAEKsXTS4iogLPfIHPCRJloixNIivVvWZ8shQoAbnMF84r5fcqsEzK/Sfz8a46jDwAAWA/+UGQQhkl6/qcoNXUY5rbme1P4gDmuNp4AYqaO4+S5RCrLw5kQSVKUWtK9Vfczv1hE5IyxKhP/pkSO8GzPVWEYpQlLSDwhLNfhIJq+7y/n0zCJvZZ/9/59W8qLi4vR+DIMw36n63iutC3Hc4EAKGbwmAIRZKbJApAol0vnUYdznXojn6cLT4tCkIX4xo2lCbJ8ewo2iIk2P7zhiCgXEwGLIoiyPOpESEACsntADtzRxLgoTW0iKkXQ7OdXizgJlVDclQxf8k5GZHYW8jnInvM7tVchdOOlK7MMbBVBfA8AKIgUAGYwgxllFKLEB19T1sovEw3XjD0nnXlUOYGV4VcCzMJVQoFfAQBQKX+eAiAnL6GMPivNANYGRbiWXqx94SZU5g1LU1Wl59moDbO7Kn9WrYQQmkyiGtpFoluMjgz1pgmcdf3mE14DUTc0REyZhMt+QshoQTSGUHOlTIDLRBirgSQAkkJAwXCSKA++RXxqi6bsNxhsgbAWtzoAmuzS3ldGHmCgtYE404K3qvlrKowe2ABK60SZajPRrekiDJBpKciwlNMY2rTb0vtVuxTftnul+nXrW1tbYRheXFwIIdrtNsfCvLy8vH//vmVZs9lsNBo9uHf4x3/8x0mS/PKXvwSlENGyRb/f73e6tm1HUTSbpd12BwAIQZGR84iVj6hFbgXlnymcoDr5mHFWeX7W7Jp5A8Wt9u3EvkxPm30zx1uLaG+LfUvNMUgSuWqAcrFzVlsu8qp0ssDoawSzcVw1j0tMrQl2zcrNm9rSaHRWlNJXaVwqusfU7q7m/hfe2dA93dCGF6oVNt1XZ8kojIYpPzfZnwCa78bifaED7xH1crkWBFUWt2ZY7/GoIpYTJmxeFyjOSXWWzO10beu6rfXLJMr/eHVIZFhWISESoGLSAdfTpWGkRs9QkP9jvvQ3LUT0LtmQmurKeleCHbdE8O+9aOzLRaNYjUqhgjbMIZguwljxONLvrGtr8N24yTybkF0IEUXRcrl0XbfT6TiOkyRJGIbz+bzf7/u+DwCWZcVxzMg4DsPhcLjTHfq+n6Ypp2sVnVaappQmmQA5H4jQ8bTRsO5jJQmsDUGrfabChoMcARd0wFABYd9O1GsWE143kQ56yOZDqNszTU2ACREKIkdjB2Y+cqJkNVPCvsVqbwHEi3xFPR4ycfDNazYr1NcSosVqAXM7XV+Y7IPK/Jc6kM8bImRqp/L8Fw3v9Z9UZGiaMH0JCJSB3s02w3vHvhvqLI3OGBEpdSNt9Hvp1UZSZv3mZoxrTruOAqfrz7ZH3VRsGF3TupsugppQxiJ5d5N5050sAZb3EyUDDKtjczq+wWIOWIeDSJJEe3qZL5hwRyMnLiUErIt2szFt5OBWsLDYT/0n35ydndm23e/3hRCclQERh8Oh67qc6vg73/mO51i//OUvx+Nxq9X67scfp2m6ChZv3ryBVHGaJtd1oygCleiuohCAKEiwjykrHEGRNhYFUoyAN/ME678MBAyFH8pQ8tuJhjHngEtYp9p/86smuHzD5gAAUZSAUXaqBZIR6r0WshsPb8rhFR/XuC+Xxr4ZSl43wAK6rd6UAKj+9pZoeNMn+bgyBIx1bD3W4eAq+DJhLlQOrNmiObSmUotObjDi91nMUbMO2JycKp5Yf9jc26b9ZnD/5dabSmmrVB+WntQPsOjU3tzD8odmQUQi1HiXlCBAynhiILbYUIjy1qBAA5Bbc8BNwzB35/oYf3Psbwm5ava3FPoKDHOh0tI2ha3nokXWJvuLiA0OO40r1CQy4iw0HP3KxPGcirjT6XC6pPl8Hoah7/uO47Crseu6LdfjtE7j8bjX6bInzLqT7BlJa9H3euMhsPIr22/FTAl1UG+NgJvKtxb1mkWDnlrIXnz4jhyw8U62T7CWjkaAnAOudk/f37it2sdlKFYaaaljteU6zFdmf6vP859vyhsZ7RZmvjr/RluCwFD9Qv2N2QE0cglvQNXmoKpI99qBbEAeX3cpbGy2NsVCT26+n29S8PYSGmhczRoOuNrV6kYy2a0NxUS9hvypIKMygBmiISm81XSZe8m6LY7MIqFUdcC8jEIgifyd25o0f12F+QnNB0Px6DICLiHC6iqW5pqDY5S2woaVuCES0q85jpOmKac9Z3QbBMFyuXQc55NPPtnZ2Xn9+vWXn/86SZL9/f2dnZ1Xr161Wq1ev9fpdHzH5W5wmFNlVE4ZAMugEsdmY18NZJN8IfSqbQYrXJMJEDcMeT3h37xwpL5gA/l/A5S8qZjwGurgERGZT2pRY+mhudmaANxmBFwaLxhgt3TTVDboGqv4qdqcfgHhGq6rqZhHGOrnGQBIiHV6TfOTKvY1x1WF6bU0OgAbcxFgDv2Qma78ITATjvq+ui7vMPYN5bb1mNTx9VRX809N+63UnQL62lhKKLMJMZeek3YPqmsifyKK//gE8Y1gd6MS407EPzFyFiBInzvSfLAQlNklX1/0JnxvIuhvbdFDpUqyccxFzaXAKFDHEpUgiD42JtEEAK5zuyltAgRBEHACRMuyOB0hInqe98Mf/pCIXr16dXR0JKXc2tpiZtf3/X6/3+t34jg+Pz+XUh4cHNy5s7eYz1Gt9VvrsRNQnvI9gy8EgJkdOxinsbaHZoXmwSwB9PcFVr6+gnWmTKVRQMNA3m10mEPopnpuSFPX9vzmfaAGjvNa1Mvl5kZY5lW3Wz1rt+r/Td5nEIminH6Km9YRjqqUBxRBgTlRJQiAiBux0rXd+8ZKNhzICAPzsH99HbvhKteSnhue3LLU79vqwM11z29yt57czw1RFN+Ha636S3C1MRRlI8XX8L4O+STyJLIcdtEStvnaOxy2zaWpNjPIBuMJ5n11gnHO0MA3LHBgFrk6FigCYn6Z69GFg2wAAOc/4Fy/3LTOAYx5XEwzFfF0OjUnR0/v48eP5/O5lHIwGBBRFEV7e3sffvih53mvXr26uLhotVofffBoe3ubG3Xy/EiWZe3s7LDg+vLyMo4iRBKAUkpl25ZlubYthAhXASISglIKdJ4JFJQvnLkja516GfVqSrC0MczgVuZXJYBV3fSlm9uet/e1u0y+zbzh6mvB9IZ69Avr19a+qjXMt1m/WXNpfjZMjmldX+lGYRGrqhbzkyZFTNN4LVnje6h7qw/dupPFakq4uVwyk4saRra0YyEHiOaWM38t+d3qcAp5tub1ptXvY7Gsh2Bkzi6ZjJRmgDK3lq+xsG2K7q3p1lF9mQCUEcTI7CqX8rccar4o3tAEjfkJGPsHQdT+VO4MEeTw2ez85i1RIIwydkLxx6Vfs3qKjBN3xrS00guaj4vj0SjEVCklpRSC7TN0FjL2hOIA+yQQdfV6sGg0Wur/NezazSkj/ZpWHNZO9PvFvhtKqcMmANJG0fyc8a65VGbnb+JZZG70VqvFTcRxXF1jbosPCReOjcWhoV3X5cDRUsp2u+26rmVZRCSE4ARKURQdHx+vVqutra3hcLi7vQUA5+fnFxcXW/2+7/tpHM9mM1vIra0ty7IWi1m30xECHKugSC5BCsrXpRoitbqCWCGhrj0e1ftvT6ntVfXh19r5m1denfyvXjbX2cjpfqMMHFSY+HfuT4n6uSG40y/UApkmnKGRxE2aeOeyGc/dqtx8hhvmcD3kG/bq2j43AaXSfS3qQaynfpo2QJF60xFyshuOmWPUAGzUCtf10CzNOuA1qZ5/zCGwG2TckpnxNUWLIovbk/UPvgkQrGdTP2HRE+fWNae7xFs0VaUpKZ0NyXwIAIiCOcg8naTkBEeM8zglhOM4fCOEGAwGpcq5uel0urW1xVkZXNe1bfvy8vLVq1ftdns2mwVBcHx83O+2Pc/jCl+/fr21tZWk0Wg0arme67qu604mk3arJYQAgSCQt1SSRdxMEZH1YwqBRB7Fp6Kz0cfPPI1QgFyblrWW7vsWltot+vXt23fG8dWFuG0xYYG5mrUvNyHgptbf11zV96dOMlxFmSYFuaH+6tgRkRSW3kHNydXBdCEQgHCt+q0dSI2u3aykqZPvUEyg9FXqqRLZfIxL1ZYo7Coag+aAkU3FBKe65tJyQ3HFC99CYSWwvGdAa39Z3csaX0TBK0OEOr4V5uxsPqU6jO5aH0yY2UXDOq5CAWxuHmyjFbT5fXFC64swgp6bk1iKsw91W/DrKE0HsrS0+k9TZKcFxWvnHONl/UntLLM3vm3bjDUdx8lS/+ZibSsv/JAF4EwQcGH8PZvNLi4umGPudrur1erq6urt27csc/Y8j1ntq6srFjtPLi/DMCRIgyDwbAcALMvqdru+7wsBOs643sS5S3QWkESPju22qpMJRXlscTJr4F0TJvumGaf6UkW0VYIsv3mfA9B1XguFzQNoQKIMMjTXXFtPQcb4bui88fx+zcRWFd1yeYcYOBtwfBMKN3EbIgKqPGBhGThU3+f6wDCw+DogYakDutSuLwGUkltW369ivupWNL/aQA9Vb27e/1LlumkqLAdqBFwWrDcMUMPw0jFXRoI7jSAQEVHmMePWiksjmgIJErUccFO5XgQNhQN/TSmhq1I90AwX3nvRRJO5ZhnVkrOnmAuZGemapIO+KcVA1s+1rqWkbvHclhYpm5mtPM/jdjmYBuccJKLFYmFWq1uxbXsymbiue/fu3V6vN5vNkiQZDodBEHie1+l0BoNBEgXz+Xw+nyPi7vZ2mqYoZL/f3+r1mYDY3d2VlgWYydiVUDaCLS0hhJKStdxEWb4Jc8bMc1UlP81dIcz0eRU4btJkv7Wlv1WhXMpUu0W/vn1bbSWb1Y0I3oTaX6VLJoLZfLo3IvJbvP/OPSw9vfbDOspvU5dKWEGnYC51oP7PXCddOsLVao36yxD1/aLhEhj56rVVb/RsVvdwZX9SFq25WTJcKhuU6FjHAFQ7nPngGNkDN+xVEzphrp5DowBllu2IgBzkHAQAq/BylkYJRZk9vFJK4BpmVmeyVG7BAW8+WibqNWtoBDS/FVao2v8gCBDRKhZE9DxPv8NjSdPU7Lx5xjTGtSzLzgtztI7tawMuVvfGcZwkyXg85pp1hEtuxbTzMpubz+dRFPm+3+12LctaLpfs72vbdpIkV1dXHGEjTdPhcLi9vd3vdqfTqZCws7PjWvZqtYrjuNfrRFFEkFKqeNRCCFtaiOi6rhBCIiqlUkqY7QYAM4MhFJev9gAwHKq+DA1L/61kgDdxwFDet19H07fggIurgO/EARfgwubTfVuE+nWv72a4UcJqm+uhIpem17eKdRrbzeByo5FRDdZBxZrCzXTPO5daIgCaOWAjGm9NVeV6Km9Wj3wVO95iPg0CtPpCLQMAlTo1bVQ/qkppYg9y+JYtkxCCQ4YholI6JeXaCo+Uwgz1rlH7tWSHJRpDJ/L/8o91hoqmYNwK8ljQkL1WDEz9W2aDajEHADC61QJhlgkzKtUosGnWTOzLZsaMgNlyiuuJozRJElOqzAZfQRBoETQjbO5Jq9XSUbq4MOK/uroaDofdbpc1vszmAkC/30+SxPf9wWBgSwyCwHGcwWAwubyM49izMrY7jmPUdt1IWQIvRABIVAoKnVzsLIQgS0ogkar6U1qk5ipEbmF6Mbc534zSvj2lyiFdS0e+16ZrWtxQrsWX71DeDRM0duC3wgGXoLyJz+qWr6ZLfIpL55203ZDxTunDQv0IQqDpaZ+/rx1UKHsva6XktfL+pdBV2PVuaN7kRPUNYeOJrqLe9WRSuZ5r+9/0Z21D5pvrpWyQEiFKDgFNCgmQQLO3gog/AdCfg+Z6QSlVilJXwrVAyKm6SqzU5lFb0JDVE/P7lEgiqjxDpgRoyBWcPScB+nkKZQXwb7+UlmFrawtzLa8WFHOqAyqmXmAkXU5bZtRJRCzKJiIOcomIq2WQJEkcp1EUpCnpnMGdTpezSBIxYg6JEEDN51Oo7B5E2N7e+vjjJ9vb2+fn54vFrNNpcVjKw8PD2WymlHJdW1HKziyI8PzlC6J0MBj4rZZj21GaSLBjldq2naemyU2ulIqiCBRZliVycsS2bZCEiEEQsE0BYfYf3wsUChQCKsgSQHGWSU4zrLdXLSH57US9pdJEfFSe8Q65eX7cW4hqiWizCFqX4q5uVH9yNmwo0M0KQGhkQ6QAiNP+FBvgJKOcPIaqEc4b3ZNu0vtCQ1k/Ob2pvkooPxEEitOOazSgJyFnhvTV7AZBZh5jXrPneaAj0kgSgRCEHrm+z+Yjy3yu8zrrmQZQuYKwsAcQycxbTHVCoGtwJEPfDZmVjV9r62k6gOvek1CozKsEyfeEtH4CKiUEVEhCQVrFAAIkoeLVQhLVQEzXkgKEqrCf89cpzz1UqkHHzGBTKk4MyDBUVQiyazdnlZIr3CgkQaQYKrINF2h7LkQkpfhUXdeOLspSwKlyyteMiSWA/Ema7R0iUASidE0VVJEyNM940wG+LbFWNtvNi5QIoNK00AoRLFZz13XbXrvb7fZ6vX6/3+12Pc+LosgkeDVdM5lMS094Pbrd7vn5eRzHru2laTo6v0DE4XAoHUmCUlISpFAgLWFbjrREmqSAxGgdQaCQfLSDIJBSSkuKovLjh5/+cHt7u9Vqdfqdw/uHjN2TJDk+ewsAURSFo5DZ6+V8sVgsgiD48MPH23vbXtvr9XqPBo/TNL0aj6eroOV6SilWNvd6vZbvK6Ucy1UKokRJKS2UAKRApSohJBKEkmnC/AhxKldUAoRi/QcCEiIgH8mqK5e0M4mCUornrpS5uVz4nFABHmYZ67Bmq2yGKVW2VQdegCIxjiRhjY2yLq9ry3eXyVHk18K/hn1b40cOxWyP5XsjM9Jm+QEiAkgweluM5YYAGYUPawQsczTAVWhvNJmmMfAHepiI+YsKsCKpTDOWzpR5KCAhxKbApJWCGoMiGxPnMAchyzUt0ECPnBS1ZqpTNh40RJS8i1KVAKksQ3mG2lFh7qBaRfySUS9nkMxyRxJCGickUAISokREgRIQQKRJkvdHw3oCYM5YwDoUVParLMZFWPe/Pp8uCAmaZkAGqFJXKzKSgRvI99qm2S4fAQQSAkCARECBUqBCEAIVP0EQRNk9XwFRQco+FUBIgAhCEUhhacgvUOZXABBKgF6akmS4hi02jOnyjJHaSRcARM6wZrWRtDJCChEBkTjIKxClnHJYCIEgc9guVAokODUoohCUh8ePcviQ2fTkXWKdPZNRgIggUAgFkHAWWYVCkRDEFjUoMhcYIYAUCgFSopDZ+rLMBQxJKgBaTI/d9IqUMUCQlq8Zq6RK199OUuCblzAMWSYcRRHHUm63257ntdttzRmbEZ7Z6hgqeyVN006nkyRJq9WKomixWGjpMUt9ERElk9iKCFttP2ND88J/Pn780PM83/cRkRePWfPBYDCdTo+P38ZxzLrkKIriOJ7Pp7u7u2manp+fIuLh4eHWVn80Gj18+ND3fZZax3F8cnbabrfvPXjw61/8crxctVqtnZ2dIAjOz89bvn94eIi5DlsIwVBXCCHQSigVqUpligoVKFKUQqp9MBQq5HDkBApVFnstFzG9L3larezoq5daKRkAAIkaFrCmW/wyj25Trtzi9esOh27UbwpHDQ2M5oMBIErNhPC8cgpAIeapfAGqo2Bf8ZuMRekZulkhArER5qBxrzbQb5V7AoN/Erx7ARHzTMiNV/2O+dX6vpgHncEoAJSuJjIuXDfp5qtPNbVh+vuVrlj89dYFienp/AoN9whAbNqUPSega96/fTFcegoh5tcSiPWrAlgggJgHjhSgGeIG0ROJMpelgVuV8a3yY5Bx4Zm8OqeEshvkXmH2Sf4h6AqLlDoBgEUbI+lXug/vvMzfkrJarYQQq9WK3Wc53KPruowCGTWabkKu67PilqNk6EAZ3W6XpdntdpttrNhmCiCztNLm0xqv6z/BkDanaczqZ9YECyG4V+fn52/fvh2NRoyPmUBm797RaJQkCfckCIJ+v//kyZPd3d1er+c4ThiGUsrhcEhEx8fHQRS2PB8ALi8v4zhr6+jo6P7de5CTexomIKKUEiDjZU0phRAlr/P1TmVNT60YTX+yfmEjZq1Kft5vuUbW90+lUn4L06WRTG1LWLm5balhs5rHVSsmgeLJvUk9jbv3Vgi4uVelvm349vehlM71WoRWKaXXTHcj84USiCvWXLgvkPJ8Q/klx9q0NrYtV3jrWNCNor/fkU2gzX2ZYWWUg4hBEPALvB46VKRlOVJKDlPluq7neYyAr66uGJ8lScKTy3ZV7Xa7qV1NcGmBLRGdnp4EQcCt+77f6XTY1Pnp06er1SpNU26Oa26320GwnM1maZoeHBx0u10iarVah4eHnudNp1MiYqvp6XQahqFj2ZxI+OpyPJvN2u323t6eStPRaLQz3AYAKaXrum3Pl1ISUZJGJso0w/XxtJRwMAKwmYXexFX0WWI6N++T8r5/f5uqxP6+L2b9d67IQqjIjDjPOTYjx4p+o84k7XeoaNawCYc1f1W4KRklvXfM18AB1+CGWhx8bWd+5xbu5sU40dfblmuJRVErpFkRKk1UbYVNh2KNbynD7gAZiMz57IJqCRFvnY7wdx0BO46jga8Za9R1Xf0OGoUPHot/F4sFGBiFI2AwvgQAxtO65pJ/EWM4ZRR+jkhXV1eLxaLdbne73VarBQDz+Xy1WnmeJ6XkyBi2bUdRdHJyslotOMoHM8EHBwedTkcpNZ/Pbduez+e/+c1vAGA4HEopzy7HoNTJ0fFqtdre3nYcZzKZCCEGW1vLYJUkiQDkYB2u6yRJkiak42EDEO9JdjnPY+1qLSbvVKU54Cba/FYbzNzu7x1kVIVL9G0NDPL1lWLACkbAAM06SL2sJmz6nYPmVdR1Ww64+snmXdocGOR6zsx8epM+NH7+e1A09i2R1xskE+bClf6shVcaMetSYkXWNec3JQQMAAz/EbPACbp7//izIZWKbdtr0UCeNCJJEtaGmmkS8kMrwdAW6A993+f5ZTdf/jaO46urK8xlznppGQFzB0oLbNsyjmN2JdrZ2el0OovFIoqifr/PFRIRexZdXV2dnJw8eHCPI0Xv7u5yngb2UGq1WpPJZDQahWHY7/fTND06Onr25VMkcm2n2+2maTqfzx3HabfbjuOsVqskSSQK13WZOuM+m0HVzVLddojaS30d1UQXk8ozN9wGOFE6A4zY3zuk/z3ngIvbT+sRs7haVQ648snvGPbVHM/74oCb3rzh83dDwE29Mv/8Jw74JhwwA/LqCxsYy5twwBqday5FeysptZYgGtrkrAZrczb12g7d6v1vWzHzAWvsyxiXWVh90nRWCb2/zSAVrJpVSnFAK2aI4zjmPMElLK5r01Xpm/l8alkWS7YXi8VqtVqtVovFwnEcdiPmb8MwVEp1Op2trS2l1Pb29v37923bPj8/j6Ko2+26rvuzn/0sSZI//dM/9Tzvr/7qr549e7Yz3J5Ppw8ePGi1WrPZbAaznZ0dL/XG4zGPxXPdRKVsic0DF0KU1FSqPs9MwQ1Gby/DRCDb6PlVP2/cbyZe/PrYX4AC2fu7vp9vW5oQ8IZPqvT+e1wdMtW/WHluPvwKC/UVse8G+lLWZX/aXP3mRktPTUK29s2bYN9/xKUWNG1+3/yz9G3pZf2riTWrPPe66VwHrEXNSikEE1pSqZVbi6DfIebqt6qwuxHkCBUMDtUskGcusiwbjAXQzjZxHLPkmRXJnItQ59IiIm3qbLYFRRBGRL7vs4KZiC4uLhidI6LneUIIz/MAYLVaLZdL3/cPDg4mk8sPPvjg/v37iMjMLsvG//Iv//JHP/rR4eHhixcvPv/88ziOd3Z2kiT5zne+s1qtxuMxUwbn5+fL2Zxdih3HcR0nTdPVagUAHLNaSplZ+xZLE1JEkIhp/U+3x6NfKw6G30uMWyo1w1/rgG9Ufnd5qXfGvvpaQodkRLIrleZZepfttxn7/lMpFWpWLVUJGmhgc2HN0TYi6Wu5bb1PcrBWrha3731484FB85I3bThxSzek226p2wophZUlP+Ap0FEkOUCV7/uc+E/jyyQpGDNrWljjCZOd1U+geG75IbPXURQFQaCUYjPmOA4BoNVqdTqdNE2n02mSJJ7nxXG8u7vbarXG4/HFxYVlWQcHB3t7e45jbW9v9/v9MAxPTk7G4zEi+r4fBEEURdPpdDKZMHHAzaVhFEWRUsp13V6v12q1BGCapm3P5+H7vt/tdNjCy3XdTsvnqRBCJEnCuD+KIqYweDhMmqRpiqRUkoocYYOxDbRgwGQ9URCota6xlp0qMFuoFNGt/IDNak2itSlfqRAIAIDKaHctIynw6wU3pJuXtSFbfiMAoJi426gzKZA+t2vJeB0bnidkJttgHbBaexxhedTa+l0pDiOT94rWCho9NCNawFctWCzGL/USFJ1EpLh/CCFhSwVdrdlE9eZa+FPiODGPNFB+3lCPuoG/m9EYb0vjBL2rDrgh7gICWSzBKtVTHwkrk2uVuQgoMma6q4SAQqq66J4mWCjcyLUwJAftAiDLrIdkdBUkAKBlwFgwJZQZvOLneRMChLN+XFxx80npHiup3M0UAOvnkoRQKFRuHMNvEOQhfjkvTl4DIuLvnQ7YTNOrrXxLDK4JuJvqqV0taDgJjOx11FDmd4koiqKtrQH/tFwuiYhzKHFMytlsxpLn7e3tbrfb7/cZC47H4+l0atu253nb29uz2Ww2m83nc8oDckGu82cJNi+5Tg5BiphyR8QwDIMgAKJut8sS9dlsxqbRnI2YbdMsyzJzGK9nwIC1WDGFMIeveWgEcVutx3ssNwdY/1R+twrWhS7iCBa1iNb8sPrwt18aQM03JmyonmWiYmzhd6rtGylm62QI86oDXI+0IoSjSql9XtuB0tjN1yym929TGnMa17/+LQN0PMtYDIgBOVeni7ESa0tpXUmJbjJx8GbOjBE885dsWc3S4OVyeXV1BQDtdpszJnmex0R9v99n46wgCC4vLz3PGQ6HLHkejUbT6VRK2el0Xrx4Yds2d8NMBWFLCxkD2zZjeiJKSdmeO51OV/OFbdvdbjeMIrYFcx0Xc9tvKNKAehSleUBjYvWNDpeqVen0TZscf0tA7T8VszSyy1j8px83o6QmMAo3cEN671uiGQ7cSoL43vBWQz24QchSi5mqSOaGPVyT4L/d06clMhr+mDgYmki3knI3B+8MV/XLRlDoDNZJuY4obgLOEvzU8/h7xwHrZAz6CrmdEU8uOwrr+cpElHmp5XerGFoXc5kZ/8VxzCwm22M7jsPN6cTAiMgOSBwsk9llFiwT0QcffLBYLM7OzjiklxBiPp+fn58HQcB/clvMAXNhOoMZYjMP43w+j1YBy6XDMJzNZoPBQEmhuecoing2OD5XicrjWhCFGftUb9bcArBocEj4TbG/m9mg359SM2rUKK7eCvpbVZpWrf455lL2G4ug31c/m5mh21hTf9NbtIqD36EGKtJGG1iU91hMMGU2rcNT36QnaIgtNYrlhwxpzT8pM7+q4ov1vTbOWiPg209H/fu/KxywqcukPOcgETHXqP+E4rGsRbEm9r22Xb0DwjDkbnAELmZ/ORAHC6I7nc7u7q5SioNULxaLi4sLpdSdO3cePnwohAjD8OLiglMyAACLkXkHsOwac32zSSUkSRLHsRDCcxzbtkejkRCi0+/ZXma2HSfJ5eUlKPI8z3Xd3HENXdd1XZ/Rv0aomJk3K0TMwTYZV75hSjC7zx5+oxY85oT8U/nHVBoRcM5BN2HZb8mWqDfmwsaY+bctjRxww/tVppDBoskB33zSvjoif+dCuXuS8edtP6+RM5cELfxcD6zImDXqJeH3kAM2JaKUR8YgItd1mdvTGy4/Eo3HtXZXVUUNpXaZT2UmWAgRBAGnUoBcRq153zdv3vzmN79RSu3s7Ny/f39nZ8f3/efPnzOWDcNwOp0qpThn8HQ6BSPOl849wH1jB99sgLYtpTw7OX348GG/318ul9PplI2uJ1dXnu0wI85WAxz0Q/PQ5mlERCSJqLDuXJUoUOP5e1nGW5dmdud31ab33crvCQecbcg88ukNOeDfRj9vNbffNFnwHjlgqlENfI2FihLvHByRyQFgnSD63YoJ9rXM2UTDuvKSHNGim4ShXw9LNM3bb0ew8NWLNrMy7a0YL2pkDDkCRsQ0JXMeoaIhgMpC1rarUSOjNPbcZUtmABBCsDE222NfXV0R0dnZ2Xg83t3dvX///v7+fhRFx8fHnFBhNBoh4mAwcF13sVicnJyw1Jq9kyE3wkrTFG1bWJIF3dxtRv/CkiCQo14vl8vz8/N+v99qtRxpAQBngNDW4Mw6l+TJiAh5eOfqKTU3mSEkoPQbM8DKelK6+afyjRfVIHw1/3ERzTCmJGnM1heB6hZ9Axp+X3CscYM1VN/Q7rfOCEsjVP3mTeatvC6/lVJLPVBRDK57ZZIFZTaj0meslPxhuQ+1D8GYrltywKhyo60b5kMF4+ZbUUoEiJ6+6sP8p030WnUhaykpxoXsCWPbdqfT4ZDRcRwfHx8Ph8PBYOB5XqvVsixrNpsdHx///Oc/HwwG3//+9+/fv+953sXFBXsovXz5HAB83yeixWLBIak5BggiWpbF2a8QMU3TVMU22GwCrekMZme3trZWq9XMmXda7dVq9ertm0NSP/zsD85ZuxwnjueyoxSjXh2EpHT2bqjg0d+mAMZ+KEmtofgnU6u3NRK8vvx2sW/D/ucErlkpq45uWzZgpkJXEADAzG6bpRsCgaA4/y9hllgc825xelL2PSIAoHXCPjRqg7V30k3Le3FYMksJnoKheNtMe73zlviqK6fr+SYRsM72XoDnRClnOCbKshp/derkWhxck/VoQ203kCdUw17pSFg5LaFTYtZYR5c+Lu2iMhpe27iI/B9zrUjEPn6cSjL7mgis2wfWyE8iknnND2Xh4OaDrM9p31D/7fyGb+t1qKgQalFvKVOHyrgqSRKllGVZQqAQ5fDOjE0xE98hZDZJRERRFHU6nclkcnR09NFHHz148ODly5edTrff779+/dp13U6ntVotkiR59uxLDubMSt/lcsl+yZeXl77v93q9jz76SCnFGt+XL196nheGsVKKKGVe1rKsdseXUi6XS4IUUCCAgewtx5bdTmuxmHMsDgBYruYJJU+ePFksFk+fPvUdN47j6XTa6bV//NO/c6VFRI5l+12fUJ2cHTuOc//+fUtIIWwAN47jOAxZJSyESKJ1OhHtH6yUIoGmcFMX12tpYTgKBADKjbz0iq6jZYMSgm4CQI39cI2NwlfleGRtit/Ggqqw/3ONOPMTZv9MZGz+u6YIzgG38R2TxeSkfkJf0UjGnj1RZn5cSJVCQEUKUQAScl5dUGmqCAiUIhAA+gpYj1Zr4QwCKJUIWoMzc6J4rtanCxERVVI/JxzwL681v5IClHrSMRcOQmUXGeKsehHNWvxjVC8gE5TlfV7XaYJy83mTH7AJW6o1lKo1/2zCEKXSTBlTfqV8ngiAA4wUNigAlfIMVkdXapGrZuoQ9SeMF7I4yRp1rZESYxWzZn1DFYAi0MrIylJ/8pz2bPXGkhQAyiW+OdJlIQmxik3D87W3sUbDtfCfij7QiEiEObgSmGXRRiKQ0uapzvx/RTbed9UB8za99krfIt6XCzU4+DJraP6Kdd44UDlaJpWEiLZtTyYTx3E+/vjjw8PDs7OzL774ggNNv337No7jR48eDYfDt2/fJknygx/8QErZ6/VOTk6m02mv13v27NloNDo4OOBIk6ygPT8/n81mQRBoNl3vCaYhtJVyFIWMnnP3cIzjeD6fz+fzIAj4kyRJ5vP58+fP0zS9uro6DQLXdfv9fpjE05OTDx9/YEt5eXl5fHqyu7t77969drs9m80sITEPscncsE4nVT0n2MwKK0X8j3L6jI0HdcTUfOKzk0AQvq+lL8L3GrHS16JG2RS+w2CO3/Ww3Ir81MOr5MQVChWAUKCAhAIATlO6vlJ+TxuyH78DR1u7ENBwvr5K+YqVlJDNWqZaASZfpaEm6LS5V1+hlHfEDa6326WZdES31xBwypzYqpqzKkPWP3FGeg2Z16c4u0eAkiCad7H+BBAloGI8W23RvDHXZRMrTyL7B5CzZ7pHOftLGX1jfQtx5NdammbNVABz0WkrStjOrEdDB33T7/cBYDqdHh0ddTodRFRKHR4evnr1ChEPDg6YDR0MBqvVamdnBwCePXs2m80sy3rz5s1yudzb27t///6jR4+m0+mLFy+WyyV7BLG617alCZp0z6UUSZJw0CshgH2CETGJk8ViwZWwmpljTy6Xy9lsxqbRruu22+0gCNIkWa1WR+NxsFzeuXPn8PBQKfXixQul1EcffMhMgG3bfm6WpZTC3PHJpAw2QBAySmn+S4hcCAHvT2dcAqDGw5wnLb75tSDjfyoNpXl1CvfvvChVGvG2RRipObkopZBqjKo2S1k3wB+z/vymVkez7k+pzndo9+ve5kRZ+A4Tk5VeqKI6xqklvAuZ8ALMJ1C0STZ/5v/V9KfYbi5gMztW0AprYFULuPRzo1r9BKGubyYS+b2zgi6JwvTURFGUWScZOQpLONjcQNrjtnQdjUb3798PguDXv/41G0+xjjZN036/f/fuXZZy+74/mUw4f9GzZ89839/e3rYs6/Hjx59++ikznZeXl8+ePWu1Wmz/LKWcTqe2Lc3+EJHKgiwKbeeVm4+lSZIkUcr5mvQYuZ/T6fT169cc1ZL9gOfz+eHh4fHxcZIkvU5ne3sbAI6Pj6eTq62tLR28k/XN7E5tWZZK1iJ9pZQ5UU3FpFpKi9K0U99jqbYOUDjt/7gxbxF+mENthPVfaymthXlTi5U311N9nEkf69q6bT9NBmsNkYsvAJQxx81LrVKD6BpNxFcZ1G+n1E5LiZNZSxRw/VXpifkQcr6yCZ0zDjSbM1GpOV0s08GCGUUZy5ZwsPmw+CdgcR2Ndms2BhH93iFgk1EzsYXjOJwUgdlEHSdLI2Bzx5g4pgTH3759u7W15bru9vY249TRaHR5eXnv3r0oioio1WoR0Wg0evPmTRzHv/rVrz788EMO0PHZZ5/t7e1xmuHXr1+zxzBbNRORlNL3fY4ymrHjkAKQShURJUlMRIiZQ3OSJHESRmEShwkAMkO8XC4BgGNMcuwONsmOoiiKotVqpZS6urri7EkvX79WSeJ53s7e7sHBwdVs6nlep9VmrySeC4Go7X+qlEpt4SktTWwJ4nBRShGp9+URU+qSAbYyDtgEoO+nyX8qNyhNCLiWw/uKrbyXqt5XT5p+MnHMTd4v3TTRrE31fN0EFxU54Dr8V3iSI9eaqYCcT9UTBbkuOX9tDajzGtYsQYl+KnWmtpUqrjUxsdln880SB2yONK9zbRP+e4eATS7NjKnN0SdarRZnBiwFqtRTj0b8rNry0UcfXVxcLBaLnZ0dKeXx8XEcx/fv3+/3+4jI+lchBMfDOj09PTg4uHPnTrfbTZJEW0F//vnni8XCdd07d+6kaXp5eRkEQafT8X03DEON4XIsRUqlnOVJiOxhGIarYBGFSbAM01S1221Od0hE7XZbCLFYLIbDYa/XU0otFgse1Pn5OUoxGo04P/HewQGLrCeTSRSEANBptZlAYQVwQmQZhgkap25eAg1zm14u7eb3WDZQTtXT/o+yFGn7bzUH/A71VB9XOWB4J32/BrtmDYhoOEl9JTRfJRA16tr8fmkCv4VF4zkTd9KawS3gP82+Gp8UqsI6TrrEWUKOevV+rp5uo2lV/ak6BLM0od5aDji/L5Md3Kj1rfIR+i2UPCuFYI9bLXNmBOz7PgeB0ujBsixt2Wt+zrVVl2p7e/vy8hIAEHE8Htu2/cEHHxwcHMznc9d137x5c3V15fu+bdv7+/uTyeR73/skCIKdnWGn03n9+vXr11dRFL148Wx3dzcMV57nXV1drVYLpdR4fNHpdKSUzPgaRQFAHMfMTMZxHITLIAjiOE4TUgktFsskSdrtNqcdZJza6/U4oDRz5PP53HGcs7OzXruzXC73d/cePHiwt7f35s2b0WjUbre3trYU0GK1VECOZWuj5RIauxYcmFRklRA262HzUkXlJBDvXEyYVe2hcbD/8aPhb09pQr3vnRi6MXash4e5keD6VyEQCZBUCQFXOaqbFK1jLnBmOZxv+urbjHdrSwl76bkqIVRTs16CFespqqOY11U164Ark1bD0Va/quDaW0vLSkQGl987DpiFsYx9GQEzTnXzwi/o9SiZRmsmWIfk5mr15P7kJz+xbfvBgwdCiOl0+vHHH9+/f//p06d37tx5/fr106dP9/b2XNc9PT3d39//5JNPHMdpt9vL5VIIsb29/Q//8A9v377d29sDgPF43Ol0VquV4zitVuvk5GSxWPT6HcizwmnsCwBEqVIURdFyNZ/P52EYEpFAy7FctrRi1p+pjTiODw4Ooig6Pz9/8uSJbduvXr3KInMtln/yJ3/y8UdPwjD8m7/5m+l0urez8+DBAw4bslgsFouF73osKrAsCxB1/uCbEOOmsZuOoUp5zgb9bf78lunbmkttr5rQ7e8cXPtdL01iCXhPdgBfhTflUuK91te6bEs36cmNykZCtpZ2aZqub2pLU50RVpUXbPq2iq1LL1Du9ENFZhoNaXD9C5UntQiypsUbGGHltW0aF72bCLopNum1/sSl7bJYLDjOg1knEbVardVqxTY+8/k8TVOOkti0SEmSsFIzSZIgCJIksSyLQzjN53PbttvtNtfPvKzlSO6t4zjMifJc6OjHZqBtnlnLsnzfBwA2RGKczV7CXC3Hl47jOI7jnZ2dKIqGwyHnEAzD8K/+6q/YwZdbef78+YMHDx4/ftzpdAaDgevaQsLZ2dnR8RvbthUlrbanKHn79u3u7m6chLP51aeffnpycmLZwvWy6YrjiDvmuq5SKooCTlC4Chbn5+dxHLfbvhBytQrTWHU6nU6nw6J1TuHQ7/cnk4nneZ988sn5+fnR0RFnP7x3796j+w8A4HIyBgCUwvW9VRh+/uWXbd9fLpe7u7vdbnd0edEKVru7u/P5fH93dzGbe57HOYlbrRY7YnU6HV4+DsLF6mff96Mk5QkkIw4o5G5gWPStAkDLshQlJQxdy8KWinkk9B6rwnc2T5MW8iImScLLriUfVDQCkEJwtwEK6Syb+oMVJTfi+2Tv0IgXXzvM6vvGFKyf5360NWC9qR6+KbAF6zbWoy65D1TqWRsz1lbb1O7mjuWvIRvUVl9rrL8hDoHQvG9GbgIAKFCYT1yVg9/cTyhOHYuUzIky9m19V6sobTMyq31u8vTmO02xqasjK2Gvwk/Faqv1a1S07jmuDaxqGjOnggQAFJMNG4pz4hvAXGCpoUqJXuG/9MFholxDGMyLeYo1+uRiaipFfsBK5KPe54hMG6xF0N9MYZSWgb+cDSWi1WqlI0b1+33HceI4Ho/HHDqqWhjXskhZ2ytx8lpOeNButzk/ge/7QggQREQc+pH5Xf6ENb7a/ApybbFt22maLpfLUuhKnbV+tVqxNdP29vbdu3cdx3nw4EEURZ9//vnx8fHW1taTJ0983//JT37i+/6DBw9+8IMf7O3tzWaz0Wi0XM7v37+/mi/YIhoAENF1Xc/z7t+/7/v+2dmZEOLFixccaZJdhrifq9VqPp/HcZymsVLK87zpbLJardrtdpJEQRBYltXv9+NACSGVUqvVKgzD5XKJiEEQfPTRR7PZ7MWLF2/evFmtVtvb251Ox7Kss7MzAJhMJldXV1LK3d1dAfjmzRuJyJSQ7/t7e3uTyeSLL77wXc+WMliukiTh/MRMl7RarfF43Gq1lFKcqLjT6RDRcrl08wkHQ4cNhm4eShoXaDzetaUEDvSfRRS4hvKaBeetqM+hiXrX0Fx3qlh5IZDIb72YnSyDs6+zUTI4iezGSDVTwiVfd3/glmxuI8F0u3SBIISFUCANmzqTvQBrd6bqHMJGfHaTsdx2nr9uXQviDaJVVUqRpLj+ZahytNdZXEPztiy9DxUAUrtYVMNP12zI0ohurQNuXuCm5/VL7DiOjuQAObuZpqnr+r7vM8/E7C9HUmzisDU7wlQk40XGPVEUcRb6MAxt2+71erZth3HAM8UIWyN+Zny1TZYGzUEQmDGcmTFioa7O7tfr9Vil6vs+Z/M9PT3lbhwdHd29e7fb7Xa73dlsNhwO7969u1wuf/rTn3qe92//7f/+61//er6YhmEohOBYGbZtX11d3b9///z8fLFYbG9vc6hn5vLTNMMTcRxPp5PFYsG8Y3/Qvby8DMNwOBwwerYsazjcSaMQc5ckDlfJwbbOzs4Y2TONsrW1FQQBC73H0yshxGw2c6Tluq7jOMtg1et0FcCrV6/G4/G9e/eurq6iIPzkO989PT3ttjtxHE8mk1arBQBJknBqRQDgWF2UM5SleJYmdtR6d8hRGofVAlRUzGC4GTaZdepjUzo/piEet5imiUa9QmQIGBGFQLOrUDycvwWkcpOiT5D+81bg+6u024RINO0C70mGfJPOQA3aQwDTTKr8fk25ZTCRLGRMM+6vIOA1nWS+ZkoBjZtbEC7vthu/JXu4WkpoMp8WkwMmaMIu+a+1OmB9n1fL9RRkVNWXwYAqmsw1IYx+AbFmz5MhdjaffGMcMHMYLPdTefodFkr7vp8kCXOW8/m81WoNh8M0bVQGarDuuu5wOLx3797e3t7Dhw/fvn17cnIyHo85rwBLmzm9LhQBKCKy0LtEqvCbzKAzKmLUwt1mFXKn02HUS0RxHC8WCzZcunfv3scff0xErVZrOp36vv/RRx9FUfTjH/94Pp/fu3fv3r178/l8OptMp1MmFJgIQMQwDOM4fv36tW3bd+/e5b65rnt1dcURoTkbUhAErOuVFrIls21Lze6z0NuyLM26Mc3h+z7bdrE8GRGTJGEEv1wuHccJw7DVam1vbweL5du3bz3PGw6HkGP9s7Oz2WwGAMPBFguud4bbSZKMRqNut9vpdJiVbLVa3CUWXbAIodPphMsVFAEQ0148Rv0kt+5WgMqMDGvu/trNoH8yJRZQFDFhwcO7sCGZkaW1JsIgorG8Z5rcl3+bxdSYQH4WmvRE773U4mCed03LwjtxwE3r28RPlfHcdQi4uVxTf+EhyaZ3mkgTEyWYuLY6XkSE62ipDYj/K5b6CvPsUl9fIcMIy8TB1yJgIkIUerYhx5RYtJktonODZKxbF/NJlaAv3Rt/6odV3ndNkuK7iaDf10onSaKjKbEE9e7du/fu3QvDeDwev337lnFPEAScqq+pXXbeZbzYarU+/PDDf/Ev/sVnn312cHDw93//93/7t38bhmEYhr7vO44TRRHHHwYAyjMgaeEz1PFPjD+UUlEUhWFoWZbneWw51W63NbM+mUy0TDsMQ6XUs2fP9vb2/t2/+3dEdHl5yewsB2R++PDhBx98MJ/Pf/GLnzmuZVmSlQiWJX3fHwwGvV43SeJOp/3w4cN+v9fv9ziGxs9+9rPRaDSfz1erBc9JkiSKEvZr6vU6RDSbzRBpZ2cohFguFxI8hsjMr1uWxfL5R48eOY5zeXl5enrKVW1vb7uuq1XprutmmQqXjhDi/PSs1+vt7++naXpxft7tdi3Hfvr82bDXv7i4CIKA3YhZ9M2BO0yeDHPLc8/zTDaXWXPNLvNRYVzIQbmVSnXcVHP7bkbA1WIiYJFndiIiRLBtW1qIudhDqYSnSFfIfZOWkFKmtE5sVT3Gv/3CRBXvBKYjiYjX+mttt4RZsxvWfgFgbufIe+/r7gy8ZwRcK2yrXV9WoBTAemmXVkiQGhVJ01hu2HNzyLeGz1+/quLdRNBQ1NE2IWBVmPN1lgUzSbkp8aqb8AwBaw51MwI21676XCPgag3GDlk/+YatoCnXiu/s7PzJn/zJP//n/7zf3/of/+N//Kf/9J9OT08PDw/ZWZalmrU1YC5MJiLXdQeDwcOHDz/99NM0Tbe2tobD4XA4RMR2u83mXZPp2BQCUMV1tTShjAz46vv+zs7O3bt3d3Z2bNsOguDq6mo0Gi0WC5Yh27bNYSPv3btnWdb+/n6n01FK7e3tbW9v/8f/+B/fvn37R3/0Rzs7O7/61a+m02m73Z5cXfq+PxwOu90u5XwDh+D48MMPDw8Pj46OhsNhq9WK4/jNmzfPnz9fLpeTyVjnOErSCACIUt93EXG5XCJSt9tlB2LP7lqWjYg6ewTlOvLlcjkajYio2+1alrVarU5OTnZ3d1npzgL8TqfjSOvy8nJ7axhF0Ww2m81mjIC3t7cXs3kURV988UWSJLu7u0mSLJfLbrfbbrfPz8/Z4Xg2mzEfjIir1Wp7sKXnmVl5xhZRFLE5G0cR4V/TNFGpQqCSFZ55DEpFo0YyMk6CwQFr7MsVOo7t+77jWprtXi7n7GytG2GTvVbb9zxvFUa8xJpu2NCZ30JhDQLb4nF/2Irit4DzoIEDtqR0HIfpVCJiCpjlInWlkZCqb7G5J3U3jQi4ecnqVV31HDCgUqnAGhWg/qo0P3q8JRxs1r8eghDXruM74t1va6EKB7z5cDUROiU/YPPlah1mK5QLpXXBosBZtwh1iFkDA8p1wCa6qeOAb6nzaFrp+rE1p5UqnFjL6vf7Dx8+/Oyzzw4P7+3v7y8Wi//yX/4LWwwxr9xUD6NGbefGkS4mkwk79ty9e5chu97HGuPylDFkZ5Fy6eTwDeNCy7I6nc7e3t6DBw8ePHgwHA7b7fbp6WmapvP5nFW/s9lsuVyyXHo4HH7/+99nITP38NmzZ1tbWwDw6tWrX/ziF5ZlDQYDxtx37uw/fvz48PCw0+lMp9OTkxO2N37y5AkROY6zu7vb7/ePjo7+4A/+4OLi3HEs25YAwAZiSRoJIYJgyRbg/X4fQDFuY4Nwz8vE0YxIeJZOT09Zqcw0CuOeJEkIIU4T1giwX5YjLcdx7j24f3V19ebNm4uLi363yzFD9vb2nn/x5dVkwtIFNjsXQoxGI2ZtGZuyfIKlo47j8MQyttBGicwfs5CfpfEcWoRygU6JbGo6lhsOhm4XDRE0Z4fsD7rMq0VRhEir1Srnv7OUU61Wazjc6na7i1XA7lgcXpsXl3IO+7dfBoPB9vZ2r9dDxDiOV6sVy1q+7nZrUS+zEa7r9no9TlYdxzEnt25GJO+HdqlHYHhrBPy+aKkSXlzfGIY5tQCn8GFDaqbaT6r3N+zo5sqLT2+9Wk0c8LVo1UCo5vsVEXRTPQ064Cqbm6tszc28lmxBBeDUImDdZ/6LmvtmjugdOeByPtE8G2j1ORCSQKSabURGVClmfRgvvnjx4qOPPvqLv/iL0Wj04x//eDQaxXHMqKu2WHlhQBnH8eXl5evXr9kqijmDi4uLo6Oj5XI5n887vS7TxUTEGS5LHLAu/JA7Zts2M3aIOJlMFovFBx980O/3P/zwQ9/3nz59enV1xZLnbrf78OFDXgkpJX/15Zdfsj3z9vb2fD5n5WgYhpPJ5d17d/b3d3d2dra2+tvbu45jLZfLdtu/c+fO3t7O2dnoo48+GA53hIC3b99+9tn33rx+2fL9bqfDiCSKIqWS3Atradv2YDBQlIzH4zAMh4Ptlj/w/ZYjrShNKEkVUhrFi2AlAUEKVCoIVrPJxPKsDx4+Ory7/+rlmzgMfd/b3d0VBOPxGFz38PAwSWIpRRCuWm3/D/7gs/39vYuLked54+nE991ut7tYLRarxePHj23X/uKLLz755JPFYsGyBynlYrGwLKvX7rBxE2Msbcum9RHmUubuKwikSHHCS0mQAgmAlBRmGYRK1zoMXfrTPITsY93rDixbsK34arWy7as4jolSdiuQUnq+2+12t7a2/Ha8WC2FEIQgVkGUxJQqBaTCqGb/82kp6KqaCyogQUgEa8uQzUUAdLvd4XDIYdfCMJxMJqyhr28hrzLL+2qkHdRJCQkxf4IKAaARB1RxMGeqY5pma2vL9/3lcsm29xsH8T5plyLmIwBRP42NcPt2KEaTHVxl1npdKM38p7IVdIl1K1zXlfDSra+IovRED/oG+XTfkV1GEuo2nyJtamqd8pJfRgDOaJl9SYgc81YZqYhTQ0SRAgmqUoEZD93Iy4JAIAKBCIy5MlVtdi8QgUXYqIAQQCASch5PQk7aiFliRMr/KSBEThqGACBQEAoCJBCUrTjrvLgpofNp394KWsrysmeLT9XnAApIEQowZyKDATJVSgopLSsIw2cvXv6P//m30na+/8n3f/bTnx8e3P1//B//58nx6enJGYLwXH+xWLCuizPRMpMUJ5GUcjqdpGl7a2tLqeTp0y+Wy/mbN6/+8A//8NNPP93e3jo5ObJt6ThWFAnLsgbdARHFcbxcLeIwtmzptNxup3t5eYmIi8UMAKTE2Ww2GAz+6I/+6HJy5fs+u+gsFotnz55JKVn7u7Ozc+fOnV6vxxZeo9GIMwET0Z07d1juats2R6E6PDz84osvjo+PGegvl0vXdT/77LP/5//r/7QssG1XCIjTqN1tfffT77DMO4hWw52tKArmy5nr2o8/fDSdXP0f//bfxVHmpsU4QymFSAzjEEvHWM5mCwTpua6QcrVcTudXjmV3+72jN69BoGNL23EsKVNKJAppW/PZ0rZdAIjjWKJg1yNm9Ofz+Sff/UhK2e/3u90uc7cffnT/v//3//6TX/2vu3fvfvDow9PLk//106PpePri1YvDw8OH9x/ato0Keea3t7fDcMVRQZjfjfLCVmOdTke7ja1Wq+WSz5IEECpViUoAFCKw5SkHwUZkS1QkQJUqRanJK2spdBzHplkQIrLce3t723HcbrenVHp0dPTq9YvJZBLHYRRFvV7HsqxUJa7nHBzsb29vJ6QSlUwXs/lynoJCiVEYx0FIApEoQ2nGFQAJUKJkKjv3uUoN6FzEPQgkFBEpzrKWKxcBAFEopWSWYVT4vn//8O7du3dtS3ieF8dhEKRBEMzn0+VyHoYrlmEw+Wg0DZaQhACKEAgJgQgJkCiDIgQIKIWUAtM4SdIErRIEzf7U/gv5bGeAL4lTAbLltQe9rVarZUsnWIZREMdhEkWR9g9kRUCSRFyDLpC7dVIK2l88yx6NiIiKEr4pWdYAqK2trZ2dnW63y5YZ4/F4NBpNp9M4iVg5xWaGaZq6rstR4apFpWR6RWrNBW9aPs5XV1dsOxLGsWtbmeab5Wp8Q2A7LkOqbrd77/Du3t6eZVlBEFxcnh8fH7Nsj70S2CoiCALbtnikw+Hw4cOHbEjx85//fLFYLBcBQerYnrQwTSiKgzQhQCXQsmxpSSdVcRjEYbTqDXqJikFlkkVETBOVpqltWayYICIL12ng0yTWc8i0po5zYICR7NTkBr6amze2BZtQUIH8TYGkhSqH/QrXax3Fse06RBAlmWtGqtRqtWq1fParZAZJJey+kWxtbRERKiLK9UqKCEUYZ3EF9Hnn/tqWy/uF8/JCbpWpCBWCBCSB+VUpBAtTAGELCQIBIAWVpmkKZNt2jsaRVVg8DqBUoMgeArdCiCgsmwQlgESQKhQAnO5aCNsYPQIpAAFKvaMO+IbZIwU00mM8iSyYYh7o9evXP//5z+/eufvnf/7ns9ns8PDwf/vf/jd2tP3r//v/HuzuMg7mUA+8V1xvy3GsIAj4oBJREAQcfnl/f39nZ4dT+jCXI4SwLGc0GnU6vV6vs7Ozww6+y9Xi+Pj40aNH4/HFeBy12+2HD+9Dbl+apuloNDo6OuJ+RlHESRE4fx9rSTnRglLq8vKSXZv0MNkraTKZvHnz5uTk5OLiwvO8drvNZ+zJkw+lRMuSjmNpno/3ehAE2mCbKAWwLUt4vuNs70VhyFjccdbhR3a3dwAAUBXBIrRbfUTpOI5liSiKgmBpWVa7419efmhZFlEaRREAOY4DqOIodRzXshwiYizIbsc8UQxzOeYGEc3n89lsdnFxPtwdvjl+MxqPOP4lJKLT7+zs7PR6PXbckiAdx/Hdlms73FueLs5VvFgs2MSMIRFPIGsQbdsOI4lZ5ArBZxwz3R4hypwVAADBuhyVAGCqN1juzpTZAWmvX54fTsbcarWklGxkFwZxEARxHGaBOFQihPB9lwkRFSfL5TKKolUUckiTJEkSUpSQbdu6H+sroSVsSJU2UTY3f+3BysIuoAIg4pHnpmNxHMdJZNu272cGgFLKXq/LOwcAONYK66dPT0/5fIk8fzPDXyEEcxlZznNWcgMmKuWGBAhEoVKlFKVpKkGqOkFoAWsag2IxfrYTmEzMRRpay64/FMJip69qMQR6a1EZFk0ZuF2Rh5Lt9/t7e3u9Xg8Arq6ulsslS78RhOt4/d5Am+j7vh9FUd38A+ReD+YyMYLkpq+urjhzSUYQQC5RZKYIkY+rdinU6i3+kxF5vtgKcnNUAOAj1m63B4MBB4e/urpqt7pAQqXA+41IKKVIoes6WfeIXeak4yBHGYIM1RUGxe4SQEJKKyMvFCqlWKpnBj9gIqC0P7n/ilQTS5tDqnrWO9M3IWhXz1UYMGKO45gw22EpqPPLC6JMq2gLKaUUKB3H5vXKLOwJGQEroDgpeKIqpRggCFyTnmwrR0QKCEEqhBQRFeorAIBlCQUJokhBZfIMnl9bEUt4gdjmiyERgCJCIgGgiPgeARKVIqEQSECCSPJrCBKAOQcEFsrzSslbI2CiBqVxrby7WcPMJLAmu+I4Pjk5IaI/+P5n48ml67rf/eQ7itKj47dffPGF1+scHN5hY+lOp5Om6XQ6ZYKUARAiMrvJKlvWPI3HY8dx0jRl+2QmqWzLSZJkMpkQEYq1LfRvfvMrz/MGg8GjRw++853vRFF0dnZGRMvlnMWS7H1kWRaAWixmk8llu+1Pp8N22x8Ment7O1dX4yBYtlqe4zj9frfT6XQ6LSJarRbL5fzs7CQMV77v7uxs7+/v7+7uPnr06OGj+9PpGEFJIRghCCHY+dR1HIGYpCnwFAnh2LYtpDvwF/N5Tr8T07lCCNd1EZFlsDlMVAAijzmVKGUxzpZSOq7luq4QEIZJFEVSisySNl4ul0spY54WRGRFrFKq3+9zFuEgCDiKGbPCb9++ZnMkpVSAASIOeoM7d+7cvXO30+l0u13bti20tBuY4zhRlOFg9qRikMRO4ZSbHGMulxMiIy3R8K4xgT5o5iM3tuLIWfyca+bKIQfcPDQOhdbr9TzPI6LVasUqeRaMZ7CSRIfptU7Htu1lGIzH48UqWC5WHGBECJGmKooiKWqPEqWUUpJqRKgpxSZQxaxkLkBj/IYGKAHf93d3d/f39/d3drvdrkpjxg0sUWCKcDAYzGazMAx1AkpWw1fbRUN2p6+aaqFmBY1Jx5hPiKORB0EQBCwcYlJ1sVgwcNfKYIb7aRrrP3VbvHCmWl3/RIbMFnL7ONYQ9fv9Xq/Hcu/FYjGfz9kmH3NN/3A45M89z2vSSZd8InQrjAB4ns/Pz1XuMWhuwmxuCQBgtQo4oo4ONsCrwOYFTJHwbOS0SBbdlj0p2LyADRH4ZTP0nkbkmqllOIASZINEkw+yJR1taYEgiYhjwHEn+RQEQcDuiObKZjsQQKDcLIXWG4yIgAU/lO0lHSaK6WDtiUD5ZmAbfn2QUxKIICxk9QoRoSrbQ6UkeVEAgJWvPJ+WzJK0ajKOEbAlHZMR14sojJDDwohwRwYhqPtm3vBOWEtoEtIEDU+pyAP8QTHcEJdvzAqaCUO2HOZwV0Q0Ho+//PLLTqfz53/+51LKR48e/ehHP/ryyy8fPXoUhuHBwYGWzGiMOBwOeG91Oh3OdU9EUso7d+5sbW2xP+tHH310cHAQx3EYhvfuPZiMry4uLlbBknlZx3EQqdVqtVre5eXlycnJT3/60+Pj4yAI9vb24jTZ399nsrrVag0GgyRJLi8vP/jgg1arxeFELMvq9XoHBwe2bbPLLxumcj/ZYIfdljqdzv7+/nA4ZA7v6urKsiSvvXZu0TuDcutrrR91LBtS4PREFxcXV1fjIAiEEK5rdzodgLXVBiIyArakAyAsy+p22zs7O8Ph0PVYkh9y+K04jm3b4gQSRNTvD9M00juMOQYievv2LWMs5lPZUzmOY5arx3G8vb29v7OPiL7dZlDI7K/neZ7t+b7faXVc1wnjSGNBMuzC+GVukYXDfJOmKTNJlMtRNSbWdLo+MEplUUryMBrZHDI3DwaCYZKl3+8zWRAEwXQ6nc/n/KaUMk3TNI1ddHzf7/f7bM0OAEEQ6hUBAA7+tcFUVSklEDngGuQ2gxsQsFKklAIeGoHIOcQ0TR3H8V2PFR/b29utVgsR5/P5crlcrVaMY9gIrtVq7e7uTiYTtmYnIj4aaDCOumBu66B7xRjLcRwQgIi1HDBTulC0dwMA7dEehiGHMWe69vT0VIMek8FiQK/Bma7QRIGadNC2lrqwcMvzvN3d3VarxbQ1C585/I6JxVnowvRBk9GcZtGgSN7pXKU6FA/PWIkj5FUDgz7QAXyISCf99LwsmnqSJIwOmVXQ6i1e7m63O58tWTvDk8BvsjqMckc+lcdMJVTLVb39HRMBrJXwfb/VarmOL4Rod3z2ImGCgOPfgaFiuHmhnDcz8RPkWEopxbPLD9nGMzMOtax2u21ZVpLGe3t7RGmapkEQsKCJB8j7DRWqgn+/AGE2B5DjchbvsyhKb3tCUEpR7olqUp+UY/3acZWwLxRdm0x1DEI5ajLlTCYaehP903tDwA0ARftjlYspp+XTztvoxz/+cbvdvn//vlLq8PDwz/7sz376058eHR396Ec/+rM/+7M//uM/ZgcbPgar1UrKglc1X6WUs9mMvWvYRpSBexRFlpVF4OLQiVIiI+bhcJim8c9+9rPXb15ejkfzxTRN0zBadXrd733/k48++kgp5fv+vXv3AODo6IhVlUmSJGmkKHE9++Bwf7g9aLVavu9vb28HQdDv95fL5e7e9mCr90c/+meMwBht82mM4yhYLaRErXPSGyKOMgtb27YtIVWSJgSWZYWrMAxX0+nkyy8//4d/+IejoyOilENQaexLpPGBABJMZxweHn7/+9//3vc/YZvnn/zkJ7/4xS/evHlj27aU4urqinXV3e7A3JRcGAC1221Gq9x5Js9fvnx+eXkphOBIn3EcpyLj9phocF230+q0Wi3X9qTEtTLSsnilAEAIoaNV6+3RarWIVJJGvGlNBFyC4wZvRFJKRYnWYOlNL/O0V5grutrt9vb2NoOAEgIGgDiOhcis39kCgH2ThsMhIfZ6vdVqxWLzOI5ns1kttOJgtY60GKOzKIVpzWsRsFIqE2kiAECSJN1ud29nl2X7Uko2wwZKV6vVbDbjmWTKj1262Sbx6uoqTVNOf8kkeW27JlOi3dxd321CwIzXwQCv2XKIDCkyl8YL3e/3P/74Y21wZ4K5JIn0yupKKA/Rw4CYLbkYPogcejIqarVa3W631WrduXOHezWbzS4uLmazGeMwLWpmID6bzVh41kQzmeSv3l1CiN3d3cFgwAF0AQosFNUZdOkQuRoBr1ar6XTKsKvb7e7s7Kg8ABER7ezsAABHqSMiRvCckFQrESB3FmB0znPFuzqKosVisQwWv/r1P9SOy/M8AJDC9n2/2+0OBoNup+84jp3Hxme9LxEtFgsOW8sfmhtmg3UX5UpfAmJjaZOcIk1J5YXnod/ve57n54AlVYntOARpFMTj8fjk5GQ0GgWzRRzHrDdh4TOjYQAgSqXNxPe6JwItQJFDAEZ7FjOlkKFkrc1FRB36kC1zs38ayGRCDTaMhNxCEkApVhAR/0tTxQZWbLElgASQRCBFQiACJPw+cdictRn2u4igb/tJUz3MubN1DAebTJLkV7/61aNHj/76r//6L/7iL6bT6U9/+tN+vz8YDKbTKR/g2WymlGJbp/l8fufOHm9HFh4y0EfEw8NDhjvsFMEnJwzD1YpVKSRlps50HEcIODk5mc4mp2fHQRDcu3fv4cOHzD0wM9fr9dh2g/EEnwEmpRk0WJa1u7vrOM54PGZx33g8Zn1ht9tlfx6GwqPRiGEcEYVhYFtC0w0mxc2zgblOlNXPrmX7rk+9HofVvLq6+vLLz2ezmeM4ihJjddYcRhBEQgjPbT14eC9Vsd9yp9NpEATn5+dffvnl06dPe72e49hnZ2dskQQgtaSLDD5Vi52jKGLQnzM3vSAIPM+bzWaTi0kQBI/uffD48eM7d+5IKV3bdV3XtuwkSeJwrlSi1QGU87JcP4Nspu515K84Hni+YyJgVQwZrWUGkAlOk/F4rCiLpKGlQExvQQ5MAYDZC0bAiMigmbXRBJnoz/OcdrvNAclZYK5SePToUZQkURQxBMntzwMzdocuAgBJMALmrXtxcTGZTFarVdM5yuB+0Wab2TI2Hdje3m61OgAQxqswDMNgyT3X7D5bIWnVI1se6YkyZ8y80fiGD2On09nZ2en2u+12uxYBa1oeiwK6MI64t7o2lnBubW0pI+6KxsHz+dQEznpNWenOetCzszM+jIiZXTBq/7Fc7Ly1tcXCgCwRZ263xfPAuBAAOFYdGyI0zX8OkdcTwrYCKg/aoweFKSCCzmdAhv0ts62c5JS5heVyOR6PiUgTf3yaknw7afUEALCWLQiCvd07GqaZW4L1C2maapo+iqJk3uRsnY3CdXxWqfT7/U675zjOfDFlqoiHrGevysY11Vx9R49CFe2QKWfZeYAsOBwMBu1ut9vt8hlRoACVLWO2U2OZQRzHjmUzzKCiIBoEBxCUa1rcSP6RGUaJjN9DIYgUQSbwF7kvou683pZa1lJDRhhTYZKSRAQChUI05Ewa0Gn6Sd9zuX0krEYSqOmD+se84YQQWhGSb/TOixfPVqvF3bsHq9XqL//yL/v9/ocfPiaiJIlGozMG045j2bbtec7JyQnLMJnU1VGxzs/P2eqSeSmW8IRhuL29TZmpSLJcLi1LcA5gVhpdXFy8ffuGNc2sQ+pv9b/88ovJZMwb/fT0hFWGPJUceoKtsba3t7e2tqIotCzLsiQACYG2bTmOTaSCYMW2S2nK7rk2ACRJDECocuv2bNMAAjnSSiFGQEdalrRUnORpV9ZyLc9z2J5TSGh5HQDQqJcoo0GZ8rAsx/fdXq+zszMcDgfT6ZTDI+SJp1rMHimlXr9+w3QMYyxm16SUzAhub28j4ng8vri4YHWRUpmaKgiC5WwZBEG4Gwoh9vb2EFGwCEhhHMdxmCRJtLu/B6DYtosnMMd2HvdB6ywdx1EqbbU9jYBNa1jM1YT6CDECDsMwSaMsQmeen0MpxdQP5bYwrusyQ8+6ND7nQRAQEYo1I8jwXQjBmyFO6c5gMFusWLfEyBsAut1+PQImACIJ2ZFjjyymYJogmhanA4DAddbqe/d2t7e3WXnBKsn5fD4ej89Ojxlo8pJx3FYug8GAecSLiwtecRMulAC6Zj0zy7Jcca55r1LhsYti/hKAzIlcc7q8UszIggGCtcKlpGvU9zqOhxCC2da8lWyHM5WwtbXFmmZE5GDmrPxWhrkAo2GWsnJnWOhVO/9KraMpUS4s1Rp0Lezl2UYBRCmDONLBfvP+a3odADiq7mw2Y6085cJkxr6z2ez8/JzNEbhvTNUtl8skVlpmY9ppcrSD1WrV6/Xu3LnjeR4bndUOCgA4GqDneYx9GYixqp5ZXgBgatK80YuSI1TFLH9hxrD8GmNd80l+ZNenmIknNjcTlsXCofl8Pp1PWRg+mUzOTs+vJtNM+JwoAADFWyhd4/goQhSICQAAsVMr40Uzc7PijQqIQlisitbUOa+4NLJRacIr2wYIaLghcTOKFALmAJuAFBGgQpUkWoKiKSo0UpZ9VQTcVBoASmMMWGZKeJDCcAB98uEHb968uby8/A//4T9wWCUhxGAweP78eRiGv/jFLxhea6TLwBTYbUZKxjcMSRnsttvtbrfLCDiO43a7vbu7e+fOHc9zZrPZ0dHRycnJbH71s5/97KOPPlyDIRW7nt1qe1dXV5wLgT2OKGfXfv7znzNhy/GZHcc5PDw8ODj4wz/8Q/ZQQkQOas2icm6ayU/XdTl5XxKHbS9DRVQUsbKIT28UBnOu647OR4vF4uzsbDK55OF3ui0+fib2pZz2XC5XACBlyHovJl94Wnj3c+W+7yNikqR7e3uMgPUM81fM5TN0mM1mcRyz/ICDfjC6kl2JiOyKnebpBaWUUkgppeegUone61YeBpxyqyjuttKmGUQA0G63GbZr2Af5edaMlJ49pRLmoZnC1UJXfmIaeVmWxVo9liSzKxTHDNHoROsXKTdOAWEx2GK35jSPZ64V2KUiCFSaqiSz+ep0OmEYauP22iIMAxCRsb5CSvnd737X933HstM0nc1mV1dXx2/enpyczKYTZWR6Zk5IKZUJ9/KQ4EyMMjlVOpLmnzyNQRDMZjPLshKVLJfLWg5YqxgYsWncIKzMnpxyTTkvHIu4qgw3bx5d9DtsB8c1YG4ULaXkA8r37FDAY4zjeDqdjkaj2WymoR5TXWBIlTC3P2hCwGSwRBo0cz2MLHkm9dlUSoExKEYyunutVotDETCxy1Qv7zdWUrLu4/LyMo5jtlicTqdswc4U//Pnz3lXMNDgwfKWMEPfsP57GSzOzk9rx8VMApNoTMMxD318cszh7ZgIzijp5VJWQvMCAJLAilu16csLeWzIakHmMPKq9vd2h8MhS/KWQcByjouLi/HVmLfKcrm8Gl9xLAFWUBIRklBFHbBSRpgmykQvRCQE2zSsOVoWQds2o2nUFlJ6y/EmKZxNRIGAxgzohTbvdVEAYIAy/avewOYOp68ogm7CrHowkFPWa0eKHP9jbrvLG0vvVEScTqcHBwcM6xlxBkHw+vXr7e1thuzaonJra2tvb+/i4kIpxZJkyG0HWCXGgj7GhZeXl/P5vNfr/fmf/9mjRw8/+OADgvRXv/rVxeX5yelRFIXD4dZsfmVZVqfbCsPw/PyKwev2zt7duweM4cbjeGtrK0miZ8+OTk6Otre30zQ9Pz8lovv373744eNer3fnzp6UUgjodFppGiNCmsatlmdZHUSKosCyrCgKoiggDjSdJqAIEASilJZeNpWkru0QURxGjmWvFssnT56Mx+PR6Oz58+c///nPnz9/Ph6P/ZarKFmtVtoFEbQPNgAAdLsdBk9CoO97vu/ZtuV5bq/XffnyRRxH4/Hctg9PT888z1MqIMJOp8OelKyIYmchADg8PIyiaDqdDgYDTo+xu7v79OkX7Xb73r17ruu+efkGETnTw3Q6tSzLd33P85I0iaJIosU22IzSuH6Rx9ww4Tj/hMj6GGLlioZ3mMvnTQSsdyZvAOawIVdV+r6/Wq00H+N53t7e3tbWFosWGZ9dXl4uFotWq4VCpmnK3mUsSOcIU6PRKE7p7fHxdL5ERCYNr66u2AvOcRw2luaDnVkCL5aPHj4cdHtcSRiGFxcXV1dXiGtduKaXMwG7xCRJkMhxnOHWzp07d+7s7fd6vTRVKo8TMplMT05OTo+Ox+Nxr9vWlfBVp7ZkeTXziLu7uy9evHj9+jVzXcz3i7XxmmJQniTJarViZfzFxcXrt6/TNF2GyyiKWJXD9sNM+TFw4SjuDx48YBxwcnb66tWrs7MztrJheaYQ4nvf+97u7i7TZ69fv3758qXMLd2YWeS148o5HQgYusk0jwhr25aUcmtr68GDB3fu3GFTxyRJPv/889PT04uLCwBgaYreYJp31MJtKSVvYMptjLl17RFkLo0mNbS1VKfT4fW1HStJEkClkXQcx5bIYsR6nre1tdVut4Plit3tmHpjqpfpNhYmjUYjZjrZI04IcXFxcXJycn5+bkmHxQmO4+zs7Dx+/PjBgwe7u7tsr6qNDyzL4vivaZoSh7DIzW65RFHEhk6sOUJElcKbN2/evn2rcksudp1i4MyECxniU94nNkFqoBbt40sIYRgSANOXqzBQSjmOEywXLMxIkiQMAh4I+4w9ePCA72E6nU6nz549u7q6uhhf7h/c8TwvTtNWpy1t62o8vbqasdrFQnA913E8IUQaJ3EcR1GsUp06li3UHCmllPZaYwUSAJIkiZNEyiy8HZACmbnnI6Ln+pjrenQBgQiSEAmFApGy8zwhAjI/DUKAEITZv2yuBIcRESmBUsRxqCzLIuDYpWType/OAes9Wot98xuwLAvFWo+lETADXFaT8AlkqOR0u8xZQs6pcFVXV1fse8f5jlhG8fr1az5pjH3n8/nl5SWLfVj7G0URC2pY2KKU6na7nW6LIM0TJMyDYDVfTKWUAGwoiK5r23aX+7x/sOf7fhyli+VstQxHF2fBKrqajqMwiZOw5XcePLy3Ndj+9HvfffzoQ893HMdBQVJIIUHgOkKTZVmWLRzbtR0p0FKUpAmpNCaF2mzSJO64TCYT5pUXi8XR0dHR0dHf//3fHx0dvXjx4uzsLAiXaea0p3U2ZfJTKaUo0edQA3q2D5/NZp9//vlkcsVc+3K5jKJEH0jzK87pxDEO2SSYDbA//vjj0Wj06tUrRHQtd2dnhxlrdolO2gkAeLZn27ZKKAzD84uRBgtazqmU4j3AvmTMMQRBEMdRnITaClrLkE0ErDcbV7larVg3xt1mybDK3QSZ4Ot0OqxyJqKzszP2WGORDOs1PM9h7MWwcj6fctaKME6n8/kyiEQe3ZrV4SzoZi8gTf/GcZxG8fjy8nD/zvb2NstmGO1lGrtc/K4PBQCMLy87nc7OcNjv93vdQb/fZ3uoyeQqiqLlfDGZTC4vJ/P5XEo5GAxMGwLz6HFgGcYZ7KWzv7/PBC6LrJlRNmVi2s6OD+ZqtbocX+7u7irMXmMtj2bKmaUWbHmXM4hYLJpmYn4LAJiJDMNQZoZpCw4Wm8Ejy8LcQkIzr7yaudWVx0aOTJ0rpabTKRN87I4lcv9G/lYHYGFgwnHXhRDsNaAPmu6tad9ksg3T6ZSBuM5CJqUUEoUQ6+AhxieaoJS5E3aS5wOtwlLeMyxDtiyLaSYOd8PeurZts5cz6yDCMDw6OmIyReeOk1L2+/3J1VgRe6sScRB1S7AajuPhM/3BA2H+UpOAYFBymsAqMnOQJAmzYGtrfgQAiFUaxzEKkajUHNQaChlOpzwzAFlek9VqdXl5eXFxsVgtHz9+vFgtnz17tlwut/qZTavnea7rCyEkZCc9jmOVpEQghc2xuA15DAtO5HoTYmbvKZVl2zYvkpZ1aZpDT4XQVm8CAcVaEmXgL40E9RgpN7RGKrsbYJ0LJd/cHgGrIgecS2xErugyu4hI0rZQgElW8K+5h4nQjAvvUceSYbhiDYTruiy0VCrxPCdJojiOw3CVpq1+v8/mV2dno8lkIvMAwqy6Z56PtS9sWM9r47p2r99ptfwgWL1+/erNm9fs/L5YLFzXJUqFAE6R6/suG5Q6nktEYRgTDKJOolTS6dBgq9Pp9La2+u1213Xt/f2Dx48f9nqDKAoQJawDo2RO+ojIITJQkBCASKiIvcpB5aElMLcwAQCAXqcTBEGwXIJSF+fnURR98Zvf/PrXv/7NF19cXl6ej0758EgplUpUlhA+X3JTZoiKCa88OFp2EwTBaDQKgmB/f39nZ/f8/Pzy8lKfCmYcNZrk+ieTyXQ65RCDXLeUMgxXLDpDxLbXFkKcnZ3N53PXcrvd7u72LgDIrhRCBEG4Wi2CKGQ3A6brNQJmazUmlhluzufz1Wo5ujiruiFpoKzKNlmJECJVWSQdbgVyxR4RMSTa2tpi/SjrJqfTKbvxWBaDeL/V8obDIY+UtXcMIMI4PTu/jFIlpfS8uci9HRj1LpeBJhH4JEvAhw86vV6PETn7VAgh2GZNI2AtBtACtHa7/emnnz756DuWZR29efvll186jhsEweRyfH5+PplMkyTxHbfdbi8XMw0fzWN6cXGhrV1YS8qeSycnJ9pii22RuLdsxK61swzjTMDB68XHSkubWNejLYqrUEYXjedMmJWvmsrtIqUo6j5SIxUKSybZfGl3d7fdbnMHOE4OW0pLIwWTKR3RgJXJO9u2WbUPALwKkNNPjPv155Dr7djqUCMSWJv4ZULX0nMWF/NYeB8y5YF1BQCiKDo9PZ3P5+ypyLh2Z2dnNl1Anmi13+8nSXJ6enp2djadTmezGVNLMncxyoIB6HBUgiWfAhFbrRbjYB2thXWumlQt7R9TKmO8gCpJKV9EZoWZCU5zvMsiKLQkEIEUfAAzMVIUMqpTSrFBDNNenE1uuVxatnV6eiosyb11bC+MoiRK+bxYlmVbrlIqZasxEFJK321plCmlzVe96FLYmEtbEYUCklKSgWibEDA/B4EEgoRG1Wu8zqbUAAgg8iAcCIBZDJTiv0wxnm/5vHsA7yyCLqHx2l2FiABKSskImE+XzN2heAld1+12u+xWkSRJkkSUqiSJOPkuSzMYbPHW4eZs2z44OPjwww/39/e//PLZy5cvOSMeG1ux/ITBh9bWsADn7t0DFvJwXoGzs7M4CS1bSIlxHEqJQohWy+MA9+zv4XiuBrIMofie9z3v1H6/3+/34ziez6NOpwMg8/3McdGYwuDYwpQkmcdRmqaUKqnYOL5gjwoAo9FoPB5rXrPVav34xz/+9a9/fTYaLZfLxXKmlLIy303mD+od8NnHSSnFN1ZeGB8opXiwl5eXSZL4vs/+vZTDFM0NLxYLxhZElCQJ26m5rrtczu/fvy+EuLy8nFxMjo+PIRF3794VJO7evTvoDfSg2ADVdh19npVhFqtRl8ztlhnzJUnCpoxVBEy5lk6sRalpHMdCmlph1sukWhnMym+95bQ83HEcRhNsnMk+tWy6PJ1OmVBjBtqCLAEXq9/4xrKseR4gRfMQLde7f//+3vaOZVlsTPDq1SvOXMnzqetkybxlWcOdvcPDw8cPH/Z6PTZNt4R8/Pjx//yff7tYLK7Gk+l0GoaxZVmuZWugWTqPkDvk8E0QBCwLZb5/tVoNBgMmp1gxyd3QBIFWj3EOriiJmHllnStLKZjd5MBwmPsNmytrLhmfdC0eYDzHHLBl9bQ/KOXmKtwTc7n5oZaEc0wrDnx9fn5+cXGRJAnbRTPQ4PyYrMZCw7+AzarjOP7Nb37Dc6XNwbQGRHfbhGwff/yxnmFh+AXlGy9zTIc8QaSOsMY7mWmdUp1mUUrN53PmgJmLYIzruS1N30wmk9Fo9MUXX7x69eqzzz6DXIOus6BeXl7mEw6gnc5VSkS2gWDQECmbITn1UcI84ph5iPhsKiIEVHl0mNTw/QXElFQcBCQyxXnOGACTbkmenSWKIj53WnbCFjyDweD12zes1/d9H0EyXBJCsIrEthIhBAMmIErT1HdbmHPVQqxdDTVlyT2xbVtKC6UQQpgIWA85zSPlFfAXypy11BitxGEWbcWh6BFlTCxVbC/o3ZIxmA2bXZG5FZleY0REJJRCI2Au/CsrJHZ3dz/44IO7d++yuDiOwzRO4jhkniAIgpOTk5OTkzRNp9Mp04+9Xu/+/fuPHz/e3d21LOvJkyfdbvfNmzcvX75kPQcispqNRW06Hs1wOPzOd77TavlxEl6OR6OLs9n8irvEhhKOY7muzQrpTrfV63f6/e72zg5XwrFk2ZjT8zxm2tjBw/McRqlCgBCZBXJO86wPQ5oCB79dTx0JJKmXzARYvuuN4uT+3XvL5TIKQtd1n3359MWLFwllCjwAJq0KXiUN6VOU9lcTuTpgZ2fn4OBgsVg8ffo0TRWrMJlc1cIJfU9Eo9FIh4CeTqe2bbfbbcdxPv744263e3Z2dnJycn5yPp1Oe60BQ3nbtlkczdPLoG2xWDDLnubWtpoDZhMVbV3CmM+yheaA9TWbPAMB538iG7kIw7hPzy0jOTbnZl0m94ohAhHxttVhlYQQs9lsNBpNJhOdFGswGCjMTLR0qC+lFIt89UFQeawPjV1WqxXHiJjP5+12m+kYtolla6+dnZ1Op3Pn8N7W1tZwMCCiyXg6nU7TOBFCvHz5crVaBctVmqa27TL1MJvNHHt97syzyVM6Ho9ZbRxF0d7eXr/fZy9wVjew7J2FtxwbTqNG/jzbPUrxgd3d3R0Oh4zhzs7OMjFszn8wVad5aMhxW2oEk0EsqNmIyPfbrBFAxDAMGQkxu4O5aCETOSqVpikLJAGACRr23maNLGsWmWblOGVpmlq5Nx3mYlUO02HSc3rJSuyg2dsHDx5wu/qFNE2jiISAVGXG+bxDWITG4hyGQuwokYnH1/u3AEKZ8uOZn06nnLuFNxifC7ZVvry8ZJTJFlVWHoWU9xK3ojj6odbLpKSUsnKlOJ84ng3P82hSQBhp7mgQxzH3R+YhCgz4jyKPcYi5vclyuZSWlZIKw1AhuK6LUqRx0m21U8Mqng8CQ3J9QqXRt8ODe5dXkxev3kgpd7aGQgjHc6VtOa6vz5d0XCJSmQ44siwHUSISi0uFICFEErPlM6+jAEDepjoSlt6NmNFhjCCRX2ZNFxEBq3hK7KzW9ZQeZtC2ygODlIK0tYpx8+4IuFRkHm7NHBgigUDWlGj2Sx9F13V3d3c/+eSTTz/9lO0a4jj0XYfRQBzHr169Wq1WvBeFEIz2ptPpy5cvmRi/c+dOq9Vhv3Lm0hjpXl5esriGW2EE3O122VV3OptwyrYkSeI4A6yWlfkix3E8nU0UJcx3Pn/xQinFuQhZb8S4h5kAjmvDPgOYc296fvRJptzpWcOmDGwJShLCPH2VlvYwxFksFpeXl7PZjO0D37x5c/Tm7c7eNhnRcWEduLHW+FAplSiFZs3cK+aQpJQffvjh48cfPH36lDmJDz74KM6TJWAu6UXE5XLJRD0fNp5z1n2en5+fnZ0ppXZ3d/f29tKQxuPx7oe77MPD+IlLkiSrYKVDS1p5LA4WSXFhU0y2DIiiMIxWbISlh4C5ZlHzTGD4DzCNxz3XUJ6H4Hlet9vtdDrMiplrRFlMLpuJBpY/K5VMJpPz8/PpdMJsYkqxkE6qMo6Hw58JIRaLBYt8TR5OKeXmhcUVaLBiLOqk3Ct0OBweHBwMBoPhzh6nFGQfGyL65c9/8fnnn3PAS66z1erYtk1JGoZhktRIpHhXMFBmuMagarFY7O/vM+nAwL3X67F1RafTubq6WiwWWhkEmQ5o7ZHFEbiUUsvlksMpc6zHbJ/lDHQtB5zk8cKSvDAO63RaPHwm2oQQTNSyRToaskGNNjgK28XFxfn5+Xw+10SY7/uDwaDf7zMloQyXUw0KWEgbxzFHtOV9zk3wLIlcKQY5z6d3ERkx6XjTajqDf2JSXns6WHn0qyAImMKTUqZrN7pCWS6XbHbAEn62d2NRv15Tdqf2PO/+/fvj8TgIAnZeYGqP1fzjySUBMb6xOewzCOaAtWMh85QckSOMhrwQJkHMC2fnsdVYx5xFjU0IIHdYAFLsHyhgNpt1ul3LsW3bVghSyjCOZrMZKmKtuVIKLYm55yHkwblYgCRzR/kkpYSU6QOpFGhmQBHzqdKyLEapSoHecvlcZe4AOeMrNWesl1Xf6/PI29hEYYhICCilxnYac0GR4TShsRnnUpdSo+bNuyBgQYCAAgvcui0tjQx0R0mQtC0h135svHeFELwqbBzI8gfbtgE833VSFSMiu7eG0SpOQmlhu9Nl4czZ2dnF5fkqWDiuZdt2t9tn8fKTJ0+SJDk6Ojo7O2PQw3pfxpdbW1uHh4cMPkaj0enpaRiGRJl7om1LlkkKCXESri4Xl5eXUgrLslzPJyJmUFiO1+v1wjBst9uarGPQlln0FalavTM05jDXIANMYZz5wE2nnFQ4iqLf/OY3DFyYtnj69Knveiw2QNAiMkbwzBrWYN98zYj/ofFvFSzmi+n+/v4f//Ef/+AHP/zP//k//83f/O1odMnnWQtkNM6z8/i0rABmQychxNOnT1nAOxwOUaFt20mgLi8vDw4OOB5TkiRJmvDBy9lT0BI88zSyiSYHkcicIJM4jGpiVpSeGJs7Owl6CKxwStOUucxer8dzyB9aeaBg5uosy9KRE6SUi8VsNBqNRiOOV0xEaZJAKoIoSZLEkk7L7+xs7wFAmpAUtkBLClsKm1Si0tiSDsMvzAN9sH0yczBMYbB6eK3vcDKT18Vs9vr168V8lSQJEjx58mQ0ukiSRCWpZVmu61uWhYqSJEmTCCqBAnjHalNnZjvOz89ZUcrBollf0+/32Yqn1+sdHR0leVARHWglTVO0kFXIw+Gw1WrN53MOCiZyvZLKM0umRpzn0o0mRku95U/Y/kvroYUQbHHJs6ehpJSSfWaYPOW4ygw9OUys5oDTNJ3P51Yeb5kL5AFYhsMhc/xUVKiR4btJhi5f5BGnyWDl0fCr0WeEM6cNBgP9XBlxzk0oXJolDkmxv7/PKJa7EUXRxXTMQEbz1oPBIE1T3kXszsSyFiEE60Q4ZYLMc8Z4rm/bdhLH/X6f4z/zVDBg7A8+THKf48VioWeVN+f+/n6r1QqC4OzsjKPqxmGClCU4UkBsipUKIKJut7uztyulJIEKaDQacfR4rrDVavW2BizpXK1WrJJgybyUkk3MbNd5/eaoP9waDAZhGKJCTQh22704jpMo0ZScZVme5SFxiHtHCMETzGC/2+lblmXbmQ4+IwQRLGlXcSTkXpGl54QAlmUiYBPjVt8HAKxDwOail+CYddtknIgCEZAzHgLbpQlAJYQAVAKzJ0Jy2G7lOjZKKGkfEVFK5IS4R0dvtre30jRmLe90MiZIeW2urq5YlMQaez4/w+EQAKSUn3/++fPnz/8//+//73w+l9K+c2fPcZx+v2tZVhiurq5mW1t923alxFardffuwf3797d3to6Pj8bji/F4HMchYwUA5fvd4XCIiOzSw7JlXvt/+S//ZSafRIyjKHEcFp4G7LouJQnB7BFnfgEAQBJCogAgoSglQgJFitiNDgVA/kQRIallsDo9O3358uXTp09fvHhxfHw8nU7ZoeXnP///E/enP5Yk2X0geo6Zu999jz0zMrIya8lams1e2GQPRXIeRA2HwAiCpJHmaR7mnxOgj8LTBwkQoNFgRE1za7LVXUtX15JVlZmRkbHH3Rd3Nzvvw8/Nrt17I7KLFKVnmbjw8OvX3dyWs5/f+eitt95qNps/+9nP/qc//AfD8YAUGzLOdSrWhrXtbmkQfcSSVpEOWrvdfuutt1rNzv7+/ttvv/1Hf/RH3e7Wn/3Zn6GsoSfcnrjUajXkMsIKDR2dCqQtqpaqw+Hw5OSk2+6989aTo6Oj/f39breL9ANE7auIhcna3KdLGWPTdLFYpHmeLRYLkcIRa4wFNZhOpzqJvQ+YAw047JsOgDyz+cxL9Eux1NEvgK4oF5obRREzQSbExs7yBSADK5XKeDzu3wzG43GSJFEUR5ERY7Pc5HlusoyIapVKu90ma8fjceGXcpp+lmUsEkVRp90rJxGMkNADwGwmkwmygzBQkAAmk0lm5Pr6+uLsbDweK44qlUo5KTHzwcE+9BNrrWblt1ISRdAzjcn9p4itVKrWGuzW2Ww+HA6yLB9Px4tXaaNRT/Os02lXq7Uo0rVSXSmep4tqvdrd6uV5tlikg0E/z02WZY12o0Y1H3gMxpA78KaQj8rdDk4MtbephHQABDpNU/in4aEnIhiorAtlRxyG1ho2WB/N5OMwADzi4dlVkE7trS9YsYDN2traUqtQ4XBjI3cxtK8sybEL6wPbs9YSS5JEJIqJFKqaJUmlXKtWq/ki9XZpWpU/Vul1cfD222/j3SG0gfoNh8PhYGwdngHcIpD433nnHQhzYMBKqdFo9OL5c6jmbDlWGv7yVrNdqVSmk0mzAdh8LRZDp6IoOrx/uEhnk8kEEgn0dREpl6rNZnOrt9Ns1UfDyXg8FsuL+Xw+XaCokIhA+bRElqTaqCNLqlarkVbzdBHpZDweL2apKFYq2traun//frPVMnk+Go8r5VoSlyfj2XyWEqlud+vhw0e1Rv3B0ePRdPLN06/OX50DY6TV6lQqle3e1mKxGPaHSOTDVKo4qpbqPvvAGl9coLy1vR1FcRRFJArurWKJaiV8+xSEfxYzToq0Egptuvya6z0Ddowy5L4K889OWcL5SEfr2dahVLi5aHz8lmUhMUIcaR3FsZAorbTWUawjHetIKdasKI6ZV9AkWDExk9Kq1m5lWXpy/CJbzH9ZLrdarfl8Phz1//k//+cXFxdJkuzu7sLcBMuwOB0lz3PEj9Rqtf/8//yn3/qt39rf2xsMb/b2dg7u7VSr1fOLk2q1Op2Ns4z29u9954P3Hz1+WClXv/nm60U6++qrL58+/aJWq9TrVSKbJMnBwQEzP3v2bD6fHxwc3Ds4hJHq/uFBr92BMg2bXrVabTVbSqlSFKdpmmdpHMe9dgcmrzzPo7KyNjd5pjVrHStm1irP82arOZ9PF4tMbC7CxmQoVcSRpkR98ezpn/zJn1xeXBS4WrWSsD2/vHzrnTdfvToZTYZbO72vn33V3eqdXZ569VcpVs6n7ox+hcyolGKORAwRRzpezPM0zYkUkbKWkqQ8ncwP9u//7u/+7v7+PjPX6/X//X//f/8f/8f/51//6//vT3/605cvXwI5hIjOzs4gYkPpPz09haiEtb63vSMio8FYkX7y9ruNRgM4tx988EGSJFESZSbrD/voYRQpYxSxFbFwV0exUqpsJRLLSpOIFTJa61I5rlTKqV2cnp15/6JSEUGlyFD/hBnFOy2hSiixtUz5YgGZerFYDAaDOI57vd7+/j5sGB7CKc/z2WyaZnNjs2qtDBfGbJZOp+XrazWdTkej0Wy20CqKdFytVJnU7LpfqzetHc/SRaw51mo+HZssr1VKiqQU65KuWJJ0miWRsnl2/PzFT+OfQjyfpzmpiHVs8nw4nmodkYpwJkrKSblCRFFiBjc3VxcXcL4cHBzcu/9oe3tbKVXEpRsSVPD16SJRBPaX52meW2tzpSKtuVyuEln4xoyR2WwyGk2m0/GXX351M7zuj4a70+1Op1cqxVGUJEn0zYtnk8loNJosFjMRFiWGbH/Ub221O53W/fv3t7a2FovF8+fPX758CRimBNUaNFkypMSSWWRzrZmoyIuDW86YjEhXKqU8T3u93mBgtOZyOen3+9VqdbHIkMVgjGm1WqVSCSnaX375JSCdYRRRSiEZbzweQpzyqUrQOw8ODnZ3d8kV9UO1EmYeDoe9Xg9mJABFbW1tAYfZs+eQ41Zq1VsFWRVp1iqJo0arWa3XBqMhs6pWq5PxCBdYS0pF1Uq92WzXa83+4hrCIXMR02CtbTabZmZKSUWreDqZp4s8S00Sl6uVeqvZQYGE2XTxqn8G3IIsy25ubsiVLoBu7Y0Wh4eHX3zxhbV2NBpBfDk8PPzii89J6TzP5tNJOSlpVkkcK9KRTpKkbIw0Gq3JZFKr1bI0b3e3RbGOS1FiWMfW0GKepYs8z2y/P6xWq5XH1UqpmsYmUvHNVf/q8ibS2hhjjVhyCUjGGLGjyezxm2/fu3fY7XZn6WI6nV5fDWfTxXQ6n0xG1Wq93em9+94He/cOFtPZ5c11o9WbzOZpTpZkq7ffqHd3tu+VSiVhuby+alWbLKpWq9lc9nb2vvOd7wDa5fr6WkXaUhE1qbWOo0ocx1GUzOfzSr1GRMaYo8ePt7a2sswkcdkYc3Z2kS9yKcB5VJCvyeydtM41i/P4b5k0RUxUoD4TA4ik4OC05MqFmAXgduAWkPIuYCKyxhvtPHsVIopQQIZWzHrecb28eulPFgmDzUCFfXTVmp2ZWaJIsSrsNii1Bzj1+XxhbDYej7N8cXV9AefWZDJ58uSJNyvFrpKXN5uAzRiXKWGM+dWvfnV6evp7v/d7Dx486Pf73W73d378I6Xpyy+/hEvp/v37Rw8Pd3Z2rLWTifl/fvJ/39zciJjxeFwqlcB6AQIMJwrkaMQxttvtTquJAEtmXiwWSZKUkxIRxTrSJUVEMGAikFVY8jy3ka9sBRXBMvPNzRUi+mq1mrUWiae1WqPRaOokfvL+ezqJj589/+KLLz788MPPv/jiyTvvLPIss0bFBZLwzaA/W0xVrFkJE7FaqeBhXE0uDqKTiFjEeMXMUxkRabfbH3744Wg0+uM//uMf//h/gFFxMpn843/8j999992nT5/+7Gc/+/TTT4HRAzwKrTXkIeuKk1Sr1WalUa1US3GZiAD11W52tre3vW4K+RSqwyydmzRTmrxrLeJItBDpKIoajUazVUcl1MlUWymiXkkXQadChL3vnLDLVugrYpIoytPC7ScusIUcfics0shgjhwogVJkDEpfGCJCKBDKYYFuJklZqUjrGAbSWEcmSTQrEVHEohRZ0VqLQbUAFcdxTARl8fPPP0ccFlLSMc7sPI54Fs4gu/TFs2eDwQCR0r1ez9vnt7a2PJ8gWjLgdL6gAqezhD2iXQ0APzjGGB0r1iouRZlJTWoWWbrI5tf9G6BlVSqVq5vCYOjZW1xKqlLZ29uBeRy5WEhcyVxpB+zTKEAqzl1+Ni6DRR1KM5zl0CABjQnRFgoNJgtOUGstynV71zXet1wuTyYj5EdYa8Gi4Bja3t72bumrq6urqysE+SP+w0dgeKoV9tkfEBGp4DhoMJzCWgt7AMjUdDwp7M9RUq1WK5Wa1tqYIrUMS047JGrQEDQf44bRhi4LxPvJZAJXFJyvylVTxgWY9729Pe//XiwWNzc3i8Xi7OwsyzKUJMFLTSaTLMWLw8tX0lrHUcKkJpPZYHAzGKo8T4sIwcur0Wg0n6fWWq1jMFgmzcwkhc0J298aES7isKy1VmyWLabT6Wgw1FrP03yxWEyn0+l0PksX8yzXebbIs9FkVumPRCSJy/P5wlqJolhrned2OoXLybKmPM2JOIriKIphl8pdiRSwGPgCSqWSVjEpzVoprZXWKtJEJMwq0krHWlhFsSVmpYmLSryWVFAyjphJKaalm6PgwQVTFl/de5l05JaMzx4NkK2EVIHNyau/ojBGJ1xvkWw42MKvQy27YLqitGZHP5Vnk9ARA2Wl8JREkSpSYHlFXvDFcyKXe4rN5msqeOc8uQBFbzLyti9jzNOnT3d2dv7kT/7k/fff/853vgOq94d/+Iflchlrd29vDzA9IB/n5+fI7kDtbni8ECj04MGD/f19vEK73UZAdaWUYBtrreGX8uwE7wgv5nw+L5VKURLlec7RshCedSFXlUplZ2cH+80YAxBEaylN01JSuX//frVarVeqYOe9Xu/q8hJmk3q93mo0K5XKaDSaz+e1qKoKgahY/aDLESuyqBxcAJyKFSIysgLu4XnwdDq9vLy8urpK09QY22q1tre3x+OxUurJkycffPDB+++//1/+y3/5+OOPP/zww5ubG4yAtwT6GLHW/Sb8pvA5YT2w88KK85mB32RZlkQRqyJyyod7iEMHRK6kp90g9OJsOeFa9Ys+bMrl2OUOFAKWZxifkecD6qa1rtfrlUoZYSwhTrKHpQUjgfMY0GCggwjkYRcW5B3JIE8IDwRVtdZeXFxgTMCEsKorlQqi+ieTidZ6PB4jJm46nQ77fYwDM0+nU5SevTU9prB85IXb1UtdhdNhNRgQDngY7sCl+v0+hgVByAC79sHeOO+zAdM0PTk5efXqFRyBHszLP0scPLIEsQ52NS0Yax5dwt6czWadTmc6nYb1juDj6PV6+C0CubFJEQXiw63zPI+iCJZP7cpDYWEj19kYg40JbEVMnzhnlidKKogb9TgSaw13hqSOkAKMJEy+URTBRIzE3MyVgfIM2I9SqFH4FADcCro7yAVKPFlrYaDy+wVbAyPfaDSIqNfrtVotrJButzscDpCXKCJITBIL8yGXSqXd3X0vMN3c3Lx69dLY3NocS3EynMBLopTKc2OtAOnJBQaTtWKy3FprsdGK+r6KiKKo2HdZlhmbE1sYQkDeocrDaRjHcbVaffz4TdANEZlMJqenp4PBoFqtKq0nk8lkPBPLcVSyhsSyyUXriJginSRxOYnLRFRKSlrrzNjQs4blph0IPOLgvCuKaRmxfBezWzt5V2PXNinSr/1heFBgTvl7hTcVD7IRrlEpZBC8sF9ksQNu9aIx9r6OirC0tWd7+BsvjfqMTHKSTvh6vjPeVI5jYNF9+OGHk8kECFkAl3j8+DHcOWCli8Xi4uLi+fPncCo3m81ut6u1Hg6HzIy0E2C8YXFjTc+nY7Ac5eJNvAQAao4XwarN8zwxibGZMksm5CzDKNy0p7W+vLw0xnS73dFo9OzZi6+++rrX3X777bd3dnZMmnU6nffee+/09PT//A//4fj4GPkVUx0hZkcpsmIwKn5IsdkQrOgZHnn4Jyuyyrf8Zi6VStfX1//xP/7H4+OXjx8//kf/6B/t7++fnJxC33rzzTePjo5++MMf/rt/9+9+9rOfHR8fExFsAEhwzPPcV7D3IUVRFEUqBnvzhBjEjojStFWOEwhkREtkdux5z8NAhgqjd557JBoKMrUiV7/Wa/ZEpMiKyaVchrgG+yTCiVHUKM9zkHu8BRHFDniLXFgmxly7IqkARmg0GlgqF1c3JVd22tPZyIWYwg6BUC8YDLwjE5xbuRKN6B6ijiGJggmBsnuv3rNnz1BJF/CrfvF75hEpvbYvtKtxEkq9fqxw0rrKuGgYEL/CIUuhjudsNkF5g4uLC0TeQoHzQd3WOeYRCQU+6r+yQQRiqVQaj8e+eg/4B2L64Hk5Pz/HGCJCWykFE8XFxcVwOPQmDeUClSuVCtyEu7u7V1dXAExFDCMcupBpkG+DRYWwcD/jvNqIKM1vKapBRNVqFZIZ1AN2se7lpISZbTab0MVJaDabiSlMF84WuJQ4xZXB9tBUs9kMHhMoJDhJTi2xrnyFBHFhoEtIy8ZmBEtuNBqLxQyCQprmcRwrjkVkMBiNx+ODgyVRvb6+fvXq1Xwxw+pI0zSdpd6BpZT2pJgUWxLEGxeWZyFh0koJs2JFlit4l2qlWq0mYuGtK5fL86wIwLy5uRkOxoif393dvX//EGHe8BMdHx9fXl5WKhVixoKEhhO5rBnsr9jhi3m2utlEimE3rqbTGlO8ldFucihmr7y+rq1xzF97/dqzIiHgvSkq9qpTw6WAFGZmpQqVl5mVgOkWdmavASdJ4Vrz3Dp4lrgcZc84CTYDrRUziVjgdBqTR5GOIi1iicRaY60RgSuL0A1jDDP533a7nbOzMyIaj0d/8if/6YMPPnj8+PHZ2Wmn0y6XS1mWRZGezabX19cvXx4/e/712cnLh2886Ha7s9lskc7vH+z1er0kSY6Ojry1qlQqVSrlwu7hnqsUYyOIAGOWrTVgwIvFvLC6iWk0azbA/VEuRvTi4gLLCMZnADBdXl7+m3/zb+7fv79IZz/4wQ8evvGAmcfjcavdaDZqv/jFLz777LOTkxOTZ0qVRYzWCVlB0QVZdV95UuuhfMI1Squ0RimF8nAIJ/nss88+/PDDSqXyD/7BP3jzzbcB4JDneafTuX//fqfT+f3f//1/9a/+1eXl5bNnz1CHACg/AJTARup2u3t7e61WK9YJyDe2kHEV04ioXq9JbmCC9oKa5yjaJYSAjZXL5W6WplkW0kcK4A48/16yJSWT0ZClqOCNorDNZrPX66F+JWALITGALkA7DHe7N1+jD+12G/iayFW97g89owo1Pyd9ExLeOp2OD/IE4fbqsiryXwskICi7njt6rUhEQIbCx21qwDqwgHkarYIQzbVt7+O9/QXgwVtbW5gR67J64HyNYw38B1hH4YrGULDTrVFoz7tCEKjh74ORhH5zfX2NdHN2+Sdglo1GAzbewWCA5CgigpyXZRnqDCILC0CSkJIRS4UMRq01Qnkhx3g4aBicULERxio/PrLRiO40QXtfADlHGAREmxullC9LrJQC8rMiCwnD31mcmQcWDjByuCdghMBXuHPkSn20Wi0bwNR4Bx9eGX40EYEppVqtRlG0WCwprbUWHsDcFUE3xiD7HGH5o0E/ilQUJZ4awFmpopIRtlRgIBthKyyklI79stdaI6bJkztsYRJrrUWM+MJVa83zfJ6lsLExL0NViGixWAyHQ5g6PAQutpK4mmZYsd686uVaMCnZsI15jmiDGwqJ2tCA/UoOz9zBOm9p35Lpykb9sYIBU8CQQ/YpQQSgl+OUUo4BL00roCnRBtIKEa3hQoR9BbfzBj1xOlxIbf3aVUFNdRvk0RLRZDKBxxFw3ojUv3fvXuTwsKBRIcl1OBwil7HT6UDe7Ha79+7da7fbnU4HlFdr7bEIoigqJ4lSChZIdgBM3qDn6Yu3MllrJ7MZ0oLLrqRanuf/9t/+WwAXNBoNa+3V1RU8W69evUIZgIuLix/85vceP368tbVVKpUO9va73e7bb789Go36V9ez2eyv//qv+4ObJEnE5Q2rZWibwrNAAgaDAWDZdVBhlFZ5MPxnXmcaj8c/+clPPvvss9///f+x3W4/fvy4XC7DTKqU2tvb+xf/4l988cUXf/qnf/rZZ5/h3Qu3t9uNyMLs9XqxToAUlrq6MeRcsEQSsWIloVogDovUrw3onY1Go6h0Fqwfr++ys+OFPImVzEcNVpLneb/fzx12ElyGk8kE1XJQUgZT7O/Aq8IvlgFcpFiiMBKSAysA6fQ7JfwhjGwwXF9eXoJ55K4wAJZupVIBP8sdQFLx6RKovCILZgNi5C/zG61SLgAKbJBlzkH67Nr56XSqCt/Q0jQKb7R15RnAU+FujCJ1fX0NAEsQcYhf0MlwJUp/wuQOyIiisKPbL0qp4+Nj5BxWq1UkHBMR0ugTBz4KPVhE5vP54eGhL7RgXNoJJDaMGNDLDw4OGo2GMWZ7ext29evra8g0+C2IDIz8YPxwRvg8bL+i8OkFqbU2m80SV2jLM+M4jnMhwPlBxJzNZsPBYDgclpMIQxpuQGttrVbDDs2y7NWrV+JMPrVaLc+XGDsUoPOGxNmvH+SJIakM1E9rPZtPlGJf94xZWwcpqp3DHm6+UqkEfj+fz5MkQtUTPC4zllbkBraWrCULUEYhEUOkSBPrmDURkRGZzKawjszn89QUPuDZbGbSLHLlwqwpyoFDhQ25kXIWVg9z4ZEQvY7nNwU5aYaZyVUCDrjaiq3Ut3CDrx1QwARv/XOzefkyPPltWHFIhMkDcaxNMK8Ca4T6ClIgYgfC4tvS6szLnhQjw8WMhgr9dDZOTDJfTGGQTNNUacpNEd/v+8OBDY1chJEXDkSkP7je2X17Pp9fXY+jWJ1fnD573uxtdcqVhNgKFV4TIVOtlXu9zvtP3rKSQ3xWrrAucGXhDkQHvOQF6uCZq3X49SjQ7ekauGyW55988snl9fVwOCyXywcHByjVaa39/PPPz8/PmXl/f19Ejo+PARAIO+SHH354fn7+6vjld77zHVSzbzabb7311ptvvlmr1c5OXn3zzTdfffXV2flprCMiUUA6rFbBqEACtra2QIY+/vjj/vXN3MyiKLKLZQEGTw5EpN/vWwdME8dJFEUvX7786U9/+vLlqzfffJOI3nvvPUSlAtPqxz/+8dHRUavV2t/ff/78OdB3QUa9SwL0N6M8z3PJxcfO5GnOha1+VklK3gccdswvtrBZpjiOQwbsN5InmnaZgF/sW+KVYhJQrYwxg8EA8NfeaCxiQ8WUnKAK3Rd+UICu5HkOVCwoMXmet1otKIKhJy8UC/CUer0OaYyclu95NsZNufiggr055RU/9+e9yL+UNrgAW7iV0ZITUkMNwOuOEBy9oxqainZeUogvaZpeXl4yC/iidQlUsJ/nQTUqCAeQMxauULzfoeKUe6wWqPWwCQMaBUyInWX14uJiNBpZa+HRgDXVi1zwDmDBAy8FLmoRgdkWIqN1oXbePwpWJA7ty8dAefItDkz/1iaBaQFMPc/z8XhcTkrOVBAj2vnm6no0GnVaDU9JPL0iIojyCNK2gfsPyVeQ7OHz9k/0IiwkQkgPGGrEhYjIaDSK4zjN5nEczedTUxR7lTRN8wwicoS74Yfk5EgRMUaYc2ttluV5bozFMmYRtsJW2FgyllAECEUBkXklpLio+kBEirkwD0DtEQfuXU5iJE1gVWOpeJcNlopX5ErlqptuHcelPLdxXCqVKqaozKHjIJXIAAEAAElEQVSVipSKmC0et8bSeFUDXNsvt5qU/UL1fy4P7uao4gJgMUHFUne64l2NV6V88hrw5jtAuvcM2IufIQMOiWb4c1rSSnBfSwTVX+GYCOQJjl4RQRhqkud5li8wJaE0EA5QKFEKGaXUL3/58f7+vU6ndXp6vrOztb29fX5+ure3pxQppYzJ8jxBsF61Wq6UNMqyom7SG2+8EcfxxcUFkvAgnsN5rLVmLjSz3GGZQgPw63jhSn/DUDaZTX/56cevzs5ubm6gZJfL5V6vp5SCyuv5B8jQYDA4Pn6xt7ePrNNPP/30+Ph4b2/vnXfe+Wf/9H/FPq/Vav2rayzcPM/zPGUHI9dut3d2dra3t2HzPDo6Ojw8vL6+7vf7v/rVrxBqRGZFwPdsGD4n1J5TSl2eXeV53u12P//8888///zLL7/87ne/+7u/+7sffPABEYHEHB0dJUmyvb398ccf/9Vf/dVXX30Fgdd7xYqgTdIiQobgKYRTE/R6Op2O+gPl8sJDJRiYveR8q7DN5nkelRIJMFr9T2zQyEmQWpHkmbEZcnkR1YLqtnEce9hCr2IiztOvNHJ8HfRaOZBIyGeQwY2LyXQR1CoNii9RgAllXaQMjPZQZfyV3qeFWQaxhlsdoT3ejwVaBu6ySQW8RrsmjmDwvadWXHAAnsguhAqz452IMFFC2oAGbEwGDzoRAQ8OIbsh3YR5EMsJOBj+cX5YclfIAVHEjUZjNptBgkRgVxzHeAqI8vPnzxF9DXLshw7VkFDO3YMtZ1kGdA5x9TaYGeCUe3t7AM/Bq0F4wsiHK8ovMCN2c5yJCCZir02maQrIzIuzc+Xsf2m6QIjfeDyuV8t21V+AR3isMaVUtVpttVqdTgcFQkQEofKXl5eIAdQu78BHiTebTVjdkyQZj8fHx8dY4ZAnjDFaq2xZ+EFnWWYNK6VKpcK5gKeTEynSNINR2hjUOMiFtVIREUJ6lQiJkBFFopiVIQN1WVlmtpaVCFmyVdeSJMmsERHM8nQ6j5ygqXWROwTLhAqKf5BTasvOzMmBToi5C1kVviJk/qy3gguG/Ii9er1qgn49m3xNW5PbiLwg8jdrURxHHKi53sgOU4fWKwZn5YI+1liv78BmF0Ryz3TDzzguGZOVywkqFtRqFSKqVstpmgqZZrMlYpCgKWSMzVDGp1ItNVt168BlmHlnZwusvVIp/fEf/9H3vvfde/cOtebLy3NrcxHWmpOkXKl0e72etfliNkEY1f7ubuRqfB7eu5em6Wgw6F9fW1gXtdZaU6ksIrP5RMiISJYv0mxRriSNeh1wsuJ8YMiaX2Qpogym0ylSI8DR4zgG3vV0Or1///5Pf/rT3/qt38rz/Ouvv/7Rj36E0NAoik5PX3366S/r9foXX3x+dXGBnCgiujw//9nPfnZ5dQHTH8KC3njjjcePHz948ACZGwcHBxiQdrv9B3/wB0+fPj0+Pk7mJWstOgDtwQvUKBfYbDaB/F5v1brdbpqmYDYAfP/qq6/+8A//8Ec/+tHOzs5sNjs9PRWR3/u93/vggw+yLHv69Gm/3//66693dnaePn16cnLy2WefVSqV3/yN7x0cHFBEQO5cLBaT8QR0h0gW01lSioCQ5XcXAkfB4VAaBSADxhjjLEnkNEi/90K13tFQaTfqVgo1ApwSds4oikB8MV+TyaTRaJRKiY/pgLQEACk4j5kZIaZxHJ+dnV1eXoKsexUQ0hiULeuyd5gZOje8m0Bdvri48BFb7LADvd7WbrehGGVZNhmN2Dl90De8L6aPiKA7Ihi41WqJsTAgQ7DwkhYcBDqAKGEXkRAOoCfuYKvkwJVEBFacKFK+k51OB37NKIru3bvnI9e87/bw8BCChacP5BindY5MTHee55CHsDInk8nNzQ0WiXU4rDCoeKWckPbWrO/t7YFjEdFgMICHfjweA8lLaz0YDKy1cPPv7u4CEAr2TC8bRdHtIIDIMSWXXgilPE3TsAJYFBXIKicnJ/P5/OLiYrFYdLtdHSmsOu0qfySu/gS7wDftShRAsKvVajCkj8fjm5ubm5sbZIIpF6yHTYEZ9PQZ5lnw7+vr68FgUCCJ9kcITIFc4giUybLs4uLqyZMn2G67u7sigtD3PM/B7JhZRUmsYxG21ma5LZXKxtjpPNVxSUgNJ9PcKkEarNLEYoSsc6DEMQsTNnLESsfJYjGDkArkqTRNwTEmk0mv18NGyBw0Co4RUIbgia2tLQhVYdTCpqYXJ3Ge55FOsNSTJCEC5EARi05BEHHhvAuc1l5/5UBpptX0n1Bv9hxurSdes/I80K5GN4e/DSW/yEuCfnYjl1CkgrzepQYcMOCwWXub5Mi+YLNd+3T5x+vngbzve+zJ6/b2NoqiY8Jg8yGy1WpzOBxWq5Xvfve7v/mbv9npdLIsnc0yrw2IaGvzwl7BthTHxhGgUMP2rxwGSWZZVm9UtYOFg3MLdmnsf+VKlSH6I7fm4OBARdFoNCqVSg8fPgSQW7lc/of/8B/CZrW7u/ud73zn7bffRu5dp9N78eLFeDzudDqx0l999dUnn3zy9OnTP/vJn+7u7m5vb9frdc2MKBit9Xvvv7ezs4Nq5IAw3Nvb63Q6WYGbahF29O6777548eKbb74RtqlBcfsiJstPtDj/Ewc2WDiHEGP1/Pnzf/2v//Unn3zy7rvv/pN/8k8gtM5ms+fPn5+enqJk/eHefdQYWCwWb7755vvvvw/7QaiWgarmee5QM8U4oGYMJqg2srkQcbO0cKoVd07uMgIjV7fVrxBshH6/n5tCe2ZXWhyaumfkiHNeLBZaq5C7iwtxxzgAMBk72SdA+3XlHy3OQohXNsYAONc7NVABAnWHrAMfzh06MYLXAIYsItsu/cbvVX+QOdBBmFuYuVqtTscT7zuUAFEnNDCEMgrc2OxMSv4VvvjiC6+Fg0260DmLoCqP4hlFEdIKEDEELRZro1ar9fv9kMpgWo1DMCUiTL3WGtDi3knsSwaxAwc1xgCGDK8DRQ3cFIVAvOocuWxvcpgV4uLkEVuLPRiGRNzFgP2mxjrxGwchBTiDvuH1wVBhcdFRkegssoxyCGVHZp7NZghR3N7ehkCDGifPnj179erV6enpwlUTwV6bTqd+WWItXV9fV6vV9957D5IH8hujKBqNRmfnr46PX1xcnAHbpFyuGmOGw/FoNGo0Wp6ickGBfZymUxbJRc6qiKiIwBJShSpc/BeHhaiIiIsVZ41drqXlvhBDgWYGiZBcusEt7MKtmZBX0bdTSUNW5+8gfxul9Pab33r8miaB1/nWnhTr0Cc1rmnAYMzhycKNr9fc1FQEOd9Rh4fI3vqVuZVhCyJR8UMRMdbm1uZE0cXFWalUqtcLr9toNEAfLi7O2p3mG4+OfuO7Hzx59+1qtYrV/5d/+ZcEsqiJWKvCxqiUKjIIJYB1pYAuKFdIxwuhwGcwJkNnmCWOfcgranBWq9VyljWE6f79+xWH2gj4ApjI3n///VarhUo4b7zxBkDwK5XK0dEbFxcX8ClOhqP/9J/+08uXL589ewbJHZbqVqOxv7///vvvHxwcHN476HY6Ozs7qMNTKpXKpZJWqlyvgyXMZrNOu/1bP/zhq5OTzz//nDWlWeoVR2wGb/n0jMo7cT35gNP05cuXZ2dniG778Y9/vL29/dlnn/385z+P4/iNN94AGbq6ukIGMKRXOPB85quPJcnzXMRqKryV6AMcWtAzIJFA7DUuJsubyyhAFlz7DBa3nWYpvBhKKehA4oKM8KDQkuy7h8VARHi0Uqper8PFGEWRRyz3O8e6zBDP9vyawW0RFg7+DQcBXKQwZUdRhERYdgDCiOJhZlo1bXm5VrnwbP9QzCZi+kxQTM1vfs99vflOKdXr9UKZw0viUObEhVIrV3JgMBhCC+l0OtDsq9Vqr9fDxZApvXCQ5zl8uv7mnnkUhXqsxa+QQQR2wi44CGYDdB5cE6IYop/wLmDAHhsLUmmpVIL5mpyvFJIQDMUweHjbA7rnfcDrFIiL/F2IhpBCsJAQrIvoJ1ihtctbg39KaV4sFmIsfh45rA9xAcxEFEXRzs4O8EfhVuj3+2C9sDwjJjyOY0iNMHplrjYz/FbIpTw6OoKfK3J52+NJrdvtLhYzeOKHw+FkMmGK2u12UZkQi0GWThx2pYKYmUiJiLHES51PESmiSDwnVuC6TAVmFJMQCXsjBxFZm4sInF/MLE5m9amqURCE70c+OMZDIRkIoyYgqfCr4My6CRobCIuc+e+SB38b1isitFp8UJyHeO1KDM6SAatVGI3YIfaB9TqZ7Nc4mb99u2NcTJJEKki6MK4aD4JX4b7t9/tInNjd3X7y7tuHh4cHBweVSsXjt4Fcenq0VOWZkTYQSqbojHKuIE/TtdZRrNJ0DpQGeHewk7XWiJ8yDgIXd9BxBIBJJJl0u11P0er1OizSROTJ8cHBAbKSEMFxdnZ2fHx8fX29WCyq5UqSJGADR4eHBwcHDx8+fPToUcRFRDHS2EFo+v3+3t4eOKsxplarHR0dPXnyZH9//+ziNMsy8NcwCUE7UF+vKWJM0nSRJAnGkJmB4ffZZ5/9y3/5L/v9/s7Ozk9/+tOvv/4aSka/36+XakophKRNp9Pj4+Mnb7/b6XS0q0zn+Zy1VsRqtcSlAs6DciEzQHgAZ0Kz1lpTpAirIAnbuOhQWuXKRDZPU6HCgqeW/pEluYGU4P2gTjIQPyDkSg1CQbHWwquHgdIugmnN+i1BMWOE4wJqEa7KTqeDShs+uAYF4ZG+4gugKqXGw6Hfj76Ri59CGIE3TmqtrbGeSfuekMu9Rj+1q++rHKQrBdxXBcY0CSxp0Nfh19da12o1jAMwYvf39yFjIbQNVl8sY1l6BArRFjKN385JkqDwQLvdBm+zDowFerCIAI3OLwMiih32APqG3KfRaATZJbS3s9O8wcJVEE0Samm30iXWRawQovqxZ+M4fvHixfX19fn5OW4I9j+bzVqNZupKTinN1lrNyseoShCojw689dZbW1tbSC4Coufp6SlqOzKz14xF5Pr6+ubmBllzRYKQa9baX/7yl/fu3Ts8POx2u4gC6fV6va3Op5/yYHADJ8VstkjTtFFv93q909NzLzWKJXbOYA19dsmAWYhEyC5VXiXElov/ShQrXO9ipMXK0kS1MsKwpZNdinTYd3EA0+bXnvtTExnHPldzau5uHHjxKYjV/XaByb/mzqEJ2t//NT8JN9FqZ5bM2LPRqFRaBiOoIPE5inxdI0aZW0B0gcz5J/26/kP9veWyO16hqHtPjrD6JITDw8M8z09PT5FY8ujRo/fee+/hwwfvf/Aukcxms+kUoS4lpPRgT0Kc8Go6XojFsGZrxb2EiEi9WnZ03Jg8N5mIycQkN8NlwdSnT5/e3NwAcDGOY/jkwIqKIp3lUrVat0QQn//e3/t76Px8Pv/www/L5TI2Uq/XOz09Be40zKFEpJQa9vuXV+etduPtd958+eK41T549913nzx58ujoIaIbqtVyp9nyXMT7UbD/kQqCzV8qle7du/eDH/zg//y//oMZSrasN1XEQ901YchqQM6lD5SAwfY//If/AGaPNA+oHXji5eVltVqFYw9pXdPR1AbhSI43wBanSqWSr7YGaR1yfVrU5VWRy4OsOFbtVfbZbJZKKnmRZU/MQmJIrIu2jePEM2wvh3nRClQAy8y4YuB4WSLCAfgiFBHo5fDMeQk19Lb6m/vXBKcEfBiCqBFNjeBVz+aVUkhtGgwGSilQdgpcR2s0xSvQUMswL9miCAGjIDdPfJLGqh9HKQWF1fNdP+/QFMHk8BPwwm63jVDby8tLrfXNzU2e58fHx9/73vdQHAn65cnJCdQyaKLsjGfswrN9FDczA0scRmwsAESYQ4DGHCFOB4ZrmKYhf0Pnhm326uoqz/Pt7e0iPMVVFcRM5a5AE7uAc0/c6DXRqophviKiRqMBPy5E0tDL7qfbY7cppSLW1lqllu4eCRCviEhrff/+fWyxq6urs7Ozm5ub6+trqKq7u7t7e3sHBwe1Wg1bDBKzcfV0/XI1xgyHQ1jO4IB//vx5t9vd3dtWSl1dXX311Vd5nrfbXQDfIgTB23uAYIVX0MzMpABvTLzBXFTBfkSRKCElyooi0gpQxWLhV72TIXkVjmgJQBvKfCvUf8MR6+frNc3fJ1zSngF75vdf08Le/lruSxvaHa8GEXsxUUSWGrDfM6HcvTZMIiKr1ZP8fb+NnBI2fXu+HXvSSaupjUhIEJFHjx59//vff/LkSaPRyPM0TdPhcJBlGRQIJKQ/evTo5ubGyxOe5LESrVYiRMhRKHJmSU8vsF77/f7Z2dn19fXl5SWK5rKrygKtCD4qZHHUGvV6vTmdz1G+cHd3FyE88/n83//7f39ycgICsbu7+/z5c5DRk5OTyOFamCwzxnQ6nUePHv34t3/n4cOHb7311tHRUbfVnk6n4/E4UhrhG0htQsQstGGwTOMculrrra2t3/md3/nZz//6etDH63s1VzmkpMiB80ELZOZ6vY6Y4W63C2IXx/He3h58wzAJItsYIbuL8Rxm/7feegue6YODg5cvXzZrzdyFjtPS3U6auFSOkVxLCNGaTGBL9OCRnkRqrZF3AS0ETkEExWDulAsmCsVMv3q95yzURSiw69qgjp6/Feq6w0NprUXVeji2xYFLh4Q4XEXWYcQjLGAwGOzt7UEegtxmAshVEUEBOLDqogN57m8VWrm9KRvBB1Bxrq+v//LP/2JNymFneQ55gGd+Po7Xe1v8fpzP54hF93dgZh8MgQ0FXzhS3d5+++1Hjx612+39/f0sy05PT4fDoTd3e7JgXXFALDysXo9EW6lUJpPJ+fk58CNFBIZfFKTCIvFTKSJYgdh9qAHq3fOYd0wouKY3V4SvLN4Se1vLjYHDAlI13MzkXCHIKYfqD8V3cNO3q+E2EgDXe5YJ/wW0XiTInp2dIbuamWHV39nZ2dvbg9iHMKtut/vo0SO/EvIl+gRVKhW4pSH+woV8db0zmYy9IxndyLN0Op0iIjq02BUasFrSYSXKkBCxLaCMlRBZJktKmITBEVBjQCvluYurQF/QVfbJBUoprTniCNIzOcfzOtH3XEZUuJH5W2jAnpiLs/GYlTxmWbv4b8GKww58Sx7MGxowr1qh/X0KBkzL8VpacsLrggcvNWBaUWT/Zq92RxAEh46TUHgXkXv37t2/f/+99957++23UTByNBo0Go0kKXInwCaZ2Sf/+RHxjWj5RqGEArEXbGyxWKDoqYhMF9Nvvvnm5OTk6urq1atXMLUZYxBWg32FQatUKq1Wy9oi4wLRE0qper2ulGq1Wn/9138N03SpVBqNRs1m8+rq6vT0tFQqIcQUyD6PHz8+OjrqtNpbW1vejwiTY6NWt8ZErJRQnuUmzUyasZWIFVuplsp5nhuTV0tlMnar0+W3337jjTcub64HgwFe3xjDbIgIqqFPA/VMCJ/Qa2GuRM7l8fExZHPjoCGMMZPJpF6qVSqVd99995133tnZ2dnZ2RmPx5eXl++8+Y5XGjDdEHxjhUpHFeh/k8kEQ+1DwcWVUMU0xS5RG1Ww8FDlonn9mvYci2gpTOArbwJVAYJg7nJRPF3zMijK1SEHOsuyfr8/GAyQfqocQqoExnBarRHCRSgmYZCRe+O9dJBpJpNJ3fns/RncUwU+5tDjC6hhdAxxYSggjcXmU2+jAK0CrywurQu3WrhSvmrV5QScTi/KFCpdFN3c3ABfCcMO/JzFYvHq1SsEEHU6nf39fYgXx8fH0B0p0LwxSq1WS0TK5TLK0gF+TkRubm6AnoFsaR+Rjt0nIj5KdLFY3NzclMuJUgoqHRYnOchGcoZ68PVms1ngP6x6CjD7dwVh5daAs8Lqzq6OtVem4d/1w4thtK7yoLVWxHqLSMiAQQ1++ctfgnkPh0NUAATfffToUezq0hMRhI88z9EHG/hKYMIZDAY+aANQnYPBoD+4Ho2Gk8kIMjHkVMVxFEWLRbpm+ioEBVVEWfMSUupW7qKIlGWIxsRawVGslLKWSZbsQykSUSKidOHBFEUYMT8m4fZcY29gw2KZRDFpImHSTJqQHiZq5f+yrMItVF3+LnzAmxz3W6qaIRteu0nY28inDYTrCdTklh6wJVl/Z/x5u1WTbTG3G4Nwm2HhFjOCl5jgAX3zzTeBDXt5eXlwcPD2229/+qtPmClNs8ViXqlU33rrrTw3/f5NmgJThqOIlEIF4ijSmiwq0i+bZ8DIo5/NJjc3g1evXl5eXs9mk3a7/dmXXxwfH2OJw4Gau9IuxgHvRVFUrVab9Uaj1W70+4W/ijlPUzFGEe3t7Ng8V0Q6ijRzEkXddns8HP7mb/zG9fU1Ed3b3//Rj3709ttvN+sNInr8+HGr1SqXy5GKFNFwNES0hYrjzGT5NBcljUaDNClRueTpLC1VS2ZkFvmiXq+rWNVHdct0eHj05dffTKdzETZGssyIZHlmxRjNXIpjk2Us1uYZGauETLqoJKU8z6eTqdZ6q9MVkZvLq2atni9SlSSdZmuxWNxc31Sr1e+8936tUi+Xy9/97nfn8/kvfvGLo6Ojre52t9v1/MnTviiKlOJynCSlyCe/ImAY7AGD5hmPcoZoImSsRlrrUilOkiSKlCmKF8XMYi1ZW5jXtC5s8j59LnfBq9pldHiWDISssJPQwOCUjaIIfj6f0bSm73oN2zgfp7c0iIPdt87/B4MqRA0YeMWFhrILkcWaCfewP0bJgcPDQxFBuaSLi4uLiwsUpRERS6KJkRapIt2q1VSkNSsjNp0vZot5tkittV6nVC7GEFwKaT8qQOIkJzbBinNzcwPGhmERkfF4DIBVeC5hxgglfXZVMpkZ0dEw8IDOYPZfvXp1fn6OQoEg0wAY91Zf2PC11qiQ0WjUmBnSQCko8Oyd6xi3KIqazWaz2bx//z564nO6/KzdQq+IhJd2EQia8JX8+Mc/RglU3ATe5SzL/uLP/ly70heFXVqWmVe+5S7q8+nTp+wCsiDQoCD39vY2ivIi8AqDNp/Pc2uUIpEi1IuZS6VSmSS3plqvKaVGo5ElmaeLi6vLV2d5t9ueLdLhaEpEjUajUqkZY2bThYggDosCryQyekH+xbJlcVWP4LEji9XFZNlatkSWFRMxk1WsvSZWsA+OAnVUmBSqMWpFWsWWl7tG7rCbymq47rdidaJ8ZcGVk0SIKVuf3SJIiyBorB2vf/LyWzDMO690YospniFrnb9FziAioiiOijzg5VkREdEuEooC77Hj4MDVxLX+rneMD9s1BwEygsCAIy6kdWttpVyt1+s7vS22plIuiUi1lHzw7pNWvXZ2dqZJWvVau1EXkVyrJEnyxfz58+cmF1ISxaU4SZj1dDYX4XKlaoyIGGOJjE10EielSrWexNpMZ2JUZrJIR1ozwhziOB6O+sPh8JNPPrm5udI6/uSTjy4urj74zncePXr0e//D74Y1w7EHBoPBYrEAacjzHLQgz/N6qwk/pVLqwf17KLg0n05qlfK/+N/+uQ8JIfouMz95+616vakdsk+73a5VqpWkFMdxvkivzi+8WgbyMVvMSQyzKNJUCLCiSAtbyzIY9VlUUo7Hs7Gw7W53OzvdP/qjP7646ktGymolqlVvzWYzsjbWUbVcOXt1WqlUxsNRt91ia2Olkji21pCYcim21i7mU6VUpZwwM4nJ0rnJVRzH9+/tJ0mymM1tLgcHB8fHx69evWLWUZSU4jJCVfv9PiqOjSZDeEONySvlitLkFRFnliAOAmTI0Ygsy7JsYW2a5/lsVu31tu/d269Wq69eqU6nc3z8fDqdtFoNIjUYXC0WWb1eXcyncRyzYpPliEfVrEhHJMTOW1WKk3q1VooTmxutdZKUK5Uas85z02q1d3f3K5VatVq/vr4+OTm5uRlkmZnPUxGu1+tpPkXAlPfIpmmKgF4iQhQSMwO3QURGoxHSkO7du9ftdr/++uvhcAgbIwXSsbcYw5cPfg8uDi3zrbfeQulMlC06OTl5/vw52BJqYOV5tsgWKo9ajUapUul0OuVqtRTHN4PB2fTVcDzWzOVyWbMyxmCJWofFEbu6n4ABR4P/tdPp5bm1NqvXm1SkDps4LsVxrFQ0my2ur/si3Gg0ut2tJCn//Oc/h9QCTQ6TCLcFENcRhaS1HgwGFxdXX375JYbRy2TgjtbVgPKGUwgxw+E4iiKUGXCxshq1Ja0lEQZSEqrz1mqNIhOiMOwpY2ye2zw3PiTer7QCq1KTBxmF3YKdeUwphdA5yByj0eibb77xE7dYLHSklFIshQ0M+xos379FtVZGJvd8Pu80Wvfv39/Z2Z7OxlneNDYzNru+uTz+6Bj1KBvNZqlauelf51mRHr29vY1k61evzp48ebK3e2BpEiXlg/sPLKmTk5Orm0kcl9rdGh6X5qRUXK5Ew+HQCsVJaTKdxRG82otSqUSGidiKdqyJmUkxIuYypalcjgeDvtJycG/7k08+qnfaaTqfjoookGq1Wm4WBpJyuSKWk1I5TdMszyrl1myapcbGpQSqNwzmvU5HxzEpzkzOzDqOjOSkJC4B/YNUxK7ItNZKCZnpbAwya/JUK4ojZXIhMUwcaS06TtO8Wq1GUbxYLCrlWpIkcVxijrXW0wmKzuVaa444t0YUK2FRiogNEVmyYmOlLZMSskwkLMwkLCwsVhDprZhRalCkuJKEjVgmTSyK8Snig7bY/S947iobRlKyg6JcU5P/K9X2gPuKNZaUCClGwQdmVBqO43g6nZKSJEmmk/l0On38qPvd3/iNUrkMkZyIms0miA4y55Br++jRIyKCwwkWXQTBMzOzdcKKZjbMhWxBRMbIfD7PUz25vmk26kmSnJ2d9Xod6AEnJyefff5plmWXl+cXFxeDweDq6qrZrNdrle9///t7e3twNSHwGKLo/v4+lAZYk6ATQ3rFQb/fr5Ur48EQXp//+R/8T3DZ+lBA7P8sK9Sv2NUVBt1ZBNVaJAi0jiMkaRlFStgycSE2KmFiqED+k0jt7e5vbe00Gi2tYybFpFjYZIVnNFJas2KyTEqzaM1iLImoIhyjSPVjLD8hRg6CFZubnDLRVuv4z/7sz7rd7ve+971SqfLVV1+Jse+//z6MeNAEoEZUq9U8z0xeVKbOXYNGkrmiC15DFRG4bIByqhTV67NGo7a11U2SaLFYpOkcVXulcPIxDBKh6omFYR3K8S1L1HKgoCufQGIckLg4RAUoOmCxMLRCg4RrnAInDsQIvAUQG2DF9emq8DSHdmzxzSFtgSdVKpVut4ssYURsnZ+f9/v9k5MT1BOMSyW8WlwqGWNQZaherz96/BjMo3p1lWUZ4nSjJImIvS3dBLlea1t+zbIVLsJCz2NGHDIRwU8PXv7ee+/BWYPq1whAi6Logw8+sNbCuQ5+dnV1dX5+Lg6kAvKKcvkXckfzhisVBPKwM054qzvub4zp9/tehPUG+czVEvVGePJeDMm9HwTh676Ilq9FqJRCnNTl5aUPbhIRMoL9EvZzTRM6Ozvb3d3t9XoIAofTV1zUN9bSs2fPnj59Oh6PibnWqsdJAg/Fzs7uxdXlV9983ev1/uk//WdJktxcD375q08/+eST8/Pz2WyRGUusiCNiRUU4MxOxIQHUxuYsCxOTIrAN6LukhQRR9HmeGptFpajZrNca1TjW0+mYbC4kzMrYbDobT6YiIp1Op1KpKKVFKI6TLMtns3meG1qNIvZLa1MjVEqxVqwE5Ue9IK4CDEtyvEkpFbEircSyipRfQrPZLNJwqzFYw8aOZyKyTOCmBc9gBuw89FcD6zYTExlhEktKsbXCzMQGoWfMqPdKQoaJ3eddmmg48uFERP49b91+fydNhDUj1l0ppRRB6qRSqRyxStM0S9Natfrw4cMPPvhg7ABj0bHd3d16vQ7QCeQbnJ+fg/BZa/v9fr3VLF5GiMJ1r4LYdOI8zbJFylbyNK1UqpPJeDKZADnv8vLym2+++eijj25ubkBbZ7MJYiIuLy9/+enHaTZHaBIzgxPP5/Or6wsEXilX1bhUKvka8pFDuiAixM54nyUCgCOHdjKdzkNTLTg6iL5/Fe2Atv14MheiFnMRWnzH2Kt79+49fPjw9OUJvJiew9EqBKMnGaHrYXNVsFPXcodEdvLqAuP8/PnzRqOhNcPQysy1Wk3rORK3XDnh1ForJOQYsOc9fnxCBoy6T8wCvRCuSpgWxUUwXV9fG2OQ0YQt50d+jQGHhi9PG7EkwR29NkNESItCyI+PS8AxXGs6SBmHXZedH8f7PhGXAKgKWAU6nQ7QHD3CoptT8X/it5Baer3e/v4+ElfIVY1FdVt4KC6urvBQz0SxLLFWoXdiOUFMzOcL5Xz8oU/OezHXKKNfKr5v+AlmGYAPYB541s7ODjMj6zfPc6A9l8vlo6MjJM4aY66vr6+vr6+uri4uLvI8w8hjzSB4yrnzl4Ega4vQd9W3kF5baxEgibrF2JvoHkQiCEzk7PAI/Cx2gS60dmbe2tpSLsXu/Pz87OzMpwCg2ibMYMvewvYoy36Gr+DHuV6vP3jwoNvtIvSyQJC+uUFSFkqplsvlm5ubyXQ6TWetdns+X4jIo0ePdnd3q9X6u+++W61WT09PP/3lZx9//PHXXz9Dfnar1RqPJl4AXSEEa2UDHBsTK1LAH6FcLhFSFSImKuojxbHudtu9TrdZr5M1mpcwI95gg8QBEYGlEDtIKQVoz3B50wYP5o18V/8T/6cJQNGdi4pYLaOXyMmRnluvNU/lhFbsw7yqc0oQpUwuvMN7Uui2xivt9mIeKx0IeO6dcDB/J42FtI5JidN9WSmlSBHRdDptNptKaDab9Xq9d9999zd/8zebzabSGumGCGUERgES3VCSE1Jzr9cDza0kpdzlBYTDEY6LcaGnNsuz2bSUJM+fP8+y9Ouvv240Gh999NFoNPjq6TfXN5c+4x5RP5988skXT788ODhA2JSPc4G1Gfm1COQB6Fqe51u9HeOqKv3+7/9+t9ttNpuVSuXZs2dEpF0OiQ/RrFRqvttYSZgh7wdSLosGBybLWa0Q7nC5bM5AqVQ6PDwcvPvuxcUFpBbCxlul/hhApZSVZV0E/y2tEj5cjPOTyeitt95RSv3sZz9rNBrf+973ms3m8fHxTm/H537EtRhZYYwoXCVEBNXBB8h4fgaN2T2Xjcm81oJ+ttvtOI4B8Lu7u4twJKhNaZpm6bJAve/zreov3iYMRMI68d7Qfr8/HA4RUAoLWJ7nidZgEuJydaIo2t7e9k4+z/7xCDByOEcRPQtUqePjY3+Zt4Uys3Vx+FrrRqOBuLZms/nq1avRaHR2dnZ1dQWSF6rOnpUaV/X2xYsX3W4XgC0oDvby5cvr6+tZNuYAYEdcpl8WlH0Md5D3B4erRURQLgnDBRzKdruNOloAK86XlWULMcU4XO5Xr17d3NyMx+PBYKAUwY4FrRTh/WYj28c/967GgdLsTdnwHNVqNZCLer3u4T4gIvgpU67wVLVegXMaJ7vdLsKghsPh6ekpiowRESzY0+k0iWLxsaKWtNZhgUhxEYVYwNba/f39e/fuHRwcJEkymUzOzs5evnz56tWrdrv98OFDYKK98847W1tbx8fHL09OBpMhgjRrtdr9+/ffeOONbnfr6Ojo5z//+dOnT7/84qvr6+vIlaOWVc07JAvBnlpual0UzVXMLAhkJlKsgF9YWM6zPI40IDP39/fzdMFiPEvzdiZUjzbGoNQSJrFUKmXzWbhyPKG7bTOyXyr+AnYOAhzDqg95VwlJQO2VUlEUwwKEFeXnZW0Bh0/cPL/WW896w+vXbuhJNK1EJd9yNz9H/vyKXv+aZ/ytWxzH8NUzM4siIShA88nUZnkSlTrt7pMnT773ve/t7uxba8Xam34fJMYaU6tWMdzz2Ww8GiVJsre7i0gNYMrkec6WjZAhLykLMZs0I60JTCvPs8ViPpvlWfbim2dfPX0KutnvD/7yL//y2bNnlUopz/Nqpd5utyfT0fX19dXVjVLRaDS6GfRvbq6VC6CFpImaB0gB0g4TJ0mSLMt73XPUGrt///4f/MEftNttWK2h9SZBhTuQP2PEA4t6GZCIoigJGbCbGktE4sL+sZZpoyLmctaZx+NRu91+6623oHnbIlO22K6elaIZY2xg9gnXgz8OyRwR7ezsLBYzEWk269Vq7eLi4rh+3Gg0bm6uGo0Gs47juFqu1Os1EYrj+Ho6ZSUSlHS0Ls2m4EBuxYsIs2jNcVJkjxDbyXQkZOI4NjZrt9tHDw+FzOnpKQjcIp2JWcmjXTK2VfnMv1yYnQIDI3iDUgroxCLi/bXWpVKwkxi8gri0QwZPD7Wx8/NzrfVwOLy+vrZBMkZ4MTPnaYr4W0QLHxwcAJ4a1aNhpBFXy2E6nUKS46AaEjtsFh95W6/Xt7a2gIWZTmd+BsPZ3FxCIQVfu4Zd3JlSaj6fZ1kGBJU8zzudDoCjkauGNDP4mxEef35+DscBES0WC+YlaqZy2WJwoPpltsbP1pp/awkCC/xyBe2uVqs7OztbW1tZlmHrPX/+HKyRHQoN/kzKcWHJjCIf2KUdGC1kbiICgVosFlGtqEstImTFWkt6iYuJe6ZBkahyeZ+ZkXt2enr68uXLr7/++uXLlw8fPsSQIn0RRY4HwyFHOrfm8ePHBwcHOzt71WqdiD777LPLi+tXJ2cXF1fwZyOXejweN+rNW+mwN7MXlMExYBsRiSJmxVzUQpYiICPLsjxPRYxScSWKdra6jx49GvZv8nSO1yFXpqVUKm1tbUEDRgQ70klCbrK24De/Cnb9CsODhQBeGBSTxso3aZZZg/hr0LRGoxlFUb3WLJfL83lurVW8ss5drtSK8n0Xy9s8v3lALnglUGnsrT5gwCm6lYzNRXSrBuyX+60T+TdseD2X2rXa736/v9Xdfu+99370ox91u12xrLXGjoVRC4ZB6Emz2axWqyG8HpbAJEnECct+2sLhtrelhyqlbq76R0dHs/nkBz/4wTfffPPWW2/XatV6vT4Y3FxdXS0Wi3arG0VRv9+fz9Ner2dMzsyAPvB7Ca4sr7SRExgBwgUm/c477xweHoJ6aqc5oedIK7KWBoORiPGMEEq2uER+zwuX43YbxChveHeKb0nG43EURb50jItGWUmN9X9aazkAf1/lVSuE2K8TJIe0Wq333nuv3e5eXV2Nx2NoV6VSCbhgtWrNWjseT/I8S7OiNp8NwoPJebh5FZDZM2CPOoLrQRCRagKUhtFohLLKSRR77hhaFNYX5QoRIE++4dHH/RHl7lUHT6mhImNI4zjGo21QB9BPitYazhRxiFRYP0jdlkBC9yYcLJ6trS3gHDWbTSAtwPHBzDC0+A7P0zR0cPr75Hk+HA5BGmB32dnZ0VpXkxJyW1GawgbZxr7/4dD4dSKBxoNrwGLxrfe5AHuVnSCF0GXEZJ2cnLx8+RIxFhgBpRRmG1bx8FnhFt7c2ptNO2zzzOUE+zUgTnJCNBwRxXF8fX0NscA6mAF2gqmXh7xYwA7zWSmFtwYwC0wauEme51aM1lolKzgnngdD5z45OSEiZJYD6wo5zQAJAHF4+PDh9vZ2rVbrbW2NppPJbNrtdnd3d5WKIEMPh8Pj42PgeICwKKW8TC+BqCQi5BRE2mAhIEHIvi3QOZi0LULBp9PpbDwxPZMIKaVardajh0ez8dYinSGygYhg967VajA4W5egSK7E1tqU0W0M2MtSWNLC2pu4YcYQEaSPx3EMNl8qlbL5YpFnxlJcSrAvWq12FEVxVOIgojMkWZtEYI2QrhDP4gyvndy8MrzPa1rI9f1BJLdx9b/DVlgPBC9siUiTIqJ2s9Npdd9+++3vfe97Dw8fgr5A2YXxmRwwL0Ra2JGstefn53meb21tAXYYL6NXuZSIRKvuhFKSVMpla20pKuUPsk6nM59Pm83mH/3RH4GUtFqtFy+e/+QnPxkMRltbOyLm+PjYh8P4TQ5UjZubm3v37mEKrbWLxcK4egOz2axeb96/f39v70CEz88vIflGURxFiVKRMZKmubPJGfB1n4Lp++/54poYEetIClc/A8bVBdTdsgiECucTcmmstUmSWGOgu7PTC0HBmdgYE0dLmH52W9cbACQI1sPBeDKCkxuD3e22a7XKaDTAhsnzPIoUykDNZlP4C/2k2CBB0ydWkjNxExFgt3XEHjySnV4LtZKIkiQBtYLyas2yNoNXbW/dM3gDE4BUkCOXEPjAV2JXshfPncxmxhgoeShYm2UZqE9Ic/3Y5g7ow7rK81AafFGEtc9Ku729vX1wcLC1teXhruB5Qeon1EoIUqVSqeZEOo+yguciE8zDe0EIi6KoWa31+/3z83NgWkmg4YXbxx97pX/tW6Caw7UPBg+or263i22CYY/jGIFXyHktlUrb29vQZqy1g8EgyxZ4cQ+7ht6GPFitekx4o1EgKORBNrN1VbDOz8/Bn7a2tiqVytbW1oMHD8LCf+Acnnn4nCU/m977DkGWnO8fwjO0YTBgzSv1W/2igs/l5fGrxTwDpC5YsuKolFTSRf7q5Mzkki7yxTzb3d2N43h3d7+Zz4XJGspyS5ReXF6en18Oh8Nf/uqzfr+fmVzHkRDrKE5K5Wq1enN17cWOcND0KvCROJ0Exbld00RCiqxInqc3/auLi4vOVi+KIhET66jT6ez0tozN4NETEcQZVCqVjz/+eDgcosQTwAFrtdpdi8o/b00AxZ86LjQEDB2YfbvdZmZUX0ZREFPJFnmmdFypVZ3EXzbGiL3dYUFOHAk5hd96a8O1ec3a6N3KdF/DicM9Hh78t/UBExFiFhQtfZnwgTebzTfeeON73/ve0dERqBgin0tRoQ9Bm/TZHYB6FhEYdbGma7WaT+zRq8g+aF4BQmMr1biyvb397NmzTqd3fHz8g+//1tXVVatdSFUvXhwD8efk5OT8/LJWq2RZliSxCIlItVrb30/SNEuSOElKMHo0m404ThqNer3esNZeXQ729/cfPnyI4FVAPQN8zpex81TSGGNt6hU+z4HYBV55Pfg1ohzarV+JcByXclfeNc/zJElQLNRTB3KhKGzXd4vfpZ61hGIszh8dHYEPQVAtlUqlOIV93ldxRxwpMpFKsQ7v5u+DLvld554iUaSIC+bHztoDzRLlfvM8R1jWYrG4vr4e3PQ9n/bkz9yR98kOPSN8UzS4JL13jRzK/8XVVblc3t7e3t3dBVQWEfmoOhXEj2CWETkPA7Wn7yICdZaDHLPC6dhu7+7u7u7uwnOBYRSR+/fvw8oHEAbggKZpOp5OwYC9ROWZvXFFZ1HlDW+0tbVFRFgPHMT3ra2fUC7fpE3MXKvVut0uqvGgOi9qBGEXG4fW4mFEEfdw7949b8I9Pz9/9uzZeDz0oRXMnDuAFFg4lzs3WKubDNhvfE/H/Storb30CaNaq9WqVCoPHz4EYhRgVsXVL7K0TAr3i4eIoMoj88K6amlxHOe58Yw/NxkiQjw7D5c6OoaYAONK8GqX/w2fOvDgQNyA8zyaT3f2toeD8XA4jKLo2bNnf/EXfzGfzweDETMXcDFpjheEzXyN6xRTtuqo8vtLaSqUX0A9I3dCBKaaq6ur6WiMV45jjqIoibVSiR8ZcVWnEO+GGMkCNajR0FpTnq0tKi8z+ZOhtmqtqMD+h4Hyz8IeJKIkScjYzBodJc12y0UUynQ6zbOlmX1NAoAJei0I69YmgaZB34IHr73UZttkveyDsDw/4KUneflUvwFEhEh+rZPZ7woclxioQExEsdKlUqlaqSdJ8uSddzqdjs3si29exFFJa81ERnJr7d72TpqmZGy9Us0XaalUUkLT0VhrHWmdL9J0NvfiUqVSESug1IqXPjnEr4aRIHjNbrtnLT169KYxGaAB7907TJKoUql8/MmHjUbj8PDBxx9/fH5+0e32rDVRzF988fnh/QcPHz48fXU+m08eHj1qtRv/13/8v3f3th8cvvEH/+PvPXrjzShWkU7q9fr1dR8PjKJoPJ6WSggwMdVqFVjnWiPf0SglSmkStkZy5AXFWrHGaos03OWiFBMTs0Kct7VFaUxrbRQVCiUsb8DshfZ2c3MzHA6Ntb/4xUf90fBXn/yy0Wj86Ec/BGkGyUa1AAxLpVI5fXnSbrfhVAkZGMwAXqYOZUZr7WQ6Go760+m02+0mcTlN0+2trcur83v7+7/xG7+xu7O/WCwuLs/iOK7VK1oztMA1wiQioTXPT66Iyc1cZLk//bKsVquLxZyZq9VKuVzOsmx7eyuOo7NXZ2dnZwjLFBFEw0ECIFerrlKpOJ2PfZUeaOeI/kOcFHrVbDaBRwb28Mbjx56vAL2rWq2iKgD8diFVBcnwrkRvmstcldNwPEGyj46OqtWqD+qOoshPsRcOKEBpzVcjhD3lCjUenGk0Gs1mk02BvQU12iNk4XGgdxCkUPHTc3dx8FhIOoJzGoglUC4hK2RZ9uLFi7OzM601KhhCPDLGtFotCKCwLuzs7Ozu7pbLiYigdgVkbpQ6Nq5qCBzMYO35KgAOXg1RP8qhXEFqUQ4aWrlSyldXV3j6w4cPd3d3UUAQ5TG0gx7KsoxUgXaJgiLiUGxtUEcSWJJgCbGOYM9nZlaUZZmYIi7J9wGzgB16//6D+Xx+czMQ5wAqlUo7O3uYHRRKQsxpqVSaz+dxkiwyU6nXUpNfXl5XG83vfv97l5eXL49fKWSyGqOiPColRCpi9fSLz2GN8OiPWK7tRnM8Hv/85z8vlUosBIZd7GI2TJqJmGDwL7JUtNY3/au//tlPu52tXq/X6XSq1Wq9USUXqG8cSDUeBKM6EYG2wDMlY8W6CG9ElECtVsvz/ObmBnFbCInHJIoIs8odvi/GHAXLP/roo2q1utXt7ezsoAK65CY1ObG2LocNSY/OZ7yzWCzSRZY5sEIiMsZoxXIbaGPIv0KeCMzrkJd7KiSrWoq7k/V3cnZK9okqzOylShQjiOg29r7W/I+ZKc8zpVfkUPQAmzOURnHebQMdx3G9Um21Wu1WF95c6A3kKoaSiLW5EctcZKGFPkuipbXQ05rlrluk0+l0Pp1d3Vz3r29mi/l0PDFixVjWKtaRjiMWEuE8L4RTKoxLRZlrZB/95Cc/6W0VG+/Ro0c///nP45Le3zvodHqLeVYuVyuVirV0cz34rd/67cViZi1FOtna2okiNR5Pp9N5HJdCesqrvqu1IaXVsrI2iP8UZ0cNt7GIkHN34Up48qAkIdolTdOrq6tnz569fPlyNB5/9NEnlml401/6gF3IfpIkiygyDgZZuZStcPpsEBIVLjgvY11dXQHAaDwep4v+fD4vl0q1Wu3s7Ozi4qJWbUC5M8ZMJpM8z1FEwf/cvzjovn93z4CNXYABh1ZQIsIro0weqibv7u52Op3xcOzzoDxpuNXShft4BRTLyWuieGUgZnjsYmNMvdn088KrSMuyqkQup2zDxiV3mLDIpVKoIB2WAs4aWjULa+3qcgof4V823ErZfB5FUa/X01oDZRNRC7BUkUuvQjEoX3ckcbWVtNbQ6bF/4TOCiQWgkj5dB6IDGHmtVjs8PASaJjLCvcS/WMyUyy7zQlilUvnss88KLNjpFIwZ6xwKEDNnWYZoDMQfQctnZuCbMjOEGEROQfWExxQ2/MFgMBqNRARWdLDV+Xw+no4g68Cp4ckrmDdKWfjaxnmek10mFiPWJVMZmA2ei8BgAAC0Wi3o3DAIbW9v7+/vv/HGGwcHB9olZyOxAlKyivTZzcVgPILL/Ojo6NGjR4ge1yq+ubk5PT27uroaDofj8Xg0mkyn0/v374f6hnVBeUAmL5xNUkgD0/mMdXwX9TeuiOR8lvb7fSRNlcrL+E3wYOwyn4oJ9RdcsIjU01q52qDalU9++fIlCtyNRiPIXn7XcIAKhT2D/TKbzS4uLjDd7Xa7WatHpWQ8meS2SNnPc5PnebrI8zxnjieTCUkhV2HpEpApebkBJYifD8+sHaxtLgwOB85167JLwr2/1vzJkKIuk03XfhNu/uBnxac3sHhO4E2mIQMWEU2cxEm9Xm80Gq1mp91ut5rNarWqRNVqtTgqkbC1hV0O4AfwGiZxEtKgzJVDJ5/AGsdKq4uz8/F4PBoOR+Nx/+bm5cnJ6atXw9FoMZ8LkVYqiuNSkugo0koJqevrPlNRb1FrbSUHpEOe50dHR4PBKI7j6WROokpJZXt7e57OW60GoqWazXYUKaWiTqf1xhuPRcy9e4cHB/fz3I7HY2ME2RfhpmVnNfU9X5sGomXEprhGAefTQcyUtZYsG6wDUdbQLF0AIgDFYa6urlDg7Ouvvz49PZ3OZy9fvkwq5VhpIoLUX0qSJElubm7Y6UnKQZkzs5cOPbtdW4VoXgJrNOpa68UihdCaG4UQs8ViNpmMZvNJo9FIkgr26mKxmAxHIffyvNZXOwh5MJG1knoG7PvAzND1tdagEb66KoiXCbJI/etYF+WBZq3FVERBOXflCgb7CcJSj1xJbOBd+JuAdfl8Kj/1r9nD4VuEB+jhy5cvUfgParpf81Dy/J2NAzscTSbh4jGrIIi5S9f2r9Cs1lBtAozHQ2LBzsGu0Faj0djd3d3e3gYn8A547ZCzsM1BDVGv4vLyEgwYlhjvPkcW+Hw+R21NZNR4zJk8L2KIfJ4uQkAeP348nU7hVhiNRmDDMDng0QgCwmviK2jzEoQWej6BEQOmNzA6jo+P4cvf29tDZjbCd5NyDGaA7MdOpwNVEho5WHsRAQotMMtT59MBA1bE+JZdFALWCcwql5fXu7u7h4eHR0dHSI7CvLzxxhvWBZEMh8NXr171+/3pfPazjz9UcdRut+/fvw8wtUq5ttXb6fcH9XrjjTeq77zzTpKUZ7MZyhqOb26m0+loNIKObq31ixXBg0QFMDBEWMUsTMSWmIk1MTFbYtYqEhExdpHP0/liMh5i+SXlUrihvDjrOYJ1gKxxHOsk9otWuXBLay3ARiAhQfQh53cXKYCC/a6xTsETkfl0hrS34XC41elWG/Wb/jDNM5hejLEiIhaFntR8PtcqYmbE6ETREnVqjc29hvUKrdAQ/0PjsgD8GUcBljZgtZpMtUr2i5NLBky3Nd8/d42N41hHy6xNv+LDx6ggBDHRUbPZ3Nra6vV6jXqrVCoB/LJaqpbL5UjH1loJlGyf7mlcHRsKLB4mxAwSEZGvnj69uLg4PT1FVS9AS2I+sPl97LFSikkPxxOEWyN/A2aTwfCm2+3u7+9/8MEHX3/99XA4Vkr96le/eueddwajfhRFr169Ukp12l2tda1W+53f+R2Ek+zt7ZXL5fFoPB6Py+WyWPLxI2tDGjLXcCY4UOVDAg2CSEH0REFK7PK2UCuvr68Hg8E333zz8uXLZ8+eIVkFdWmipAggyq30+/2PPvpoPB7XqtV6vf75559Dq8AjxMGAMEdhn5fbILA9cBCnHZdiSLsg8XEco9LLo6OH8/kc4cGaI79UoGn5m3il3/v8bOAQEjFC+SYD9t3ACFxcXIxGI+1Cl336jWefwfZYWinn83kUaXiYPGMmF39gXJUFD3wBDVhcOSbw3TUG7Dum1O2WK39mQw4rvjo5OfHmX3KMlhysgRdcvAasAuuILVJHCn+zJ5EU6NBvHD6AORH6Dd4Or4D7e1UMBXGhuKy9AnKvIfOdnp4CwRgNwBQgpux80lmWffXVV4g+Q6vX6/V6vVwut9tN2EvRmBn9b7fbykGa5K4ohSd86DzmF2o6no63QKgwFhUmQrsCFUCzAjxWkiRgbEdHR7CLpmma5oU/GPn6ytX9vbq6AsunIHaBAleotZbV0qJDLrDfG1EQ2f7bv/1jhHw2m80QjvT6+hqjenNzc3Z2Bojs0WQ8zdOkUhaRKIoglu3u7O/v73c6HYjdRARa9PbbbzPzn/7nP7m6uDw5OVEu3CRL08ViEXFhijfGKFfzplwu55aIl7Qo3Jvhsgklv5A+rLEZDswzIqJJOCri+7ATMT5eZIHwAdVZKUWsXbXiIOJEhIhcjIiGNm+MmU+mcbk0nS0yU8jTSmkQeWaGcyGOEmaGdc1aa8go1vABy6pGEb5ReOANb6/ZyOEGAV5WyGU9bQ+H0X+uaMBrPGON+3pmASAFu1q2z1u6ICAjNA6+h0aj0etut9vtKsAlLFtrS0kl0nGktWUmFUdRpJitzaNIAf8IG88TndiVAAPJQBTJbDY7Oz29Or84Pz9HNdk0TU2ea2LWke9MpCPFSrGyxI16K8tgmWQiRleFTKPRaLU6WWYGg0GtVpvNZl999c3Ozl6URPV6s9fLoyg6PDwC2sB7731gjEHwEczOBwftLMuurm52drasWAHADBMxiSUBuDkRufQvZhZbgHeJMJESscYst64tyqsRpAitNXMuwlYEeYrANDg/Pz85Obm5ufnlL395fn4OE433LQGzd5FnubHn5+d//ud//otf/KJcKjUajSxLB4MBioeDkxXTrXhzta0tRA7Cs51YLVGk4zjJsszaPMsWaZpeXFykaT6bzbDhJ5MRPLLhcvdC9Bq7Cre0EFkrTEXZb3yV5QusN2K7SGdpNs/z3OQiRmB/JmcRYpe0ys5ijFWUJIlSDIYNsgLObRwIpXXhuMwM7Mk8z4Hp6KEwvCFOubJ3FMgotMpoX/OnHw1ywf9rAxIKduLkUWvtZDTyWzXcj15n9QwbrRzFvqwkipp4WCjvorbW4prJZFIKNH4TFKb95ptvwJaQuReSV72aAwZmDJ7npSIvFtfrVSBpI1kZNm1kKMExfHV1hSRjdghH6A+MuiKCYlngdl6SgFUZ9Uusc9+yk9hGoxEcjYjgazQa5Jj03t6ep7CA+kL8+fPnz7FmEDHquREHlhJW5LZtgQ4Nuw4MyBA73nvvPRRMm07n4/FUKYV7fvnlV9jOCFCHWYKYjTLjyWSRpucXF1mGkMMW2G2j0eh2tprNZppl1zc3REqxVKvVkSupXlD/KLLWRlxY40D6rMMDMHmGEBNGToU7xjuivoJmpbhIVVqxFa+yGXYKAwYW5LoUV40jHT5AjwKB2PuARCQ3qDiwanJzy15rHUeFTXSxWNgs56nOcmvJr7qlruLpFXYKpj41aUmX13zAvFoAce2AAiUkPOCNttzHVIAp4MC6PQvGCfJPztMXhYNIGy0kKO7C9UgZcjY3DD0wybCsS6VSq94olUqVctXbOQv7VYGnCGqu4zhWzNZyqZKg9JZ3rVnnxkMB1MvLy8vLy4uLi6urq9FoZNNsNBohLsMPEDMjR9NDKxcTQyqKyrP5wntoYBfSLtrir/7qr8rl8tHRYb/fb7VaT58+3TvYv3fv8N7BkZC5d3D0+M036rVmmuaRToiUNcpaqpSr7XZnMc+m0zm7IiGeVoYTFs4iZj18TU9hPR30w+vF7Zub/uXl5dnZGTANXr58eXJyMhgMzs/PwXjgtAMRLwJMTB4nRRG32WwWaV2r1crlUpqmpTgB07UOfyqcdwn0+FuXBxEBzrNarbbb7TQtnGe9Xg8WsPF4yszddqdarRb8MipGIBQ1vNlKgoYZEzJCZm29kUutCa1eRQ0Dq725ybp0ILjclMNrRK45EYkUNZWtS8LGsMO+bRw+lFIKil2appljuuGetC7FaHOsgkWu/JLYlDP8qvA8JtxZzIxQlDWDkHXytl85yoFas8vXUi5OAu8I0whizeAAtq7sgbdj4YKzszNrLUBOvMCBnKs0TWHbgHeTXRlEP4/kJDZPc8P+YC+70CrlA7YRn5wkCcKqESmG0k9AwPDTBGszGOTFxQWWH2IPLy8viQp0LT9K3sbjZQg/0TCbMTOUsEU2hyEB1ObVq1cQcAFCCZ0VFawRd1aKC5+0UkWNeuUSWIGacnV1hQhnTEGr1cNSQTj0eDyGLQGYaz5nGoyZ46jVao1mU4wz5OnZbHZ8fDwYDJIkQc5rHMedTufg4P7Odu/Vy1c3V5dY1f6VRcTKMuUP9ZqIyJIopUhxQWOcLEFEcOggpYfEyd/CYoviOrg0FLOss/ZjMIlQTInZ4UCs0ZPN9Zwb0ToOM2+lsAELqFPoKFFKsVJE1i+twtZlCu6rAlg6/KqgsXdXzpW7NWBaZcM+GsO/FzMDhQM6DG2wbX+rkBdEt3LfkHl43quUYhZrc2MLgghuCvkFDA8BF/V6HTmCcRzHHEdRlBT81TKzeMhqF97KSlljhDnPs1hF5KRRKCLYbEhaPzk5OTs7g7UZZWFiVghEYgdNgN2+Pisg9ySRjpOE89zmebFcarV6o1Gv1WrPnj0zxuzt7Rkj9Xpzf38/TdO9/Xt7uwdvvvl2FKkoSvb37nU6vZOT4/k8rdfr9VazTvU0zfs3wyhKtno7VtJwSNnRx1UpKYiyCyIO/PL1cw96h4uh+j/7+vnx8cnTp0+Rho/4W4QgWEuVSrlarSNAdzqdGpMnSZKavFKu1Go1pHwgQ3o2m5VKpVhHSinipRfHuiVy66pY4xzizJ6gj2maTqcFYP1wOETPF4tZq9Hc29uDtXA6mnrJI1zuofwUMmAr6wzYMyrMLOipz7WNOAklG6+NRQ6qAsqcC5LPwGvDBYMH+bRacnmcBY8P5CTlfPxrs7wmaYVEx79guLFDBiyrdmPrsrEBTRoSLBD02WIRCnne0QUi7nmzv8Zbj6EgQubwt8VAiQjcE/DCYn7xLdieV3mVw3+AgQrLwM8mlBssbO/DVq5kL16zVCoxM1C6vC6OyGpjDHChYTgF3w2RouFKFJGbm5tms/n06dOvvvrq6uoKtcWQEWRcUFthj80y6wA0FovF5eVlmqYvX74EtTHGLLI5OwMP2DAGoV6v45URvgSKF65JEHZ//Omnn7548aJcLg+HQ+jQw+FwMpmUSjXIc/1+H2IQSBzCu7yMQkR5nltreMypyY0RZiqXdaVSEaH5fH766hyhcHEcM+vLy+vPP/+SxHRbbZOlmC8MplMBi13gg7CyLMutSaoVBxAV6sGsdSwiZHkp9QoTjBCOKnifRcgUyYWgKqVIq3meEa1QwnB5kwv1KrYAKTxXbiM+WZaRLZaodvA4WmtLS6GcmUVcXciAcxWZfkZ7BhxuurVtGB6EF6wxVFn1iymlmEkpFRbf9VvP39Ou5rW/LgqaVxU4JeCaRMQkpFhFOqqUKxBaD/bvgwF7yJJi/1sbx3G5VI0TrWlZKCZynbDW5tb53kya5gut2WswUFbm8/nTp08vLy+Pj4/Pz88L+wyRVoqUIq3YcC62yAxjVnGU5zlHOjaGtGIruVi2IqxVpMu6CHlN0yKOQ4QePHjwl3/5lz/4/m91uq2/+unP6vX6gwdHjx49arW7RHR4eNjpdK6vr0ulSq/XY+arqytmrZSqVusAllNq0Wo1mDSzVUTCpIhZKRayhdShFClLAosqCwtzHJekgN3ImY0n4sYY2PiNmU2ncx+3eXx88vXXX3/11VeXl5dQy+BC89g0UEEgkSQ6tmQkL6qCp4uFyfOoWi2Xy8DUVKSsNSIsYrWOoyhJ82XKbLgwlEtDWluyOzu78/lsMBhqreM4ieOEiObzeTpLYaIYDAb1elOYtrq9arWqk5jt0qrhpSXPwNbXICkiy6QhrxMZJi1krCGRXCznJs1SYyVXHCmlQ1YNUgvNyceyYc9DSkjThXF5a3g0EmAEUDuuFJKPkVZKkbXElkkpTYphVVJWciJiJWKZ2BJpYisWhVI0sSXYn9ynkBFhnCe25I7FZVXawODvbSS+/9bZvY0D6lpu0g0MUUxZQYOUyo0xC0OirOTWAMcvYmbFkVVWqziKIpNLnk/TRYF27nOrIlcVSq+CqXniIiI+Ognvohy2hmecmStTCBp9fd0vleI8hzAaAX6YaPby5asoUp1Or1IpicCMYWezKbO2NieCjTcbDl/NZovBYJBl5vz89PT0PMsWzWY7jmMRtja31gqTFsmtydPMiC0npVKlTFbSPIuUVpG21hqxi/k8zbPFYuaHDsOOtKvIVfCF/OGFMBNEvamAwfznP/lJpVpqNtpJKUrnC+hjIvxnf/7n4JrWxS7BLFlvNLBJszyHWFMql6NSMskW1VodhfaMsVmWa60VR0+ePEFPiEjrIrU6necvXrxIkqhSqsalSHNkTGaFrYgozZGKknK5krBlmU2ywoCifUkDJCDh9bMsE7uk/0rBwapya8I4TT8OeZ77aHnjInWoCMUgxRFWeG5Ta8gYozgiRSTKGJNnlpVEOlFAAWJLtIIMw8wwUxFRrGL0MDW5zUgniVOSLUlRTVupCEYOx/wkilQURZEpxFMl2G8sRVE5wRlNyjK+IyVMQqqwQxdKoz9wqAlSeBU1odpQpBTuZsj6T5zxu5yMYN9bFv5f/rd/6rVGqLMsZK1VQt65BYkD5zWLiGHWURQhGR/pXL3eNmINIofwnmcWhE9HHOlER6xIe7IOVQNTi+diV0/nEwSmQ/a8ubk5e3V6dXV1c3Mzn8/Hw9FoNPKJ81EUjcfjKInJyjxdaFbVei2J4twaRVyuVqrlCmslxgpTpLSK4sP7R81mq1QqXV9fP3/+fDgcokZNs9kkoiRJ2u3Wzs5ur9dF5sn29nacaLhwEDiNKfQ+ORs0UDoiq1mxVpoVKdashAl9UMSk2H8KqyQuw6uEHHzoE8aY2WwWx/H5+fnz58/v379/eXmJ6uUfffyLly9fIIUXRWchjlxfX29vbxMR8Ghubm4ePHhwdnYGuxKgiFrN5nQ6jeN4OBy2Wi1rc2atFOV5XkRnBOG7FIhv4kxAstrIZbXGcandblardWA2Iclvb2+v2Wz+8Ic/fPPNN6vV6tH9wyzLWAoWCC6ILBF0wHMOz2CstZoNHOr4hGZOhHzBLM+NtcZvDa8brUkPzIxgIk9f3BtYMM7Nn3hfF62KyVrD0KSYxX8SWZHi/NqnodvPiwAeduW3IiL5MsglbGuSeNBWXtMf+8CTUG+21s5nsNAUY0iuoHgcJ+HY+vPE6ya4sCebJxGDGtpyfEfBfb1Oj5OlOBEyoWiSZ9bYbDZdKE1axUqTNWRslqUmNymxJrbWUJYvstQYm4llVsKkIYoR20gnSSmKo5LSFEXKkEGdV4hAqNvKVixTxErFUSmKOdKa2JDkrkZ1aG4hp414jcfLOiZdwb4gIgUTqogfQ01LwUjFcRinzs7UBJ+9tyvgoZZExUmoybkVrPkOrclaS2yVKMtWyXK9RVFCZJWKmMW5yVQxXw4b2e8OqFtEtzzCX7wm6nmvh6chRCREhpUwhescn5DAQX/8J5HVOmYWrQsuuxwognCgYSovnqKUVrFlFfacmUmKXIawh8yFdi5MYLyKNDkmbDLrz/hPXi0q5am9OFXB2wA8/9J6eU8WZcn4T8/ow8+IXZQEFlCe55HSWutY6czhqtjcQJlTmrJZGie6Wq40m812u93r9brdLWQNRlEUxyVYb4wx1hARjcYDRVoxK9LhnHkjiQT2cSuiI15kGZya19fXFxcXZ69OgZhKVIRnFRpMlmXGtFotZs7FiIiKNWQ0YKnLjJRSlUpFx5G1NkriSqUWRRF0RJQJEoc5UK/XYdKpBa1cLpVKpShWvniRX1trPktyOqJmFAnHPCsWFuKiNi9MMbD4FJ+0WKSz2RyciIizLJ/PF1mWnZ6eArP366+/Hg5HmF1vYLTWIizQugp6+/v7SNh48ODBo0ePnj59Ci6+1evAyre7swNODH+5UsrZP5Tbk+AlS6a7NkHhef9tFCXWZjAeGiNxHO/u7iJZHiICMwOt7OL6SmtdL9UkCOL1mtwaa3SbR0gKBJKQvzDDfIK//ectQU/+IDRJBW+xYg5aYy230jci7Q5ATYgLFvV38vlrO7De/Fit9T+cIwmMaUrpzSFaPV6OKjMX2vxtljr/p2x4znjVdvf6/oswkYghZrGWrLXWECZHWKxhEcEZEiWEIq+iOFJKiGKntjERUZwLdGuOCoMFE0mROrCsAkskiotPACDDYWmXgxl++qaCiFbPFCUAMiIqDJCr67P4b5c1Dxhszt8K1IL8fzyOmEkvS6+QwsGvG1Nl/SeBlysjgl2jCheli9IHdSqQItj32f66h3zrpoIDCT6t7xsRYQ0wK7+zmBUYHIZIXIdECqxJEhQ6JrKQy4v0JWz3tUlkZndSMxdGScuWLDFbt/9YWNiysCisNKKg8lwhUxVlf4XwLf6zFWZRarnOoGmwKBFhUiJCwli94ecSdgAuLmMMr+JsWGuJCz1VR2xZV2tlYKN0u91Wq9VotIA2501ShdHMMjMjyc/7NpZbxdEIcFM8Pcvzi8uz0/Pz4+fPT05OkFHTv75B7aMkSSqlMjII4zi2eZ4hasZFwXnUQ8gpPr3MGyEbjQZcDkj8Pzo6QkIkeojYMa01DG71er1erxljAJfq3w6d95Y3vw9xXrtNRXcSOD+dAkEEDjDQrOl0itgTlGwDou94PD48PNRat1rN5y++mc0mPuUR9gkiajQakL8QVNLr9eAFhHixWCw6nc50Om2322mahhFJwQIF1fi2DBhvAU+biMCPhRA8FKdDnOrNzY2IdLvdfr/fbDabD98kR6AlcITLRisGLai3uMZs7uI9txJQH75x28uKrHKL8AVX5w49WU6ff/Sd/ObvjJC9rm0+fY0B+zOboXZ020J1Z26xDYTDTps+M7KshMTCOkcM44QFA3L//c3FyrLcIRNbsVbywvEvlkUQwmMlx3nH+kgRRVTQaJzSEWdZkXYfRQp4QY64rkQU3vrKTkApYuX8El0b0vBXzGy1IhEVLkj39fJKJ81I8MNw8NdaaIhipTavuXXGN98rbH7Wbl0qa7N552K++/5/ixYOQtBJnHFqDDOTFhEjoLf4CXsnLBnDwd3CF6FgMfuv8P1t5zd7Uhzd9da3nlwbwDVautkixPWFPicwsJyKguFxHJfiouZUFKt6qVKrV9qtbrvdRpnxyKFZQeQUn7dniIiS0jL92dNcCaqXIBlxNBqNRqPpbPbV119e9/uXLhYfACv1ej1y5ep86mqpUqkotVgsYqfBw1BsHJ47OBmyA0UE8Fu1akMpXa1WUS0VZbFBlRqNBvIZEMELNq+jFbwIP47KBXWHjYg0r5fa3VwQwTyxSBHgw8zT6dQXfEVo95MnTxqNRhRFT548mc1mRHLTv2o0apVKBWD6UIVxUCqVnjx58umnnz579uzhw4fD4RDI+OyCEpVS8BNrreHb8wa3YI5WkFT9AgoZcPheCGLy4tdisYAYdHJyMh6PUYEHeaLGmJ2dncO9++H92SGN+Pzv0MrnbMu3Wz7XqKHcvVX8Cl+jYsyFyW5tml5zH1qlZX+H9Oi/pr1myYWT6AncrUTn1vPsdIrXv+xd376G9KDZIKiNXZ6MXwPWxbEvdx80D9fYB8QyQ+L0VneFoFFZn6nwHUP666c1fJFwF4Sj6q8pdGIp9gh5FTKAaOAVJMKVMQmZbtiWhOIODvG3aK+fi9dvn7/ztvmgkHWFSyIcSac0r03QShkbHINNbLJYXrW3++NwzMktPP+kb0kWRFYq/lGwB0NTdtgi+PBAi7GabW6yLEtz46Oaa5VqgRwZq069Wa4ktWoDyDXsXCMO0qVIpc+yzBS0fVkgHQ38xlqLsPvBYHB9fX11dXV9fT2eTF6evJgtFovZDOkN5XI5qtaYGabjdL7Isgx1AI3WDGC8ShnGWGDuEJEHf4cShveCnfmNh4+gpSFSrFRKSqXCdR3HsVJsrUSRJhJj8ul0qvSaUXQlMz1U7gtJXFbg+P0ErM4c9iERISiOo0hnWTadTgaD/nA4yPPcmLxarfR6XWQ0bm9vXV1dWWvfe++9TqdVLpdfvHgByCEkM1Qqlc8//xxly5i50+n86le/Ojw8TGKtlELZvnK5fHNz4z3uNghGvZVqh5wvFNHC5YXifcCfIyJEyUZRhHJviIESEVRGiuP47OwspDL+QWu7YoVt0LIz4Q55/ZYIOy+BkTCcu+K2fMurvea2oQb835ls3dVCyvXtf7XJljYPPAP+G92TV5WAjfW/bGtrb3PjrJ2hVZuTCmJicd5H1eH2ipfZX7za6LaJCy/YfPSt7ysillms1cFbA5dySY65CDJe22i+aYd/subqsrTez9cM5mva3ctjhbWEg/w3fcTfqK3d38+RtayU87yKYiZmiOmxLPVj389lwPHaTlxxCgTTzWz5NgYcrhBvzhQR3hj/sP/+/HJ4NywNr18/kY+DJ7cIRGljDOuoUqm0Wq1Wq9Wo1b0GXNZxnGiPRUcu3cq58ZaR6Jha711WLgAVmDWvXr1CoVP4d6+vr/v9/mQ6nUxHeeEIKoyThhUzI0hKakHYN5EwR1FUqVUjh2kM42qapr1er91uQ4HGLt3d3T042ANiMDzWCLQBTGZYTgT29iJiM1/mmUWuqQ1wE79nmNYVIz+vt06AdQm4sHvXajV4RpVSvogvcPjyPGcl+/v7pVLMzICTnM1mACt48OABUh3yPN/f3//Od75zfX09nU6bzSYz93q9fr+P8KtaraaUgs1fHOqTTwrapH10N2diZtS1FSmiV6xLyVUOkAgKLjRja22/31cuK8BbNcgBfnm+uEL+AjrMqzKy74Yf803CTY4B+z2jXIIgF9iwK3vVr2paJVsiQmTDqQ8feuce+29MyNSqDzvc+ZtcJ+zPrRxo8+TrGfAmM+DV+/uhez1BD4fUTzRvSBXoTLjd1oS5NbFMRBSLSKCMhkOx8ScFwUT+Dms6ethnWS0iQiZAS5XlRf5BBdNwizDsQMiA18g6bczsawj6XV+F47l6fn0Kfu197nr036Jt0klxIfTWWsVLZ6hSK1euHLvZWbuPf8Tqhl3pwBrRQwvjReSOPbLZ1p4bnn/NfEXj8RhfYwXEcRyVtFKqVq4AMq3RaFTLFdgVdcTKiI440svaZ8y5iLjUwJQ93grqPLNCziL4GSKcR6PRN998gyz40WiExMT5fJ5mWRSrWOsocApqh+ESRVGSlBAPhdVkRIBoAwdwpVKBKzRN0+3tbXhAod2ifFur1SqX6s1ms9VqoYQAESHeSkS8GcCD5uR5biX3GyPMSLF35HHaAMNojXyszQoOoCMiGBjQJTCbl8tllBzGs4rXL0XValXrbXA+oDhdX1+fnZ1tb28fHR39xV/8BerPVCqVH/7wh3/6p38KH4HWemtra7FYIIcqSZLZbKYdcoIJ6rGH3Q6P71qFpVLFh7ayq468WCy63S7SkeGVn81mnU4HPDi8eUjpKGDAaNZasVbIhj1Z6+etZNHTTU89/Xz56dNaF7vaCcUqiFFQAThocFurVgxif0t15O+wrRFWCbQ9Cui7fy9v0PMXvObO5HieBKLGGqWj9UGwwC5zqpWI+GPr/heGvdX/TEXEuHU3Wao3/gwrVgpaETop7n9x7LUXvEQooIQDchdZdOT+lnSAcBiXCzjSIiIusllEkKBSrDcr4uLU/UhSAIXon+UNdaH6RasLPhzq17CBuybUv+Aqq1hZMBuz+d+q3fqI5Ta3zEyW4daMmMm7xoio4CxERGKspdWp8XcOD8LtQMEyoFXqfWtH/RTQqtjtbx7uPqLXhapstmgymSB/y4t+UHa3uz3YbCuVSilOikjriGNSShMc47bAnrVgdeKEF3RRo4D5ZISKJSioCRQnIMvA++tBqUCO5/M5a00OvkoppZXWWheAACpFvoqIkLVGpNVq4aEA4Wq321rr2WwGPtTtdmFtRkuShEhVK3WPgY63mM/nAArwwcbaVxwiw4Ga6wc6TMsJD6xZp9r4SUi4w8kGZI+4fFPELo1Go2q1enR0dH5+7gWjcrlcKsdxrOO4zsxegECslrX28PDw+fPnT548GQ6HX3zxBWoEEVEcx4vFYnt7e7FYtNtt6KDaVWryQXObFCfs7dqa88eAdIAABDszgLghQ/T7fU9TgL2+NgL+INwMIS0QWdeAb98nq2OLi32OJgUYtmDA7mLM7PLnm9t4lQGjD8X529jPf++2+WgJdLU1QiPOEEUbQ/16k37IMNamTDYUjs2ffxuusHl8K2UM38jf/DX39+x27edrDHXzJ8qlXIf9WdOMiQgasCjr07WVkLgihuidZ8BCxFrxqqYe6ty+P2tvtPmCf1MGvMZf77pMZJ0f/3doa+yfiODW5UJOwgGRfwvxr6OsISqqKK6w2NArgTvfSmFuHfAV+vPa4sF37b61G4b7bv0O/6//5X9G7g1ijiqVSrvZqtfrW52uT2/SDkVFR2zmqZBBJLhTDWPYTpVSzAXfyvMcmQOT6Wg4HF5eXsLLC0AruH5lozycEM0X03qzmS0W1Wo1z/Moig7v3b+6usrzvF6vf/6rz/r9/vvvv18qldL5/I3Hj6vV6nQ+A1duNpt7e3uHh4fb29tIU0ZFUo8KopRKkjKtGprC/bZGsMgVKqANjRDRTGuzS0RiiORb0yCsK17X8MTVwiwAXALYM2NuqbmN/gBLaDabjUaji4uLk5OTq6urPFsAhhcBWYPBgIg6nc5HH33UbDazLPvss88gge3u7v7iF79ot5vgoIit82AUdy2gIM1gpdXr9TRNW63Ww4cPm82mtbbVat27d++zj3/FLm3//v37jx49KpVK4/HY19jxoWHW2jxPJTfGZg5sfck77Wp+nm/efmWDZGJxeBGhmw2WSxGBBoy/vQ0w3EUBiUcK4FLzvotiLjumbt+93hKw9lvJ10UTHKyZxH2XABwYelLD+/u+LV9BNN3W7qDIopQSMmvXhDxv7QDKYDjy6IM3SPgJKvqZryCg+c/cFSOh1fnVcRHUuUlJxYlc5BibUiqXPLzYz5daBZbxFwQ2zxUSEZolQ7JrZD1+giEw5oUqYq0N9WMJ8mjDtobd7ddwbm/fX2uKsm9rnGzZq9UWSBK30z3/lLX3XRsW/4i1QSvOE+Wk/SvTHRxr5b04Cq4JlRZiZpcNjEcjFXhlPaw9Yu2tiQjFZjbf19PYtesjVmtn/CoNX3n5UH171PqaT9p/Ru12G7FLwF+Fx7dcLtcr1SW9c2hr1q7IuZ5gaYfDLlKUBptOp/NZmuf5s+dfj0YjlMkbj8eTyQTIVn70w/UhRNVqdT6fa+bd3V0gu857c5DmarX6+PFjRP3MZjOyFoUhEYQFTPZer6jYDGB3oHSFm2o6nYdzHxKIWyfGmNuBEXh1oS/pqVkvCbm5rIO7EKjb2ioXEbxyqKqGZGutA8wMEapSqeR5DucxrO7XVxfw0AM5T6miODZKzRNRr9ebz+da63K53Ov18jxd6/xa375l8+wQcw2xAGF3SGBj5jzPz8/Pq9WqiPg6r+H4M7Ns3DMc9vD8kt6tCnYhnfVjKIVXmJgZaWa0Ydn2Z4LXX1Ed1pbKZsfW+v9f2WSD+4p3Y68+mtfNjMs73DWRcgcDNsbAR37XyG8ebI752mbZ7NVdX202tQwxobWDcDt7yqjuZjBrq3rtz81u39V4wzzAzNohmimlxAV3E5F1GpXaaGuLMFztf4u2Ofu/tsn/X205vg/BBrxTMy6Ig5OGaWOOggtWNOO7FK1bVwit+oPXSK7cZvu5dd+9ZlSj7e1tkPtWqwXWVatUlVIapAkOyNw4EdsPTfGtb5B589xOJpN+v399fT0aTubz+dffPEUFRwCfronAIfdFi+JoOB63m81qtZqmKcqDtFotaC2PHz82xlxcXEyn02q5DB2x3e3s7OzA8dlsNnu9Hqp9+XhCG6TQYMIwy5vrc3MyvBS2tm/X9slSBTErxi5/fZh/udFuMaFAAcUqDOG2XiP5+rAmMOMkSVqtVrvVAIhYHMeA0UeFFh/55R/kHQHhIlujd9++AWcY5nSkmTHzYDCQ1CKwwBiDVdFsNpvNJqzl5AjTXdQndCGH+yH09a6MV2hfcd8GuB8o023Dh0rgmb51qNfoVDjLt26/W9ut16wz7+DPNY05ZG9rLVyZvle0tBnc3rdQtgt7RES3asC3dltECmxZC/wyf0+WQlP0bmD/E7N6Q39QuIoxTf5/6OJ1Xy2vUYqcrZIUYCxoxRTpD9YsKJuvEw7d69c/JHdxsV5FrQKtmUgZKyJWWSUukgHfckE/19ARQp7xLRnwWt/Cbm9eILcZSDe/+lvs979d21xR1hqXAUzO1KSIiISdkZSIFFYUEfEGUhvapsXO0dWV4ombPFg51yfOeMtZyIB9swHSnOvDygBuDunaTSKYB4Et7msHKaXyNPM8Y/kAtjYQ6q21eZ6juhYsh7PZot/vo2DRcDBeLBYvT45B9BFyrFw4sVeC/dhh8cH42Wg0Tk9PW63WgwcPfvnxJ8icuby8bNYbgFlvNBrNen0wGsVx3Ov13njjDVS3RhxWrVYzLhjKW8NMgP6/SUCtq+AdknW6bUGH34aEj4jEsgROo7Wp2jxJLOLCSWgV3W2Nu/gW9ifsFYaXHZod9N1yudxpN/v9PoreoATQaDQiIkRHj8djIoIJBKFSSRKFlt67Lc+/psEpAEB/ZgbXF5GtVg8l8ICThedeXV3Bja0DkGGnPawMiOeyxfg5LrWm7IbEa40AhayLUA3GMWAEomtXsYdWdyYVZHYphm6SqpBrSkAwNls4d+FP1mqxBYTp9vxsdCmUM/z1IU33rSjZ89r+hKettWtpWptrLzgDKWG5XDc7cCu38y0kWOHgr84arf3kNTdX+k6Ce9etwq33elYkTExMnlAEPAxrmIOQflQLvksDDpf3yuzf8Xy5TQq89UVeP+C0kYZ068L+b9rCV85zhNRxwPbwOkUnsQGXPVyd9M2NQ+uvvIRDXiMsOPbWXGf9XUZorvWZVgcNF4iTGlXgM6JgglY2O3OkXMlooKinaapZRVEkTtliZvJGfEQ4i/EQgtYC1VZQMmw8niKpt9/vTyfzPM/PL879VgzliMBqHZAY5ixLob9+9NFH3//+999///2PfvGhtbbb7U6nUzCVw8NDEO9XZ2d7e3v37t27d+9ep9PRrrYzcp9w20KeyHNxzmlebRRQ2LX2Gtq9NrLFZ0G4i2xxkeXSybJbTdmWI4RurmPor2ER+yeGmlB4QTiezAyor2q1mmeLSqWCYslIavII0lEUpWnabrcHgwFixev1ujEZuuHhfCXI4fkbNeUQ1vwKtta2Wq1Xr1598803e3t777///s7ODuriQfCCzKccyEnBjB0iHd7OL6e18Qk50Nr8+ssoEGiwk621HulMBT5g2BLWSCRIA5KRrF1JSVqbqeWz7iagm8RCREIMwJDl3MWAiUz4Rmv33OyYvYOib9Dl5e9CE/TKbt3oJGw51lVL81+FPHLzWZtLK+QBa5TLbCzFW+/pn0u3UVtyayx8izW+6z9vHa7N/mPjiYgKZQLYCosSY8TEasPmpwK/qd9r32a7bb7g2ldra2Bz0IjIf/mai/+btvX9YpeDI0KqSFTwqxrnMaCKiJheh52+SSeZ81vXA65Uy/wI57UU8uJ4yINDEuRnUCklQd6wWk1ZvLWHETMj9gphwNAUgZSkXTUxm3ujscznc2OzxTybu5amOZSYNE0nk9lwOBwOh9PpNEuNOHNf5GqUhkyCV6GuiYiYlVL1eh0MwBjTarXee+89VBcGVGS3293b2+t2u7HW4+m0Vqsd3L+3tbUFAQJ8COhX7paFguslhnDI/ASEGg/dtgnXFko4ssEGWKee/gXv8CWjYqZ4E6in9b7/a+01Pmn0yrhKcBCq0kXRw8ViAUNCqVSaTCbD4TBJktFoZK3t9/uwRVer1SxbiAhSw8Fj7EaAz7dp6ABCSyTIAj8/P7fWttvtWq2GqPhut/vo0SMIBD7iLCRMQgW0ug3ct2sUMzwZzlQ4HbJqS8CJPM+9hseBrUk7aPVlKKLWzJIkCZ7gd2NALyTsXsEwXmvy9Z1cEkTLa2fWltza2xmzXqsxoK23iI9yByLPHZRXRNaBSjY/gwO7qZEv+dNt8xIStU0WsilCiV13BlFgKqQN1w8FPr/wQO7gN2sb/9cypKWR3UdWuzWwfK/bFKDwFXhVYbKreaivb3e9l9wtQKyeXHnTWxfPf5/mV0cw9bAJrVwVHBcaKq1pQXerUp7j3Loe8K0KwNSIKFbai9rhejCr5eeVq2TK0VKFWFvtm5udAEVZcg0JtSKS53mpHKtb4gLseDxOs/lsuhi6NpnMgANsjEnTfD6fz2azNE0h/CellTIGXn0JGWS42uKkYq29vLw8OjqaTqcvXrz4+3//7w+Hw9PT0wcPHtSrtW63e3R0VC6XxZiHjx6JSKvTRk1v64KMAJW8xm7x6FV7/RIgyUOXhV0iIo/ktTZnmyvbWgtEuoJs3aKsbDYrufEMONC01hm5f7QnNGsNqBoiS2VaufxCoIOBI/rqrQcHB7PZDNHI1Wq1Xq/DSFCtVnE3PEu5cnLrotKva1mWIfzNvwLm5XJxiSJa9Xp9sVggzwrJV5AYQmOAUoo0hVHia4pgeOxZWsBrljxpLTo6ZMBCK3G5frSVAwxJksSlgCNaciWpwEtOm88lIns3A97ckCKiHGrE2rdrn+G6WpMqaEM3Cq83d1RbuqPB8HcLA95cokQkYrRmK8aKtYEDmBAaTEJFdm9gqPDerWDZMxeI92rVGU9Eim4PwlLLspYc3srI8rLwK2/goTv2pnw7TszMqPFDgaLDzLZQ1paXhQfrfV1993D9f5ttt0qvlmMrG2yVgrXhurpy5q7X/DtvIQldW8/M7NiwZWYsFmPCOQqswYGAFRJPFQSvhKTbxxGvvbVnBGrND6jspqeAAp8XfuVNd2yZXcz8Go269TNClT006KnW2jzPS1G8XEx+FYiazmfpfDEejxFpdX19PRpNUHqdiOAPLq5nS77MUaC7hB0Kx04pRcxJKVrMZhcXFz/+8Y+//vrrL7744p/90//16dOn5XL5t3/7tyulqtb66OgI0dT37h0OBjdArYJT2b9CkiRQ48TZFnzYkZ+7cIDuYsDGCKNmplb4RDFBRlEWVH11nyK5CMMquEaI1R3OVDFWlJBl0QafijQpG+tEUDESpazIKtIhJAXRChH0osYaIw91R/ga0LFOp/PNN98YY4D4sb29fXFxcXp6Wi5XiSyzzvOiWCGRUiorQjjXy+rd2QDBEUo81to0TXtb3eFwCLjKBw8eRFE0HA5/8YtfHB4eriWuMDPq/d1KqtZoyhpn2hz/kAFbFxAgYq3JwvMrvFApcF+yQomQFaVIKkK0lNvuevSSptxhhb7DhAub1bLWkz+gIgCb3V2LM3a1hUSBAgYTjIOzh/qCg8s6p7J5HnXpVjtJFAQVrpIY9E3E4giFDZmYxBI+i3JG2D+rCSphn29lTiISRxGvMjO0NbnTf7XIbscEDmcwPKANPiS/ji0BLUmkqNu11jc8da3Pa11du//Ki6M47bduzGxJXH3bouwdKVuU/ynOM4rlCfsOKxEblESzsvyUlTKaKJi2el7IUHBe3HmUolpv4vCcsY5FSIiKdVMsIYyHWAgwlkg5ha1AGMXqsixKaVJUlH21wpoV/iaNCrwoAVv8I/KBgSH3lcDLZoPgCWZWKM7rFFxPzcI9HjJ7lIBkjkRQZ90wR0QW7FFcQUaUIhUxETNXq9UsyyaTSbfbFZEsTYEnBQI6Ho9tbpRSMBien56enJycnp6ibs9gMAARB6AEEVmxrClSWkRIlA+yxTt4Nn9+fg6UD0A3RFHU6/V6vV69UZ2OJ9fX1+enZ8164/Dw8Msvv3r06PHf+3u/X6lUFEd5nudGdnb3UTI9ScpiyIqNVMzM2SJnZs2RyawY0hwJCVmyItYaEaO1RlTnGk1hGJMEg7tcNpFOmIv4CeUKlomwYrZWCnJonSfcIiJDYT2xW2Gg9CEd8S2KotwYY6woSTRxxBGzZSuUWraaNEccKS2syBSFnKGxEZENq6cV3Je0IsXCqIklBaGBDocnNhqNg4ODF89f3js4/OEPfnRzc1Ov18fjMdP1m4/ffv78+dHDw/Ozy5OTk1azU6tXTl6eXlxeNuqtUjlG1VXFulQui+XZfDKdTre2urPZbD6fI7F4Pp9vbW1dXV3UapVmsz4cDq+vr5VSlUqlXC7P53NmrXU8HI6/+OJpHMf37t17+PANIppO57PZAosE2nAUaVFExhSosJZJrM0ps4bVRugNEzErWUnZ8rzWQ8SEGjBZy0IklqwRY+xSMy5kJjE5WUPWiMnzwDOkA1zSIk9MFTNrQwQuXklvCw/IwYtQiDMiJs1vN6NprUWWnpSlsOJksjUeHJL18MCIJSJriFARF7X42JpciK3iCHV5/bdRFAV2VvJbJs/N5s2JKM8R4awUkxA2DIuxRIrEMismZqXZVaUVfXtMA+sVRSQYwHXpsyB8Srs/icgHNqowvzP8RGiCn1A/6SEb3mS9EkiHONBcxF5YsUwbDnYmghuiuKtlxYRIHSbLQiysigOsBiMmt7kRk4uxS/Fr3b66tqL8sSUmERaGqd4S6AXFSguqo7Iqau4RC5NxNVJZKSgWYFnWEiu2ljBXSmsmzUqUiomteEbOTL6SolO5rS1EOGEiRRZVFAt5gpG5XoxmYVsDrRJispQzc2FFdCUuxFqtI09rg9gjI6REScQRRxyrmCOOOBIlTBFpUkqzZkVaIHCIuDAOoiA3XTYAanyzSRKuirVtBaamSDSTNTlbZpMqxc5tJSIIphERUoqV0l4kNiaz1gDaUHt8Y/RmMplE9QbqpQ8Gg/l0lud5v9+/uLj45quvRGQ+n19dXWVZhmjb8Xgcx3G4Px1NXEZy4SlQtbXWwFkkIthFrbVQah8/elgqlY6OjoBstbW1tb9/r9VqJXFZLOtE12q1OE5EaLFItVaQI/xDNzfM6kmFvEZ/vR9Zs1HIvVjTahlXXhgWYLVRak04FCJia3IrkvvZ3dzYzOucw4dn55JHFOWcExWinmGjSdvIki0mzXIBzbh2T7qDdoQjE77a3t4ebAaDwSCO4/Pzc0zo9va2yaVUKjXqrTRN21F7Z2en3+8TEZPWWlsbEZFYVkqVkop2MOBQcO/du5fn+bNnz1qt1ng89hlQ1Wp1MpkMh2f/w2/9eDye5nmOPCVIYCYojBiqhnnO5XISzukaGVqbRCLyox6O/12NRKIoIkua2eC/49N5njO2e55nIjbPtdbCnNsiUhoebi9QevlmZdKt5OZ2y4rv/ArDFs5zQ3Srg2MhgfTgeK1lKUz0a++7RlCWwh+vKMRExFzIo0xsKWcYz2CpE9pIE6K1P/39g5NegSavzYNLEoHvEhdVfCNjMuL1bcvOiEd3tE3Z4tY/iezm4vHHa9uHVmkXr5oQN39V/FBQzXg9wfrW+xPpW1Fcbn07fCly+8veyoBJMSiuiLCvN8+iCanuUAnDTxLUt2YmVsQsCBVjTUz+DKvgW8VEmpjWPoWISOG3QvgUQVncwE5BHipfWKyQMImXWwoq6/90+pAiktV9zYW+I1ipbLRoUYZEizJFVV9SpCyTJiWKCdL7baJtOPW0sdQRDBtOpaerfFuQZhRFSnEUWZFIVh2CRCRil1KFWCKKlFKIjy2VSvACKqVmsxlbMcaMx+Orq6vRYDiZTFA14ezVK0TPooStchXBfBrM+mcRYspac1KKSqUYbsharYYwHwBeMvNoNBoMBq1Wq1artdvtTqfTbrebzSbqDZdLFQ8foXWErGKlkiWx26AR4f7xBybAag4PINGHaxrTD6yzteVOq1Hc4bPStKhvusaAgzFZsVham4KYqiD3xk9YuPcEQD9sN/MyfcckqJC42We3UzUzt1rler2O5GARLpUqw+EYJUdGo5FSqtlsjsfjdrvd7XaPj4/JSVHi8IaiKKpUKouUrLVbWzuNxuLy8nI6nTebzTguAeUDJgpgpDBzqzUfj8fD4Rg1iYFTDd9wq9WiWywlpJRSNlIsKCXLJEyanL4JY0UYWGRNTo6xeV4V8qeVeRHROhJFYgzIgEL4twgpJcxGxBqTW8suljvNM6UUijmWSiVjDARKH9Owtuoyk29OgZ9QCeKVrLVEKl3kngGHn9hf/spCcyAhe3uUckjcV1YRE62GgPn1HO4d/xleubmbNh9xl6slvIxXW6g3LuljSEOIi0L0gIO+gyFtHt/F4TZ/EjJdDlivtevo3xttuQfl7rBk33xRAZBl9z8cBMY+IAJQDNFtPHjTx4nJVVrzqox1K3kMh4HunkfPY1bn67ZR2HB8MLMss7Q3Li5sjejknYMmzu1ibejXK6AdpMidWYZNra3/18z+8v2ZacMl5Bv2tX+7dVrqmnMPW8eAiyL0PvpYrYZl+aUSTSYT1K7RWk8mE6WUZjUej89fnaLSzs3NzWQ0BpbCdDplkevra2stIB4Xi4WIgFhv7CsvyQpzjACWJInK5XIc606nY0wWRUm73dze3q1USvN5OplMep0u9Akw5nq9DliJSCe+th2CgzxIJK0uMnFO9TVznJs/clZ4Z8QQJrJ5jgxn5YxjBqL6Il9sLiz/ucmAnQa13jzUnO+wFIw/X7OBiLN/ro1nQaPZCq/seX99SDskENBwpY8gQKYpFhwqQVlLIrJYLFqtVrVaHg6H8Cy0Wq3t7e3RaARO41N0cP8Cf1QTMn3hvxiNRlmWtVqtzz//fHd3t9vtwss7mUx6vd7e3l7/YoCIa19rC/WYESnGQeAxGLDfdf4t0DZHGBdY4+3zNrQ2e4v06m+WYxUuWlktXL9y/1TWFp7diOwLN7CRFZfS2gKQddMxQ97bfLVbFXqoqLjlGrnZPC4IDS1XS/jtrf5IEbF2pZzo5gtu0u7XHK/1J3zc2mW3avBMigolaf25a6/jvlUbDy/eZe01w7v5feRGYBkTK6ugvhLYme4alrXjQj1dlcbWfhi8gsJrb7zXmqrgDlgpVh6+OOzVXdkTm8wv/NykQpsdvvU1l+9CKwWF1kYs3EdrL0irK82sFLm58+mbS+uu5bf2CBsI8bc+N7wtM4cW36BPVmQlO9EPo11NCvADW1StV0qhNpG1loVubm7OX51CJZ3NZnmaoWgdESXREnsTnJKIksBQvvZ6eZ468ClmlihSSRKVSnG9Xo1jXanUms16q9Wq16ulUkVrXU4qcHEppcrlsk+OikoRuO9kMiFiFKDNspSd8Bh678jR39ADF4wUi7C1ToMqbALkXEfChaANwlfwu7WJDInR6kJfxuKGXYLqEa5gXCIiVnIRIVGWxbIwaSISUgJCaUkssVJirZAB2Ed4k+IdfaockSglSrFSJIpURMRIP3ek3BBRqVQxxkRRorXOMtPp9Jh1p9N5+PDBfD7/xS9+YYzpdrvVavXy8hJFEjnIjkURqkajMZ2NtdYXFxftdvvevXvw+Far1e3t7a2trZ2dHSJCbWAgb0RbMXh5w7VerwdUEDTPYvE68EFif4KbgkP79OI1nhRWo1rTejd3FxfR7+sZtK8hpuJ0a6+Sog782p1981HQa0Rh2eFVE7RWsQTKwRqdCvvDzEQSxxGwrNcoztpm9AfGrtM+cnt5k8IKmTxfSUK7k/6ttlvpafjtXcMVjs/ayaCp225+i2a8xi7uumd4rG5DYfMjxs427uVdb9C668XX/lRqedlruJo7WZDrzfv4QfAra+0a/4jNd195tSCUcq0ba/e8tZ+bbeWhJJs/clQoNAQuv13TQ4jI2PWZZXaVyoiUJmHWWmnNWrNSBKc/uzJZRNZf72m774v7U3ygw9ri9Fkw7vkoUUwuFkHc9eH7iSeSvuco+AvKFmDRqwgVZBHGPBqNZrNZOl9MJpOby6vBYDCZTJhZsxIRn0JaqVSstdPp1BgD0jmdTr3GEE45s4A8MbO1EbNoreNYJ0miFJXL5Xq9Wq1Wo0jFcdxqNer1us1F6yIqG6s8ywpiB3djlmVgwPhWKyUu+dVzI0/ajKvO7RdlUSXMGinKKTkuWEhAGG6YEy0RxaRuXbleolybsFs9j7j+FgInlpX4eDmllLWwWIqIJrIiEZJeimusFbbejUSrpME/a+VAgasFdN+yiDQaDUBAM2tjpgDNrlQqUaSMyVCuqtfraa3r9fr9+/cnk4mv1bhYLNI0bTQanU7n008/3dndEhFcUK/XjTGNRuP999/H1CPKejqdpmlKoqJqUcwYVSBbrRZ8DdVq1Y+Pt3oRUZ6nngGD/HkGHC75kNcu98Ft3GttleY2V7SikbBDYA2vLEabi63mlxwkRQ7MX2vrIb/DhXGHEq8oKvxka7e6tT8YyZAB+/O+A2uEOze3DEtI4PxNRISKtLd1gFK5g7uv3XCzP/6Tl0kyK7Zf/9s7Db+88ojNp6+97/JdbhsW35nw57eunDWfdPDnynSv3WptEOg2DRhtbTrcr4oLb33fsOfBvNzOaTfnyH/z+mvCybprN632+c6HyiqbuvU+eqPKHCqmrEL5hnikWjaKrPBqW3ti2Ct/clOGFmdZXD41QI+4S2OmVfOVdy+iJ96IiGatjV6+fKG1Rg1aOH0no7GITEfj4XCYZVmlUtFxbK3JjY1FK1UIca5aV26tnc+nkdOM3cCBEZIxRshqpNoo0pGKkyhOom6vU61WAQJcQauW4zjOxSAsCzbbPEdiD4kIklMrlQpeMssydrQSYxqanb0GHK5+ETHGIkdizTXo5zs89u+5uWJCpSq8AImN4QR4AnrbmrAsxtpcBOKbcnmRlsgQWa0FLiKlSIStzcWV7vGv7COY/EO9kVYplRVBQ4B3iTApuCzP8yhKFGtrRLEuVyskTGysLb3zzrtRlCwWCyKylsrlKqQxEUF+12w2azQa3W631+slcbnUqaRp2r8ZKqVqtXq91pyMZ9PpVEQqlQriDJi5Ukl6vS4gu+v1erlcRgVJoEMHi2c5L+PxkIhEmCgnUlFEWhutLXMuYnEtgjKsXcq2d21v2qQyzMQKAyoirC2vlutZu95aW0DVKiXMwsoSM/GqRL9cRT7fdIMQr3QVa4eKBKc7AZjCm3jutfbKHCCfEK0zJLWxzv3WWOMHzCwEe/tSog00v/XxXDu4iwKGn7zE0S34DTn8OL4dMtOuuRVfT/H9aPh3XDtPG+TYC0YhhaW7oGSJwinY7MnaIPjDYAyZi3rnBgvAXUmFyOcY8NpbyyrORnEM19ttrHZzZNz52403a3fefOJd9/dv6ocX39z6CLcIVywQ/qviV0IQu4OnFCYBrwGroPFqWxmf2xaMBIzWd4mdtBHegQPdIDTph/f0iMt5nqOaH/QQD4qABssZEUUvX75EhOpsNiuVStPpdDwclctlXxUODjncLsuyVrM5Ho9FpNlsKqWAcQiP3RrpERERI2SliP8uXgA3fPToEVQuxH8hy2o2m1XLNSICmgeeHselSqUilubzOcyezApBWHEcbW7yzXAbP0Yi4nz562kbmz8XEUWUri5HWqUgayeJCIm2IoSAN7c/C1OM/3QKBsyqxq29TKmYGW5/H5UdKZUjmtRKLlY4iL8I5QxZNQDgIJfiHUFX/VgNBoP5PGWKkGwWRUkcl+I4T0o6z9MPPvhgZ2fns88+w+ReX1+zM6oAvRm4lfV688c//vFf//Vfp2na7XZHo1GappVKZTgcoiRzu93+/zH3tz+2JDd+MPgjI885VXXfb7e61VKrNSNrRmsY/mY/gP8BY43FLgz4L/YnAwYM78IvwGBm7fFoNKOxpH6791bVORlB7gdGMJkRmedWz9jPPqGr7Kw8mREMBoMMMhjk4XD4/vvvL5fLV1999Y//8T9+8+L169evP/30U8u+YOZoc4PXZrQw4rZ++fE7DpHCRvWodVlViNY2i453rGcgEU3E6odVJITCGQcXQNHlwH6c7TZZOsLTta4Tb0aGTkRQjssIrPnaWNCyFcX6YytjoS0tcHy5Ig2JKJsFzzGzAfZV6etTz5EZAdhtehv/DLVoHhtgj90hqhuQvaBavxafcPOF7MDu7gM5LT/twRM7Gx/uDZPjMB4h7/CMtTXCQRI7PrbV9NgQGqmNnUVgdLQvdzfLAj86jPWVj8UFYdBYQEQpxQVls2gC02EJF7+5jbJDSBvQYjCb0TpyViwdC/JWHh8f/H1jZTln36t1uvLTFtP5/PD+/ffn8znn/N13IiJQff/he5Vy9+yGiIh0ns+WREIk/+EPv7u7u1PFf/tvf/HVV1+9efPqv/yX//LZZ5/d3r60Jk2WM7PFj/z+3XevX79+/vz527dvv/zyS9vwe/PmDRFZ9H9qvGyaJuUloIQdpJqmKaUDM0uB6cTn8xmgFjd4CUAYXWN897ebyQBM3rjJOlo2XB77FcD5fHZ+HsfPB6a7XorYGUpFgXLNnUzCNCmJnaXzK0hYxWzdbEfmJIsuZnNSQKAFqiwi2bi/FLQs9yqiUqBa8pJpioimaYJOkphSTYtmtoSUUuLEPJVSddmcZZqOAN/fPxKl+ZJB9PBwvrm5+0f/6E++/vrraTr+s3/2f/z5n//53d3z3//+95fLxZyqpun4+Pj4k5/85C//8i//83/+z7/97W+//PLL+/v7L7/88uXLl3/2Z382z/Pr16+/+uqrFy9efP7551988QUR/fHP/ohbjkXbUHh8fHz//j23rQQJR13XK6WVAzAHNyKfLaqYpskjZ/k88ZqdWny463StCigSp8SVlzkDivfTITlXbE8BhdrJDVUC3LoMQM27Hm31NTCIFVOgHQPiULySqi4NmqWZHMYiomhpqLFmsg2qGocIsEVkXRAYJteWQHRYwtpkN8pab8WfR6YZ7zuhiIVF9lLTyjTt5DmGRhESQfL6I+uIXq/AQgkderXpyp1HmL9WD4EO/VqN3WCZj6QekLkQjN+MoWoNRB/GjjWlnWxsRbZN7hFCCeGpo2WF1qIr9qjVgpyzLPAstOF2u665qEK0KUzMPM/LcT7HpwLMpLyYFqpcroQMhYgvghkE4tUCcum75yDvULHWjGHI8Ofd4DoqrBcufZ1gzDvahJc9r6eFDcV1eauqqpeHR680haTEGWTHfy1xr1kRiejx8fHu7u7Nmzeff/651CxJAuCf/JN/8vbtWzvN8sUXX7x48eJ0Oj1//tw2dEtL9lSBbgbX2J+O8sZCFM0dfUGYTiKilFU2JHSMlOQjITX9pFhoAlI2sQoSLQoSaOqvRICYyVghdmBWVUSzRSaAags9pAQFCaFf8qPtGbuYr5QhIi0OojbLtgsSWWd8AsBISUmWnPBMRFBlFiKq4XDMsbSuhOV4nIpkIrKUUzY6Zhqxw0L/43/8j9vb23/37/7dn/7pn85zsfzKz58/P51OX375pcXrtmOyP//5z//4j//49evX5p91c3Pz+9//3vzy0Yzntodqh5E6zmhlk/1ZcW3YxLmImEVK24fOyOKM6lG9s5Dq9oRqPQRlipwscmqsjwmhmTQ7mLHFszb73hF5hxb7r21BjvBH/rieSivW39UZn9tNSglb+Rvia90APaUjDk83VePN5lWVO5l3vYziNj6PrMNvxs832xrn7BOh6lqMEI6yUNfGmzgFPtriGm8fGRoHZq+z2ELX+PnqIaBNC77yvjcYCWlz4HqhwLZRsls64nehHv/cKwioG4GJNpKI59PpFOWII8oksYPk28mT5S2w4Mm1lpEsgiXh9evX33///SeffEJE7969OxwOX3zxBVpKu+fPn79588ZE8jzP8zx//vnnlqD3zZs3n3zyiXkyT+lg54AtGkPJAi3TRHaCCtg+dtb9GYEcWc/oFKN1YVJvRH0Tl4hItKhqDVdLLWitIpGSWpya6mxsYrVZRBSLK51YTCWQqCVxEwKK3UspxEqQcE1gZSbblIYIxPYT65Ff64U5aJn1r6go96tmD3ndmaMBkIokweIEzwBSDS8Ogjk4FKYJYffUXraTQjc3N5ar4+bm5g9/+MOXX375+Pj43Xff/fSnP71cLp999tnr168/++zH9/ePL168+OKLnz579uL58+eXy+Uf/+N/8qd/+qefffbZ4XD49NNPP/3001LKmzd6e3tr5GvkYSLKnKo2eUQ7dE+mW/o/5gkQS7pK5PmqhVUQTKZx8oz0A2AVQKHZtYyAgFUYfathL7mCLyJtYUFtzcTNHjjaxzpWi/Wk2+Sw8V7bAaTYO69/U+NRVYQNRV2rhrGJBtiSzm+zxC5oWxFeeR9hFNp1yddLRA3lvYBpfxrX21Tm9uRQf+KZ2lIp4sHfjpaV2M2ro9AvoK+J4RaI0RvRusVOIgplFYW2tMatiQgnDWuF6zL4GjBPKJsSN/Zag7IedWXYglU9rJUuNiNVQlzgquqiV0SOHTvi3Vk04Lr5Ij5xGxkYDJW9B5oHSLxSOwEj4SiK3Yd2yUKaL54H7Z9WcwNb9ZwsuBtZRMguXB0A82CNtg2boZNv6bnt14A9vl6cqmyNYV++uHv24cOH169fp5QeHx+fPXv25s0bu0Ej37u7u7dv39r5TmZ+9erV7e2z29vTNB1VJWdhnh8fLzlfzMpoQe9Pp9tpmiY+OMYjq0KzmEWWoZV4V5GhOkJpcrdz0YL5Fdup3+ZXrAAxw56IEJOUXIAqUNtBIFYUMyNDFaRQAQkUIMml+LGWnjGVxWTBykQZgmlKPioSth6jgr4YRkRMbZV29jT0SGTtkAWApZqpUzJ8FigjFeYJjX0zWRjFykEeHy+KZbF2OByeP38+TdOrV6+++eabX/ziF7/5zW/evXv3r//1v/63//bffvnllz/60Sf39/f39/fPnz+3wCkmgH/1q1+ZX9XpdPrRj35kSurPf/7zCUQt4BQ1A5dvLnZjF4csTsU4CbmFaVVV1cIqOsTidvQ6qr2qK/IiznmnRl3Hkh3fj0Cqagr5Qdf1LGWzj3EK7LfVmwTivaNxQawuxwEQZHCkvYHP8p5s07UyQYNWNL7cgQpAdQPJscvrvm+ovyPYYxnfj8jpnsdx2fz1Ch4GgPuig+iKNDCYQM17ZtWKj91mW6q6wuaW4rjCzJqvjh0Z0RXBcGws4iO0pQSVumaNBA8g8UItqtWAR8EE3cG5bUlahxsa8byJ/3jfmTwjhNfpOUIS/b/S2lUlRsCNc9xgm6ZpymUuUorkOgZtSA6Hundoe2de0T3h4fH+Mp+LZJBOh3Q8HRTyyadvbeH//MWzFy+ff/Lp2zdv3pyONzc3d7e3tzZzLNC/Kt3eni6XPE1Hs0da8sPj8WaaJtQIJwPBrYbcbfeiIuavFLdPZF0cBa1kbeGwXQAzT1RD4pHviJHiUma0wNkaAncQJZQxOYHY0aZIcH6Pxoij/46iOu+wWbZRvYGqbbFKeELNJyOAclMJAZAdCzbNWwRS6olmiEA0JTApUVPqxCIoiwgx1wVp9ck0S6NcLpdp4jRNUJovWVWZ0t3ts5TSyxevbm/uvvjxT0qWf/7P/o+//vVv3r59+/bt25/+9Kffffedneg1Mayqv/zlL839KudsPu23t7efffbZ1//zdxqWC64HRwEQ+VEngyND8QkQ6JtZRcHjtxRM9JszvHsS/6KmmQFYXLzW0756FcB8qu1/agtu2irx4Jx7OUWyH2XSFrRqoSgpGLWoKdyBwa2lozIN06qGmtKFvRLM4KPYUvpj5ZHOxzfHsuaYG3yz45JjzRFLT2lrr/UodbSpJk6B8Vt/fp3RXxF1Yx+vlBW02rYthpBYPcIJXeDuPfkUvqjU4paz7v293nVNG/PXFnzRHgpUhWSJzLWsOQjLAjE2Ie3YXmh0jPnV5C4r0QaoEcN9h9d0G1nEiPwNdAW2MzJzIqox4Fs+U27WR7fw+cQ3/9Ppm2++sb1iqz2lZLE7H95/WEDMS5D64zQ9PDx888035jV9PB6Nw9pBUuO/r1+/fvHihR3kffbs2fF4c7lcchbb8GPmaTqmdHDu6fwx53xzPHm7AWmqzftUVQFXCqXkbDTnDNd3c6MRQBcHn9mZO4JqMk3GKG3WaaUfSJEZYgE8lyugIhczeqhKXB7Yud4osE0wl2IHw+0clwVZnAAkqSEtPbalM9A4S51iNOwKIyziYn/9ZVJVsnNjRjSFUGrd5r8gJFCbMioEUiMBbul7pZ27nabpl7/85ePj449+9KOf/exnAP7lv/yXf/jDH968+cS281+9enU8Hk0DJqLT6WQ5B3//+9+LyKtXr25ubr777jvb7LDdBztPbA6AMVKYd6ebHiMXcD2vvUYkRdf61kBICzLjRm/H9+NX0gxrUlcyKwHpNcer3/A69q8v5P2djrN3lcc94636BaKgHhgK2lX8sFW17Ra+VYOR9zZLWpHlDxHA/nnks923jv+uC/EQPA1K21j2fh1xRUEx9UYleO1d6UjX0PX3MaBOtzQwAE26rMYlgk1Xtf9IUXtOWJDVNo3urGz20LVUszY+VzQSXAADUO1H08nYCdgmrs/fCEAoiwAGtkfH9emuHt5HftffEQ9xjetJhnwIbAlS8urkknOYuB1WWnoYIlqdHaovAWg2a2t1Oiav9PH+/uXLl/b+z372s5/+9Kd/+MMfPvnkE+O2r169uru7szBd8zyr4Pvv39+cqlBkmlICM/IsthcIP8kqqkQgdqcwBEbjCHX6bO9ojGTUab1uiHcEmYxXrBLcGhJFJ1pZxlREUESkkIWuRBGBeTUrSswnE68kiq20Uxbq0gRwSsIMokJEOVeCaGlnPH3FpGorU4jUJaqIAClOeG15fiylgQvgqgCJmAA2LBJZFGUWAVJSrcpxjXQBePYbX88iGMO/+OKL//7f//vnn38+TdO7d+9++ctfArB4GpdLfvXqlSVEsgjPRFSK3t09f/1azuezKuUsf/jD1zdTenx8NJeuEnKy+p43urWUkBSoUIv/zP6PyBgUAbbtqy0b2iIPqC3y/NopmhaovREbnDtUyVmZIGCbxQo/jxgL1scn4pzysGXjHnCUIs6MOqdKJ8g4HIFNs+RCTFhz2zhxsMWgiVYyGGsTn39CxLKKBLRRyV6FV8qaC/OSbq/afuy35c3lSrbrvwHJXlvRFN/h+TqocRS6Gdf1pRvQJxTfVte2rIe23GloiV1QGXdy/MSGNEjfTbRHeqCwJdEVMxeNlXROiB1aRuqKom6hVa0asH/dfdXqibVto3Q0QROR0rIf0YHq8MSOa9hO1bWRrBvl2JfY8ajvep0SzOYWx9CxFKYqHDMum1R1un9/X0rOuaiKBckiYlZNaSKCnQPidGDmlCZmSuC3n7wxm+Qv/vgf/fTLnxD4y5/99Nnd85vb083pFqSPD2di3N0+Ox1TzlmPepgmTun5s2cWk+l4PE62LQlkVNPe6Xg8HA4e/rcfZhSAoDF+kMDiQmt13q5+TCIQUREtBUVQE6AWEpAW0axaRGCGaDMmE6mITATlyTLgimQRqGSQhYIklQJq+QghnBii4LrQA9rVGFYFyeL+ZQBSZiIiy/6mM5jNhc/iMJMdkiNWYjJDc42wDxAbIUMVoiIzM4uanGYRUZGi4gd4pEWWVlVKByqFirKSIikVIdFcNJGQqBYwW/CvuhGucjxQKWI+e+5dHEwX06tXr/7yL//yj/7oj77++usf//jHl/l8urk5nU7H02me55vb21evXt3e3T0+PHz/7t2L589fvX793bfffv/u3fFwKKXodMilXM75PF8STZzS8XADyyNpiSJZVahOXcvdQ6vp6sTdzZA6w5uG1zGLjjt0M9w2odC83N3vHWqZn+s9SMz5y9DlV0BSOjhdlaJGXYSilAhCSIDVo1D2lH92FC3eJ8/sPQhgbFkvwTUZQ4eKpwhCBF7Z8Z3l5+DD0pWIxie22zHumoB2S5OLTC0KYMN/k1JsR1FUNzaD7Mo82XLYDh2IKqCiypa6x5bJVJfMNJhwR0RtdApwo5nDND6nKu0EDFKInZAiaI0MZLmSVSzeGvlpZwRqaTTJirboJ1Zb+kM5Yga24K7ppyzX74ifqmRHe4/3d7PXcaUVr05Fq/dVNXjwRnNxHNZIRPHz+nLtmunEVokCYr9Hn4xxWQD00qQdUVrGxRZBNmpEZPdYTwTaKnEaug5d41FpAokqieaqtini2IlmU+ToJ3/0JScwTXa1wSbI4+PFjKjMOB5v7u5u7u6eH4/Tp28+ffH87s3rT16/efn82cu7Zzd3t8+Pp+l4uPEzr4SUJkp8SCmp0jRNN8fT8eZ0czwdTseJEyW+Pd1UImOyFPd+2KkzJvsKCwDQ6xmUV2ZYETF/tnmexazn1ZJZxe1cLkJqYgtFRYsKFHJzum2HiuvQTpSIoHIxJfN8fsiXIpqndDwcEyGRFnfmchJPKeU8V/UuWjsVpqQS0XGqgTYBlFKzPddDz4cpEVNq66x1fDUAc8nMTFNKKUG5Wp6bAM7Z1GRlZqaJmcGHVGN7ng6Hw3Q4JZoo8fFwC4B5IkpgZk6V5FIxX9OuALBYK3bAjJons6oazAI9pMnGt6gYbpowKvZEVcucHx7vz/ePs+SJLN2vXsqcwEJKgqxFs1zKLHOZJaPILLO2XFWWx/ByucyXy+L8oNEDYHbbNdbWbF97xpXv3KKgEC0MHZCcxS0ZUdDe3T2Pz20rgWhx6+uWdwYFliQflU5K0c5GYu/71hGH6HrUHPEk7J4Yb8rnswvgWLalhXqotb64iTLKPABaw7Lu2iQjA1rBtlVGokoe17fN9I6V69p7q32ZOCEuXxIfEJZQdhUqmtgFVjIhphCC5uL3Zr2yvX1bKBu0EvZByk4yA1FSpgRS457t/sBp8zmKWItZhUQL1K7lMo9wEqWJzfpossfkEAPaArbULbB6BYiScsVPa7le3Urn+AFqrPJlPddGdm8c47jHeSTrgBX+pseaJiLLQdkGN1SqQUlt5py4BrUgyvFhBVIVzY2yo9sRYGqWNlXjOjUWsd0bBhORXavzIRFk8aP0DmKYDkvTySTUMq/j7HYvIr9OTMRgv8LymYMsdJZJBUvN++zZs5ubm88++ZFFODJj4+l0spMqRKRq2dDtJG9lHId0sDhZhzSlZPEWmWoW6ErCAiWlQstiqmOaWGfz8AlPQhAiQfXGEpFSar76IqQFNX1fJlWoCNfTva3a5c/Hh3uqaTBBRApkLSDxQA5ERFxdpUTI0tagpk01v6cM4JJnCxlWSmFd+Mjj+VJKUfPsOIiKHCyFuJINECYIiEEWBFpS26H00a2DrwRly9UAIbJFcPXER1t0FttzVrCUOi5UJB2kFKPokmZCUi3MCrTUlawQjSfrqKkmURWLVFhUJiJKnAAwichFxOKmtZmmRYo9EZFynnMuAhC4qGo2a4xoSHxUSpGixRZeFKZlmFxrAeAcxJZEK3kQ2US3YlVVZwrrSZVSIq18IdqcTUotSkZQNUp4LkQpqmVErkf6V2Wox+9/QCE3kT9F5SXpmhj5yJq/qOo0Cvj4lS0O3LY/VuivdfRT31yPqa6NE/HPWpQBC4lvvHgyLyIiApJCCUnN2ACYw6KUqi5lo1gACgFD7YiS6QAAASQsSyAXh5a3QoD5p0JVO7NdkeZLAqgWAoMKlBSFQFrzMZvYK674NgpXJmrKnRIUQjQFvKGtn6j6ywLmwdfyDRhWE1HDTz36TzAnj4oTdU0GQ37o//8WH/H1Osx8JyuloR3iMvtBWe8rx+Lsl8J6DkAJHS7tCVomAAsKasYF2tl06OhhWbBqtNzkcF3WlxHhk1NY5/VzOBwsyMbNzY3dvHz58tmzZz96/dbuba/X41UhWMMBeIXH4zG19K4xAoi91m3IUTugMgpgrzkyVhJiJGoxKkSzqz6lzK2eeWHHkFIKSKu0tpdFVfXx8bzoHJ5eCcXy7xn7rKNYRERN+TOREFmaSHEBDFlMH/cfPogIiTJzORzK4TCnxMwTT6bsAJgAaVsgjCQiSRPpSv5pjfChIDN6Nx4B0jr9YLqmEIgkteBiRKQ8A1AmtojeKszKzODmBsnKk/OQ5Sw1rW2hkUc7M3WpZr7NlmKLW9xU83bOOctlOe8rg6OcrH2vxvnplBN3YkZZGyXu2ET8ldI0CuAo3WMf40zrBUlYrPifnTgZu/MPL9QEcAT4ShlB6vgdVoZoNR693W57w4jn+q7q5gIOgBahTUE7NNc+2wj8ixUHXBiuMUoZLO1j9+uVyVTCyHncL2EDmQBqFnryKYP10Hc4cY4Xn+s6y2EEqdv7jL92XQYgCrJtrHUZ8TMCFoH530SrTyzdDLJ7aUmoJJ7VhNr4jp0aJ2b3QpSC3XAsLwOoG/PL587rsNWun7kam4sNLQLY++Ns1Gu3WEgvXrx49uyZxY988eLFs+PNzc3Ns2fPjsdjJBFznnIy8rF3CW03ng7C+x9pHSGWUCeAYzech5IqKVMh9ZPU7TxoKUV1ZWwEoFJUakK25aciqjVrk7mGs0sylFKESNM6/LKqPj4+iojkoloDZVi/pqmmbMqX2RcTKmL5pljBzPM8H49HMwngCGZOgdEnQBMjg1oMhGRGEhsgQ69FJGACwKBopHNEiRSgKJtbc8k5I01EZALYPmER5skEMFvaESKiJZ2DhrXRBsMimqzO5mdPQdw6zxURX5RIzlaVS1//SoNvgq4Nj3EiRU5XSXmt4MZBj63EX53Iac2yEbjnyIkiJCNsGgZxZPf/mwozxzg5eKIqPIxj5FxBsKmq7mnYEVEIJ0H34IztbgLTQRKbCOhejn94Yhm0WTkiYZuzDoJtIa2E5k+/en5FANPaC31v0J3zYkBm92GsgQPnia1ExriMHQhgAa0Hsdf/Nm8wTIT/rWWNpXi/OlzefSXrs2FqoS7Dy/5JVPM2R2QPIbH7UQDrekUVBXAci0vJXRN7jVYBbLuSaBPPqzOPGwsb+fLlS4sl+fLlSy5qp49s02jRJAIzit1OQzHUeOzNTt0hf7gWwNwiE6lWZystRVQh0ixGJn0rq805VwGsOSJoeUeq1RoiVE33IqVImRluElDRzGzhG5ktNJVIEVEpJed8vuScIepBPpVIi5Q5z5eL6Xw5Z5TqV2w1axGI8kFpUuWppEr3wmw5C40gzaPX9mMZrKiuuapqPrmsrASiZEZPZk4CIRYLEBM4hmpRNc8yJoiqoroRGoMDlCETQUnZV4fdaK5Iv9GQCWA7WeRqkITkEF7qIAZvYT+Va4sSHVZj/RTSnlPXl3UD4AiqhhVrN/HEzQPbtq+N2uJ8GTlvFL2jCMfWtPyHFKOO2KmPMlBdL6oiqN6pxr9Umwbc8Y6OHpY/dVsAqyyvAiEHgC4CJvy+tGh/eggic1ZgNseIFOAHkRvhFC0MIoG9wrWc86ubtwhQZm5G2lXZXFgoQJQIiyxHRwbwMOMVyNrfOou1krV3cxDhW6dgq3GC2rgQRaIiPxqwAnVP9A4m6E2i/T+tRDSODCcWVTUN+IoJGlso7Sp5Omx7DCEicIzRHVuPNVQBbFu8Nzc3lpJI24GWaH+2SIQmdBnLkSbnnh7NPPbWrlata8BOna4dRnsgUENsdGdAEWa+rvVjc8KyjwIqSinFjxsF8lJV2yQm80wjLaIw5xeRLKKQkpXcuQYkJChcexrpg/001GLPlvP5PF8u58fH8/k8z7PMde/TXMxgkSdFBZSpLkRYmERL43omu5DqtgQVEoUmJSJzXRQm85ustgTz1jLHARsLQinFxpwnS7bn60EL5lXHS9cGk3bfxiLsTXTUhmaH8NE0snHzxkpAGiQ1jeVKJFuJi7AVIwuHPiM1RxmjTQB7K5a4Iopq/8Qrd3tpaZXQWm2NkHfzLdYZoY0/xarGufe/sGxqwB+VwVYi/HExEenc3hvr7Hr99NIRW6Le9OrVdmg0+nHYomqIMKAr+UdkLp5j/TtQ1a9ct3Yy2A7tCSZazkVFvufd3BQAToqdtLsCYVcicmIlqjy2Nd6PNwhI+99Eq7GsgYwafy9BOpDiTTxw2P0qIbBPh6sr8PjVJwYF4LpWdChY6+iRbY5kYPfT27dvzcfq9vbWAjWbyc5CFz179ux0Otmxzpzz4+PjiScT8uYV1RIWJcvOS8E2aMXCMkQ+7gbATvrWXi17tysN2HHX1OMqXLks7NJEr79Wd2tjeE8ShqiKKEgKKVQKmceagCAMEKntZOYa4kPNd2NJuZhLyVlzgWgiVk4KgWrJWUUeHh7KZbbgEiKipR4fAoSYLR9DdRYTUZF8mZkZkxKRgHKlQoYqMYsoiZJna+GavkEZihpTg8GmdSQ75GTnWEAW9SodDsyceDIHuEoBZFrCWkTxBi/wMV0GaH3TxTitY9AMy+5YgGAkjAqxqnowrA36oXV4ikBXK7pfOK8xNXbzjG8aOb/rlo8eGbabNt28dYGknUV0jbGO7WrQqv838bXGJRa0/JAPl/s43N07usUBN9n6FQDilsEeMJuyCmv8d2jvEI5BeDPXPdrutbj+Cz1SMBESEUBCbJ4loqq85T2ui1fUqvKxF+OfuhBthWEEkoi0+ZFsvtnDE+wT3aCMI1hv/i/mhLW3VdH9WQFeL602u0lr6dt1s8N5R2Nk3n3SY5VCJDtdH/2/UuHYWSKa3rx5czqdLJyCOTN78IrD4WBBBI1nmY0xnW6tycUtKEj+bh0K4Obmxpt3cdvJXQn2dJfKnQD26+rXIgmpvRMEebVKLyc3qienSttFFQDUDvUSUDQDSDXcm9hZWM8eTE0AJ5BZlcvcQkgSiR+WVXz4/l1pWi8RTcQAJU5SSuWUdvKeRIsgmROfEhHN9SCWgkqq8WAIMPdmrh4loEQWOyIKFRLU/eC4B6+kTUuuCyDuzH1qcRWDNNHGb9eEGJTCOCKqOs+zH131ETT6cU8ZH+WV6cLs+WbIP5+PxyOazr1MlYFoIxtaiD78GjVgbtnjxwngTdAwW/bK3hQaq9187X8Tg6MggDXI+6d/3vGITXR1Rdfy4CktXq9wfK2TOhHaETwf5e5GzaRMfc0RbF0LKrAdFVstOnfhH6y9e92MzzsOudf9rnfxfhPbqur+oN1rtCd9t8qI9v8zy95MdKi67hv+NYjYj5JubMj/HPfa659h2yti1XmdM7eu5lUlO/0iosn8md+8efP69WuLTmUCxjIdHY9H29qktqVnGo83ZizVNnTdLQKAWbbt86jpuq2yA32RtSLQQlAmVUg9X+SKi7S94ZqVUfPlUtERDg9Aa6LyhUm3EM1sCuk6xCaJLgfT7GUpKgVFVKSUGQDKVC6kIrY7Ph0nsrOwj+eH88VWJwDOD49m+SZaFE5VnThBYUozMydizWWW83Q6kdZt6jpmnJhh5wJFJFlSeuMIAFGCQBPQzscohfW+gollqicNFLaEoyp8SdvObzNBq4JtU7yShB877izP1FZm0rkuA+3I9eLZ5OkFfVj9fadC89Wy1yybgtNYZLL+soiQLPQzz7PDxupTy/b+t1XtWHwCpJ3jVV7/OJH8ifcXa74Q7+OeUJy6IxvtaogL3CviTUScnuPn+5Jg1d9NrhH6aL+uZO0uH7Q1jSitvXARNAYAHVzRBB07GslgjQ0CSMR8GNjbanl8CbCXFYACFy1Ng3eeCwClGKdeDS6zD6i2ziZLBZ+b8+BqdjCpQNaBYpz2InLCeBWM68r16Pi4o52QqpipP60kh/1Qh4ngL8bRuUJCOkAYqt1YEJiV1GaWI8Trj4RnBDkdlmCLIiWQhDoyKaa3WstFJwA3qo39ih1zDcSj7G3OshFgt+B2pMtNTYtfqarnje64StwP0p3VQJwX0/Pnzy25r230Gikbh3XHIg4xASwokutVPvNvbm5MR7TaLSOsS1/nv6PZWaNCjIJcPJtNJAKT8S4Alg5LwYr0VU0YQ2sybo212TFRe9wEdhFxsd3AQ5EEUmY7AYx2FNz2WdvpeLCitLDJ8zxrEduRqxNd7Vgt+XF0E8fOSKCquUjzPvZM9GiMgUgt9lelG6CmLAyoI2qh1Ag1QGMtqRJ08xtFZRxss9iZCVkItG7nL5C4TycXvYvEhWpYRfmH7tyHdUADZy5RHm96uOyVOOni7DKElVJ806GbG10l9au259TxmpUtIfwka2eurkIflLHdzfe7sjldrxcTwF2LI9/xEvfY/IbWGlJgQBu7EjroIvE5DwuaDp9XcEKB+Xak2HEDBN6H1fTH5s1Y4p5uaIhs12nEYdedvbLZ4lPg8TnejcXm51fGN9Z25YU9gK9XizXa9z7xjjwFNlWNL45dNpz78yhB/RgSBo22g+0KVrEmqtXnQBFhXT4ZSa57aDInDuXIT2JHJlN8X7x4YS5XAIzJcovCwc1t1RAhpfJNDoWIzFh9OBxMNkcBHBUm591oXDgWRUHQXBe1OKhZsTbAnIP7uO2ocRwXM70ZqAHYYWDVJW6lugG81NrVFtJEzJRngSFRlWD7wwLIdDgAKBDLHaxF8mWWnBlV0zWIiJQsmp/ZwWChA5XJ8uSglIICSkkUQlyYM5GIpJRYuR3tJSiIWQkiAiIOaS1ZaBG75pBVvbinGtC4KsGMxIRkSSBANkycEvPEKVX/UsUiYHyYaH1MyKUpgKLLsEbylXhcLwjgKaQq8sHl9fG+MJrreb62lzhBh8+WydmxiaHm1dxzxjoKDH8ev+3mYXzerS+7Fj/K4H5ocWfD2Nx1OdEJSH+ua2lqz8Z6ovTtRCawxMrebAJr1HU/RVRHRMWuMTOUYe6RCiiIQEQqUTVsN9UAhUVHbMUixwwMt4ytj31ZMLDuV4fGPVK5XrqOiwgQ9/UIIHUH6u7bdT3XBeHYaAchhQWW35SQxcunof/aoQhbE6dBtQ3kOHPtJwnHZf2dKICBfqpuYnXsaSfdVy8DKjJqwCOuxoZi7xw5cYba/fTixQvzgj4ejxZPw8Wqab1oItmap+Zq22nG0gzFJoPNBH04HMq8YtB+0818bdJUgvSN0rrGUVprYECNE7ZUVd1ao0heqISgJn1pICCZcyP3enDBiUmbCghozpkVmophRtt5rWmajtOURUm5UDWKsqKm+0L9k9CYgCigytAiFkDKiDvlosQWPKk49bdYREKQArMm29+qWhM4JyYiC1XvI4gUrdAMU3NhMroe52jRyepCqoTVTBwpWwB1qx8AWYoOAhXryeArJ7XFS6snzoFREtTxjKTfCKNrJdxQBwMFvwQfx24mbEqL62yr4xGNPBYHQ4d57Nfm1A2v/TAJbXgZq9qDf1xqbHLMyOxGL+K9+lVWzsnxyjthOsL8uMY627cM5ei6L10G+CcvdGitbtZrCxU8Li+8X5urCm+0Iwxdl70+dp0N95Z6dFkHe+k6uBJgg5p1BSERmd3ccYB1lExDv0ZI9jqra7HUdVxCnd4utbxGsRJVlRbyr4Nzs+b4YXxnnKQL0qrUWH01jNGCLjKv+53u69aCtZ4OQmOL1IxsLlkdoFIzGiUJYT/ROPXj4yPaDgGFnZKCDXW2KwsvAKWUUCySk6CUFt1YUApESASlaCnabMmFKIbKa302URp0MlJVJQVESIuK2YcFWrOQlHwhUyAt/L6qBamc2Gy7SARIgeRSRIVVjgAYekhEN8eJcZ5SzvLw/T2RZhsPgIC6RPC8vrCINVW9Y0YN60rq1AdVS4fARO0AMVRh+3dqZmgiLQKijAyACiGxKh1aMG3rBqoWPbkJmi34ogWM5cTMVVGABZVdLWkdq6MAtlErawHsdBZNjl5EJIu4E8DmrIgTALQ2567fcQi7We4/RX8unyGRf2kLHTPK4Aje+CeGeRj7GKndF2rjV/9LCjP7Oeiu+5vvjz2loLl2rzV+Xp9sMri+diKYjz7QNNF2r66KVvV6hMqvgQ61gwTt780ORlQvqu9QfEC9IVUFafWV2BASGlDXzOw1+Yc2Xi2BSKsurjXZkf1T5t1x0Rq5Nn4rJCvdsSNgB2lTisSbvRK7H+/jrlAc9MgfOl4RKaprfRw714BtiDt5GYHxdmNfnCPEVkYANrs5Yv4KVftvtF70rGlj+ZaYunc20ehw1hCSdQtzZ9gi52XSaZqkebpK2xq0pLCRC0c2FDWkyFJ7rmHG2pAzTsL+sTe3qgRljT5pMIcD0dSUBJVSLqzLQktEjOTd6h7HW0RsVxvGoWo3wtIJYOYabjNNJefLhzNUZX36hYjM+bl+pTWrU40WEIlPVERSIRBJ20mNVhZzTqMwZ6RNGJIadrgtgVlV0oF9N9wCiXDdD05ADRPgNEC+9hhIzT0DSoie4YjqBjQOayQAESnzYsTuphPGQusz34F8xxlrf8Udk1izy8XSedfvGxhj/QtEcUwHALru793Hh11zP1QDZmY/SRJr22O7FhD4Sn+vlO6djU+MST1ttUGNB4/cM75jXRtFTjcEHaO/3vTmC0QEWz3QEh3TX+P+BIGZpvoKI7358z3aGAHwRr24APZubnacmo+IXSmI5I+2uzmFOwNVhHCvHgyzw1AaMdkNUNeRrvLx5fhEVdWCcK/bjQB0sy/WYO+448se/fsPupaJe/dIva/D5mT3FidtnskGnPtQOJOiEJ9ZRHR9JBQtv4QdF/Zozwg7f3HjEE2sdkRZ29qC2EpnxHbwiuTQMR/UpvvW9I9WmZBC5izNLYtk6Ygp+VIEIThDjcmtShbpoiqQICLbMM7m7YwFvJSSikzEQiAQg1LzY2xhccCEuswWwJJaqgAkLCLCRfzokR1wYpHG2oCWx4LcNUBULbkGkVDLXWP+ZkxKiVLVfZ2ObWIQESxjT13BUwVuWBGjbUO4ADaE7zlPEZHnk8b66HaZ5079tV9dcK4r6kKyLPWneEpkoSJL57e9TjdSN0dC/zbrisycljoNPs7n+OYm49gsmxPyH16q2BhE72bgCAB5XjG+TdYTINySUsObjhwiKkXiR11nRzx3jHiEp0evbkjrjtuubvbXFR0PrZiU5YkOCkmH5K5EeutMJk+kEBpWtFGQx0a7P7vueFV7cmUETIfSVTtWtSmEOnmm4whu9HqB5KOzqXuBgs99dCP1gYt92YSTw3GPDi1eRUSR3e/6ja63WnQQ2x2Ek5OLhOObaLqXFW+YFWDihBjS2apuCe9O03Ss7jxKqhu5BSNhOZMjmFZHFhsysloOoSv923qFbSVKS/RmfRPyw1sUyYhJJefsWh45QkUtCGwpgmZgN3utGQbqlmrAYz0NbNJIoZanrxRiZbbDu5bJxAbATk2JpdgQJEVhTASoSnWwEpAypJCySjNJ1UAiomrKKdX0Jnb0qBKKypLlMhy3VUVirkGjVdtxnUYCLWupKkSEYImwEhKqJ5blGZGa7qwo1OKKCARgC1pZjQsV4WYcMMW+iOcvhWiRYnGza8hoXVIFGfElIksN5s/tnTbm9ldN50eUCQlkiecylLXm16y74KpmC6npwABRJZEMMDAD3NIFgqQogUBKLa0OSKmF+VQIlMTqUzCpiE1Nass9VUWzHIJJxXwN2j8bGNGlNnsoaskPtSXJoWqB7FMtNXx4AqWyPFFYAlMsc77qA0zTNoNAf7zKOUV3Y38R0SohfJiYWlfMqlqdgrTddzy0k7J+T231gMCbrjN61+C763XpG+nKrp54LqafAxhUFIgZoC0/tKc+bNl5LW1r3UNlNWpD1prqz1Mc2lVbksHuuV9jqkS25OdSSaX2iGARaKmuwWH6XwimCbgBMGD+KTLYlw4df3YOT2vdcW90AAY4hs/cl6au+VC0YrJyARIoQ3v8oF655g03Ng0zY5k9Lp6dhBFk+NfBzOEc2wZwAABRy2ulEUvYWSliwL9juPMh8Penm0MNlEHS8JVARKfpZNkGc84fPnzQrId0ON5M5/PD3/3u746n093z27/5m785nU6ffPKj3//+99+9e//lT786nm6Jy7PjHRE9PDxouajkpsFQAoNQpIgI6+popve3XIqiSDFbrDryuAVtuFwuuVy0WWNOh0kkWzJcbf5HibntoTIRsaLkIiJFymG6/XD/bpqm081NvszIRUTOj+fjdKAWliMZggRCSmoKVblc6na2iOSsVXMCUENj5VKKZGUoMeiQpkPNV2oZEiklELhOaRBDVZQERSfiiaeJKbEmFBUFaDocQEqounKRLEqlKOFIx+nAh8QTE6litk1pYiVOEBG95JLUvOSmUmZWNuGuJVFi4mIMn0LM2+pRqjWoF8DQAgG0EFilaFFo8TiTUNGStQCcqnClmuLcEoxbVmDJJUvxfMAAyjwDVRSpVlFEhHyZTSAqQbTF/KLqMwdbzKkCbFECGawqpQgJGdhIBCTR3KYVEyWiyVhuKXbaGCmlUrQULkWzCrEd36otCuA+BcaslcBsMYUhdWdgNUUrl695PFUAgRG5qmrNX6gkqAnUjQVPlKowUCSQ2GY96DAdiJQoESFc2YxHlXEwtTyjAKc6ddfsYw4HlOOE58PkvK/tiQhsETcwCLZjtSEf6sKAYLwfkbtZFkCTwLJeyOacYXYXDzgf4LP/2pNxuz0WWwa3gw/kItwyiQAhu4bFvBGjIgGTKCgxgRJTorrAYloWkQCkzjlWUVvi2FVqOlA7xcWoC0hIzoq2D2QqhqhCGW0lqAqLvI5qCktVGyACaDmUyNyW0rb+hrJacqYo/FBzVutyNCNGmobU1Ijk8b+0jfW24AxpjjVkQ0HbY0KzcfpoorVuLrfTdHSr5xhGe0palQFVprpFqqqVXZPlMq0500xQCJTUVhttJjNBhNSC+GrNx60gJrkUyy5DSJwSE1JiarH5bBEs9b+qqsmpl4hUSJS0hi6IqxAXuUVqwvC61lmm2OKvF2eHJcbRwdzdkbHPsundu3ceNKMKYhFV9QPXTeTky+VyPuPdu++K5N/8zV//9u/+9ttvvlPV29vfnM/nTz/58etXb1+9enM83tzc3Krq+Xy55AxZAmCRmV1FTOOC26KLTzlxPTmc2K06ehTYjSbkkCZAmIl5iutnroeBRQFR1LwLCiRQOhymI9OU8zmfZ1JzLSbAeD/V7EIgkOYiSm23MgBALYWoLwWIiFlJjMvYRFBgdd+qUgWIlZSYhEmZlFjZ+L9JIUg7vSSqYGXL3SMiKMWUc0KyVYWI1OV83Xhe/H6rDd5XOaKqyqJIJmgnV1rqa1JzWC5rHxSoUj2eZCGmzcJfQASheqJaVLl534gW3yvIK8dp3zczJcIwZM37rLMaKmMFSlW0nMmgCT2qTAzqXSaktgxdlEiiqmATqSuRS55OCzemapb/AoVCoNWlfn3FjhMNFrXXho1tnBeGBVO11GJPGDu0XAGKqnxrnaver9gLae75CVAgVRYM93hag7OYT+t0V8WY2bdjl10RQhJ2sdyqrcPldbR/dr8RmQRrBTfyo9j8Ion/11npTTcF1ZBGpCAmO6pvpBiv/hGAFqymM0XAt1+WKWYGjibFnZJtJdKGs3bbzC3VVYfM3NRMU6GGJURqo6yGoypi4a4e1RLTsKfYxPNYnK+iSsTgGDEoxD4i8X5dXTTJLrShcnU0SUDSuiAWpZ98vtgYUcVGh2FbfwBIUIYmaIKyCtvBzxAMwJSC2im0BZ+tB9s6sjnFBnI25t0IWgcrTt+V+lO/yokfxnGxm+n77789nU6qdynRNDkS1fxViQ6HQ7q7uyGqGXAvl8uz53fv37+/v79PPM3z/OHDw+vXr/1sUp9tsFj2IKk8tglgbp7VIgKPiQGxLEaoEQZWVmtqq2mUmuGulDlzSol8+1mDBUz9JGtZ0VYCHQ8HIpI5n8/nietRKxNXRFTUV38qImB14eHE1Eaxnq/gGpNEbSXuADvVduMUP2diShzdASg4s9i3pc5oiIgWzw0lqlXU2Wy3I0VEFapICxqWU0DV5Gq0zhYUBEBZBzaJmKwDuvZeti1+kRXb1bZCimfGOqNWV5xM442ujT9PL5H7bM6ZFYv5gTVfadE3s9G64McSIguL9UR6GJvQtf/RwAWiqO472H1ljyMMfw+sxq498SsHoxMMHWPabOgfXqIY6njf3vsO9nVa7Qa0e7P7kzzVyvrcFNazrGvxyjCt2Mi6X530tWr37AougGPxJ1E8Y2dcurbWNys2MsLfdb97JzLbPXozLj1WO97X+fixc+qrb4dfOv4fK28Pl0VkpPO90xaT+T9fLhczI5hDL5ngbbDax/M8z/P5m2+/Pp4O33zzzffff//5Zz9mZlX60Y9+9MWPf/rpp5/e3NyUUh4eHtyxKzJfidw8WjyyC9q6p1uHBMXHxq0cqlpkbpn+5ks5p0QWA6TG4TKktBpFBLJQwOPl0XFRdfE0HabpfP+AtrBKMIHIVfQOnGLBI4AQR4KZWXtapLCJ3rEbUrSgGYz6j4hICUJ169J2VE32CyGTkNRT0aaVWigMqBkJqJ7/Db4AcWqJ1E1usz01rXiBELrgPPais0BQs265hO06q2OUFVVV5S3mhR0uox1rCD+tBNiWv0ycGP7+5iTXwPs+yvqvM+6401Mn3kbgwJ1e2M0w6bsefRSSzdIGl1oejo9gPjTSc/Y9dhmhGjkmh1xG/uY4Ka6w2r9fMRJwHI7yaRN+3ZK+459RRGE95WW9BWDFu+8AOF3XMwid9KVwDRBukkTHW0bI+8oBBG/nbob5NB+fd43WvgTchE9WmvQI3tbMXqC98nLkwM28sKHtIExMq5Bb2Msr6Fow3GJ9jxCOH/ogb74Qhz62O51OloBhPp8BCCA3NzfTdDgeJ9Vyf//+crnc399///3333777f39/Tfffq2qv/vd7969ezelw+FwOB5vADx//vz29naappyzyNn8gJhXRFaZQCM48WTp2VVkcwRrNE0LcR+PB++VCWDr0pxzKTUlQM17aNGMTbRYtcGe83B/n1K6O54OhwNESWGHcx4eqgBm5gSyQ9Bg5QMjzN4wCBvYBxFPSYvb/tpwifDWhGGAVZOlP+N6RIioZ8JqnlqAao0HYqgwAWw/SbHMP7CUkZ4gUrUadV0iqln5ZRmLTQHsTftNnACNQCk+lBbj4qNls3IMpRPA2GE66Ln5tQMDcaIaTrRRKW2omH35qNCzbw3/qrongHWPh8rGUZOhgx04G0DxTuALe19VsY4bFSqkrtpOZth1T6PakCLt+chP41fddbf8fWwhfS+uje8WH4+Yd/r3G+wTcGyxQ2BHuiPevMauZg2LxQ6wsSqfO+NCIV5HDIwAYz2IXb8it9NlS2thCGMrDsCabyxdHhEyIBYugLGmnIiK+HwvUEanQjSicRm/WndGhA/juyzEY9k5Z4/Jol9pyytnoEzT9PDw8Pj4+PDwYHlt7+/v3717d39//+z5nUm7m5uby+VyOp1evnyZUnr//v3j4+PNzV2ET2QxXep6D9iNk6UULBqScX8nbumQbobu4/EoNeg/k8K2GS2rHTMfLERXjLATBvVyuSTQ/eH+dDqZmdT8mR8eHojI9rsOnJh5SgmsBz5gOZ3Vs8Uep0SJk7iXdRin8WA7mQC247DMlBhMthGoTNTUX7btCAWAbJFfpI2fWmoFQ00SEWaoaqKNUxA+D42YJCxOowkaWrBVOo4T+xIZEwZd2e3e2hZesU5sTbaF3HfWyFgzBWzxo47PdkCuIey1Z10HIujwsPkc68m5IOeqvFj1Yn3jdTpHGLuDq4JwpNUOkyNPuQJnB8BTOtU1tAlMaufs/XoFw3+PorxAM/DKp9XwtPXoFRKNf47Eqev1fdecAq4B05ao3gQ1Vu7TsHsSwR7Jj1tBUOgjH6P1IdoRoCutxM6OjGWzdx2tNgTaPO2t+l1f4p+jhWksy4eW3TX0okN+HM1W+nSK1FTwzbamIrmiklEkPzzm+4cPqjrP8/v379+/f29BNmDBCLUcj8d3796llJ49e/b4+Pjy5cs3b964jbHDLFXfto09YN8jrCxwGAlVjUEhzuezY3maptPplFIq5Xg6HHO+nM/ny+VidumSMxFx28dl5iW6hGoC5cv88OFei8yXy3y55Mfz5XLJlxmN7EBiTnGcQJnA9RiS+zfAnOC3iMMN15GbYx0jdxkVtEUWc7RRaPW8ohTWwqqqhFyytIlpArhGmpxsfhIASuzDEcGTEBskxxSQQQOmtSN9x8S7Ga6qzInWu90+xBqSZyx7wOsIRAiTCuu5Fwmp0cP2pEK3bt0JsdSN1wIwNsoej9vTwEb2URtd66/jV1cYx+avWHVnl5vEc8y0UolWAO92c6d8VG7RoHN0E8EfbjIy7OOqfhiumz9t/joKv492ZIQhdiSWzU+ioIq/7rW7HqNe5Y0A+FXXS3wKlokRTq+h47SdYkBBYY1nLx11vA4n0E3ArhVsDXoc5e4dh7TD1R49kHui7ZeeIJ9G79Q0YN/3HDHZvR8RsncdP5wul4u5L1lKuMvl8vDwYHEHv/vuu2+//TbnbDusIjLP8/l8/u1vf/vq1avb29tvvv6WmX/yky/v7+9/8cd/aiboy+VipmVVJeJFzQWiBjySQocyrAfycrlwKLZFrTodp0POFwtMraqXy8X2R00A14CarV1SnThd5DxfLnZUaZ7nfL58+PDhkJZzk+rBmRXImVLjFxR3sFYrGgfYabTrUdyEx4poxM5KmdO9pU9Q2ImXeopUtIaaF2jOxd3DobZ5TERkCZrckNPR04puTOjWCOOkaw2YsPK20LVw7QmobnuvIqtY8XGP0StVNV0VAB3j1h8uIcZ6NmVwV3zCdzN/782nP39i0bUatPmTlY9KjvE1jba4wOCeWCLRPrH1rumxxU0qHVvcrPAHARBF1PWa/f2OGPZav8KOr5RNhGx+rnotisheixJCnUceGx/G2dqJ2IioqnU0D1N/6PdxQbO3io3tbnG/az3yJ5skFH76iOvG6td1Hft1LvebVe/hf1zqjS/Eb6d3776z95i5lPLhw4dvv/32/fv333//vbTQTk3aqYi8fPn65cuXh8Ph17/+9csXr373u9/9+te/fv78uao+f/78crmcz+dpOjw+Pqrq+SwfPnzwPV0OXsQl52VgFohZ1tqw99bgNjs5J5gMZj48ygPz0R2w379//5DzPM+n6fD4+EhEt7e3DHp8fJzn+ZCSmmvYnO/n9yIiOUsux+lQlwiiylpc4iqIU1CTFnotHpWqITqlRJxEBHbS0Fd8oJSawrxWXUhRSkHCYaoeZEIoIlml5fasocBrHEgVxZRLNmgTH1JKdjDW8ln6rgS3YGQpJTC57cFkIxFJzUpkDnG8fKULwiMtbtu0gZw1r/MguQD2mxhC67A+pB8ZxFiwduLTxj5ERGk5Rx4qWof/DJCvtkJaZ5tNY8VNxkmyV0b+6416W1PIuhP5UUfb/qfnnaWwaKMQjH79vnVnIUIMLCACA0DV8rP6uYAGcIXf/zWkBJVr02FHw5Lal9qOjTgKkSs5YNGMGREVURTxozWdInf153WcH39/ksXy5G11dcayl+d1BYOqzXe0LRVtNl6XbXG+mJOmte6WsAi8Y9gH194XqIo40+je9z8j6rp++Qh6fusOyRHajvC02cx4K+Ov11yD1OYOYVU0qiqoH2VqNu3FSNmOv8YJ2BGMrhOxWJEiRGro0qa4d+iNA7ow4LWMjHvkS++oz8LkbKS0SMzUViotRW+P4a65jrqmh4eHeT5fLnmezznL5fJ4f/94Pj+cz7NFDjoc0jRNRGrxrf7iL/7iq6+++uSTT5j5dLz59NNPP/3002ma/vZv//anP/3p7e2z0+nEnGxTNuc55+xezR76EYDGhNUL3WwYNLo/bYAdFxZ6MBoKmOh4PObzxQbVQl9Z7ncGJOcEym3IlzjDRnkrWwLFyD4i4odNDaYRs6B6sJ3WupRurf6ICIR0PEzEzGwqb1N2KU2TMcICy2FQipSsoqBgUUilFJeeHUkt6Gp7nBYHwoKl1ZgBLcKjkzjvbFp2dF8frpl7ZBCbRUQKtIPwWhP7QjDSA+381OFhsx6yY/5PU5THVvaqHQGIXBKD3MXAVa9UFR/SWgPY7EUcl73adGsN1EEe2UdX4RUg90qHQF1rS13T18HerHacerGe6wPX9Si+fIWQNhvtIN8cYn/o84jWEmisZIRkJLBNaD9Kt5ug7vXLiSFOcyz+fdv6ZVdzN006ZtJBu/4Q2DEedMNxpZKu3fimMUZdQ7VH5DosvGJxCeUvW5m+++6b8+Xh4f58vjzkWeZ8Pj/Ol/mRaVIUponTQSSDTErqNHFK6eHh4dmzZy9fvPrqq68eHy9//dd/rZK++uqrH/3o89PpBFANWZXn+XLx1AeetwUtpkAbip5rdPQRhxlAU+SESE1kmkJslRymaZ7n9999b7bonLMWuVwuIsKAXGYQSyNxbSv3ujIdTA6lFCzzdhm/Gpd5LfOIaqAkZ2cdMY03KU1EBLYUp1C2wEM6TZMCBVpUipRZSi5ZVWv4zSqACzOT7eymZXUfi4ioewka5CJElMVW60lVTQOum9DcOji4U27QXK9d1XHxGy+uAWdZMfoIp2Ny2b8MOxVos3n5e4e/eK0fZUYw7YQ2xmWPQ428uBMPe5x3ZCublfNAUd076+c9EXYd6ZgyERFZHA//tzROQ0Gg+djfMAJ/TxnsVcWQt7QvF70jNDiz6CBi/XqlwqeUOH+vANYRwN7zNVZ7+vc3vZgGTLSK1uk3I6PX9XnfDXFydS534CEQYSzu3h/btQcdYQCmQRDWnbI6XavucKK6S0K6pcaM72yO+0ISV7kxOiInJIttu4ZhZCze8T0B3E1//2T6/t23thVaSlGIiORymef55ibZqUEREc2lMJFIwRdffHF3d3d/fz9N0+9///ubm5uvv/7222+//ckXXz0+Pr5//36e58PhaJYcZvbAGqpqATfQGM0y68hdkMzYWIGTxVC2Th+mnkjH4vSoD+rhcCBgmqZymU0A27fVG4hZSgFpAgkTmsnIcSqD/wig5IPHQbLa2ZVhwagMkmuEEkkcFlqPLC6kESihBiSmolJUZiml7alrOzldDSbB22tsyN+3HV4igvWUiIguWSqOdRHAzEzT9l715qRVQHUxu1Ezo8m6xJ1gKovRJoLtNVB0+g1RTbAjgHVtVhrh7JhyHCwy20lYV4193Oj1Fkfrvv0op+heWP7cWtV1X3VP7K+Re262boFiRjYRG40lzDVgbcnfw89e6ag0zutIDB2rwhpXOjzEsCYYn1vlnWPaJpCb1DJ25PoTp+HNF7oB2mwlzgLdGs1Iyd3s617Ye3MT/tXQhyHzD11wBpxXpQhBU9qsc+U1HYbGtcPYWQd+E9QGmCG5V8HHbi7Pt4Y9NrHC4daI6CAjrEQz+FhkSFxtb06Xy9kEsNaNDT6djimxeV2pWmgnyXlWtUD0/PDwICKXy+Vv/+a333333cuXr3/1q1/9/Oc/f/PmDYCHhweTGGgrNd9wIlmM+LkBxMwCHxt7sW3nrCJhLXtg4Xyw5V1YkjgRkYXsuLm5MVcyEdHSmIgoKUjVA/5aAF4FSi7wPEi8wMMHqmd7UqIlQlbN8kthd2cZEu5ny6o0ftlGogbyrYTIREQgmue5QJdwnO0Tl2qbUzdSg7Y9OaVGS/XYEgGYgwC2uCP2fuLFGS1O+03C2hTALnS1LReiAEYuviUW8aZbJtBuWb2nAXfzBwPHGV/zF8KkeFJZzc8dAdzd6xa7jw3HXzngfPOd9c3ChiLT3Hw/wsktdIw/8RGMjBJYHdsYeVyHB1wh+zVC/MNxb9ghiYhaOtXO3XUmEI8E2mLX9G0hMMHNuRP7Mvaug6qDULeUM/8wdjDOFNqSEHuAPbH4aI6YjD3apIpxAl7pZgc/1lOjfW6UCSeqbqVl70dXr67R+No2YBu6eJWFXZcbcj9iYvH37WmsAVvDRI3ptTn1cQFMYak0mSh9fHwspVhQ6JubG2o+F5FRGmN9eHh4//7eDLYvXry4u7v7yU9+8otf/OLTTz99+fLl4+Pl8fHR3gRgB5AMbBGhcCy4tGPHRJbfz4AzU3CzYWqOzHZBOi2hK7ktizsMmuf26XQSEVKcz+faHWooUtUwunETXtUjxmliNjFARBJM5cUyeDQvkpF0xpvt58V0WWKqB4oUEOilZIuHtfS/CdQogDuKcfqrQ1BD+KMJYAVqBtycF+tC8/eyY2O0B/lYTADrOnfKnvpbvdMtGHUwQHXIibN0ybazI4DtT9pbH3xM+kKXFfTmOz+0Tuyrtgu0TQRukspoPhmZ/vBkl2OOAKAJ1MjvltbHIgtynLri2H2USEbMII7pWkpFlI6wmIFet8arY4v+UNYa/PhhV66YEDenuYOqw65TR+Hx105CRKzGjtSbLYV1pAoEDXVEpqyPJ2Gg4Q7OSFHenc0ut2+jsO/rsP90di/vpmOGmaUsq8mOSGK9m2PkyMQwxAGtS6N+E7+KUGkzyfaVbK0+pTpkXVvYjZicahqfNjzmTtxMx9U+YE+YOSWd0s379/fH43Gapi9/+jOT2X/1V381X/TVq1eHw4kCF266bwtBXJZYxO49iNWZWjmdTi6AowYcl1GRGxh9rgRSo3vz31ZVLVK9uFU4oBJ1ivbkKEtmGI+GaSOxrBNt7wohvmvtC/Un5+Iw0Jo5AtDEaFq7Od0VkYI68RCoM0sRERNjNoELh/O1VFcMUfjV3fItAexc1KAxYDp3x0ig21QVeEdHoHV4hgJZJq0O7CmSMhEp7R5g7ybY5k8R8nEmVxial6kOZHC98itv7nHnWEmMjBNJpUutMBLM+qeF3/nD8ZP1t0Q7kYFpp3TsJs4dGvjyXtmcDuO3XUeccduN0EajXSURts0mrhc33Y2A6c56cXOaRCKPTzp+7e9vTyLjA1s9iN9G3C6MZbDT+J8RkkgJm6PftRtra7/SMLj91+NoItg/aIsVdB3pXhiBGV+LPXWi9f52A0RrfaYCsNZ/NKyTNnGL/QVcB5u3OJnx2QIpm1htzQA1qlaN8KiqUnA83rx48erzzz8XkWfPnn3zzTfv3n349ttv//rXv3327NmPf/yTly9fpjSVsoQUXnh0dOOu+ZGgWrKS7xMfDqlha2V/thWAiHACgmVSSkYk7vb8mCZquwvmIA6glJzAYkkeSVRIYTllC5HdE0jaZkRSramvDGJtXtCsNScJB+mrqjXXzs6U6Kin/cqUmInBBFVRnaVU32Z/BxWAknMps+WJQjNHaxEBJjaFvqgmT5to4ShN9gLV81wrrgxVy8GSNljbidyvUJVqy9ZLVFCjcHdXESH1BMx15AFWtQRQyjau4HitHYeQfQV2gqL6jmu/dg9F6aym2JonXSEhJSUhgbCykrKykIxX1HyvDFIVS2XnuWO15oQJ2WSducR5HvlLzzf9eBtVtaK72mqpPanJoghLJmPCKscwmDx9m6XiMdKVGhha2b4S+LfVGFPxaX1Z3DFULeUxQATVRf1B8BodIEeAPF7N+d/CX9LibwTi5USUcrIbJsoyW57pAiWFjYDt4vh9y7lrHg+wdEPa8vJywEmXj7kjmFHIrWhmPYKRd195eEXDds6+3IelYWx3k4SsxP3U7maz3c0SRWBXj4O3+XLDG5rWay/A5gUh1Xuy/MiFkKjOmkJIBBBtROLrmgtIsLyQCLme0Sxi9ZX2vNQEVjvLEWCDVzDIssMiDEocprGSeAwvlm6b0gdxenh4SCkdDseUJoCKWUQVKsQ0cWJmhrIUMB+miU6nWxM5z58///Wvf/3w8HA83pRSvvnmmz//8z9/+fL169ev7+/vLSqkpXYQIdVZREyHE8kAhMQSCKiWhiYQ0/3lfq3EL51UlJDqWT1QJVdFlAC4lXu+nJkwJc45J8Lt6Xh5fLh/eODTgSGUEprrluXvLCIgAbNCkiVyVxWSedYkiUFMyfMbFsDilsy5AKgrlGmyh2GVF0XdYmxpLzAzz0UZNRVdEbkUN0jUBJml+jhItmCb59k+ZxC05HJRFC4MUZ6S5aPNpICQ4YdZqabes2lgSm8TvOb6Zf90Saq63p0i1/gHgWHnXCUXgkoRhUouKoVBEClzLvNc5qyllNmOLyuxJj1wAmMitqmopWS0tOee/NyuSsLtuaVDZ7LzkW0htYiSlhZqyG5raFRLTEbWb7OKENlOlYttBSklThyEgD1kcJ3xNri2OiKyicNKzFAhYiIkYm58ARFjca1NgwZpo+Mp3C3Lck2oRnXlZynK7UkiCGxfxcQbkSWZZ5IqaECAWFMA1OwKIBNLlufDsx0rlEkt2akCJMykyhApICgJMScGoVxmS40upKzQyiyoXOaagp6p1mwC3oWlZU5my4EsiUlIlLg0xm1UamATlr5ps9KJqiiUyFYr1FLJefpM+9IeijFogogmIhVKRFJgS0Zn29IyDo6C1orbCHkduaLzu3HWHAVtlFj+QjebRgFpJGm+0P7EjQEt0lHkJxUerzZKC2+ikx/UDJbxTbOAemejMDY/Ygsl394XANPBQjgsVoFiW07VdApCAsgSkGpBc7cignnSwJQyh1zWIWwdcll7M008rRZ7WrPGERn/s9Qj7YQFyLK9ufyLeFhhfkHUYoLTqp/Fvq/K5ohXhIgSASrUZJX9PkWyqDctYw0Ay00NWG7zRETv3r370Y9+9Jvf/OZ8Pr9988n5fP7++/d/8id/cphuP//8c9sJvlxmaWGzInAjtSlbll4jVkmgNhFalx0pbFgW1AgSZuIu8W0O1prY6EKjCQCkEY2ttsTWXObYVQVThYdr3vjqPsYu532cwlKUtfLNiM+uxDlDRGCaaFIglyIij/PFhKyIFK3bvdQ8rm2zgLGOnR/Tf6pBVYdNVVWLFFJq1v7KgQEgR/pQASZAVJlWeT1bN7dYht1ISzbl8URNKS+llDnnnOs155xzKdnijYAEplrBDA9oeVnrKFBzk7N7v0IpZuw1ddu+rfV0xBy6sFmImFVBcMVXSROSTVeFMth+rXHf2yrQtwhUNZgq0EKkeYtMtHJS6+Dx+4hnOzkmqFdgUfL6K+qVwjU+d04Q66mrOgK0r5Pqc8uOzI2NqV8tL/JSj8JsqaaSViPSUkN4EtsFhKAW643r4ZM2Wu1KALESo4paBgnIFhVqqK3GFNR5as9D/uhKA8Ux2Ww+0p531+ljsYJ1rVy6vBmvWPOBbug3xTa2WEdHIbQWtNQ5zQ2c1koHZ9f6OEH2RAtasoFxcnVUTSxQgcndJm+CZFmMJo05/QAdvQN2vFZqqD1dFgre5Qgq1qMzTtJVYx/bm4vrnq65kTBWkdC1IamDL5bPPvvsu+++K6Ucj8c//OEPzPyTn/zk5ubmix9/+eMf//jt27cvXrx4eHg0TdwOI30EeUGiXQm80OhAncubi4/xu7qhEmMihYJAqT4e1lNs0X3k4ObE4VuYHTmONHpF9GJNrI5529nNOV/mFs66lEuepQXc0JBUKlk6rWGMYp3+cilFQL6CbiZoqGoUwNwifzGzUi+ArYxr4UozxH7GV4ObmK0klgDdpZgAPhwO2B9m3TedRb42Ija+tjkE42vdQERq0bDa8IFTVdRzjdvAj8iJP3VQjWwrMtbup793Gfu7R+cITCBiPsy1/gDSZp3YGcRtDJDlk98+FKSqFDPxqWI/6ODYr82mKag+f+8S64x0MlIsgjLXYWCPILsS6S2ymqjyjmKy4wOxrW4QS6mnEnS9wh6dllvHe9E7IjPOgji5ZDiK46917HqE84eWkVwxyM5xHMeJH3fgu6qwNdNlHRCtw5IMR5WWjdLYTERNV54/f/4//+f/PBwONzc39x8ebm5u3rx5Q0QvX768vb0lotwsDymlKIBDxyLXW2Gtp6P1TwijIvWgi2QVp0Bf8ogE79t1rCsRAdrJh60x1rVXrbflJzcoxDTpAneobY9tobRDb6y/RQ3LvnEeEV5KKSGCmDvvdBQQP7H6bSNZEHyJhYCavjA3ExkzKxhOc2EPpiO4bsLbQzs8HqM9u89zLCNFdk14x/cYWbzp5nC8EVkZiCLOR+RjvWcW+ZR/uJqrZLvWvTrbjVoceqqW375rtGZeO1T/FBl8bcEX79sT2nq4PKG1iIqMLA7i2ArWw9H1dJMPwsXwEH5BR4klYnvqwAry2JGR4UYYNqv9+xWvodtzxRbVrUhoWPR3wKzwQGCiTRP0ngas6+XFyG26h25a71ofG2o1c1enf+VV+cscQtvGX71CDRZEp7EOyf7aDxieNd2OfGycuZvEjyCAx3ndXTuEx3Y75hxnRHVvXg5+bHHJOMy//e1vX758+bd/+7e///3vf/7VH93c3Dw+Pv7Jn/yJpWcQkfv7+8ulipN5nn15FsHdbOW6Bhy7F9fjtmHazgIt600J0tcZB1kwkSaAOeCLsdr17LBpMqbCv1ofrahERKpBbNCVEWhOl+OPpKp2Dnue5zn6Oat08MRRwHrUOgxXAERQCji5m7cKaZMfEqc9ifs/u5s+BjYR21p8CtaxQXzlI+sQ0Ajsw/WYcV7psCYdCQBrnrUHbQfz+GFrotcbrlVOoJbauQM1spKu0Nb7m5punBodqH+/slnbJp3vwfnEmr37e6zNy4pQmxKMfcPvMnDK5oyJgUTjax1RdfNi5JhDe7u/jH3HIMDGnupa/O/VucnfzVgVU4l/tHRD2VUesWT3fii/88oeuUr7dbenHPI0hD8rJFELcvDirOkcjP2FPYxdLwtDbsV70RFDd9MhIfppj12+PkEicfrxsG4sJjsAY9eKl3UzXTkej5fLxfylp2n62c9+9uMf/+Tt27effPKJJWkwrXeapiqxBk38igZcW9zvj59ocrSqSJvL1QTdrX0iy0spaS7eSGxIw3lHGmhXQ6B5WmmfHACp+7W6lU3Wh3BlG2ECUFoyY/N/toRO2o5d+SiYxJ3SRETa/uxkcCRuEdECaNF2zNcFsLadsIpsEoffXQziXMXaCYXColvKEmbSiv/pajEWyyqvKXwhRN3RmTp67b6Kn0c62aSfsZI4Opv3PWzBBE3r3T6sqCLAvBauXR/xtJm899OV0jEyv/HHI0Ic4Ss4rzbRYTs2NPa0+8naoupk1C992p/LnqWKgExD6temXXf20BsnyC7Cy+4xkgi/t+sa28jNxx7Rlqa1SXtOb1JKZ4Lu/Gk72DYhGUchtrjY9tY8JFZCgwQa64n39j4zSzOodU0gRCH09zUoylfaekrpOJj/GVcJ3Q0ChTgD3+v0EyGMdBIH3QGrAthZuTR5hoFqDay3b9/+7ne/++KLLw6HA4F//OMf/+pXv/q7v/u7Dx8+fPjwIaWDiBDx8Xi0o00yzx3Rdx12gh4486pE27pjk6jagCPVkAvkALc7KmsunkkwasBFMraYXcSXIbGs6l70WmkO8deJ1furquYQanFAo+1FRAT1qCgzx8Pj0zQB0GF7BsPMt6FUXdIXqpDVDMBnAwBQ1e+JyLlbR7j+gq5Xc1Jo1IClJQN2P7JG09pB27Fvf0gDE+le6ORHvBm58NjQ5v0e31kG9Gl7wN1DoJ/q8c04NcZyhXW2F678WGvobrBF53vtdkzgehNWukGMCOwwHAKqb/guRQrxUY0CeLNHHcYi84k92j1fvi+AIytAG80ogEeiHeHsatskmOUhU15nQ/Li0qvryN4Y9ZTcnnTSd4WKwaesK3GIR4uO1xk5CYWEhnF13s3r2MRHafWjJY6CC/gIfAS763ucvLHOEc8d0XZI8+53nGra0qI2YPLheffu3VdfffXu3bt37979o1/88ubm5i//8i9zzv/z775+eHj45JP7w+Fwe3vHLbThR7ETcHEN0Z1U8OfM3Angak2NZKGwxMDTNEnK7m65EsBz3uTmxKsFi4hQWEatvL6sO/7hmnY7blKfK0UpZWmZrbksxQAGIJaGoUXNRRDAGJjaavKrFqkasIhEAawxP3GLLNZxt1g6o7rPN0to6AsjGcomfW8OLtZTOvaumy0jcV+57rW1+VPHQ7WTf00ARzC6abxZNlnYyK/j2O1xvR9URpLefDjyDox931ozbVYy1vCRvrdt8k34O7ZoJBpb3OxabCjKA296jxr3ML7HcDuGvsd/MaC3u8YhWOpZL/i62e2yrWtlHLjrZRMtkYOtu7xieF139mCIcEbIe3643+4P6hEGWt0j2vFNrIdjr+nrhD1yhk4ieN8nfyks5Tiw15gnMqWU3rx581//6399//79L37xi2ma/uqv/ur9+/tSiko6n885y9u3b0+nG1Vl5tvb28vDQ2TBzGz2ySrDKIxns/2qrujAPjcN1edeRKUBp1qz21pDroeJiEWsIKJpmuh08ljTCOuDGjPLonrSMuRCQkSWMbCa65mJ6Hg8EpFHh6Y2URzgmNiViPxcna94ADg5x7mUUjoej1QygLqHDbXWLZ7XNE2UEgARnec5NV25lFJUpmmapgMzS31ST2iIiJmD6jGkUpa1F6sPyjREyPO1tlNSNBO1eNuLAPbtf/sq7nHYOWPiFa/x8eoo2P60JUgdC1mZu6UNa/RgKOvsOl7GqWKl6EajuDp1dXjH6GesnIhLKRRCpfqewniu2uHfhDNOoggqcx84pWPlHfxlS8Ojlqd2LFKkzvyWEdxPoGLNVqx4JJ/oeBFhc1Inquk+ta4VvZLFhkSU/EMFuSnIOYY0n7toaInjPh0W+qE1J+2YjAHMTa/1+dh96Pd2E8cxlj16Ph6Pm3iObS1jN3B+R12sNg5xMGWtPung9xeWgPlBlMZWhomwnazF2R3WfMODMvnQr/g/AA8o1LThTZgj84m927NkxLzOcaA7PIz4RwhSRjUy7MaCqbRIjh2RxPkb6crm1/j+Enm/K5Gq4pP/8B/+wyeffPLzn//8crn8p//0nw6Hw8uXr589e3Y8nW5ubu7u7p4/f353d+e5HDar8g53GLHRG1ET4O6nhIjAHTtF4oJ0+TC0klLSprdS878HUDQQt6zQxMxM1YidmiSp2Q8Dc2uNrZZyXrohobDCdbo0Qq1ipuWWTykxVw6YUqJWq64Za/0zquSAGkrgDIVU67YeDZvHkXON7KC795HdJG5/v2MW7eVdnXh80ndw617XK5jr3262ODKU2MfrHbzaxMbidxy7WMMVULuXfxD2aBFpO5rfTrschq+r6okldrm7MfpUC6iydOojg2UL9FgPNUvsnkvE5vjGzwM+n2Q1uY6Eju938F/v3QbAtA05tvT77n7swmZfPjqmgacBgfzGbtJ6QTa21Umg7s/utZHLdZPo+pQfr11be4TR1TM+dG7zRABKCL0c35m6bqPRd0cN/trlcnn79u2nn37629/+9v7+u9evX59OJ1U9HA6WyOHm5sYCYJlz7zg8K7yvaMtZ3SJBo2oFICoTARetn8Mxifqye2BpYk7a0vhFKW1e0FUel6UeIWHmA9ctZIt3xcx1L7Z01laNR4a8v7SOgLoggSDtELClP3LM2DtV6E5p4Sxtv6ZiqhtpMRWwmqkFWoq6Bmz5C83zy32prCyuAEussSVkvH5sN8Fh6ARzpJ8F7H2TTnf/9Mm2OSU6Otn8NsRW2wAjVkhtz3IP7Cslth4JwP/s5NMOqCuW1z5ZfuqAGWUetnHfQ9g1GufsHmON9UT869W1UQ1Oa6E91811JGS/Nei31dlurm1WMpIZraRvr3t5WRbNO1va/nm832xuE2979J9SkjXVjVOsG5HNAYr9ijebJNchBGGKuaVEt2TwOOsxRNgYB27ERofG+HLHEPb4UscKRp5whSS6ejZHvOM2HWAjJH4YpIOq2mfGMduED8CXX375+Pj461//ep7nTz/99O3bt6r09ddfv33zmblGm23BjJCXy+UUTIgdHrsnlklCRfxVH/Wwldh9hSo0zC2I2TVg3jqty2xejs0RLnDtwskFKaWFWAsKMx/TNE2TuZUdpnp4WlUFBc2bQMz1F6prirFiwyPr3IVKmOc5l2J+WKWZmpnZvaBTSkg12Egp5Zg2TFgLoqpXyyKARdidsBCokIL0XclguPtfddDgkKFhbNdvupnA61g5K5exq2xr5FN7/DHeuyly5B3YmeTdT3vtrkQIbX/7xLLJs7BG4yaeMeiOH+XjXVtPQcVm2UuPuFdiQ5FD+XUFFVRK0eWk6zJzN9hRE8B29MC9TBwhsYweEpvyL35b4Q8d6foyon0cx9hrv9901+jKnhwiC2w6rJAc5si9I210lDbS2KaouDLT469j5R2eHasi2/S82esOVAkut7SFBFzNXrV5P8q1PcEXv41Tz593P40Y2Kynmw4rE/Te3I7Y//LLLy0E9E9+8pOfffnVNE3394/TNL18+fLZs2e2MyrhMOhIKPsddhBXWOtQGSeDqkzJ9mRJVTVk2onhMpjqHEiJZM7uZ0SB2joN2NuyPWDXgE0l5Sbp0WSPNGekUYsdKHLZw5BqNV/RENUcyUuvjSW11UHd9eymRF0EVPFPqgpmgdrO2QrbA6uKpdt32uNfYVD6lbK/T2vOaNWLCDYq2WgL+xrwJhMZH0ZoN9kK9jXCWFaIXkN7haXGd8cKN+c5BpysQd3QwMbOYs0C4qS+UvneXhrW6fy2MNF3DcO4YNAga1UkQnABvAck+bqtEVUEW5uQw44u8YPKZgfj6NBaGYo01sHWjfLTCWZpLjwcJwW2+P4Te+fDNLKRTUa9iZCRtMYZ3eFzDyEj8B2Em2DgavahrqHu/gretrhc31bsb1f/5gBxOzkSkU9EU4duah6Jre3af5cTl8vlcDjc3d19/vnnx+Px/v7+dDr97Gc/k8Ld/nnV3rY6uYzutrC/hs31YIgyiVRdTYMwI3NBMsW/GdWZGRw0vMistaJYVSmFVR4rESUsySztNXMykrzE21LTgHURzBG38c+lpy2ihnm45JKXvf3gCIa0NF1fqES/QouItJ1jbhBAIEskna2xQGBbRB+JBtoNxKov65FzgsOwM4er8uM6n7rOHGXtqr057ccKO0jic++X0ZUtyBFA/Rj7W6wOezP2CnO5AqpzPdXFTdKFUGxFe4G9zbD2BfDSesesrwA5PvSvVvBDFdCGpU0crjAcNGANRpfIvjbhGcc3PtzsV/zzKVx77PsVzn7l23ijBMsZ1/WO156SOwO9sVodB7HbQnMSinN2xNjYYpzsWI94B+eIxrX02V5Y01Unj71yfeCus5or5Qok0X4Tt/D82q0YNpyw1l1djDz25d/8zd+YqfkPf/jD73/3B1W9u3uec5bCqjpNRwDMyQy2p9MJg5dgBFQXrVcNQhHpDlrWz1cRvX10BUXID+PmsjgfBgGcQK1TS/IGWiN+OXSrymEX3EIvc2BD1sT5fFZV2wOuur41EPIwRqlj9XfiwfaAddDtVGsW7qo3JLZWcs4t3DwRkchqsWZqOLkHpiqYihYPnUggZtagi2zSUFd8Amz+FEORxDo7Qo9FP6rrbLTS6xZYc5YOzg7gq1N3+3msc3FE2Ho58p2NygMxjB0Zm3siO/A3VwaVkOa9q2eTtY0AXPlJ1+X6yyPyt3vdBLC/fVUGr/aAN1+OBOBNXAF1lMGxF2O12GHl8c1N4CP9bwITLWp+rQnVaeEbXklcfDjON/E8PuwmrK5jC2oz77ubyAj2ONF8r8rlhTfStd5NgU3MrJhkK3EG7c2jEe3ddfx1bHGsZ3w+NmeFeXcLj8LS09+ZUHN3MZqUsn2fluzPhDbsAImqHo/HF69ekerXf/hmmqZPP/20lPIXf/HfAMzzPE3HlNLpdLq5uWHm4zTN4dwegCUOMxFIuIqt2pwCqpaIrOGIa5IxErJ0LyIZsmQem8s8MajZvSUvCW5NHqtqIUptJcJp59w6LU/Sgk9FauhriQ5ru/Osqp55txKaNYpt8upQD0BFz3lOiU2MWYbMqhMfppzzpeSsojkDyDlfLpfT4QYikwiYLLVcihXW+VNxCuUsxfPCsqWi03X041X6o43cak8vkXdgy1bm99073VjEm45LbjbasR5VJSS7rvP1Dtc1g6ZBzPeld0FYscVtnJAASmLZhklQCKwksCe63EOW5IMxQ60VETEPRG2H9NR9FVGSkiZmJbX0jeCJuaiyzTet2ZpVlSwG2oANkm0sOT56TOzTQDd8/m1k2bUGYsAa2sBqX2oGs6XxPea4opn98e3o6qO926RPYMkovJFjGEtqJopX7a+AJWpcrlZbUYGu9KoRjf5kREXsV4ereiNk/9Si/BEBpAriegWImIfoM5Z3MwGWmVsgpCqqTKywNL1q5rTtNesmkmNH4jvetSdK3w5XnbgNubGXdJk+dn2uaHasKi0RMjT2i5hBlmdwY+G7CZ6PyAS6AdWEmoTF5T2lhamJYi4yFyGix8t8LuU4HShNBfS7r78pWS+Xy4tnz75/9y2TPty//+Zr/uzTHzFzvlxKmYmUGfOcJWfzmIJkQFUKLZ2p3ZsmRktJaFKEVBWlBk9U5ZqbTAFlCLNokcs8my8xmrvWu3fvGMvpndQON6ss9jom9jWLtDSvRBXnRBACM6lIkQIoGKoKO5oLVZUSGmUQmNLEljtpiYFsvSmWGnNxO6+7tpJVlJDA1cqtpEWyZgiBeCrQ82Wuc4j4nEtRFEULH8oKtgjUSsScVLVkLTkTUeFZOBViElWmBCWkZAFgL4VZcWAm5aw2WUQsN3NP9B0n9cUyACkFzcjsP0X68xU0MwM6TZPlfOz4Qre/4lMxWrR8ne7vczhIZn8mNUsDG023pM/GC6oBk4ihRMRC1Q7RlgAL2GpGCBNAXG/UKvbDYzCWRdpSXIwTDSSAELNCFCiatUBQGMkFMFNSEkuaaszYwr8QohNKlUBAEMwqWjKR9ZLtCmaCXh4vYAIxmFRIobmIqkouREqUqEZ5lFqPZGZU2WeRW7SoqKepr7szqgnEVJO/si+Q2xv13LafiRdFEVLVXFrtixQtUEpsEFgPwAouRMQTgQAqhvUFoxVAUtsRENWgugFhdlearF1yuR7YejSiQJUAynk5WBzbbCyYnEvYDSo2CUyJyQSQMqGIEBgkBAWJEkAiOKZk4Q4ELSexoqhCyZZKBSCxK+xbEIESMdvRQcMGJ48FBK1pushyjFOTDo3/VKQ3dgq1SPNi3AykxFrnr4rl01U+sHFZFRVatjmaJY+BmkvSZlYCQ221CcXCH+bLZZlb5qZi9bipxtBpKbfrfOLaS05FQZUJKxFB1HzSnIuWtgXjENp9Simujxd+BVWAyILhqyhExZanomBA1FQboioMO/XAKKFfycFUR10s8DBMUBVtJlRMfgHEyk0A18SbC5bazQJ6mzWE5jdUBKrZ6Pdyzufz+XQ4/PN//s+/++b7zz777Fe/+tU33/zhdLw9Xx4cL4EriTEX/5sbH1ZYumShGpoOFWmoXIfU6EMAZSggEBXNkKxlVttDNa00zzZi9V9KLCYJlhTIJnSr4/SiWCg8Mi2RJVwy3k9NsZYmYq2gul4zCcTksyyB1qihUUQACRKFVKUmH0/W5TroagtTVSUyL0LbDQIhmxuWuzItXl3B38F1IyKxGWfLal5SmbShXgwmGnSXSF57Ja5DNxfjcdwjAWx+2NX80dZHSNpkW20y+ENVCT/F1/pGOzaNFcu2SUbaDHRPhNC85au3ESuZklP143hlX2CjMou2ixyy2UaznrgAFlJmEhILZCHEYCGhyiVrYRWCzYvGhdp8JCHmmhFYREkEqnVtty4R7XsIxEBFnYzEDx/osdHup+sP4zg2ubvSfcf72JeOy5ssZGahuqyzq/XNeDratf7aMihXlbddCWQpPpSbycYsUwRG7zUZ+9LhX1Q54OYKrvwFak6aYhssuhAfEdUo3R7wF+S51Vwg1ZpUibh9biqSsZ3e6aBjBd3934Mklr7sE8BCnEBVd5tBSdt4wUysRA0BANnx1LJeARoCevg7GCI/xOCE5UOzG4hjr3sf7j+IiByQUjKfYEIios8+++wv/uIv/p//j//X7373u3//7//9v/pX/+r7797/v/8//98ff/b5YuV9AnLrQDcR4ujouLahUhUuC+0obc7ZjuLO82z7nT6jiBMR8WGCy5tgV5k4aVC8/MZO1krOpRTYKj8XEdEidnpXctYizEwJzFxkEcBVtNcdW1MBl6hSIlAtoMWysYkon2liictVSzXuaF0y2jJaWp8WuxfQQgdp0xopLAa7suauS9OOkMiPHE4p3Qpx9WvHNfqBDk3s0cZHlwIjYB2j7B5er61rd8SVhvXNkyo09mzLWQWDRAXrOuuvTUkgStW1qplc2hgqK6lqUXMtVACai7az4YpETdtjZhFimojZgGAlVWzH2Vr3GgsxaGknAiI2RnrwPzcR0gnv6HS/Yzno+RewwnkU5CMh7YERIZFwbi3edM8xcB4K6jXsnO6wRxtv/MMOw13pMNl4Vn00rvYcbH/iC/HNaruO+J8cYkHHOp9K3u2duPfpGBb0QNKwJtgcrIiNEf+bk72rbWQ+lUTNq2fZBLm6RqnOQBvuHR1j8Ru3INKa05qbbcSP3T9JAMc2okmQiE6nU7o9iMj79+//xb/4F7/5zW8eHx+/+uqr77///nS6/fTTTw0DjUH8fUpt3TRU2hhOl8GWUig6Rmk4w2quVWwZjRo83XDqIIOrpptzztlasq1lBpU5SzVBL4jWIqJSymKMtdrbVjx1GjCxCqmBgrAiiNPAmZQAzAQmMFWjse3ztZYCakBESilWRbQa/tjr8Hx7id2hPdyvFIXuhW4aRLTHSdUBs/nnOG83AaP1r5tN+Fe22RbhiWyomzDatlQVIAUxmcnWth66vdu2g6tZclsyr/q7y4iVtTq59wBrC7YqktW0YBUt88KDdGHBLWxqcDlWX+hfK7HXIuKhK0eAe9a2ZULQreLvMLPIbuhNZ2GRfp6CQ38nftXBEJ0lV2O9lU86Ui+HqDUugGNz2CJpBzU6zY2fxEmnWpm5rn1rO/rs8bbGQLx3TNZp2PrIzLrFEDbridNkr8thpHsgdX8pPwI84n8EidYvdKwmPmmNrvjJCMkWn9noY7cr7z81/1eNBNy1GOnt4wLY27Amnz17xswqNM8zAYfDYUrHw+Hwf/vTP/3+++8nPvzTf/pPv/zyy9///ven4+0vfvGLv/6rXxOtQLw+bRDYaH1/A55FA/Y6XQxLXrLgSYhazAnM7DFLmTmZTlz3hhfC0sW9BaKCUjVsMaGeixZR1HDTImIxas0ApKqQ9q8ushSwdOLmp+AjRAJh+DZPS92ods/aYkUDrE3DRUpgVkq2caNMyizL0qSKe7INPaYrLDfSh924z143UuMsGuvp2DEG6nTq797vmN1eu3scZ2v+qBPROA26Ure8nLv5TQwdp3WHUAnVK8UoikBAixxuA7m+2uC3dRiZU5MBGMChdq1nz5rRBFqsaajtukAqyWkphbTYFmu1lhFZ4EjU/TxhZk2akKp24t52JJXeAj9CXR9To3wRLap9TGMv0d16HM3/JaVjjpELO824LNmUahFsZ3zSUnWNDNHeizB0fNylr11NAEfEjGQ8ystN0bWJxppIdN1rkWXjrJs7qsrryG7xntba3ijtaFiGYhj3UYJujsuq5jCU8Rrb7X7qGu1g3qOKriqsh6/1qBeHsWub95uwjQI4YqDjq7Ha+BOumKC7ofIGLvMFQMkW2lDv7u6Ox+PxePzjP/7j//gf/+P//V/+q88///zrr79++fLlN19/h2oc+whah65ud97HfgSsm3WxLZcrlWRbUgQAgtWwYUsDNpzVuCKloEgLP1lRaV4PFUhxvrCx/ATcJGy1ooWhhzszF3NGswhuBFEowTMdKgFMwuBEyiQEMApqTgvfzSciaQzWdpjiBBhxNZIFBgLtjq+NozbOyW74ljeHGT6ifYRnbHH8lYhUVuQeRzY+9xuPu9YB33HM+pJoSqxEbDqumGC0fbENDVhIVMuIjb1+kZrjlcnRRTVkoKjaobd6uC4X0awqojW7FxERNccTrd6FvtpjZpj5ua5d+9JhvnoIauk0NgwD2t17coKIvY424nn6EZIOnrEJDKPfsQXs07lPXV+jd5RGa9EeoaVmfI4a8BX4EWZWx4gj+96DFoDSEtzfK4kukH5damhMqxssom0NuCv+0zh8I5PsvooD0T5XtV2xNfPZI6HuSXx57FF7r38f+8NnIGFN7bFTUcTs4ecKnGTRHMNgdbU5bv2nH7AHbFcL72zxHg6Hw83NzbNnz06n05//+Z//m3/zb/72N7/967/+61/+8pdff/313d3dd999czocXe/x2rQ5du21ReHNFQwVcY4pMDMrx6K0nIbEsMg1BlFRwMps+WrokJbEUFg5YVUagum4DZIihVG9QNN6ZSQidmqrgb3aQujI1+CqMlLdsZBanigCuEWAIBBlECtRPW/HXMGixVwFEAkzmzj21p0WI6XulZH4oi2hG65ldLbK+Jy2pG+c3tchGauiIMJlHZsQYWohMCy/Cq1q82snGxaAIdWoUKCABSM1+nbNNv5jBsKID9qv1Vmv0pqgev5OgQJVUkCKipAoJEOKlFxKUZVcHqkl6DAfeyIySQxlAQnA07Q0xR8f91FsOPxxpDZHvPs1DnRXiaqK6BV4OqEVR6eD6sp9rKqTYfH5Uue6C77d69w8/jkKYCebjp67vvhDR8vI2bEWwFgvIGI9XU+7Sq7M9CgPsO743nzv6u/gt7IsL7T6rsaXHYHYGrVNPFz/1R9273Slvm0DNOwBu3aszUZlPLkZrIah2bcldLgdv4p86QfsAVs3nj17RkSmOJxOp8PhYOj+oz/6oz/7sz/7k3/0p4fD4W//9m+/+OKL+VJAcn54XIvUH1ZoZ+eYqBpsI4qrAG5uBXHye//tUT1TIcuS8JAmDcsiChotBkIntbNyrdHwY6rssOdZm4LEPJPRTqRVtRV+LrA2LahuVwKFCJiKCpPZPoXAdlZgaQ7Q+gJSACOiy2HoWG3sbLyJLGM9EBzfvD7bxz+vy9fNFsdqdVjTbNazeZWBlW8ytdZYtf+RsqCwslD7H01CYk/iNSGNMmavRwxUjVkYrIDCPAS0qCpDCkrTS22fRXLO5lrfVn4mg4V5AiCcVZMNn9EFttTfPVypqvnz7rHd693pKowsciGnHXg+Ou4Rko9SUay2o/YI4TLx1yVqUZszaKzKt3IiffpAXMHnqte01NmtHuL0ifwN+6jrEEVr6bvR+tYaaPPlDiH+xM55dgJ4JJ6nDPRe8XHZ/HN82HH4K2QWkLkhbiPHW9fG8fNuaDDgcIq/xR+6dZ+j6ZNPPimlME3Pnj379JNP3r59++L5q2maROT29vbrr79+/vz527dvSylsKYNOp5wvpZRpmkjVtmCnacrzOXapAkoo2s7ihJ8HbBJamHgtS/BStEWiNeedinRGK/1m+fZ8PqeULN1CAknLa3suFxFhRQIVRcnFTNBqUYeIWMGNlEW15BkAIU3ETYuFihxcC9F25LB6r1r/mSkpWJVEpChUqRC0HSWzo4YCpZSUWIkFZPVAkIgUNfMuUbNApjQxp7YnVGdEQ9TUUkp09OcmR3vzmjTy7vSjs7oZ2ZO5+PpwcIt0rVsFay4QmRrCpFroO3lzZmWpgsecfbxCv07VO9HGaamwSHTWa2CIpkQmAlXznNXP9R7SUUkYiRIUCayKBJKsSz0+BgC8fqAtv4kAJEJ1gVZlIjBBIdCi1cmwyKwFtj0sZRbJIJVSSMiEcJAWSZUURe2EvyXsS31igIBnZy7S/tlpq+0wy7FTjlJu4ZBcThg8lr6zH1yq+Q6dEjbpR9suUmxIgyLLzPM8+4eRq7r3abdpGi0csVM65whD5IEOj7Qz6BWSLQbeQej1mL0tWuwiVuPUA2ChaqXB34nDjq1ZVV5/N15xjqwGqOkgvA4vb/nR/UMfTQqKXexF5BteiQXm69DjunXErX8b8/jGnnpfeurdosaOfmQdIU7XfiEdeejKyK9ZiluYNmG2jgfLR6/T2mudbw01afVUE7TXdTqdUkq3N89evnz59s2bV69e3d48m6bpw7t3y8GbioXtxCYRsz+oaQDRC9r55ljhQgTBYcFsenYQuqIey9QihaVyYmYCmU91znkWi/mcc84l51KaAC5FixCxEsUk3oAdF1rCPapCW6J4rOeYDaWSwnZtAaAeOpFmCJHm26yVizLavEWbeMxs/tV1a6ptAVJKtE4uOd4PE6RfFfo83BwXz3bi/d18rT3XbhD/4YX6FejGihgB7V3T3YfYWc5XjtOi+7QzuuYkJYDOl0cwMTLK4hcNJkrwYypr66UYOjpWQtVTL5s1hEVFxSKNi2ZF0ZJVVaSIFEVG2PQWEYtKRETAjLoh0inBq/k/ogIrEq2r34iQPUoYvl1YlW6pfQBoHXo8cvwwfdRZpFWoQ7kOm1cYCT5qqKu3Q8jYvQkSod0rexBeL46oBXQiWksg71FXeeNpywKlk2EfhSeSR6xWWzycru9eW2T7XSv2n4XIt0xlHWV2g9XREoXFkPX3CkF2pE5hAdd1ZA9pezVji1yvfBIREj/5AV7QVk6n093d3csXr1+8eHFzOuWc3717p6qHlDz2shUKebhGID7ablfqhDGFoX2tYbXi6NijA4tFFff8BMugljkfDgeDVkA55/P5PM+zaNHghGVe1qSaLzMRabOxqKoWEWhKlrK38lbVehBdpNQZZC0CBNSvCQB7TIwamERroCWgxdICiLAWwCZwJ+YayDrxgagquilNbIbIZRB3LWmB8kaF9eNUiC3hh4HQq8LA25Owa/R6uxHyOHttP2AUG1aZVoNSXb0BMHm2aovsCJedxKV2SNfi2hBEoAVSIALJKqKliGqZZyWyDQi/CsDHOjREpEHjYYe/c0SqTaiSgKiI0d5cchaZS54hoiIQIS0iRbUsCrQJbwbIoq9AGJpIS6o94dU+yx5WAwdEMyH0q7cr49L9Guemrhc6av6DO+wYQd3s2o0c0/8caTu2hWBu0UGiLPAHn4ZYOnRFNG6SaWfRiWBfLxrkn4V/ooDVkbl5nU0v314t6U4ZtwBokFIIq59Y+bjh3TeHax33hx16KchgDlGp/cmacUWVbxufq97ZiRV7ri0LCKAe3jj889XMZu8iqGGIP54eEYEgn7oH7MVsyMfj8XA4iMjDw8P5cS6l/PizzywYpB3OKaXwDlI+ylv3YABGDXiFC2rnBKglJHBiLaXADg4FiLSFoiSqZzrP57OIpKYBl1JMAC9GCVGK0fkrUBAxs6QS2Qp6mSrQ5TijpS6u0IJMeYbZjIGW/ERFtQCqNWwpyMMfroYDWLhSJFC4ZCazOPr02DXx6TqVkN90PG4snQY8jvV6xI2tbOcs6hjKk+ghNGRPODidxRtnFl39OfcCeHOet2aKlNlwJWFNpqpmOrOOqYlbIiGlywxW5okZrBMrmCcT0Hauu0ahXe7rWMDi7Xpmz5JLKTJn0VwBkAyRFuKuV1BaNSufHfMi0BqKYFeIus5aI2sNAvhKcY7pn2yOqT1nIsHKSxZryrQbn8tdH7s6NQrFNuN0LfJdAO8naluZpr2MTnnXCTVOqwjbR+XEipuv34k1RIt6hMeUiu4TfyGe38BW5R08fh05g6M6Otz0vRv6uok0H6xuzvpMdNbkRntHFGGDchzmrjlVJe2Zku6boJXq4cMOeBrkbnw+dhCBALr3p6fzOyvn89lkcEqJjS61xuM1ASwtJkZiixm5wvIPYrIjDNqiSrX+L4CllKZpUlUGLUQWbQuiaE43VpvocuyVmK0LpZTUvKCZ2RSUVCPNKXgRVETNRCdLVgZz+vN5DgDaD7a1zhanlwTUzjMoW+xV084UUF3RPRGgzMq24QeAUAPoEjgh1UhIYAZbCsWUohFiIfFIQD6LEHTHyMuwM3MAlLIYBkcGHWldm9JJvFaDtur/KJHsSoKPkVZHh3HiIbAVv1nBoyVfLhbkzAORVmJr3pUgsj0nBcBUJINVWZShLEi29V8FsHoYeAjA5mylqmI7x3XzYrZwp6XM9Yw76qKwiMCSi7T1GTO1yGtqftl2VEnUnOUIUFBB9fYl1XoKvcFesWS0JaXXvXbRPowLr1PBxNEMlahLmU3Kwf7KqXt/BMxJruN9dh93x9bXxUG6qzYCEHt0heBiVWOFH/+ENgCITcfpo4MAjlNSd0rXepRtsVq/iQusPZAQGIgz2HGCbwrIFecMUHVlaSgI4BGTtNJNlajuecTuxy4M2FGgWvX3KGEoHxnfrjzJBB1xejweU0rGgMBMRObuZCtKhzznrEzEPX73Bv4ppZvDxjRSSiChSUXEIBFi9YP2LdaaqlZ7cAje3dIfMLUg4wZbjWxeo80pAFgEopboHgBScvcrhIPOV9aDIwFR05SNWassZpEmgBVEtpmo7EHKe603Fmam4B7hK30DISDT8FmHI/DWXo5eHzLVDdG7N+eNWZn3dtyeGOfAR4nBm+iQLKWOr66FqAtaCcdAr7AG02hrna5HapnPDxSjvjS/B4fKSq1TVThbDZwsgwH5/rBnvlkC81MIdEXCmhQm4zMKRHLJubmwFVVSEW5nvh0AphViSylE1bGFuaTpCI0ksY3YRktEIRB/ZHxPH6MrZdmBGUocHVfivdHVDFrredR0Jl4HWezoM3rlrKZki6kehzXSZKTVj/aRgqr0UWzEDtZWrPsNnnGCjDPIvF8dNu+afzjWo8FiESFHSHYSAfPXIiV0LzRmVO0cezgkP+f5FH/PNWLRxus6PmktgxkrbHgrcSEeitB6qTF2PD78KMAjeD9YAyaieZ7fv38vIodpSikxTcx8PB5t7DkkxhnP+HnPftg6Yav/BBCxMoMSSUvDQqScVPViWTgC2VEl5MUrNQXP4RjyPIVV1cQJgDkLQpSZtZEma9UjzOWKxtXWFvb6CU8tDvvSVV52J2J/LcxCjAOAakJntjwonHhyPwXzKdtjl22hMG6wbQvgzgQXBiaN8zY21xM00SbFbdKh7rMtWotYf62szy/6zbJrsBbAtnHQURe1LQz/pGq6knW+WGaDaM3TwBAN+YZEIaEkQoUoSVHmiXLdI2aeVEvTeu2gEQGSpSgKRAGp7xQpkkmp1LO/ltNaoCQi1UZm2dB4IlKuwYlTRYdoEUUpmjNSuk0H7EsOJ+NKA5USeordG6+957Reki5NE/YYgeO/w7NG/t6KDlIhdqej57G/8R1uEXtobTzvKMR/ulIzggDoOz4UHYQTGk+IROvYiMOhQcxoiDvkWwmx2lhGgL13vE7uu4e6DuCuHqzf2es1rTmn33SD7s/j+yNKu3Hpmlaz7mjd3NPGaKmtmOM/gUL1uiG9wz/vnGuPAxSvk8WYgHmZhKtALeqekoKpRnogvH///nK55JznfH5+9+Lu7u54Mx0PNyb/zEOodl4LaXpK+NnQM2aU4eQvW9ap4DLJqR7RTcwCZTDbeQNVVVQCgvVkmbSq1WVFiTgBzInZbOmQosQ4UOJEpp3Y5m+dloaTbm63wbD4GfaP6q+RPjacMOvMAYgSW1KmMN9s/F2vscjDFl8p0cTsAtjW8ovQjQp9VX8DOVa6qfuFNcT/gvu1FInPdV8A0+I71vNZn+TrKW/NbrvAjMBouPZNW/bOcL4IAFq+oAZPA0YyAMuAYSGVDaDLfHFBCHCtkxRShaJILsXswEpatBTGSh7Evo2igQClDBLTbpfd3yRmfO4EcJFihmhikChIixSRTMoiWaQoSk2ZZbnUcvV/hyopGekQeQws2/QtiiTKkOl2A9dL/ihLxLNIi7XYuCI/Nsdua8g66VgTL+59FdEb9WBD7p4j1VrAKOwEBIMVQmBFsUUu14hmfqW2yRWBiSIhOI8rPQAAfMhJREFUitWlrX1UPBFjI/z1qjUapXUpXj35UuXXgR1FDOzx/QX4BmDMjIu2OU9avU+WrRI0w7gdJ7FUTmaqpRpcPZnJjix5Ur/ijtiL/d0U1eOftF5vsQXMEIQodAApRNHya1uuZlvrcaC3sfJ+RhNA4gx8b7DWfM/q7OUpEVtviMxvxkZSppqVfPhAGULCzMqqDLuCcCnzRId0nE53Nzd3p8NpYoaifPvt14fD4dntzc3xMDFpyUXEPLGOx6Oq3t/fXy4Xmzk55ylt94dpajzUDuIIgxQJIK5JB1qsCoLFNaFmQLZiCQHv7u7MR8bmbUrJ8gQ+O0yWsZOd1YITVImJTJvTtnRL2izYEIGIEk3MaMbqtrkLgWX9Y3DNZ2yPDVaiREyliJmQwcTpkKYppQMSlFDUUnOqkLJqshiTxEoQJmUipHa+O6mSHX4hahmBiaFgYggEhQBOycRMUWVajnpHejPTsS+P3OdIdUX0kf2NjJiIzEDmOzf+057ZEIAKhBR5MZyGRm2RU/cLlBQ8iZ2ENb6jsIw+CaxaSKgabNvgS7mYu7HP52LOcWZJLsW2+d1d/zxfiJRbunqRbNcDH0SzFIid9JVcd2olRzQSQIRo2vf/GoFMkzlUKyCKos3ZquDijlfxym2lCxURtfNOTHq5nImVEyybSw3NoRCRKVGy/X9ANed5VtWUDj4iQqxiyaOny/l+Otwcj8fqraZs67mKkKKkxAkgYlYBDsdkElrbcs2sJszGVkCE6O11nYF2SxRmKororek1LIuA9UM/zteRVlRbY2OsgJ1LaJn66kwgIkHdpQmuw1nE16/erlYvzlCtrYArkINmv44cF9EyTX4eVHxaxQlFwUtfVSUXgVqKFwvAAlG1w2eWHa0Knbr44jTFdiPAnQy2cimZiAqKe+8bLSYiC8mgRCAtogQtljJXLcU0wbLWmb+9ioImZk5pSskwq0SHadKguzuddNTiJCGDBWuv1BdUoMKUUE+VAombq48CdhqA6xXQsjI1O0K4ZZiGwnJw2vJTRJQKEUkpsek1KP6cUk1lvywLVACSMgtICAkkbK6iyiBMSp4JcX1lS4WsYFYScKquPlMCUynlfD4fOAEK0WkSmYWIPrx//+vL/2DlaZo+/fTTzz77bG5w+/RoPd9ZG4pajur2t9GD6Vk1ni1ZKAvbOWXGGiFCixGpxEal7g/Z2VqiunDkOgBtqUfEtm6DEIEsB84a4z45CaS2BgATWbgHZbW1Ymkni3qjSuit5dchW5uTKJuYpEZ8iqqS2HwDUj0MnIi4/dugy8AIrOnlaviU4EVJbeuabIGwtWUVp0S80bAmjb2L87x7QmS8YJW/LLa1fAvNJbeFkkJ1rsdvxNSXWqFnqiBlFSJ152zVGvak7tnmXFraShFRLcrmrZSp7ZuQqIpk9ejLQtpUS8SdgScUEi0EyMb82roqKTX+vODNon1z1BIWr+CU0PytpMGnopJqcHEjJmcGtmmdRSZmgXpqTrIwXBUSZTRnYGoKDIVlfhyvcYi7cVyQMRRd69lOdRq0CgSSwxYddi2umYzHR29eZwA8R6+Zi9qvsdq4CNDBQZcWBxFjv8uH/poMwfjs3hem/rzzGPeOLCLKrqKq5jJQrRyQqrMDLanvVtlE1ILttuA13oemTBvHk7qAa4MVfq22TYeYmYlc7iZm3cqzFEcqMITdU14j2CsuR0piYJUKl4JC5uwa/3x13XZ76gZlRerOjUPrcdRcewmAirEju/drNV+RaINz2uxn114sDw8PpRTzz0zgw+E43U43Nzd8YhE5P87v391rKbe3t7e3tw8PD+lw0OYZ0aH+etmb1V1JKSnAVPc8UkqSiqrafUnJWK1IdX+uW6dMaHOSG3+B/eAottalxFnE7ZiThNMFYXImlZzbastYf7XfoCo2AhZiISYCW7vVPU+JEqnWidAM1JU5NJOO7y+62dlnvoNR1xsWoqXkTXx2BNeRVzclxhkS27Kbzu0lQoItNn2dazcOqipqKw9rBPWgVt3s5ZZUW0QgRSTnckEwEXu1fjrONeDGCrlbIaGIVleB+oRaukk1J45wUuVjRUTaPHxCUQUTpK0+I4ra9Fn2/xAGLk6uyOLj7FZVQHO+gHiaRFU9glspc90z3io9PwrsezVYA13FEhmWA2zn4WWLnDCwvNijSPN7HKzCs3Zh9ZsIT/yEhnVAfN/vaxdqZzbWpjGJQoQqZmGyr6XF1YpTz/HZ7X9vorfDf+zCWBb+ViMrLFN+nP4+oxeVYwfnkR0t+FmDF4HvKGdEeNeRDrzWGcTJGyvZo8MdrKxAckR1q5pIkHsCuF89D2B0cO56QXcd9iIiOefHx0dmPk3Hm5ubOkLEl8tlfphTSq9evXr16tXd3d3lcpmag7Q3v4eaFSJoeZkM1v33DUqzGokIuJqOc87z5WIPSykt7MZCPfYfF8DUTgPHX538I1rGxYRfc7EnBrnV0SYGUuwHUc1iUw83E0Esrw6IoNxco7uJ0Vy348DTmolosBiX3J/iGPEfq4reoeNr4008eB6boC3u3AG5+jPsgIf5qmheqYCw2bg4ASJzJlYGq6qoFMky51Lm8+VBQ74qxJCBDTMAUkopJUDPeca6YYToaeue1oM9m6bjnatZz/HEYm5VOhx2D73oxyK1yE06lPha7ThhnmdwyvmSUuJJCGwcPkxSH3f2HQoaZDACzY/jG4ln5CFelCk0Vy0x41edNXisbQOTa+cjr3MPmPjmZme7Hi2FWbcEcAeVAx8FcKxK10v52N9uTP1Kg5gxzoOFXFeAjfMXAA0r+A7yOH02kTzi36F1suzwML6PMGQOw2YfV1PSOAd6+b3ZireGLeNrxC0i+WE5+7oe9tWC0kvOPT/shruDc1cD3isvXrwAkCiJ6OPj4/v37yc+5CyT6aDKh8Ph7vb5i+ev7m6fpZQez48+tzdHcQ8RIw/af7/itOElUDZg+1vMLDAxs9EcgJpS0MxKtoqBoqaBU7XYuM2YaGOuZkeoyRJIiaRuPrTtlqr1qnmyWTJBsEXgYANHq8xlUpWayqoNOVDhMT24NzavShzmuE8mZXWubkSmTyGfafGn8f7KZB5n2sijnfL6l8OSS33TCKszGBMzEqU0TZQyCyCkEClacrnM8/lS5Hx+vFe0r9cMDmEaGKkojLnbIEvdAK8Kq1S8E5mXkoqtBsoT7cleyQ/wQySqrlXDeNFa8nVMc10Hbf5pfSwyY06Jp2k6MgsvuTTcCzCUeoRkQykcJURsbvO6QbRrAbwpM6JbcuxUV3kHQ6xnEyHXJUHAWM+OaJBtnkjZY4lDkZp2ZtYT/2cG5PoIgK/CA9sC1mdl1pMlEnMci5E8ItqxpqXlRvtA37HObuJ0c7ab+7ReBUavkbELtJas/vlIZl0rPoWdaThg3dDsFw3/gLD0Dw+rHDBOEZHzhPp3+e0I50fyAY9zrBo2tVrzcs7zPF8ul9Pds2fPnp3SqZTy4cMHADnn29vbh8uDaRtxbbvnvrg0/WQNWFUjQoiq2mm2Yg0R4ZXYDI/ACju1ftlYzam5R6G6pGu7FhXyU5ztuYgoVIirYCAkkFJisBASMVINkMTMSGxXkALFdV+t/zffh7YmWJ2T6lHXza6O1hFOmsWbOAQdifvDjpo75rj5eRuRjxgnOyC7QVy+UpVStxBIixBNnCiJJEguluxM5jzP83x5nOfZ8hXUNGIBGArHihxmYwIpcTGpSqIC84X2kFYEJlZS85z9wdIXbVvtqRoz+TLPkaMhRmlTZNcjGDEZfW3ieNXJq0oiKHMu0zyfU0qUViIwjAEDK7ejFeNrZXSliRaUUUwiuPsRkdASuczrjy1Wig9biZ2FJrL4rfFdpk03u92y2vXb2VRHupH1RyBpXYOGtRHWc2Gz+AtRXMUeYWveUXNG2+LvvQl6c9ou3wYBHG/iHnZcmkf8xNq6m67jIwfYE2OjiPJ2Rw0B1SfjScbn66XjSA0bmjhpCAbX4cHpc7OSkSePcH5cAMcC4PHxkSkdpomIVEilrnoOh9M0HaGc83yZL6WUy+WSUrp5dnM8Ho/HY2cS3FMLNpn1FcQREWKcRgB1X1AAJaTExKRCApKUspkcKyTirAoihUCpncpSrZtHqqpkvnQqKkXFri6Ai4qqFBWFWflqxCwAogwyFzJTsBN4ojQpM3GiKRHzRTOQ1HVfFVtCVOdQpKYEN3eJLSd+GhaqPl4dgxsxTMNSFOsJsOB564aD/W0k046L7bWobZmFNcmZx5DFNibzts3lMV8gIua1m0vJl3meZb7knBXlUmZAxl2ZmC2nzRkCiCCsoqooWZVUC6mChJTsGB4sfJQUVYhkInr6ni4AQOoxjadcIaCC6lpcVSMVj7xhju3aRrwyytBW7RoRldwDqWrKVxGlnC95mkopzBPq2YftQvtOWB1niCLT8dyJPVoXZhKsqDnSxiYRbooT7AhgBK+OKBsAeMAK39Dp6ulufE/XqvJRoGFB0GFD11LhuuAZ34lI6JoYWcFeGQEI3/bjdQUGbeeDRwDcJNv5pvh6sWPj3M4Z72GjG31t55tXA12NUxsa8xMxM7a76jXWMcO1cZMqX1C5vdYb+66rquN1HZy7JugdQuQp1ZRBAEzK3t/fl1Kqq1yhlNLLZ88Ph8P5fP7w4cPnh8/nefZDut63j26MdQO2U8xEtjj+6NYiiOyckr1Ucni+0Wj83M6vmHxQqWpuUXG6qQbSphYryM4GELWzUmZkowTLC+v2Z66qLYQLtQhYAGw5rCA25xQs4eEBwhKuC8MY61AAcFq98ASULni48sk4UTu6HyEc2XfX4ua1YYuBhCKzXPJlllLmeUbJUkrOF5mbnx1Kni/KumRmbJXP82zj7yekU0rMmMtFIWb4FVGFMJqT/ZKer1SztpbEP+xou6JYuqSnXBVCZhExp3pVW0FFhEQVhNbHNmzVUQUwFoGxDIGNg2TRJJJFMnDwOG57hQbRG+eIiyJ/uWPoViQGcG1Fnf2HtkbWH3vR6ayr3q2556ZcQaAuCZEyvaodvrdKPEAh2NZIxnEijOxohGQP+Zto33wzzvfuze6TUaqNWOrwv9fiCK2XlZFjL37AFrvYbGhzXPz38c3rkO/JnU0AmCgxBGupD6BZCOJ9/ZNTfHOstoNzivuFCGOZalzhftV5czoQpURVBl8ul3wpRO/Pz+Znz569ev5imqYPHx7evftwPB6Px+l0Ol0uF2v15ubGQvmoam4aibUa+2xNdd1oK81ls6f9qkRVsgGwROXu9kyUfJKYi/SlXETttI9algRWUlWL5CUiqpJQjUKWVzirSCkiKFD7Vw3OrdT7KkMVxAxWJlZG4kSTMiUkMKc00TRRmsATOClb2sG2kQwLTJWIIcUiodhpOwab6KbSBrtbksfITXFClmBIjMUEUueviGEqdhPJRyTwo5URJRLcJkNE4wK61pZ0bfBsVyEIEx+YmVlQJHMBRPL8+KCSRURzISAxppQoTfP8mAjHiU+nk6o+Pj5ago1pmgBlYoaQQouKlgwol1LmMlv+C2ZSMkFeQKyTHoiUlC3zYDvR++TiAzv8o2pqke6ay0XsvGBFRbuSgpQYjBbsmRQQ5skIFYD5yhs1Whjn5mxcQ8Yq4XE+c5qYec5nvkyqejjcMk1WA9fQg8vwyeJLCFVdYl+3we0Mg1HQRhJyfhJpQwFiwkC0TiFebSczIpe35xagvmNZRJSaiXXVbouE6uqvfzJNUxdhVIOi1gm5CDDWc6c6vmg9BefKplnSrNJGJE1DqG0Efhjc8WJnY+tuK26CapEKXU8jJhfh0abdKOzjjX/l+Yb919h3bsXrt/j8kU9KcHKMXWvw9ysYl0cdnEQEMr640a+RbFppClirXszPxPhP+7oOQmk+usNKsUYlUa26r81GVPbg3XFUjIzRMLBwTxoUx80yzzORWPJv21VNNAHIpzzP8zzPrl6klIjS/f3j6XRg5sPhQOaZPM+llLS/C2zCbMRj34ct/XW8+iRRhZgw25FJS1utHWUqG4rlbqkttgknZBzd1GIiTu6TQ7AASCxQgSq1BMD2ISBWSQw6ssZGx87oY8vksZvdlB6LDnrwOD+vNzG+vDlPxq4Zv6jcXFQgpZQy5/nyOM+XMp9N+qKIbQLUT4oeD6lozjkb3yhlJqLDIamCmncVWg4iJs0lWygPRSGYnCOouA9Nm1ICKHqL6UdL+8pO1sZrjSC0fm4jTwBKnfI1Vmhq/lxgZpWPDx+Fgp5aTGTPpcwyTSKZEhEStiL++FIAu1rIMnD+Qmx0hLMjXb+J95vvx5sOAzG70eo12Zgsm9Xu0XPkJ1jPNYS90sjrY4+i7EE7huSd/ehsXTzRd0rXenz+lPr33uwkYscBOtQ9keeMYHcVbpJr11D3k67dRzaB3KzqKcLuymsdnEsXPHhZaMgZWqzNfp26iq6DDsDEqrnwWhRoQiIi2/E9H24Oh9PxkKZpYk5EOJ/PRHo4HKZpMpI076103D0QbcK1m+fdNAjdDl8Foq8LwLhaV0g98MOiastLqBa7B7Rtl5rwU5AFzyrDv/5wnus1qgDXo+GExKxMxKyUoKxMIFImJRSLgxJEr1i0BbXwafCQCAqPLko2UFc479PFMO2UcTj8faxnY335hyiEe5UDiEsMB4aZUwG05Hkupczny+XyWOa5zLmUQlp35G0FrKIgnSaWWct8kQIoiZbE0+EwXc4zEUiFULP8QCGkgqxi+6WSTB0zWlBlBrSQEYMUYgu68gOjq5IAUgd0daWN5yREsNg4BAvuY8ml7ays7VsjBl/ZYyNxQOOwEilILEMD08w8MR2IUg3TNliefFxcfTGS3ySwkXvs0ZUXbckG1vNpu0fYolv7dUwv6AwR2OC5e4y+gyFyFf+T1n43DlW86SxSro/KOq7AHlo+Wp4uRa7j/7rAi392MnjvTWAlfq4DhmDS2CRXR2D8af1mT43dm0OzdUlNhGad8utGkbaA61qJWv4Cqv13EMw6ZHnymyVuWWzmyria98rKtxBERKb7ng/n0+l0d7o5HA7MCVDPznu5XMzwO01TSgm6rYYGidY6MLyAQcf1HCaV2EXqebs2uQ0xQjUhquNq+TVMsyhjStnOQ4ItGjV12bx32FYp6UB84MSQGhlVCUJgQoFCSIiViqhaJCxpQ6hbui8GBtT9tMEZd0Zy8wzxCslDu+PDcXTGGsabsX5VbYmzVnhmhSiKSM75cnmcz5d8uZQ5i2RStaNhRIT2MgGlmpWC6d2OUrNPV1vnQFFASlKaDwGYQAwoaQ1wpqYUtvlJzc5kK7YnXn+I17R5bIXDSy6GTfBb7LNwSO0jAhg9lQqR+RKqzmobNIknIuIDt7w1G2PkE6tjiN7WFRiuEJgCFpV7NfAD+47NRbq9IloiT4sPNwhvyyVQg9k5Gk69uKVd19qbNxedjwILUpHSvX+tF/vxrWKP1hN/e1z2msAWohwV440G3jveRNoY2UUHebxxJLgzFwLVRRR1hKFq7pmKdTCT/V73ovEKkFhbOHS9azb2KDU2MXZzVwDH6vzp7mgBj48X04CJaKJMRIxERKfpRJQfHh6Ox+Pt8eZwOKZERPTs2TNAROR8PjOQUjoej9M0fXj//V6HDcLu+ThzTEu2mIPOGkTEhG7Olj9uMf6wQlnFEsjIQi52L9a0xbnV1fDXTV+CMiExqG5cAfUIkxMf16wRxGBKPPGBp5RoImYL9c+UiCdQUttbIAi0KClYVAgQNIuKhqy5S0C9j6gUG2O/k/aAhjLWgK25143FFT9GbBGcf9U7sNSAHss4ighJyeUieZ4vl/lyyZdLzhfzf65e5kSAcOunkpRivkXiRmzVIpKJFC3+qHWFiFTElE4WZSZLJQKBCLgaNUShbK54ULHQGpsm5b0rsLO43hxBpZbHtw2EVOPwckyu+UM8OaPYMgphXSsqOecplZwzUZqmI23OMtT51fwqeukYGUhkUv5OJ60jYYiqWFy+QddcYWrQfrqNRuzTG6OPAtQ10f0pLdOzP/S+R3iWIi1pCtW9wApqaWtp3ywEoGrLxLovG+LubXrWy/4IO8emzWX3YDC4Xs9G01vb3hiG6UrN1wttrV02QaVhhRSeKAYC66hlqI1HIrlWpBmBTNFs89bHa+GcsPDYCw2MkFBQ7eym14A/ClkpheqJc7Klui3IE5KqPoBSSgeejP0dDtPhcChlthPD3MA6HA579UfMbv6qTeuyN3POqkoSdvhleY5g/Ek1BjQENZRmra0pwyLSFAoiIrHUM+Fctru0dKunZa5CU8WvsYglXWNhcxazmPh2ILjmR7cP/VpJfIe09yh1byaM54bHevZIP3azc8JabnbMsXvve1salq4AavTUxu7MEYakzPOj5DnnfLlcJF9KKSpimZMBtG1dENWde2OdFLxadDhkGXiKINWM9I4As4kSAxZCudlLGopEgRjf9epVfpi9uhWqGjBXnVu5ppnZpYuN4gPnzNoDv9cVqVpMzkx5Iw6lajUA+Hpoj+dGXtOR0+ZM8aknBAHpDpffZEe0drL1d2L+5liPBvr3vjhUHuUjomtTAHcwUHNEWmc878vmJKW189fYx6cX+gdrwHsCabyPkqMTGT7E4/pmv90VbD6s3WsfgY1WakpXYddKA3sD4deR3y0UYn/750ymvnUzwt/UYB6wmx/shJVSAmoUVNN9bcfSXKuQbU1bK2S+nefZQgPasNgG8Pl8/mhDm4jwG9W6MKmCtvk+q1azc70PGrAolAmsPuFRw9MoALIQhEWJLAMXtMCEs0WwMgphpBFL8YmElZElCqw75XbmAmnFO4iKqMcqjMzC5b5TjOVdn540qcKaC9sCGFuyfKTXDYnbMdMnCOCRebVGw4KjxhwrzgFLKSj5fL7XkrXMpRTJRUz6AlLEksklEGoWE+jim13LdQUdMGfUVSe0SCmlD5m27tz+T1tlU7vZXl4JVIgVUCjHcbFz6ltq3zaca/7SzKEkSFKXzyGMdllyU8blOWFr69dKCUlWHEis485akSEiUieAHZsdnfifEQ9j6d7ppmfHNJxLOHl0sPn6rxPAkW+65DaGHgVSfGETHmDDVVjX2ZZ+UKH/RU5Ycb5rO+87NtQdKEUblL2J9tF2sV5U+a9dKzuyyci2l4UrBrjv1/0U5Ix12o2TxOp5EMDx5SsN9U5YHWq2elyqyaTas5IhxnyTlEQgxJhOh9PdzVTS+fzgHTAF53K5DNRmvJgAlbrj1fqgzTHYJBs1NzMYDJJLURT1lOmq1IzSwMoJSxQqSJQWk9faGqYiLXhkjcTls7DCP6zs+tkOYlWBB5dJliYdVN1ZQQwiT22jgKd4UlXse8DJkmnYhjcZj940e1rSqxrjhgn7Mrj1ldtNa1oZJCpkqRvt3urUqpCVlqVOuhGs49j2UDspTKmFH6zbtxXHIrYJIChZci45l1JU8uXxTFq0ZKlZiQSiIFGpSRmlLgFNxkgiFTPhQqA1lnZiaIBiuSWBSEt+wIYokVKkWFo3E0hmCazBBate+8RY0D+MK5GCKFlMUuXECiFO5jMtqlwYDDCrCFkmGBt1YYgArKJEHrVLmcwIzi0XktYYp2YqI0ssISKE3PzCVlStCpUiUAs5Q2CQME1gJT+1QcS0YjlwQt1axq2Ky/yno2jHsjrOmtq0nTrTtkrTYtfmTJc89pm/I5JJtGhmZSFBgaKwJiFhZUFJSKqFNRFhcZHcgnMATIiSMjExUs3LZnMLpeYqjteP4mFHLNUTmza1W9bg7QqZax62YvHe2jURCVdaSkqWQRm1UQC9Cpil8hFu3KSOArDk6MVw1UqFICVajrzXT2tMBDuqia6/qsoCJOM5Kcw485aIV3tO8drSJXVOGHXmKgm1UwmwLHQgr4eokorZFw3DRMZtFQQGKwmDQcpISgIhMeySGMbtfqoxkBfp5t68IECL5SRVpARWZi12pDIdiBgiIip2+lDLYTowU9F8//jucM8vX9+mw91cLnG2iGK+gIhuDkd7mFJKxKJ5vpRcZkVp7ieEIDrSZAecWWs6dSmlKAoSRCQjFxSBiIrkXEqBLJIshUVGmXueuCy0maixKagFsyLmRME84qxjDGJnpVj+XVmmnw3S4XSMz1VVwUKiBIu4y8z2a10fWLZRImUimqgFPyGehGBpOQXElFrMLAaIOAHCygSG6dN03IRztdisPrbsy3lzuCVurNtSJlaDgZklzVQrpRRiTXwgVhP8KqQoIurCG2BFURMYFl8GFgybGFSKiGRAi8wyZ8klm815fixzlocHgvuzuFYtpMXcGC1leOuUEuRoPFEKiLhm9SxQ1cba3FanhCJzrV/qth0AZhS99HQCqOVdJoVKd5WSN55XjXBDOaMlCHM0qRFhsm1CP5dotppDmpRsZ1rqFBUlAiFNDKJUU2OqQquLAskS6SJxvWaL8UWkSKyAqOgM1vv79ymlw+F0OBwsnUkpRTMulwsARj2ADECRVSo81fRgxnIz5beueJ9Gtk+osp7Bs9RcBh0qKqdvq+QlvhIroKImtYLiG5ZYUXlqPtakWkRMABvzLUTacuA29i2FlVWliDCEQQS1tEfz+QJWRWJLCApLYApujgXwA9PGUZcoe+Twg5Nyy7/GFl5eYYfSSZQgdeW4XAmctuxP9geHFQwRQTmjGb3UaFBsPZpSWoxzbJuGRERFqunepGYCyNw/JaOtoLGSqUItfxvsZSIiUEsKQqibCpXflrlupkCScaQpmWEFKkSJ7PSlYa+In9M1FmNR8Ba9IBCJSWWilKB2ujQRmCwcjXmh24k+AdTsmTXTvC12NKOAtABEbPy87kCqqkDYtM1JAFEILLktCMrmyGsTi+q2phLqoFv0NSWdCELKKlLTJQupKoTU1oWqkKlLVRTvO9MKmtKgsPWjqURVwaryiUm1CFhQssxZZtub8XrY0n8z28H5ZnmwJSqIltxkNvbuhmDn9cnuDFNMUFIWVQG3dK0mo9VmdTMihYO9biPulsymgFaaWkpKzBQSvGPf7QiAgAlUWoKFRpEMJ19u2kD1X11ZFMl2lSom636z7Rm7iUYpESxSNKcYJRxoMTFSMJ/WVE9bwC4KSisGjG94sAtaAIsZU+PV57RUerB7XZ4QtYQiyQbVFBJRYsByKZsFImvOZZ5zuZQ553yRnLVkqhqMEik1U0U11dYVo8QFN0PcoqwVM6a5mkFgpZaSybOnhZZsdLO1kN+/Rk1l07axbqMO5ea1puoyP0FAqeaF9U42axHX3K0uG8ikI1qycoZJAQJJwcSqJZcZpEmSVL9yVVUhX0gqLV4mBmV1YFj6+kNjdKIRSXywsxG2aNK6/Vq/lPH7Ko6l2T602WxGZagQoGqWFYGoUs3eSgpTVkFiZgC7gsgyr0R1fwSDVpYtAcxWWV1AFv8PRRMsyzWSzMifu9UGVAnJ1RbyKQlqWWIAEjtkbtmzuZ6U7ulNqqxCd7W4rb6xZToUUVuIaLOi1KtyjYduMG56/kfF1EbW0l3XXtJK91XvPQBSyFyUiFmM79eAPQ22Js79MExhmOOJbYkKTB4D0OLkIqR2QkWhCbYlVABUu2Ot3EyZsAg5oGqPA5jVTCZcm4OwQkgTCGSGhHqfQFNapyrq3A26kV6GKJQqGOJqVNWTNBzSMVaVUjpM0zRNl1yIaJomU7cWKeCL/nXpoGqCh5gmW2OYYxNQkKhioc1JWqSFqmgbm9XZslhzqB9pCqAFYOT/1967plluI1mCxwy8NzykzOmqrt7BbGB+z/7XUTuY7nqkFO6XgJ35YYDB+LruLoWkVFZB8VF0XhIEAYO9H8ncGNcNWFtoi/d9xnmg46B//QPdtxddAu5UcRDgcAGL4W0a54skqrJALSuW35vP42fuQCKm9IjvTl+RTTvOPxlsp7Tz7YBmzWsp1E6A2ZrVVckRDfTc6zc04Z9rHeJ+dXtOXN8nvR/uf0fRxxKEFj1ayEUy9GZlCMQM3gRwj3Ssb69sd6/7USgkm7W09IKuggtwKsAgvx/2xz79rpGDdaPV3IHc/NKBJk9pXobMLQHeEqoL0KVRe5Yq292cccVxNRN93V+fn0k6L8TtDadYN98CDvkzm8y6iwuGstJJlhT13PgzlqT7sfh3iblk7FotBwc7q2/NC+cOM1cYjV8lPBiLbyQJ/DP4wd28cI64CwEdlY/PjYS7MvgQABGhSZeV+3KatSZKoyjV/3lSZI0UWWmeSUcl3UtoKAgYnrae9t9FbpLSAwIDLKdqVsS9eRi+FF2I8yw5qiDTZoEkyp1bJ8ABB4dlYP5VVeNbhrg48UInBIT1wM3H6+vry/2r3+O+DLXWbpetLRwZdEnlFTeWyCM/O9+lI5c6pDCxmc2Xv+UUHHMzc8uQpmEz/xlko2gR3dzsXbkZe7frDFrb6pM/d1QijUP6na/bFEvoafOAvQRcJiVLw9M0Tk0LofONSsphCwBDjt9NQvYcGfvcLNnRDyjsGQHedeuoZ7U2hmOkCBubGWtrtdnqZQU9zwbbys7NEME+e0aqfs4tNw2XjJ8M6Y9qGU5iO/yCTvJ2QyYzZ1RhtzcBiBtYMFfZzBRqsNoeJMULdlEANGtsNvZOkY0Y9x2bhTZeklwrWxl3Xm802XOx+Z4drI5SFhsafAHM7ieyL/EUv+5ocOCNWIvjqOLiRF/syxDXj4PJzWXN3Tcev2iMJOJNY5k6iTYziLn/YjDrQorqabDTKXwq4dV34tOiuXPW+PCBSzHt2b3emGyO8St1b/9u/Vk5Xve6ZWIEaGigiJNNo8GKqIn7wrj83e37bIbucuLFdBpNDJUmIgaquflMsg8HwkfwCvidNo5Z6yyuGFthQbej76z7Ho/j58uyLN6LU5SdU36GEt+uI2FCApEtYfBO3NX59fW1/WXtmfFBa5VGsJlVNCfn92VRK+L1+tCm991uQ+bvT5hIluXemqCoiZGkUlnMzFAThHZPThHYst9CMlW4++siI4Hh1uIhIkWXPFF+VA9Swma3HJHv+LTDHLpbk8slg3QdVNAnDYMvC7gf79oyNNtVyyezn0NEwYzvOjz7pMXYssMnAR0Vll3Od1u+1Wq1xT+YwSrdpj/f1cfl33WSouCgz/xIk99FAuZWhPplLYPTKeKWbgKAiKCbSCfHLUKGuookTQCKmFWhNKNU0ceqmL7BE5l25wAd3yoblUO30vmbnvFkh+Zgj/kKxHvdhNlFN4diajhjzq+afQ2xachJSdeIQZSI+RNcMNmQNMA83x27n0dsE8JdhjZOPTPzvmyJcV6yeUKALnwq+jdZsvkemht5qTEtEt5PVCQZMRBTqIY7eAyFE9H9Fj2y0vHP0JyevvpkHQ3o1G5MM0TcUZOoIuL+KKMeHd0WJ92JyjMueKkZ9KMbVSFd7BSQ5p5MKvCicn4098npzi/svQmLKMYE9bp4rsy3NlE32fNvk2bV/QAM4Q2g5keMdLAkegpTD2odUyE7iX54IlMDWxYUEakQKeq1edyz1+j2x2FlGtcXz1nv0m04J0fgeebUfFsW3XCmY2CXBPj19fXLly/3+92xcGttbWZmSpRSwnU593MqAQf8+bmOMiaii+dPCL61eN3fzDpoTmW1Z2Pnq8+vzzFMTxBHY1vWBPA0GqIiZmnkFxJwf8VUF4+ToVo4bafoLbDY+U9PCfDuPC9uxvIhMcfgd7hm12KuQnneYYNSOwZpRrb6sLXWurb6aOvDrAqbsY06U01H9aHhi+GIj8NfcFx5lvLij2w50GWz7r+0n4zTx/psJMI9OKWLKu5vp72WJptYoRpNlGKstT5ERGkqpatDXLcmbcQbPvGoB4bx+cONSrELCTJ/b7/uzqRpn25QxxZimSTgXT84wO3EDj0QyYBZg3Y3n2lKDYIRDnAiAe+ugybWU4/mTb3rPE9oHmEe9k5QiaY6YxZIFjqr6t68BlDMOzFAxYwCykf/A8IWxKE5BtEEZeAHBJYS0a6skrGbB6rvU+1uTSImEhmIVIE2F9X5i54Dwx+2rqsgCTNRE6GykE1YiAoUKGFqYgXFxMQ9kE1MrLU1L7cNdRpZRQVoNhhZr2OWAWws5YTAvjQdZEw40rR3Z70yHVFTfIpf9/NJgN0C56voFPQCcDfge7rhmWzAf/vb3zjST6bkRLxpiVnIYM3Dvur3HKK//bSUpTURKbFnDE3VKEtMdNbXXKn+jl7Nxy2RA/gk8SU21VxdAo5HY0Pmzxkn5/cgbc7Toc4ZSxMehJzsSJDkE1Q/VFUTzWXccSSrgUd2iOMjBHj3aUXo6k0zY23r+qhd7byyNTaDebhRM6suA/R523yPDdZmenBMgexs2ubp7MZ9xC5n6aSXK1XhxVw7W/jraXDGtvnPCAMT6QJZ51dUAY5SCoEQ4fSV7Ew+CbFCoWoROA2WwoJyg1cSM2dlha7LVBtidTa6u7JuUN9Tieq8aQx4DDJmmBiUY8AeYmkzNO5maZLe9yjujkwGAolO9JByB4d9akagDVKEONlqaKRPPGE2Ur6fsf6HJuLVk7J87xOnw51oY5VyTpS9iCqGldhj53rZlyGGwQyk9eiEjxwB9AJ9vnZkWIDZNROjOhAAJ34iM9l3mtuYLgYRGVOXvLFiWlzCoKsKSE/qb4CxrvRErco4Usyo8EQpYkI1NA8EAtB97vpkOk0nADSMgcwVMQ4nYo6BIIdfOuRMU50QClFdpFJ1idAmh6MIjorz5Xa78ZAqKChKjOODWCNh8y4Hf/v27Xa7tdaiHllfAdkHekcPad73Ahn29L6o5HotGOwX9JaKOKY9eZoxB4kA70aVP1+2It1u3jqMSSIYZwLobj/niY23PJ3j2aKf3RptTnQ/gHd7O/0TZzDwZKjBpgQ0k0SvdwshDA3WWqvWVqtrrSvbarV5iQRjM6v0GAoPuXO33Ul0w3s5aZ6nWPz30o7TuAOAX9bPjro8addrZID2RJBUvXsQkzZb/SlFAYxs6J4mztFad8UaGPnwPguk8+5RPG6AGULO52fCntdHfG8OE4NyedsJ95k0ZU+m8TilV+gxMwokuydWSi+8I8O7ppRGQiidkEiQk6KLuautx5sq/Th4MoGXnnEfZDM3E0jjEA+sB5Sb0XXE/MARUBkmtpFuyXWWiSAFQfUAKHXxllaFAjZ2kVhE6GGJgiJD/dt5CKqoJ2Ft/UjliNUGYWxKIazWKtLARie9Sif8JR09ervLwSOaoq94BzkAwwMLIhjpIACa+z8LoT2Ako3ithCB0UA2a25ZNVOIiGuUXWtFh3YzyAh7gxiERBPh8sMPP9jIPeQFFUi21v7yl794/sgQGZ3Il+WOENG8BA1IsqiGLHi73QB7fX19PB76L/9zWcr9fgsq41GevN1ut9vtvhhbaxXoMLrck1f2YCsQBJJnmIieY1lJGq0UKWVhndVIkhc0IBtCG73NP/eKPqhqKVOSM7KNzv0GQtDjyekLtuVwpwIWG5cKJbloygbKnqISY4rDCSt6y+rcTOHyV2DquLR9AEf3WRLJeC2jIdnWDI65GhqnzT15bPHU4BAbYLRqdW11bWtd69v69qh1Xd/e2CphaO6m15YiKAuteSwTff91rt9uyxIhFu7Z7+/xgfeXdnVNvzQZHY2leYad3523j7RdZqiY1Z2zRbTTeT79szeZKz6+uAAeFCBwbEudywQSbDCASogULCJeclygNIEQrRnt1d1JW0FRXbSgFBHA63mTdOSo6nk4MCyjvIg2OT2CFB0JLzEIsAC1VZIwczkNAERMpdv7grudaz1lhkxEr9JZeM3vLJP5A9YqkxZaVV11V2uTnt+UwKSdY2hdbOBGEYW4Iax7y/2mpJh10SluOsuEZTAP01UUKBUFAhWhqCKieyi0/iE2inl4EtNR7NZvgFcPGx/aJTPBqHK9OZq1M7YJtdUePEmVuX/qVoPoJBkisj4eoYDtyXxMIFZkSfFsw7CsHn9MeOAsGkDAqYMCngKiRXI2RUOrlDYMxOxHoRY3n3u2ABOhQIrAbKWkTaQiIkuvp+40DdIjoc0twWSjAVqc+aBZMxchBOaFxa1ZX01xNzCvzQYb2WzN8ykI1Mu9iEKgEC7LslgqN2FmXkK5tZZFPXxYCN41V0Q/Ho9lWTRlXg2n6HVdPUBbe77GSWwkSaVugLpAmJ7Lpme08cUO8Z+e4yXy3SQ43xGbHaaLpqoiRRWuNHDTPSkizpG1GcjvHNZOPxauL2dsdVaAPmeHc+NBbs7nW/J/KQEf8bsckvMdB4yN3YGdERtXdiOP8XgzM0hl7T7PzSlxW9nWSX3RRtSmQ/DsrHPHQLe9hepynv/52g4wjkD4LisgMj0ATlch9xPc0nw54MKKk0J4/qGEswhzfZYHXYj0yhBPNSsfpMEdveTd96RtrDvbGRhfd+lH/bz1AcjGZTrOLaWPzj/5e/AZ3GiJQc+PnJrGhCPnhYi7HrmjGjwJi2figUEwjzTrqSfc0OB5c5pZw0gJbGY68CS7RLinwdadj3bXEUaHgDqRAgzhIXmKdDGAjeEQ0zc1aDTUeadUNoh4Vgye7mWXr5B4Mv8u2goxogD0oyufDZvMUxyZp8iWYio8v4w7XNEzH7kOvXtz0YqADnejBpmJCFpwUE4r+5YxqhYxGz6xdL8gsAkUzuiQIsVduEW43O/3kICD3KqqZ8DJoJYsnQGF78Pc29ubqt7vdxFxdTeGB4HXKJTh3XG73TIXpaoYZhiyJ5g8EuDWmqdbYxdyiqtEio50jFIyAZay4Ay7BYt6/ElE3Kge5nR2UcFoYnR85LrnLGgFytvQwtjJG/BKpHrqU7fIVIb8nfftpLjDPdLnITDCc0SZV9abbXOc7j4Bifr6UVU9j8zO1osEPL7WZmbWhLXVR6trXde21nVd27q2dTU2cS/NXk6j5wHwyLcOygH/XaZlf4/PHRA4/c/Vjrj7FAivngZGhtOu+WBIDCNNREeBnVfquQK84JYjBiEazMVRQ+vO/+xMsLbm839TV6OhXAwGyTTwgUYTlmHdmx8v0l8vydlOsAHk4+7Ie2qyocluej4EDkMpQ/SdErA3x0I7GhlMTPy5o6mnra1VRKAbAnzsfL4F0idBTAhR+rmH42xTz9LEU7A1UsRaku+bjfrZYa104uypWY4Ggq7sPRoO+hzPwn/dA15vgfuY8gtZnRJPpiYb7JpRyoUDAXV6GgcbAZi1VYSkUSmw6Xgl1t2vxn+eWJSDWR+GDBHVEcQp8CxhbnShM6BCsa5X94+nMygN5GB4XMFAI82oQgiNFOmJTofARkBH4o5+vtxuN3f2y05SAb4cUlFg6syrBg1+AnZed8EVcS8vL6X0UoaORHs+LEgMgGyxKBt1XG04I8BmzqpQdXFKLQJSi+jwSSkCjHMopvY4n2RZP59YSyAig91DQQFJKlsTlEbS8+YE3Zqo4cwGPESPHR6RIMDxNzYSJ6JzDo5jN+B825AOnzXbekaEOuSITeTQADoBPt6fqW9weGhVUF3t4fG+Vh9W12ZVrOf0Jps/V7rF1zkAh7RRKDflGeiJt/6EEvCOrfGTzPFkXu0dAhwA+h45L8WRtd/e71TaahQhjNSCMNkIxXSofArJUm6lx+aom5B/JceT+fj40iMw909lt23uqG//9eA7rcP68O4YBlo3s14OhNuGtCjbFUnz/z4N7sPLSsf87fvvHZNLVIEQ1cNd4UmXCPRqbVl9bQ29OA1J56sAs1oHgzFCDEiIiZ3HAY/Wtkcv3mwDDbr2RQFgyeMfBJWDuXfLfXe+R4/KdY2nujpW/HoRZSRZS0drHaFEbZAuT7OBRikwM3EDO0Fb2XZZvpufq/gI+ltLH6dq8aS2bksUEBBTN60OyMSEzPP1opNOg5HqklmXvz0ppVCpVPivamJTBR1OgF4rMJNhF1/GizO+fl8CrrWK8Ns3UYUIv3z5op79yrXpZrWyqtRalkVLuddapziVkjDbhQRcXDBNGwPJ9tMnLs7FihYb9zBxx7GZxlzHFBNSfMNlxyKfHDNjrSa1x6EZpHvNTRq8TcjSwXNS6AsCHMatHZp27V8m8CfA8ZQwp8FsOMogmbuXng5jh3COeGdyMGZeAisIcFtrWx9trZ6124b5KiIFxYMjJD7E8hj8fcwSMBHEeGh+YibGiQCyW218BIB/oxbTtaMlV4zg9To6X5KtvN5V5kiSw6N5jmkFID3uvIHqSQjUrVeehRSkNaOCMIrZMiBNjyEDaTj8BDMk3TQ4N+9kXrn91x8Qx/05g32XuqbuJ5AzLqPzNtQ0RN7hdVIttLUjxVLf8lAdOdt7Pxg5kLclfa7IsLiQJ9Ld1Ae3oWc6BSFgJr23rujpI7e5W5H2QK8qNqrDYVTLttrVpETjSHpOjHTUH26e3JHu+M2ZANFa7iWBhwk8pUYq5NKDpCDCvjO7ykXZc7u7N1Q6srZeNAKciTjEDSWENVGXlnzN0NbWPcNGkg4Pt6JKZyBI1UX960soSkRc7SruH0G3tVvQgu6qZV7fZWA/+D2q2p20PdGWNROW/lUwYy82QdKdyMHFnZODADNptDHEo1hjkekn+KHlEgJwVfOyLLfbbVmW27KIdO8Jf8u6rsuyuJFYutNTEa+yEvKfO/gcXntbbtgCYgf8NhXm4YTF4cIjScTvIHMIyxlbaKZ+dBk9u3N7UbZ4EWCecHtDroaVCHvJBtgj4kmAJQm+R2Y5+sl95mkJ7uEkYcVoSRk11/RISueHnLTNG3cj4VjioMGsK/ho9W1d17W+sTaz1pXPM8lz50kAiNLr/Pg5fJuG+LuL/f1E9Et80h8sN18t8RZOrveaIyfIph8cV2Eiaxv54uMVLtOYUaRwqxoFHC0YoCMAt6gXZeoSmAvBTzTS77TMxwcEXn/yhivdAapstQWZoTy2vG2DBg8CbCEEy7D7zmQy+90NDJ9/pD1y+cGfTBejxZ2k9qrgXiRnW2QCcJ2oeYy9f3txXauT3o1GwGXF93T0m2agFzkQcqa3A5Tw+usZ9tRzaAAwKtCM6k4mXtsLZvRaOigC9nMR0Ay6OzYzeDYONnMJmgJa0SFeJaUYGoyte+xukxJ6DRPpgc29KJZQoSY9hacCppDBXWwoRYgrnWhyrriqChvYK9I40RaC0rPZK4QwzymuvR4DugTszV8TEjCH6S5gVEa8V8fXH1g5VQWmDNTpoIjnmvH3KmRdV09mVm4a4XflUJPy2DyTV7QgwDLDjDs/6AS41pqhcNf/cfN7/z6/pZRSijMK/i05HsnMgFKb7DfYJqpq8y5gc+8GrTz9cA7qjS1GCFjJV656OFJfbKns6ZxkgMNWw5dRXhxjclprbK3VR6uP+ni0urIZYa4u065XFJ1FMegeN5F8TUIOOLY/mpT+gpZnOCZ8B5l5Xc66iB8JeJCDdLlq41TrbLSHCVBExPMEuZ2rWz+9/paKOnfccwWbrc5qihRBK2WfpvHXtx0EPm/Hecjztjv/YJ8BpZkAB55F2mJZdeyd5xSwOEsncHjXfGk+uaDZ1g0F7OmJ48ja3Le2WbVGY6Pt+QkFVMU6c9BIwksu9f2lItJYz198apMWqkydROxHgcC8lMXEXZ52323YAMEGEHSnJDNrHtnQTdwkPVsIcCL/dr27x0R7BaBeUwjtHNFJUmQOlSLNpVc0FfVkZFt0IurljdwO3K9ZT31JYviHBq2E7DGPG85UxGLnRu/jGBRCRJZFtYkoUN2Lt5RbKWa2qDbVRbWKFBEPHrbtp45vq704Uoy4T6WHQ8Ad1gKmfX0re5iBiFTD2rQ0hRLayX+HoS0NpuyF4LL4kg/3qDHv3UYbobomZgZKwwrK0FUz47DtlSkZi4hAtchSbsutLOVWFn28rQ70pVirpqoqnnYmnIYwQtdPUAaHykhGMGW/TSUUqRjjkM2DduyQrs5hT1FjMpzpBdMFcdsy9c3XI5RoRw+OnwBARM2TtnJ4AOGQxd6aWEOrYs3YWn2s9a2ub601kGQTI92TnE3YYwl813q+vrHihmFYEhHrsRMxxiBUp+PtWSJ8OweOJEZEy4fblYPRFS/6vPMduyNJEdUff06AGRzJdHs5Bw9g+IMEBuhoj0lEUBpQirAJtduttI0sQk7I3edU2WMMACoqUdzL8xR5PjkGosijvaBG4svLs888Jbfbe0KCnK8amGoEyNHABhv/+rYy6VVz0z90MuOBn0brMSp9vx648Pl1/WMnEr9ovhdEdNTblAysPb0uTYywxrYGdnVZgGwUIZRQUQ8GgZdxzvyetIsNfgq57GFOBcXEyw95rmNTiFcIb6DnUx4htgsEZIWgMwECAEY32zVX3/o5OpSejacbGrg72lYVH+eJE2I6cTXbxHsimpMrdy7KrdFbPIzEKWbOzHua//PyUmPLZaHKIWN3XB6vb1Lcb4sgvtxu2iNze6mlIlLKIiKuv1iWH8zs8Xi01r4sX5YFEDHW2+0ewK0L4Dk7rRVRLShFifb2+La8almk3L54cTp6zneaGAq90KAAKFpMzNAMoiJlWeqgF7LJibG4N8CwvgzXAKKug1UXoRYxcy+6kh6f6zouuoybsycW8SSXIkLVxcmcWS3lVt/q6+vj7e1brWZWSRGoLrdAKMF5kFyrZxkbtI0AKFT22O++qxu7LqW7czunNnwt4E40ARCSbDCdHweAvmdVfK8p9/vc0ajfW7QMaHGa1/d612ROpsRztkIJJcSoKgZKuVdrVldfhVtR8VJrVltr9ByTrbX6WB+Pur6+Pf4mrEQjVmsN3V5k61pVUCDdz7MDtynmDvGdq6NWycBGzmEPRrhEsYeE1wXipcJ6yt0xG4am7yfOyu0qrvSKALcpK2z0zJOyjk+VxELFzo4HtwEeYfKAAwkNw9awG9+kNx3hyg3oZXw36y5QocDEmmGkCmLPmwBKURUqa1vt0VorX1CW+7JouUmR4tGQoHmIcenuNeJShY10vu46KIDZ4B3MkzNYiFwO+V4dTqGJBVeX8TziBcxbqUOt0yTnKczjOImujBXSLYW+r7XU2qw9rDWwaq+yurKtQFMhtHNC/gpa5dCEoSMfAdW3nCMM/+ejOrJQvmCqd3oJFnEzcAc/OVDi7n5lxfX+gH+IsTZjFQ88tWqt0UxgKoQYzVTd1ubjr60Ov9ZRCM55CPHiyvVNyyj6kkBLl5HUI+WP7NOt6sopr3orvq1a3ezKAZ8V+7rafZVjI7n2JkiVnu+kwWX3xQ5C3qTGU5OvOCPkfZzW68NRrJFWmzQB1JMlq1B0ESpcDQJaqlwSxKeUMorSwwGklGJmdFdTz79dunkGoIEqneh2l9GxI5YEASB6zdEiWkpZWmmlLGY9dYiaa4lnbgex7lCwk8QTLBmrUl2WHWJtbW1x3G7oabzUZG1FTW7YFJDPQDBwkleN7nA+qCl0ZqtQAMvQQZsZU4WTUm47scPb7XZzKOylI+IFQ32NwWWbmVmp9duIYnSGyn3FPdhc8uAHmG1eOmAdFAiKY2mP+hZPI5d8t8Ltk4ddumuholARkgUzPGkDi4P/yD/FbORpia8+/tSZGwGhGFS8GoWNbDRDq9bW1lpbH2191MfbWt/QmqG6AWLAJwBoZ02YVsUAUVhUVz4krM8CzU64OWmajvECdwn5eLu69/Q6MfnoD67gL2mfMeL5Xh0qRMSQwuQkSfdIUrBQAPg6uMHRUGE3k7Y2hTR2Fog20NuohJ6PgwJh5Po1uAFSOMzYwQd3wjrnSsf/ev6H82lI2y09awBEQWJQX+cqG7m67Es0r4gJL4pAo3XHMGdCJ6YBxHe4q2a6x0cZSi8m3Hd2MiT+yW+dmYo0PeZU21rPX9kV5J7BiiNqoOcqaQCV9OoHISZyeG85jA52xQVsFvdMcq56coMcfcw6A50fLwq3fLqA3jtt7m3l/lLoiqW9bHrRJJ9c3X7K0Gynt18MEnzxtnDxywPY5WOhqxClu48BY+PIqMLn8dzHEQ59OYAPHbuBMwgMRvIXtw1HiSQRcY7A80XHK5nsoJPNj9GQ4TXtPa/r6ube+/0W2jbSPOuWqr7cNrieQ4e8Uc0leUK3zScNAMt8PJNvTV6L+cRdq0L8DQ2GS8DZEN4jatIMhMLcPzbjh8k2nFH9UKzkXzhC74+IRuRcM3RsscOxIeWzXYZdpTKFuKC+6eZuVRqCuDuAVFpFs9ZWl4Pb6nV+3+r68DRsgInQg+slSnnL+G5Hmj2229+VkgAIx+4YVzYpAn4DCvcr2m4ZT1mZ37MFKMaVUPbEno2fRISOmsWpjsKMbCbWysP1WmJEMV9Bkr3I+UgLhTPOI0GdEEsXDEBV35t0QicC73PE3AHoOa5Ht2Tqf8bzItfqQFJxZAvuCDTCJuKoB7n1qpdTSuvvHeLjUDVviqnHTn8+/2Dr2fGSqdEHmfUrwwjjiTOIBjHS4+lpVpv7M3cE1yOFLnmxWFxfaO2lYGc20R2lzIJvIHDEHnvvM3fvPbYnXNS7XeWTYz/PxyZCCnMge8xJIPMknQ9ozIVi2WPrRQTihm3djO0zTPaSBTKHzpKauyA5vSml1EH2J9APGhwkNja3RXjJmI6o0KCqX77c02bohM0dtTTFXHN4ZTMz5tjgiKDEI5TZc7JIjGEMxCPS5Pisf3Um5zEhS+mMCJMgvnNf4qTB50HGu3dNqtan5oQ0M83zR2A9U8q8c8DB+qYO8YFwl4w3d+Ob/atYazastgIRWmNrbbW1ejLn1lavOGhmxqqaVKkOM96b5+Txpcx5wp8B8+/teHWlf37SMl8V84Y/lACHG0Eeg+cDsI0q2Ndok14UY/zrurbWXOvW4xeks7aUTc95FxzAzD0zgC0KygOWQ8DCbuQcEjwSRhpT3bpmDwByWSqSINqO+nIGo5yszslFtihGfvq9+x7SL2T32/A/e8RHvhlwjhYwNLi3hNFdWa21B2BeSzu9kVdOChknyEh4p/Bc8Yzrcf/RCYtDtxxgIFvJ7cl7T6bik/D/BFMdO3yyBDgTY4I69KgWtk4CRpABBhyqelj8Sf/s0s7n2uL0hkl+DSLkNDhWLuhcwHf4SR0/KQ9rArdIzz1ZShS0V1Xf5P7xLmF78QZ2Nk2sJ+XvzVLgaQwGUybugYKcnIFnx3aSc7JyIrIsy5EApz77SPJqHT+wPzLZK1d2Gcip1O7zDNJzem9C8jNwx4owgde73FVsDDMbHpT7TbLpPGnXd7/uxrPrH0BPzYput/A4PTNDM2urWW1rNTZWjwB2hxVBz9PUHT08W4JJHdPj4UaRyQgp0Cij4GAsdnEdnyaSp2T1t1AV/z00M5NRjScfr+6XTQO640FPoeNMp+/WzsIu927TO21bIZvC1qq74w4BQ3w1ZepLx2bpvOpe8TgQ1EZiy2/pWugO8FR3NclEa/4zLSC35UQnF7XRXtJZW1LFxM2D9EBT3YT+x/09FaG52UZEkRwqS/pqb64vr7VODoM0q2ZD9mXzN4LESNQqvVzBtMX6ibhrSb+ILsd3LcUGhfYpS3gp7/04Zsh5TvC+S7ui+jvIPX7IsR8CHokKznQCHirj95RCT+YlIqQrrQsI7V4aSjC8jj2KjyBhjeGA/NHW44CDqMSMu1XZCXAQ/6DWHFJgp5TsSNOflbRUmR6YdVWzVwvOgibQqyd5ckqPGLYkPZckFbXgac1KqaFDs5Q8BGfq0yDJ+Xomt7GK6as3lDj3vKNYjkS0G4Q2S57vybSNpKCkbtMYts9+fEWR6CXZ+Y3cTz5yy8/md+WtlYedEZyJG828qqanaKvtsdb6qOtb86obdTUzsHnk4gYbRm+RgxfmuGHMnR3oqzc7nPzCJvg8xf5Mm1CyxVl/VDtlpEiu67qjW50L1O6lnGEAPcGOOPldFvPd6q5Icc+uq/xnPxHQikUqUzVOnWinlyObZqBd7wFORtgr/zj6mlfienLGmSnePHGj9MqKm6aivl9CB4uzbTjWkade3LjYs9pH7cMhRm11JbhlA0fJIbqASxNjZTOzZqyAeVyvRLl4MZwF1kQ7h7pU/infmW/WkR5x9+iRFn7ivZ9vuZ8tYr+UiS/7ORvRoB1d05ndgCBS1CBF3UXYjDJKa3d5mkY3xmHKBR9rnQC79JmxrZdk8Fl2BWwppVgJq3Boqzqkjm/Pq7djkeKp1tq3b98i+YY7ffoUPPShql6FKRNg1UnzwlnA7wwCPGatE6/dUs0FSCQ2LsYM2C7UT5kXeIekYrpCozv0sR3VWoqk2hHg8Xim0GnGMDf/RgL+2LrOcW6h4SMU/TDCvQ5wclcABGLNzFarZsa6ruva6qOttba1vr22+gBQhKCaNrER+QeAnj+u0QhyFBwEeyLoniYHCNl3irwiMmmzZIXExybo92oBPb+Ml/ruTVUDQ2QwjpQyOzLD1lS9+g4FHnPvFp1GgFaontLHYDcuZbVveY8cceLmV+ht+Uq633Qmq1TJOdsn6fW1FigxqutQvYBHnOcS6D7geLtoz8ODuR85/6l7L07pMU1Ipm0yflWQQLfppl8loWFuT0woMGr3JR6cqMs/3D5DZTd+0cx9xat7jSkxSuT6hFAAmcgqRttPHH8iUGjnHsTcjyPRYFUVuLN3BGFAvfIxe7RFoMeM3k/bE+X0p+4f+o+9HIwDkn+fJ+iJTYDurzBYPbK730nhIMBu0kQTKQu8SAMdT5kKRmixx/axRxd8hqVfcjROfICT23A+Qpc1i7Zu4vXBZUOoYMPdHylf9Ozt8XjM+6cJnLXUkbh/k4Srp8cb74rrkRAj9OGdE9/wE0ltYvvMi/G9QUrzV6zrmj/BuYc8trhzmAdqnoEjMZuklBwV3HA6b78AWcdHSRJurliyHUHdjfN0PLtHxEyKNDRr1e2+tT3Wt0erK8n69vp4+1brQwHPIeYyh0nPrudrAWsktaeJ7d2Pt7TrKfgOBuD3Pad/dXM4C2DIq/8bv/m8lVIGddkBSYfhcD/0fbGu0+1jalPn3pkBAioFFWurKQzyhAZvQIhafryBy9zaEeo66w2bZJ8AAJ5Kgr04B139goYoqyfo15n0gWKimregdGdA3/YUEm1UOx4FGI7D3u7KrqHhqJBGNpFliMWb3FVdJGATr2I+mgN9a02HJ1V6gXteGQm6F4VVormQji7cGwAFVdTdyq0n28rvjdCweb3XdkMkV+7r2M3D23reOGz/D2Knq9uu4P/q/i3a3FCr3dieDabXRdoNI/EQnPvCmTO3MEBVUU2KiLCX3YM48PSMV0P5/MmMQEspZV1XtzTEZ6hqr1M0PJtGAJLVWn2LlFJ0kEAze7l/iU5DeF2Wxfk2n6NRboG11mUp7h5LsiyDFyVdC/329ubScSS6iupMvl1iDVwiT7R208Z4PECrucdH/ORA5kd/UWZHvM/7/R5k3lJynKwDyDCqeuNMjIUxqlMBqE/jhI/E5YVbsIywifH5+7R/HBzSyUWajALamb2IGeAh56iPZ8edxPnxYqurtZW1WnvUdX17e317fX08Xlkb2MhWPOWCVTNCubJixEq6XcrNUEOTQVUtnQcyER21fkP2xRh2E9clDo0IsKFwH2+feuAjCGW31nLmsrDrZwfPu26TBwMDArtu8DsRcs9/d/yQZdG543rYmOb7AZDSWmvtGwAZAWm7rsLnI3+vibvKm5m2piMGYRGR1mlh72g7IU6TWpA99PQvgwD3owJWFolNqtpdWFrrCY39H4QCqkK0oHn96S4ZpfUCjviB6tSxF3n1hBKMyBGMwRBOIIFeiGB4TiDFxYXdOfu7OGYmBVaF7LUH2IgRvOjZQ0cBALInA4CKQCAFFNFS19bDZ1W6eo02LMNeN74HWjmG8NT9jucwvVk7dxMQHtB4FEjmep3TpPha2Zxc7a95suHqstvTGY49aZ54YDzTocvT84woZIYW8qf//M/H401Evn79WsriJteXLz+oaltX1YVsP//0k4i8vLzcyrL2OsqH8V/s05nHMUhgH6UqEhvoJGpX0ze6foKVZPhtJXIYBFr6mc3VKiOFZB4Y0OtBHl/E1Ea/NDPPkCXDjht9ZveKzHPY8PYKCWAQwqmAjbfEuzI+7fckarcb6hHn7r4o/x5qBkcc8y0XxWdOF3gEM0VPGwDdzfNHOjy5rTba2qzWuta6Wuv1fb26SUc54keKeMa8zR4WAJHl6jBvnBXQkLbh7+3//Gta/qjM98R5AOFuD+YevtdgvPC4n+cX1VpP74/tsBtqvojk0yRdC7fpP5/kr4ZjdiWowlvY4UZR8MF4ifXw3x6dar4vQc4jvBLf4diNdJmHIzbKu5Y3Anlitck8U15KT4so3e3DDc/9fGsymt5bXcqle/qrjpQMMA8f9Wz+U4XOTnSlF06AR/2ZMrwH/TkQ7KoBAiMeVTASAUS2Bz/GlY2HmoRMsmM1BrLizHhzxkceVvzT3OFzBveDSOnpC56hjiN8vrx8cY7ky5cvpZTWrJTy5eX2+u1RSvHAHS/pW2stRT6Lmnoc8Bzexjoyr7vf8q3eSimW5K0Y7maP5e9NUCvDyNpa05S7JHR0JG86KVzGSkFqMSSeTBejnrGIABN6Bkab4FK2CxwSYSheti+F863xvUF6Q6Te3R9ZSj6CNMVz4sy/80+Y86NZSXLebUaU8yJVRYV7wXeHRs9Q6sn109ba6sV9a32r67qub15hUJdF4RGcvY7pQIiY6rg+GA5WIWPJnaIPJ3/OvfR9pMDfogX/s7uetSnRMlI7pXa/vpnNuc1vv3qReL5bGIxeQ6b7rosCGCawNmTzLl+cgFB8WjoRorXVfYcBQIqQoGuKPTxt+GkqBwGzWWMVQjbPtTVosO6OIsqumvZZnVp3s2pWB1oxgmSTkXEzJgaTM+YoENM/TXrE6JxSso/8sLL+fx2lmYnGqScXl2kJ+GcH89E1xOi+zQY2z2EnMHQebujgAzewr+48YusFnU+2OP+oed6tYyLGkq+cL/r3a6f4KvFVn2qBNxzqchYBF/O8c4gIjGADPF8b/WC1tvr4+vXr4+1bq+3HH39c1/Xnn38uX7+ya2I+2mYcMLeUg8NkJcPDyCXg2+1mW4VS0MW+B5OweErSnJiV0gmb9eQ5fVqdqoUZOAQFTWvMTescnLtlOgHmqFPk8MRUswLvGVnjVx/nciv5S4M5iBFiC7scNzzvPPpU3aug+9u764QC2BLgcwn49HVKFXp+/Ul65TCT0UNeyhjws+0kZm1tttb2qPVR68q2slUIhZFf11l+UU+hJuFCBQCj+ifQc2Dt9v4RmjP1/W5y4W/XrgjwnIGDWiKu/xY0ONDl7njFL0Yu8Stq3TdhOIskMwHew8V0ybvsra1UArd4r4jbVhWgdR+LFns/6Z9xfRytm1Rba93N00bVoL5tyczPnU5LushRzHYzOcenxgc2puFIsiB44frIu5SfHP9v4zO9aJj17ME9DanH1WDUH9prVo5fsfuiXTvDtI4KNuxFRvjbL/WTK/3zs5E8v2EHV+/ev+udF8z6mKh+PpCkNVudsriNbF3Xordw/jWzWh/3+z+b2bquLy8vUb3tg22vgs4DisVz0uJ5Oe73eyPD9xiJLMWfkjrZLW7cP9RNGwzvhNPjlNZ1XZcl0JB7+HVJ9DCHTm4H6IiqFq0ydSkTRLLNGGlwHgecv6jfdptjOyVaMUVD7C6ucRqhw5sF9qfzbOc5v1JBRy5DfpLj6ymd03Tt9skT+voO6Z23GVu1ulptGFKdqpIN9Hgxr63ZXycqSdsc/UeICBNlJblLK3Ml+4bc/Om6eFe5Na4+/dOR9hctb7d8MgEv0eDv80oAwwv6lAaf3m89jb7Dntune15iAISrRhMKPkfE542AeT5F6Mhn2UopZkKvwZ0ioNz7wbKfS4SgCK4QvausXeVqNLGeMtbMms26v9212yv2nEkwY5b6vzF8Z59NICoqMHX9HIeGYOwySRqsOTOWtlhf98N75z0mQE9s6mmGXPcgJgRofgeRVcf7k4Q6A4Mxf1cw5zbemwGS7GVhMwLkVhrGyYo/tQF/LBVlVkukk09vjm2eEh9YEmnY4FA35OCbFhbPbQwVeABSEXq5DqEpqDT/180Zp++9GOhwf9rKhRkjBHW53W63Vu/3ezXzPDiJgp5IwDFZ0UkAAclw+5IkIgQB9oRZt1Lg3l6qNgCaZMFk0ERwGHn383Kmgd3ZuN/mzlZ5lnafP9ciAVP+HD93L/GdsSSmLpyx89jswBupaqa6OwKMLXIcc3u6jheseqK+uw88/TpJnO/utksE3Xqr7QEzgFpEG9gVzr34DsZWX1RtaLnQ/SkagLgm8l66zWc89S+hwb9pO7IxsU1wkIOjbVf8e0rAvheP14/AGaPFFjKx4VDlOZ92SoYnmAFmDKMrDZHxpjXXiMzg3e60vJzw30+aJY2UiMuc7NR3+lF2GuMcH4YbR4w2TjK2dADmrAKZp0u2dGtiSE/+2GGA8yfpx8MHtC7Hi0ivlg0vLMHwuZ2xzh5bvJHh9pB2bPFReai2DRUZnWgOQ0IC0TPqSHT/j0+0K2A6YiT/63O9v/fe/b4TK3quo5IhEy/L4tRwVxj3g+3SCStTzWVZnJLdbrda61Jrr1iQlip/SZBZDOZru8Ygua7r3BVDyw2gWdORMnpdFgAum97KjEsOpino39i0bgbuXoixmYc1REiW4eUVE+0/e4LMaJJCe6PFzPionMzvpf8LlLqD1Hjv8/aE8n2wxcAzcc0L/UGIv2qurrD6YG1sBmGBUBVS2Mtlek1TAgIVqBeQsUF9OxIq6vS4yxmHt2fycCQJf78+WVcEOM6vSO+TK7+mHXu7Ir3RlB71qOgZypoAHiAm8PhgEoSou9SOF+34444N/Mc+EoGzy30MCveIdlQkIuL50VBiXy+l7JZ7TOAVAzGDMpwFdLXzIL41rEi9KgPZ+ROJzmPDThmx6yrtmRkrU99YdHWnTgIjS37nqedho5VpVsfwqKOQgIw7JRfjY5Sb+kTbjd8OIZrxk6qKaABLpli7D8evANfn0Hjs9nPoUaQLBieKLGdlZkmoedID3ytQHMVZXRVc316LqC63t9dvZvbldheiXnz51Tj3RDvuy6Qo8kIv60gSLbKMlFLPEQRTz0GAMVTBzkRI+Ex5fXYqrLHVWtfpq5W6l2HUJOk+k+HDHABEtKwLD67f44+PBNWJt0cDI5N29NwavbNe8K+UcquNqosoYd2JdxCQRtevm8gmR8RhinbOUJt5m6i5JxcgTD4H2SRDadXF0JH68Xw8J4/ncfU/BaQ0IYDa2luzR2u12QMeou055gmIwji4H4gUiEEB6fGLpAhrL4wnRUVGODlnLRfBhUpw0uARTq0i4cPyd9E6T2GUXM8qKyQBJBdZh2qTXqOpeI1VIq4cj4MtT6knnhwBuITnwavpWHQ5r9lrrnIoiZo6qI/yr8DYaBY+Ux4aJFLI5vGmHszi5x4gRC+ozqYqAhFjD+0V1z43iPTybNrIoSfjtA2P+SOAS7WJmZj1Ek8iEAoptv0nMhZFIW2Q1dgxrrIfZX6GAp9kEbSuoAdkhMsZuy8zCKOlY18oB2z0LJJMBDh90DgfscEDSOKYN4WSbbimNi+w5gskH9CgiEiPCe7lU0Ga6oIe8GpeDtHDw1zLRXY7UjqPb+DQbkzG6AkMc3vlarSnMgM9f8DHm3s07N7g098vur/9CG4kFi1NFdSiN6TIVVXWWu/3FwDfvn0DcL/fm9MKoVJsdwT2Vygm7MGvnkkuCFh8HkeGCg+Evd1ut/Xt65cb6ovQXl9frTWSi8rSU3qYiFJptGbdM3kRvQFIPu4k7/e7iKgIzWBSii7LskgXuG+Lgq2ubwKT28uyLI/H2zLfgWUpUF1bU9ALU5gYCIUqGoC3+rYUyuK0eyREFa1t+lxkOlRKobsMWU/vpqSIlHITVYJrXdvaDCK6GFajUBRSoAYWG9GIzdaBYiT0L468kqXFvKyu72QbzEGmG6PMCtzJUcTzx+JUf3gJbwKNZDZjJC3YpgHumeNeRz0Jku6H2j09vcyqmMstZmtda21vP3/7d+FKmKr1L3ZE6em7ywzS7TVq+OhVBcUE2qWmAi/SwClA9aNn7t1+1iESGugTTrbk/pZPjjJl57Q42cejXJjv7PcP/Jv5Tno0aJrlOS6Jj98NeuoGhxFGRMTjNX2tQahACAVabZ5CGF58SsXYWSGHDYj/J15fOh97jKPbU/tsKUAUBQyq3cDmGYJ2R4UJOSoZiBNFwGSuFSYAj5opdBEYEAUF4suotDj6TlDBStJjdPuU2so5OUVEhqbMJ3wGOOyWZsxrRtNa18QWOH0iAaxvb76ayQtSoSamZLNmEJRSivaw+F6mi740TnxEpBQxQGG0RiOhUAhtEt1GDzCiQiDG+jphwVVEk9BneI5zGy9GDwOiBxf5qDy75bChk2TrCeTIRAXRNiltUjY056GMg+VSqIooqVARqnieTDHrxNg14TICzs5YPcxzMQ7NOfJRYhDEHK8XnNxGOvnJFg/0kwLDoSzgszbcabZXfafHjEciNACojUUWQNtqABQFxON1JSnA+vYKYFEBYHWFoIiOauybI1vzmVSloqjHaCsW7dUNNyUBsA1rCZ9el4aXVpbRcFAahGrCbwa6E29cHIqsflvYhpVgr8cppJJipmbWbBWjyhLEO48Tw+PJGQX3Rqu13m43ETFbhiN0D10ANm/fMVzctvg6rwQ+jq7nUVDHcfJVgjJ69LppA2pPOXQx+5jNMhQAn61fy1FbyzUGQK/Yeexjx3iNDAPsgjwr2dDogRG11sfjtbZXYaMHgQhHvV7pgoCMUjQzI6BBar+NOBVtd8qhD/O3HY39DsLvDgKfSxhPnLwC8cY/JEPg8ahD2BoI2bFuGYKHiqAXL0IhIgy7X5cN7nnuMzy8i5HXTkaQRtDdUOXlVSr94v7IdERUkhWhjn8Yag94kj8R9PLa/R+Atp5wRUj4asNLUdk4uKax2Z25PF8VD72NqAHt/I3LNQefJoZNN6+UdM80f5EbeqUz0r1e75jA00Fsm2eP43sP0LmiNvgDx299aHKxLXqlZ84enh0duqT2UX3oOIvoXEF1PkJMubRTVHndDmXCr9uHOt6Rs8/5lHQvaEkec37ULsw4JGIINssu81EslcvE2FJfl5Ud/95uN1cj59w9iXXtDlAW5ZJGy0CcFd19e0hP9+gbICh0ud0mqR764fnZo4daqzO5OrJ3Jd75BHXGn5kkd/rTP7xPQii3h0lpM4BMywWlR9D/BnGcv6wdBYWtyouH86EkMOtTYmbW2KxZba2t69vj2+tav3FQkmCtfo/v+e3b8UPm+h5sLr9+ld/tIcZzPMl/vjv/Vy86vd4FgSA2ku+8epHhXEmTtAKIDqaLxm7wwQHnKyKSsc2VwDBpMLsa63nPJ5+QeNRst+aEcD9ON5H86oxGtq/7zbGBjEibKzj5B2vsgv7fe8s6m7xVOwGeptORV3lZlp67cYC+P+Devx4Q3A7qXCQQxAEpZIIn2xYDDSJXa+VImUTyfnvZPYWECn0ko58u03OExAxHLZA04Y7uHt8efEPMTHKY7PfIZjCbr44PHDTYL5wlHr5gxL9v+8gO3JITI2msnmrBFQG02lprda314T7qToCX29wA/9hbPbcAm+/CYH2cV/uV1PcXDw8Jnr9v57H74y0xsTvm2E+yaSDPWOjhNoiI6tQ3I5mnBNjc+etI+/NLZarQrGsiDvdkxLghwL/LFvkvQn3/RO1IfTsB1qUYqCwU+D+hKYoUdYuHUxvr9SaFAimqSym3pbRFrSnYWusalrC2BnniBu7NTAeIB2ELpb8M5cmgoDXG6nceMzqZuRTeVeillMj4GqDvf5aiJCnnNoMjBR1bKIUoHOyIuz8TXnZLlTMBZQSxDRpM7f9++92x4YTGdznLMD8z4QthLyYq3a5pQkDMamu1PtbH4/Fqa13X19rW1lpZQt8luUjGn33fHyEhru/o5S+jwTsWMHVyPnNHlDpOzq9/dlSn9x8l4OM2ObQrfeBBAh6eRLsZOGUm4r05d/qOAB9XhL2ganP93ejfb3k2PzvavyW6kra5m1mG2bV/nm+FUMNmEe03d9ePXbiHk8vPtXewUFJQPO3nj2n8jSXg3xqPzSr0QWMcxPOVHVsqIynH7XYLhtTjekPulBRZhMS6qg6LqVPfHjsEVRUV9dJmIWWKePJrDw7OKuUAr7HxumoagEgh6fd7kAzJiNY9xR27G/IrYgfm+4/szA4xSTK/DNIbSQx+75iZI69wjm0nl9E8LR/JXo+Inuv58fb27e3tm9U6tCPs+Wm38/AP3AIXPwGPX9Dn/OOpyHIl2ZwSrfff9ZnhHb76u6GmYG0Db+ywR26qG3EzE90TAgw36A5HqjR7V/PQpYbrWToS4/zSjCd5ojCQ34eCXcHJf7c/ql2BTQ9DyiRztxkyhLlZNIy7AfSq6krgTGj9KP5ghsUkAXsmVtUe7BsEGH1H9VjeIKjuaZWZ5VA9ef+uu8YgzIJC0uOYVRdV9bxxeW9kor6j7rJNfLGhu0S6J2Mon8Mh6QLSg+WD9P6uNHiDmtO8ZbfcSXp7UVXrMYrNhhsVO/X9+dvb45vnLlBI0WkaFOkuuf8YNDjTV29HOpSv/8o2O7lGmEesKiLHB35TnHtGVI7tSeajrQTsxRVyTUPVQD+7946Tjao5jja8YbcUceZ47698Tz0gLrx2+RVwTAVmwyrzySxE4cdIfIsh+2YR7SoT4vdsp3DyD7Ep/8QtRMrd9X0ijiC6TIJv0EuSpfQcT+6l5ZLQDh9l+B5q1qlzlpSu3QlzttcyZZBBog1hhd3tpeAJgnWN5Bgi4mW9b7fbsiylUFW/lBceVF6nNPgU0eQ52V0/7up+cRYGzzT4j2mnXFhuiRg3j+txF89aH+v6tta3dX0zs1JUgxniJPP/GNT3d24fmbQL6ntyw3ccw1EFjXdosLvsfiZSbrT8J86qMO1uiGP+M1/vf4EAM964+tgY/5OZPPt2VzJrxDdH7Cx6tHUT0REPPU0/v1EL3voKTv7BGn9rFfR3mrwrorOsXlbBhUszJdVsIR+PB1ujx8U6F1nKbVlqe1AF0KK3e1EvV8x1laWgQaRXWYB7Dtd6Kwswc447dItIa5Wk9uxUEgT4/uVrikieNFVldaqf3bC/fPmyrr1OcLACpdwinwaGA3Nrzd0xHo9HT+OVpjank2Tk5QEAfPny1UVqv8cF/VJKq1NP4LaxLY8Tu927Kq7RNYvcqqED+EQcGwVFb8GA52Me8xU/JAlJefJ3P9f5lKe0IGEqQmOr7bG+1fr4+T//9lhf17eHteZu656cSNRz+7C1ZinTfPEp7VJB+obPxDH/gnbKaT6hGZ6ytA8tnWRt0PEkv8XvPO3nOLY4butAz/tbQtDHVX7+4bu3nDLQV7TnigCXokd5dmK940Nh/tw/MmTK1DloouIq5+h5VCgq28cTT9/jJrqPpFdFSeQ1m8ZVxTMcNEBLKYCXLm6RES/YdwAk1nWNjvKeOkJCH4w7fdO0eC0EgmY0tuZFgsd1t5//quIE/dWHaA4ZHh4qU7uWBuxpJa7Cad4djxxO/oBmV/H9F9T3yoRx1f4oyWHJaylJvZYFwR30ZxzkxuDQ82RfRJIGFrCX7Dx0vhuKJI135hSiw0RKVUSi3pGrnT38V1WXxTwI2C3H++ySdmLQPUIzEuJ7IhBftTRpw+rznVTQgUGfYOedxDAvbq/k+4/XyWbWmlWXfR/ra60PswqhKLxe95MN8F+tXelL/IQHsvq7te8rKH+gZSCfR9X9lVEMDiIng8wztsM/+fq7eFNLD3/w+KVI7Xd+t9gnwdl9r2TG/xJ+HKWCu08LukH605sliOu88tku/qGbiMifYUp21CSuLBZJCj1db1GaCUSKqngunKF66vYmASCqbtUsy1JaK2ZlWVgrUhbyHr9iJuzpkPI283AmntS0t51Z14/Ot67r6sxvfIb301p7e3t7PB4icr+nWoHs6vSwJWMkQM+EVraGveeiDA9t+/t0tiS9XgUSDQ5XrF8INEcCfDXIPoyL6yfdkgBFDTSF272s1kd7rHV91Mdq1ghTxSJYFCrkJkPofzfggiU6kWN++2F8lmv8fo1DMTuPA1IOxwFBu6Hu0vvsX3Dgoa9mVUQ8WaxjJE8rrT2emF3BLiOv6WBWz954NR5XMocxnoOdbYDk/Te22J+AWvx3++4t090NAQ7iF0dXzoS7UxaPsmAaN/c00cuCUc13HxIwpKR80eOJjir2Y7nAeKRGEYgNAZ7nobiu1ZNFrPU2rdSey7QwF3U4YUnwYbqbJ2c34JjV8aLLXfc5FCk9LvfIMTxB8Zu1GCeS1JLYrrIIKHSHNrPqtX5FqEWEoopl6RoFQ/vNjVp/qnakvhmAf09ymKGaW/vT1f0X1zPsHiWx/VOjoO2e0A4Ctr0udlrP4EphcArneccdmtW6Dq+oD7IjnxSBO/XdDOBUNO9o50+iGv3v9n3bkfoOCXhoj3cwypHdwlukoXBAil5CwfvlyxcATvmOrw/8bmYyipLmkWGQzyz+7jZb0GBLSak86tcLFZuZF1nyG1prHGkbVVV1AfClqGxppCTl+RGB7shtsk/v/Ud2000SUJeD00QoiOSTFTLxr22nWMD/H3/KByTg+MjWGpqF2fh+vxubsohS1W337un2XYb/p29H7u1UFP49x5NHEoD6+fF8ngCc24DP+iGa2cjPJxmGtUxNEjZ8TE/sSmBE37ogeyahUtd1VS3ZSfNqz/7iNuKM++BH57aTgPs4P1kuUy5U0N/1E55bxCSd/GHeo1fNxbg/ehS/vC07ZjNJlj14jiPiCNi4nAVAu1fU/X4nua7rXiyzDUlwAhy4fmq/vUhna9baTirNjPwOj3SmUuR2u8VPrUV+jw3hdJPwfYQ3y6HhDI1muhvyfWstlNu7CY2BhczxHOV9DiFK7PRzrWa8NI5yUtpv3+Yj0tr6cLtva83aw8xEqCplKWaeHlhC8ayndb3+C7cj0b1a3+9LA05HkmE4Tq7Gcw2HnxnntUvAJeyhPYGhIx6wbcard7dYuIyE19Uvm/mLp2wwBHveN2OVv+P2WWcU+zukwX+KtqMIAS3LDo6jxXUbhXX9gXAYDqHZYdozU+4cjAVQFdhG9jKb9deKTnnUCSS4DzTK78UWuGNzqkq4g0UGEReL7/f77XbzgSFJtNHzbqvscKhtW2udCkf838mOpspJLuhniuiPNx/pjj/ISzZHsbUacOuEJek2STc/3t6aPbomw6qb8PMy+UMiIqBcFoH7L9eOqDYDUlC+fPI7jOcIFZ8mwJsf3lNBd9H0M/1z3nDKU2I7h3Kwiz3rPLXgyB13nSrq4t53e9t8wUEkwAGr7L7ls+03loD/xM3R4fH639v8BOztri9GUVGi/xPR4ZXg9h96WDphhBpq8Qp/s4qRmsEMpVgpTXURKdITsLmDRRMR6/uShWhextRERIwsY1NVmramqgYWFGKUGQEI+HVgVOSdKvONtFFKCZnvnpqXMnzXN31HfQGQbfuPZNuGPewfz9owOQ2Bp0JGsZrPcJTCDnHFi34RBhZIP4qIedUntIu6SVu4DNP8yGklWNe12bquq1n18CJPnOKly9kV9dTJZ7WNI5aYUr8jp+x9fxBpKT0U7qyu7cXRq+2M+rupUilgXtYm1Sv9SDvFsKfM7+Unx+iQjmdfsHEnfncY/LS9QwBSIdZfc6znCppg1jfFUAkfmp6Fn1GgJjiYwJwdl0OA/niux9dufapx4mUtFmVaOPIIeZmWCwL8OaztmOiUYHckmW1DXXF07iV+cXynOU+d++0v30GL28IGLk3wg/bpbepVzk4q3p7VwAXVPumoaQCVXhhSTEypDaZUio3rxa9DALH8RXE8xQG/pMlIqbSvri1n9baHmHqs223mhYapopQ4X6hqGcRjD6gSQCni3s4iKIW8F0OPem0wGklF+XIrMGHD47bWpVlls174b61VexXJPrtAt+AsZbl9ud9ut7KoLgUqVEFRI2FY2Rah3pa7Fs8+DcGipdxvy33RW7ktN1W16oytiYhTWY86UFmSyCtRoQFl8tohDe+2ekYHzVZRlsVLd0MaRViKPB5rMkVPttesX985o0UwcSlLnwKi1TBS72l/5qPzr6L0oMU4qgiBuq5Bq9QXsoBAtdm/dEUCAXiWKzYQzS0AnsLLyW0pIlKErLWttdZatddg9CwqzcwaDWLuhJIYshGXIQS6y3lkyAVw5dz6hDeinCDGUaHv2I8ECqMjMlHAREucs/8q6L76zTXrZC8TTwJl6edAM1LgSZvQ5nYZkyoi0lo9HbxHxGXywR7HeYIPTJRGg6qrF2R4KYoU1ebB40BRdZA1M1trJqu+yAAiG4Yz0hAUFYAeKZBu7ifLchsKnh6u48VwW2vqgf8OWuzHolBAxK9H3dNeeu1kEc9twMZSAANtJJOP9R3abOl1CvtoVRJb3r2s03zuj1/uC9jz6xXf9a2trWmeXrP03vPxn7fhzOExA0pE2cQZJj3nWEbhPEu41jNlivvbDG7Y7cokyUpxI5CIoykviiqjxuv8XAAo/kkHYq6dZfcr0o/NremyR4l5veI1XfIWDvbU8rGIUChkPnb55XTmLjQBzVYANIUaTEUNRnUqThNCxWDQQiEoprowRYHFUYucEuBLzUcaZdYa6qwdafm4tvbjX77+n//973/56w8C+ennn//pf/zPv/30H8uy9J2yPabxcPjPgMRSG9WrA6sIRdAj3MWlVE+tKITHEpHi1ZU3Bk4C8MzQX768tGaADGeotiyLD1qkV6bVLtoayqg2qOpcMPpOZ3MGQzqNKMCj1U4ma3m0KrWIiFILp0pqt8BbrrlP6Cn2ka1cMvWEaGOroG+Y7jCyy3oxn3XPtWyEzn3u3gKgTeevvTUrsDyGYs+sKRXSMlVmto35OTvFo0sBB2DrkMTAIL3mIGC32601iLC1xjYt+s5AOAel09OVQoi4GImRBt5iPHkGROT9zO+HlqvKymGTbGRv/2iV4edyPOrhiGVZiGaDhxIRLSrBPqS3X9VRTl930uxQaTvYvpOPDY85X0enM+Pct61qqimalDrpFUf6MbHocZx+xS0rUd9sDEj1QvDP8094LZYGoq3nCO6UwRrFcs+mYqswmHtn41r47vGX6Hs/0ZLQs2v5xT6FfqWN9fTdD4jzT8NV07bndAP2Dlr2aC2YeALsE92RbeJTT6B/9LbDilfwCYwMOwONx7FZA9GwOXre/1N25irMbNEyxldOjgCgsgggsghQoJ3r3x2b2QlEXLf4cGyhbvxsu6Mo13Vdbp4XEvf7vbZHKcWBGoGOxrHZfl84t7Ws1hSkSvDcCIbIaRBVWPxXNGOzU527llKW5Xa/31vzsT8eD7Oqqhg2S+XAwsB9ud1ut5fb/Xa7LUtPcXVbFra5/M6Gu/nWN3CcJwu0ZRQvIqpXmivk2zLMhZGbexMdr1ruIxaMvSCxi8V7AnycNG6zbu14gtyCqDupZpLa97CyF6bnD7KRMYYe44BB/CvWdbX6WNd1XR+1VrcZIB03tsEDo7N13IsbfqHXdEbRXeGWCICEtBFMwMd7FqGpNXMpyGuCiGhnUMf3xEnMqgNOAqQrQuXJgTNq64+/O7A8q9mpEBlUUhK3rvvw945ggl23mRBmOMnrdcVMbL/r/HuvJIzTPj3H8qnj1g625+vew6R/inbAM5fzufNcCYTwJE769IcnK+pecBM+QQgaDjDv5vPvZFt9wrBeXd9xCeg78Lz/q36uKtAFOsXZ6hxbKeX19fV+vzvAf/369fX1dVmWK03Y6feKyJIllQD33ILgkRSI6wGOG8NG+mUvkeSZMWqti+bUdD4IR9/LcUBMiCa69S/0OOMdVnJC57cnxCQijkYPN5MuhmfUE7R8N00kRdBaDdWc11ZyL+tR5Ztx9AHsilIcqeNu3nbLEycx7blgc79hO8j8YBw7AAFmgz5she8jbMmUtns95ubk9/Ford0W5VD0zamVoCzIne+Of8dNa10zD8eR0/Q0WdLgaGQHWrhGKLsViXaKIEgRLfm+eNwZu927dmCUwbjZMwJ5hDd3EsaWmzQzXkh4V+0qydSFBHwiGcxf03gmY/FZJ6+/r6ZA40EZhovvEinZ2TPD0vn3CuxTGgj0+uh5GE/4sJ3A8G7jdfnjKzixi9S8V+t7xfBdmbSeMOifAiFVfTweP/zwg+eGut/vP/300/1+v9RwXzgMLse1zOsRNLinS21mMC9h0pl5Oh/LRvNSwQXLXdBo5fGGhzzqCpjjaucNSjePmJPqm7XbKi4Bl1LEJmjmyAEPdgrhDGPq76PWGCa28pGH3UQAc1tEN6KJEW2E9AjERFU7PAw1APbgntok8++uWSaouYuA8kU3qDDfH9J/WpdiZqdK5VPX8RgiYUP10BVcWxo8qLR4OFlbV43xRH5Q78uDgFW102ubUvUVO5++W3mpF7zYMAfZt9+d1HrbXi7iLM/xtrGZiNzKTQbPa2Y0iiYP7yQJj+mVLAQj6xl241HNKxvrfkpHBCCgjCQ14uYeEGgnSlYjPE+7i0boNsdyUM/H3Nou3j3G8eXLF24dF/pP1991ev1+P2GscQCJflEgPMnGs2uZXfiHSH0aYqSDBACQm217eo7n1PdpO59/l3gPkYoc5VmxnXzgo66IY7hJe/Sxdr/fT6/bWXpEAOuFxHnpU3LBmJ5yG0c+KfcvSUPzrntv7LvMSAFYQqkbCKJLitvg/f4+VRn2wl2srWtxnYjebjcA67q2Vn/622OH/Ij9Yg9q7v30CHwR8a48iMgJcHwDh5+kbeEjUU3VrWslAHdg3gnZ3uEO4OIRD68akwMAKjSzdd1ouXNvImKHqk3O8eXx+CMqm+XPJ7nzscastXZFUZrDaLJt/QrEhaUDPJ0kp90GXLUwCo7J9JH07kXVrO0wQiwlfsvWw+F+dTfLcldFMDoR7f3kmd0Mx8WrcZ7Oz+krBIotQUrwfKKxzLs17j+VXaK9vHzF1ttgt3FyzCGeOsddfe/p9UsCbO8Q4P04/8xZF3AtwWcRggeJ/woMjk2Knrr7noKEKVRoYrlnpjIeOMx/uZj/S9PDBcN0df+in0tU4rTm2K7n+cJmnyZ8t1uvXn2/373WgBeed0rx5GYc1lFEljLic9wTyv138+snERYRUpbFw1EIGtFAAwmagCqEqBYRuYP39VHr+va6DMNhFxlcEFvut2VZRqFA0VJ0WRYtiq6CU1Wnvvf7PQgwySwUmlkvmsLhOCYynMgoQtGZIidzFZlCJPK2WYnx53xXKZZCcfbONd5cat9tJDnLNYbOaZ5LwDG8nSq7lIK0VVI/Gwm7M0yAGUQw/IhCjIKHVPkb0sfKVtneNqrvNOw4kyF47drOtj1m+BdS5aPsq2k8mSu/MjJfbST3Uk5yKkUK2RmdeHp231GK5H9XkwBAUKYg6yDR+zlVOfZvPZL2XL1HUs3QkG5FRFAw2MLBnOzJYazCDrHmKkBbUDxHWFfzaZc5k886cT3TNQE+IQMXmpKL8fxdZ40ILgpbeWu31/K+BnC8J7eynBOw0/tVDMJ2IMCnD/odVxLwp1TTuIafDIenbc8Q6LnG5bNtNz/PuVi/4eXl5aeffnp5eSmlvL293e/3n3/+OYoB7loWwPLJ4rrlnD0jg8VufCISXnbZKh5MfQBH11ovZbnfZCSL0bSlT6kXgMfjoYmEeMi8iOwk13j7Xe84SELH70zjnFJ7Hv/VeLKocZwQHFYuO03kG05V0AB2YUhxEjmxdwO43+/ETJcdR1/geEsQ4Le3M4CeSuy9O3fovUspMM8H3kOPTqbFTA/4IhYo9+xyVdehfI+2IVH5+ln085OmurgmIHREV5Dw7jCu2ilPffpgAUKhthuGE2BLiWPRVbJ7aH86fv35558zAd5MY2oBSPVCxXfVrpxQTm1+v4gAn7cnksrfbduN+YiRkKAlEAuekocr2+o5chNpwmVbjBVn6EuGBGXrZ5yMrj0IrjQlp/Hix7fEyaec/j7S5GO50wHc7/d/+7d/++GHH0opj8fj5eWltXZFgN/e3nCmoVxeX18xRCtX+Xo07bqunl3y8XgA+PLli4jUdfX6mr3wEWlkbc3MfvzxR5doAby+vrb/+A+IlFLu97tZ7a5Jrn9nM7Pl68vampkt1m63soCttZW4lXsUDw4tqPeJBA0xv1//17+8vLxgSIHfvn2jyV//+tdh1CXZmoHwcMby888/e4asZVnycobHk7f4ySfOnZIej0et1RpI3u9fWmsCDzqaCvmdfhtbnJ4Jav+5V2cR7z9qLDoQmJmzhLfbTVVJq7V62EwQDH/c6xz7R80B+Lk6ye+lU0V74J8bg0VEC80XqTU2d71a13VFFLYycycstzxEwn1RLiq07lfiw46xBUOWbPntigCrTu29P95d/7I61A2ffr4UDAHHp9Kj/Xy6jhhkhFHtmrXHyqFjz+nSPJzmhBnV+U4fb8aMvdMECe48iAPu2Nm05jhtzwQECo5xSoGbqIVqUdUHAJqOUiU///w6xzxigiHm9v7ceYw84F+G1z2wp3j55rP5xNX67tiOfiJC45UXNLZ0qO99nXszxu/s9OlrP4uGryS5S3Qcm0gVkO69mFLqquoyPFklxS/K1FL0jvzy7vj29i0gM8eDhAokyxIi4njyg+OnWC1CseNtO18BjAGJnUaN7dc3Bvblkyri5fbMh2DHLwKo6+P0/isCfJWO44qQ767Hxnw8Hu6EZWZvb2+llL/97W/Lsnz79u1T41nCK3K3Fb9+/eoE2OHGCfC6LNZWMd5ut5eXlyyVvr29kXx9fQ2/2ZeXl+VW3t6+FRl7ybsnnGBgxJ+6CNI14KUr9UL/6S6pTto37NiZDCpDJvjpp59izdxj2T/kh/tfPGNlUFmf0x9//DE6yYvtCHRQFGLIrK+vDzNzj+gdWd2xrjsCsIe8kVSqjjY2M1z69wedQ3KDa8St+vWwN/v5jgA/Hg8nwGbG2ow9H8h9mc5rCQPy7e2bF3astUovp7hhikVEZeATZemZRjvYIFXEkmS9Hvi6mGXV7mxHW6k/taQNnNVfx0QcfuXlr3/Ny7EDlWNztgCjrtdRgQFsyHBYBGSr2MikgoNVIZnhFmcYZHdSezj3hmlDIsAOCTHOUNlx6ipVRH78cVsCpL/Avn37adC+zWhHLe0lssn66769/nQ6b9cJDT4hwVDMzHjmVHj86rEiGyDJs30xnnKVqerJOD9+/48//sVPnNAGAV6SZvGm8/w1EYyAMW4VMPE6EfmXf/rnI1VzTCtblbUff/jhh49/VFPDUp7M/+7bVYS1fYoA365KL1+0+jhXQec9iAQPweDu2tNUo59of/nLX/KfsUABb9l9xMz++Z//6bSfK9X68qgrBgdXaJ6TppTy4+0v5bYAaOwmW//mBkOBJNDpPZUOfFSRpdxevpBU8N//z/9mm3I9AFDNTAfKKMtyu3XRDUZPu5Oxz3Fr5c3www8/vLzcx/Xiqs4ffnwZYvGcNSfDC+4uAWcxV8SFZg02Mwbw008/+eBLKU5KXAJ+fa2qqjctpZgxW0x36Nu7CpKJ7caTpb/Ox+PwFHaB1ppfcU80s/b169cdAQ5d/RMJ2OGDbR1P8f/8f/9bpOdGkZ5hiRj1rLx/HXy9M0kifWWKYlmWUlQLhEYokw7f8/xFJ1sCjFqvlFKCDlYhGYgn+dZ8S/CwWcWdMcIQCDe8xTFYajxpkXrCTKhO5Dmc8vpPow9sJGkm7oeJeRD2KAEh8be//RxgsDuZo0gItNoQc68l4ABdkrWuc5yYcN7HuSGHnhHM0ztsqG9u8VJvOwQULUN4bldRRaecB5Srrc8JwH7etMR5HvMVAVaeS2DfiwCvj6mZ8xQG7sf39vqKYCKTQ2i533CYeZw5afrRNXB+W7C2ZhblZ3YTteITKmJz5Z1emm/zSAAIUULbsm07eIixfdY2fEU4r/r59nYu8V95U1/FAV+1rHnNJy6d5oH5ury+XhDaC0ZB/u//9/9BEOBSnAUupfzrv/6rf4NDwMvLi7jY2maiiSwBu+I6glZdDmarP7x8YVtdBT0IcDOz2wC4ZVm+JAJ8l1sQ4KwTc4l5NwuASbNMgL99+0byr3/967ef30gO4jHl4PVbCwKcO/y3f/u3eGm21379+lWGCLuu9fF4OAH+8cf/oaoqTvA63cpeS/He8fYLE8sgwKHijg93AhwqaCfAj8fDWKOHGG0oz1VTmSaRZVmcAJNkW81cCSk/vHw1MzcnK937t7bW/q8ff3h9ff327ad1XYu4aZkkW30EGb4VDxsrWgBrHKGNMnhDF9yzCjrRs0svxJ306Z92P3hF9uQyKZwgE2AnPFktkWf+2DhcwQPYdguUnyVZbeaKCoCJY9wWLatqMzrLiCZv7yDAu5F/igBjbNsjATY+tgQYNhK1l4JlKb6RMXhfT/12OtTT+Xy8vWMzzpNAscp6qibOyzVEdmDL6WBO9WWK03IRlnY1/s9et5YEGAjJWmut0JE+T1VCBQ2AOv1AE/7Ely/3jCui7ZyediQt0+BOgN9zYtoMXuwtFTTLn3h6rsBdz72sx517xuizXnC3ck6okgZxc/2zNobPjie8l3cAEPgt9v7487yfKwbi/wfCm0XAUZt7yAAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, - "execution_count": 79, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -632,18 +596,18 @@ }, { "cell_type": "code", - "execution_count": 80, + "execution_count": 12, "id": "c19b0460", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, - "execution_count": 80, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -655,18 +619,18 @@ }, { "cell_type": "code", - "execution_count": 81, + "execution_count": 13, "id": "3a0cc9c5", "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ - "" + "" ] }, - "execution_count": 81, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -678,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": 82, + "execution_count": 14, "id": "74c4de2e", "metadata": {}, "outputs": [], @@ -701,7 +665,7 @@ }, { "cell_type": "code", - "execution_count": 107, + "execution_count": 15, "id": "5ef0439f", "metadata": {}, "outputs": [ @@ -751,14 +715,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "id": "53de29a5", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -785,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": 162, + "execution_count": 16, "id": "25c72ee5", "metadata": {}, "outputs": [], @@ -817,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": 163, + "execution_count": 17, "id": "3f83ae8d", "metadata": {}, "outputs": [], @@ -839,7 +795,7 @@ }, { "cell_type": "code", - "execution_count": 164, + "execution_count": 18, "id": "3729d4f2", "metadata": {}, "outputs": [ @@ -946,10 +902,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "281047ed", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([45, 49, 27, 7, 32, 48, 19, 38, 35, 30])\n", + "tensor([44, 37, 21, 39, 29, 13, 8, 31, 33, 5])\n", + "tensor([34, 28, 2, 23, 15, 42, 43, 40, 22, 6])\n", + "tensor([36, 3, 46, 9, 26, 16, 12, 17, 18, 1])\n" + ] + } + ], "source": [ "#构建输入数据管道\n", "ds = TensorDataset(torch.arange(1,50))\n", @@ -964,17 +931,12 @@ ] }, { - "cell_type": "markdown", - "id": "e2856881", + "cell_type": "code", + "execution_count": null, + "id": "1f75b5e1-ca5d-4ed3-b961-c82ccdd38627", "metadata": {}, - "source": [ - "```\n", - "tensor([43, 44, 21, 36, 9, 5, 28, 16, 20, 14])\n", - "tensor([23, 49, 35, 38, 2, 34, 45, 18, 15, 40])\n", - "tensor([26, 6, 27, 39, 8, 4, 24, 19, 32, 17])\n", - "tensor([ 1, 29, 11, 47, 12, 22, 48, 42, 10, 7])\n", - "```" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1005,7 +967,7 @@ "formats": "ipynb,md" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1019,7 +981,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/5-2,\346\250\241\345\236\213\345\261\202.ipynb" "b/5-2,\346\250\241\345\236\213\345\261\202.ipynb" index 30f22a52e..b4474bd77 100644 --- "a/5-2,\346\250\241\345\236\213\345\261\202.ipynb" +++ "b/5-2,\346\250\241\345\236\213\345\261\202.ipynb" @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 1, "id": "8cf76da1", "metadata": {}, "outputs": [ @@ -145,7 +145,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "channel mean: 1.1920928955078125e-07\n", + "channel mean: 1.043081283569336e-07\n", "channel std: 1.0000009536743164\n" ] } @@ -171,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "id": "a2f406b8", "metadata": {}, "outputs": [ @@ -314,7 +314,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 3, "id": "74ea2f3c", "metadata": {}, "outputs": [ @@ -395,7 +395,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 4, "id": "27bdd558", "metadata": {}, "outputs": [ @@ -475,7 +475,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 5, "id": "d22c1c29", "metadata": {}, "outputs": [ @@ -650,7 +650,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 6, "id": "7ce71a78", "metadata": {}, "outputs": [ @@ -728,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 7, "id": "ea31566a", "metadata": {}, "outputs": [ @@ -738,7 +738,7 @@ "0.75" ] }, - "execution_count": 14, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -851,7 +851,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 8, "id": "f468519a", "metadata": {}, "outputs": [ @@ -923,7 +923,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 9, "id": "df6ad71e", "metadata": {}, "outputs": [], @@ -1026,7 +1026,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 10, "id": "664b53bb", "metadata": {}, "outputs": [], @@ -1094,7 +1094,7 @@ "formats": "ipynb,md" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1108,7 +1108,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/5-3,\346\215\237\345\244\261\345\207\275\346\225\260.ipynb" "b/5-3,\346\215\237\345\244\261\345\207\275\346\225\260.ipynb" index d95e53345..0bff88a7f 100644 --- "a/5-3,\346\215\237\345\244\261\345\207\275\346\225\260.ipynb" +++ "b/5-3,\346\215\237\345\244\261\345\207\275\346\225\260.ipynb" @@ -203,10 +203,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "254502ae", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.0184)\n", + "tensor(0.0184)\n" + ] + } + ], "source": [ "import numpy as np\n", "import pandas as pd\n", @@ -214,7 +223,6 @@ "from torch import nn \n", "import torch.nn.functional as F \n", "\n", - "\n", "# nn.BCELoss() 和 nn.BCEWithLogitsLoss() 关系\n", "\n", "y_pred = torch.tensor([5.0,3,10,-5,-3,-10.0])\n", @@ -230,10 +238,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "dedc03d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.5493)\n", + "tensor(0.5493)\n" + ] + } + ], "source": [ "# nn.CrossEntropyLoss() 和 nn.NLLLoss() 关系\n", "\n", @@ -252,23 +269,35 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "3fb27a27", + "execution_count": 3, + "id": "2faac800-94c6-4809-b1f5-c2e61b38beb5", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(0.5493, grad_fn=)\n", + "tensor(0.5493, grad_fn=)\n" + ] + } + ], "source": [ "# nn.CrossEntropyLoss() 和 KLDivLoss 关系\n", "import torch.nn.functional as F \n", "\n", - "y_pred = torch.tensor([[10.0,0.0,-10.0],[8.0,8.0,8.0]])\n", + "y_pred = torch.tensor([[10.0,0.0,-10.0],[8.0,8.0,8.0]],requires_grad=True)\n", "y_true = torch.tensor([0,2])\n", "\n", "ce = nn.CrossEntropyLoss(reduction=\"mean\")(y_pred,y_true)\n", "print(ce)\n", "\n", + "\n", "#KLDivLoss要求target为向量形式编码且preds经过LogSoftmax激活\n", - "kl = nn.KLDivLoss(reduction=\"batchmean\")(F.log_softmax(y_pred,dim=1),F.one_hot(y_true))\n", - "print(kl)\n" + "pred = F.log_softmax(y_pred,dim=1)\n", + "target = F.one_hot(y_true).float()\n", + "kl = nn.KLDivLoss(reduction=\"batchmean\")(pred,target)\n", + "print(kl)" ] }, { @@ -341,12 +370,12 @@ "$$focal\\_loss(y,p) = \n", "\\begin{cases} -\\alpha (1-p)^{\\gamma}\\log(p) & \\text{if y = 1}\\\\\n", "-(1-\\alpha) p^{\\gamma}\\log(1-p) & \\text{if y = 0} \n", - "\\end{cases} $$" + "\\end{cases}$$" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "7fba9823", "metadata": {}, "outputs": [], @@ -374,10 +403,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "c046690d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "focal_loss(easy samples): tensor(0.0005)\n", + "bce_loss(easy samples): tensor(0.1054)\n", + "focal_loss(hard samples): tensor(0.0866)\n", + "bce_loss(hard samples): tensor(0.6931)\n" + ] + } + ], "source": [ "#困难样本\n", "y_pred_hard = torch.tensor([[0.5],[0.5]])\n", @@ -404,19 +444,6 @@ "# 因此相对而言，focal_loss可以衰减容易样本的权重。\n" ] }, - { - "cell_type": "markdown", - "id": "aa888688", - "metadata": {}, - "source": [ - "```\n", - "focal_loss(easy samples): tensor(0.0005)\n", - "bce_loss(easy samples): tensor(0.1054)\n", - "focal_loss(hard samples): tensor(0.0866)\n", - "bce_loss(hard samples): tensor(0.6931)\n", - "```" - ] - }, { "cell_type": "markdown", "id": "e9e66b5b", @@ -466,10 +493,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "7144bbc7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor(8.2502)\n", + "tensor(4.5786)\n" + ] + } + ], "source": [ "def ce(y,p):\n", " p = torch.clamp(p,min=1e-4,max=1-1e-4)\n", @@ -493,12 +529,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "1f047c74", "metadata": {}, "outputs": [], "source": [ - "\n", "import torch \n", "from torch import nn\n", "import torch.nn.functional as F \n", @@ -600,15 +635,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "e159348a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "e83537c6", "metadata": {}, "outputs": [], @@ -632,22 +659,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "958b6522", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "888f57ae", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -684,10 +695,7634 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "4a4685b9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T16:23:30.844482\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# 结果可视化\n", "fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))\n", @@ -936,7 +24822,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "8b677c0e", "metadata": {}, "outputs": [], @@ -986,7 +24872,7 @@ "cell_metadata_filter": "-all" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1000,7 +24886,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/5-4,TensorBoard\345\217\257\350\247\206\345\214\226.ipynb" "b/5-4,TensorBoard\345\217\257\350\247\206\345\214\226.ipynb" index 631405d6a..f69916db7 100644 --- "a/5-4,TensorBoard\345\217\257\350\247\206\345\214\226.ipynb" +++ "b/5-4,TensorBoard\345\217\257\350\247\206\345\214\226.ipynb" @@ -82,16 +82,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "b7a386b5", - "metadata": {}, - "outputs": [], - "source": [ - "#!pip install torchkeras==3.2.3 -i https://pypi.python.org/simple" - ] - }, { "cell_type": "code", "execution_count": 1, @@ -102,8 +92,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.__version__=1.10.0\n", - "torchkeras.__version__=3.2.3\n" + "torch.__version__=2.0.1\n", + "torchkeras.__version__=3.9.3\n" ] } ], @@ -243,19 +233,19 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "id": "9e0a5eac", "metadata": {}, "outputs": [], "source": [ - "writer = SummaryWriter('./tb_logs/example')\n", + "writer = SummaryWriter('./data/tensorboard')\n", "writer.add_graph(net,input_to_model = torch.rand(1,3,32,32))\n", "writer.close()\n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "8545c243", "metadata": {}, "outputs": [], @@ -266,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "id": "453aacc8", "metadata": {}, "outputs": [ @@ -286,41 +276,14 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "id": "00ef2d81", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " " - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "#启动tensorboard程序\n", - "notebook.start(\"--logdir ./tb_logs\")\n", - "#等价于在命令行中执行 tensorboard --logdir ./tb_logs\n", + "notebook.start(\"--logdir ./data/tensorboard\")\n", + "#等价于在命令行中执行 tensorboard --logdir ./data/tensorboard\n", "#可以在浏览器中打开 http://localhost:6006/ 查看" ] }, @@ -352,7 +315,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "id": "d3df8c70", "metadata": {}, "outputs": [ @@ -383,7 +346,7 @@ " result = a*torch.pow(x,2) + b*x + c \n", " return(result)\n", "\n", - "writer = SummaryWriter('./tb_logs/example')\n", + "writer = SummaryWriter('./data/tensorboard')\n", "for i in range(500):\n", " optimizer.zero_grad()\n", " y = f(x)\n", @@ -425,7 +388,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "f175bcf7", "metadata": {}, "outputs": [], @@ -440,7 +403,7 @@ " t = std*torch.randn((100,20))+mean\n", " return t\n", "\n", - "writer = SummaryWriter('./tb_logs/example')\n", + "writer = SummaryWriter('./data/tensorboard')\n", "for step,mean in enumerate(range(-10,10,1)):\n", " w = norm(mean,1)\n", " writer.add_histogram(\"w\",w, step)\n", @@ -482,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 12, "id": "e8b981de", "metadata": {}, "outputs": [], @@ -503,7 +466,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "e4ac84c8", "metadata": {}, "outputs": [ @@ -529,19 +492,19 @@ "images,labels = next(iter(dl_train))\n", "\n", "# 仅查看一张图片\n", - "writer = SummaryWriter('./tb_logs/example')\n", + "writer = SummaryWriter('./data/tensorboard')\n", "writer.add_image('images[0]', images[0])\n", "writer.close()\n", "\n", "# 将多张图片拼接成一张图片，中间用黑色网格分割\n", - "writer = SummaryWriter('./tb_logs/example')\n", + "writer = SummaryWriter('./data/tensorboard')\n", "# create grid of images\n", "img_grid = torchvision.utils.make_grid(images)\n", "writer.add_image('image_grid', img_grid)\n", "writer.close()\n", "\n", "# 将多张图片直接写入\n", - "writer = SummaryWriter('./tb_logs/example')\n", + "writer = SummaryWriter('./data/tensorboard')\n", "writer.add_images(\"images\",images,global_step = 0)\n", "writer.close()\n" ] @@ -574,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "id": "99e152a5", "metadata": {}, "outputs": [ @@ -615,7 +578,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "id": "19ef9c9f", "metadata": {}, "outputs": [ @@ -625,640 +588,645 @@ "\n", "\n", - "\n", - "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", - " 2022-09-03T23:02:33.342869\n", + " 2023-08-02T16:27:07.696420\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib v3.3.4, https://matplotlib.org/\n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", - " \n", + "\" style=\"fill: #ffffff\"/>\n", " \n", " \n", " \n", - " \n", + "\" style=\"fill: #ffffff\"/>\n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", - 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" ] }, "metadata": {}, @@ -1284,12 +1252,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "id": "223dbb27", "metadata": {}, "outputs": [], "source": [ - "writer = SummaryWriter('./tb_logs/example')\n", + "writer = SummaryWriter('./data/tensorboard')\n", "writer.add_figure('figure',figure,global_step=0)\n", "writer.close() " ] @@ -1323,15 +1291,7 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "dd1e5463", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "id": "bf4d2161", "metadata": {}, "outputs": [], @@ -1359,7 +1319,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 60, "id": "dfcf8541", "metadata": {}, "outputs": [ @@ -1369,46 +1329,45 @@ "\n", "\n", - "\n", - "\n", + "\n", " \n", - " \n", + " \n", " \n", " \n", - " 2022-09-03T23:03:16.294231\n", + " 2023-08-02T16:37:14.244070\n", " image/svg+xml\n", " \n", " \n", - " Matplotlib 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" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], @@ -6102,7 +9952,7 @@ "%config InlineBackend.figure_format = 'svg'\n", "\n", "#number of samples\n", - "n_positive,n_negative = 2000,2000\n", + "n_positive,n_negative = 4000,4000\n", "\n", "#positive samples\n", "r_p = 5.0 + torch.normal(0.0,1.0,size = [n_positive,1]) \n", @@ -6130,7 +9980,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 65, "id": "23218dac", "metadata": {}, "outputs": [ @@ -6138,17 +9988,16 @@ "name": "stdout", "output_type": "stream", "text": [ - "torch.Size([200, 2])\n", - "torch.Size([200, 1])\n" + "torch.Size([16, 2])\n", + "torch.Size([16, 1])\n" ] } ], "source": [ "ds = TensorDataset(X,Y)\n", "ds_train,ds_val = torch.utils.data.random_split(ds,[int(len(ds)*0.7),len(ds)-int(len(ds)*0.7)])\n", - "dl_train = DataLoader(ds_train,batch_size = 200,shuffle=True,num_workers=2)\n", - "dl_val = DataLoader(ds_val,batch_size = 200,num_workers=2)\n", - "\n", + "dl_train = DataLoader(ds_train,batch_size = 16,shuffle=True)\n", + "dl_val = DataLoader(ds_val,batch_size = 16)\n", "\n", "for features,labels in dl_train:\n", " break\n", @@ -6174,7 +10023,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 66, "id": "1019bfae", "metadata": {}, "outputs": [], @@ -6182,8 +10031,8 @@ "class Net(nn.Module): \n", " def __init__(self):\n", " super().__init__()\n", - " self.fc1 = nn.Linear(2,4)\n", - " self.fc2 = nn.Linear(4,8) \n", + " self.fc1 = nn.Linear(2,16)\n", + " self.fc2 = nn.Linear(16,8) \n", " self.fc3 = nn.Linear(8,1)\n", " \n", " def forward(self,x):\n", @@ -6196,7 +10045,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 74, "id": "9cab0b5e", "metadata": {}, "outputs": [ @@ -6207,48 +10056,36 @@ "--------------------------------------------------------------------------\n", "Layer (type) Output Shape Param #\n", "==========================================================================\n", - "Linear-1 [-1, 4] 12\n", - "Linear-2 [-1, 8] 40\n", + "Linear-1 [-1, 16] 48\n", + "Linear-2 [-1, 8] 136\n", "Linear-3 [-1, 1] 9\n", "==========================================================================\n", - "Total params: 61\n", - "Trainable params: 61\n", + "Total params: 193\n", + "Trainable params: 193\n", "Non-trainable params: 0\n", "--------------------------------------------------------------------------\n", "Input size (MB): 0.000069\n", - "Forward/backward pass size (MB): 0.000099\n", - "Params size (MB): 0.000233\n", - "Estimated Total Size (MB): 0.000401\n", + "Forward/backward pass size (MB): 0.000191\n", + "Params size (MB): 0.000736\n", + "Estimated Total Size (MB): 0.000996\n", "--------------------------------------------------------------------------\n" ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/liangyun/ProgramFiles/anaconda3/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:261: UserWarning: Attribute 'net' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['net'])`.\n", - " rank_zero_warn(\n", - "/Users/liangyun/ProgramFiles/anaconda3/lib/python3.8/site-packages/pytorch_lightning/utilities/parsing.py:261: UserWarning: Attribute 'loss_fn' is an instance of `nn.Module` and is already saved during checkpointing. It is recommended to ignore them using `self.save_hyperparameters(ignore=['loss_fn'])`.\n", - " rank_zero_warn(\n" - ] } ], "source": [ "from torchkeras.metrics import Accuracy \n", - "\n", + "from torchkeras import KerasModel\n", "net = Net() \n", "loss_fn = nn.BCEWithLogitsLoss()\n", "metric_dict = {\"acc\":Accuracy()}\n", "\n", - "lr = 0.03\n", + "lr = 0.0001\n", "optimizer = torch.optim.Adam(net.parameters(), lr=lr)\n", - "lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, step_size=10, gamma=0.0001)\n", "\n", - "model = torchkeras.LightModel(net,\n", + "model = KerasModel(net,\n", " loss_fn = loss_fn,\n", " metrics_dict= metric_dict,\n", - " optimizer = optimizer,\n", - " lr_scheduler = lr_scheduler,\n", + " optimizer = optimizer\n", " ) \n", "\n", "from torchkeras import summary\n", @@ -6273,343 +10110,1519 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 75, "id": "8ef27156", "metadata": {}, - "outputs": [], - "source": [ - "import pytorch_lightning as pl \n", - "from torchkeras.callbacks import TensorBoard" - ] - }, - { - "cell_type": "code", - "execution_count": 23, - "id": "6a5a9042", - "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "GPU available: False, used: False\n", - "TPU available: False, using: 0 TPU cores\n", - "IPU available: False, using: 0 IPUs\n", - "HPU available: False, using: 0 HPUs\n", - "\n", - " | Name | Type | Params\n", - "----------------------------------------------------\n", - "0 | net | Net | 61 \n", - "1 | train_metrics | ModuleDict | 0 \n", - "2 | val_metrics | ModuleDict | 0 \n", - "3 | test_metrics | ModuleDict | 0 \n", - "4 | loss_fn | BCEWithLogitsLoss | 0 \n", - "----------------------------------------------------\n", - "61 Trainable params\n", - "0 Non-trainable params\n", - "61 Total params\n", - "0.000 Total estimated model params size (MB)\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Sanity Checking: 0it [00:00, ?it/s]" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/liangyun/ProgramFiles/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, val_dataloader 0, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================2022-09-03 23:05:16\n", - "{'epoch': 0, 'val_loss': 0.6782772541046143, 'val_acc': 0.6100000143051147}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<<<<<< reach best val_loss : 0.6782772541046143 >>>>>>\n", - "/Users/liangyun/ProgramFiles/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/connectors/data_connector.py:240: PossibleUserWarning: The dataloader, train_dataloader, does not have many workers which may be a bottleneck. Consider increasing the value of the `num_workers` argument` (try 4 which is the number of cpus on this machine) in the `DataLoader` init to improve performance.\n", - " rank_zero_warn(\n", - "/Users/liangyun/ProgramFiles/anaconda3/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py:1933: PossibleUserWarning: The number of training batches (14) is smaller than the logging interval Trainer(log_every_n_steps=50). 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" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================2022-09-03 23:07:16\n", - "{'epoch': 6, 'val_loss': 0.19714665412902832, 'val_acc': 0.9241666793823242}\n", - "{'epoch': 6, 'train_loss': 0.19003979861736298, 'train_acc': 0.9285714030265808}\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "<<<<<< reach best val_loss : 0.19714665412902832 >>>>>>\n" - ] - }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, + "text/html": [ + "\n", + "\n" + ], "text/plain": [ - "Validation: 0it [00:00, ?it/s]" + "" ] }, "metadata": {}, "output_type": "display_data" }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================2022-09-03 23:07:28\n", - "{'epoch': 7, 'val_loss': 0.21902619302272797, 'val_acc': 0.9066666960716248}\n", - "{'epoch': 7, 'train_loss': 0.18537959456443787, 'train_acc': 0.9292857050895691}\n" - ] - }, { "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "", - "version_major": 2, - "version_minor": 0 - }, + "text/html": [ + "\n", + "
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epochtrain_losstrain_acclrval_lossval_acc
010.7309810.5310710.00010.6878670.547500
120.6712470.5637500.00010.6601810.545417
230.6541050.5416070.00010.6484380.538750
340.6450790.5364290.00010.6400900.526667
450.6370270.5360710.00010.6319320.551667
.....................
64650.1832120.9285710.00010.1842220.923750
65660.1825790.9303570.00010.1839040.922917
66670.1822440.9285710.00010.1831200.923333
67680.1819060.9292860.00010.1829380.922500
68690.1815130.9282140.00010.1838880.920417
\n", + "

69 rows × 6 columns

\n", + "
" + ], "text/plain": [ - "Validation: 0it [00:00, ?it/s]" + " epoch train_loss train_acc lr val_loss val_acc\n", + "0 1 0.730981 0.531071 0.0001 0.687867 0.547500\n", + "1 2 0.671247 0.563750 0.0001 0.660181 0.545417\n", + "2 3 0.654105 0.541607 0.0001 0.648438 0.538750\n", + "3 4 0.645079 0.536429 0.0001 0.640090 0.526667\n", + "4 5 0.637027 0.536071 0.0001 0.631932 0.551667\n", + ".. ... ... ... ... ... ...\n", + "64 65 0.183212 0.928571 0.0001 0.184222 0.923750\n", + "65 66 0.182579 0.930357 0.0001 0.183904 0.922917\n", + "66 67 0.182244 0.928571 0.0001 0.183120 0.923333\n", + "67 68 0.181906 0.929286 0.0001 0.182938 0.922500\n", + "68 69 0.181513 0.928214 0.0001 0.183888 0.920417\n", + "\n", + "[69 rows x 6 columns]" ] }, + "execution_count": 75, "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "================================================================================2022-09-03 23:08:09\n", - "{'epoch': 9, 'val_loss': 0.2253556251525879, 'val_acc': 0.9141666889190674}\n", - "{'epoch': 9, 'train_loss': 0.18573816120624542, 'train_acc': 0.9275000095367432}\n" - ] + "output_type": "execute_result" } ], "source": [ - "#1，设置回调函数\n", - "model_ckpt = pl.callbacks.ModelCheckpoint(\n", - " monitor='val_loss',\n", - " save_top_k=1,\n", - " mode='min'\n", - ")\n", + "from torchkeras.kerascallbacks import TensorBoardCallback\n", "\n", - "early_stopping = pl.callbacks.EarlyStopping(monitor = 'val_loss',\n", - " patience=3,\n", - " mode = 'min'\n", - " )\n", - "\n", - "tensorboard = TensorBoard(\n", - " save_dir='./tb_logs/',\n", - " model_name='lightmodel',\n", - " log_weight=True,\n", - " log_weight_freq=1, #没两个epoch记录一次权重可视化\n", - " log_graph=True,\n", - " example_input_array=features,\n", - " log_hparams=True, #记录超参\n", - " hparams_dict={\"lr\":lr},\n", + "tb = TensorBoardCallback(\n", + " save_dir='./data/tensorboard',\n", + " model_name='model',\n", + " log_weight=False,\n", + " log_weight_freq=5,\n", ")\n", "\n", - "#2，设置训练参数\n", - "\n", - "# gpus=0 则使用cpu训练，gpus=1则使用1个gpu训练，gpus=2则使用2个gpu训练，gpus=-1则使用所有gpu训练，\n", - "# gpus=[0,1]则指定使用0号和1号gpu训练， gpus=\"0,1,2,3\"则使用0,1,2,3号gpu训练\n", - "# tpus=1 则使用1个tpu训练\n", - "trainer = pl.Trainer(logger=True,\n", - " min_epochs=3,max_epochs=10,\n", - " gpus=0,\n", - " callbacks = [model_ckpt,early_stopping,tensorboard],\n", - " enable_progress_bar = True) \n", - "\n", - "\n", - "##4，启动训练循环\n", - "trainer.fit(model,dl_train,dl_val)\n" + "model.fit( train_data=dl_train,\n", + " val_data=dl_val,\n", + " epochs=100,\n", + " ckpt_path='checkpoint',\n", + " patience=10,\n", + " monitor='val_acc',\n", + " mode='max',\n", + " callbacks=[tb],\n", + " plot=True,\n", + " quiet=None,\n", + " cpu=True)\n" ] }, { @@ -6713,7 +11838,7 @@ "metadata": {}, "outputs": [], "source": [ - "#!tensorboard --logdir=\"./tb_logs\" --bind_all --port=6006" + "#!tensorboard --logdir=\"'./data/tensorboard'\" --bind_all --port=6006" ] }, { @@ -6734,7 +11859,7 @@ "metadata": {}, "outputs": [], "source": [ - "notebook.start(\"--logdir ./tb_logs --port=6006\")" + "notebook.start(\"--logdir './data/tensorboard' --port=6006\")" ] }, { @@ -6756,14 +11881,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "1892bc02", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "d4c58721", diff --git "a/6-1,\346\236\204\345\273\272\346\250\241\345\236\213\347\232\2043\347\247\215\346\226\271\346\263\225.ipynb" "b/6-1,\346\236\204\345\273\272\346\250\241\345\236\213\347\232\2043\347\247\215\346\226\271\346\263\225.ipynb" index 8aa95df79..9de0406e5 100644 --- "a/6-1,\346\236\204\345\273\272\346\250\241\345\236\213\347\232\2043\347\247\215\346\226\271\346\263\225.ipynb" +++ "b/6-1,\346\236\204\345\273\272\346\250\241\345\236\213\347\232\2043\347\247\215\346\226\271\346\263\225.ipynb" @@ -22,10 +22,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "536f4c77", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__=2.0.1\n", + "torchkeras.__version__=3.9.3\n" + ] + } + ], "source": [ "import torch \n", "import torchkeras\n", @@ -34,17 +43,6 @@ "print(\"torchkeras.__version__=\"+torchkeras.__version__) \n" ] }, - { - "cell_type": "markdown", - "id": "8267bcfb", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__=1.10.0\n", - "torchkeras.__version__=3.2.3\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -71,11 +69,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "b7852e18", "metadata": {}, - "outputs": [], - "source": [ + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (dropout): Dropout2d(p=0.1, inplace=False)\n", + " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (flatten): Flatten(start_dim=1, end_dim=-1)\n", + " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", + " (relu): ReLU()\n", + " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], + "source": [ + "from torch import nn \n", "class Net(nn.Module):\n", " \n", " def __init__(self):\n", @@ -108,69 +126,54 @@ "print(net)" ] }, - { - "cell_type": "markdown", - "id": "2fdfcf14", - "metadata": {}, - "source": [ - "```\n", - "Net(\n", - " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (dropout): Dropout2d(p=0.1, inplace=False)\n", - " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (flatten): Flatten(start_dim=1, end_dim=-1)\n", - " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", - " (relu): ReLU()\n", - " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", - ")\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "c76ceb33", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Conv2d-1 [-1, 32, 30, 30] 896\n", + "MaxPool2d-2 [-1, 32, 15, 15] 0\n", + "Conv2d-3 [-1, 64, 11, 11] 51,264\n", + "MaxPool2d-4 [-1, 64, 5, 5] 0\n", + "Dropout2d-5 [-1, 64, 5, 5] 0\n", + "AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0\n", + "Flatten-7 [-1, 64] 0\n", + "Linear-8 [-1, 32] 2,080\n", + "ReLU-9 [-1, 32] 0\n", + "Linear-10 [-1, 1] 33\n", + "==========================================================================\n", + "Total params: 54,273\n", + "Trainable params: 54,273\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.011719\n", + "Forward/backward pass size (MB): 0.359627\n", + "Params size (MB): 0.207035\n", + "Estimated Total Size (MB): 0.578381\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "from torchkeras import summary \n", "summary(net,input_shape= (3,32,32));" ] }, { - "cell_type": "markdown", - "id": "d0d91d44", + "cell_type": "code", + "execution_count": null, + "id": "a321e10b-fb08-4cd3-a6f9-39e79ea8b626", "metadata": {}, - "source": [ - "```\n", - "--------------------------------------------------------------------------\n", - "Layer (type) Output Shape Param #\n", - "==========================================================================\n", - "Conv2d-1 [-1, 32, 30, 30] 896\n", - "MaxPool2d-2 [-1, 32, 15, 15] 0\n", - "Conv2d-3 [-1, 64, 11, 11] 51,264\n", - "MaxPool2d-4 [-1, 64, 5, 5] 0\n", - "Dropout2d-5 [-1, 64, 5, 5] 0\n", - "AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0\n", - "Flatten-7 [-1, 64] 0\n", - "Linear-8 [-1, 32] 2,080\n", - "ReLU-9 [-1, 32] 0\n", - "Linear-10 [-1, 1] 33\n", - "==========================================================================\n", - "Total params: 54,273\n", - "Trainable params: 54,273\n", - "Non-trainable params: 0\n", - "--------------------------------------------------------------------------\n", - "Input size (MB): 0.011719\n", - "Forward/backward pass size (MB): 0.359627\n", - "Params size (MB): 0.207035\n", - "Estimated Total Size (MB): 0.578381\n", - "--------------------------------------------------------------------------\n", - "```" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -200,10 +203,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "81633480", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (dropout): Dropout2d(p=0.1, inplace=False)\n", + " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (flatten): Flatten(start_dim=1, end_dim=-1)\n", + " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", + " (relu): ReLU()\n", + " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], "source": [ "\n", "net = nn.Sequential()\n", @@ -220,27 +242,6 @@ "print(net)\n" ] }, - { - "cell_type": "markdown", - "id": "5f09fcc3", - "metadata": {}, - "source": [ - "```\n", - "Sequential(\n", - " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (dropout): Dropout2d(p=0.1, inplace=False)\n", - " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (flatten): Flatten(start_dim=1, end_dim=-1)\n", - " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", - " (relu): ReLU()\n", - " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", - ")\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -261,10 +262,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "19f77a5d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (4): Dropout2d(p=0.1, inplace=False)\n", + " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (6): Flatten(start_dim=1, end_dim=-1)\n", + " (7): Linear(in_features=64, out_features=32, bias=True)\n", + " (8): ReLU()\n", + " (9): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], "source": [ "net = nn.Sequential(\n", " nn.Conv2d(in_channels=3,out_channels=32,kernel_size = 3),\n", @@ -282,27 +302,6 @@ "print(net)" ] }, - { - "cell_type": "markdown", - "id": "856b470c", - "metadata": {}, - "source": [ - "```\n", - "Sequential(\n", - " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (4): Dropout2d(p=0.1, inplace=False)\n", - " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (6): Flatten(start_dim=1, end_dim=-1)\n", - " (7): Linear(in_features=64, out_features=32, bias=True)\n", - " (8): ReLU()\n", - " (9): Linear(in_features=32, out_features=1, bias=True)\n", - ")\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -321,10 +320,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "989fde07", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (dropout): Dropout2d(p=0.1, inplace=False)\n", + " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (flatten): Flatten(start_dim=1, end_dim=-1)\n", + " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", + " (relu): ReLU()\n", + " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", + ")\n" + ] + } + ], "source": [ "from collections import OrderedDict\n", "\n", @@ -344,70 +362,47 @@ "print(net)" ] }, - { - "cell_type": "markdown", - "id": "fd554f04", - "metadata": {}, - "source": [ - "```\n", - "Sequential(\n", - " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (dropout): Dropout2d(p=0.1, inplace=False)\n", - " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (flatten): Flatten(start_dim=1, end_dim=-1)\n", - " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", - " (relu): ReLU()\n", - " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", - ")\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "b0aac6e2", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Conv2d-1 [-1, 32, 30, 30] 896\n", + "MaxPool2d-2 [-1, 32, 15, 15] 0\n", + "Conv2d-3 [-1, 64, 11, 11] 51,264\n", + "MaxPool2d-4 [-1, 64, 5, 5] 0\n", + "Dropout2d-5 [-1, 64, 5, 5] 0\n", + "AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0\n", + "Flatten-7 [-1, 64] 0\n", + "Linear-8 [-1, 32] 2,080\n", + "ReLU-9 [-1, 32] 0\n", + "Linear-10 [-1, 1] 33\n", + "==========================================================================\n", + "Total params: 54,273\n", + "Trainable params: 54,273\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.011719\n", + "Forward/backward pass size (MB): 0.359627\n", + "Params size (MB): 0.207035\n", + "Estimated Total Size (MB): 0.578381\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "from torchkeras import summary \n", "summary(net,input_shape= (3,32,32));" ] }, - { - "cell_type": "markdown", - "id": "09554a10", - "metadata": {}, - "source": [ - "```\n", - "--------------------------------------------------------------------------\n", - "Layer (type) Output Shape Param #\n", - "==========================================================================\n", - "Conv2d-1 [-1, 32, 30, 30] 896\n", - "MaxPool2d-2 [-1, 32, 15, 15] 0\n", - "Conv2d-3 [-1, 64, 11, 11] 51,264\n", - "MaxPool2d-4 [-1, 64, 5, 5] 0\n", - "Dropout2d-5 [-1, 64, 5, 5] 0\n", - "AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0\n", - "Flatten-7 [-1, 64] 0\n", - "Linear-8 [-1, 32] 2,080\n", - "ReLU-9 [-1, 32] 0\n", - "Linear-10 [-1, 1] 33\n", - "==========================================================================\n", - "Total params: 54,273\n", - "Trainable params: 54,273\n", - "Non-trainable params: 0\n", - "--------------------------------------------------------------------------\n", - "Input size (MB): 0.011719\n", - "Forward/backward pass size (MB): 0.359627\n", - "Params size (MB): 0.207035\n", - "Estimated Total Size (MB): 0.578381\n", - "--------------------------------------------------------------------------\n", - "```" - ] - }, { "cell_type": "markdown", "id": "5be6178a", @@ -438,10 +433,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "39699388", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (conv): Sequential(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (4): Dropout2d(p=0.1, inplace=False)\n", + " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " )\n", + " (dense): Sequential(\n", + " (0): Flatten(start_dim=1, end_dim=-1)\n", + " (1): Linear(in_features=64, out_features=32, bias=True)\n", + " (2): ReLU()\n", + " (3): Linear(in_features=32, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], "source": [ "class Net(nn.Module):\n", " \n", @@ -470,31 +488,6 @@ "print(net)" ] }, - { - "cell_type": "markdown", - "id": "5e9dd029", - "metadata": {}, - "source": [ - "```\n", - "Net(\n", - " (conv): Sequential(\n", - " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (4): Dropout2d(p=0.1, inplace=False)\n", - " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " )\n", - " (dense): Sequential(\n", - " (0): Flatten(start_dim=1, end_dim=-1)\n", - " (1): Linear(in_features=64, out_features=32, bias=True)\n", - " (2): ReLU()\n", - " (3): Linear(in_features=32, out_features=1, bias=True)\n", - " )\n", - ")\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -515,10 +508,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "2bbddd64", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (layers): ModuleList(\n", + " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (4): Dropout2d(p=0.1, inplace=False)\n", + " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (6): Flatten(start_dim=1, end_dim=-1)\n", + " (7): Linear(in_features=64, out_features=32, bias=True)\n", + " (8): ReLU()\n", + " (9): Linear(in_features=32, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], "source": [ "class Net(nn.Module):\n", " \n", @@ -544,72 +558,47 @@ "print(net)" ] }, - { - "cell_type": "markdown", - "id": "be03b9a8", - "metadata": {}, - "source": [ - "```\n", - "Net(\n", - " (layers): ModuleList(\n", - " (0): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (4): Dropout2d(p=0.1, inplace=False)\n", - " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (6): Flatten(start_dim=1, end_dim=-1)\n", - " (7): Linear(in_features=64, out_features=32, bias=True)\n", - " (8): ReLU()\n", - " (9): Linear(in_features=32, out_features=1, bias=True)\n", - " )\n", - ")\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "4c8053ee", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Conv2d-1 [-1, 32, 30, 30] 896\n", + "MaxPool2d-2 [-1, 32, 15, 15] 0\n", + "Conv2d-3 [-1, 64, 11, 11] 51,264\n", + "MaxPool2d-4 [-1, 64, 5, 5] 0\n", + "Dropout2d-5 [-1, 64, 5, 5] 0\n", + "AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0\n", + "Flatten-7 [-1, 64] 0\n", + "Linear-8 [-1, 32] 2,080\n", + "ReLU-9 [-1, 32] 0\n", + "Linear-10 [-1, 1] 33\n", + "==========================================================================\n", + "Total params: 54,273\n", + "Trainable params: 54,273\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.011719\n", + "Forward/backward pass size (MB): 0.359627\n", + "Params size (MB): 0.207035\n", + "Estimated Total Size (MB): 0.578381\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "from torchkeras import summary \n", "summary(net,input_shape= (3,32,32));" ] }, - { - "cell_type": "markdown", - "id": "7a1374e3", - "metadata": {}, - "source": [ - "```\n", - "--------------------------------------------------------------------------\n", - "Layer (type) Output Shape Param #\n", - "==========================================================================\n", - "Conv2d-1 [-1, 32, 30, 30] 896\n", - "MaxPool2d-2 [-1, 32, 15, 15] 0\n", - "Conv2d-3 [-1, 64, 11, 11] 51,264\n", - "MaxPool2d-4 [-1, 64, 5, 5] 0\n", - "Dropout2d-5 [-1, 64, 5, 5] 0\n", - "AdaptiveMaxPool2d-6 [-1, 64, 1, 1] 0\n", - "Flatten-7 [-1, 64] 0\n", - "Linear-8 [-1, 32] 2,080\n", - "ReLU-9 [-1, 32] 0\n", - "Linear-10 [-1, 1] 33\n", - "==========================================================================\n", - "Total params: 54,273\n", - "Trainable params: 54,273\n", - "Non-trainable params: 0\n", - "--------------------------------------------------------------------------\n", - "Input size (MB): 0.011719\n", - "Forward/backward pass size (MB): 0.359627\n", - "Params size (MB): 0.207035\n", - "Estimated Total Size (MB): 0.578381\n", - "--------------------------------------------------------------------------\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -630,10 +619,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "8264bafd", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (layers_dict): ModuleDict(\n", + " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (dropout): Dropout2d(p=0.1, inplace=False)\n", + " (adaptive): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (flatten): Flatten(start_dim=1, end_dim=-1)\n", + " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", + " (relu): ReLU()\n", + " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", + " )\n", + ")\n" + ] + } + ], "source": [ "class Net(nn.Module):\n", " \n", @@ -659,28 +668,6 @@ "print(net)" ] }, - { - "cell_type": "markdown", - "id": "09b4e071", - "metadata": {}, - "source": [ - "```\n", - "Net(\n", - " (layers_dict): ModuleDict(\n", - " (conv1): Conv2d(3, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (pool): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (dropout): Dropout2d(p=0.1, inplace=False)\n", - " (adaptive): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (flatten): Flatten(start_dim=1, end_dim=-1)\n", - " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", - " (relu): ReLU()\n", - " (linear2): Linear(in_features=32, out_features=1, bias=True)\n", - " )\n", - ")\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -719,7 +706,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -733,7 +720,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/6-2,\350\256\255\347\273\203\346\250\241\345\236\213\347\232\2043\347\247\215\346\226\271\346\263\225.ipynb" "b/6-2,\350\256\255\347\273\203\346\250\241\345\236\213\347\232\2043\347\247\215\346\226\271\346\263\225.ipynb" index 762f89207..d3aac1d40 100644 --- "a/6-2,\350\256\255\347\273\203\346\250\241\345\236\213\347\232\2043\347\247\215\346\226\271\346\263\225.ipynb" +++ "b/6-2,\350\256\255\347\273\203\346\250\241\345\236\213\347\232\2043\347\247\215\346\226\271\346\263\225.ipynb" @@ -26,10 +26,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "24b101ed", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__ = 2.0.1\n", + "torchkeras.__version__ = 3.9.3\n" + ] + } + ], "source": [ "import torch \n", "import torchkeras\n", @@ -37,17 +46,6 @@ "print(\"torchkeras.__version__ = \", torchkeras.__version__) " ] }, - { - "cell_type": "markdown", - "id": "2586a627", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__ = 1.10.0\n", - "torchkeras.__version__ = 3.2.3\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -66,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "04d2d65b", "metadata": {}, "outputs": [], @@ -80,15 +78,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "5fa1c7d4", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "60000\n", + "10000\n" + ] + } + ], "source": [ "transform = transforms.Compose([transforms.ToTensor()])\n", "\n", - "ds_train = torchvision.datasets.MNIST(root=\"./data/minist/\",train=True,download=True,transform=transform)\n", - "ds_val = torchvision.datasets.MNIST(root=\"./data/minist/\",train=False,download=True,transform=transform)\n", + "ds_train = torchvision.datasets.MNIST(root=\"./data/mnist/\",train=True,download=True,transform=transform)\n", + "ds_val = torchvision.datasets.MNIST(root=\"./data/mnist/\",train=False,download=True,transform=transform)\n", "\n", "dl_train = torch.utils.data.DataLoader(ds_train, batch_size=128, shuffle=True, num_workers=4)\n", "dl_val = torch.utils.data.DataLoader(ds_val, batch_size=128, shuffle=False, num_workers=4)\n", @@ -97,23 +104,819 @@ "print(len(ds_val))\n" ] }, - { - "cell_type": "markdown", - "id": "bb15a7e4", - "metadata": {}, - "source": [ - "```\n", - "60000\n", - "10000\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "f7130d4e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T17:02:27.116950\n", + " image/svg+xml\n", + " \n", + " \n", + " Matplotlib v3.6.2, https://matplotlib.org/\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "\n" + ], + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "%matplotlib inline\n", "%config InlineBackend.figure_format = 'svg'\n", @@ -133,30 +936,6 @@ "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "55d3e3f5", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "6af1b177", - "metadata": {}, - "source": [ - "![](./data/6-2-minist.png)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b42ca4a2", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -178,15 +957,34 @@ "id": "4d305aa4", "metadata": {}, "source": [ - "脚本风格的训练循环最为常见。" + "脚本风格的训练循环非常常见。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "9dad7971", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sequential(\n", + " (conv1): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (dropout): Dropout2d(p=0.1, inplace=False)\n", + " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (flatten): Flatten(start_dim=1, end_dim=-1)\n", + " (linear1): Linear(in_features=64, out_features=32, bias=True)\n", + " (relu): ReLU()\n", + " (linear2): Linear(in_features=32, out_features=10, bias=True)\n", + ")\n" + ] + } + ], "source": [ "net = nn.Sequential()\n", "net.add_module(\"conv1\",nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3))\n", @@ -226,8 +1024,8 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "68444322", + "execution_count": 11, + "id": "417a87bd-8e15-4872-a5c2-f6d41f2247ce", "metadata": {}, "outputs": [], "source": [ @@ -240,18 +1038,160 @@ "import torch\n", "from torch import nn \n", "from copy import deepcopy\n", - "from torchmetrics import Accuracy\n", - "#注：多分类使用torchmetrics中的评估指标，二分类使用torchkeras.metrics中的评估指标\n", - "\n", + "from torchmetrics import Accuracy\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "bc9fb1ae-2e9e-45ed-a12b-007068e2fd33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:52:38\n", + "Epoch 1 / 20\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:23<00:00, 20.37it/s, train_acc=0.906, train_loss=0.289]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 33.64it/s, val_acc=0.976, val_loss=0.0822]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:53:03\n", + "Epoch 2 / 20\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "<<<<<< reach best val_acc : 0.9758999943733215 >>>>>>\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.78it/s, train_acc=0.967, train_loss=0.11]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 32.63it/s, val_acc=0.979, val_loss=0.0773]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:53:28\n", + "Epoch 3 / 20\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "<<<<<< reach best val_acc : 0.979200005531311 >>>>>>\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.75it/s, train_acc=0.973, train_loss=0.0915]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 33.50it/s, val_acc=0.977, val_loss=0.0741]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:53:53\n", + "Epoch 4 / 20\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.87it/s, train_acc=0.972, train_loss=0.0959]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 33.15it/s, val_acc=0.977, val_loss=0.0782]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:54:18\n", + "Epoch 5 / 20\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.46it/s, train_acc=0.969, train_loss=0.103]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 32.36it/s, val_acc=0.978, val_loss=0.0748]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:54:44\n", + "Epoch 6 / 20\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.46it/s, train_acc=0.971, train_loss=0.0955]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 32.96it/s, val_acc=0.974, val_loss=0.0871]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:55:09\n", + "Epoch 7 / 20\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.65it/s, train_acc=0.969, train_loss=0.105]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 34.38it/s, val_acc=0.976, val_loss=0.0843]\n", + "<<<<<< val_acc without improvement in 5 epoch, early stopping >>>>>>\n" + ] + } + ], + "source": [ "def printlog(info):\n", " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", " print(str(info)+\"\\n\")\n", " \n", - "\n", "loss_fn = nn.CrossEntropyLoss()\n", "optimizer= torch.optim.Adam(net.parameters(),lr = 0.01) \n", - "metrics_dict = {\"acc\":Accuracy()}\n", + "metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)}\n", "\n", "epochs = 20 \n", "ckpt_path='checkpoint.pt'\n", @@ -371,48 +1311,11 @@ { "cell_type": "code", "execution_count": null, - "id": "018920b1", + "id": "2605b75e-15f9-4a4a-a31f-00a6a5d0b228", "metadata": {}, "outputs": [], "source": [] }, - { - "cell_type": "markdown", - "id": "2f7bc866", - "metadata": {}, - "source": [ - "```\n", - "================================================================================2022-07-17 19:21:50\n", - "Epoch 17 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [01:01<00:00, 7.67it/s, train_acc=0.985, train_loss=0.06] \n", - "100%|██████████| 79/79 [00:07<00:00, 10.45it/s, val_acc=0.985, val_loss=0.0659] \n", - "\n", - "================================================================================2022-07-17 19:22:59\n", - "Epoch 18 / 20\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9851999878883362 >>>>>>\n", - "100%|██████████| 469/469 [00:57<00:00, 8.14it/s, train_acc=0.983, train_loss=0.0692]\n", - "100%|██████████| 79/79 [00:07<00:00, 10.89it/s, val_acc=0.984, val_loss=0.0849]\n", - "\n", - "================================================================================2022-07-17 19:24:04\n", - "Epoch 19 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [01:08<00:00, 6.82it/s, train_acc=0.984, train_loss=0.0679] \n", - "100%|██████████| 79/79 [00:06<00:00, 11.39it/s, val_acc=0.983, val_loss=0.087] \n", - "\n", - "================================================================================2022-07-17 19:25:20\n", - "Epoch 20 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:57<00:00, 8.14it/s, train_acc=0.986, train_loss=0.0553] \n", - "100%|██████████| 79/79 [00:06<00:00, 11.35it/s, val_acc=0.983, val_loss=0.0813]\n", - "```" - ] - }, { "cell_type": "markdown", "id": "c726168a", @@ -431,10 +1334,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "0a1a41d9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (layers): ModuleList(\n", + " (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (4): Dropout2d(p=0.1, inplace=False)\n", + " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (6): Flatten(start_dim=1, end_dim=-1)\n", + " (7): Linear(in_features=64, out_features=32, bias=True)\n", + " (8): ReLU()\n", + " (9): Linear(in_features=32, out_features=10, bias=True)\n", + " )\n", + ")\n" + ] + } + ], "source": [ "class Net(nn.Module):\n", " def __init__(self):\n", @@ -459,29 +1383,6 @@ "print(net)" ] }, - { - "cell_type": "markdown", - "id": "b66cb36e", - "metadata": {}, - "source": [ - "```\n", - "Net(\n", - " (layers): ModuleList(\n", - " (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (4): Dropout2d(p=0.1, inplace=False)\n", - " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (6): Flatten()\n", - " (7): Linear(in_features=64, out_features=32, bias=True)\n", - " (8): ReLU()\n", - " (9): Linear(in_features=32, out_features=10, bias=True)\n", - " )\n", - ")\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -492,7 +1393,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "6c97a606", "metadata": {}, "outputs": [], @@ -627,15 +1528,172 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "feb456ac", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:58:14\n", + "Epoch 1 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.85it/s, train_acc=0.901, train_loss=0.304]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 31.61it/s, val_acc=0.971, val_loss=0.0954]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:58:39\n", + "Epoch 2 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "<<<<<< reach best val_acc : 0.97079998254776 >>>>>>\n", + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 21.04it/s, train_acc=0.966, train_loss=0.113]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 31.05it/s, val_acc=0.98, val_loss=0.0678]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:59:04\n", + "Epoch 3 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "<<<<<< reach best val_acc : 0.9801999926567078 >>>>>>\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.99it/s, train_acc=0.971, train_loss=0.0969]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 31.92it/s, val_acc=0.979, val_loss=0.0709]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:59:29\n", + "Epoch 4 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.74it/s, train_acc=0.97, train_loss=0.101]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 29.90it/s, val_acc=0.981, val_loss=0.0652]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 16:59:54\n", + "Epoch 5 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "<<<<<< reach best val_acc : 0.9811999797821045 >>>>>>\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:23<00:00, 20.27it/s, train_acc=0.975, train_loss=0.0832]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 30.07it/s, val_acc=0.984, val_loss=0.0561]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 17:00:20\n", + "Epoch 6 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "<<<<<< reach best val_acc : 0.9836000204086304 >>>>>>\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:23<00:00, 20.24it/s, train_acc=0.977, train_loss=0.0788]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 29.13it/s, val_acc=0.981, val_loss=0.0756]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 17:00:46\n", + "Epoch 7 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.74it/s, train_acc=0.975, train_loss=0.0834]\n", + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 31.43it/s, val_acc=0.98, val_loss=0.0824]" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 17:01:11\n", + "Epoch 8 / 10\n", + "\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 469/469 [00:22<00:00, 20.53it/s, train_acc=0.975, train_loss=0.0828]\n", + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 79/79 [00:02<00:00, 32.60it/s, val_acc=0.981, val_loss=0.0708]\n", + "<<<<<< val_acc without improvement in 3 epoch, early stopping >>>>>>\n" + ] + } + ], "source": [ "from torchmetrics import Accuracy\n", "loss_fn = nn.CrossEntropyLoss()\n", "optimizer= torch.optim.Adam(net.parameters(),lr = 0.01) \n", - "metrics_dict = {\"acc\":Accuracy()}\n", + "metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)}\n", "\n", "dfhistory = train_model(net,\n", " optimizer,\n", @@ -649,51 +1707,6 @@ " mode=\"max\")\n" ] }, - { - "cell_type": "markdown", - "id": "c1ddb3cb", - "metadata": {}, - "source": [ - "```\n", - "================================================================================2022-07-17 19:27:50\n", - "Epoch 1 / 10\n", - "\n", - "100%|██████████| 469/469 [01:00<00:00, 7.71it/s, train_acc=0.905, train_loss=0.296] \n", - "100%|██████████| 79/79 [00:07<00:00, 11.18it/s, val_acc=0.96, val_loss=0.129] \n", - "\n", - "================================================================================2022-07-17 19:28:58\n", - "Epoch 2 / 10\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9603000283241272 >>>>>>\n", - "100%|██████████| 469/469 [00:57<00:00, 8.19it/s, train_acc=0.966, train_loss=0.115] \n", - "100%|██████████| 79/79 [00:06<00:00, 11.55it/s, val_acc=0.982, val_loss=0.0639]\n", - "\n", - "================================================================================2022-07-17 19:30:02\n", - "Epoch 3 / 10\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9815999865531921 >>>>>>\n", - "100%|██████████| 469/469 [01:00<00:00, 7.71it/s, train_acc=0.971, train_loss=0.0982]\n", - "100%|██████████| 79/79 [00:07<00:00, 10.21it/s, val_acc=0.976, val_loss=0.0831] \n", - "\n", - "================================================================================2022-07-17 19:31:11\n", - "Epoch 4 / 10\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:58<00:00, 8.06it/s, train_acc=0.972, train_loss=0.0932]\n", - "100%|██████████| 79/79 [00:06<00:00, 11.32it/s, val_acc=0.978, val_loss=0.0786]\n", - "\n", - "================================================================================2022-07-17 19:32:17\n", - "Epoch 5 / 10\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:57<00:00, 8.21it/s, train_acc=0.971, train_loss=0.0992]\n", - "100%|██████████| 79/79 [00:07<00:00, 11.04it/s, val_acc=0.978, val_loss=0.0774]\n", - "<<<<<< val_acc without improvement in 3 epoch, early stopping >>>>>>\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -715,7 +1728,7 @@ "id": "0b68de55", "metadata": {}, "source": [ - "### 三，类风格 torchkeras.KerasModel" + "### 三，类风格" ] }, { @@ -731,10 +1744,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "eab1d853", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Net(\n", + " (layers): ModuleList(\n", + " (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n", + " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", + " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (4): Dropout2d(p=0.1, inplace=False)\n", + " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (6): Flatten(start_dim=1, end_dim=-1)\n", + " (7): Linear(in_features=64, out_features=32, bias=True)\n", + " (8): ReLU()\n", + " (9): Linear(in_features=32, out_features=10, bias=True)\n", + " )\n", + ")\n" + ] + } + ], "source": [ "from torchkeras import KerasModel \n", "\n", @@ -763,41 +1797,1388 @@ "print(net)" ] }, - { - "cell_type": "markdown", - "id": "794e7ab9", - "metadata": {}, - "source": [ - "```\n", - "Net(\n", - " (layers): ModuleList(\n", - " (0): Conv2d(1, 32, kernel_size=(3, 3), stride=(1, 1))\n", - " (1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (2): Conv2d(32, 64, kernel_size=(5, 5), stride=(1, 1))\n", - " (3): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (4): Dropout2d(p=0.1, inplace=False)\n", - " (5): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (6): Flatten(start_dim=1, end_dim=-1)\n", - " (7): Linear(in_features=64, out_features=32, bias=True)\n", - " (8): ReLU()\n", - " (9): Linear(in_features=32, out_features=10, bias=True)\n", - " )\n", - ")\n", - "```" - ] - }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "cf6ae428", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/svg+xml": [ + "\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " 2023-08-02T17:06:30.867934\n", + " image/svg+xml\n", + " \n", + " 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epochtrain_losstrain_acclrval_lossval_acc
010.3686900.8792170.010.1408360.9576
120.1203570.9635500.010.0821370.9774
230.0996940.9706830.010.0636540.9814
340.0881440.9734830.010.0786260.9781
450.0847750.9752670.010.0713850.9808
560.0805020.9774500.010.0511280.9856
670.0765120.9791500.010.0726390.9803
780.0679800.9808670.010.0905250.9780
890.0738430.9804000.010.0677650.9827
\n", + "
" + ], + "text/plain": [ + " epoch train_loss train_acc lr val_loss val_acc\n", + "0 1 0.368690 0.879217 0.01 0.140836 0.9576\n", + "1 2 0.120357 0.963550 0.01 0.082137 0.9774\n", + "2 3 0.099694 0.970683 0.01 0.063654 0.9814\n", + "3 4 0.088144 0.973483 0.01 0.078626 0.9781\n", + "4 5 0.084775 0.975267 0.01 0.071385 0.9808\n", + "5 6 0.080502 0.977450 0.01 0.051128 0.9856\n", + "6 7 0.076512 0.979150 0.01 0.072639 0.9803\n", + "7 8 0.067980 0.980867 0.01 0.090525 0.9780\n", + "8 9 0.073843 0.980400 0.01 0.067765 0.9827" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from torchmetrics import Accuracy\n", "\n", "model = KerasModel(net,\n", " loss_fn=nn.CrossEntropyLoss(),\n", - " metrics_dict = {\"acc\":Accuracy()},\n", + " metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)},\n", " optimizer = torch.optim.Adam(net.parameters(),lr = 0.01) )\n", "\n", "model.fit(\n", @@ -806,82 +3187,10 @@ " epochs=10,\n", " patience=3,\n", " monitor=\"val_acc\", \n", - " mode=\"max\")\n" - ] - }, - { - "cell_type": "markdown", - "id": "d960e9ad", - "metadata": {}, - "source": [ - "```\n", - "================================================================================2022-07-17 21:01:18\n", - "Epoch 1 / 10\n", - "\n", - "100%|██████████| 469/469 [00:58<00:00, 7.98it/s, train_acc=0.906, train_loss=0.291] \n", - "100%|██████████| 79/79 [00:07<00:00, 10.60it/s, val_acc=0.974, val_loss=0.0773]\n", - "<<<<<< reach best val_acc : 0.9739999771118164 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:02:24\n", - "Epoch 2 / 10\n", - "\n", - "100%|██████████| 469/469 [01:00<00:00, 7.78it/s, train_acc=0.968, train_loss=0.105] \n", - "100%|██████████| 79/79 [00:07<00:00, 9.92it/s, val_acc=0.971, val_loss=0.0882]\n", - "\n", - "================================================================================2022-07-17 21:03:32\n", - "Epoch 3 / 10\n", - "\n", - "100%|██████████| 469/469 [01:11<00:00, 6.60it/s, train_acc=0.973, train_loss=0.0868]\n", - "100%|██████████| 79/79 [00:08<00:00, 9.25it/s, val_acc=0.978, val_loss=0.0769]\n", - "<<<<<< reach best val_acc : 0.9779999852180481 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:04:52\n", - "Epoch 4 / 10\n", - "\n", - "100%|██████████| 469/469 [01:13<00:00, 6.34it/s, train_acc=0.973, train_loss=0.0888]\n", - "100%|██████████| 79/79 [00:12<00:00, 6.47it/s, val_acc=0.979, val_loss=0.0789] \n", - "<<<<<< reach best val_acc : 0.9793999791145325 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:06:18\n", - "Epoch 5 / 10\n", - "\n", - "100%|██████████| 469/469 [01:04<00:00, 7.30it/s, train_acc=0.977, train_loss=0.08] \n", - "100%|██████████| 79/79 [00:12<00:00, 6.19it/s, val_acc=0.975, val_loss=0.0828]\n", - "\n", - "================================================================================2022-07-17 21:07:35\n", - "Epoch 6 / 10\n", - "\n", - "100%|██████████| 469/469 [01:03<00:00, 7.44it/s, train_acc=0.979, train_loss=0.0719]\n", - "100%|██████████| 79/79 [00:08<00:00, 9.51it/s, val_acc=0.981, val_loss=0.0664] \n", - "<<<<<< reach best val_acc : 0.9805999994277954 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:08:47\n", - "Epoch 7 / 10\n", - "\n", - "100%|██████████| 469/469 [01:01<00:00, 7.57it/s, train_acc=0.979, train_loss=0.0738]\n", - "100%|██████████| 79/79 [00:07<00:00, 10.12it/s, val_acc=0.982, val_loss=0.0707] \n", - "<<<<<< reach best val_acc : 0.9817000031471252 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:09:56\n", - "Epoch 8 / 10\n", - "\n", - "100%|██████████| 469/469 [01:02<00:00, 7.50it/s, train_acc=0.979, train_loss=0.0747]\n", - "100%|██████████| 79/79 [00:07<00:00, 9.91it/s, val_acc=0.983, val_loss=0.0667] \n", - "<<<<<< reach best val_acc : 0.9833999872207642 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:11:07\n", - "Epoch 9 / 10\n", - "\n", - "100%|██████████| 469/469 [01:03<00:00, 7.44it/s, train_acc=0.98, train_loss=0.0748] \n", - "100%|██████████| 79/79 [00:07<00:00, 10.20it/s, val_acc=0.985, val_loss=0.0658]\n", - "<<<<<< reach best val_acc : 0.9850000143051147 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:12:18\n", - "Epoch 10 / 10\n", - "\n", - "100%|██████████| 469/469 [01:02<00:00, 7.51it/s, train_acc=0.979, train_loss=0.0742]\n", - "100%|██████████| 79/79 [00:08<00:00, 9.58it/s, val_acc=0.982, val_loss=0.0751]\n", - "```" + " mode=\"max\",\n", + " plot=True,\n", + " cpu=True\n", + ")\n" ] }, { @@ -892,223 +3201,6 @@ "outputs": [], "source": [] }, - { - "cell_type": "code", - "execution_count": null, - "id": "c5a56a18", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "a13efa44", - "metadata": {}, - "source": [ - "### 四，类风格 torchkeras.LightModel" - ] - }, - { - "cell_type": "markdown", - "id": "cfae718b", - "metadata": {}, - "source": [ - "除了torchkeras.KerasModel,torchkeras还提供了torchkeras.LightModel 来支持更多的功能。\n", - "\n", - "\n", - "torchkeras.KerasModel 更加简单, 推荐给新手用户。\n", - "\n", - "而LightModel借鉴了 pytorch_lightning 库中的许多功能，并演示了对pytorch_lightning的一种最佳实践。\n", - "\n", - "\n", - "尽管存在着一些差异, torchkeras.KerasModel 和 torchkeras.LightModel 的使用方式和特性是非常相似的。\n", - "\n", - "详情参考：https://github.com/lyhue1991/torchkeras \n", - "\n", - "\n", - "\n", - "\n", - "|features| torchkeras.KerasModel | torchkeras.LightModel | \n", - "|----:|:-------------------------:|:-----------:|\n", - "|progress bar | ✅ |✅ |\n", - "|early stopping | ✅ |✅ |\n", - "|metrics from torchmetrics | ✅ |✅ |\n", - "|gpu training | ✅ |✅ |\n", - "|multi-gpus training | ❌ |✅ |\n", - "|tensorboard callback | ❌ |✅ |\n", - "|simple source code| ✅ |❌ |" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c628b28a", - "metadata": {}, - "outputs": [], - "source": [ - "from torchkeras import LightModel \n", - "\n", - "class Net(nn.Module):\n", - " def __init__(self):\n", - " super().__init__()\n", - " self.layers = nn.ModuleList([\n", - " nn.Conv2d(in_channels=1,out_channels=32,kernel_size = 3),\n", - " nn.MaxPool2d(kernel_size = 2,stride = 2),\n", - " nn.Conv2d(in_channels=32,out_channels=64,kernel_size = 5),\n", - " nn.MaxPool2d(kernel_size = 2,stride = 2),\n", - " nn.Dropout2d(p = 0.1),\n", - " nn.AdaptiveMaxPool2d((1,1)),\n", - " nn.Flatten(),\n", - " nn.Linear(64,32),\n", - " nn.ReLU(),\n", - " nn.Linear(32,10)]\n", - " )\n", - " def forward(self,x):\n", - " for layer in self.layers:\n", - " x = layer(x)\n", - " return x\n", - " \n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "99073760", - "metadata": {}, - "outputs": [], - "source": [ - "from torchmetrics import Accuracy \n", - "import pytorch_lightning as pl \n", - "\n", - "net = Net()\n", - "loss_fn = nn.CrossEntropyLoss()\n", - "metric_dict = {\"acc\":Accuracy()}\n", - "\n", - "optimizer = torch.optim.Adam(net.parameters(), lr=0.01)\n", - "\n", - "model = torchkeras.LightModel(net,\n", - " loss_fn = loss_fn,\n", - " metrics_dict= metric_dict,\n", - " optimizer = optimizer\n", - " ) \n", - "\n", - "\n", - " \n", - "#1，设置回调函数\n", - "model_ckpt = pl.callbacks.ModelCheckpoint(\n", - " monitor='val_acc',\n", - " save_top_k=1,\n", - " mode='max'\n", - ")\n", - "\n", - "early_stopping = pl.callbacks.EarlyStopping(monitor = 'val_acc',\n", - " patience=3,\n", - " mode = 'max'\n", - " )\n", - "\n", - "#2，设置训练参数\n", - "\n", - "trainer = pl.Trainer(logger=True,\n", - " min_epochs=10,max_epochs=20,\n", - " gpus=0,\n", - " callbacks = [model_ckpt,early_stopping],\n", - " enable_progress_bar = True) \n", - "\n", - "\n", - "##3，启动训练循环\n", - "trainer.fit(model,dl_train,dl_val)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bcf4a5be", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "b0cff3ac", - "metadata": {}, - "source": [ - "```\n", - " | Name | Type | Params\n", - "---------------------------------------------------\n", - "0 | net | Net | 54.0 K\n", - "1 | train_metrics | ModuleDict | 0 \n", - "2 | val_metrics | ModuleDict | 0 \n", - "3 | test_metrics | ModuleDict | 0 \n", - "4 | loss_fn | CrossEntropyLoss | 0 \n", - "---------------------------------------------------\n", - "54.0 K Trainable params\n", - "0 Non-trainable params\n", - "54.0 K Total params\n", - "0.216 Total estimated model params size (MB)\n", - "\n", - "================================================================================2022-07-17 21:30:14\n", - "{'epoch': 0, 'val_loss': 2.330533266067505, 'val_acc': 0.1015625}\n", - "<<<<<< reach best val_acc : 0.1015625 >>>>>>\n", - "Epoch 9: 100%\n", - "548/548 [01:08<00:00, 8.04it/s, loss=0.0785, v_num=2, acc=0.948]\n", - "\n", - "================================================================================2022-07-17 21:31:23\n", - "{'epoch': 0, 'val_loss': 0.12257852405309677, 'val_acc': 0.961899995803833}\n", - "{'epoch': 0, 'train_loss': 0.3137461841106415, 'train_acc': 0.8978333473205566}\n", - "<<<<<< reach best val_acc : 0.961899995803833 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:32:37\n", - "{'epoch': 1, 'val_loss': 0.08038929104804993, 'val_acc': 0.9764000177383423}\n", - "{'epoch': 1, 'train_loss': 0.1108873263001442, 'train_acc': 0.9664333462715149}\n", - "<<<<<< reach best val_acc : 0.9764000177383423 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:33:46\n", - "{'epoch': 2, 'val_loss': 0.07084609568119049, 'val_acc': 0.9800000190734863}\n", - "{'epoch': 2, 'train_loss': 0.09520334005355835, 'train_acc': 0.9722499847412109}\n", - "<<<<<< reach best val_acc : 0.9800000190734863 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:34:55\n", - "{'epoch': 3, 'val_loss': 0.0732991024851799, 'val_acc': 0.9796000123023987}\n", - "{'epoch': 3, 'train_loss': 0.0875784158706665, 'train_acc': 0.973550021648407}\n", - "\n", - "================================================================================2022-07-17 21:36:04\n", - "{'epoch': 4, 'val_loss': 0.07758694887161255, 'val_acc': 0.9775999784469604}\n", - "{'epoch': 4, 'train_loss': 0.0757126733660698, 'train_acc': 0.9774666428565979}\n", - "\n", - "================================================================================2022-07-17 21:37:13\n", - "{'epoch': 5, 'val_loss': 0.05971676856279373, 'val_acc': 0.9832000136375427}\n", - "{'epoch': 5, 'train_loss': 0.07704824209213257, 'train_acc': 0.9775000214576721}\n", - "<<<<<< reach best val_acc : 0.9832000136375427 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:38:22\n", - "{'epoch': 6, 'val_loss': 0.054445140063762665, 'val_acc': 0.9847000241279602}\n", - "{'epoch': 6, 'train_loss': 0.07280954718589783, 'train_acc': 0.9792166948318481}\n", - "<<<<<< reach best val_acc : 0.9847000241279602 >>>>>>\n", - "\n", - "================================================================================2022-07-17 21:39:29\n", - "{'epoch': 7, 'val_loss': 0.08798510581254959, 'val_acc': 0.9764999747276306}\n", - "{'epoch': 7, 'train_loss': 0.07288103550672531, 'train_acc': 0.9790499806404114}\n", - "\n", - "================================================================================2022-07-17 21:40:32\n", - "{'epoch': 8, 'val_loss': 0.08194874972105026, 'val_acc': 0.9761000275611877}\n", - "{'epoch': 8, 'train_loss': 0.06978205591440201, 'train_acc': 0.9799833297729492}\n", - "\n", - "================================================================================2022-07-17 21:41:40\n", - "{'epoch': 9, 'val_loss': 0.07893478125333786, 'val_acc': 0.9810000061988831}\n", - "{'epoch': 9, 'train_loss': 0.06853801012039185, 'train_acc': 0.9806166887283325}\n", - "```" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "654f7576", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "597ea3df", @@ -1122,29 +3214,29 @@ "\n", "![算法美食屋logo.png](https://tva1.sinaimg.cn/large/e6c9d24egy1h41m2zugguj20k00b9q46.jpg)" ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "fdc05012", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9270eea", - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,md", - "main_language": "python" + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/6-3,\344\275\277\347\224\250GPU\350\256\255\347\273\203\346\250\241\345\236\213.ipynb" "b/6-3,\344\275\277\347\224\250GPU\350\256\255\347\273\203\346\250\241\345\236\213.ipynb" index e0e15be15..1bd82b1d3 100644 --- "a/6-3,\344\275\277\347\224\250GPU\350\256\255\347\273\203\346\250\241\345\236\213.ipynb" +++ "b/6-3,\344\275\277\347\224\250GPU\350\256\255\347\273\203\346\250\241\345\236\213.ipynb" @@ -2,16 +2,13 @@ "cells": [ { "cell_type": "markdown", - "id": "2ca7bf1c", "metadata": {}, "source": [ - "\n", "# 6-3,使用GPU训练模型" ] }, { "cell_type": "markdown", - "id": "cadcb375", "metadata": {}, "source": [ "深度学习的训练过程常常非常耗时，一个模型训练几个小时是家常便饭，训练几天也是常有的事情，有时候甚至要训练几十天。\n", @@ -26,36 +23,65 @@ { "cell_type": "code", "execution_count": null, - "id": "14993aea", - "metadata": {}, + "metadata": { + "tags": [] + }, "outputs": [], + "source": [ + "!pip install -q torchkeras \n", + "!pip install -q -U torchmetrics" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:21:37.080604Z", + "iopub.status.busy": "2023-08-02T09:21:37.079630Z", + "iopub.status.idle": "2023-08-02T09:21:44.145337Z", + "shell.execute_reply": "2023-08-02T09:21:44.144019Z", + "shell.execute_reply.started": "2023-08-02T09:21:37.080551Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__ = 1.11.0\n", + "torchkeras.__version__ = 3.9.3\n", + "torchmetrics.__version__ = 0.11.4\n" + ] + } + ], "source": [ "import torch \n", "import torchkeras \n", + "import torchmetrics\n", "\n", "print(\"torch.__version__ = \",torch.__version__)\n", - "print(\"torchkeras.__version__ = \",torchkeras.__version__)\n" + "print(\"torchkeras.__version__ = \",torchkeras.__version__)\n", + "print(\"torchmetrics.__version__ = \",torchmetrics.__version__)" ] }, { "cell_type": "markdown", - "id": "a67c1c4c", "metadata": {}, "source": [ "注：本节代码只能在有GPU的机器环境上才能正确执行。\n", "\n", - "对于没有GPU的同学，推荐使用kaggle平台上的GPU。\n", + "对于没有GPU的同学，推荐使用\n", "\n", + "在Colab笔记本中：修改->笔记本设置->硬件加速器 中选择 GPU\n", "\n", "可点击如下链接，直接在kaggle中运行范例代码。\n", "\n", - "https://www.kaggle.com/lyhue1991/pytorch-gpu-examples\n", - "\n" + "https://www.kaggle.com/lyhue1991/pytorch-gpu-examples" ] }, { "cell_type": "markdown", - "id": "3ed179b3", "metadata": {}, "source": [ "Pytorch中使用GPU加速模型非常简单，只要将模型和数据移动到GPU上。核心代码只有以下几行。\n", @@ -95,7 +121,6 @@ }, { "cell_type": "markdown", - "id": "cfa2354a", "metadata": {}, "source": [ "## 〇，GPU相关操作汇总" @@ -103,10 +128,26 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "98f0023a", - "metadata": {}, - "outputs": [], + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:21:49.406397Z", + "iopub.status.busy": "2023-08-02T09:21:49.405676Z", + "iopub.status.idle": "2023-08-02T09:21:49.469074Z", + "shell.execute_reply": "2023-08-02T09:21:49.467906Z", + "shell.execute_reply.started": "2023-08-02T09:21:49.406358Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "if_cuda= True\n", + "gpu_count= 1\n" + ] + } + ], "source": [ "import torch \n", "from torch import nn \n", @@ -121,10 +162,27 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "ae2a699e", - "metadata": {}, - "outputs": [], + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:21:50.949675Z", + "iopub.status.busy": "2023-08-02T09:21:50.949297Z", + "iopub.status.idle": "2023-08-02T09:21:55.584912Z", + "shell.execute_reply": "2023-08-02T09:21:55.583660Z", + "shell.execute_reply.started": "2023-08-02T09:21:50.949642Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda:0\n", + "True\n", + "cpu\n" + ] + } + ], "source": [ "# 2，将张量在gpu和cpu间移动\n", "tensor = torch.rand((100,100))\n", @@ -138,10 +196,27 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "7daa73c5", - "metadata": {}, - "outputs": [], + "execution_count": 7, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:21:55.587734Z", + "iopub.status.busy": "2023-08-02T09:21:55.587050Z", + "iopub.status.idle": "2023-08-02T09:21:55.597566Z", + "shell.execute_reply": "2023-08-02T09:21:55.596260Z", + "shell.execute_reply.started": "2023-08-02T09:21:55.587689Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n", + "True\n", + "cuda:0\n" + ] + } + ], "source": [ "# 3，将模型中的全部张量移动到gpu上\n", "net = nn.Linear(2,1)\n", @@ -153,10 +228,37 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "029cd019", - "metadata": {}, - "outputs": [], + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:21:55.599678Z", + "iopub.status.busy": "2023-08-02T09:21:55.599312Z", + "iopub.status.idle": "2023-08-02T09:21:55.621575Z", + "shell.execute_reply": "2023-08-02T09:21:55.620550Z", + "shell.execute_reply.started": "2023-08-02T09:21:55.599640Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cpu\n", + "[0]\n", + "cuda:0\n" + ] + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# 4，创建支持多个gpu数据并行的模型\n", "linear = nn.Linear(2,1)\n", @@ -175,7 +277,6 @@ }, { "cell_type": "markdown", - "id": "91dde858", "metadata": {}, "source": [ "## 一，矩阵乘法范例" @@ -183,7 +284,6 @@ }, { "cell_type": "markdown", - "id": "1f750b06", "metadata": {}, "source": [ "下面分别使用CPU和GPU作一个矩阵乘法，并比较其计算效率。" @@ -191,9 +291,16 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "a729766e", - "metadata": {}, + "execution_count": 9, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:21:58.132801Z", + "iopub.status.busy": "2023-08-02T09:21:58.131981Z", + "iopub.status.idle": "2023-08-02T09:21:58.138927Z", + "shell.execute_reply": "2023-08-02T09:21:58.137799Z", + "shell.execute_reply.started": "2023-08-02T09:21:58.132746Z" + } + }, "outputs": [], "source": [ "import time\n", @@ -203,10 +310,27 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "f4b0ef82", - "metadata": {}, - "outputs": [], + "execution_count": 10, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:21:59.222992Z", + "iopub.status.busy": "2023-08-02T09:21:59.222619Z", + "iopub.status.idle": "2023-08-02T09:21:59.871529Z", + "shell.execute_reply": "2023-08-02T09:21:59.870275Z", + "shell.execute_reply.started": "2023-08-02T09:21:59.222960Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6000046730041504\n", + "cpu\n", + "cpu\n" + ] + } + ], "source": [ "# 使用cpu\n", "a = torch.rand((10000,200))\n", @@ -222,10 +346,27 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "893d1a27", - "metadata": {}, - "outputs": [], + "execution_count": 11, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:01.349894Z", + "iopub.status.busy": "2023-08-02T09:22:01.349166Z", + "iopub.status.idle": "2023-08-02T09:22:02.226856Z", + "shell.execute_reply": "2023-08-02T09:22:02.224728Z", + "shell.execute_reply.started": "2023-08-02T09:22:01.349856Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.8443384170532227\n", + "cuda:0\n", + "cuda:0\n" + ] + } + ], "source": [ "# 使用gpu\n", "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", @@ -243,14 +384,12 @@ { "cell_type": "code", "execution_count": null, - "id": "63fa7654", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "1babcdf5", "metadata": {}, "source": [ "## 二，线性回归范例" @@ -258,15 +397,21 @@ }, { "cell_type": "markdown", - "id": "90b7b1e8", - "metadata": {}, + "metadata": { + "execution": { + "iopub.execute_input": "2022-07-17T14:58:49.525724Z", + "iopub.status.busy": "2022-07-17T14:58:49.525304Z", + "iopub.status.idle": "2022-07-17T14:58:49.546334Z", + "shell.execute_reply": "2022-07-17T14:58:49.544588Z", + "shell.execute_reply.started": "2022-07-17T14:58:49.525694Z" + } + }, "source": [ "下面对比使用CPU和GPU训练一个线性回归模型的效率" ] }, { "cell_type": "markdown", - "id": "eb77c320", "metadata": {}, "source": [ "### 1，使用CPU" @@ -274,9 +419,16 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "cfecdb45", - "metadata": {}, + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:05.233030Z", + "iopub.status.busy": "2023-08-02T09:22:05.232639Z", + "iopub.status.idle": "2023-08-02T09:22:05.297141Z", + "shell.execute_reply": "2023-08-02T09:22:05.296102Z", + "shell.execute_reply.started": "2023-08-02T09:22:05.232997Z" + } + }, "outputs": [], "source": [ "# 准备数据\n", @@ -290,9 +442,16 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "add72b0d", - "metadata": {}, + "execution_count": 13, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:06.677267Z", + "iopub.status.busy": "2023-08-02T09:22:06.676242Z", + "iopub.status.idle": "2023-08-02T09:22:06.685850Z", + "shell.execute_reply": "2023-08-02T09:22:06.684746Z", + "shell.execute_reply.started": "2023-08-02T09:22:06.677187Z" + } + }, "outputs": [], "source": [ "# 定义模型\n", @@ -310,10 +469,35 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "091eef39", - "metadata": {}, - "outputs": [], + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:08.718803Z", + "iopub.status.busy": "2023-08-02T09:22:08.718085Z", + "iopub.status.idle": "2023-08-02T09:22:13.949452Z", + "shell.execute_reply": "2023-08-02T09:22:13.948304Z", + "shell.execute_reply.started": "2023-08-02T09:22:08.718765Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'epoch': 0, 'loss': 258.9547119140625}\n", + "{'epoch': 50, 'loss': 33.212669372558594}\n", + "{'epoch': 100, 'loss': 9.038525581359863}\n", + "{'epoch': 150, 'loss': 4.485360145568848}\n", + "{'epoch': 200, 'loss': 4.017963409423828}\n", + "{'epoch': 250, 'loss': 3.994182825088501}\n", + "{'epoch': 300, 'loss': 3.993659734725952}\n", + "{'epoch': 350, 'loss': 3.9936563968658447}\n", + "{'epoch': 400, 'loss': 3.9936563968658447}\n", + "{'epoch': 450, 'loss': 3.9936563968658447}\n", + "time used: 5.222184896469116\n" + ] + } + ], "source": [ "# 训练模型\n", "optimizer = torch.optim.Adam(linear.parameters(),lr = 0.1)\n", @@ -337,7 +521,6 @@ }, { "cell_type": "markdown", - "id": "0099bc25", "metadata": {}, "source": [ "### 2，使用GPU" @@ -345,10 +528,27 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "dc8b66cb", - "metadata": {}, - "outputs": [], + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:13.952419Z", + "iopub.status.busy": "2023-08-02T09:22:13.951355Z", + "iopub.status.idle": "2023-08-02T09:22:13.998524Z", + "shell.execute_reply": "2023-08-02T09:22:13.997457Z", + "shell.execute_reply.started": "2023-08-02T09:22:13.952376Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.cuda.is_available() = True\n", + "X.device: cuda:0\n", + "Y.device: cuda:0\n" + ] + } + ], "source": [ "# 准备数据\n", "n = 1000000 #样本数量\n", @@ -368,10 +568,25 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "4f368c13", - "metadata": {}, - "outputs": [], + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:14.856923Z", + "iopub.status.busy": "2023-08-02T09:22:14.856519Z", + "iopub.status.idle": "2023-08-02T09:22:14.867761Z", + "shell.execute_reply": "2023-08-02T09:22:14.866595Z", + "shell.execute_reply.started": "2023-08-02T09:22:14.856887Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "if on cuda: True\n" + ] + } + ], "source": [ "# 定义模型\n", "class LinearRegression(nn.Module): \n", @@ -395,10 +610,35 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "46296b4c", - "metadata": {}, - "outputs": [], + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:21.232004Z", + "iopub.status.busy": "2023-08-02T09:22:21.231614Z", + "iopub.status.idle": "2023-08-02T09:22:21.785143Z", + "shell.execute_reply": "2023-08-02T09:22:21.783907Z", + "shell.execute_reply.started": "2023-08-02T09:22:21.231970Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'epoch': 0, 'loss': 153.66574096679688}\n", + "{'epoch': 50, 'loss': 32.86173629760742}\n", + "{'epoch': 100, 'loss': 9.03520679473877}\n", + "{'epoch': 150, 'loss': 4.485783576965332}\n", + "{'epoch': 200, 'loss': 4.018568515777588}\n", + "{'epoch': 250, 'loss': 3.994813919067383}\n", + "{'epoch': 300, 'loss': 3.9942924976348877}\n", + "{'epoch': 350, 'loss': 3.994288921356201}\n", + "{'epoch': 400, 'loss': 3.9942891597747803}\n", + "{'epoch': 450, 'loss': 3.9942891597747803}\n", + "time used: 0.5444216728210449\n" + ] + } + ], "source": [ "# 训练模型\n", "optimizer = torch.optim.Adam(linear.parameters(),lr = 0.1)\n", @@ -423,14 +663,12 @@ { "cell_type": "code", "execution_count": null, - "id": "6921a4fe", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", - "id": "978fdb3f", "metadata": {}, "source": [ "## 三，图片分类范例" @@ -438,9 +676,16 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "34de7cf7", - "metadata": {}, + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:24.911548Z", + "iopub.status.busy": "2023-08-02T09:22:24.911130Z", + "iopub.status.idle": "2023-08-02T09:22:24.917106Z", + "shell.execute_reply": "2023-08-02T09:22:24.915927Z", + "shell.execute_reply.started": "2023-08-02T09:22:24.911513Z" + } + }, "outputs": [], "source": [ "import torch \n", @@ -452,18 +697,130 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "6130e226", - "metadata": {}, - "outputs": [], + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:22:26.656218Z", + "iopub.status.busy": "2023-08-02T09:22:26.655272Z", + "iopub.status.idle": "2023-08-02T09:22:27.893139Z", + "shell.execute_reply": "2023-08-02T09:22:27.892010Z", + "shell.execute_reply.started": "2023-08-02T09:22:26.656155Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to mnist/MNIST/raw/train-images-idx3-ubyte.gz\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8f4936d7cceb439494d46f71f02f7518", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/9912422 [00:00>>>>>\n", + "\n", + "================================================================================2023-08-02 09:23:33\n", + "Epoch 2 / 3\n", + "\n", + "100%|██████████| 469/469 [00:35<00:00, 13.29it/s, train_acc=0.966, train_loss=0.109] \n", + "100%|██████████| 79/79 [00:03<00:00, 22.50it/s, val_acc=0.975, val_loss=0.0814]\n", + "<<<<<< reach best val_acc : 0.9749000072479248 >>>>>>\n", + "\n", + "================================================================================2023-08-02 09:24:12\n", + "Epoch 3 / 3\n", + "\n", + "100%|██████████| 469/469 [00:34<00:00, 13.50it/s, train_acc=0.971, train_loss=0.095] \n", + "100%|██████████| 79/79 [00:03<00:00, 23.03it/s, val_acc=0.964, val_loss=0.12] \n", + "<<<<<< val_acc without improvement in 1 epoch, early stopping >>>>>>\n" + ] + } + ], "source": [ "import os,sys,time\n", "import numpy as np\n", @@ -531,14 +948,14 @@ "\n", "loss_fn = nn.CrossEntropyLoss()\n", "optimizer= torch.optim.Adam(net.parameters(),lr = 0.01) \n", - "metrics_dict = {\"acc\":Accuracy()}\n", + "metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)}\n", "\n", - "epochs = 20 \n", + "epochs = 3 \n", "ckpt_path='checkpoint.pt'\n", "\n", "#early_stopping相关设置\n", "monitor=\"val_acc\"\n", - "patience=5\n", + "patience=1\n", "mode=\"max\"\n", "\n", "history = {}\n", @@ -551,7 +968,7 @@ " \n", " total_loss,step = 0,0\n", " \n", - " loop = tqdm(enumerate(dl_train), total =len(dl_train))\n", + " loop = tqdm(enumerate(dl_train), total =len(dl_train),file=sys.stdout)\n", " train_metrics_dict = deepcopy(metrics_dict) \n", " \n", " for i, batch in loop: \n", @@ -595,7 +1012,7 @@ " net.eval()\n", " \n", " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dl_val), total =len(dl_val))\n", + " loop = tqdm(enumerate(dl_val), total =len(dl_val),file=sys.stdout)\n", " \n", " val_metrics_dict = deepcopy(metrics_dict) \n", " \n", @@ -638,91 +1055,25 @@ " if best_score_idx==len(arr_scores)-1:\n", " torch.save(net.state_dict(),ckpt_path)\n", " print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", + " arr_scores[best_score_idx]))\n", " if len(arr_scores)-best_score_idx>patience:\n", " print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n", - " monitor,patience),file=sys.stderr)\n", + " monitor,patience))\n", " break \n", " net.load_state_dict(torch.load(ckpt_path))\n", " \n", - "dfhistory = pd.DataFrame(history)\n" + "dfhistory = pd.DataFrame(history)" ] }, { "cell_type": "markdown", - "id": "b0308b51", "metadata": {}, "source": [ - "================================================================================2022-07-17 15:07:03\n", - "Epoch 1 / 20\n", - "\n", - "100%|██████████| 469/469 [00:57<00:00, 8.15it/s, train_acc=0.909, train_loss=0.279] \n", - "100%|██████████| 79/79 [00:04<00:00, 16.80it/s, val_acc=0.956, val_loss=0.147] \n", - "\n", - "================================================================================2022-07-17 15:08:06\n", - "Epoch 2 / 20\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9556000232696533 >>>>>>\n", - "100%|██████████| 469/469 [00:58<00:00, 8.03it/s, train_acc=0.968, train_loss=0.105] \n", - "100%|██████████| 79/79 [00:04<00:00, 18.59it/s, val_acc=0.977, val_loss=0.0849]\n", - "\n", - "================================================================================2022-07-17 15:09:09\n", - "Epoch 3 / 20\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9765999913215637 >>>>>>\n", - "100%|██████████| 469/469 [00:58<00:00, 8.07it/s, train_acc=0.974, train_loss=0.0882]\n", - "100%|██████████| 79/79 [00:04<00:00, 17.13it/s, val_acc=0.984, val_loss=0.0554] \n", - "<<<<<< reach best val_acc : 0.9843000173568726 >>>>>>\n", - "\n", - "================================================================================2022-07-17 15:10:12\n", - "Epoch 4 / 20\n", - "\n", - "100%|██████████| 469/469 [01:01<00:00, 7.63it/s, train_acc=0.976, train_loss=0.0814] \n", - "100%|██████████| 79/79 [00:04<00:00, 16.34it/s, val_acc=0.979, val_loss=0.0708]\n", - "\n", - "================================================================================2022-07-17 15:11:18\n", - "Epoch 5 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [01:03<00:00, 7.42it/s, train_acc=0.974, train_loss=0.0896]\n", - "100%|██████████| 79/79 [00:05<00:00, 14.06it/s, val_acc=0.979, val_loss=0.076] \n", - "\n", - "================================================================================2022-07-17 15:12:28\n", - "Epoch 6 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [01:00<00:00, 7.77it/s, train_acc=0.972, train_loss=0.0937]\n", - "100%|██████████| 79/79 [00:04<00:00, 17.45it/s, val_acc=0.976, val_loss=0.0787] \n", - "\n", - "================================================================================2022-07-17 15:13:33\n", - "Epoch 7 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [01:01<00:00, 7.63it/s, train_acc=0.974, train_loss=0.0858]\n", - "100%|██████████| 79/79 [00:05<00:00, 14.50it/s, val_acc=0.976, val_loss=0.082] \n", - "\n", - "================================================================================2022-07-17 15:14:40\n", - "Epoch 8 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:59<00:00, 7.85it/s, train_acc=0.972, train_loss=0.0944]\n", - "100%|██████████| 79/79 [00:04<00:00, 17.21it/s, val_acc=0.982, val_loss=0.062] \n", - "<<<<<< val_acc without improvement in 5 epoch, early stopping >>>>>>\n" - ] - }, - { - "cell_type": "markdown", - "id": "6c88222e", - "metadata": {}, - "source": [ - "CPU每个Epoch大概1分钟" + "CPU每个Epoch大概40s" ] }, { "cell_type": "markdown", - "id": "30f523ad", "metadata": {}, "source": [ "### 2，使用GPU进行训练" @@ -730,10 +1081,45 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "53792236", - "metadata": {}, - "outputs": [], + "execution_count": 23, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:27:40.875092Z", + "iopub.status.busy": "2023-08-02T09:27:40.874670Z", + "iopub.status.idle": "2023-08-02T09:28:18.788065Z", + "shell.execute_reply": "2023-08-02T09:28:18.786947Z", + "shell.execute_reply.started": "2023-08-02T09:27:40.875053Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "================================================================================2023-08-02 09:27:40\n", + "Epoch 1 / 5\n", + "\n", + "100%|██████████| 469/469 [00:14<00:00, 33.06it/s, train_acc=0.91, train_loss=0.283] \n", + "100%|██████████| 79/79 [00:01<00:00, 56.00it/s, val_acc=0.972, val_loss=0.0912]\n", + "<<<<<< reach best val_acc : 0.972000002861023 >>>>>>\n", + "\n", + "================================================================================2023-08-02 09:27:56\n", + "Epoch 2 / 5\n", + "\n", + "100%|██████████| 469/469 [00:09<00:00, 50.56it/s, train_acc=0.968, train_loss=0.105] \n", + "100%|██████████| 79/79 [00:01<00:00, 40.13it/s, val_acc=0.98, val_loss=0.0672] \n", + "<<<<<< reach best val_acc : 0.9800000190734863 >>>>>>\n", + "\n", + "================================================================================2023-08-02 09:28:08\n", + "Epoch 3 / 5\n", + "\n", + "100%|██████████| 469/469 [00:09<00:00, 51.60it/s, train_acc=0.972, train_loss=0.0926]\n", + "100%|██████████| 79/79 [00:01<00:00, 55.83it/s, val_acc=0.964, val_loss=0.121] \n", + "<<<<<< val_acc without improvement in 1 epoch, early stopping >>>>>>\n" + ] + } + ], "source": [ "import os,sys,time\n", "import numpy as np\n", @@ -757,7 +1143,7 @@ "\n", "loss_fn = nn.CrossEntropyLoss()\n", "optimizer= torch.optim.Adam(net.parameters(),lr = 0.01) \n", - "metrics_dict = {\"acc\":Accuracy()}\n", + "metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)}\n", "\n", "\n", "# =========================移动模型到GPU上==============================\n", @@ -769,12 +1155,12 @@ "# ====================================================================\n", "\n", "\n", - "epochs = 20 \n", + "epochs = 5 \n", "ckpt_path='checkpoint.pt'\n", "\n", "#early_stopping相关设置\n", "monitor=\"val_acc\"\n", - "patience=5\n", + "patience=1\n", "mode=\"max\"\n", "\n", "history = {}\n", @@ -787,7 +1173,7 @@ " \n", " total_loss,step = 0,0\n", " \n", - " loop = tqdm(enumerate(dl_train), total =len(dl_train))\n", + " loop = tqdm(enumerate(dl_train), total =len(dl_train),file=sys.stdout)\n", " train_metrics_dict = deepcopy(metrics_dict) \n", " \n", " for i, batch in loop: \n", @@ -837,7 +1223,7 @@ " net.eval()\n", " \n", " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dl_val), total =len(dl_val))\n", + " loop = tqdm(enumerate(dl_val), total =len(dl_val),file=sys.stdout)\n", " \n", " val_metrics_dict = deepcopy(metrics_dict) \n", " \n", @@ -885,134 +1271,33 @@ " if best_score_idx==len(arr_scores)-1:\n", " torch.save(net.state_dict(),ckpt_path)\n", " print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", + " arr_scores[best_score_idx]))\n", " if len(arr_scores)-best_score_idx>patience:\n", " print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n", - " monitor,patience),file=sys.stderr)\n", + " monitor,patience))\n", " break \n", " net.load_state_dict(torch.load(ckpt_path))\n", " \n", - "dfhistory = pd.DataFrame(history)\n" + "dfhistory = pd.DataFrame(history)" ] }, { "cell_type": "markdown", - "id": "b7d6009c", "metadata": {}, "source": [ - "```\n", - "================================================================================2022-07-17 15:20:40\n", - "Epoch 1 / 20\n", - "\n", - "100%|██████████| 469/469 [00:12<00:00, 37.07it/s, train_acc=0.89, train_loss=0.336] \n", - "100%|██████████| 79/79 [00:02<00:00, 37.31it/s, val_acc=0.95, val_loss=0.16] \n", - "\n", - "================================================================================2022-07-17 15:20:55\n", - "Epoch 2 / 20\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9498000144958496 >>>>>>\n", - "100%|██████████| 469/469 [00:12<00:00, 37.04it/s, train_acc=0.964, train_loss=0.115] \n", - "100%|██████████| 79/79 [00:01<00:00, 43.36it/s, val_acc=0.972, val_loss=0.0909]\n", - "\n", - "================================================================================2022-07-17 15:21:10\n", - "Epoch 3 / 20\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9721999764442444 >>>>>>\n", - "100%|██████████| 469/469 [00:12<00:00, 38.05it/s, train_acc=0.971, train_loss=0.0968]\n", - "100%|██████████| 79/79 [00:01<00:00, 42.10it/s, val_acc=0.974, val_loss=0.0878] \n", - "\n", - "================================================================================2022-07-17 15:21:24\n", - "Epoch 4 / 20\n", - "\n", - "<<<<<< reach best val_acc : 0.974399983882904 >>>>>>\n", - "100%|██████████| 469/469 [00:13<00:00, 35.56it/s, train_acc=0.973, train_loss=0.089] \n", - "100%|██████████| 79/79 [00:02<00:00, 38.16it/s, val_acc=0.982, val_loss=0.0585]\n", - "\n", - "================================================================================2022-07-17 15:21:40\n", - "Epoch 5 / 20\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9822999835014343 >>>>>>\n", - "100%|██████████| 469/469 [00:12<00:00, 36.80it/s, train_acc=0.977, train_loss=0.0803]\n", - "100%|██████████| 79/79 [00:01<00:00, 42.38it/s, val_acc=0.976, val_loss=0.0791]\n", - "\n", - "================================================================================2022-07-17 15:21:55\n", - "Epoch 6 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:13<00:00, 34.63it/s, train_acc=0.977, train_loss=0.0787]\n", - "100%|██████████| 79/79 [00:02<00:00, 39.01it/s, val_acc=0.97, val_loss=0.105] \n", - "\n", - "================================================================================2022-07-17 15:22:11\n", - "Epoch 7 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:12<00:00, 37.39it/s, train_acc=0.975, train_loss=0.0871]\n", - "100%|██████████| 79/79 [00:02<00:00, 39.16it/s, val_acc=0.984, val_loss=0.0611]\n", - "\n", - "================================================================================2022-07-17 15:22:26\n", - "Epoch 8 / 20\n", - "\n", - "\n", - "<<<<<< reach best val_acc : 0.9835000038146973 >>>>>>\n", - "100%|██████████| 469/469 [00:13<00:00, 35.63it/s, train_acc=0.976, train_loss=0.0774] \n", - "100%|██████████| 79/79 [00:01<00:00, 42.92it/s, val_acc=0.982, val_loss=0.0778] \n", - "\n", - "================================================================================2022-07-17 15:22:41\n", - "Epoch 9 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:12<00:00, 37.96it/s, train_acc=0.976, train_loss=0.0819]\n", - "100%|██████████| 79/79 [00:01<00:00, 42.99it/s, val_acc=0.981, val_loss=0.0652] \n", - "\n", - "================================================================================2022-07-17 15:22:56\n", - "Epoch 10 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:13<00:00, 35.29it/s, train_acc=0.975, train_loss=0.0852]\n", - "100%|██████████| 79/79 [00:01<00:00, 41.38it/s, val_acc=0.978, val_loss=0.0808]\n", - "\n", - "================================================================================2022-07-17 15:23:12\n", - "Epoch 11 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:12<00:00, 38.77it/s, train_acc=0.975, train_loss=0.0863] \n", - "100%|██████████| 79/79 [00:01<00:00, 42.71it/s, val_acc=0.983, val_loss=0.0665] \n", - "\n", - "================================================================================2022-07-17 15:23:26\n", - "Epoch 12 / 20\n", - "\n", - "\n", - "100%|██████████| 469/469 [00:12<00:00, 36.55it/s, train_acc=0.976, train_loss=0.0818]\n", - "100%|██████████| 79/79 [00:02<00:00, 37.44it/s, val_acc=0.979, val_loss=0.0819]\n", - "<<<<<< val_acc without improvement in 5 epoch, early stopping >>>>>>\n", - "```" - ] - }, - { - "cell_type": "markdown", - "id": "3fc1cc4f", - "metadata": {}, - "source": [ - "使用GPU后每个Epoch只需要10秒钟左右，提升了6倍。\n" + "使用GPU后每个Epoch只需要10秒钟左右，提升了4倍。\n" ] }, { "cell_type": "markdown", - "id": "4a52c7b5", "metadata": {}, "source": [ "## 四，torchkeras.KerasModel中使用GPU" ] }, { - "cell_type": "code", - "execution_count": null, - "id": "4951f88d", + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ "从上面的例子可以看到，在pytorch中使用GPU并不复杂，但对于经常炼丹的同学来说，模型和数据老是移来移去还是蛮麻烦的。\n", "\n", @@ -1027,20 +1312,25 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "fcc49be8", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install torchkeras==3.2.3" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b8ba42bb", - "metadata": {}, - "outputs": [], + "execution_count": 24, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:31:06.804407Z", + "iopub.status.busy": "2023-08-02T09:31:06.803922Z", + "iopub.status.idle": "2023-08-02T09:31:06.819012Z", + "shell.execute_reply": "2023-08-02T09:31:06.817856Z", + "shell.execute_reply.started": "2023-08-02T09:31:06.804365Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "cuda\n" + ] + } + ], "source": [ "import accelerate \n", "accelerator = accelerate.Accelerator()\n", @@ -1049,10 +1339,211 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "32ba126f", - "metadata": {}, - "outputs": [], + "execution_count": 25, + "metadata": { + "execution": { + "iopub.execute_input": "2023-08-02T09:31:12.618443Z", + "iopub.status.busy": "2023-08-02T09:31:12.618036Z", + "iopub.status.idle": "2023-08-02T09:32:49.031087Z", + "shell.execute_reply": "2023-08-02T09:32:49.029676Z", + "shell.execute_reply.started": "2023-08-02T09:31:12.618408Z" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< ⚡️ cuda is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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780.0720740.9801330.010.0731260.9795
\n", + "
" + ], + "text/plain": [ + " epoch train_loss train_acc lr val_loss val_acc\n", + "0 1 0.327403 0.893783 0.01 0.105610 0.9660\n", + "1 2 0.109891 0.966483 0.01 0.086295 0.9746\n", + "2 3 0.092442 0.972733 0.01 0.058266 0.9825\n", + "3 4 0.085473 0.975367 0.01 0.072749 0.9806\n", + "4 5 0.079213 0.977350 0.01 0.059756 0.9832\n", + "5 6 0.077976 0.977800 0.01 0.081202 0.9768\n", + "6 7 0.074797 0.978950 0.01 0.076534 0.9821\n", + "7 8 0.072074 0.980133 0.01 0.073126 0.9795" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "from torchkeras import KerasModel \n", "from torchmetrics import Accuracy\n", @@ -1060,7 +1551,7 @@ "net = create_net() \n", "model = KerasModel(net,\n", " loss_fn=nn.CrossEntropyLoss(),\n", - " metrics_dict = {\"acc\":Accuracy()},\n", + " metrics_dict = {\"acc\":Accuracy(task='multiclass',num_classes=10)},\n", " optimizer = torch.optim.Adam(net.parameters(),lr = 0.01) )\n", "\n", "model.fit(\n", @@ -1072,136 +1563,15 @@ " mode=\"max\")" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "60cfd732", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "a7e6d799", - "metadata": {}, - "source": [ - "## 五，torchkeras.LightModel中使用GPU" - ] - }, - { - "cell_type": "markdown", - "id": "230a863d", - "metadata": {}, - "source": [ - "通过引用pytorch_lightning的功能，\n", - "\n", - "torchkeras.LightModel以更加显式的方式支持GPU训练，\n", - "\n", - "不仅如此，还能支持多GPU和TPU训练。\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a075749c", - "metadata": {}, - "outputs": [], - "source": [ - "from torchmetrics import Accuracy \n", - "from torchkeras import LightModel \n", - "\n", - "net = create_net() \n", - "model = LightModel(net,\n", - " loss_fn=nn.CrossEntropyLoss(),\n", - " metrics_dict = {\"acc\":Accuracy()},\n", - " optimizer = torch.optim.Adam(net.parameters(),lr = 0.01) )\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "84d8dd0b", - "metadata": {}, - "outputs": [], - "source": [ - "import pytorch_lightning as pl \n", - "\n", - "#1，设置回调函数\n", - "model_ckpt = pl.callbacks.ModelCheckpoint(\n", - " monitor='val_acc',\n", - " save_top_k=1,\n", - " mode='max'\n", - ")\n", - "\n", - "early_stopping = pl.callbacks.EarlyStopping(monitor = 'val_acc',\n", - " patience=3,\n", - " mode = 'max'\n", - " )\n", - "\n", - "#2，设置训练参数\n", - "\n", - "# gpus=0 则使用cpu训练，gpus=1则使用1个gpu训练，gpus=2则使用2个gpu训练，gpus=-1则使用所有gpu训练，\n", - "# gpus=[0,1]则指定使用0号和1号gpu训练， gpus=\"0,1,2,3\"则使用0,1,2,3号gpu训练\n", - "# tpus=1 则使用1个tpu训练\n", - "trainer = pl.Trainer(logger=True,\n", - " min_epochs=3,max_epochs=20,\n", - " gpus=1,\n", - " callbacks = [model_ckpt,early_stopping],\n", - " enable_progress_bar = True) \n", - "\n", - "\n", - "##4，启动训练循环\n", - "trainer.fit(model,dl_train,dl_val)\n", - "\n" - ] - }, { "cell_type": "markdown", - "id": "fad61a3e", "metadata": {}, "source": [ - "```\n", - "================================================================================2022-07-18 00:18:14\n", - "{'epoch': 0, 'val_loss': 2.31911301612854, 'val_acc': 0.0546875}\n", - "<<<<<< reach best val_acc : 0.0546875 >>>>>>\n", - "\n", - "================================================================================2022-07-18 00:18:29\n", - "{'epoch': 0, 'val_loss': 0.10364170372486115, 'val_acc': 0.9693999886512756}\n", - "{'epoch': 0, 'train_loss': 0.31413567066192627, 'train_acc': 0.8975499868392944}\n", - "<<<<<< reach best val_acc : 0.9693999886512756 >>>>>>\n", - "\n", - "================================================================================2022-07-18 00:18:43\n", - "{'epoch': 1, 'val_loss': 0.0983758345246315, 'val_acc': 0.9710999727249146}\n", - "{'epoch': 1, 'train_loss': 0.10680060088634491, 'train_acc': 0.9673333168029785}\n", - "<<<<<< reach best val_acc : 0.9710999727249146 >>>>>>\n", - "\n", - "================================================================================2022-07-18 00:18:58\n", - "{'epoch': 2, 'val_loss': 0.08315123617649078, 'val_acc': 0.9764999747276306}\n", - "{'epoch': 2, 'train_loss': 0.09339822083711624, 'train_acc': 0.9722166657447815}\n", - "<<<<<< reach best val_acc : 0.9764999747276306 >>>>>>\n", - "\n", - "================================================================================2022-07-18 00:19:13\n", - "{'epoch': 3, 'val_loss': 0.06529796123504639, 'val_acc': 0.9799000024795532}\n", - "{'epoch': 3, 'train_loss': 0.08487282693386078, 'train_acc': 0.9746000170707703}\n", - "<<<<<< reach best val_acc : 0.9799000024795532 >>>>>>\n", - "\n", - "================================================================================2022-07-18 00:19:27\n", - "{'epoch': 4, 'val_loss': 0.10162600129842758, 'val_acc': 0.9735000133514404}\n", - "{'epoch': 4, 'train_loss': 0.08439336717128754, 'train_acc': 0.9746666550636292}\n", - "\n", - "================================================================================2022-07-18 00:19:42\n", - "{'epoch': 5, 'val_loss': 0.0818500965833664, 'val_acc': 0.9789000153541565}\n", - "{'epoch': 5, 'train_loss': 0.08107426762580872, 'train_acc': 0.9763166904449463}\n", - "\n", - "================================================================================2022-07-18 00:19:56\n", - "{'epoch': 6, 'val_loss': 0.08046088367700577, 'val_acc': 0.979200005531311}\n", - "{'epoch': 6, 'train_loss': 0.08173364400863647, 'train_acc': 0.9772833585739136}\n", - "```" + "### " ] }, { "cell_type": "markdown", - "id": "026f3faf", "metadata": {}, "source": [ "**如果本书对你有所帮助，想鼓励一下作者，记得给本项目加一颗星星star⭐️，并分享给你的朋友们喔😊!** \n", @@ -1215,12 +1585,24 @@ } ], "metadata": { - "jupytext": { - "cell_metadata_filter": "-all", - "formats": "ipynb,md", - "main_language": "python" + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, - "nbformat_minor": 5 + "nbformat_minor": 4 } diff --git "a/7-1,\346\216\250\350\215\220\347\256\227\346\263\225\344\270\232\345\212\241.ipynb" "b/7-1,\346\216\250\350\215\220\347\256\227\346\263\225\344\270\232\345\212\241.ipynb" index 3db53e37c..64c97728b 100644 --- "a/7-1,\346\216\250\350\215\220\347\256\227\346\263\225\344\270\232\345\212\241.ipynb" +++ "b/7-1,\346\216\250\350\215\220\347\256\227\346\263\225\344\270\232\345\212\241.ipynb" @@ -222,6 +222,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/7-2,\345\271\277\345\221\212\347\256\227\346\263\225\344\270\232\345\212\241.ipynb" "b/7-2,\345\271\277\345\221\212\347\256\227\346\263\225\344\270\232\345\212\241.ipynb" index 603ae0a2a..99c0030c4 100644 --- "a/7-2,\345\271\277\345\221\212\347\256\227\346\263\225\344\270\232\345\212\241.ipynb" +++ "b/7-2,\345\271\277\345\221\212\347\256\227\346\263\225\344\270\232\345\212\241.ipynb" @@ -222,7 +222,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -236,7 +236,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/7-3,FM\346\250\241\345\236\213.ipynb" "b/7-3,FM\346\250\241\345\236\213.ipynb" index fee96a6a0..62abe9630 100644 --- "a/7-3,FM\346\250\241\345\236\213.ipynb" +++ "b/7-3,FM\346\250\241\345\236\213.ipynb" @@ -45,23 +45,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "4f3e9c86", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__=2.0.1\n", + "torchkeras.__version__=3.9.3\n" + ] + } + ], "source": [ "import torch \n", - "print(\"torch.__version__=\"+torch.__version__) " - ] - }, - { - "cell_type": "markdown", - "id": "e3bef545", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__=1.10.0\n", - "```" + "import torchkeras\n", + "print(\"torch.__version__=\"+torch.__version__) \n", + "print(\"torchkeras.__version__=\"+torchkeras.__version__) " ] }, { @@ -201,7 +202,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "9a530e37", "metadata": {}, "outputs": [], @@ -365,25 +366,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3ff538b7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 2, 4])\n" + ] + } + ], "source": [ "##测试 NumEmbedding\n", "\n", "num_embedding = NumEmbedding(2,1,4)\n", "x_num = torch.randn(2,2)\n", "x_out = (num_embedding(x_num.unsqueeze(-1)))\n", - "print(x_out.shape) " + "print(x_out.shape) \n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "ca9ef57f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 3])\n", + "torch.Size([2, 3, 4])\n" + ] + } + ], "source": [ "##测试 CatEmbedding\n", "\n", @@ -396,10 +414,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "bb503f2f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 3])\n", + "torch.Size([2, 1])\n" + ] + } + ], "source": [ "##测试 CatLinear\n", "\n", @@ -412,10 +439,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "b09022e1", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.Size([2, 4])\n" + ] + } + ], "source": [ "##测试 FMLayer\n", "\n", @@ -428,12 +463,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "050b8e23", "metadata": { "lines_to_next_cell": 2 }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([[ 0.4033, 1.3612],\n", + " [ 2.8410, -4.4903]], grad_fn=)" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "##测试 FM\n", "\n", @@ -481,7 +528,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "4ada2b70", "metadata": {}, "outputs": [], @@ -497,11 +544,7 @@ "from torch.utils.data import Dataset,DataLoader \n", "import torch.nn.functional as F \n", "import torchkeras \n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(info+'...\\n\\n')\n" + "\n" ] }, { @@ -522,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "d72dcaeb", "metadata": {}, "outputs": [], @@ -550,7 +593,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "ec01fe7b", "metadata": {}, "outputs": [], @@ -588,7 +631,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "fb9b516d", "metadata": {}, "outputs": [], @@ -609,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "cb40472b", "metadata": { "lines_to_next_cell": 2 @@ -635,10 +678,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "d6b54af6", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Linear-1 [-1, 1] 14\n", + "Embedding-2 [-1, 26, 1] 1,296,709\n", + "NumEmbedding-3 [-1, 13, 8] 104\n", + "Embedding-4 [-1, 26, 8] 10,373,672\n", + "FMLayer-5 [-1, 1] 0\n", + "==========================================================================\n", + "Total params: 11,670,499\n", + "Trainable params: 11,670,499\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000084\n", + "Forward/backward pass size (MB): 0.002594\n", + "Params size (MB): 44.519421\n", + "Estimated Total Size (MB): 44.522099\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "def create_net():\n", " net = FM(\n", @@ -656,33 +724,6 @@ "\n" ] }, - { - "cell_type": "markdown", - "id": "9446731b", - "metadata": {}, - "source": [ - "```\n", - "--------------------------------------------------------------------------\n", - "Layer (type) Output Shape Param #\n", - "==========================================================================\n", - "Linear-1 [-1, 1] 14\n", - "Embedding-2 [-1, 26, 1] 1,296,709\n", - "NumEmbedding-3 [-1, 13, 8] 104\n", - "Embedding-4 [-1, 26, 8] 10,373,672\n", - "FMLayer-5 [-1, 1] 0\n", - "==========================================================================\n", - "Total params: 11,670,499\n", - "Trainable params: 11,670,499\n", - "Non-trainable params: 0\n", - "--------------------------------------------------------------------------\n", - "Input size (MB): 0.000084\n", - "Forward/backward pass size (MB): 0.002594\n", - "Params size (MB): 44.519421\n", - "Estimated Total Size (MB): 44.522099\n", - "--------------------------------------------------------------------------\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -700,202 +741,23 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "6cd40c3a", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e8cdc3b2", + "cell_type": "markdown", + "id": "72f5bc0a-5d9f-44af-904c-275c35b62b98", "metadata": {}, - "outputs": [], "source": [ - "import os,sys,time\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime \n", - "from tqdm import tqdm \n", - "\n", - "import torch\n", - "from torch import nn \n", - "from accelerate import Accelerator\n", - "from copy import deepcopy\n", - "\n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(str(info)+\"\\n\")\n", - " \n", - "class StepRunner:\n", - " def __init__(self, net, loss_fn,stage = \"train\", metrics_dict = None, \n", - " optimizer = None, lr_scheduler = None,\n", - " accelerator = None\n", - " ):\n", - " self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage\n", - " self.optimizer,self.lr_scheduler = optimizer,lr_scheduler\n", - " self.accelerator = accelerator\n", - " \n", - " def __call__(self, features, labels):\n", - " #loss\n", - " preds = self.net(features)\n", - " loss = self.loss_fn(preds,labels)\n", - "\n", - " #backward()\n", - " if self.optimizer is not None and self.stage==\"train\":\n", - " if self.accelerator is None:\n", - " loss.backward()\n", - " else:\n", - " self.accelerator.backward(loss)\n", - " self.optimizer.step()\n", - " if self.lr_scheduler is not None:\n", - " self.lr_scheduler.step()\n", - " self.optimizer.zero_grad()\n", - " \n", - " #metrics\n", - " step_metrics = {self.stage+\"_\"+name:metric_fn(preds, labels).item() \n", - " for name,metric_fn in self.metrics_dict.items()}\n", - " return loss.item(),step_metrics\n", - " \n", - " \n", - "class EpochRunner:\n", - " def __init__(self,steprunner):\n", - " self.steprunner = steprunner\n", - " self.stage = steprunner.stage\n", - " self.steprunner.net.train() if self.stage==\"train\" else self.steprunner.net.eval()\n", - " \n", - " def __call__(self,dataloader):\n", - " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dataloader), total =len(dataloader))\n", - " for i, batch in loop:\n", - " features,labels = batch\n", - " if self.stage==\"train\":\n", - " loss, step_metrics = self.steprunner(features,labels)\n", - " else:\n", - " with torch.no_grad():\n", - " loss, step_metrics = self.steprunner(features,labels)\n", - " \n", - " step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n", - "\n", - " total_loss += loss\n", - " step+=1\n", - " if i!=len(dataloader)-1:\n", - " loop.set_postfix(**step_log)\n", - " else:\n", - " epoch_loss = total_loss/step\n", - " epoch_metrics = {self.stage+\"_\"+name:metric_fn.compute().item() \n", - " for name,metric_fn in self.steprunner.metrics_dict.items()}\n", - " epoch_log = dict({self.stage+\"_loss\":epoch_loss},**epoch_metrics)\n", - " loop.set_postfix(**epoch_log)\n", - "\n", - " for name,metric_fn in self.steprunner.metrics_dict.items():\n", - " metric_fn.reset()\n", - " return epoch_log\n", - "\n", - "class KerasModel(torch.nn.Module):\n", - " def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):\n", - " super().__init__()\n", - " self.accelerator = Accelerator()\n", - " self.history = {}\n", - " \n", - " self.net = net\n", - " self.loss_fn = loss_fn\n", - " self.metrics_dict = nn.ModuleDict(metrics_dict) \n", - " \n", - " self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(\n", - " self.parameters(), lr=1e-2)\n", - " self.lr_scheduler = lr_scheduler\n", - " \n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer = self.accelerator.prepare(\n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer)\n", - "\n", - " def forward(self, x):\n", - " if self.net:\n", - " return self.net.forward(x)\n", - " else:\n", - " raise NotImplementedError\n", - "\n", - "\n", - " def fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt', \n", - " patience=5, monitor=\"val_loss\", mode=\"min\"):\n", - " \n", - " train_data = self.accelerator.prepare(train_data)\n", - " val_data = self.accelerator.prepare(val_data) if val_data else []\n", - "\n", - " for epoch in range(1, epochs+1):\n", - " printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n", - " \n", - " # 1，train ------------------------------------------------- \n", - " train_step_runner = StepRunner(net = self.net,stage=\"train\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " optimizer = self.optimizer, lr_scheduler = self.lr_scheduler,\n", - " accelerator = self.accelerator)\n", - " train_epoch_runner = EpochRunner(train_step_runner)\n", - " train_metrics = train_epoch_runner(train_data)\n", - " \n", - " for name, metric in train_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - "\n", - " # 2，validate -------------------------------------------------\n", - " if val_data:\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " with torch.no_grad():\n", - " val_metrics = val_epoch_runner(val_data)\n", - " val_metrics[\"epoch\"] = epoch\n", - " for name, metric in val_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - " \n", - " # 3，early-stopping -------------------------------------------------\n", - " arr_scores = self.history[monitor]\n", - " best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n", - " if best_score_idx==len(arr_scores)-1:\n", - " torch.save(self.net.state_dict(),ckpt_path)\n", - " print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", - " if len(arr_scores)-best_score_idx>patience:\n", - " print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n", - " monitor,patience),file=sys.stderr)\n", - " break \n", - " \n", - " self.net.load_state_dict(torch.load(ckpt_path))\n", - " \n", - " return pd.DataFrame(self.history)\n", - "\n", - " @torch.no_grad()\n", - " def evaluate(self, val_data):\n", - " val_data = self.accelerator.prepare(val_data)\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " val_metrics = val_epoch_runner(val_data)\n", - " return val_metrics\n", - " \n", - " \n", - " @torch.no_grad()\n", - " def predict(self, dataloader):\n", - " dataloader = self.accelerator.prepare(dataloader)\n", - " result = torch.cat([self.forward(t[0]) for t in dataloader])\n", - " return result.data\n", - " " + "我们使用梦中情炉torchkeras来实现最优雅的训练循环。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "17c5c1d4", "metadata": { "lines_to_next_cell": 0 }, "outputs": [], "source": [ + "from torchkeras import KerasModel\n", "from torchkeras.metrics import AUC\n", "\n", "net = create_net()\n", @@ -913,46 +775,93 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "c621ef79", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + " ████████████████████100.00% [79/79] [val_loss=0.4748, val_auc=0.7674]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< val_auc without improvement in 5 epoch,early stopping >>>>>> \n", + "\u001b[0m\n" + ] + } + ], "source": [ - "dfhistory = model.fit(train_data = dl_train,val_data = dl_val,\n", - " epochs=20,\n", - " ckpt_path='checkpoint.pt',\n", - " patience=3,\n", + "dfhistory = model.fit(train_data = dl_train,\n", + " val_data = dl_val,\n", + " epochs=100,\n", + " ckpt_path='checkpoint',\n", + " patience=5,\n", " monitor='val_auc',\n", - " mode='max')\n" - ] - }, - { - "cell_type": "markdown", - "id": "e3438d09", - "metadata": {}, - "source": [ - "```\n", - "================================================================================2022-08-11 19:39:44\n", - "Epoch 7 / 20\n", - "\n", - "100%|██████████| 313/313 [01:14<00:00, 4.18it/s, train_auc=0.768, train_loss=0.475]\n", - "100%|██████████| 79/79 [00:03<00:00, 23.94it/s, val_auc=0.767, val_loss=0.477]\n", - "<<<<<< reach best val_auc : 0.7665905952453613 >>>>>>\n", - "\n", - "================================================================================2022-08-11 19:41:02\n", - "Epoch 8 / 20\n", - "\n", - "100%|██████████| 313/313 [01:13<00:00, 4.23it/s, train_auc=0.768, train_loss=0.475]\n", - "100%|██████████| 79/79 [00:03<00:00, 23.92it/s, val_auc=0.767, val_loss=0.477]\n", - "<<<<<< reach best val_auc : 0.7671190500259399 >>>>>>\n", - "\n", - "================================================================================2022-08-11 19:42:20\n", - "Epoch 9 / 20\n", - "\n", - "100%|██████████| 313/313 [01:13<00:00, 4.25it/s, train_auc=0.769, train_loss=0.475]\n", - "100%|██████████| 79/79 [00:03<00:00, 23.37it/s, val_auc=0.768, val_loss=0.476]\n", - "<<<<<< reach best val_auc : 0.768292248249054 >>>>>>\n", - "```" + " mode='max',\n", + " plot=True,\n", + " cpu=True\n", + ")\n" ] }, { @@ -981,75 +890,32 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "540b7e7c", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "036913fe", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"loss\")" - ] - }, - { - "cell_type": "markdown", - "id": "703ea78e", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h532tdrdvsj20f40a6dg3.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "92792236", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"auc\")" - ] - }, - { - "cell_type": "markdown", - "id": "a0c00a66", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h532uca1oij20f40a9aab.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 23, "id": "24a5e739", "metadata": { "lines_to_next_cell": 0 }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████| 98/98 [00:04<00:00, 24.16it/s, val_auc=0.769, val_loss=0.475]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.47518228997989576, 'val_auc': 0.7691175937652588}" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "model.evaluate(dl_val)" + "model.evaluate(dl_test)" ] }, { @@ -1071,29 +937,39 @@ { "cell_type": "code", "execution_count": null, - "id": "b5720a7f", + "id": "90f929be-f31d-4c24-af2f-b9e75427e1c2", "metadata": {}, "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "b5720a7f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7691173430695828\n" + ] + } + ], "source": [ "from sklearn.metrics import roc_auc_score\n", "model.eval()\n", - "preds = F.sigmoid(model.predict(dl_val))\n", - "labels = torch.cat([x[-1] for x in dl_val])\n", + "dl_test = model.accelerator.prepare(dl_test)\n", + "with torch.no_grad():\n", + " result = torch.cat([model.forward(t[0]) for t in dl_test])\n", + "\n", + "preds = F.sigmoid(result)\n", + "labels = torch.cat([x[-1] for x in dl_test])\n", "\n", "val_auc = roc_auc_score(labels.numpy(),preds.numpy())\n", "print(val_auc)\n" ] }, - { - "cell_type": "markdown", - "id": "8c71b32b", - "metadata": {}, - "source": [ - "```\n", - "0.768292257292055\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1120,30 +996,24 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "55758fb9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "net_clone = create_net()\n", - "net_clone.load_state_dict(torch.load(\"checkpoint.pt\"))\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "6a99c8a6", - "metadata": { - "lines_to_next_cell": 0 - }, - "outputs": [], - "source": [ - "from sklearn.metrics import roc_auc_score\n", - "preds = torch.cat([F.sigmoid(net_clone(x[0])).data for x in dl_val]) \n", - "labels = torch.cat([x[-1] for x in dl_val])\n", - "\n", - "val_auc = roc_auc_score(labels.numpy(),preds.numpy())\n", - "print(val_auc)\n" + "net_clone.load_state_dict(torch.load(model.ckpt_path))\n" ] }, { @@ -1176,7 +1046,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1190,7 +1060,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/7-4,DeepFM\346\250\241\345\236\213.ipynb" "b/7-4,DeepFM\346\250\241\345\236\213.ipynb" index d9b18e474..42d6a7948 100644 --- "a/7-4,DeepFM\346\250\241\345\236\213.ipynb" +++ "b/7-4,DeepFM\346\250\241\345\236\213.ipynb" @@ -54,20 +54,19 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "0b61a84a", - "metadata": {}, - "outputs": [], - "source": [ - "!pip install torchkeras==3.2.3" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "56892bf7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "torch.__version__ = 2.0.1\n", + "torchkeras.__version__ = 3.9.3\n" + ] + } + ], "source": [ "import torch \n", "import torchkeras \n", @@ -75,17 +74,6 @@ "print(\"torchkeras.__version__ = \", torchkeras.__version__) " ] }, - { - "cell_type": "markdown", - "id": "bf724455", - "metadata": {}, - "source": [ - "```\n", - "torch.__version__ = 1.10.0\n", - "torchkeras.__version__ = 3.2.3\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -162,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "1b01f881", "metadata": {}, "outputs": [], @@ -366,10 +354,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "5d20a06d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "tensor([-4.4453, 0.1339], grad_fn=)" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "##测试 DeepFM\n", "\n", @@ -406,7 +405,7 @@ "\n", "这个数据集的目标是通过用户特征和广告特征来预测某条广告是否会为用户点击。\n", "\n", - "数据集有13维数值特征(I1~I13)和26维类别特征(C14~C39), 共39维特征, 特征中包含着许多缺失值。\n", + "数据集有13维数值特征(I1 -> I13)和26维类别特征(C14 -> C39), 共39维特征, 特征中包含着许多缺失值。\n", "\n", "训练集4000万个样本，测试集600万个样本。数据集大小超过100G.\n", "\n", @@ -423,7 +422,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "13245a35", "metadata": {}, "outputs": [], @@ -439,11 +438,7 @@ "from torch.utils.data import Dataset,DataLoader \n", "import torch.nn.functional as F \n", "import torchkeras \n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(info+'...\\n\\n')\n" + "\n" ] }, { @@ -456,7 +451,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "1a89c17a", "metadata": {}, "outputs": [], @@ -483,7 +478,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "b6f30f3f", "metadata": {}, "outputs": [], @@ -522,7 +517,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "8b0eba3e", "metadata": {}, "outputs": [], @@ -542,7 +537,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "04f56e49", "metadata": {}, "outputs": [], @@ -566,10 +561,48 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "11d303b8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Linear-1 [-1, 1] 14\n", + "Embedding-2 [-1, 26, 1] 1,296,709\n", + "NumEmbedding-3 [-1, 13, 8] 104\n", + "Embedding-4 [-1, 26, 8] 10,373,672\n", + "FMLayer-5 [-1, 1] 0\n", + "Linear-6 [-1, 128] 40,064\n", + "BatchNorm1d-7 [-1, 128] 256\n", + "ReLU-8 [-1, 128] 0\n", + "Dropout-9 [-1, 128] 0\n", + "Linear-10 [-1, 64] 8,256\n", + "BatchNorm1d-11 [-1, 64] 128\n", + "ReLU-12 [-1, 64] 0\n", + "Dropout-13 [-1, 64] 0\n", + "Linear-14 [-1, 32] 2,080\n", + "BatchNorm1d-15 [-1, 32] 64\n", + "ReLU-16 [-1, 32] 0\n", + "Dropout-17 [-1, 32] 0\n", + "Linear-18 [-1, 1] 33\n", + "==========================================================================\n", + "Total params: 11,721,380\n", + "Trainable params: 11,721,380\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000084\n", + "Forward/backward pass size (MB): 0.009438\n", + "Params size (MB): 44.713516\n", + "Estimated Total Size (MB): 44.723038\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "def create_net():\n", " net = DeepFM(\n", @@ -587,54 +620,6 @@ "summary(net,input_data=features);\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "9be7ebf2", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "dd85feec", - "metadata": {}, - "source": [ - "```\n", - "--------------------------------------------------------------------------\n", - "Layer (type) Output Shape Param #\n", - "==========================================================================\n", - "Linear-1 [-1, 1] 14\n", - "Embedding-2 [-1, 26, 1] 1,296,709\n", - "NumEmbedding-3 [-1, 13, 8] 104\n", - "Embedding-4 [-1, 26, 8] 10,373,672\n", - "FMLayer-5 [-1, 1] 0\n", - "Linear-6 [-1, 128] 40,064\n", - "BatchNorm1d-7 [-1, 128] 256\n", - "ReLU-8 [-1, 128] 0\n", - "Dropout-9 [-1, 128] 0\n", - "Linear-10 [-1, 64] 8,256\n", - "BatchNorm1d-11 [-1, 64] 128\n", - "ReLU-12 [-1, 64] 0\n", - "Dropout-13 [-1, 64] 0\n", - "Linear-14 [-1, 32] 2,080\n", - "BatchNorm1d-15 [-1, 32] 64\n", - "ReLU-16 [-1, 32] 0\n", - "Dropout-17 [-1, 32] 0\n", - "Linear-18 [-1, 1] 33\n", - "==========================================================================\n", - "Total params: 11,721,380\n", - "Trainable params: 11,721,380\n", - "Non-trainable params: 0\n", - "--------------------------------------------------------------------------\n", - "Input size (MB): 0.000084\n", - "Forward/backward pass size (MB): 0.009438\n", - "Params size (MB): 44.713516\n", - "Estimated Total Size (MB): 44.723038\n", - "--------------------------------------------------------------------------\n", - "```\n" - ] - }, { "cell_type": "code", "execution_count": null, @@ -652,193 +637,22 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "df4eb363", + "cell_type": "markdown", + "id": "f19a40a0-bbb6-4164-8f47-451842664326", "metadata": {}, - "outputs": [], "source": [ - "import os,sys,time\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime \n", - "from tqdm import tqdm \n", - "\n", - "import torch\n", - "from torch import nn \n", - "from accelerate import Accelerator\n", - "from copy import deepcopy\n", - "\n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(str(info)+\"\\n\")\n", - " \n", - "class StepRunner:\n", - " def __init__(self, net, loss_fn,stage = \"train\", metrics_dict = None, \n", - " optimizer = None, lr_scheduler = None,\n", - " accelerator = None\n", - " ):\n", - " self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage\n", - " self.optimizer,self.lr_scheduler = optimizer,lr_scheduler\n", - " self.accelerator = accelerator\n", - " \n", - " def __call__(self, features, labels):\n", - " #loss\n", - " preds = self.net(features)\n", - " loss = self.loss_fn(preds,labels)\n", - "\n", - " #backward()\n", - " if self.optimizer is not None and self.stage==\"train\":\n", - " if self.accelerator is None:\n", - " loss.backward()\n", - " else:\n", - " self.accelerator.backward(loss)\n", - " self.optimizer.step()\n", - " if self.lr_scheduler is not None:\n", - " self.lr_scheduler.step()\n", - " self.optimizer.zero_grad()\n", - " \n", - " #metrics\n", - " step_metrics = {self.stage+\"_\"+name:metric_fn(preds, labels).item() \n", - " for name,metric_fn in self.metrics_dict.items()}\n", - " return loss.item(),step_metrics\n", - " \n", - " \n", - "class EpochRunner:\n", - " def __init__(self,steprunner):\n", - " self.steprunner = steprunner\n", - " self.stage = steprunner.stage\n", - " self.steprunner.net.train() if self.stage==\"train\" else self.steprunner.net.eval()\n", - " \n", - " def __call__(self,dataloader):\n", - " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dataloader), total =len(dataloader))\n", - " for i, batch in loop:\n", - " features,labels = batch\n", - " if self.stage==\"train\":\n", - " loss, step_metrics = self.steprunner(features,labels)\n", - " else:\n", - " with torch.no_grad():\n", - " loss, step_metrics = self.steprunner(features,labels)\n", - "\n", - " step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n", - "\n", - " total_loss += loss\n", - " step+=1\n", - " if i!=len(dataloader)-1:\n", - " loop.set_postfix(**step_log)\n", - " else:\n", - " epoch_loss = total_loss/step\n", - " epoch_metrics = {self.stage+\"_\"+name:metric_fn.compute().item() \n", - " for name,metric_fn in self.steprunner.metrics_dict.items()}\n", - " epoch_log = dict({self.stage+\"_loss\":epoch_loss},**epoch_metrics)\n", - " loop.set_postfix(**epoch_log)\n", - "\n", - " for name,metric_fn in self.steprunner.metrics_dict.items():\n", - " metric_fn.reset()\n", - " return epoch_log\n", - "\n", - "class KerasModel(torch.nn.Module):\n", - " def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):\n", - " super().__init__()\n", - " self.accelerator = Accelerator()\n", - " self.history = {}\n", - " \n", - " self.net = net\n", - " self.loss_fn = loss_fn\n", - " self.metrics_dict = nn.ModuleDict(metrics_dict) \n", - " \n", - " self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(\n", - " self.parameters(), lr=1e-2)\n", - " self.lr_scheduler = lr_scheduler\n", - " \n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer = self.accelerator.prepare(\n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer)\n", - "\n", - " def forward(self, x):\n", - " if self.net:\n", - " return self.net.forward(x)\n", - " else:\n", - " raise NotImplementedError\n", - "\n", - "\n", - " def fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt', \n", - " patience=5, monitor=\"val_loss\", mode=\"min\"):\n", - " \n", - " train_data = self.accelerator.prepare(train_data)\n", - " val_data = self.accelerator.prepare(val_data) if val_data else []\n", - "\n", - " for epoch in range(1, epochs+1):\n", - " printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n", - " \n", - " # 1，train ------------------------------------------------- \n", - " train_step_runner = StepRunner(net = self.net,stage=\"train\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " optimizer = self.optimizer, lr_scheduler = self.lr_scheduler,\n", - " accelerator = self.accelerator)\n", - " train_epoch_runner = EpochRunner(train_step_runner)\n", - " train_metrics = train_epoch_runner(train_data)\n", - " \n", - " for name, metric in train_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - "\n", - " # 2，validate -------------------------------------------------\n", - " if val_data:\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " with torch.no_grad():\n", - " val_metrics = val_epoch_runner(val_data)\n", - " val_metrics[\"epoch\"] = epoch\n", - " for name, metric in val_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - " \n", - " # 3，early-stopping -------------------------------------------------\n", - " arr_scores = self.history[monitor]\n", - " best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n", - " if best_score_idx==len(arr_scores)-1:\n", - " torch.save(self.net.state_dict(),ckpt_path)\n", - " print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", - " if len(arr_scores)-best_score_idx>patience:\n", - " print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n", - " monitor,patience),file=sys.stderr)\n", - " break \n", - " \n", - " self.net.load_state_dict(torch.load(ckpt_path))\n", - " \n", - " return pd.DataFrame(self.history)\n", - "\n", - " @torch.no_grad()\n", - " def evaluate(self, val_data):\n", - " val_data = self.accelerator.prepare(val_data)\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " val_metrics = val_epoch_runner(val_data)\n", - " return val_metrics\n", - " \n", - " \n", - " @torch.no_grad()\n", - " def predict(self, dataloader):\n", - " dataloader = self.accelerator.prepare(dataloader)\n", - " result = torch.cat([self.forward(t[0]) for t in dataloader])\n", - " return result.data\n", - " " + "我们使用梦中情炉torchkeras来实现最优雅的训练循环。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "e9f5987a", "metadata": {}, "outputs": [], "source": [ "from torchkeras.metrics import AUC\n", + "from torchkeras import KerasModel \n", "\n", "loss_fn = nn.BCEWithLogitsLoss()\n", "\n", @@ -855,13 +669,93 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "2d3ec3f5", "metadata": {}, - "outputs": [], - "source": [ - "dfhistory = model.fit(train_data=dl_train,val_data=dl_val,epochs=50, patience=5,\n", - " monitor = \"val_auc\",mode=\"max\",ckpt_path='checkpoint.pt')\n" + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< val_auc without improvement in 5 epoch,early stopping >>>>>> \n", + "\u001b[0m\n" + ] + } + ], + "source": [ + "dfhistory = model.fit(train_data = dl_train,\n", + " val_data = dl_val,\n", + " epochs=100,\n", + " ckpt_path='checkpoint',\n", + " patience=5,\n", + " monitor='val_auc',\n", + " mode='max',\n", + " plot=True,\n", + " cpu=True\n", + ")\n" ] }, { @@ -874,48 +768,39 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "8937aa90", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()" + "execution_count": 12, + "id": "4cb64f90-8df9-4045-b371-458646316756", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████| 98/98 [00:04<00:00, 23.15it/s, val_auc=0.782, val_loss=0.464]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.4642997295880804, 'val_auc': 0.7817735075950623}" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.evaluate(dl_test)" ] }, { "cell_type": "code", "execution_count": null, - "id": "17f0ebe1", + "id": "cead8403-a736-4e0f-8a40-5363f9a20720", "metadata": {}, "outputs": [], - "source": [ - "plot_metric(dfhistory,\"loss\")" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5adcb4b7", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"auc\")" - ] + "source": [] }, { "cell_type": "markdown", @@ -927,17 +812,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "b1fd052c", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7817740760992791\n" + ] + } + ], "source": [ "from sklearn.metrics import roc_auc_score\n", - "preds = torch.sigmoid(model.predict(dl_val))\n", - "labels = torch.cat([x[-1] for x in dl_val])\n", + "model.eval()\n", + "dl_test = model.accelerator.prepare(dl_test)\n", + "with torch.no_grad():\n", + " result = torch.cat([model.forward(t[0]) for t in dl_test])\n", "\n", - "val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())\n", - "print(val_auc)" + "preds = F.sigmoid(result)\n", + "labels = torch.cat([x[-1] for x in dl_test])\n", + "\n", + "val_auc = roc_auc_score(labels.numpy(),preds.numpy())\n", + "print(val_auc)\n" ] }, { @@ -949,31 +847,33 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "c578b927", + "cell_type": "markdown", + "id": "961764d9-2459-43b8-87a0-ed97bfdcd247", "metadata": {}, - "outputs": [], "source": [ - "torch.save(model.net.state_dict(),\"best_deepfm.pt\")\n", - "net_clone = create_net()\n", - "net_clone.load_state_dict(torch.load(\"best_deepfm.pt\"))" + "模型最佳权重已经保存在 model.fit(ckpt_path) 传入的参数中了。" ] }, { "cell_type": "code", - "execution_count": null, - "id": "2948189d", + "execution_count": 14, + "id": "c578b927", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from sklearn.metrics import roc_auc_score\n", - "net_clone.eval()\n", - "preds = torch.cat([torch.sigmoid(net_clone(x[0])).data for x in dl_val]) \n", - "labels = torch.cat([x[-1] for x in dl_val])\n", - "\n", - "val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())\n", - "print(val_auc)" + "net_clone = create_net()\n", + "net_clone.load_state_dict(torch.load(model.ckpt_path))" ] }, { @@ -1002,8 +902,24 @@ "metadata": { "jupytext": { "cell_metadata_filter": "-all", - "formats": "ipynb,md", - "main_language": "python" + "formats": "ipynb,md" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/7-5,FiBiNET\346\250\241\345\236\213.ipynb" "b/7-5,FiBiNET\346\250\241\345\236\213.ipynb" index 73a8b3944..ad5911c8e 100644 --- "a/7-5,FiBiNET\346\250\241\345\236\213.ipynb" +++ "b/7-5,FiBiNET\346\250\241\345\236\213.ipynb" @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "a704b92e", "metadata": {}, "outputs": [], @@ -287,7 +287,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "09d50922", "metadata": {}, "outputs": [], @@ -351,7 +351,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "93153b47", "metadata": {}, "outputs": [], @@ -590,10 +590,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "6b39cb4a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([ 1.4711, -0.9382], grad_fn=)\n" + ] + } + ], "source": [ "##测试 FiBiNET\n", "\n", @@ -660,14 +668,13 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "70d99f21", "metadata": {}, "outputs": [], "source": [ "import numpy as np \n", "import pandas as pd \n", - "import datetime \n", "\n", "from sklearn.model_selection import train_test_split \n", "\n", @@ -675,12 +682,7 @@ "from torch import nn \n", "from torch.utils.data import Dataset,DataLoader \n", "import torch.nn.functional as F \n", - "import torchkeras \n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(info+'...\\n\\n')\n" + "import torchkeras \n" ] }, { @@ -693,7 +695,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "edf49cd4", "metadata": {}, "outputs": [], @@ -720,7 +722,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "86e22fca", "metadata": {}, "outputs": [], @@ -759,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "a77fcdf2", "metadata": {}, "outputs": [], @@ -779,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "77e4b9cb", "metadata": {}, "outputs": [], @@ -803,7 +805,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "6107212d", "metadata": {}, "outputs": [], @@ -843,192 +845,22 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "f3fd5a09", + "cell_type": "markdown", + "id": "a3353895-6d7d-4504-84cd-7817badca0cc", "metadata": {}, - "outputs": [], "source": [ - "import os,sys,time\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime \n", - "from tqdm import tqdm \n", - "\n", - "import torch\n", - "from torch import nn \n", - "from accelerate import Accelerator\n", - "from copy import deepcopy\n", - "\n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(str(info)+\"\\n\")\n", - " \n", - "class StepRunner:\n", - " def __init__(self, net, loss_fn,stage = \"train\", metrics_dict = None, \n", - " optimizer = None, lr_scheduler = None,\n", - " accelerator = None\n", - " ):\n", - " self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage\n", - " self.optimizer,self.lr_scheduler = optimizer,lr_scheduler\n", - " self.accelerator = accelerator\n", - " \n", - " def __call__(self, features, labels):\n", - " #loss\n", - " preds = self.net(features)\n", - " loss = self.loss_fn(preds,labels)\n", - "\n", - " #backward()\n", - " if self.optimizer is not None and self.stage==\"train\":\n", - " if self.accelerator is None:\n", - " loss.backward()\n", - " else:\n", - " self.accelerator.backward(loss)\n", - " self.optimizer.step()\n", - " if self.lr_scheduler is not None:\n", - " self.lr_scheduler.step()\n", - " self.optimizer.zero_grad()\n", - " \n", - " #metrics\n", - " step_metrics = {self.stage+\"_\"+name:metric_fn(preds, labels).item() \n", - " for name,metric_fn in self.metrics_dict.items()}\n", - " return loss.item(),step_metrics\n", - " \n", - " \n", - "class EpochRunner:\n", - " def __init__(self,steprunner):\n", - " self.steprunner = steprunner\n", - " self.stage = steprunner.stage\n", - " self.steprunner.net.train() if self.stage==\"train\" else self.steprunner.net.eval()\n", - " \n", - " def __call__(self,dataloader):\n", - " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dataloader), total =len(dataloader))\n", - " for i, batch in loop:\n", - " features,labels = batch\n", - " if self.stage==\"train\":\n", - " loss, step_metrics = self.steprunner(features,labels)\n", - " else:\n", - " with torch.no_grad():\n", - " loss, step_metrics = self.steprunner(features,labels)\n", - "\n", - " step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n", - "\n", - " total_loss += loss\n", - " step+=1\n", - " if i!=len(dataloader)-1:\n", - " loop.set_postfix(**step_log)\n", - " else:\n", - " epoch_loss = total_loss/step\n", - " epoch_metrics = {self.stage+\"_\"+name:metric_fn.compute().item() \n", - " for name,metric_fn in self.steprunner.metrics_dict.items()}\n", - " epoch_log = dict({self.stage+\"_loss\":epoch_loss},**epoch_metrics)\n", - " loop.set_postfix(**epoch_log)\n", - "\n", - " for name,metric_fn in self.steprunner.metrics_dict.items():\n", - " metric_fn.reset()\n", - " return epoch_log\n", - "\n", - "class KerasModel(torch.nn.Module):\n", - " def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):\n", - " super().__init__()\n", - " self.accelerator = Accelerator()\n", - " self.history = {}\n", - " \n", - " self.net = net\n", - " self.loss_fn = loss_fn\n", - " self.metrics_dict = nn.ModuleDict(metrics_dict) \n", - " \n", - " self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(\n", - " self.parameters(), lr=1e-2)\n", - " self.lr_scheduler = lr_scheduler\n", - " \n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer = self.accelerator.prepare(\n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer)\n", - "\n", - " def forward(self, x):\n", - " if self.net:\n", - " return self.net.forward(x)\n", - " else:\n", - " raise NotImplementedError\n", - "\n", - "\n", - " def fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt', \n", - " patience=5, monitor=\"val_loss\", mode=\"min\"):\n", - " \n", - " train_data = self.accelerator.prepare(train_data)\n", - " val_data = self.accelerator.prepare(val_data) if val_data else []\n", - "\n", - " for epoch in range(1, epochs+1):\n", - " printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n", - " \n", - " # 1，train ------------------------------------------------- \n", - " train_step_runner = StepRunner(net = self.net,stage=\"train\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " optimizer = self.optimizer, lr_scheduler = self.lr_scheduler,\n", - " accelerator = self.accelerator)\n", - " train_epoch_runner = EpochRunner(train_step_runner)\n", - " train_metrics = train_epoch_runner(train_data)\n", - " \n", - " for name, metric in train_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - "\n", - " # 2，validate -------------------------------------------------\n", - " if val_data:\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " with torch.no_grad():\n", - " val_metrics = val_epoch_runner(val_data)\n", - " val_metrics[\"epoch\"] = epoch\n", - " for name, metric in val_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - " \n", - " # 3，early-stopping -------------------------------------------------\n", - " arr_scores = self.history[monitor]\n", - " best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n", - " if best_score_idx==len(arr_scores)-1:\n", - " torch.save(self.net.state_dict(),ckpt_path)\n", - " print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", - " if len(arr_scores)-best_score_idx>patience:\n", - " print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n", - " monitor,patience),file=sys.stderr)\n", - " self.net.load_state_dict(torch.load(ckpt_path))\n", - " break \n", - " \n", - " return pd.DataFrame(self.history)\n", - "\n", - " @torch.no_grad()\n", - " def evaluate(self, val_data):\n", - " val_data = self.accelerator.prepare(val_data)\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " val_metrics = val_epoch_runner(val_data)\n", - " return val_metrics\n", - " \n", - " \n", - " @torch.no_grad()\n", - " def predict(self, dataloader):\n", - " dataloader = self.accelerator.prepare(dataloader)\n", - " result = torch.cat([self.forward(t[0]) for t in dataloader])\n", - " return result.data\n", - " " + "我们使用梦中情炉torchkeras来实现最优雅的训练循环。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "81598c6c", "metadata": {}, "outputs": [], "source": [ "from torchkeras.metrics import AUC\n", + "from torchkeras import KerasModel \n", "\n", "loss_fn = nn.BCEWithLogitsLoss()\n", "\n", @@ -1045,22 +877,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "cd242b51", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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"cell_type": "markdown", @@ -1072,64 +984,39 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "26b68fdb", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "27b334a2", + "execution_count": 13, + "id": "21a1a856-fe49-412e-bcfd-2e1c10424f1b", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████| 98/98 [00:16<00:00, 5.99it/s, val_auc=0.781, val_loss=0.466]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.46646365553748853, 'val_auc': 0.781028687953949}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "plot_metric(dfhistory,\"loss\")" - ] - }, - { - "cell_type": "markdown", - "id": "29c19734", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h2t3tup3hxj20gc0af74n.jpg)" + "model.evaluate(dl_test)" ] }, { "cell_type": "code", "execution_count": null, - "id": "6efb4d50", + "id": "9bbe2633-638b-4eb7-a024-9954b5d91e91", "metadata": {}, "outputs": [], - "source": [ - "plot_metric(dfhistory,\"auc\")" - ] - }, - { - "cell_type": "markdown", - "id": "4169cb05", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h2t3tuemikj20f70ait90.jpg)" - ] + "source": [] }, { "cell_type": "markdown", @@ -1141,26 +1028,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "8b356206", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7810287729660677\n" + ] + } + ], "source": [ "from sklearn.metrics import roc_auc_score\n", - "preds = torch.sigmoid(model.predict(dl_val))\n", - "labels = torch.cat([x[-1] for x in dl_val])\n", + "model.eval()\n", + "dl_test = model.accelerator.prepare(dl_test)\n", + "with torch.no_grad():\n", + " result = torch.cat([model.forward(t[0]) for t in dl_test])\n", "\n", - "val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())\n", - "print(val_auc)" + "preds = F.sigmoid(result)\n", + "labels = torch.cat([x[-1] for x in dl_test])\n", + "\n", + "val_auc = roc_auc_score(labels.numpy(),preds.numpy())\n", + "print(val_auc)\n" ] }, { - "cell_type": "markdown", - "id": "e56ee74b", + "cell_type": "code", + "execution_count": null, + "id": "f021a0bd-1ee5-43d6-85e3-432b7a9e1f8f", "metadata": {}, - "source": [ - "0.7806176567186112" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1171,15 +1071,11 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "13bda74f", + "cell_type": "markdown", + "id": "b9ce7957-6042-44ca-bee9-dee0b3b42d55", "metadata": {}, - "outputs": [], "source": [ - "torch.save(model.net.state_dict(),\"best_fibinet.pt\")\n", - "net_clone = create_net()\n", - "net_clone.load_state_dict(torch.load(\"best_fibinet.pt\"))" + "模型最佳权重已经保存在 model.fit(ckpt_path) 传入的参数中了。" ] }, { @@ -1189,38 +1085,8 @@ "metadata": {}, "outputs": [], "source": [ - "from sklearn.metrics import roc_auc_score\n", - "net_clone.eval()\n", - "preds = torch.cat([torch.sigmoid(net_clone(x[0])).data for x in dl_val]) \n", - "labels = torch.cat([x[-1] for x in dl_val])\n", - "\n", - "val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())\n", - "print(val_auc)" - ] - }, - { - "cell_type": "markdown", - "id": "f922378d", - "metadata": {}, - "source": [ - "0.7806176567186112" - ] - }, - { - "cell_type": "markdown", - "id": "22370240", - "metadata": {}, - "source": [ - "可以看到FiBiNET在验证集的AUC得分为0.7806,相比之下DeepFM的验证集AUC为0.7803。\n", - "\n", - "不能说纹丝不动, 只能说了涨了个蚊子腿大小肉的点。\n", - "\n", - "并且这是以较大地牺牲模型训练预测效率为代价的。\n", - "\n", - "DeepFM训练一个Epoch大约需要20s, 而FiBiNET训练一个Epoch需要大约2min.\n", - "\n", - "尽管如此, FiBiNET的结构设计依然是值得我们学习和借鉴的, 集神经网络结构设计三大主流高级技巧于一体, 闪烁着穿越时空的才华与智慧光芒。\n", - "\n" + "net_clone = create_net()\n", + "net_clone.load_state_dict(torch.load(model.ckpt_path))" ] }, { @@ -1243,6 +1109,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/7-6,DeepCross\346\250\241\345\236\213.ipynb" "b/7-6,DeepCross\346\250\241\345\236\213.ipynb" index b3d53e518..de853a789 100644 --- "a/7-6,DeepCross\346\250\241\345\236\213.ipynb" +++ "b/7-6,DeepCross\346\250\241\345\236\213.ipynb" @@ -138,7 +138,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "1e109057", "metadata": {}, "outputs": [], @@ -234,7 +234,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "46e93a0a", "metadata": {}, "outputs": [], @@ -316,7 +316,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "c0db9756", "metadata": {}, "outputs": [], @@ -425,7 +425,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "c607ceba", "metadata": {}, "outputs": [], @@ -653,15 +653,26 @@ " if self.n_classes==1:\n", " x_out = x_out.squeeze(-1)\n", " \n", - " return x_out " + " return x_out \n", + " " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "4fb91e6f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "tensor([ 0.1160, -0.1248], grad_fn=)\n", + "tensor([0.4309, 0.2105], grad_fn=)\n", + "tensor([0.0336, 0.4770], grad_fn=)\n" + ] + } + ], "source": [ "##测试 DeepCross\n", "\n", @@ -735,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "4d6b5ddc", "metadata": {}, "outputs": [], @@ -750,12 +761,7 @@ "from torch import nn \n", "from torch.utils.data import Dataset,DataLoader \n", "import torch.nn.functional as F \n", - "import torchkeras \n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(info+'...\\n\\n')\n" + "import torchkeras \n" ] }, { @@ -768,7 +774,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "2c810d87", "metadata": {}, "outputs": [], @@ -795,7 +801,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "21d7ef14", "metadata": {}, "outputs": [], @@ -834,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "dc41f88f", "metadata": {}, "outputs": [], @@ -854,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "51a784c7", "metadata": {}, "outputs": [], @@ -878,7 +884,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "63435a35", "metadata": {}, "outputs": [], @@ -921,192 +927,22 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "a422cebd", + "cell_type": "markdown", + "id": "b16c0558-af36-4a41-a96f-661a2f11a811", "metadata": {}, - "outputs": [], "source": [ - "import os,sys,time\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime \n", - "from tqdm import tqdm \n", - "\n", - "import torch\n", - "from torch import nn \n", - "from accelerate import Accelerator\n", - "from copy import deepcopy\n", - "\n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(str(info)+\"\\n\")\n", - " \n", - "class StepRunner:\n", - " def __init__(self, net, loss_fn,stage = \"train\", metrics_dict = None, \n", - " optimizer = None, lr_scheduler = None,\n", - " accelerator = None\n", - " ):\n", - " self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage\n", - " self.optimizer,self.lr_scheduler = optimizer,lr_scheduler\n", - " self.accelerator = accelerator\n", - " \n", - " def __call__(self, features, labels):\n", - " #loss\n", - " preds = self.net(features)\n", - " loss = self.loss_fn(preds,labels)\n", - "\n", - " #backward()\n", - " if self.optimizer is not None and self.stage==\"train\":\n", - " if self.accelerator is None:\n", - " loss.backward()\n", - " else:\n", - " self.accelerator.backward(loss)\n", - " self.optimizer.step()\n", - " if self.lr_scheduler is not None:\n", - " self.lr_scheduler.step()\n", - " self.optimizer.zero_grad()\n", - " \n", - " #metrics\n", - " step_metrics = {self.stage+\"_\"+name:metric_fn(preds, labels).item() \n", - " for name,metric_fn in self.metrics_dict.items()}\n", - " return loss.item(),step_metrics\n", - " \n", - " \n", - "class EpochRunner:\n", - " def __init__(self,steprunner):\n", - " self.steprunner = steprunner\n", - " self.stage = steprunner.stage\n", - " self.steprunner.net.train() if self.stage==\"train\" else self.steprunner.net.eval()\n", - " \n", - " def __call__(self,dataloader):\n", - " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dataloader), total =len(dataloader))\n", - " for i, batch in loop:\n", - " features,labels = batch\n", - " if self.stage==\"train\":\n", - " loss, step_metrics = self.steprunner(features,labels)\n", - " else:\n", - " with torch.no_grad():\n", - " loss, step_metrics = self.steprunner(features,labels)\n", - "\n", - " step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n", - "\n", - " total_loss += loss\n", - " step+=1\n", - " if i!=len(dataloader)-1:\n", - " loop.set_postfix(**step_log)\n", - " else:\n", - " epoch_loss = total_loss/step\n", - " epoch_metrics = {self.stage+\"_\"+name:metric_fn.compute().item() \n", - " for name,metric_fn in self.steprunner.metrics_dict.items()}\n", - " epoch_log = dict({self.stage+\"_loss\":epoch_loss},**epoch_metrics)\n", - " loop.set_postfix(**epoch_log)\n", - "\n", - " for name,metric_fn in self.steprunner.metrics_dict.items():\n", - " metric_fn.reset()\n", - " return epoch_log\n", - "\n", - "class KerasModel(torch.nn.Module):\n", - " def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):\n", - " super().__init__()\n", - " self.accelerator = Accelerator()\n", - " self.history = {}\n", - " \n", - " self.net = net\n", - " self.loss_fn = loss_fn\n", - " self.metrics_dict = nn.ModuleDict(metrics_dict) \n", - " \n", - " self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(\n", - " self.parameters(), lr=1e-2)\n", - " self.lr_scheduler = lr_scheduler\n", - " \n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer = self.accelerator.prepare(\n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer)\n", - "\n", - " def forward(self, x):\n", - " if self.net:\n", - " return self.net.forward(x)\n", - " else:\n", - " raise NotImplementedError\n", - "\n", - "\n", - " def fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt', \n", - " patience=5, monitor=\"val_loss\", mode=\"min\"):\n", - " \n", - " train_data = self.accelerator.prepare(train_data)\n", - " val_data = self.accelerator.prepare(val_data) if val_data else []\n", - "\n", - " for epoch in range(1, epochs+1):\n", - " printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n", - " \n", - " # 1，train ------------------------------------------------- \n", - " train_step_runner = StepRunner(net = self.net,stage=\"train\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " optimizer = self.optimizer, lr_scheduler = self.lr_scheduler,\n", - " accelerator = self.accelerator)\n", - " train_epoch_runner = EpochRunner(train_step_runner)\n", - " train_metrics = train_epoch_runner(train_data)\n", - " \n", - " for name, metric in train_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - "\n", - " # 2，validate -------------------------------------------------\n", - " if val_data:\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " with torch.no_grad():\n", - " val_metrics = val_epoch_runner(val_data)\n", - " val_metrics[\"epoch\"] = epoch\n", - " for name, metric in val_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - " \n", - " # 3，early-stopping -------------------------------------------------\n", - " arr_scores = self.history[monitor]\n", - " best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n", - " if best_score_idx==len(arr_scores)-1:\n", - " torch.save(self.net.state_dict(),ckpt_path)\n", - " print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", - " if len(arr_scores)-best_score_idx>patience:\n", - " print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n", - " monitor,patience),file=sys.stderr)\n", - " self.net.load_state_dict(torch.load(ckpt_path))\n", - " break \n", - " \n", - " return pd.DataFrame(self.history)\n", - "\n", - " @torch.no_grad()\n", - " def evaluate(self, val_data):\n", - " val_data = self.accelerator.prepare(val_data)\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " val_metrics = val_epoch_runner(val_data)\n", - " return val_metrics\n", - " \n", - " \n", - " @torch.no_grad()\n", - " def predict(self, dataloader):\n", - " dataloader = self.accelerator.prepare(dataloader)\n", - " result = torch.cat([self.forward(t[0]) for t in dataloader])\n", - " return result.data\n", - " " + "我们使用梦中情炉torchkeras来实现最优雅的训练循环。" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "id": "32761275", "metadata": {}, "outputs": [], "source": [ "from torchkeras.metrics import AUC\n", + "from torchkeras import KerasModel \n", "\n", "loss_fn = nn.BCEWithLogitsLoss()\n", "\n", @@ -1123,22 +959,102 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "id": "306a3dc9", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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\n", 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"cell_type": "markdown", @@ -1150,64 +1066,39 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "1a577c97", + "execution_count": 16, + "id": "91871493-8ce5-41e6-8966-6ed1f852925d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████| 98/98 [00:04<00:00, 22.22it/s, val_auc=0.781, val_loss=0.466]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.46555993021750935, 'val_auc': 0.7811386585235596}" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()" + "model.evaluate(dl_test)" ] }, { "cell_type": "code", "execution_count": null, - "id": "a1b52108", + "id": "81bde359-249f-44b2-a810-2ec7f6eb05cb", "metadata": {}, "outputs": [], - "source": [ - "plot_metric(dfhistory,\"loss\")" - ] - }, - { - "cell_type": "markdown", - "id": "4edc1d36", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h2wizlzzsij20fv0a73yt.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "acfd031e", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"auc\")" - ] - }, - { - "cell_type": "markdown", - "id": "3f540023", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h2wiymw5nzj20fj0aaaad.jpg) " - ] + "source": [] }, { "cell_type": "markdown", @@ -1219,26 +1110,39 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "2f99aaf7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.7811383534406475\n" + ] + } + ], "source": [ "from sklearn.metrics import roc_auc_score\n", - "preds = torch.sigmoid(model.predict(dl_val))\n", - "labels = torch.cat([x[-1] for x in dl_val])\n", + "model.eval()\n", + "dl_test = model.accelerator.prepare(dl_test)\n", + "with torch.no_grad():\n", + " result = torch.cat([model.forward(t[0]) for t in dl_test])\n", + "\n", + "preds = F.sigmoid(result)\n", + "labels = torch.cat([x[-1] for x in dl_test])\n", "\n", - "val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())\n", - "print(val_auc)" + "val_auc = roc_auc_score(labels.numpy(),preds.numpy())\n", + "print(val_auc)\n" ] }, { - "cell_type": "markdown", - "id": "8a79be59", + "cell_type": "code", + "execution_count": null, + "id": "dba04d27-8887-4cde-a6a5-04664fc1ef43", "metadata": {}, - "source": [ - "0.7820486224544348\n" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1249,39 +1153,33 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "0a3cc987", + "cell_type": "markdown", + "id": "6c717899-cd41-4770-8f5e-f1c507fba044", "metadata": {}, - "outputs": [], "source": [ - "torch.save(model.net.state_dict(),\"best_dcn.pt\")\n", - "net_clone = create_net()\n", - "net_clone.load_state_dict(torch.load(\"best_dcn.pt\"))" + "模型最佳权重已经保存在 model.fit(ckpt_path) 传入的参数中了。" ] }, { "cell_type": "code", - "execution_count": null, - "id": "8cae86f6", + "execution_count": 19, + "id": "bf7f187f-ec7d-4ea5-b95f-9e65cc781ffe", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "from sklearn.metrics import roc_auc_score\n", - "net_clone.eval()\n", - "preds = torch.cat([torch.sigmoid(net_clone(x[0])).data for x in dl_val]) \n", - "labels = torch.cat([x[-1] for x in dl_val])\n", - "\n", - "val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())\n", - "print(val_auc)" - ] - }, - { - "cell_type": "markdown", - "id": "7fd06a8e", - "metadata": {}, - "source": [ - "0.7820486196785761\n" + "net_clone = create_net()\n", + "net_clone.load_state_dict(torch.load(model.ckpt_path))" ] }, { @@ -1304,6 +1202,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/7-7,DIN\347\275\221\347\273\234.ipynb" "b/7-7,DIN\347\275\221\347\273\234.ipynb" index 8d27af91e..8e8271a7f 100644 --- "a/7-7,DIN\347\275\221\347\273\234.ipynb" +++ "b/7-7,DIN\347\275\221\347\273\234.ipynb" @@ -211,7 +211,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "129dfbd7", "metadata": {}, "outputs": [], @@ -263,7 +263,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "4079669c", "metadata": {}, "outputs": [], @@ -408,7 +408,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "d4512b9c", "metadata": {}, "outputs": [], @@ -522,7 +522,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "d005ea71", "metadata": {}, "outputs": [], @@ -597,7 +597,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "2873e610", "metadata": {}, "outputs": [], @@ -629,7 +629,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "219af657", "metadata": {}, "outputs": [], @@ -789,7 +789,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "d46756cb", "metadata": {}, "outputs": [], @@ -925,10 +925,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "15291fb8", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "preprocess number features...\n", + "preprocess category features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 353.26it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "preprocess sequence features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 63.96it/s]\n" + ] + } + ], "source": [ "from sklearn.preprocessing import QuantileTransformer\n", "from sklearn.pipeline import Pipeline \n", @@ -1021,7 +1051,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "d172c2de", "metadata": {}, "outputs": [], @@ -1048,7 +1078,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "d1944dc4", "metadata": {}, "outputs": [], @@ -1090,10 +1120,59 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "a8656b3f", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Embedding-1 [-1, 16] 64\n", + "Embedding-2 [-1, 16] 4,480\n", + "Embedding-3 [-1, 16] 368\n", + "Embedding-4 [-1, 16] 1,984\n", + "Embedding-5 [-1, 6, 16] 320\n", + "MaxPooling-6 [-1, 16] 0\n", + "Embedding-7 [-1, 10, 16] 61,888\n", + "MaxPooling-8 [-1, 16] 0\n", + "Embedding-9 [-1, 10, 16] 28,656\n", + "Linear-10 [-1, 16] 1,040\n", + "BatchNorm1d-11 [-1, 16] 32\n", + "BatchNorm1d-12 [-1, 16] 32\n", + "Sigmoid-13 [-1, 16] 0\n", + "Dropout-14 [-1, 16] 0\n", + "Linear-15 [-1, 8] 136\n", + "BatchNorm1d-16 [-1, 8] 16\n", + "BatchNorm1d-17 [-1, 8] 16\n", + "Sigmoid-18 [-1, 8] 0\n", + "Dropout-19 [-1, 8] 0\n", + "Linear-20 [-1, 1] 9\n", + "Linear-21 [-1, 32] 3,648\n", + "BatchNorm1d-22 [-1, 32] 64\n", + "PReLU-23 [-1, 32] 1\n", + "Dropout-24 [-1, 32] 0\n", + "Linear-25 [-1, 16] 528\n", + "BatchNorm1d-26 [-1, 16] 32\n", + "PReLU-27 [-1, 16] 1\n", + "Dropout-28 [-1, 16] 0\n", + "Linear-29 [-1, 1] 17\n", + "==========================================================================\n", + "Total params: 103,332\n", + "Trainable params: 103,332\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000801\n", + "Forward/backward pass size (MB): 0.006302\n", + "Params size (MB): 0.394180\n", + "Estimated Total Size (MB): 0.401283\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ "from torchkeras.summary import summary \n", "summary(net,input_data=batch);\n" @@ -1108,190 +1187,72 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "4dc20f48", + "cell_type": "markdown", + "id": "cf63bab5-6c77-4641-bbac-80603cbc7bda", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "我们使用梦中情炉torchkeras来实现最优雅的训练循环。" + ] }, { "cell_type": "code", - "execution_count": null, - "id": "f54e9220", + "execution_count": 45, + "id": "e05c3365-fd6b-49c9-9b11-1cc500ed49ec", "metadata": {}, "outputs": [], "source": [ - "import os,sys,time\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime \n", - "from tqdm import tqdm \n", - "\n", - "import torch\n", - "from torch import nn \n", - "from accelerate import Accelerator\n", - "from copy import deepcopy\n", - "\n", + "from torchkeras import KerasModel \n", "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(str(info)+\"\\n\")\n", - " \n", "class StepRunner:\n", - " def __init__(self, net, loss_fn,stage = \"train\", metrics_dict = None, \n", - " optimizer = None, lr_scheduler = None,\n", - " accelerator = None\n", + " def __init__(self, net, loss_fn, accelerator=None, stage = \"train\", metrics_dict = None, \n", + " optimizer = None, lr_scheduler = None\n", " ):\n", " self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage\n", " self.optimizer,self.lr_scheduler = optimizer,lr_scheduler\n", " self.accelerator = accelerator\n", + " if self.stage=='train':\n", + " self.net.train() \n", + " else:\n", + " self.net.eval()\n", " \n", - " def __call__(self, batch):\n", + " def __call__(self, batch): \n", " #loss\n", - " preds = self.net(batch)\n", - " loss = self.loss_fn(preds,batch[\"label\"])\n", + " with self.accelerator.autocast():\n", + " #loss\n", + " preds = self.net(batch)\n", + " labels = batch['label']\n", + " loss = self.loss_fn(preds,labels)\n", "\n", " #backward()\n", - " if self.optimizer is not None and self.stage==\"train\":\n", - " if self.accelerator is None:\n", - " loss.backward()\n", - " else:\n", - " self.accelerator.backward(loss)\n", + " if self.stage==\"train\" and self.optimizer is not None:\n", + " self.accelerator.backward(loss)\n", + " if self.accelerator.sync_gradients:\n", + " self.accelerator.clip_grad_norm_(self.net.parameters(), 1.0)\n", " self.optimizer.step()\n", " if self.lr_scheduler is not None:\n", " self.lr_scheduler.step()\n", " self.optimizer.zero_grad()\n", " \n", - " #metrics\n", - " step_metrics = {self.stage+\"_\"+name:metric_fn(preds, batch[\"label\"]).item() \n", - " for name,metric_fn in self.metrics_dict.items()}\n", - " return loss.item(),step_metrics\n", - " \n", - " \n", - "class EpochRunner:\n", - " def __init__(self,steprunner):\n", - " self.steprunner = steprunner\n", - " self.stage = steprunner.stage\n", - " self.steprunner.net.train() if self.stage==\"train\" else self.steprunner.net.eval()\n", + " all_loss = self.accelerator.gather(loss).sum()\n", + " all_preds = self.accelerator.gather(preds)\n", + " all_labels = self.accelerator.gather(labels)\n", " \n", - " def __call__(self,dataloader):\n", - " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dataloader), total =len(dataloader))\n", - " for i, batch in loop:\n", - " if self.stage==\"train\":\n", - " loss, step_metrics = self.steprunner(batch)\n", - " else:\n", - " with torch.no_grad():\n", - " loss, step_metrics = self.steprunner(batch)\n", - "\n", - " step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n", - "\n", - " total_loss += loss\n", - " step+=1\n", - " if i!=len(dataloader)-1:\n", - " loop.set_postfix(**step_log)\n", - " else:\n", - " epoch_loss = total_loss/step\n", - " epoch_metrics = {self.stage+\"_\"+name:metric_fn.compute().item() \n", - " for name,metric_fn in self.steprunner.metrics_dict.items()}\n", - " epoch_log = dict({self.stage+\"_loss\":epoch_loss},**epoch_metrics)\n", - " loop.set_postfix(**epoch_log)\n", - "\n", - " for name,metric_fn in self.steprunner.metrics_dict.items():\n", - " metric_fn.reset()\n", - " return epoch_log\n", - "\n", - "class KerasModel(torch.nn.Module):\n", - " def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):\n", - " super().__init__()\n", - " self.accelerator = Accelerator()\n", - " self.history = {}\n", - " \n", - " self.net = net\n", - " self.loss_fn = loss_fn\n", - " self.metrics_dict = nn.ModuleDict(metrics_dict) \n", + " #losses (or plain metrics that can be averaged)\n", + " step_losses = {self.stage+\"_loss\":all_loss.item()}\n", " \n", - " self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(\n", - " self.parameters(), lr=1e-2)\n", - " self.lr_scheduler = lr_scheduler\n", - " \n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer = self.accelerator.prepare(\n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer)\n", - "\n", - " def forward(self, x):\n", - " if self.net:\n", - " return self.net.forward(x)\n", - " else:\n", - " raise NotImplementedError\n", - "\n", - "\n", - " def fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt', \n", - " patience=5, monitor=\"val_loss\", mode=\"min\"):\n", + " #metrics (stateful metrics)\n", + " step_metrics = {self.stage+\"_\"+name:metric_fn(all_preds, all_labels).item() \n", + " for name,metric_fn in self.metrics_dict.items()}\n", " \n", - " train_data = self.accelerator.prepare(train_data)\n", - " val_data = self.accelerator.prepare(val_data) if val_data else []\n", + " if self.stage==\"train\":\n", + " if self.optimizer is not None:\n", + " step_metrics['lr'] = self.optimizer.state_dict()['param_groups'][0]['lr']\n", + " else:\n", + " step_metrics['lr'] = 0.0\n", + " return step_losses,step_metrics\n", + " \n", "\n", - " for epoch in range(1, epochs+1):\n", - " printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n", - " \n", - " # 1，train ------------------------------------------------- \n", - " train_step_runner = StepRunner(net = self.net,stage=\"train\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " optimizer = self.optimizer, lr_scheduler = self.lr_scheduler,\n", - " accelerator = self.accelerator)\n", - " train_epoch_runner = EpochRunner(train_step_runner)\n", - " train_metrics = train_epoch_runner(train_data)\n", - " \n", - " for name, metric in train_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - "\n", - " # 2，validate -------------------------------------------------\n", - " if val_data:\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " with torch.no_grad():\n", - " val_metrics = val_epoch_runner(val_data)\n", - " val_metrics[\"epoch\"] = epoch\n", - " for name, metric in val_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - " \n", - " # 3，early-stopping -------------------------------------------------\n", - " arr_scores = self.history[monitor]\n", - " best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n", - " if best_score_idx==len(arr_scores)-1:\n", - " torch.save(self.net.state_dict(),ckpt_path)\n", - " print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", - " if len(arr_scores)-best_score_idx>patience:\n", - " print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n", - " monitor,patience),file=sys.stderr)\n", - " break \n", - " \n", - " self.net.load_state_dict(torch.load(ckpt_path))\n", - " return pd.DataFrame(self.history)\n", - "\n", - " @torch.no_grad()\n", - " def evaluate(self, val_data):\n", - " val_data = self.accelerator.prepare(val_data)\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " val_metrics = val_epoch_runner(val_data)\n", - " return val_metrics\n", - " \n", - " \n", - " @torch.no_grad()\n", - " def predict(self, dataloader):\n", - " dataloader = self.accelerator.prepare(dataloader)\n", - " self.net.eval()\n", - " result = torch.cat([self.forward(t) for t in dataloader])\n", - " return result.data\n", - " " + "KerasModel.StepRunner = StepRunner \n" ] }, { @@ -1304,20 +1265,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "78f8b329", "metadata": {}, "outputs": [], "source": [ "from torchkeras.metrics import AUC\n", - "\n", "loss_fn = nn.BCEWithLogitsLoss()\n", "\n", "metrics_dict = {\"auc\":AUC()}\n", - "\n", "optimizer = torch.optim.Adam(net.parameters(), lr=0.002, weight_decay=0.001) \n", "\n", - "\n", "model = KerasModel(net,\n", " loss_fn = loss_fn,\n", " metrics_dict= metrics_dict,\n", @@ -1327,21 +1285,93 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "597c3158", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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\n", + " \n", + " 33.00% [33/100] [00:31<01:03]\n", + "
\n", + " ████████████████████100.00% [10/10] [val_loss=1.3538, val_auc=0.6238]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< val_auc without improvement in 10 epoch,early stopping >>>>>> \n", + "\u001b[0m\n" + ] + } + ], "source": [ - "dfhistory = model.fit(train_data=dl_train,val_data=dl_val,epochs=100, patience=5,\n", - " monitor = \"val_auc\",mode=\"max\",ckpt_path='checkpoint.pt')\n" - ] - }, - { - "cell_type": "markdown", - "id": "3238d807", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h3sbs7wl0lj20r107nab5.jpg)" + "dfhistory = model.fit(train_data = dl_train,\n", + " val_data = dl_val,\n", + " epochs=100,\n", + " ckpt_path='checkpoint',\n", + " patience=10,\n", + " monitor='val_auc',\n", + " mode='max',\n", + " plot=True,\n", + " cpu=True\n", + ")\n" ] }, { @@ -1354,85 +1384,32 @@ }, { "cell_type": "code", - "execution_count": null, - "id": "86f8e31a", - "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "503f5d04", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"loss\")" - ] - }, - { - "cell_type": "markdown", - "id": "1d43b72e", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h3sbs8ryajj20h20a1gly.jpg)\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8251d9f9", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"auc\")" - ] - }, - { - "cell_type": "markdown", - "id": "b7217d1a", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h3sbsf8b1wj20f30a70t3.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "a69e3841", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████| 10/10 [00:00<00:00, 61.64it/s, val_auc=0.647, val_loss=0.686]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.6855947375297546, 'val_auc': 0.6471151113510132}" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.evaluate(dl_val)" ] }, - { - "cell_type": "markdown", - "id": "e901f47e", - "metadata": {}, - "source": [ - "{'val_loss': 0.6842133283615113, 'val_auc': 0.6392135620117188}" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1451,33 +1428,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "d1a3c8bd", "metadata": {}, - "outputs": [], - "source": [ - "labels = torch.tensor([x[\"label\"] for x in ds_val])\n", - "preds = model.predict(dl_val)\n", - "val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())\n", - "print(val_auc)" - ] - }, - { - "cell_type": "markdown", - "id": "11a31ef1", - "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6471136914166838\n" + ] + } + ], "source": [ - "0.6392135469811272" + "from sklearn.metrics import roc_auc_score\n", + "model.eval()\n", + "dl_val = model.accelerator.prepare(dl_val)\n", + "with torch.no_grad():\n", + " result = torch.cat([model.forward(t) for t in dl_val])\n", + "\n", + "preds = F.sigmoid(result)\n", + "labels = torch.cat([x['label'] for x in dl_val])\n", + "\n", + "val_auc = roc_auc_score(labels.numpy(),preds.numpy())\n", + "print(val_auc)\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "ffcd29df", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "code", "execution_count": null, @@ -1496,10 +1472,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 50, "id": "5df6b615", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 50, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "torch.save(model.net.state_dict(),\"best_din.pt\")\n", "net_clone = create_net()\n", @@ -1508,10 +1495,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "6297c73d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6471136914166838\n" + ] + } + ], "source": [ "net_clone.eval()\n", "labels = torch.tensor([x[\"label\"] for x in ds_val])\n", @@ -1520,14 +1515,6 @@ "print(val_auc)" ] }, - { - "cell_type": "markdown", - "id": "c6694f06", - "metadata": {}, - "source": [ - "0.6392135469811272" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1558,7 +1545,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1572,7 +1559,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/7-8,DIEN\347\275\221\347\273\234.ipynb" "b/7-8,DIEN\347\275\221\347\273\234.ipynb" index 8fafbb589..d4ddc21c0 100644 --- "a/7-8,DIEN\347\275\221\347\273\234.ipynb" +++ "b/7-8,DIEN\347\275\221\347\273\234.ipynb" @@ -262,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "950dd47b", "metadata": { "lines_to_next_cell": 2 @@ -336,7 +336,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "3b7bd9e4", "metadata": {}, "outputs": [], @@ -1105,7 +1105,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "e09e2ac7", "metadata": {}, "outputs": [], @@ -1238,10 +1238,40 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "7c2549a7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "preprocess number features...\n", + "preprocess category features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4/4 [00:00<00:00, 393.16it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "preprocess sequence features...\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 3/3 [00:00<00:00, 67.73it/s]\n" + ] + } + ], "source": [ "from sklearn.preprocessing import QuantileTransformer\n", "from sklearn.pipeline import Pipeline \n", @@ -1335,7 +1365,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "1f78510e", "metadata": {}, "outputs": [], @@ -1346,10 +1376,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "73039199", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'gender': 4, 'movieId': 280, 'occupation': 23, 'zipCode': 124, 'genres': 20, 'histHighRatedMovieIds': 1791, 'negHistMovieIds': 3868}\n" + ] + } + ], "source": [ "print(cat_nums)" ] @@ -1372,13 +1410,80 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "5483c9d7", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "--------------------------------------------------------------------------\n", + "Layer (type) Output Shape Param #\n", + "==========================================================================\n", + "Embedding-1 [-1, 16] 64\n", + "Embedding-2 [-1, 16] 4,480\n", + "Embedding-3 [-1, 16] 368\n", + "Embedding-4 [-1, 16] 1,984\n", + "Embedding-5 [-1, 6, 16] 320\n", + "MaxPooling-6 [-1, 16] 0\n", + "Embedding-7 [-1, 10, 16] 28,656\n", + "Embedding-8 [-1, 10, 16] 61,888\n", + "GRU-9 [-1, 16] 1,632\n", + "Linear-10 [-1, 100] 3,300\n", + "Sigmoid-11 [-1, 100] 0\n", + "Linear-12 [-1, 50] 5,050\n", + "Sigmoid-13 [-1, 50] 0\n", + "Linear-14 [-1, 1] 51\n", + "Linear-15 [-1, 100] 3,300\n", + "Sigmoid-16 [-1, 100] 0\n", + "Linear-17 [-1, 50] 5,050\n", + "Sigmoid-18 [-1, 50] 0\n", + "Linear-19 [-1, 1] 51\n", + "Linear-20 [-1, 16] 1,040\n", + "BatchNorm1d-21 [-1, 16] 32\n", + "BatchNorm1d-22 [-1, 16] 32\n", + "Sigmoid-23 [-1, 16] 0\n", + "Dropout-24 [-1, 16] 0\n", + "Linear-25 [-1, 8] 136\n", + "BatchNorm1d-26 [-1, 8] 16\n", + "BatchNorm1d-27 [-1, 8] 16\n", + "Sigmoid-28 [-1, 8] 0\n", + "Dropout-29 [-1, 8] 0\n", + "Linear-30 [-1, 1] 9\n", + "AttentionUpdateGateGRUCell-31 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-32 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-33 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-34 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-35 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-36 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-37 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-38 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-39 [-1, 16] 1,632\n", + "AttentionUpdateGateGRUCell-40 [-1, 16] 1,632\n", + "Linear-41 [-1, 32] 3,136\n", + "BatchNorm1d-42 [-1, 32] 64\n", + "PReLU-43 [-1, 32] 1\n", + "Dropout-44 [-1, 32] 0\n", + "Linear-45 [-1, 16] 528\n", + "BatchNorm1d-46 [-1, 16] 32\n", + "PReLU-47 [-1, 16] 1\n", + "Dropout-48 [-1, 16] 0\n", + "Linear-49 [-1, 1] 17\n", + "==========================================================================\n", + "Total params: 137,574\n", + "Trainable params: 137,574\n", + "Non-trainable params: 0\n", + "--------------------------------------------------------------------------\n", + "Input size (MB): 0.000801\n", + "Forward/backward pass size (MB): 0.012115\n", + "Params size (MB): 0.524803\n", + "Estimated Total Size (MB): 0.537720\n", + "--------------------------------------------------------------------------\n" + ] + } + ], "source": [ - "\n", - "\n", "def create_net():\n", " augru_attention_groups_with_neg = [\n", " AttentionGroup(\n", @@ -1411,14 +1516,6 @@ "\n" ] }, - { - "cell_type": "code", - "execution_count": null, - "id": "15722f65", - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "id": "10909969", @@ -1428,208 +1525,82 @@ ] }, { - "cell_type": "code", - "execution_count": null, - "id": "64381bd1", + "cell_type": "markdown", + "id": "9b16c8af-d31c-49e4-8038-d8736187df49", "metadata": {}, - "outputs": [], - "source": [] + "source": [ + "我们使用梦中情炉torchkeras来实现最优雅的训练循环。" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "653ab7c6", "metadata": {}, "outputs": [], "source": [ - "import os,sys,time\n", - "import numpy as np\n", - "import pandas as pd\n", - "import datetime \n", - "from tqdm import tqdm \n", + "from torchkeras import KerasModel \n", "\n", - "import torch\n", - "from torch import nn \n", - "from accelerate import Accelerator\n", - "from copy import deepcopy\n", - "\n", - "\n", - "def printlog(info):\n", - " nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')\n", - " print(\"\\n\"+\"==========\"*8 + \"%s\"%nowtime)\n", - " print(str(info)+\"\\n\")\n", - " \n", "class StepRunner:\n", - " def __init__(self, net, loss_fn,stage = \"train\", metrics_dict = None, \n", - " optimizer = None, lr_scheduler = None,\n", - " accelerator = None\n", + " def __init__(self, net, loss_fn, accelerator=None, stage = \"train\", metrics_dict = None, \n", + " optimizer = None, lr_scheduler = None\n", " ):\n", " self.net,self.loss_fn,self.metrics_dict,self.stage = net,loss_fn,metrics_dict,stage\n", " self.optimizer,self.lr_scheduler = optimizer,lr_scheduler\n", " self.accelerator = accelerator\n", + " if self.stage=='train':\n", + " self.net.train() \n", + " else:\n", + " self.net.eval()\n", " \n", - " def __call__(self, batch):\n", + " def __call__(self, batch): \n", " #loss\n", - " preds,aloss = self.net(batch)\n", - " loss = self.loss_fn(preds,batch[\"label\"])+aloss\n", + " with self.accelerator.autocast():\n", + " #loss\n", + " preds, aux_loss = net(batch)\n", + " labels = batch['label']\n", + " loss = self.loss_fn(preds,labels)+aux_loss\n", "\n", " #backward()\n", - " if self.optimizer is not None and self.stage==\"train\":\n", - " if self.accelerator is None:\n", - " loss.backward()\n", - " else:\n", - " self.accelerator.backward(loss)\n", + " if self.stage==\"train\" and self.optimizer is not None:\n", + " self.accelerator.backward(loss)\n", + " if self.accelerator.sync_gradients:\n", + " self.accelerator.clip_grad_norm_(self.net.parameters(), 1.0)\n", " self.optimizer.step()\n", " if self.lr_scheduler is not None:\n", " self.lr_scheduler.step()\n", " self.optimizer.zero_grad()\n", " \n", - " #metrics\n", - " step_metrics = {self.stage+\"_\"+name:metric_fn(preds, batch[\"label\"]).item() \n", - " for name,metric_fn in self.metrics_dict.items()}\n", - " return loss.item(),step_metrics\n", - " \n", - " \n", - "class EpochRunner:\n", - " def __init__(self,steprunner):\n", - " self.steprunner = steprunner\n", - " self.stage = steprunner.stage\n", - " self.steprunner.net.train() if self.stage==\"train\" else self.steprunner.net.eval()\n", + " all_loss = self.accelerator.gather(loss).sum()\n", + " all_preds = self.accelerator.gather(preds)\n", + " all_labels = self.accelerator.gather(labels)\n", " \n", - " def __call__(self,dataloader):\n", - " total_loss,step = 0,0\n", - " loop = tqdm(enumerate(dataloader), total =len(dataloader))\n", - " for i, batch in loop:\n", - " if self.stage==\"train\":\n", - " loss, step_metrics = self.steprunner(batch)\n", - " else:\n", - " with torch.no_grad():\n", - " loss, step_metrics = self.steprunner(batch)\n", - "\n", - " step_log = dict({self.stage+\"_loss\":loss},**step_metrics)\n", - "\n", - " total_loss += loss\n", - " step+=1\n", - " if i!=len(dataloader)-1:\n", - " loop.set_postfix(**step_log)\n", - " else:\n", - " epoch_loss = total_loss/step\n", - " epoch_metrics = {self.stage+\"_\"+name:metric_fn.compute().item() \n", - " for name,metric_fn in self.steprunner.metrics_dict.items()}\n", - " epoch_log = dict({self.stage+\"_loss\":epoch_loss},**epoch_metrics)\n", - " loop.set_postfix(**epoch_log)\n", - "\n", - " for name,metric_fn in self.steprunner.metrics_dict.items():\n", - " metric_fn.reset()\n", - " return epoch_log\n", - "\n", - "class KerasModel(torch.nn.Module):\n", - " def __init__(self,net,loss_fn,metrics_dict=None,optimizer=None,lr_scheduler = None):\n", - " super().__init__()\n", - " self.accelerator = Accelerator()\n", - " self.history = {}\n", + " #losses (or plain metrics that can be averaged)\n", + " step_losses = {self.stage+\"_loss\":all_loss.item()}\n", " \n", - " self.net = net\n", - " self.loss_fn = loss_fn\n", - " self.metrics_dict = nn.ModuleDict(metrics_dict) \n", - " \n", - " self.optimizer = optimizer if optimizer is not None else torch.optim.Adam(\n", - " self.parameters(), lr=1e-2)\n", - " self.lr_scheduler = lr_scheduler\n", - " \n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer = self.accelerator.prepare(\n", - " self.net,self.loss_fn,self.metrics_dict,self.optimizer)\n", - "\n", - " def forward(self, x):\n", - " if self.net:\n", - " return self.net.forward(x)[0]\n", - " else:\n", - " raise NotImplementedError\n", - "\n", - "\n", - " def fit(self, train_data, val_data=None, epochs=10, ckpt_path='checkpoint.pt', \n", - " patience=5, monitor=\"val_loss\", mode=\"min\"):\n", + " #metrics (stateful metrics)\n", + " step_metrics = {self.stage+\"_\"+name:metric_fn(all_preds, all_labels).item() \n", + " for name,metric_fn in self.metrics_dict.items()}\n", " \n", - " train_data = self.accelerator.prepare(train_data)\n", - " val_data = self.accelerator.prepare(val_data) if val_data else []\n", + " if self.stage==\"train\":\n", + " if self.optimizer is not None:\n", + " step_metrics['lr'] = self.optimizer.state_dict()['param_groups'][0]['lr']\n", + " else:\n", + " step_metrics['lr'] = 0.0\n", + " return step_losses,step_metrics\n", + " \n", "\n", - " for epoch in range(1, epochs+1):\n", - " printlog(\"Epoch {0} / {1}\".format(epoch, epochs))\n", - " \n", - " # 1，train ------------------------------------------------- \n", - " train_step_runner = StepRunner(net = self.net,stage=\"train\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " optimizer = self.optimizer, lr_scheduler = self.lr_scheduler,\n", - " accelerator = self.accelerator)\n", - " train_epoch_runner = EpochRunner(train_step_runner)\n", - " train_metrics = train_epoch_runner(train_data)\n", - " \n", - " for name, metric in train_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - "\n", - " # 2，validate -------------------------------------------------\n", - " if val_data:\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " with torch.no_grad():\n", - " val_metrics = val_epoch_runner(val_data)\n", - " val_metrics[\"epoch\"] = epoch\n", - " for name, metric in val_metrics.items():\n", - " self.history[name] = self.history.get(name, []) + [metric]\n", - " \n", - " # 3，early-stopping -------------------------------------------------\n", - " arr_scores = self.history[monitor]\n", - " best_score_idx = np.argmax(arr_scores) if mode==\"max\" else np.argmin(arr_scores)\n", - " if best_score_idx==len(arr_scores)-1:\n", - " torch.save(self.net.state_dict(),ckpt_path)\n", - " print(\"<<<<<< reach best {0} : {1} >>>>>>\".format(monitor,\n", - " arr_scores[best_score_idx]),file=sys.stderr)\n", - " if len(arr_scores)-best_score_idx>patience:\n", - " print(\"<<<<<< {} without improvement in {} epoch, early stopping >>>>>>\".format(\n", - " monitor,patience),file=sys.stderr)\n", - " break \n", - " \n", - " self.net.load_state_dict(torch.load(ckpt_path))\n", - " return pd.DataFrame(self.history)\n", - "\n", - " @torch.no_grad()\n", - " def evaluate(self, val_data):\n", - " val_data = self.accelerator.prepare(val_data)\n", - " val_step_runner = StepRunner(net = self.net,stage=\"val\",\n", - " loss_fn = self.loss_fn,metrics_dict=deepcopy(self.metrics_dict),\n", - " accelerator = self.accelerator)\n", - " val_epoch_runner = EpochRunner(val_step_runner)\n", - " val_metrics = val_epoch_runner(val_data)\n", - " return val_metrics\n", - " \n", - " \n", - " @torch.no_grad()\n", - " def predict(self, dataloader):\n", - " dataloader = self.accelerator.prepare(dataloader)\n", - " self.net.eval()\n", - " result = torch.cat([self.forward(t) for t in dataloader])\n", - " return result.data\n" + "KerasModel.StepRunner = StepRunner \n" ] }, { "cell_type": "code", - "execution_count": null, - "id": "41baccf7", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, + "execution_count": 56, "id": "25c23e40", "metadata": {}, "outputs": [], "source": [ "from torchkeras.metrics import AUC\n", - "\n", "loss_fn = nn.BCEWithLogitsLoss()\n", "\n", "metrics_dict = {\"auc\":AUC()}\n", @@ -1638,27 +1609,99 @@ "model = KerasModel(net,\n", " loss_fn = loss_fn,\n", " metrics_dict= metrics_dict,\n", - " optimizer = optimizer\n", + " optimizer = optimizer,\n", " ) \n" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 57, "id": "551bf220", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< 🐌 cpu is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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\n", + " ████████████████████100.00% [10/10] [val_loss=0.7237, val_auc=0.6365]\n", + "
\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[0;31m<<<<<< val_auc without improvement in 10 epoch,early stopping >>>>>> \n", + "\u001b[0m\n" + ] + } + ], "source": [ - "dfhistory = model.fit(train_data=dl_train,val_data=dl_val,epochs=100, patience=10,\n", - " monitor = \"val_auc\",mode=\"max\",ckpt_path='checkpoint.pt')\n" - ] - }, - { - "cell_type": "markdown", - "id": "116d2931", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h3z8ccgn9ij20p507qmy7.jpg)" + "dfhistory = model.fit(train_data = dl_train,\n", + " val_data = dl_val,\n", + " epochs=100,\n", + " ckpt_path='checkpoint',\n", + " patience=10,\n", + " monitor='val_auc',\n", + " mode='max',\n", + " plot=True,\n", + " cpu=True\n", + ")\n" ] }, { @@ -1679,91 +1722,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 58, "id": "8e81c1d8", "metadata": {}, - "outputs": [], - "source": [ - "%matplotlib inline\n", - "%config InlineBackend.figure_format = 'svg'\n", - "\n", - "import matplotlib.pyplot as plt\n", - "\n", - "def plot_metric(dfhistory, metric):\n", - " train_metrics = dfhistory[\"train_\"+metric]\n", - " val_metrics = dfhistory['val_'+metric]\n", - " epochs = range(1, len(train_metrics) + 1)\n", - " plt.plot(epochs, train_metrics, 'bo--')\n", - " plt.plot(epochs, val_metrics, 'ro-')\n", - " plt.title('Training and validation '+ metric)\n", - " plt.xlabel(\"Epochs\")\n", - " plt.ylabel(metric)\n", - " plt.legend([\"train_\"+metric, 'val_'+metric])\n", - " plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "bc705d62", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"loss\")" - ] - }, - { - "cell_type": "markdown", - "id": "1d16172f", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h3z8afwf3zj20f20aiglx.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a5d9136b", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3c03069e", - "metadata": {}, - "outputs": [], - "source": [ - "plot_metric(dfhistory,\"auc\")" - ] - }, - { - "cell_type": "markdown", - "id": "64ac7283", - "metadata": {}, - "source": [ - "![](https://tva1.sinaimg.cn/large/e6c9d24egy1h3z8ddym3bj20f20ab74o.jpg)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9806e6a", - "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████| 10/10 [00:00<00:00, 53.85it/s, val_auc=0.65, val_loss=0.709]\n" + ] + }, + { + "data": { + "text/plain": [ + "{'val_loss': 0.7085483193397522, 'val_auc': 0.6495699286460876}" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "model.evaluate(dl_val)" ] }, - { - "cell_type": "markdown", - "id": "055d6c18", - "metadata": {}, - "source": [ - "{'val_loss': 0.7020544648170471, 'val_auc': 0.6469045281410217}" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1782,29 +1766,32 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 62, "id": "8db6ff42", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6495558111992674\n" + ] + } + ], "source": [ - "from sklearn.metrics import roc_auc_score \n", + "from sklearn.metrics import roc_auc_score\n", + "model.eval()\n", + "dl_val = model.accelerator.prepare(dl_val)\n", + "with torch.no_grad():\n", + " result = torch.cat([model.forward(t)[0] for t in dl_val])\n", "\n", - "labels = torch.tensor([x[\"label\"] for x in ds_val])\n", - "preds = model.predict(dl_val)\n", - "val_auc = roc_auc_score(labels.cpu().numpy(),preds.cpu().numpy())\n", + "preds = F.sigmoid(result)\n", + "labels = torch.cat([x['label'] for x in dl_val])\n", + "\n", + "val_auc = roc_auc_score(labels.numpy(),preds.numpy())\n", "print(val_auc)" ] }, - { - "cell_type": "markdown", - "id": "f4f6f35a", - "metadata": {}, - "source": [ - "```\n", - "0.6469045283797497\n", - "```" - ] - }, { "cell_type": "code", "execution_count": null, @@ -1823,10 +1810,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 63, "id": "98e0dc20", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 63, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "torch.save(model.net.state_dict(),\"best_dien.pt\")\n", "net_clone = create_net()\n", @@ -1835,10 +1833,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 64, "id": "795fa7f3", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.6495558111992674\n" + ] + } + ], "source": [ "net_clone.eval()\n", "labels = torch.tensor([x[\"label\"] for x in ds_val])\n", @@ -1848,14 +1854,12 @@ ] }, { - "cell_type": "markdown", - "id": "c4b62168", + "cell_type": "code", + "execution_count": null, + "id": "ce8f8acd-8ac2-489a-8c77-e51bed6eec72", "metadata": {}, - "source": [ - "```\n", - "0.6469045283797497\n", - "```" - ] + "outputs": [], + "source": [] }, { "cell_type": "markdown", @@ -1877,6 +1881,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/A-1,Kaggle\345\205\215\350\264\271GPU\344\275\277\347\224\250\346\224\273\347\225\245.ipynb" "b/A-1,Kaggle\345\205\215\350\264\271GPU\344\275\277\347\224\250\346\224\273\347\225\245.ipynb" index 5cf4a7098..dd349d1a7 100644 --- "a/A-1,Kaggle\345\205\215\350\264\271GPU\344\275\277\347\224\250\346\224\273\347\225\245.ipynb" +++ "b/A-1,Kaggle\345\205\215\350\264\271GPU\344\275\277\347\224\250\346\224\273\347\225\245.ipynb" @@ -733,7 +733,7 @@ "formats": "ipynb,md" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -747,7 +747,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/A-2,Streamlit\346\236\204\345\273\272\346\234\272\345\231\250\345\255\246\344\271\240\345\272\224\347\224\250.ipynb" "b/A-2,Streamlit\346\236\204\345\273\272\346\234\272\345\231\250\345\255\246\344\271\240\345\272\224\347\224\250.ipynb" index 0ef803c4b..918570f96 100644 --- "a/A-2,Streamlit\346\236\204\345\273\272\346\234\272\345\231\250\345\255\246\344\271\240\345\272\224\347\224\250.ipynb" +++ "b/A-2,Streamlit\346\236\204\345\273\272\346\234\272\345\231\250\345\255\246\344\271\240\345\272\224\347\224\250.ipynb" @@ -1173,7 +1173,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1187,7 +1187,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" }, "toc": { "base_numbering": 1, diff --git "a/A-3,\344\275\277\347\224\250MacM1\350\212\257\347\211\207\345\212\240\351\200\237pytorch.ipynb" "b/A-3,\344\275\277\347\224\250MacM1\350\212\257\347\211\207\345\212\240\351\200\237pytorch.ipynb" index 43c8e717b..67ab93d16 100644 --- "a/A-3,\344\275\277\347\224\250MacM1\350\212\257\347\211\207\345\212\240\351\200\237pytorch.ipynb" +++ "b/A-3,\344\275\277\347\224\250MacM1\350\212\257\347\211\207\345\212\240\351\200\237pytorch.ipynb" @@ -158,60 +158,102 @@ "name": "stdout", "output_type": "stream", "text": [ - "Sequential(\n", - " (conv1): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1))\n", - " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (conv2): Conv2d(64, 512, kernel_size=(3, 3), stride=(1, 1))\n", - " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", - " (dropout): Dropout2d(p=0.1, inplace=False)\n", - " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", - " (flatten): Flatten(start_dim=1, end_dim=-1)\n", - " (linear1): Linear(in_features=512, out_features=1024, bias=True)\n", - " (relu): ReLU()\n", - " (linear2): Linear(in_features=1024, out_features=10, bias=True)\n", - ")\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to mnist/MNIST/raw/train-images-idx3-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 9912422/9912422 [00:09<00:00, 992848.32it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting mnist/MNIST/raw/train-images-idx3-ubyte.gz to mnist/MNIST/raw\n", "\n", - "================================================================================2022-12-02 21:27:39\n", - "Epoch 1 / 20\n", - "\n" + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to mnist/MNIST/raw/train-labels-idx1-ubyte.gz\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████| 469/469 [00:36<00:00, 13.00it/s, train_acc=0.766, train_loss=0.81]\n", - "100%|████████████████████████████████| 79/79 [00:01<00:00, 42.58it/s, val_acc=0.956, val_loss=0.144]" + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 28881/28881 [00:00<00:00, 14564830.33it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Extracting mnist/MNIST/raw/train-labels-idx1-ubyte.gz to mnist/MNIST/raw\n", "\n", - "================================================================================2022-12-02 21:28:17\n", - "Epoch 2 / 20\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ "\n" ] }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to mnist/MNIST/raw/t10k-images-idx3-ubyte.gz\n" + ] + }, { "name": "stderr", "output_type": "stream", "text": [ + "100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 1648877/1648877 [00:01<00:00, 1245879.83it/s]\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Extracting mnist/MNIST/raw/t10k-images-idx3-ubyte.gz to mnist/MNIST/raw\n", "\n", - "<<<<<< reach best val_acc : 0.9558000564575195 >>>>>>\n", - "100%|██████████████████████████| 469/469 [00:33<00:00, 13.93it/s, train_acc=0.959, train_loss=0.138]\n", - "100%|████████████████████████████████| 79/79 [00:01<00:00, 44.61it/s, val_acc=0.968, val_loss=0.108]\n", - "<<<<<< reach best val_acc : 0.968000054359436 >>>>>>\n" + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|██████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 4542/4542 [00:00<00:00, 2365643.71it/s]" ] }, { "name": "stdout", "output_type": "stream", "text": [ + "Extracting mnist/MNIST/raw/t10k-labels-idx1-ubyte.gz to mnist/MNIST/raw\n", "\n", - "================================================================================2022-12-02 21:28:52\n", - "Epoch 3 / 20\n", + "Sequential(\n", + " (conv1): Conv2d(1, 64, kernel_size=(3, 3), stride=(1, 1))\n", + " (pool1): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (conv2): Conv2d(64, 512, kernel_size=(3, 3), stride=(1, 1))\n", + " (pool2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (dropout): Dropout2d(p=0.1, inplace=False)\n", + " (adaptive_pool): AdaptiveMaxPool2d(output_size=(1, 1))\n", + " (flatten): Flatten(start_dim=1, end_dim=-1)\n", + " (linear1): Linear(in_features=512, out_features=1024, bias=True)\n", + " (relu): ReLU()\n", + " (linear2): Linear(in_features=1024, out_features=10, bias=True)\n", + ")\n", + "\n", + "================================================================================2023-08-02 20:30:22\n", + "Epoch 1 / 20\n", "\n" ] }, @@ -219,9 +261,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████| 469/469 [00:33<00:00, 13.98it/s, train_acc=0.964, train_loss=0.123]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.79it/s, val_acc=0.978, val_loss=0.0732]\n", - "<<<<<< reach best val_acc : 0.9783000349998474 >>>>>>\n" + "\n", + "100%|███████████████████████████| 469/469 [00:34<00:00, 13.48it/s, train_acc=0.84, train_loss=0.559]\n", + "100%|████████████████████████████████| 79/79 [00:02<00:00, 35.89it/s, val_acc=0.903, val_loss=0.315]\n", + "<<<<<< reach best val_acc : 0.9033000469207764 >>>>>>\n" ] }, { @@ -229,8 +272,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:29:28\n", - "Epoch 4 / 20\n", + "================================================================================2023-08-02 20:30:59\n", + "Epoch 2 / 20\n", "\n" ] }, @@ -238,8 +281,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████| 469/469 [00:33<00:00, 13.96it/s, train_acc=0.968, train_loss=0.11]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.51it/s, val_acc=0.977, val_loss=0.0863]" + "100%|██████████████████████████| 469/469 [00:34<00:00, 13.72it/s, train_acc=0.963, train_loss=0.122]\n", + "100%|████████████████████████████████| 79/79 [00:02<00:00, 37.16it/s, val_acc=0.954, val_loss=0.146]\n", + "<<<<<< reach best val_acc : 0.9538000226020813 >>>>>>\n" ] }, { @@ -247,8 +291,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:30:03\n", - "Epoch 5 / 20\n", + "================================================================================2023-08-02 20:31:36\n", + "Epoch 3 / 20\n", "\n" ] }, @@ -256,9 +300,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "100%|██████████████████████████| 469/469 [00:33<00:00, 13.98it/s, train_acc=0.966, train_loss=0.118]\n", - "100%|████████████████████████████████| 79/79 [00:01<00:00, 44.28it/s, val_acc=0.971, val_loss=0.103]\n" + "100%|██████████████████████████| 469/469 [00:33<00:00, 13.80it/s, train_acc=0.828, train_loss=0.732]\n", + "100%|██████████████████████████████████| 79/79 [00:02<00:00, 37.19it/s, val_acc=0.114, val_loss=2.3]" ] }, { @@ -266,8 +309,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:30:38\n", - "Epoch 6 / 20\n", + "================================================================================2023-08-02 20:32:12\n", + "Epoch 4 / 20\n", "\n" ] }, @@ -275,8 +318,10 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████| 469/469 [00:33<00:00, 13.98it/s, train_acc=0.958, train_loss=0.15]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.15it/s, val_acc=0.972, val_loss=0.0903]\n" + "\n", + "100%|█████████████████████████| 469/469 [00:33<00:00, 14.07it/s, train_acc=0.981, train_loss=0.0615]\n", + "100%|███████████████████████████████| 79/79 [00:02<00:00, 38.03it/s, val_acc=0.977, val_loss=0.0748]\n", + "<<<<<< reach best val_acc : 0.9774000644683838 >>>>>>\n" ] }, { @@ -284,8 +329,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:31:14\n", - "Epoch 7 / 20\n", + "================================================================================2023-08-02 20:32:47\n", + "Epoch 5 / 20\n", "\n" ] }, @@ -293,8 +338,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|████████████████████████████| 469/469 [00:33<00:00, 13.99it/s, train_acc=0.245, train_loss=inf]\n", - "100%|██████████████████████████████████| 79/79 [00:01<00:00, 44.49it/s, val_acc=0.114, val_loss=2.3]" + "100%|█████████████████████████| 469/469 [00:33<00:00, 13.90it/s, train_acc=0.983, train_loss=0.0551]\n", + "100%|████████████████████████████████| 79/79 [00:02<00:00, 34.32it/s, val_acc=0.982, val_loss=0.059]\n", + "<<<<<< reach best val_acc : 0.9815000295639038 >>>>>>\n" ] }, { @@ -302,8 +348,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:31:49\n", - "Epoch 8 / 20\n", + "================================================================================2023-08-02 20:33:23\n", + "Epoch 6 / 20\n", "\n" ] }, @@ -311,10 +357,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "100%|█████████████████████████| 469/469 [00:33<00:00, 14.18it/s, train_acc=0.982, train_loss=0.0575]\n", - "100%|████████████████████████████████| 79/79 [00:01<00:00, 46.04it/s, val_acc=0.981, val_loss=0.065]\n", - "<<<<<< reach best val_acc : 0.9807000756263733 >>>>>>\n" + "100%|█████████████████████████| 469/469 [00:34<00:00, 13.71it/s, train_acc=0.982, train_loss=0.0552]\n", + "100%|███████████████████████████████| 79/79 [00:02<00:00, 36.55it/s, val_acc=0.984, val_loss=0.0541]\n", + "<<<<<< reach best val_acc : 0.9844000339508057 >>>>>>\n" ] }, { @@ -322,8 +367,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:32:24\n", - "Epoch 9 / 20\n", + "================================================================================2023-08-02 20:34:00\n", + "Epoch 7 / 20\n", "\n" ] }, @@ -331,8 +376,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|█████████████████████████| 469/469 [00:33<00:00, 14.20it/s, train_acc=0.985, train_loss=0.0474]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.78it/s, val_acc=0.979, val_loss=0.0678]\n" + "100%|██████████████████████████| 469/469 [00:33<00:00, 13.88it/s, train_acc=0.98, train_loss=0.0644]\n", + "100%|███████████████████████████████| 79/79 [00:02<00:00, 36.86it/s, val_acc=0.979, val_loss=0.0734]\n" ] }, { @@ -340,8 +385,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:32:59\n", - "Epoch 10 / 20\n", + "================================================================================2023-08-02 20:34:36\n", + "Epoch 8 / 20\n", "\n" ] }, @@ -349,8 +394,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|███████████████████████████| 469/469 [00:33<00:00, 14.18it/s, train_acc=0.981, train_loss=0.06]\n", - "100%|████████████████████████████████| 79/79 [00:01<00:00, 43.97it/s, val_acc=0.98, val_loss=0.0664]" + "100%|██████████████████████████| 469/469 [00:33<00:00, 13.91it/s, train_acc=0.941, train_loss=0.296]\n", + "100%|█████████████████████████████████| 79/79 [00:02<00:00, 36.79it/s, val_acc=0.103, val_loss=2.31]\n" ] }, { @@ -358,8 +403,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:33:34\n", - "Epoch 11 / 20\n", + "================================================================================2023-08-02 20:35:12\n", + "Epoch 9 / 20\n", "\n" ] }, @@ -367,9 +412,8 @@ "name": "stderr", "output_type": "stream", "text": [ - "\n", - "100%|██████████████████████████| 469/469 [00:33<00:00, 14.12it/s, train_acc=0.98, train_loss=0.0642]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 45.20it/s, val_acc=0.975, val_loss=0.0896]" + "100%|█████████████████████████| 469/469 [00:33<00:00, 14.07it/s, train_acc=0.976, train_loss=0.0782]\n", + "100%|███████████████████████████████| 79/79 [00:02<00:00, 38.00it/s, val_acc=0.982, val_loss=0.0677]" ] }, { @@ -377,8 +421,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:34:09\n", - "Epoch 12 / 20\n", + "================================================================================2023-08-02 20:35:47\n", + "Epoch 10 / 20\n", "\n" ] }, @@ -387,8 +431,8 @@ "output_type": "stream", "text": [ "\n", - "100%|█████████████████████████| 469/469 [00:33<00:00, 14.18it/s, train_acc=0.975, train_loss=0.0832]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 45.62it/s, val_acc=0.978, val_loss=0.0702]\n" + "100%|█████████████████████████| 469/469 [00:33<00:00, 14.09it/s, train_acc=0.982, train_loss=0.0588]\n", + "100%|███████████████████████████████| 79/79 [00:02<00:00, 37.45it/s, val_acc=0.983, val_loss=0.0631]" ] }, { @@ -396,8 +440,8 @@ "output_type": "stream", "text": [ "\n", - "================================================================================2022-12-02 21:34:44\n", - "Epoch 13 / 20\n", + "================================================================================2023-08-02 20:36:23\n", + "Epoch 11 / 20\n", "\n" ] }, @@ -405,8 +449,9 @@ "name": "stderr", "output_type": "stream", "text": [ - "100%|██████████████████████████| 469/469 [00:33<00:00, 14.20it/s, train_acc=0.969, train_loss=0.101]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.81it/s, val_acc=0.979, val_loss=0.0701]\n", + "\n", + "100%|█████████████████████████| 469/469 [00:33<00:00, 14.04it/s, train_acc=0.977, train_loss=0.0761]\n", + "100%|███████████████████████████████| 79/79 [00:02<00:00, 35.79it/s, val_acc=0.982, val_loss=0.0642]\n", "<<<<<< val_acc without improvement in 5 epoch, early stopping >>>>>>\n" ] } @@ -658,35 +703,24 @@ "id": "8527a167-02c8-4980-ac1d-ecece961616b", "metadata": {}, "source": [ - "我在最新的3.3.0的torchkeras版本中引入了对 mac m1芯片的支持，当存在可用的 mac m1芯片/ GPU 时，会默认使用它们进行加速，无需做任何配置。\n", + "3.3.0以上的torchkeras版本中引入了对 mac m1芯片的支持，当存在可用的 mac m1芯片/ GPU 时，会默认使用它们进行加速，无需做任何配置。\n", "\n", "使用范例如下。😋😋😋\n" ] }, { "cell_type": "code", - "execution_count": 5, - "id": "7cb81b0e-5a0a-42cb-831d-b701f2b768af", + "execution_count": null, + "id": "96be06af-186c-4d48-b15c-3a0f79f8ac5c", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Collecting torchkeras\n", - " Downloading torchkeras-3.3.0-py3-none-any.whl (17 kB)\n", - "Installing collected packages: torchkeras\n", - "Successfully installed torchkeras-3.3.0\n" - ] - } - ], + "outputs": [], "source": [ - "!pip install torchkeras>=3.3.0" + "!pip install -U torchkeras " ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "id": "dfa97081-8940-41c3-9283-ea0c5444efda", "metadata": {}, "outputs": [ @@ -729,86 +763,79 @@ "Params size (MB): 3.172401\n", "Estimated Total Size (MB): 4.276550\n", "--------------------------------------------------------------------------\n", - "\u001b[0;31m<<<<<< ⚡️ mps is used >>>>>>\u001b[0m\n", - "\n", - "================================================================================2022-12-02 22:17:29\n", - "Epoch 1 / 15\n", - "\n", - "100%|██████████████████████████| 469/469 [00:33<00:00, 13.89it/s, train_acc=0.903, train_loss=0.304]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.44it/s, val_acc=0.973, val_loss=0.0884]\n", - "\u001b[0;31m<<<<<< reach best val_acc : 0.9730000495910645 >>>>>>\u001b[0m\n", - "\n", - "================================================================================2022-12-02 22:18:05\n", - "Epoch 2 / 15\n", - "\n", - "100%|█████████████████████████| 469/469 [00:33<00:00, 14.09it/s, train_acc=0.974, train_loss=0.0823]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 45.71it/s, val_acc=0.979, val_loss=0.0662]\n", - "\u001b[0;31m<<<<<< reach best val_acc : 0.9794000387191772 >>>>>>\u001b[0m\n", - "\n", - "================================================================================2022-12-02 22:18:40\n", - "Epoch 3 / 15\n", - "\n", - "100%|█████████████████████████| 469/469 [00:32<00:00, 14.22it/s, train_acc=0.982, train_loss=0.0553]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.59it/s, val_acc=0.981, val_loss=0.0699]\n", - "\u001b[0;31m<<<<<< reach best val_acc : 0.9808000326156616 >>>>>>\u001b[0m\n", - "\n", - "================================================================================2022-12-02 22:19:15\n", - "Epoch 4 / 15\n", - "\n", - "100%|█████████████████████████| 469/469 [00:32<00:00, 14.21it/s, train_acc=0.986, train_loss=0.0434]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.89it/s, val_acc=0.988, val_loss=0.0382]\n", - "\u001b[0;31m<<<<<< reach best val_acc : 0.9883000254631042 >>>>>>\u001b[0m\n", - "\n", - "================================================================================2022-12-02 22:19:49\n", - "Epoch 5 / 15\n", - "\n", - "100%|█████████████████████████| 469/469 [00:32<00:00, 14.22it/s, train_acc=0.989, train_loss=0.0326]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 44.56it/s, val_acc=0.987, val_loss=0.0429]\n", - "\n", - "================================================================================2022-12-02 22:20:24\n", - "Epoch 6 / 15\n", - "\n", - "100%|██████████████████████████| 469/469 [00:35<00:00, 13.13it/s, train_acc=0.99, train_loss=0.0313]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 43.52it/s, val_acc=0.987, val_loss=0.0486]\n", - "\n", - "================================================================================2022-12-02 22:21:02\n", - "Epoch 7 / 15\n", - "\n", - "100%|█████████████████████████| 469/469 [00:32<00:00, 14.32it/s, train_acc=0.991, train_loss=0.0255]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 43.23it/s, val_acc=0.986, val_loss=0.0591]\n", - "\n", - "================================================================================2022-12-02 22:21:36\n", - "Epoch 8 / 15\n", - "\n", - "100%|█████████████████████████| 469/469 [00:32<00:00, 14.27it/s, train_acc=0.994, train_loss=0.0199]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 45.32it/s, val_acc=0.985, val_loss=0.0542]\n", - "\n", - "================================================================================2022-12-02 22:22:11\n", - "Epoch 9 / 15\n", - "\n", - "100%|█████████████████████████| 469/469 [00:32<00:00, 14.28it/s, train_acc=0.993, train_loss=0.0228]\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 43.70it/s, val_acc=0.987, val_loss=0.0471]\n", - "\u001b[0;31m<<<<<< val_acc without improvement in 5 epoch, early stopping >>>>>>\u001b[0m\n", - " train_loss train_acc val_loss val_acc epoch\n", - "0 0.303751 0.903283 0.088422 0.9730 1\n", - "1 0.082336 0.973917 0.066228 0.9794 2\n", - "2 0.055266 0.982233 0.069914 0.9808 3\n", - "3 0.043367 0.985983 0.038230 0.9883 4\n", - "4 0.032618 0.989317 0.042920 0.9869 5\n", - "5 0.031270 0.989533 0.048605 0.9871 6\n", - "6 0.025532 0.991283 0.059145 0.9862 7\n", - "7 0.019919 0.993550 0.054234 0.9853 8\n", - "8 0.022779 0.992517 0.047057 0.9874 9\n", - "100%|███████████████████████████████| 79/79 [00:01<00:00, 42.95it/s, val_acc=0.988, val_loss=0.0382]\n" + "\u001b[0;31m<<<<<< 🚀 mps is used >>>>>>\u001b[0m\n" + ] + }, + { + "data": { + "image/png": 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\n", 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"#================================================================================\n", - "\n", - "model.predict(dl_val)[0:10]\n", - "\n", - "#================================================================================\n", - "# 六，保存模型\n", - "#================================================================================\n", - "# The best net parameters has been saved at ckpt_path='checkpoint.pt' during training.\n", - "net_clone = create_net() \n", - "net_clone.load_state_dict(torch.load(\"checkpoint.pt\"))\n", "\n", "\n" ] @@ -992,14 +1008,6 @@ "\n", "不过目前看和企业中最常使用的高端的Tesla P100 GPU相比，还是有2到4倍的训练速度差异，可以视做一个mini版的GPU吧。\n", "\n", - "因此Mac M1芯片比较适合本地训练一些中小规模的模型，快速迭代idea，使用起来还是蛮香的。\n", - "\n", - "尤其是本来就打算想换个电脑的，用mac做开发本来比windows好使多了。\n", - "\n", - "有需要的小伙伴推荐买这个，京东自营的渠道，Mac Book Pro M1芯片，16G统一内存，小型炼丹基本够用。\n", - "\n", - "![](https://tva1.sinaimg.cn/large/008vxvgGgy1h8pw5ijnfuj30qi0z30uf.jpg)\n", - "\n", "\n", "\n", "\n", @@ -1015,7 +1023,7 @@ "formats": "ipynb,md" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -1029,7 +1037,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git a/README.md b/README.md index 74b63c66f..3feac7552 100644 --- a/README.md +++ b/README.md @@ -1,51 +1,18 @@ # How to eat Pytorch in 20 days ?🔥🔥 -🔥🔥 B站讲解：https://www.bilibili.com/video/BV1Ua411P7oe - -🐳🐳 和鲸专栏：https://www.heywhale.com/home/column/5f2ac5d8af3980002cb1bc08 - -🌺🌺 公众号：https://mp.weixin.qq.com/s/0YdveETOZkq2SFtDkIANEg - - -### 一，本书📖面向读者 👼 +## 一，本书📖面向读者 👼 **本书假定读者有一定的机器学习和深度学习基础，使用过Keras或TensorFlow或Pytorch搭建训练过简单的模型。** -**对于没有任何机器学习和深度学习基础的同学，建议在学习本书时同步参考阅读《Python深度学习》一书的第一部分"深度学习基础"内容。** - -《Python深度学习》这本书是Keras之父Francois Chollet所著，该书假定读者无任何机器学习知识，以Keras为工具， - -使用丰富的范例示范深度学习的最佳实践，该书通俗易懂，**全书没有一个数学公式，注重培养读者的深度学习直觉。**。 - -《Python深度学习》一书的第一部分的4个章节内容如下，预计读者可以在20小时之内学完。 - -* 1，什么是深度学习 - -* 2，神经网络的数学基础 - -* 3，神经网络入门 - -* 4，机器学习基础 - - - - - - 🔥🔥**号外号外，《20天吃掉那只Pytorch》视频版本登录BiliBili啦，吃货本货倾情掌勺，只为最纯正的乡土味道，欢迎新老朋友前来品尝** 🍉🍉！ https://www.bilibili.com/video/BV1Ua411P7oe - - - - - -### 二，本书写作风格 🍉 +## 二，本书写作风格 🍉 **本书是一本对人类用户极其友善的Pytorch入门工具书，Don't let me think是本书的最高追求。** @@ -70,7 +37,7 @@ https://www.bilibili.com/video/BV1Ua411P7oe ``` -### 三，本书学习方案 ⏰ +## 三，本书学习方案 ⏰ **1，学习计划** @@ -83,9 +50,9 @@ https://www.bilibili.com/video/BV1Ua411P7oe **点击学习内容蓝色标题即可进入该章节。** -|日期 | 学习内容 | 内容难度 | 预计学习时间 | 更新状态|B站讲解| -|----:|:--------------------------------------------------------------|-----------:|----------:|-----:|-----:| -| |[**一、Pytorch的建模流程**](./一、Pytorch的建模流程.ipynb) |⭐️ | 0hour |✅ | +|日期 | 学习内容 | 内容难度 | 预计学习时间 | 更新状态| +|----:|:--------------------------------------------------------------|-----------:|----------:|-----:| +| |[**一、Pytorch的建模流程**](./一、Pytorch的建模流程.ipynb) |⭐️ | 0hour |✅ | |day1 | [1-1,结构化数据建模流程范例](./1-1,结构化数据建模流程范例.ipynb) | ⭐️⭐️⭐️ | 1hour |✅ | |day2 | [1-2,图片数据建模流程范例](./1-2,图片数据建模流程范例.ipynb) | ⭐️⭐️⭐️⭐️ | 2hour | ✅ | |day3 | [1-3,文本数据建模流程范例](./1-3,文本数据建模流程范例.ipynb) | ⭐️⭐️⭐️⭐️⭐️ | 2hour | ✅ | @@ -121,7 +88,6 @@ https://www.bilibili.com/video/BV1Ua411P7oe 本书全部源码在jupyter中编写测试通过，建议通过git克隆到本地，并在jupyter中交互式运行学习。 - step1: 克隆本书源码到本地,使用码云镜像仓库国内下载速度更快 ``` git clone https://gitee.com/Python_Ai_Road/eat_pytorch_in_20_days @@ -130,14 +96,8 @@ git clone https://gitee.com/Python_Ai_Road/eat_pytorch_in_20_days step2: 公众号 **算法美食屋** 回复关键词：**pytorch**， 获取本项目所用数据集汇总压缩包 eat_pytorch_datasets.zip百度云盘下载链接，下载解压并移动到eat_pytorch_in_20_days路径下，约160M。 -救命方案：如果环境配置遇到了困难，也可以在和鲸社区fork项目后直接运行。 - -和鲸《20天吃掉pytorch》专栏地址：https://www.heywhale.com/home/column/5f2ac5d8af3980002cb1bc08 - - - ```python import torch from torch import nn @@ -152,7 +112,7 @@ print("[[2,1]]@[[-1],[2]] =", c.item()) ``` ``` -torch version: 1.10.0 +torch version: 2.0.1 [[2,1]]@[[-1],[2]] = 0 ``` @@ -161,28 +121,10 @@ torch version: 1.10.0 ``` -### 四，项目更新记录 - - -#### 1， 2022-06🎈🎈更新pytorch模型训练工具库torchkeras - -相关章节代码进行了对应优化调整。 - -|features| torchkeras.KerasModel | torchkeras.LightModel | -|----:|:-------------------------:|:-----------:| -|progress bar | ✅ |✅ | -|early stopping | ✅ |✅ | -|metrics from torchmetrics | ✅ |✅ | -|gpu training | ✅ |✅ | -|multi-gpus training | ❌ |✅ | -|tensorboard callback | ❌ |✅ | -|simple source code| ✅ |❌ | - -详情参考项目链接：：https://github.com/lyhue1991/torchkeras - +## 四，项目更新记录 -#### 2，2022-08🎈🎈更新 **pytorch与广告推荐**章节 +### 1，2022-08🎈🎈更新 **pytorch与广告推荐**章节 适合对广告推荐领域感兴趣，且需要进阶的同学😋😋 @@ -201,7 +143,7 @@ torch version: 1.10.0 -#### 3，2023-03🎈🎈更新 彩蛋章节 +### 2，2023-03🎈🎈更新 彩蛋章节 介绍一些与pytorch相关的周边工具 @@ -220,20 +162,38 @@ torch version: 1.10.0 - +### 3， 2023-07🎈🎈更新pytorch模型训练工具库torchkeras + +相关章节代码进行了对应优化调整。 + + +|功能| 稳定支持起始版本 | 依赖或借鉴库 | +|:----|:-------------------:|:--------------| +|✅ 训练进度条 | 3.0.0 | 依赖tqdm,借鉴keras| +|✅ 训练评估指标 | 3.0.0 | 借鉴pytorch_lightning | +|✅ notebook中训练自带可视化 | 3.8.0 |借鉴fastai | +|✅ early stopping | 3.0.0 | 借鉴keras | +|✅ gpu training | 3.0.0 |依赖accelerate| +|✅ multi-gpus training(ddp) | 3.6.0 | 依赖accelerate| +|✅ fp16/bf16 training| 3.6.0 | 依赖accelerate| +|✅ tensorboard callback | 3.7.0 |依赖tensorboard | +|✅ wandb callback | 3.7.0 |依赖wandb | + + +详情参考项目链接：：https://github.com/lyhue1991/torchkeras + + ```python ``` -### 五，鼓励和联系作者 🎈🎈 +## 五，鼓励和联系作者 🎈🎈 **如果本书对你有所帮助，想鼓励一下作者，记得给本项目加一颗星星star⭐️，并分享给你的朋友们喔😊!** -如果对本书内容理解上有需要进一步和作者交流的地方，欢迎在公众号"算法美食屋"下留言。作者时间和精力有限，会酌情予以回复。 - -也可以在公众号后台回复关键字：**加群**，加入读者交流群和大家讨论。 +如果对本书内容理解上有一些疑问或者建议，可以在公众号"算法美食屋"后台回复关键字：**加群**，加入读者交流群和大家讨论。 ![算法美食屋logo.png](https://tva1.sinaimg.cn/large/e6c9d24egy1h41m2zugguj20k00b9q46.jpg) diff --git a/push-to-github.ipynb b/push-to-github.ipynb index 923133732..84b76913c 100644 --- a/push-to-github.ipynb +++ b/push-to-github.ipynb @@ -34,7 +34,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 15, "id": "aa131303", "metadata": {}, "outputs": [], @@ -49,12 +49,72 @@ "metadata": {}, "outputs": [], "source": [ - "!git pull origin master " + "#!git pull origin master " ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, + "id": "9e7b346d-0298-44b1-9235-4a95769f0ea7", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Unstaged changes after reset:\n", + "M\t.gitignore\n", + "M\t1-1,结构化数据建模流程范例.ipynb\n", + "M\t1-2,图片数据建模流程范例.ipynb\n", + "M\t1-3,文本数据建模流程范例.ipynb\n", + "M\t1-4,时间序列数据建模流程范例.ipynb\n", + "M\t2-1,张量数据结构.ipynb\n", + "M\t2-2,自动微分机制.ipynb\n", + "M\t2-3,动态计算图.ipynb\n", + "M\t3-1,低阶API示范.ipynb\n", + "M\t3-2,中阶API示范.ipynb\n", + "M\t3-3,高阶API示范.ipynb\n", + "M\t4-1,张量的结构操作.ipynb\n", + "M\t4-2,张量的数学运算.ipynb\n", + "M\t4-3,nn.functional和nn.Module.ipynb\n", + "M\t5-1,Dataset和DataLoader.ipynb\n", + "M\t5-2,模型层.ipynb\n", + "M\t5-3,损失函数.ipynb\n", + "M\t5-4,TensorBoard可视化.ipynb\n", + "M\t6-1,构建模型的3种方法.ipynb\n", + "M\t6-2,训练模型的3种方法.ipynb\n", + "M\t6-3,使用GPU训练模型.ipynb\n", + "M\t7-1,推荐算法业务.ipynb\n", + "M\t7-2,广告算法业务.ipynb\n", + "M\t7-3,FM模型.ipynb\n", + "M\t7-4,DeepFM模型.ipynb\n", + "M\t7-5,FiBiNET模型.ipynb\n", + "M\t7-6,DeepCross模型.ipynb\n", + "M\t7-7,DIN网络.ipynb\n", + "M\t7-8,DIEN网络.ipynb\n", + "M\tA-1,Kaggle免费GPU使用攻略.ipynb\n", + "M\tA-2,Streamlit构建机器学习应用.ipynb\n", + "M\tA-3,使用MacM1芯片加速pytorch.ipynb\n", + "M\tREADME.md\n", + "M\tpush-to-github.ipynb\n", + "M\t一、Pytorch的建模流程.ipynb\n", + "M\t七、Pytorch与广告推荐.ipynb\n", + "M\t三、Pytorch的层次结构.ipynb\n", + "M\t二、Pytorch的核心概念.ipynb\n", + "M\t五、Pytorch的中阶API.ipynb\n", + "M\t六、Pytorch的高阶API.ipynb\n", + "M\t四、Pytorch的低阶API.ipynb\n" + ] + } + ], + "source": [ + "#!git reflog \n", + "!git reset e8be8c24" + ] + }, + { + "cell_type": "code", + "execution_count": 11, "id": "18e7aece", "metadata": {}, "outputs": [], @@ -64,7 +124,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 17, "id": "4abc318a", "metadata": {}, "outputs": [ @@ -72,19 +132,25 @@ "name": "stdout", "output_type": "stream", "text": [ - "[master fd9242b5] update wandb&gradio\r\n", - " 1 file changed, 18 insertions(+), 501 deletions(-)\r\n" + "[master dc53aa0c] update torchkeras\n", + " 47 files changed, 109199 insertions(+), 29201 deletions(-)\n", + " create mode 100644 \"5-2,\\346\\250\\241\\345\\236\\213\\345\\261\\202.md\"\n", + " create mode 100644 \"7-6,DeepCross\\346\\250\\241\\345\\236\\213.md\"\n", + " create mode 100644 \"A-1,Kaggle\\345\\205\\215\\350\\264\\271GPU\\344\\275\\277\\347\\224\\250\\346\\224\\273\\347\\225\\245.md\"\n", + " create mode 100644 \"\\344\\270\\200\\343\\200\\201Pytorch\\347\\232\\204\\345\\273\\272\\346\\250\\241\\346\\265\\201\\347\\250\\213.md\"\n", + " create mode 100644 \"\\344\\270\\203\\343\\200\\201Pytorch\\344\\270\\216\\345\\271\\277\\345\\221\\212\\346\\216\\250\\350\\215\\220.md\"\n", + " create mode 100644 \"\\344\\272\\224\\343\\200\\201Pytorch\\347\\232\\204\\344\\270\\255\\351\\230\\266API.md\"\n" ] } ], "source": [ - "!git commit -m \"update wandb&gradio\" " + "!git commit -m \"update torchkeras\" " ] }, { "cell_type": "code", "execution_count": null, - "id": "2933b845", + "id": "64ef63cd", "metadata": {}, "outputs": [], "source": [ @@ -113,7 +179,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "id": "60bb0e8d", "metadata": {}, "outputs": [ @@ -121,15 +187,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "Enumerating objects: 21, done.\n", - "Counting objects: 100% (21/21), done.\n", + "Enumerating objects: 7, done.\n", + "Counting objects: 100% (7/7), done.\n", "Delta compression using up to 8 threads\n", - "Compressing objects: 100% (14/14), done.\n", - "Writing objects: 100% (14/14), 26.28 KiB | 5.26 MiB/s, done.\n", - "Total 14 (delta 8), reused 0 (delta 0), pack-reused 0\n", - "remote: Resolving deltas: 100% (8/8), completed with 6 local objects.\u001b[K\n", + "Compressing objects: 100% (4/4), done.\n", + "Writing objects: 100% (4/4), 1.18 KiB | 1.18 MiB/s, done.\n", + "Total 4 (delta 3), reused 0 (delta 0), pack-reused 0\n", + "remote: Resolving deltas: 100% (3/3), completed with 3 local objects.\u001b[K\n", "To github.com:lyhue1991/eat_pytorch_in_20_days.git\n", - " f44a8033..fd9242b5 master -> master\n" + " fd9242b5..e8be8c24 master -> master\n" ] } ], @@ -169,7 +235,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "id": "a95d42b7", "metadata": {}, "outputs": [ @@ -177,333 +243,64 @@ "name": "stdout", "output_type": "stream", "text": [ - "Enumerating objects: 37485, done.\n", - "Counting objects: 100% (37485/37485), done.\n", + "Enumerating objects: 7, done.\n", + "Counting objects: 100% (7/7), done.\n", "Delta compression using up to 8 threads\n", - "Compressing objects: 100% (24038/24038), done.\n", - "Writing objects: 100% (37485/37485), 63.37 MiB | 818.00 KiB/s, done.\n", - "Total 37485 (delta 13456), reused 37459 (delta 13441), pack-reused 0\n", - "remote: Resolving deltas: 100% (13456/13456), done.\u001b[K\n", + "Compressing objects: 100% (4/4), done.\n", + "Writing objects: 100% (4/4), 1.18 KiB | 1.18 MiB/s, done.\n", + "Total 4 (delta 3), reused 0 (delta 0), pack-reused 0\n", "remote: Powered by \u001b[01;33mGITEE.COM \u001b[0m[\u001b[01;35mGNK-6.4\u001b[0m]\u001b[0m\u001b[K\n", "To https://gitee.com/Python_Ai_Road/eat_pytorch_in_20_days\n", - " + b766d8de...fd9242b5 master -> master (forced update)\n" + " fd9242b5..e8be8c24 master -> master\n" ] } ], "source": [ - "!git push -f gitee master " - ] - }, - { - "cell_type": "markdown", - "id": "54e87f74", - "metadata": {}, - "source": [ - "## 创建pages分支" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e7f08287", - "metadata": {}, - "outputs": [], - "source": [ - "!git checkout -b gh-pages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "5ae54a4b", - "metadata": {}, - "outputs": [], - "source": [ - "!git rm --cached -r *.md" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "59ff5563", - "metadata": {}, - "outputs": [], - "source": [ - "!git clean -df\n", - "!rm -rf *.md" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2c931586", - "metadata": {}, - "outputs": [], - "source": [ - "!cp -r _book/* ." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b3f9fd6c", - "metadata": {}, - "outputs": [], - "source": [ - "!git add ." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "be150ae8", - "metadata": {}, - "outputs": [], - "source": [ - "!git reset" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e154b99d", - "metadata": {}, - "outputs": [], - "source": [ - "!git pull origin gh-pages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "53f955a1", - "metadata": {}, - "outputs": [], - "source": [ - "!git commit -m 'add gh-pages'" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c3b3ad6d", - "metadata": {}, - "outputs": [], - "source": [ - "!git push -u origin gh-pages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d62cf830", - "metadata": {}, - "outputs": [], - "source": [ - "!git checkout pages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "3a9ecb74", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "f5b83762", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "1ffd9079", - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "markdown", - "id": "f36dbd94", - "metadata": {}, - "source": [ - "## 更新命令" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "a51a2599", - "metadata": {}, - "outputs": [], - "source": [ - "!git checkout master" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e927df0e", - "metadata": {}, - "outputs": [], - "source": [ - "!git add ./data/* *.md *.py" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c58df61b", - "metadata": {}, - "outputs": [], - "source": [ - "!git commit -m \"revise readme\"" + "!git push gitee master " ] }, { "cell_type": "code", "execution_count": null, - "id": "9161b444", - "metadata": {}, - "outputs": [], - "source": [ - "!git push -u origin master" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8bfe64ef", - "metadata": {}, - "outputs": [], - "source": [ - "!gitbook build" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "9cd493c3", - "metadata": {}, - "outputs": [], - "source": [ - "!git branch -D gh-pages " - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0c16c162", - "metadata": {}, - "outputs": [], - "source": [ - "!git checkout -b gh-pages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e85dce6e", - "metadata": {}, - "outputs": [], - "source": [ - "!git rm --cached -r *.md" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "e1d20017", - "metadata": {}, - "outputs": [], - "source": [ - "!git clean -df" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "d9fc8613", - "metadata": {}, - "outputs": [], - "source": [ - "!rm -rf *.md" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "8973a899", - "metadata": {}, - "outputs": [], - "source": [ - "!cp -r _book/* ." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "b96c2f15", - "metadata": {}, - "outputs": [], - "source": [ - "!git add .\n", - "!git commit -m \"add postscript\"" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "0e6ce145", - "metadata": {}, - "outputs": [], - "source": [ - "!git push -f origin gh-pages" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "2efa3175", - "metadata": {}, - "outputs": [], - "source": [ - "!git checkout master" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "id": "c51d9a03", + "id": "69928150-d226-4b8a-9cd5-46ce48b0e7cd", "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": null, - "id": "809ad208", + "execution_count": 16, + "id": "df3e49de", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Overwriting .gitignore\n" + ] + } + ], "source": [ "%%writefile .gitignore\n", ".DS_store\n", ".ipynb_checkpoints\n", ".ipynb_checkpoints/* \n", - " __pycache__/*\n", - " eat_pytorch_datasets\n", - " *.pt\n" + "__pycache__/*\n", + "eat_pytorch_datasets\n", + "*.pt\n", + "data/*.pt \n", + "data/*.zip\n", + "data/*.pkl\n", + "data/tensorboard\n", + "data/mnist \n", + "mnist\n" ] }, { "cell_type": "code", "execution_count": null, - "id": "eb232048", + "id": "9458fc79", "metadata": {}, "outputs": [], "source": [] diff --git "a/\344\270\200\343\200\201Pytorch\347\232\204\345\273\272\346\250\241\346\265\201\347\250\213.ipynb" "b/\344\270\200\343\200\201Pytorch\347\232\204\345\273\272\346\250\241\346\265\201\347\250\213.ipynb" index 9a8a7479b..8785b88b7 100644 --- "a/\344\270\200\343\200\201Pytorch\347\232\204\345\273\272\346\250\241\346\265\201\347\250\213.ipynb" +++ "b/\344\270\200\343\200\201Pytorch\347\232\204\345\273\272\346\250\241\346\265\201\347\250\213.ipynb" @@ -56,6 +56,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/\344\270\203\343\200\201Pytorch\344\270\216\345\271\277\345\221\212\346\216\250\350\215\220.ipynb" "b/\344\270\203\343\200\201Pytorch\344\270\216\345\271\277\345\221\212\346\216\250\350\215\220.ipynb" index 23a843b78..effc34c2e 100644 --- "a/\344\270\203\343\200\201Pytorch\344\270\216\345\271\277\345\221\212\346\216\250\350\215\220.ipynb" +++ "b/\344\270\203\343\200\201Pytorch\344\270\216\345\271\277\345\221\212\346\216\250\350\215\220.ipynb" @@ -64,7 +64,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -78,7 +78,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/\344\270\211\343\200\201Pytorch\347\232\204\345\261\202\346\254\241\347\273\223\346\236\204.ipynb" "b/\344\270\211\343\200\201Pytorch\347\232\204\345\261\202\346\254\241\347\273\223\346\236\204.ipynb" index 0c0d81ef1..2f8a8d8bd 100644 --- "a/\344\270\211\343\200\201Pytorch\347\232\204\345\261\202\346\254\241\347\273\223\346\236\204.ipynb" +++ "b/\344\270\211\343\200\201Pytorch\347\232\204\345\261\202\346\254\241\347\273\223\346\236\204.ipynb" @@ -57,7 +57,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -71,7 +71,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/\344\272\214\343\200\201Pytorch\347\232\204\346\240\270\345\277\203\346\246\202\345\277\265.ipynb" "b/\344\272\214\343\200\201Pytorch\347\232\204\346\240\270\345\277\203\346\246\202\345\277\265.ipynb" index e0ef2dc9d..b7ecaf8bb 100644 --- "a/\344\272\214\343\200\201Pytorch\347\232\204\346\240\270\345\277\203\346\246\202\345\277\265.ipynb" +++ "b/\344\272\214\343\200\201Pytorch\347\232\204\346\240\270\345\277\203\346\246\202\345\277\265.ipynb" @@ -60,6 +60,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/\344\272\224\343\200\201Pytorch\347\232\204\344\270\255\351\230\266API.ipynb" "b/\344\272\224\343\200\201Pytorch\347\232\204\344\270\255\351\230\266API.ipynb" index 727ad7e15..75c0163dd 100644 --- "a/\344\272\224\343\200\201Pytorch\347\232\204\344\270\255\351\230\266API.ipynb" +++ "b/\344\272\224\343\200\201Pytorch\347\232\204\344\270\255\351\230\266API.ipynb" @@ -51,7 +51,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -65,7 +65,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/\345\205\255\343\200\201Pytorch\347\232\204\351\253\230\351\230\266API.ipynb" "b/\345\205\255\343\200\201Pytorch\347\232\204\351\253\230\351\230\266API.ipynb" index a3bc47c3b..997a7fcda 100644 --- "a/\345\205\255\343\200\201Pytorch\347\232\204\351\253\230\351\230\266API.ipynb" +++ "b/\345\205\255\343\200\201Pytorch\347\232\204\351\253\230\351\230\266API.ipynb" @@ -9,7 +9,7 @@ "\n", "Pytorch没有官方的高阶API。一般通过nn.Module来构建模型并编写自定义训练循环。\n", "\n", - "为了更加方便地训练模型，作者编写了仿keras的Pytorch模型接口：torchkeras.KerasModel/LightModel， 作为Pytorch的高阶API。\n", + "为了更加方便地训练模型，作者编写了仿keras的Pytorch模型接口：torchkeras.KerasModel， 作为Pytorch的高阶API。\n", "\n", "本章我们主要详细介绍Pytorch的高阶API如下相关的内容。\n", "\n", @@ -40,6 +40,23 @@ "cell_metadata_filter": "-all", "formats": "ipynb,md", "main_language": "python" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.0" } }, "nbformat": 4, diff --git "a/\345\233\233\343\200\201Pytorch\347\232\204\344\275\216\351\230\266API.ipynb" "b/\345\233\233\343\200\201Pytorch\347\232\204\344\275\216\351\230\266API.ipynb" index 4cfbcbb42..ddc2313c9 100644 --- "a/\345\233\233\343\200\201Pytorch\347\232\204\344\275\216\351\230\266API.ipynb" +++ "b/\345\233\233\343\200\201Pytorch\347\232\204\344\275\216\351\230\266API.ipynb" @@ -54,7 +54,7 @@ "main_language": "python" }, "kernelspec": { - "display_name": "Python 3", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -68,7 +68,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.8.8" + "version": "3.9.0" } }, "nbformat": 4,