FedML-experiments.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\h5py\\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n",
      "  from ._conv import register_converters as _register_converters\n",
      "Using TensorFlow backend.\n",
      "WARNING: Logging before flag parsing goes to stderr.\n",
      "E1109 09:58:19.659200  8364 ultratb.py:147] Internal Python error in the inspect module.\n",
      "Below is the traceback from this internal error.\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2963, in run_code\n",
      "    exec(code_obj, self.user_global_ns, self.user_ns)\n",
      "  File \"<ipython-input-1-18477ef90465>\", line 16, in <module>\n",
      "    from fedml import run_experiment, run_PIL, run_baggingPIL, tune_federated_averaging,run_FedAveraging\n",
      "  File \"C:\\Users\\ma10s\\Documents\\fedml\\fedml.py\", line 20, in <module>\n",
      "    import keras\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\keras\\__init__.py\", line 3, in <module>\n",
      "    from . import utils\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\keras\\utils\\__init__.py\", line 26, in <module>\n",
      "    from .vis_utils import model_to_dot\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\keras\\utils\\vis_utils.py\", line 7, in <module>\n",
      "    from ..models import Model\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\keras\\models.py\", line 10, in <module>\n",
      "    from .engine.input_layer import Input\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\keras\\engine\\__init__.py\", line 8, in <module>\n",
      "    from .training import Model\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\keras\\engine\\training.py\", line 14, in <module>\n",
      "    from . import training_utils\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\keras\\engine\\training_utils.py\", line 17, in <module>\n",
      "    from .. import metrics as metrics_module\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\keras\\metrics.py\", line 1850, in <module>\n",
      "    BaseMeanIoU = tf.keras.metrics.MeanIoU\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\tensorflow\\__init__.py\", line 50, in __getattr__\n",
      "    module = self._load()\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\tensorflow\\__init__.py\", line 44, in _load\n",
      "    module = _importlib.import_module(self.__name__)\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\importlib\\__init__.py\", line 126, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "ModuleNotFoundError: No module named 'tensorflow_core.keras'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 1863, in showtraceback\n",
      "    stb = value._render_traceback_()\n",
      "AttributeError: 'ModuleNotFoundError' object has no attribute '_render_traceback_'\n",
      "\n",
      "During handling of the above exception, another exception occurred:\n",
      "\n",
      "Traceback (most recent call last):\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 1095, in get_records\n",
      "    return _fixed_getinnerframes(etb, number_of_lines_of_context, tb_offset)\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 311, in wrapped\n",
      "    return f(*args, **kwargs)\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\IPython\\core\\ultratb.py\", line 345, in _fixed_getinnerframes\n",
      "    records = fix_frame_records_filenames(inspect.getinnerframes(etb, context))\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\inspect.py\", line 1483, in getinnerframes\n",
      "    frameinfo = (tb.tb_frame,) + getframeinfo(tb, context)\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\inspect.py\", line 1441, in getframeinfo\n",
      "    filename = getsourcefile(frame) or getfile(frame)\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\inspect.py\", line 696, in getsourcefile\n",
      "    if getattr(getmodule(object, filename), '__loader__', None) is not None:\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\inspect.py\", line 733, in getmodule\n",
      "    if ismodule(module) and hasattr(module, '__file__'):\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\tensorflow\\__init__.py\", line 50, in __getattr__\n",
      "    module = self._load()\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\site-packages\\tensorflow\\__init__.py\", line 44, in _load\n",
      "    module = _importlib.import_module(self.__name__)\n",
      "  File \"C:\\Users\\ma10s\\Anaconda3\\lib\\importlib\\__init__.py\", line 126, in import_module\n",
      "    return _bootstrap._gcd_import(name[level:], package, level)\n",
      "  File \"<frozen importlib._bootstrap>\", line 994, in _gcd_import\n",
      "  File \"<frozen importlib._bootstrap>\", line 971, in _find_and_load\n",
      "  File \"<frozen importlib._bootstrap>\", line 953, in _find_and_load_unlocked\n",
      "ModuleNotFoundError: No module named 'tensorflow_core.keras'\n"
     ]
    },
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'tensorflow_core.keras'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m"
     ]
    }
   ],
   "source": [
    "from utils import compute_errorRate\n",
    "import numpy as np\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from classification_dataset_preprocessing import *\n",
    "import partition_dataset as partitions\n",
    "import uuid\n",
    "from collections import OrderedDict\n",
    "from collections import Counter\n",
    "from sklearn.model_selection import GridSearchCV\n",
    "import matplotlib.pyplot as plt\n",
    "from random import sample\n",
    "import copy\n",
    "\n",
    "from fedml import run_experiment, run_PIL, run_baggingPIL, tune_federated_averaging,run_FedAveraging\n",
    "%load_ext autoreload\n",
    "%autoreload 2\n",
    "\n",
    "%pylab inline\n",
    "\n",
    "def moving_average(a, n=3) :\n",
    "    ret = np.cumsum(a, dtype=float)\n",
    "    ret[n:] = ret[n:] - ret[:-n]\n",
    "    return ret[n - 1:] / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "x, y = load_spambase_data()\n",
    "#x, y = load_covertype_dataset()\n",
    "#x, y = load_breast_cancer_data()\n",
    "#x, y = load_adult_dataset()\n",
    "#x, y = load_Phishing_dataset()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sklearn SGDClassifier base learners (Federated SVM)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Simple partial incremental learner  (orchestration scheme)\n",
    "Each member trains a partial fit on local data sequentially. This is the simplest form of orchestration/global traing, but has limited parallelization potential  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "score, training_loss, test_loss = run_PIL(x,y,20,parameters = {\"nr_global_iterations\": 1000, \"nr_local_iterations\":1} )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x12b923ba8>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(training_loss)\n",
    "plt.plot(moving_average(training_loss,10))\n",
    "plt.plot(moving_average(test_loss,10))\n",
    "plt.ylabel('error rate')\n",
    "plt.xlabel('Global iteration')\n",
    "plt.legend(['Training loss', 'sma_train','sma_test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Bagging partial incremental learners (orchestration scheme)\n",
    "A number of PIL-models are trained on a subset of alliance members data (member_fraction). The global prediction is obtained by hard voting. This model can be parallized over subset learning. Also, member contributions can be purged from the global model at any point in time without having to start from scratch.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = {\"number_of_models\":100, \"member_fraction\":0.2, \"nr_global_iterations\": 10, \"nr_local_iterations\":1}\n",
    "score, training_loss, test_loss = run_baggingPIL(x,y,100,parameters=parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a0610320>"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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io6O79ud1iMIZLJYAw0UkEydIXAFc1ZkTVfXqptcicj2QE65AAc4S5fWNfvx+xeWyjeWNOZq88cYbDBgwgNdffx1w9qO47bbbSE9PZ8WKFfzkJz/h+uuv55NPPqG2tpZRo0Zx4403EhUVxcsvv0xcXBxFRUVMmTKFCy+8EJG2z4j777+fNWvWNG9k9P7777N8+XLWrFlDZmYmAHPmzCEpKYmamhomTZrEN7/5TZKTk1tcZ/PmzTzzzDP87W9/47LLLuOll17q8k2MDlXYgoWq+kTkJmAh4AbmqOpaEZkNLFXVeSIyCXgZSAQuEJF7VPWIr2Ec6T2wtWqUq2uW8zXGhHCQGkC4jB49mp/97GfcdtttfP3rX+fUU08F4MILL2w+XllZSWxsLLGxsURGRlJaWkqvXr244447+PDDD3G5XBQUFLB371769evXqftOnjy5OVAAPPzww7z88ssA5OXlsXnz5jbBIjMzk3HjxgEH3yujO4R1uQ9VnQ/Mb5V2d9DrJTjNUwe7xlPAU2EoXrNIjxMg6hr8RHktWBhzNDnuuONYvnw58+fP584772Tq1KnAgf0mXC5X8+um9z6fj7lz51JYWMiyZcvwer1kZGSE3N+iPcF7Zbz//vu8/fbbfPbZZ8TExHDGGWeEvFZwOdxuNzU1NV/684aLrQ1F0D7cvkaga3eXMsZ0r127dpGUlMSMGTNISEjgiSee6NR5ZWVl9OnTB6/Xy3vvvceOHTvazRsbG0tFRcVBr5WYmEhMTAwbNmzg888//9Kfo7tZsCA4WFgntzFHm9WrV3Prrbficrnwer389a9/5ZJL2iwY0cbVV1/NBRdcwOjRo8nJyeH4449vN29ycjInn3wyo0aNYvr06Zx//vktjk+bNo1HH32UkSNHMmLECKZMmXLYn+tIs/0sgHkrd/GjZ77g7Z+ezrA+vbu4ZMYc22w/i57jcPaz6KnzLI6oCHdwM5QxxpjWrBmKA6OhrBnKGHMwxcXFzR3kwd555502I5uONhYsCOqzsN3yjDEHkZyc3DyX4lhjzVAcGDpb32jBwhhjQrFgQXDNwvosjDEmFAsW2NBZY4zpiAULgmZwW7AwxhyCw1miHOChhx6iurq6C0vU9SxYEDwaypqhjDFfngWLY0RTM5TtaWHM0aeqqorzzz+fsWPHMmrUKJ577jkyMjK4/fbbGTduHDk5OSxfvpzzzjuPoUOH8uijjwJQWVnJ1KlTmTBhAqNHj+bVV19t9x7BS5TfeuutAPzud79j0qRJjBkzhl/84hftluXhhx9m165dnHnmmZx55pnh/4EcIhs6izVDGXOk/Hbxb9mwf0OXXvP4pOO5bfJt7R7vjiXK33zzTTZv3szixYtRVS688EI+/PBDCgsL25QlPj6eBx98kPfee4+UlJQu/dl0JatZABE2z8KYo9bo0aN56623uO222/joo4+Ij48HWi5RfsIJJxAbG0tqamrzEuWqyh133MGYMWM4++yzm5co74w333yTN998k/HjxzNhwgQ2bNjA5s2b2y3LV4HVLAC3S/C4xPosjAmzg9UAwqU7lihXVW6//Xa+973vtTnWuix33313iCv0PFazCIj02NaqxhyNdu3aRUxMDDNmzODWW29l+fLlnTrvcJYoP++885gzZw6VlZUAFBQUsG/fvnbL0tES5z1BWGsWIjIN+CPOTnlPqOr9rY6fBjwEjAGuUNUXA+mDcXbQc+FsMPEnVX00nGWN9LqtZmHMUag7lij/3e9+x/r16znxxBMB6N27N08//XTzHt/BZQGYOXMm06ZNY8CAAbz33ntd88G7WNiWKBcRN7AJOAfIx9mT+0pVXReUJwOIA24B5gUFi4hA2epEpDewBjhJVXe1d7/DWaIc4MTfvMOpw1N44JKxh3wNY0xbtkR5z3E4S5SHs2YxGchV1a2BAj0LXAQ0BwtV3R441qL9R1Xrg95GcgSay6wZyhhj2hfOYDEQyAt6nw+c0NmTRWQQ8DowDLg1VK1CRGYCMwHS09MPq7ARHpeNhjLGHJQtUd4DqWoeMEZEBgCviMiLqrq3VZ7HgcfBaYY6nPtFeqzPwhhzcLZEeXgUAIOC3qcF0r6UQI1iDXBqF5UrJGuGMiZ8jpbtm7/KDvd3EM5gsQQYLiKZgQ7rK4B5nTlRRNJEJDrwOhE4BdgYtpLirA9ly30Y0/WioqIoLi62gNGNVJXi4mKioqIO+Rpha4ZSVZ+I3AQsxBk6O0dV14rIbGCpqs4TkUk4Q2QTgQtE5B5VzQZGAr8XEQUE+D9VXR2usoLTDFVe4wvnLYw5JqWlpZGfn09hYWF3F+WYFhUVRVpa2iGfH9Y+C1WdD8xvlXZ30OslOM1Trc97C2fuxRET4XZZn4UxYeD1esnMzOzuYpjDZDO4AyK91mdhjDHtsWAREGlDZ40xpl0WLAIiPW7qGy1YGGNMKBYsApyahfVZGGNMKBYsAiJsnoUxxrTLgkVApMeNz6/4rCnKGGPasGAREOkN7MNtwcIYY9qwYBEQaVurGmNMuyxYBER63IDVLIwxJhQLFgERVrMwxph2WbAIaG6GsiU/jDGmDQsWAQeChdUsjDGmNQsWAZFep8/CahbGGNOWBYsAq1kYY0z7LFgEWLAwxpj2hTVYiMg0EdkoIrkiMivE8dNEZLmI+ETkkqD0cSLymYisFZFVInJ5OMsJNhrKGGMOJmzBQkTcwCPAdCALuFJEslpl2wlcD/y7VXo1cG1g17xpwEMikhCussKBeRbWZ2GMMW2Fc6e8yUCuqm4FEJFngYuAdU0ZVHV74FiLr/Oquino9S4R2QekAqXhKqw1QxljTPvC2Qw1EMgLep8fSPtSRGQyEAFsCXFspogsFZGlh7u/b/PaUBYsjDGmjR7dwS0i/YF/Ad9S1TZPcVV9XFVzVDUnNTX1sO51oBnKgoUxxrQWzmBRAAwKep8WSOsUEYkDXgd+rqqfd3HZ2rAZ3MYY075wBoslwHARyRSRCOAKYF5nTgzkfxn4p6q+GMYyNotw22goY4xpT9iChar6gJuAhcB64HlVXSsis0XkQgARmSQi+cClwGMisjZw+mXAacD1IrIi8GdcuMoK4HIJEW7bLc8YY0IJ52goVHU+ML9V2t1Br5fgNE+1Pu9p4Olwli2USI/LmqGMMSaEHt3BfaRFel02GsoYY0KwYBHEmqGMMSY0CxZBIr1uCxbGGBOCBYsgkR4XdQ3WZ2GMMa1ZsAjidHBbzcIYY1qzYBEk0uO2Dm5jjAmhU8FCRAaLyNmB19EiEhveYnWPSK8NnTXGmFA6DBYi8l3gReCxQFIa8Eo4C9VdbDSUMcaE1pmaxQ+Bk4FyAFXdDPQJZ6G6i1OzsGBhjDGtdSZY1KlqfdMbEfEAGr4idZ9Ij9uaoYwxJoTOBIsPROQOIFpEzgFeAP4b3mJ1j0iPzeA2xphQOhMsZgGFwGrge8B8Vf15WEvVTWzorDHGhNaZhQT/V1X/CPytKUFEbg6kHVUiPC5botwYY0LoTM3iuhBp13dxOXqEpj4L1aOyS8YYYw5ZuzULEbkSuArIFJHgTYtigf3hLlh3iPS48Cv4/IrXLd1dHGOM6TEO1gz1KbAbSAF+H5ReAazqzMVFZBrwR8ANPKGq97c6fhrwEDAGuCJ4VzwReQOYAnysql/vzP0OV6S3aWtVP163TW43xpgm7QYLVd0B7ABOPJQLi4gbeAQ4B8gHlojIPFVdF5RtJ06T1i0hLvE7IAanU/2IiPS4AZwRUZFH6q7GGNPzdWYG9xQRWSIilSJSLyKNIlLeiWtPBnJVdWtgnsazwEXBGVR1u6quAtr0KqvqOzi1mCMmwtNUs7C5FsYYE6wzbS1/Bq4ENgPRwHdwagwdGQjkBb3PD6T1WJFNwcJGRBljTAudaphX1VzAraqNqvokMC28xeocEZkpIktFZGlhYeFhX6+pGcrmWhhjTEudmWdRLSIRwAoReQCn07szQaYAGBT0Pi2Q1mVU9XHgcYCcnJzDHu8aac1QxhgTUmce+tcE8t0EVOEEgG924rwlwHARyQwEmyuAeR2c062aRkPZkh/GGNPSQYNFYETTfapaq6rlqnqPqv400Cx1UKrqwwkwC4H1wPOqulZEZovIhYHrTxKRfOBS4DERWRt0749w1qGaKiL5InLeIX/KTopwHxg6a4wx5oCDNkOpamNg46OI4JVnO0tV5wPzW6XdHfR6CU7zVKhzT/2y9ztckd6mPgtrhjLGmGCd6bPYCnwSmMVd1ZSoqg+GrVTdxEZDGWNMaJ0JFlsCf1w4S30ctQ50cFuwMMaYYB0GC1W950gUpCdoaoayDm5jjGnJFkAKYkNnjTEmNAsWQSKsGcoYY0LqcOisiPzkSBWmuzXVLGobrGZhjDHBDhosVLURZ12oY0KE24XHJVTVW7AwxphgnRkN9YmI/Bl4jpZDZ5eHrVTdRESIj/ZSVtPQ3UUxxpgepTPBYlzg79lBaQqc1fXF6X4WLIwxpq3ODJ0980gUpKeIi/ZSbsHCGGNa6MzmR/Ei8mDTUuAi8nsRiT8ShesOVrMwxpi2OjN0dg7OjnWXBf6UA0+Gs1DdKSHGS2m1BQtjjAnWmT6LoaoavCT5PSKyIlwF6m5WszDGmLY6U7OoEZFTmt6IyMlATfiK1L3io72U1zbg9x/2XkrGGHPU6EzN4kbgn0H9FCXAdeErUveKj/aiChV1PuKjvd1dHGOM6REOGixExAWMUNWxIhIHoKrlR6Rk3SQuECDKaxosWBhjTEBHM7j9wP8LvC7/soFCRKaJyEYRyRWRWSGOnyYiy0XEJyKXtDp2nYhsDvw5YjWZpgBh/RbGGHNAZ/os3haRW0RkkIgkNf3p6KTAlqyPANOBLOBKEclqlW0ncD3w71bnJgG/AE4AJgO/EJHETpT1sFmwMMaYtjrTZ3F54O8fBqUpMKSD8yYDuaq6FUBEngUuAtY1X0R1e+BY62VezwPeUtX9geNvAdOAZzpR3sNiwcIYY9rqTJ/FDFX95BCuPRDIC3qfj1NTONRzB4Yo30xgJkB6evohFLGthBgnWNhcC2OMOaAzfRZ/PkJl+dJU9XFVzVHVnNTU1C65ptUsjDGmrc70WbwjIt8UEfmS1y4ABgW9TwukhfvcwxLtdeN1iwULY4wJ0plg8T3gBaBeRMpFpEJEOjMqagkwXEQyRSQCuAKY18lyLQTOFZHEQMf2uYG0sLNlyo0xpq0Og4WqxqqqS1W9qhoXeB/XifN8wE04D/n1wPOqulZEZovIhQAiMklE8oFLgcdEZG3g3P3AvTgBZwkwu6mz+0iwlWeNMaalDkdDBZqfrgYyVfVeERkE9FfVxR2dq6rzgfmt0u4Oer0Ep4kp1LlzcBYxPOKsZmGMMS11phnqL8CJwFWB95U48yeOWhYsjDGmpc4EixNU9YdALYCqlgARYS1VN0uwYGGMMS10Jlg0BGZjK4CIpAKtJ9EdVaxmYYwxLXUmWDwMvAz0EZFfAx8D94W1VN3Mlik3xpiWOrMH91wRWQZMBQT4hqquD3vJulFc0zLltT7iY2zlWWOM6czaUKjqBmBDmMvSYwTP4rZgYYwxnWuGOubYkh/GGNOSBYsQLFgYY0xLFixCaGp6smBhjDEOCxYhJEQ700gsWBhjjMOCRQjWDGWMMS1ZsAghyusiwu2itKa+u4tijDE9ggWLEETEVp41xpggFizaER/tsWYoY4wJsGDRDlsfyhhjDghrsBCRaSKyUURyRWRWiOORIvJc4PgiEckIpEeIyJMislpEVorIGeEsZygWLIwx5oCwBYvASrWPANOBLOBKEclqle0GoERVhwF/AH4bSP8ugKqOBs4Bfi8iR7QWZMHCGGMOCOcDeDKQq6pbVbUeeBa4qFWei4B/BF6/CEwN7MyXBbwLoKr7gFIgJ2wlrSmFuooWSQkxEZRVW7AwxhgIb7AYCOQFvc8PpIXME9izuwxIBlYCF4qIR0QygYnAoNY3EJGZIrJURJYWFhYeWinL8uG3g2H1iy2S46K9VNT5bJlyY4yh53Zwz8EJLkuBh4BPgcbWmVT1cVXNUdWc1NTUQ7tT3ECIiIV961okxwctU26MMce6Ti1RfogKaFkbSAukhcqTLyIeIB4oVlU2kxu0AAAgAElEQVQFftKUSUQ+BTaFpZQi0Gck7G0bLABKa+ptmXJjzDEvnDWLJcBwEckUkQjgCmBeqzzzgOsCry8B3lVVFZEYEekFICLnAD5VXUe49M2CfWtBDzQ52ZIfxhhzQNiCRaAP4iZgIbAeeF5V14rIbBG5MJDt70CyiOQCPwWahtf2AZaLyHrgNuCacJXTuVs21JRAxe7mJAsWxhhzQDiboVDV+cD8Vml3B72uBS4Ncd52YEQ4y9ZC38CI3r3rIG4AYMHCGGOC9dQO7iOrTyBY7FvbnJRge1oYY0wzCxYAMUkQ279FJ7fVLIwx5gALFk36ZLWoWUR53UR4XDYxzxhjsGBxQN8sKNwEjQfmVQxKjCZ3X2U3FsoYY3oGCxZN+mRDYx3s39KcNHFwIst2lqBqs7iNMcc2CxZN+mY7f+890BSVMziJ0uoGthRWdVOhjDGmZ7Bg0SR1BIi7xbIfEwYnArB8R0l3lcoYY3oECxZNPJGQPKzFiKghKb1IiPGyrFWw+MHcZdz96pojXUJjjOk2YZ2U95XTNwsKlje/dbmECelOv0WTXaU1LFizh7TE6O4ooTHGdAurWQTrkw2lO1rsbTFxcCK5+yopra4H4LVVu1CFvP01VNTasFpjzLHhmA8WRTVFzPpoFot2Lzqw7Me+Dc3HJwb6Lb7YWQrAvJW7iPA4P7aNe1pumPT51mKeX5qHMcYcbY75YNHb25uF2xby6a5PQy77MTYtAbdLWLajhC2FlawpKOfaKYMBWL+7vMW1Hnp7E7+ctxZfo/+Ild8YY46EYz5YRHmiGJk8khX7VkDCYOjdFza92Xw8OsJN9oA4lu0oYd6KXYjAd08bQlyUh/VBNYuGRj8r88qorm9k016byGeMOboc88ECYFyfcawtXkuDNsKYy2HzQqg8sE3rhPREVuSV8sqKAk4ckkzfuChG9o9rUbPYsLuCmgZnM78VeaVH/DMYY0w4WbAAxqWOo66xjvX718P4GeD3warnmo9PHJxITUMjO4qruXCss4T5yP5xbNxT0bxH99Id+wGI8rpYkWfzMowxR5ewBgsRmSYiG0UkV0RmhTgeKSLPBY4vEpGMQLpXRP4hIqtFZL2I3B7Oco7rMw7AaYpKHQEDc2DF3Oad83IynE5ur1uYPqo/ACP7x1Jd38jO/dUALNtRwoD4KE4amtLcGW6MMUeLsAULEXEDjwDTgSzgShHJapXtBqBEVYcBfwB+G0i/FIhU1dHAROB7TYEkHPrE9GFg74GsKFzhJIy/2pnJvesLAPrHRzM4OYapx/dt3o/7+H5xAGzY4zRFLd9RwoTBiYwblEBuYSXlNqzWGHMUCWfNYjKQq6pbVbUeeBa4qFWei4B/BF6/CEwVEQEU6CUiHiAaqAfKCaOxqWNZsW+Fs2jgqG+CJ8qpXQQ8N/NEHrh0TPP74/rG4hJYt7uCXaU17CqrZeLgRManJ6AKq/LKwllcY4w5osIZLAYCwZMO8gNpIfME9uwuA5JxAkcVsBvYCfyfqu4PY1kZ32c8hTWF7KraBVHxMPICWP0CNNQC0C8+irgob3P+6Ag3GSm92LC7nOWBGd4TBycyJi0BwPotjDFHlZ7awT0ZaAQGAJnAz0RkSOtMIjJTRJaKyNLCwsLWh7+UFv0WAOOuhtoy2PBau+eM7B/H+j3lLN1eQrTXzcj+ccRHexma2qvFiChVtWYpY8xXWjiDRQEwKOh9WiAtZJ5Ak1M8UAxcBbyhqg2qug/4BMhpfQNVfVxVc1Q1JzU19bAKOzxhODGeGL7Y5/RTkHk6pBwHC/4fFG0Oec7IfrHk7a/hw82FjB0Uj9ft/DjHpyfyxc7S5n0w7vnvOqbc906bSXzGGPNVEc5gsQQYLiKZIhIBXAHMa5VnHnBd4PUlwLvqPGF3AmcBiEgvYAqwgTByu9yMSR3DysKVToLLBVc+Cwj862Io393mnJH9nU7urYVVzcuCAIwblEBxVT35JTUs2lrMU59up6ahkR/MXW7rSRljvpLCFiwCfRA3AQuB9cDzqrpWRGaLyIWBbH8HkkUkF/gp0DS89hGgt4isxQk6T6rqqnCVtcm4PuPYVLKJqobAZkfJQ2HGi1CzH56+GGpa9kM0BQugRbAYn+70W3y2tZhZ/1nNoKRonrx+Ejv3VzPrP6tt5z1jzFdOWPssVHW+qh6nqkNV9deBtLtVdV7gda2qXqqqw1R1sqpuDaRXBtKzVTVLVX8XznI2GZ86Hr/6WVUYFJcGjIcr5jpNUXOmtdhJr398FHFRzirvE9IPBIsRfWOJ9rq597V1bCuq4v6Lx3DGiD7ccu4IXl+1m399vuNIfBxjjOkyPbWDu1uMTh2NS1x8tuuzlgeGnAFXvwDV++HxM2Hx30AVEWF0WjzH9e1NQkxEc3aP28XotHgqan1cMWkQJw9LAeB7pw3hrOP78KvX1rOzuPrIfTBjjDlMFiyCxEbEctags3g592VqfbUtDw49E77/KWSeCvNvgeevhXqn1vDXGRPbXOvMEX0YlBTN7V8b2Zzmcgm/uXg0IvDn90J3mhtjTE9kwaKVq0ZeRWldKfO3zW97sHcqXPUCnDPbGVI7ZxqD3CUMTe3dJuv3zxjKB7ecSXy0t0V637gorjohnZeWF7C9qCpcH8MYY7qUBYtWcvrmcFziccxdPzd0R7TLBSff7IyU2r8N/nYm7Pg05LVcLgmZ/v3Th+JxCX96N7cri26MMWFjwaIVEWHGyBlsKtnE0r1L28943HnwnbecZUGenA5zpsO6eeBv7PAefeKimDFlMC9/kc82q10YY74CLFiEMD1zOgmRCcxdP/fgGfuMhBs/hnN/DWX58Pw18JcToWBZh/e48fShRHhcPPxOePsuXliaZ1u9GmMOmwWLEKI8UVxy3CW8l/ceBZWtJ523zhwHJ90EP/oCLn0K6ivhiXPg3V+Br77d01JjI7n2xAxeXVHAjuLw1C5qGxqZ/do67n1tHXW+jms8xhjTHgsW7bh8xOUIwhOrn+jcCW4PZP+PM2JqzOXw4e/g0VNg6ZNQHzoY3HBKJi4R5i7aedjlfeyDLXywqeX6WAvX7qGi1kdFrY8PNh7e2lnGmGObBYt29OvVj6tGXsWLm17k012hO7BDik6A//krXPEMuCPgtR/D70fC/Fth24fQ6GvO2jcuinOz+/L80jxqGzr3zX/z3oo2tYSVeaX8ZsEGZr20qsWxF5bmMzAhmuReEby6clfnP4MxxrRiweIgfjT+RwyJH8Jdn9xFef2XXATw+K/BjR/Bt96A4WfDsn/APy6A3w2FV34Au501qGZMGUxpdQOvrWq79lRrq/JLOfehD/nZ8ytbpD/09iYiPC52l9Xy3BKnf6KgtIZPthRxycQ0zh/Tn3fW76WyzhfqssYY0yELFgcR5YnivlPuo7immPsX3f/lLyACg0+ES+bA/9sKl/0LRgRGTT12GvzzG5zoX8FxKZEdLgGiqsz+7zoAXlu1m7fW7QXgi50lvLexkJunDmdyRhKPvJdLbUMj/1mWjypcMjGNC8cOoLbBz1vr9nz5z2CMMViw6FB2SjbfHfNd/rv1v7y8+eVDv1Bkb8i6EP7nUfjJGjj7l7BvHTL3m7xecw0/2vNzds9/ANa+DNs/hsJNzvIigbker6/ezdIdJcy+MJvj+8Vy5yurKa9t4KG3N5MY4+W6kzL48TnD2Vtex78X7eTF5fmcOCSZQUkxTEhPZGBCNPNWWFOUMebQyNGyAmpOTo4uXXqQeRGHocHfwA0Lb+CLfV9wetrp3Db5NgbFDur4xI746mDzm9RvfpeCZQvIlLZNUeryQkwyRZX1eMVHfKRQHTeEp3elsT81hzf2xHLt2TnccNYYEOHyxz5jZX4ptQ1+HrxsLBdPSAPg/gUbeOKjrSz++dkk9Ypocx9jzLFJRJapapv9gtrks2DROQ3+Bv69/t/8ZcVf8Pl93DzhZq7NvrbLrn/7f1bx9tJ1pFBCilQwNrGe6pI9jIytJTOmhs37qjgjayD946Ngz2oa85fh1qA+CE8U9BtNXvLJ/HBxMjsjhvLZz6cRHeEGYP3ucqb/8SN+9Y1RzJgyuMPyqCofbi5i2Y4SZp42hN6Rni77rMaYnsOCRZjsrdrLrxf9mvfy3uOGUTdw84SbEQm9rMeXkbuvkpv+vZwzRvThupMG0z8+mgWrd3PXq2soqqzn7JF9eeK6A7/P2uoKHnzqWc4b2MDEFB9U7oWdn0HBciDwO43oDVEJ0H8MOvl7nPsK7CipITbw4E9PjuHei0YxamD8ges2NPKf5QXM+WQbufsqAcgeEMeT10+iT1zUYX9OY0zPYsEijPzq51ef/4oXNr3A5SMu544T7sAl4en+2V9Vz9zPd3BpziD6xXfiYV1VBFveg5JtUFPqbNy0+S2oLqIqYQSfRJ9BSUR/yjypvLbDxfrqOGaePowrJqXz7JKd/HvRTkqqG8geEMcNp2TSO9LDj59bQWJMBE99axLD+8aGvO2eslq+2FnClsJKLho3kEFJMV38kzDGhEOPCBYiMg34I+AGnlDV+1sdjwT+CUzE2Xv7clXdLiJXA7cGZR0DTFDVFe3d60gGC3CaaR5c9iBPrX2KsaljGZs6lmEJw8hKzmJ44vCwBY9D0lALa16Ez/8Ke9e0OOTDQ54/mR3aj02ahqf/aCZOOY0x46cgLqcJa01BGd96agnlNQ0h+zvqfX6Kqw7MVh+YEM1/fnASfa0m0iXy9lfzsxdWcsaIVL5/+tDmmmxZdQO3vLiSAfFR3Pn1rOY94A+F36/srailf3x0VxXbfEV0e7AQETewCTgHyMfZHvVKVV0XlOcHwBhVvVFErgD+R1Uvb3Wd0cArqjr0YPc70sECnIAxd/1c5m2Zx9ayrdQ11gGQEJnApH6TmNh3IsMThjM8cTiJUYkdXO0IqS2H8l1Qng+leVC6g307N0JxLqk12xF/4KEfkwyZpzsbPw2bSl5jEn/7aGvIyYMuEY7rG8v49AT8qlz798UMSorhue+dSHy0l7LqBl5ZUYDbJYxPT2BE31g8h/FgO1rV1Dfy9Oc7yMlIZHxg58XV+U6gLq2ux+dXrpg0iF99YxR7ymu5/sklbC+qwudXTjsulb9cPeGQ+paq633877+/4J0N+/j510bynVMzu6Rp1Xw5ZdUNfJFXwpqCMlJjI5mQnsjQ1N7UNDSyKr+M1QWljBoQz0mBzdS6Sk8IFicCv1TV8wLvbwdQ1d8E5VkYyPOZiHiAPUCqBhVKRO5zTtOfH+x+3REsgjX6G8mryGN10Wo+3/05i/csZk/VgXkNfaL7MKn/JE7odwJT+k+hf+/+3VbWdjX6YP8Wp99j2wew9X2oCIzQ6pMFQ89ytpntkwUpw8HtDXmZjzcX8a2nFjNuUAIj+sXy0rICaoKCTEyEm++ckslPzjnu6HsoqTp7tVfug6pCqC1zfk4uD7jc0FDjLP9SVwHVRU6zYVUR9XU1rNxRSE1NDV4aSYiCpChhV3kDuDyMGJjMlroEXt/Vi4h+I1haFsdOXyL/d83pbC+u5uevrOH4frFt+pb8fuX3b23k9aBJnym9I7l6Sjrnjx5AWU0D3/nHElYXlDFuUALLd5Zy/UkZ3PX1LNztLLHfU72xZjdzPtnOlMwkZpw4mD6x3VizbfQ5v+92/n2/u2EvD761icpaZ5BKQ6NSUFrTJl/vSA/V9T78QY/pSyemcefXs9rslXOoekKwuASYpqrfCby/BjhBVW8KyrMmkCc/8H5LIE9RUJ4twEWq2rL9xDk2E5gJkJ6ePnHHjp6zt7WqUlhTSG5pLrkluawpWsPiPYspri1GEM4fcj7fH/t90uPSu7uo7VOFwg1On0fuW7DjM/A3OMdcXuibBf3HQv9xkJYDfbKdNbKA/67cxY+e/QKvy8WF4wbw7ZOd/o8v8kpYsHoPb6zdw6zpx3Pj6QetMPZsDTVQuhNKtsO+dZC/FPKXOIMNOisynoaoRPIqlOpGFwOSYqlXD/nlPqoaIDbKzai+0URoPZTltb22JxqSh7Gr1wieyI1jtyeNK8/K4bRxWdR6evOzF9fx+po9nDo8pbkJcXVBGVsLq+gTG4nX7aK4qo4/XTmBqcf34TcL1vO3j7ZxTlZffnFBFmmJR7Dvye+H+gqn2dTtpVFc7GuoYH3RPpYV5FNcU8zQvkp5QymVDZXER8STFJWEh168/MUevthZSkJ0BCVlcbh8fTl/1GDSk3sBIMCQ1F5MSE8kLTGaOp+fNQVlzUGyqSbXKfXVUJwLxZud2nltqdM/WF0cqLXvgso98LNNzoZpQUqr67nnv+t4+YsChqb2ah5c4hJhWJ/ejE9PYPTAePZV1PHFzlJW5ZeSEO1lfHoiWQPi+Men23nsw62k9I7g0omDmvfM6R8fxZWTD+1ZclQECxE5AaevY3RH9+vumkVnqCpby7by6pZXeWb9MzT4G7hg6AVcPPxixqaO7Vn9HKH46p3/IHvXwd7VzpIlu1Y4/1kAvDFOzSPzdBh+Nmt0CH3jY0iNjWxxGb9f+fFzK5i3chcPXDKGy3K6YM7KIdhfVU+vSDeRHvfBM6o6D+mS7bBndSAoLIb9W1vmSxoCaZOh32j2u5JYsL2RN7fUUFJZixcfHvzUuSKZfNwgzhqTybaaaJYV1PD2eicA/P26HCYOTgKg0a8s21HCqIFxxEQENS3VlrFj40qSG/fRu24flBc4gWrXCmcwQwiN4sEVEYNEJUBUPBqdyD5/LF/sj2BHTTRfmzySQQMGOE2PyUN5ck0Dv5q/EYBp2f349ikZTEhPbFsL9Dc6c4X8DdDYAA3VzoO0vhJ/xV42bd5I7pZNRPmr6dvLTWqMi+TIRryNgdpVQzVaX8WWxmq2+GvY769lv8vFLo+b3AgvW71eal1t/09EKvTCRTl+fAep/LjrE2mo60dDXX/8tX1prBmM+hJI6hVBRW0DDY3Os08Erj8pg1vPG0GMq9GpTZcVQMk26vZuZOu6L+hVt5eBUXW468oO/Htv4vI4ow5jkiBuAMQNhLgB7B/1bf61qoqFa/fg8/sB2FteR1Wdjx+cMZQfnjWs4397IazKL2XWS6tZt/vAEkTjBiXwyg9P/tLXcj5/9weLw26GEpE/AIWqel9H9/sqBItgRTVFPLH6CV7c9CJ1jXX079WfqelTSY9LJykqiaSoJJKjkkmKSiIuMq7nBhJV5yFasMz5Vr3z88C6VwoxKZA2yamB9MmCfmMgeRi4XNT7/NzwjyV8uqWY+y8ezcUT0to0e6g6VfPlO0spq65nTFoCI/vHEeE59J+FqvL51v38/eNtvLNhL16Xi+yBcYxPS+C6yX0ZHCvOt9s9a5wdEHd8AoUbwRfURNCrDwya7HyepExIzHA+V0wSy3eW8PePtvHGWqcJcurxfThxaDIT0hOJjfIwd9FOnluS17xOV1KvCCYOTmTW9ONDbs/7JT4YlOXj27+dd5auZtHqjfSmhq9np3BcapTzEG/6Blyz32kiqyx0Pmtrnmga4tPZWxfBzkqhqtFLQrSLwfFeUqLBVbPfaWZrJzgF8+GmxBXNXjwUuz0Uur3s90ZQHhnJ9khhpaeeUldQP5hCVGMkcXUx9NfeDPH0YmiEiyFeiK2ro2hXGZ6GBvpGu6iqraFOfLiilJRoP15/HQ31VWwXH7kRXjZHRJDr9bLT66ExEOjSfDC2TjizzsMUPER73JRVVuKrraS31BFLyxWiG/Cwzd+X3aRS6erNiIxBDM3IpCRmMKtq+1IS0Z9zxw2lV9SBJqGNeyqY8/E2Xl5RQL3PzwmZSc21uiivm++cmkn2gHh6ip4QLDw4HdxTgQKcDu6rVHVtUJ4fAqODOrgvVtXLAsdcQB5wqqpubXODVr5qwaJJZX0l7+W9xxvb3+DTXZ/i87dd7M8jHhKjEkmKSmr+OznaCSQRrgOjkyb2nUh2SvaRLH5oVUWQ+w5seccJHEWbQQMPhIjezkM2dQR1cek8tKyB1UXKgFg3541IIj3exa59Rewp2k9eSS07amMoIp4SjaWaSHzuaIb0TyF7UApjM1KZmJFyYNSVqtM01FDt9AlUFQUeinvxlRWQt30Lxbu3E1NfTKqrnCQpx60HWe3XE0XjwByqk7LxxQ+mMT6dhqTjaIxNa9MW/cXOUv7+8TZW5JUSF+XhysnpXHtSBgMT2o4uqqhtYMn2/QxN7U16UkxY+m12FFdR7/O3O9S5ma8OakqpqtzNzqJ1FBdtYH/pNvZV7Sa3sZLN/jp2io/W/yoVCfxN87SewPd0540ILpcgQsh/0wD+hlgaq4fiqxpKlH8w2X0HkjMojQnpyYxPTww58q6yzsf9C9bz+qrdnJvVj2+fksmIfkGfURUq9jg14OItUF1MXU0xW6p2s6y+mMW+EpY0VlCFn2ESydWuFKZFpFJdF8lHO6rZURPNbpIo8/ZhU10iUalD+O2lE4iN8nDbS6tYsr2EuCgP5bUHPlPT73vcoAT+vXgnH20uIsrr4uIJaXz75AyG9engd9DNuj1YBArxNeAhnKGzc1T11yIyG1iqqvNEJAr4FzAe2A9c0RQYROQM4H5VndKZe31Vg0WwRn8jJXUl7K/dT3FNMSW1zuvgP8W1xeyvcV5X+6rbXOOMQWdw07ibGJE0ohs+QTt8dVC0CXavgt0rnCaT4tyDfjPdGOFlWWQk+91u9rtd1IuQ2Ognyd9If18jE2trSW3041cBcR5Kov52r+dHKNR4Sj0p9EpOo1//NDyxfZxl5IGiyjqeXrYXd2QvvnVmNr37H8/CkgH8/LVNFFW2v4lVsMyUXnz75AwunpBGrx4y492vfirqK1r8uwn+s7tqN1tKt4Tc5Kt/r/4MSxhGRnwGXolgx/4qNu2twBdovkEgMSaCfnFR9I2LbNGkElxL7OXt1VxbToxKdL7oRCbhdR0IpB6XtLtnfVer9dWyYNsC5q6fy8aSjbjFTXZKNpP6TiYzJofq8oGszC9nUFIM3zk1s/lz+f3K3MU7WZlXypi0eMYPSqS+sZE5n2znjTV7aPQr/eKiuPakwVw5KZ3Er8iyOj0iWBxJR0Ow+LJqfbU0BDqc6xvreWnzSzy15ikqGiqY1G8SU/pPYXK/yYxKGYXH1TMeXi3UljtNWA3V4PaysaiadwpX8mn1x6wscSqggpDo7YUXFyW+KuqDagLprliGNsSTtK83iZXRJMZEIhG9qJMoyhsjWVsWwT5/HEUaT9Zxw7j+1OGcMiyl3W/yi7ft55q/L+L4/nGkJUTz+urdZA+I45opgzt8kPWLi+KUYSlH7IHXpKGxgW3l28gtySW3NJddVbtaBIWS2hJ82vabvSAkRCaQGpPK0IShDE8YTmZ8JinRKSRHJZMcnUyM9+ieWKmqrCxcyYf5H7J4z2LWFK2hURvp16sf0zKmMTV9Ktkp2XhdHY86KiitIXdfJScNTT6s+S7dwYLFMaqsroy56+fy7s532VjidFKmRKdw2YjLuPS4S0mOSia/Ip9Fexaxs+LADn0e8TQ3bfXr1Y/s5Gwi3OH/ZuRXPysLV7Jg2wLe3P4mxbXFDOw9kCuPv5LpmdNJjkrGHZgcqKpU+6rZXr6dxbsXs2jPIpbtWUZtYy2xnmSiGsbi9afg0VgiXXEcl9KfnEHpnJyZQf/4Xp0qz1vr9vK9fy3F43Jx89nDmXnakB73n7/B38Ci3YtYsG0B7+58l8oGZ1kWj3jo26tv8+8xuN+r+Rt9ID0hMqFnfoHoRhX1Fbyf977TJFzwKT71EeOJYWLfiZzQ/wQm95vMiKQRneo/rG6o/soEWwsWhv21+1m8ZzGv5L7CJwWf4HV5SY5Obp7/4XV5m//hN/gb8Ac140S6IxnfZzxT+k/hnMHndNkQX1VtDlaLdy9uHk4c6Y7ktLTTuGDIBZyWdlpzgOhIdUM17+e9z4LtC/hs12fNEyODCUJabBrDEoYxLGEYE/pOYEKfCe3+Z162Yz+JMREMOZwO5y6kquSW5rJ4z2IW7V7E0j1LqWioINYby9TBUzmx/4kMTxxORlwG3nbmvpgvp6yurPnnvWj3IraXbwcgPjKe7ORsUqJTSIpKIjYiFgn001Q2VLKldAu5pbkUVBbwweUfkBSV1I2fonMsWJgWtpVt49kNz1JUU8SkfpOY3H8ymXEHZur61U95XTnFtcXsKN/Bkj1LWLRnEZtLNgOQnZzN9MzpnJdxHv169ev0fasbqtlWto1NJZtYtncZi/cs5v+3d+fBWVVnHMe/vyQYCNQk1oBhayABraOoKEQrtRSdKtWq7UirY0fHseu0bq3T0tqxttPO1FqrdWrttLhOHZdaFKYdFsfi0kXWKItUEnaUJQJhMxAgT/84J3iJxJuEbLzv85lhwr3vzXvPk5O8z73n3vucjXvCA2IlfUoYWzqW8weez4ShE+jbq3VH/y0xM3bv333YNZ+te7dSW1/LqrpV1NTVsHbnWg7aQfKUx+klpzP2pLFUllYyqmQU+bn56TvpAjsbdrKybiUrtq1gweYFzNs0j217w/Wdwf0GU1layQWDL2DcoHFdcvbnQgHRpuRRXVd96Hpi8uAkT3mUFZYxomgEFcUVTBo5qedUbvgInixch9i0ZxOz1sxixuoZLNsariOM7j+ai8suZlTJKIYXDqegVwH7D+5nzc411NTVUL29muq6amq2hyMsi/fJNJVBqTypkjGlYw5LVl2l/kA9VVuqQjLcOJdlW5fRaI3k5+Zz3sDzmFg2kfFDxrd41tFwsIHFtYuZt2neES8KN5eXk0dxfjHFvYspzC8kV+GM6UDjAer21X3oxoX36t+jtr720Pf379OfytLK8HMrrWRgv4Ed84NwR83MDrvTK0c5rT4j7kk8WbgOt27nOmaumcmM1TOoqasBwhBPSUEJ2+q3HbqQmqtcyo4vo7yonIriCkYWjaS8qJyhxw/tcc+L7GrYxcLNC3l942i6RHgAAAiKSURBVOu8uPZFtry/hd65vRnUb9CHtjWMd3e/y96De8lRDv0L+pOTMtnk/sb9bN+3vcXbR/Nz8w9dR2j6V1ZYxsjikVQUVVDatzTzSqK4HsWThetU63etZ8X2FdRsD0M7A/oOYETRCMqLyhlWOOyYHB5ptEaqtlQxe83sw47uk/oX9GfsSWM5e8DZFOa37sEqM2PX/l3s2Lfj0DMJOTk5FOcX0yevjycD1608WTjnnEvV2mTRs8YEnHPO9UieLJxzzqXyZOGccy6VJwvnnHOpPFk455xL5cnCOedcKk8WzjnnUnmycM45lypjHsqTVAusPYq3OBF4r4Oac6zIxpghO+POxpghO+Nua8yfMLOStI0yJlkcLUkLWvMUYybJxpghO+POxpghO+PurJh9GMo551wqTxbOOedSebL4wJ+6uwHdIBtjhuyMOxtjhuyMu1Ni9msWzjnnUvmZhXPOuVSeLJxzzqXK+mQh6RJJb0uqkTS5u9vTWSQNkTRH0luSlkm6Ja4/QdKLkqrj154/w3wbScqVVCXp73F5mKS5sc+fkXTsTeuXQlKRpOck/U/ScknnZXpfS7ot/m4vlfSUpN6Z2NeSHpG0RdLSxLoj9q2CB2L8iyWNbu9+szpZSMoFHgQmAqcC10g6tXtb1WkOAN83s1OBc4HvxFgnAy+Z2QjgpbicaW4BlieW7wbuM7MKYDtwY7e0qnP9DphpZqcAZxDiz9i+ljQIuBk4x8xOA3KBq8nMvn4MuKTZupb6diIwIv77BvBQe3ea1ckCGAvUmNkqM2sAngau6OY2dQoz22hmi+L/dxE+PAYR4n08bvY4cGX3tLBzSBoMXApMicsCJgDPxU0yMeZC4ALgYQAzazCzOjK8r4E8oI+kPKAA2EgG9rWZvQpsa7a6pb69AnjCgteBIkml7dlvtieLQcD6xPKGuC6jSSoDzgLmAgPMbGN8aRMwoJua1VnuB34ANMbljwN1ZnYgLmdinw8DaoFH4/DbFEl9yeC+NrN3gN8A6whJYgewkMzv6yYt9W2HfcZle7LIOpL6AX8DbjWzncnXLNxHnTH3Uku6DNhiZgu7uy1dLA8YDTxkZmcBe2g25JSBfV1MOIoeBgwE+vLhoZqs0Fl9m+3J4h1gSGJ5cFyXkST1IiSKJ81saly9uem0NH7d0l3t6wTnA5dLWkMYYpxAGMsvikMVkJl9vgHYYGZz4/JzhOSRyX19EbDazGrNbD8wldD/md7XTVrq2w77jMv2ZDEfGBHvmDiOcEFseje3qVPEsfqHgeVm9tvES9OB6+P/rwemdXXbOouZ/cjMBptZGaFv/2lm1wJzgKviZhkVM4CZbQLWSzo5rroQeIsM7mvC8NO5kgri73pTzBnd1wkt9e104Lp4V9S5wI7EcFWbZP0T3JI+TxjXzgUeMbNfdnOTOoWkccBrwBI+GL//MeG6xbPAUEKJ9y+bWfOLZ8c8SeOB283sMknDCWcaJwBVwFfNbF93tq+jSTqTcFH/OGAVcAPh4DBj+1rSz4CvEO78qwK+Rhifz6i+lvQUMJ5Qinwz8FPgBY7QtzFx/p4wJPc+cIOZLWjXfrM9WTjnnEuX7cNQzjnnWsGThXPOuVSeLJxzzqXyZOGccy6VJwvnnHOpPFm4HkWSSbo3sXy7pLs66L0fk3RV+pZHvZ9JsdLrnGbryyTVS3ojVv/9o6ScuH5p3GZ8U3XcriDpLkm3H+02LvN5snA9zT7gS5JO7O6GJCWeAm6NG4Gvm9lnj/DaSjM7ExhFqHR8zBe2c9nBk4XraQ4Q5hC+rfkLzc8MJO2OX8dLekXSNEmrJP1K0rWS5klaIqk88TYXSVogaUWsHdU038U9kubHmv/fTLzva5KmE54Gbt6ea+L7L5V0d1x3JzAOeFjSPS0FGYvb/QeoaOsPSNLLku6LcSyXNEbS1DiXwS8S230vtm2ppFsT6++I8f8LODmxvlzSTEkLY9ynHGHfN8ezosWSnm5r292xqy1HS851lQeBxZJ+3YbvOQP4JKF08ypgipmNVZjk6Sag6cOyjFCavhyYI6kCuI5QBmGMpHzg35Jmx+1HA6eZ2erkziQNJMyVcDZhnoTZkq40s59LmkB4WrzFJ2UlFRBKUtzZhhiTGszsnBjftNiObcBKSffFOG8AKgEBcyW9QjhAvBo4k/D3v4hQnRVCkv6WmVVLqgT+QKinlTQZGGZm+yQVtbPt7hjkycL1OGa2U9IThMls6lv5bfObat5IWgk0fdgvAZLDQc+aWSNQLWkVcArwOWBU4qylkDBZTAMwr3miiMYAL5tZbdznk4Q5JF5IaWe5pDcIVUGnmdkMhZLxbdVUw2wJsCwR+ypC4bhxwPNmtieunwp8mpAsnjez9+P66fFrP+BTwF9DhQgA8o+w38XAk5JeID1Wl0E8Wbie6n7CUe+jiXUHiEOnknIIdY+aJOv9NCaWGzn897x5fRsjHHnfZGazki/EelJ72tf8FjVdszhayfiax96ev+scwtwPaW27lJAUvwDcIen0xHwRLoP5NQvXI8UCd89y+DSYawjDLQCXA73a8daT4h1I5cBw4G1gFvBthRLuSBqpMFnQR5kHfEbSiQrT814DvNKO9nSW14ArFaqw9gW+GNe9Gtf3kfQxwoc+cW6T1ZImwaG5m89IvmFM0EPMbA7wQ8IZWL8ui8h1Kz+zcD3ZvcB3E8t/BqZJehOYSfuO+tcRPuiPJ4zP75U0hTDGv0hhDKaWlLuUzGyjpMmEEtgC/mFmHVX++kJJGxLLk8zsv215AzNbJOkxQqwQruFUAUh6BniTMOfB/MS3XQs8JOknhET8dNyuSS7wF4VpWwU8EKdrdVnAq84655xL5cNQzjnnUnmycM45l8qThXPOuVSeLJxzzqXyZOGccy6VJwvnnHOpPFk455xL9X+ASTOYkBP9eAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(training_loss)\n",
    "plt.plot(moving_average(training_loss,10))\n",
    "plt.plot(moving_average(test_loss,10))\n",
    "plt.title(\"Member fraction: {0}\".format(parameters[\"member_fraction\"]))\n",
    "plt.ylabel('error rate')\n",
    "plt.xlabel('Number of PIL models')\n",
    "plt.legend(['Training loss', 'sma_train','sma_test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Federated SGD/Averaging (orchestration scheme)\n",
    "Federated averaging as descibed in \"Communication-Efficient Learning of Deep Networks from Decentralized Data\n",
    "\" https://arxiv.org/abs/1602.05629 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters = { \"nr_global_iterations\": 1000, \"nr_local_iterations\":1}\n",
    "score, training_loss, test_loss = run_FedAveraging(x,y,20,parameters=parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x1a0a76320>"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(training_loss)\n",
    "plt.plot(moving_average(training_loss,10))\n",
    "plt.plot(moving_average(test_loss,10))\n",
    "plt.ylabel('error rate')\n",
    "plt.xlabel('Number of iterations')\n",
    "plt.legend(['Training loss', 'sma_train','sma_test'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# A FedML Keras CNN model for hand-written digit classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "import keras\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "from fedml import KerasSequentialBaseLearner, PartialIncrementalLearnerClassifier, FedAveragingClassifier"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## First develop a base model (model initiator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from read_data import read_training_data\n",
    "def scale_and_reshape(x_train,y_train):\n",
    "    img_rows, img_cols = 28, 28\n",
    "    num_classes = 10\n",
    "    x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)\n",
    "    input_shape = (img_rows, img_cols, 1)\n",
    "    x_train = x_train.astype('float32')\n",
    "    x_train /= 255\n",
    "    y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "    return x_train, y_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "#from read_data import read_training_data\n",
    "\n",
    "# input image dimensions\n",
    "img_rows, img_cols = 28, 28\n",
    "num_classes = 10\n",
    "\n",
    "# input image dimensions\n",
    "dim=img_rows, img_cols \n",
    "input_shape = (dim[0], dim[1], 1)\n",
    "num_classes = num_classes\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Conv2D(32, kernel_size=(3, 3),\n",
    "           activation='relu',\n",
    "           input_shape=input_shape))\n",
    "model.add(Conv2D(64, (3, 3), activation='relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "model.add(Flatten())\n",
    "model.add(Dense(128, activation='relu'))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(num_classes, activation='softmax'))\n",
    "model.compile(loss=keras.losses.categorical_crossentropy,optimizer=keras.optimizers.Adadelta(),metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "# All this will be as implemented in fyrai/federated\n",
    "from fedml import Alliance, AllianceMember\n",
    "\n",
    "alliance = Alliance()\n",
    "M=2\n",
    "for i in range(M):\n",
    "    x_train, y_train,classes = read_training_data(sample_fraction=0.01)\n",
    "    x_train,y_train = scale_and_reshape(x_train,y_train)\n",
    "    member = AllianceMember(x_train, y_train, classes=classes)\n",
    "    alliance.add_member(member)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "base_learner = KerasSequentialBaseLearner(model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "pil_model = PartialIncrementalLearnerClassifier(alliance=alliance,base_learner=base_learner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "600/600 [==============================] - 4s 7ms/step - loss: 1.7833 - acc: 0.3933\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 1s 1ms/step - loss: 1.0961 - acc: 0.6517\n"
     ]
    },
    {
     "ename": "TypeError",
     "evalue": "unsupported operand type(s) for -: 'float' and 'NoneType'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-84-13615c0ba3ed>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mparameters\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"nr_global_iterations\"\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m10\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"nr_local_iterations\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mtraining_loss\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtest_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpil_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/github/fed-learn/fedml.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, parameters)\u001b[0m\n\u001b[1;32m    162\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    163\u001b[0m             \u001b[0;31m# Training loss, mean error rate over all alliance training data\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 164\u001b[0;31m             \u001b[0mtl\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malliance\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0malliance_training_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpartialModel\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    165\u001b[0m             \u001b[0mtraining_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtl\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    166\u001b[0m             \u001b[0;31m# Test loss, mean error rate on a  validation set\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/github/fed-learn/fedml.py\u001b[0m in \u001b[0;36malliance_training_loss\u001b[0;34m(self, alliance_model)\u001b[0m\n\u001b[1;32m    394\u001b[0m         \u001b[0mmember_loss\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    395\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mmember\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmembers\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 396\u001b[0;31m             \u001b[0mmember_loss\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscoreLocalData\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0malliance_model\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    397\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmember_loss\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    398\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/github/fed-learn/fedml.py\u001b[0m in \u001b[0;36mscoreLocalData\u001b[0;34m(self, partial_model)\u001b[0m\n\u001b[1;32m    530\u001b[0m             \u001b[0;32mreturn\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m;\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    531\u001b[0m         \u001b[0my_pred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpartial_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__x_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 532\u001b[0;31m         \u001b[0merrRate\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcompute_errorRate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__y_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_pred\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    533\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0merrRate\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    534\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/github/fed-learn/utils.py\u001b[0m in \u001b[0;36mcompute_errorRate\u001b[0;34m(y, y_hat)\u001b[0m\n\u001b[1;32m     12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mcompute_errorRate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_hat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcount_nonzero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0my_hat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for -: 'float' and 'NoneType'"
     ]
    }
   ],
   "source": [
    "parameters = {\"nr_global_iterations\": 10, \"nr_local_iterations\":1} \n",
    "training_loss, test_loss = pil_model.fit(parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "fedavg_model = FedAveragingClassifier(alliance=alliance,base_learner=base_learner)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1\n",
      "600/600 [==============================] - 1s 2ms/step - loss: 1.8537 - acc: 0.3500\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 1s 2ms/step - loss: 1.8084 - acc: 0.3783\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 1s 2ms/step - loss: 0.9547 - acc: 0.6850\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 1.0261 - acc: 0.6717\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.6192 - acc: 0.8200A: 0s - loss: 0.6193 - acc: 0.81\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.6712 - acc: 0.7933\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.4309 - acc: 0.8483\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.4643 - acc: 0.8567\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.3287 - acc: 0.8967\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.4384 - acc: 0.8650\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.2744 - acc: 0.9150\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.3306 - acc: 0.8983\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.2783 - acc: 0.9150\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.1991 - acc: 0.9350\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 3ms/step - loss: 0.1910 - acc: 0.9400\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 4ms/step - loss: 0.2426 - acc: 0.9300\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 4ms/step - loss: 0.1602 - acc: 0.9533\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 4ms/step - loss: 0.2074 - acc: 0.9400\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 4ms/step - loss: 0.1902 - acc: 0.9350\n",
      "Epoch 1/1\n",
      "600/600 [==============================] - 2s 4ms/step - loss: 0.1181 - acc: 0.9617\n"
     ]
    }
   ],
   "source": [
    "parameters = {\"nr_global_iterations\": 1, \"nr_local_iterations\":1} \n",
    "training_loss, test_loss = fedavg_model.fit(parameters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "index 13 is out of bounds for axis 1 with size 10",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-179-3b15878889dc>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mpil_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_train\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/github/fed-learn/fedml.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x_test)\u001b[0m\n\u001b[1;32m    169\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    170\u001b[0m         \u001b[0;34m\"\"\" fdfsd \"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 171\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcurrent_global_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx_test\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    172\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    173\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/github/fed-learn/fedml.py\u001b[0m in \u001b[0;36mpredict\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     80\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     81\u001b[0m         \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 82\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0margmax\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     83\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     84\u001b[0m \u001b[0;32mclass\u001b[0m \u001b[0mSGDBaseLearner\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mBaseLearner\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mIndexError\u001b[0m: index 13 is out of bounds for axis 1 with size 10"
     ]
    }
   ],
   "source": [
    "np.shape(x_train[0:,::])\n",
    "y=pil_model.predict(x_train[0:2,::])\n",
    "y"
   ]
  }
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