Projects/mnist/mnist_prediction_of_numbers.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "MNd2dXnp6u9m"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Loading the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "2auciuks6tb9",
    "outputId": "7350344b-a375-4e25-dbfa-7346e957da15"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/fedoradushev/anaconda3/envs/tensorflow_new/lib/python3.10/site-packages/sklearn/datasets/_openml.py:1022: FutureWarning: The default value of `parser` will change from `'liac-arff'` to `'auto'` in 1.4. You can set `parser='auto'` to silence this warning. Therefore, an `ImportError` will be raised from 1.4 if the dataset is dense and pandas is not installed. Note that the pandas parser may return different data types. See the Notes Section in fetch_openml's API doc for details.\n",
      "  warn(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_openml\n",
    "mnist = fetch_openml('mnist_784', version=1)\n",
    "X, y = mnist['data'], mnist['target']\n",
    "X = X.to_numpy()\n",
    "y = y.to_numpy()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kf9Yr_VgDUjQ"
   },
   "source": [
    "# Displaying the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "TtRr_k9x7dcu",
    "outputId": "20c3d01f-9b06-441a-fd91-4e260368fbb8"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "this is the shape:  \n",
      " (70000, 784)\n",
      "\n",
      "these are the labels:  \n",
      " ['2' '1' '3' '1' '4']\n",
      "\n",
      "this are some values of a datapoint:  \n",
      " [  0.   0.   0. 110. 246. 254. 254. 254. 254. 254.]\n"
     ]
    }
   ],
   "source": [
    "print('this is the shape: ', '\\n', X.shape)\n",
    "print()\n",
    "print('these are the labels: ', '\\n', y[5: 10])\n",
    "print()\n",
    "print('this are some values of a datapoint: ', '\\n', X[10, 400:410])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fFRQlZHA7spt"
   },
   "source": [
    "The dataset consists of **70,000 images** of numbers **28*28 pixels**\n",
    "\n",
    "  pixel intensities range from 0 (Black) to 255 (White)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 488
    },
    "id": "wMFV5HYw9GYW",
    "outputId": "dbe9155d-940a-4fd3-dacc-ee6cfb1d8f6e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 25 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "m, n = X.shape\n",
    "\n",
    "fig, axes = plt.subplots(5,5, figsize=(5,5))       # 'fig': overall fugure (8rows x 8columns with total size of 5x5 inch)  & 'axes': subplots\n",
    "fig.tight_layout(pad=0.005,rect=[0, 0.03, 1, 0.91]) #[left, bottom, right, top]\n",
    "\n",
    "#fig.tight_layout(pad=0.5)\n",
    "for i,ax in enumerate(axes.flat):                  # By passing an iterable object (.flat allows to enumerate through index & subplot)\n",
    "    # random index for each subplot\n",
    "    random_index = np.random.randint(m)\n",
    "\n",
    "    # reshape the random datapoint\n",
    "    X_random_reshaped = X[random_index].reshape((28,28))\n",
    "\n",
    "    # display image\n",
    "    ax.imshow(X_random_reshaped, cmap='gray')\n",
    "\n",
    "    # label above the image\n",
    "    ax.set_title(y[random_index])\n",
    "    ax.set_axis_off()\n",
    "    fig.suptitle(\"Label on top of image\", fontsize=14)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5AvLd7LADibb"
   },
   "source": [
    "# Preparing tha data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "Qrl5u1_7e81B"
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X, y = mnist['data'].values, mnist['target'].values.reshape(-1, 1)\n",
    "\n",
    "# normalize the data set\n",
    "X = X.reshape((70000, 28, 28))\n",
    "X_normalized = X.astype('float32')/255\n",
    "\n",
    "X_train, X_temp, y_train, y_temp = train_test_split(X_normalized, y, test_size=0.4, random_state=42)\n",
    "X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.2, random_state=42)\n",
    "\n",
    "\n",
    "def label_to_np(label):\n",
    "  label = label.to_numpy()\n",
    "  label = label.astype(int)\n",
    "  return label\n",
    "\n",
    "y_train = label_to_np(y_train).astype('float32')\n",
    "y_test = label_to_np(y_test).astype('float32')\n",
    "y_valid = label_to_np(y_valid).astype('float32')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "io-sIMTCeYVT",
    "outputId": "307925a1-ef83-41b3-e493-4736e82d1b8f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(42000, 28, 28)\n",
      "(42000, 1)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training the models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def gelu(x):\n",
    "    \"\"\"Gaussian Error Linear Unit.\n",
    "    This is a smoother version of the RELU.\n",
    "    Original paper: https://arxiv.org/abs/1606.08415\n",
    "    refer : https://github.com/google-research/bert/blob/bee6030e31e42a9394ac567da170a89a98d2062f/modeling.py#L264\n",
    "    Args:\n",
    "        x: float Tensor to perform activation.\n",
    "    Returns:\n",
    "        `x` with the GELU activation applied.\n",
    "    \"\"\"\n",
    "    cdf = 0.5 * (1.0 + tf.tanh(\n",
    "        (np.sqrt(2 / np.pi) * (x + 0.044715 * tf.pow(x, 3)))))\n",
    "    return x * cdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# swish, mish, selu, gelu \n",
    "\n",
    "from tensorflow.keras.layers import Conv2D, MaxPooling2D, Flatten, Dense, Dropout\n",
    "\n",
    "def train_model(X_train, y_train, X_valid, y_valid, ACTIVATION, num_conv, large_model, ARCHITECTURE,\n",
    "               training_times, validation_accuracy, architectures):  \n",
    "\n",
    "        model = tf.keras.Sequential()\n",
    "\n",
    "        for _ in range(num_conv):\n",
    "            model.add(Conv2D(32, 3, activation=ACTIVATION, input_shape=(28, 28, 1)))\n",
    "            model.add(MaxPooling2D((2, 2)))\n",
    "        \n",
    "        model.add(Flatten())\n",
    "\n",
    "    \n",
    "        model.add(Dense(8, activation=ACTIVATION))\n",
    "        model.add(Dropout(0.2))\n",
    "    \n",
    "        if large_model:\n",
    "            model.add(Dense(128, activation=ACTIVATION))\n",
    "            model.add(Dropout(0.2))\n",
    "    \n",
    "        model.add(Dense(10))\n",
    "\n",
    "    \n",
    "        model.compile(metrics=['acc'], \n",
    "                       loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), \n",
    "                       optimizer=tf.keras.optimizers.legacy.Adam())\n",
    "\n",
    "        import time\n",
    "        t_start = time.time()\n",
    "        history = model.fit(X_train, y_train, validation_data=(X_valid, y_valid), epochs=10, batch_size=32)\n",
    "        t_end = time.time()\n",
    "        training_time = t_end - t_start\n",
    "\n",
    "\n",
    "\n",
    "        # add average of the validation accuracy to a list\n",
    "        average_accuracy = sum(history.history['val_acc']) / len(history.history['val_acc'])\n",
    "        validation_accuracy.append(average_accuracy)\n",
    "\n",
    "        # add training time to a list \n",
    "        training_times.append(training_time)\n",
    "\n",
    "        # teack the architecture\n",
    "        architectures.append(ARCHITECTURE)\n",
    "    \n",
    "        return validation_accuracy, training_times, architectures\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# lists for tracking the training time & validation accuracy\n",
    "training_times = list()\n",
    "validation_accuracy = list()\n",
    "architectures = list()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-17 12:27:53.530501: I metal_plugin/src/device/metal_device.cc:1154] Metal device set to: Apple M2\n",
      "2024-01-17 12:27:53.530530: I metal_plugin/src/device/metal_device.cc:296] systemMemory: 16.00 GB\n",
      "2024-01-17 12:27:53.530539: I metal_plugin/src/device/metal_device.cc:313] maxCacheSize: 5.33 GB\n",
      "2024-01-17 12:27:53.530696: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2024-01-17 12:27:53.530712: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/10\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-01-17 12:27:53.978537: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:117] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1313/1313 [==============================] - 9s 6ms/step - loss: 0.8663 - acc: 0.7268 - val_loss: 0.4523 - val_acc: 0.8751\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 7s 5ms/step - loss: 0.5757 - acc: 0.8249 - val_loss: 0.4033 - val_acc: 0.8891\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 7s 6ms/step - loss: 0.5362 - acc: 0.8393 - val_loss: 0.3988 - val_acc: 0.8904\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 7s 6ms/step - loss: 0.5330 - acc: 0.8436 - val_loss: 0.3958 - val_acc: 0.8953\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 7s 6ms/step - loss: 0.5299 - acc: 0.8441 - val_loss: 0.3916 - val_acc: 0.8946\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 7s 5ms/step - loss: 0.5363 - acc: 0.8433 - val_loss: 0.3877 - val_acc: 0.8950\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 7s 5ms/step - loss: 0.5363 - acc: 0.8411 - val_loss: 0.3978 - val_acc: 0.8948\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 7s 5ms/step - loss: 0.5334 - acc: 0.8427 - val_loss: 0.3996 - val_acc: 0.8913\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 7s 5ms/step - loss: 0.5314 - acc: 0.8429 - val_loss: 0.3976 - val_acc: 0.8927\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 7s 5ms/step - loss: 0.5348 - acc: 0.8436 - val_loss: 0.3981 - val_acc: 0.8945\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 9s 6ms/step - loss: 0.7746 - acc: 0.7494 - val_loss: 0.4289 - val_acc: 0.8796\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5985 - acc: 0.8245 - val_loss: 0.4236 - val_acc: 0.8830\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.6374 - acc: 0.8166 - val_loss: 0.4520 - val_acc: 0.8758\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.7167 - acc: 0.7971 - val_loss: 0.4917 - val_acc: 0.8639\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.8298 - acc: 0.7775 - val_loss: 0.5808 - val_acc: 0.8513\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.9898 - acc: 0.7560 - val_loss: 0.6933 - val_acc: 0.8431\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 1.2040 - acc: 0.7326 - val_loss: 0.6896 - val_acc: 0.8444\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 1.4111 - acc: 0.7176 - val_loss: 0.8860 - val_acc: 0.8040\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 1.6672 - acc: 0.7099 - val_loss: 1.3445 - val_acc: 0.7600\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 2.0139 - acc: 0.6899 - val_loss: 1.8538 - val_acc: 0.7339\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.9262 - acc: 0.7009 - val_loss: 0.4006 - val_acc: 0.8977\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.5150 - acc: 0.8299 - val_loss: 0.2964 - val_acc: 0.9244\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.4422 - acc: 0.8498 - val_loss: 0.2630 - val_acc: 0.9280\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.4114 - acc: 0.8559 - val_loss: 0.2424 - val_acc: 0.9363\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.3999 - acc: 0.8557 - val_loss: 0.2418 - val_acc: 0.9348\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.3961 - acc: 0.8543 - val_loss: 0.2408 - val_acc: 0.9339\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.3848 - acc: 0.8558 - val_loss: 0.2375 - val_acc: 0.9310\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.3818 - acc: 0.8545 - val_loss: 0.2272 - val_acc: 0.9333\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.3767 - acc: 0.8578 - val_loss: 0.2265 - val_acc: 0.9349\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.3738 - acc: 0.8580 - val_loss: 0.2168 - val_acc: 0.9403\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.7061 - acc: 0.7581 - val_loss: 0.2749 - val_acc: 0.9222\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.4435 - acc: 0.8521 - val_loss: 0.2391 - val_acc: 0.9348\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.4117 - acc: 0.8617 - val_loss: 0.2337 - val_acc: 0.9314\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.3890 - acc: 0.8698 - val_loss: 0.2064 - val_acc: 0.9441\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.3852 - acc: 0.8741 - val_loss: 0.2049 - val_acc: 0.9453\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.3971 - acc: 0.8717 - val_loss: 0.2190 - val_acc: 0.9392\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.4240 - acc: 0.8653 - val_loss: 0.2813 - val_acc: 0.9229\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.4999 - acc: 0.8505 - val_loss: 0.4046 - val_acc: 0.8871\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.6679 - acc: 0.8236 - val_loss: 0.4918 - val_acc: 0.8771\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.9894 - acc: 0.8047 - val_loss: 1.0704 - val_acc: 0.8440\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 12s 8ms/step - loss: 0.7866 - acc: 0.7451 - val_loss: 0.2513 - val_acc: 0.9279\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.3517 - acc: 0.8798 - val_loss: 0.1649 - val_acc: 0.9489\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.2587 - acc: 0.9100 - val_loss: 0.1108 - val_acc: 0.9658\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.1898 - acc: 0.9377 - val_loss: 0.0899 - val_acc: 0.9725\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.1621 - acc: 0.9460 - val_loss: 0.0878 - val_acc: 0.9744\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.1506 - acc: 0.9490 - val_loss: 0.0765 - val_acc: 0.9779\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.1427 - acc: 0.9520 - val_loss: 0.0662 - val_acc: 0.9808\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.1392 - acc: 0.9535 - val_loss: 0.0649 - val_acc: 0.9810\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.1348 - acc: 0.9547 - val_loss: 0.0716 - val_acc: 0.9795\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 10s 8ms/step - loss: 0.1297 - acc: 0.9553 - val_loss: 0.0654 - val_acc: 0.9808\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.7095 - acc: 0.7632 - val_loss: 0.3050 - val_acc: 0.9140\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.5404 - acc: 0.8421 - val_loss: 0.2851 - val_acc: 0.9258\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.8302 - acc: 0.8333 - val_loss: 0.5138 - val_acc: 0.9217\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 2.9850 - acc: 0.8044 - val_loss: 2.7394 - val_acc: 0.8827\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 13.1435 - acc: 0.7711 - val_loss: 14.8296 - val_acc: 0.8113\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 60.4218 - acc: 0.7515 - val_loss: 43.9600 - val_acc: 0.8489\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 153.9348 - acc: 0.7530 - val_loss: 105.7288 - val_acc: 0.8741\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 338.8522 - acc: 0.7454 - val_loss: 246.4086 - val_acc: 0.8360\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 694.5872 - acc: 0.7423 - val_loss: 363.3127 - val_acc: 0.8521\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 1099.1310 - acc: 0.7483 - val_loss: 720.9758 - val_acc: 0.8315\n",
      "training relu models done\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.9458 - acc: 0.6955 - val_loss: 0.4910 - val_acc: 0.8733\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.6451 - acc: 0.7952 - val_loss: 0.4010 - val_acc: 0.8908\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5920 - acc: 0.8134 - val_loss: 0.3817 - val_acc: 0.8946\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5632 - acc: 0.8248 - val_loss: 0.3596 - val_acc: 0.8971\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5450 - acc: 0.8309 - val_loss: 0.3589 - val_acc: 0.9007\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5340 - acc: 0.8365 - val_loss: 0.3537 - val_acc: 0.9002\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5223 - acc: 0.8365 - val_loss: 0.3526 - val_acc: 0.8997\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5115 - acc: 0.8424 - val_loss: 0.3551 - val_acc: 0.9003\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5060 - acc: 0.8460 - val_loss: 0.3496 - val_acc: 0.8995\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 8s 6ms/step - loss: 0.5013 - acc: 0.8452 - val_loss: 0.3414 - val_acc: 0.9013\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.8214 - acc: 0.7252 - val_loss: 0.4402 - val_acc: 0.8681\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5210 - acc: 0.8392 - val_loss: 0.3606 - val_acc: 0.8921\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4601 - acc: 0.8571 - val_loss: 0.3480 - val_acc: 0.8948\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4298 - acc: 0.8680 - val_loss: 0.3263 - val_acc: 0.9006\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4087 - acc: 0.8745 - val_loss: 0.3117 - val_acc: 0.9063\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.3935 - acc: 0.8782 - val_loss: 0.3171 - val_acc: 0.9009\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.3863 - acc: 0.8807 - val_loss: 0.2987 - val_acc: 0.9087\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.3782 - acc: 0.8847 - val_loss: 0.2929 - val_acc: 0.9120\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.3686 - acc: 0.8870 - val_loss: 0.2855 - val_acc: 0.9123\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.3683 - acc: 0.8865 - val_loss: 0.2843 - val_acc: 0.9128\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.7476 - acc: 0.7559 - val_loss: 0.2939 - val_acc: 0.9329\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.4287 - acc: 0.8636 - val_loss: 0.1622 - val_acc: 0.9586\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.3126 - acc: 0.9044 - val_loss: 0.1303 - val_acc: 0.9639\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.2663 - acc: 0.9193 - val_loss: 0.1167 - val_acc: 0.9679\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.2303 - acc: 0.9303 - val_loss: 0.1108 - val_acc: 0.9700\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.2103 - acc: 0.9353 - val_loss: 0.0976 - val_acc: 0.9737\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.1954 - acc: 0.9402 - val_loss: 0.0923 - val_acc: 0.9743\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.1788 - acc: 0.9440 - val_loss: 0.0922 - val_acc: 0.9742\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.1679 - acc: 0.9479 - val_loss: 0.0900 - val_acc: 0.9748\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.1598 - acc: 0.9509 - val_loss: 0.0917 - val_acc: 0.9743\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 13s 10ms/step - loss: 0.5928 - acc: 0.8135 - val_loss: 0.2155 - val_acc: 0.9364\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.2465 - acc: 0.9284 - val_loss: 0.1344 - val_acc: 0.9598\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 12s 10ms/step - loss: 0.1819 - acc: 0.9468 - val_loss: 0.1102 - val_acc: 0.9648\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1487 - acc: 0.9551 - val_loss: 0.1045 - val_acc: 0.9685\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1280 - acc: 0.9610 - val_loss: 0.0962 - val_acc: 0.9706\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 12s 10ms/step - loss: 0.1182 - acc: 0.9644 - val_loss: 0.0886 - val_acc: 0.9721\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1090 - acc: 0.9652 - val_loss: 0.0823 - val_acc: 0.9741\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.0994 - acc: 0.9682 - val_loss: 0.0803 - val_acc: 0.9758\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 13s 10ms/step - loss: 0.0921 - acc: 0.9716 - val_loss: 0.0761 - val_acc: 0.9775\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.0845 - acc: 0.9738 - val_loss: 0.0753 - val_acc: 0.9766\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 13s 10ms/step - loss: 0.7089 - acc: 0.7963 - val_loss: 0.2596 - val_acc: 0.9483\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3260 - acc: 0.9090 - val_loss: 0.1414 - val_acc: 0.9704\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.2339 - acc: 0.9325 - val_loss: 0.0959 - val_acc: 0.9759\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1881 - acc: 0.9441 - val_loss: 0.0819 - val_acc: 0.9776\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1602 - acc: 0.9522 - val_loss: 0.0681 - val_acc: 0.9814\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1406 - acc: 0.9583 - val_loss: 0.0626 - val_acc: 0.9821\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1244 - acc: 0.9628 - val_loss: 0.0609 - val_acc: 0.9830\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1128 - acc: 0.9665 - val_loss: 0.0628 - val_acc: 0.9828\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.1086 - acc: 0.9681 - val_loss: 0.0554 - val_acc: 0.9844\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.0977 - acc: 0.9718 - val_loss: 0.0568 - val_acc: 0.9831\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 15s 11ms/step - loss: 0.5147 - acc: 0.8297 - val_loss: 0.1265 - val_acc: 0.9613\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.1783 - acc: 0.9465 - val_loss: 0.1102 - val_acc: 0.9668\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.1306 - acc: 0.9620 - val_loss: 0.0726 - val_acc: 0.9778\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 14s 10ms/step - loss: 0.1043 - acc: 0.9691 - val_loss: 0.0700 - val_acc: 0.9790\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0865 - acc: 0.9740 - val_loss: 0.0618 - val_acc: 0.9817\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 14s 10ms/step - loss: 0.0776 - acc: 0.9768 - val_loss: 0.0520 - val_acc: 0.9837\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0681 - acc: 0.9796 - val_loss: 0.0529 - val_acc: 0.9841\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 14s 10ms/step - loss: 0.0626 - acc: 0.9811 - val_loss: 0.0508 - val_acc: 0.9858\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0562 - acc: 0.9824 - val_loss: 0.0571 - val_acc: 0.9815\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.0496 - acc: 0.9842 - val_loss: 0.0558 - val_acc: 0.9839\n",
      "training swish models done\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.9157 - acc: 0.7173 - val_loss: 0.4537 - val_acc: 0.8783\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.6120 - acc: 0.8110 - val_loss: 0.3891 - val_acc: 0.8968\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5708 - acc: 0.8201 - val_loss: 0.3714 - val_acc: 0.8996\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5447 - acc: 0.8295 - val_loss: 0.3547 - val_acc: 0.9030\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5292 - acc: 0.8350 - val_loss: 0.3552 - val_acc: 0.8991\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5138 - acc: 0.8389 - val_loss: 0.3604 - val_acc: 0.9001\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5033 - acc: 0.8415 - val_loss: 0.3471 - val_acc: 0.9015\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4936 - acc: 0.8459 - val_loss: 0.3384 - val_acc: 0.9039\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4866 - acc: 0.8492 - val_loss: 0.3437 - val_acc: 0.9025\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4843 - acc: 0.8496 - val_loss: 0.3439 - val_acc: 0.9012\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.7429 - acc: 0.7572 - val_loss: 0.3791 - val_acc: 0.8892\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.4787 - acc: 0.8510 - val_loss: 0.3254 - val_acc: 0.9032\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.4269 - acc: 0.8672 - val_loss: 0.3000 - val_acc: 0.9087\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.4000 - acc: 0.8749 - val_loss: 0.2844 - val_acc: 0.9141\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.3851 - acc: 0.8793 - val_loss: 0.2826 - val_acc: 0.9148\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.3745 - acc: 0.8818 - val_loss: 0.2783 - val_acc: 0.9147\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3638 - acc: 0.8858 - val_loss: 0.2706 - val_acc: 0.9168\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3562 - acc: 0.8861 - val_loss: 0.2598 - val_acc: 0.9203\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3463 - acc: 0.8895 - val_loss: 0.2571 - val_acc: 0.9226\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 11s 9ms/step - loss: 0.3436 - acc: 0.8910 - val_loss: 0.2531 - val_acc: 0.9238\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 19s 14ms/step - loss: 0.7224 - acc: 0.7703 - val_loss: 0.2175 - val_acc: 0.9436\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.3583 - acc: 0.8926 - val_loss: 0.1482 - val_acc: 0.9609\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.2902 - acc: 0.9119 - val_loss: 0.1333 - val_acc: 0.9622\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.2541 - acc: 0.9233 - val_loss: 0.1133 - val_acc: 0.9680\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.2350 - acc: 0.9285 - val_loss: 0.1139 - val_acc: 0.9681\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.2113 - acc: 0.9355 - val_loss: 0.1096 - val_acc: 0.9684\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1995 - acc: 0.9387 - val_loss: 0.1204 - val_acc: 0.9666\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1878 - acc: 0.9444 - val_loss: 0.1015 - val_acc: 0.9716\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1702 - acc: 0.9501 - val_loss: 0.1039 - val_acc: 0.9712\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1606 - acc: 0.9540 - val_loss: 0.1066 - val_acc: 0.9713\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.6025 - acc: 0.8084 - val_loss: 0.2169 - val_acc: 0.9375\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.2651 - acc: 0.9203 - val_loss: 0.1351 - val_acc: 0.9604\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1909 - acc: 0.9422 - val_loss: 0.1153 - val_acc: 0.9649\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1593 - acc: 0.9523 - val_loss: 0.1089 - val_acc: 0.9669\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1323 - acc: 0.9600 - val_loss: 0.0948 - val_acc: 0.9708\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1212 - acc: 0.9629 - val_loss: 0.0881 - val_acc: 0.9731\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1068 - acc: 0.9670 - val_loss: 0.0945 - val_acc: 0.9728\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1028 - acc: 0.9680 - val_loss: 0.0886 - val_acc: 0.9742\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.0903 - acc: 0.9713 - val_loss: 0.0970 - val_acc: 0.9708\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.0850 - acc: 0.9733 - val_loss: 0.0911 - val_acc: 0.9733\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.6784 - acc: 0.7914 - val_loss: 0.1932 - val_acc: 0.9562\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.3196 - acc: 0.9097 - val_loss: 0.1263 - val_acc: 0.9692\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 20s 16ms/step - loss: 0.2454 - acc: 0.9295 - val_loss: 0.0959 - val_acc: 0.9766\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.2029 - acc: 0.9400 - val_loss: 0.0813 - val_acc: 0.9793\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1766 - acc: 0.9473 - val_loss: 0.0721 - val_acc: 0.9810\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1571 - acc: 0.9537 - val_loss: 0.0658 - val_acc: 0.9820\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1438 - acc: 0.9568 - val_loss: 0.0650 - val_acc: 0.9825\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1310 - acc: 0.9609 - val_loss: 0.0601 - val_acc: 0.9837\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1232 - acc: 0.9623 - val_loss: 0.0571 - val_acc: 0.9839\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1143 - acc: 0.9643 - val_loss: 0.0525 - val_acc: 0.9850\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 24s 18ms/step - loss: 0.4910 - acc: 0.8385 - val_loss: 0.1498 - val_acc: 0.9561\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.1877 - acc: 0.9458 - val_loss: 0.1039 - val_acc: 0.9696\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.1345 - acc: 0.9615 - val_loss: 0.0776 - val_acc: 0.9763\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.1079 - acc: 0.9688 - val_loss: 0.0701 - val_acc: 0.9776\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0871 - acc: 0.9735 - val_loss: 0.0844 - val_acc: 0.9738\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0801 - acc: 0.9752 - val_loss: 0.0600 - val_acc: 0.9812\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0696 - acc: 0.9797 - val_loss: 0.0648 - val_acc: 0.9801\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0617 - acc: 0.9812 - val_loss: 0.0619 - val_acc: 0.9815\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0584 - acc: 0.9823 - val_loss: 0.0635 - val_acc: 0.9805\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0543 - acc: 0.9836 - val_loss: 0.0569 - val_acc: 0.9833\n",
      "training mish models done\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 10s 7ms/step - loss: 0.8259 - acc: 0.7357 - val_loss: 0.4310 - val_acc: 0.8839\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5298 - acc: 0.8364 - val_loss: 0.3641 - val_acc: 0.8994\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4847 - acc: 0.8512 - val_loss: 0.3388 - val_acc: 0.9050\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4646 - acc: 0.8591 - val_loss: 0.3346 - val_acc: 0.9051\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4511 - acc: 0.8645 - val_loss: 0.3299 - val_acc: 0.9079\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4454 - acc: 0.8657 - val_loss: 0.3274 - val_acc: 0.9061\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4381 - acc: 0.8666 - val_loss: 0.3277 - val_acc: 0.9071\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4289 - acc: 0.8711 - val_loss: 0.3195 - val_acc: 0.9094\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4271 - acc: 0.8708 - val_loss: 0.3189 - val_acc: 0.9086\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.4238 - acc: 0.8705 - val_loss: 0.3183 - val_acc: 0.9093\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 13s 9ms/step - loss: 0.6436 - acc: 0.7975 - val_loss: 0.3542 - val_acc: 0.8998\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.4599 - acc: 0.8630 - val_loss: 0.3333 - val_acc: 0.9051\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.4253 - acc: 0.8735 - val_loss: 0.3153 - val_acc: 0.9097\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.4052 - acc: 0.8804 - val_loss: 0.3079 - val_acc: 0.9119\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3906 - acc: 0.8828 - val_loss: 0.3077 - val_acc: 0.9098\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3747 - acc: 0.8875 - val_loss: 0.2975 - val_acc: 0.9112\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3712 - acc: 0.8883 - val_loss: 0.2803 - val_acc: 0.9201\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3576 - acc: 0.8929 - val_loss: 0.2808 - val_acc: 0.9186\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3498 - acc: 0.8946 - val_loss: 0.2746 - val_acc: 0.9198\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.3397 - acc: 0.8967 - val_loss: 0.2647 - val_acc: 0.9217\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.6106 - acc: 0.8153 - val_loss: 0.2544 - val_acc: 0.9294\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.3134 - acc: 0.9089 - val_loss: 0.1737 - val_acc: 0.9505\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.2350 - acc: 0.9315 - val_loss: 0.1336 - val_acc: 0.9606\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.2004 - acc: 0.9395 - val_loss: 0.1269 - val_acc: 0.9618\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1775 - acc: 0.9467 - val_loss: 0.1229 - val_acc: 0.9624\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1659 - acc: 0.9513 - val_loss: 0.1194 - val_acc: 0.9661\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1529 - acc: 0.9534 - val_loss: 0.1183 - val_acc: 0.9657\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1473 - acc: 0.9553 - val_loss: 0.1097 - val_acc: 0.9671\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1378 - acc: 0.9575 - val_loss: 0.1105 - val_acc: 0.9678\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.1306 - acc: 0.9594 - val_loss: 0.1114 - val_acc: 0.9696\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.4823 - acc: 0.8491 - val_loss: 0.2008 - val_acc: 0.9395\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.2581 - acc: 0.9212 - val_loss: 0.1448 - val_acc: 0.9560\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1948 - acc: 0.9393 - val_loss: 0.1333 - val_acc: 0.9609\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1706 - acc: 0.9478 - val_loss: 0.1183 - val_acc: 0.9634\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1502 - acc: 0.9547 - val_loss: 0.1086 - val_acc: 0.9676\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1390 - acc: 0.9568 - val_loss: 0.1119 - val_acc: 0.9667\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1303 - acc: 0.9586 - val_loss: 0.1054 - val_acc: 0.9695\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1243 - acc: 0.9608 - val_loss: 0.1048 - val_acc: 0.9688\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1147 - acc: 0.9637 - val_loss: 0.1051 - val_acc: 0.9689\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 20s 15ms/step - loss: 0.1084 - acc: 0.9655 - val_loss: 0.1043 - val_acc: 0.9689\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.4587 - acc: 0.8610 - val_loss: 0.1395 - val_acc: 0.9598\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.2009 - acc: 0.9425 - val_loss: 0.1000 - val_acc: 0.9717\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1627 - acc: 0.9525 - val_loss: 0.0861 - val_acc: 0.9748\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1383 - acc: 0.9604 - val_loss: 0.0930 - val_acc: 0.9737\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1248 - acc: 0.9640 - val_loss: 0.0840 - val_acc: 0.9747\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1171 - acc: 0.9670 - val_loss: 0.0781 - val_acc: 0.9778\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1063 - acc: 0.9696 - val_loss: 0.0758 - val_acc: 0.9784\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.1031 - acc: 0.9693 - val_loss: 0.0703 - val_acc: 0.9808\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.0970 - acc: 0.9712 - val_loss: 0.0755 - val_acc: 0.9787\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 21s 16ms/step - loss: 0.0900 - acc: 0.9730 - val_loss: 0.0726 - val_acc: 0.9796\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 24s 18ms/step - loss: 0.3760 - acc: 0.8796 - val_loss: 0.1192 - val_acc: 0.9641\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.1590 - acc: 0.9530 - val_loss: 0.1013 - val_acc: 0.9697\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 23s 17ms/step - loss: 0.1265 - acc: 0.9614 - val_loss: 0.0857 - val_acc: 0.9744\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.1147 - acc: 0.9656 - val_loss: 0.0728 - val_acc: 0.9778\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0991 - acc: 0.9701 - val_loss: 0.0889 - val_acc: 0.9732\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0948 - acc: 0.9707 - val_loss: 0.0784 - val_acc: 0.9777\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0893 - acc: 0.9729 - val_loss: 0.0773 - val_acc: 0.9772\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0811 - acc: 0.9747 - val_loss: 0.0664 - val_acc: 0.9811\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0760 - acc: 0.9770 - val_loss: 0.0825 - val_acc: 0.9755\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 23s 18ms/step - loss: 0.0730 - acc: 0.9771 - val_loss: 0.0692 - val_acc: 0.9798\n",
      "training selu models done\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.9813 - acc: 0.6825 - val_loss: 0.5094 - val_acc: 0.8674\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.6913 - acc: 0.7730 - val_loss: 0.4249 - val_acc: 0.8811\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.6260 - acc: 0.7974 - val_loss: 0.4001 - val_acc: 0.8898\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.6057 - acc: 0.8034 - val_loss: 0.3906 - val_acc: 0.8913\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5848 - acc: 0.8127 - val_loss: 0.3901 - val_acc: 0.8936\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5642 - acc: 0.8195 - val_loss: 0.3692 - val_acc: 0.8953\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5563 - acc: 0.8224 - val_loss: 0.3726 - val_acc: 0.8966\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5447 - acc: 0.8258 - val_loss: 0.3705 - val_acc: 0.8952\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5433 - acc: 0.8282 - val_loss: 0.3673 - val_acc: 0.8955\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 9s 7ms/step - loss: 0.5348 - acc: 0.8273 - val_loss: 0.3607 - val_acc: 0.8995\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 12s 9ms/step - loss: 0.7767 - acc: 0.7452 - val_loss: 0.3695 - val_acc: 0.8957\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.4705 - acc: 0.8538 - val_loss: 0.3180 - val_acc: 0.9085\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.4185 - acc: 0.8724 - val_loss: 0.2998 - val_acc: 0.9121\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.3917 - acc: 0.8787 - val_loss: 0.2917 - val_acc: 0.9133\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.3758 - acc: 0.8831 - val_loss: 0.2812 - val_acc: 0.9158\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.3592 - acc: 0.8890 - val_loss: 0.2867 - val_acc: 0.9124\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.3486 - acc: 0.8929 - val_loss: 0.2752 - val_acc: 0.9157\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.3397 - acc: 0.8955 - val_loss: 0.2716 - val_acc: 0.9180\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.3327 - acc: 0.8974 - val_loss: 0.2709 - val_acc: 0.9184\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 11s 8ms/step - loss: 0.3259 - acc: 0.8986 - val_loss: 0.2715 - val_acc: 0.9178\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 15s 11ms/step - loss: 0.7722 - acc: 0.7456 - val_loss: 0.2459 - val_acc: 0.9408\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.4146 - acc: 0.8690 - val_loss: 0.1705 - val_acc: 0.9563\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.3231 - acc: 0.9013 - val_loss: 0.1485 - val_acc: 0.9593\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.2820 - acc: 0.9136 - val_loss: 0.1311 - val_acc: 0.9637\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.2580 - acc: 0.9211 - val_loss: 0.1145 - val_acc: 0.9680\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.2356 - acc: 0.9272 - val_loss: 0.1149 - val_acc: 0.9689\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.2220 - acc: 0.9320 - val_loss: 0.1060 - val_acc: 0.9706\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.2069 - acc: 0.9373 - val_loss: 0.1039 - val_acc: 0.9712\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.2035 - acc: 0.9386 - val_loss: 0.1033 - val_acc: 0.9723\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 14s 11ms/step - loss: 0.1883 - acc: 0.9417 - val_loss: 0.0999 - val_acc: 0.9727\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 17s 12ms/step - loss: 0.5884 - acc: 0.8075 - val_loss: 0.1796 - val_acc: 0.9482\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.2237 - acc: 0.9337 - val_loss: 0.1198 - val_acc: 0.9635\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1598 - acc: 0.9529 - val_loss: 0.0947 - val_acc: 0.9706\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1338 - acc: 0.9612 - val_loss: 0.0845 - val_acc: 0.9737\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1115 - acc: 0.9667 - val_loss: 0.0838 - val_acc: 0.9740\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1020 - acc: 0.9686 - val_loss: 0.0722 - val_acc: 0.9780\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.0911 - acc: 0.9713 - val_loss: 0.0757 - val_acc: 0.9762\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.0850 - acc: 0.9730 - val_loss: 0.0782 - val_acc: 0.9763\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.0804 - acc: 0.9748 - val_loss: 0.0802 - val_acc: 0.9762\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.0758 - acc: 0.9767 - val_loss: 0.0721 - val_acc: 0.9777\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 17s 13ms/step - loss: 0.7514 - acc: 0.7689 - val_loss: 0.2304 - val_acc: 0.9512\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 16s 13ms/step - loss: 0.3719 - acc: 0.8885 - val_loss: 0.1424 - val_acc: 0.9650\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.2698 - acc: 0.9257 - val_loss: 0.1130 - val_acc: 0.9717\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.2267 - acc: 0.9374 - val_loss: 0.0929 - val_acc: 0.9756\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1992 - acc: 0.9455 - val_loss: 0.0838 - val_acc: 0.9783\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1835 - acc: 0.9496 - val_loss: 0.0759 - val_acc: 0.9791\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1664 - acc: 0.9535 - val_loss: 0.0828 - val_acc: 0.9776\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1494 - acc: 0.9555 - val_loss: 0.0713 - val_acc: 0.9807\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1376 - acc: 0.9585 - val_loss: 0.0720 - val_acc: 0.9807\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 16s 12ms/step - loss: 0.1253 - acc: 0.9617 - val_loss: 0.0704 - val_acc: 0.9805\n",
      "Epoch 1/10\n",
      "1313/1313 [==============================] - 19s 14ms/step - loss: 0.5873 - acc: 0.8034 - val_loss: 0.1514 - val_acc: 0.9555\n",
      "Epoch 2/10\n",
      "1313/1313 [==============================] - 18s 13ms/step - loss: 0.2136 - acc: 0.9333 - val_loss: 0.1030 - val_acc: 0.9693\n",
      "Epoch 3/10\n",
      "1313/1313 [==============================] - 18s 13ms/step - loss: 0.1458 - acc: 0.9562 - val_loss: 0.0794 - val_acc: 0.9762\n",
      "Epoch 4/10\n",
      "1313/1313 [==============================] - 18s 13ms/step - loss: 0.1106 - acc: 0.9670 - val_loss: 0.0791 - val_acc: 0.9765\n",
      "Epoch 5/10\n",
      "1313/1313 [==============================] - 18s 14ms/step - loss: 0.0886 - acc: 0.9741 - val_loss: 0.0591 - val_acc: 0.9823\n",
      "Epoch 6/10\n",
      "1313/1313 [==============================] - 18s 14ms/step - loss: 0.0782 - acc: 0.9766 - val_loss: 0.0576 - val_acc: 0.9830\n",
      "Epoch 7/10\n",
      "1313/1313 [==============================] - 18s 14ms/step - loss: 0.0669 - acc: 0.9800 - val_loss: 0.0549 - val_acc: 0.9823\n",
      "Epoch 8/10\n",
      "1313/1313 [==============================] - 18s 13ms/step - loss: 0.0624 - acc: 0.9813 - val_loss: 0.0548 - val_acc: 0.9832\n",
      "Epoch 9/10\n",
      "1313/1313 [==============================] - 18s 13ms/step - loss: 0.0573 - acc: 0.9830 - val_loss: 0.0593 - val_acc: 0.9821\n",
      "Epoch 10/10\n",
      "1313/1313 [==============================] - 18s 13ms/step - loss: 0.0509 - acc: 0.9851 - val_loss: 0.0542 - val_acc: 0.9834\n",
      "training gelu models done\n"
     ]
    }
   ],
   "source": [
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='relu', num_conv=0, large_model=False, ARCHITECTURE='CONV=0 | SM | relu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='relu', num_conv=0, large_model=True, ARCHITECTURE='CONV=0 | LM | relu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='relu', num_conv=1, large_model=False, ARCHITECTURE='CONV=1 | SM | relu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='relu', num_conv=1, large_model=True, ARCHITECTURE='CONV=1 | LM | relu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='relu', num_conv=2, large_model=False, ARCHITECTURE='CONV=2 | SM | relu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='relu', num_conv=2, large_model=True, ARCHITECTURE='CONV=2 | LM | relu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "print('training relu models done')\n",
    "\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='swish', num_conv=0, large_model=False, ARCHITECTURE='CONV=0 | SM | swish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='swish', num_conv=0, large_model=True, ARCHITECTURE='CONV=0 | LM | swish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='swish', num_conv=1, large_model=False, ARCHITECTURE='CONV=1 | SM | swish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='swish', num_conv=1, large_model=True, ARCHITECTURE='CONV=1 | LM | swish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='swish', num_conv=2, large_model=False, ARCHITECTURE='CONV=2 | SM | swish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='swish', num_conv=2, large_model=True, ARCHITECTURE='CONV=2 | LM | swish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "print('training swish models done')\n",
    "\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='mish', num_conv=0, large_model=False, ARCHITECTURE='CONV=0 | SM | mish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='mish', num_conv=0, large_model=True, ARCHITECTURE='CONV=0 | LM | mish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='mish', num_conv=1, large_model=False, ARCHITECTURE='CONV=1 | SM | mish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='mish', num_conv=1, large_model=True, ARCHITECTURE='CONV=1 | LM | mish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='mish', num_conv=2, large_model=False, ARCHITECTURE='CONV=2 | SM | mish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='mish', num_conv=2, large_model=True, ARCHITECTURE='CONV=2 | LM | mish',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "print('training mish models done')\n",
    "\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='selu', num_conv=0, large_model=False, ARCHITECTURE='CONV=0 | SM | selu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='selu', num_conv=0, large_model=True, ARCHITECTURE='CONV=0 | LM | selu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='selu', num_conv=1, large_model=False, ARCHITECTURE='CONV=1 | SM | selu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='selu', num_conv=1, large_model=True, ARCHITECTURE='CONV=1 | LM | selu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='selu', num_conv=2, large_model=False, ARCHITECTURE='CONV=2 | SM | selu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION='selu', num_conv=2, large_model=True, ARCHITECTURE='CONV=2 | LM | selu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "print('training selu models done')\n",
    "\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION=gelu, num_conv=0, large_model=False, ARCHITECTURE='CONV=0 | SM | gelu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION=gelu, num_conv=0, large_model=True, ARCHITECTURE='CONV=0 | LM | gelu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION=gelu, num_conv=1, large_model=False, ARCHITECTURE='CONV=1 | SM | gelu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION=gelu, num_conv=1, large_model=True, ARCHITECTURE='CONV=1 | LM | gelu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION=gelu, num_conv=2, large_model=False, ARCHITECTURE='CONV=2 | SM | gelu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "validation_accuracy, training_times, architectures = train_model(X_train, y_train, X_valid, y_valid, ACTIVATION=gelu, num_conv=2, large_model=True, ARCHITECTURE='CONV=2 | LM | gelu',\n",
    "                                                               training_times=training_times, validation_accuracy=validation_accuracy, architectures=architectures)\n",
    "print('training gelu models done')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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YWLBgQYPnJiUlsX379jpFGgBpaWlERUXRq1cvq5/n/PnztG/fnieffJIePXpYvb453NzcOH78OLt27bIYT0tLIyAg4Krm8nM8PT3x8vJq7TQAFWCIiIiIiIiIiIiIiIiIiEgzBQVB377w73/DZfULddTWmuL69jWts5VHHnkEg8HA3r17iYuLIzQ0lG7dujFz5kx2794NwLlz55gyZQpjxozh1VdfJSoqiqCgICZPnsz69evZuHEjmZmZFvtOnz6d119/nQMHDtR77qhRo/Dz86vThaGsrIyMjAySkpKu6HmCgoJYsWIFCQkJeHp6XtEeV8rBwYGJEyeSlpZmHvvuu+/Iyclh4sSJzd6/srKSadOm4e/vj7OzM0FBQSxduhSAWbNmMXr0aHPs8uXLMRgMvPfee+axrl278sorrwCma0ViY2PNcxs3biQyMhIXFxd8fX0ZOnQo586dszj/+eefx9/fH19fX6ZOncqFCxea/UygAgwREbGFkhIoKrL+VVLS2pmLiIiIiIiIiIiIiIiNDB0Knp4//5//S0pMcbbsfnHy5Ek+/PBDpk6dipubW535ix0Stm7dSklJCbNnz64TM3r0aEJDQ9mwYYPFeHR0NKNGjWLu3Ln1nu3g4EBCQgLp6ekW3TPeeustKisrmTRpknnM3d290de99957JY/fIpKSksjIyKCsrAwwXU0yYsQIOnTo0Oy9X3jhBd555x0yMzP56quveP31183XmgwaNIjc3FxqamoA2LFjB+3atWPHjh2A6ZqZw4cPM3DgwDr7FhUVMWHCBBITEzl48CA5OTmMHTvW4vclOzubgoICsrOzWb9+Penp6Ta7wsTBJruIiMjNq6QEFi2C0lLr13p5wfz54Otr66xEREREREREREREROQqu9gF4/33Tf/p32CoG3Ox+8XIkbbtfpGfn09tbS1hYWGNxh0+fBiA8PDweufDwsLMMZdaunQp3bt3Jzc3l/79+9eZT0xM5LnnniMnJ4fBgwcDpus6xo4di7e3tzkuLy+v0fxcXFwanb+aoqKi6NKlCxs3buTBBx8kPT2d1NRUvv7662bvfezYMUJCQujXrx8Gg4HAwEDz3IABAzhz5gz79++nV69e5ObmMnv2bN5++23AVEDRoUOHen+vi4qKqKqqYuzYseY9IyMjLWK8vb1ZuXIl9vb2hIWFcd9995GVlcWUKVOa/VwqwBARkeaprDQVX7i4gKtr09eVlZnWVVa2VGYiIiIiIiIiIiIiInKVDR0Kn35q+vnNdu3qzrdE9wvA3OHAUF/VRyPx9Y3Xt0dERAQJCQnMmTOHnTt31pkPCwsjOjqatLQ0Bg8eTEFBAbm5uWzdutUiLjg4uEn5XSsSExNZt24dAQEBnD17lpEjR7Jy5cpm72s0Ghk2bBhdu3ZlxIgRjBo1ipiYGAA8PT2JiooiJycHR0dH7Ozs+PWvf83TTz/NmTNnyMnJqbf7BUCPHj0YMmQIkZGRDB8+nJiYGOLi4iyKYLp164a9vb35vb+/f4PXy1hLV5CIiIhtuLpC27ZNf1lTrCEiIiIiIiIiIiIiIteFi10w/v1vU7eLS13sftG3r227XwCEhIRgMBg4ePBgo3GhoaEADcYdOnSIkJCQeucWLlzI/v372bJlS73zSUlJbNq0idOnT7Nu3ToCAwMZMmSIRcz1dAUJwKRJk9i9ezfJyckkJCTg4GCbHg+9evWisLCQRYsWUV5ezrhx44iLizPPDxo0iJycHHbs2MHAgQPx9vamW7dufPrpp+Tk5DBo0KB697W3t2fbtm188MEHRERE8OKLL9K1a1cKCwvNMY6OjhZrDAaD+bqT5lIBhoiIiIiIiIiIiIiIiIiI2MzQoaYuFyUlluMt1f0CwMfHh+HDh/PSSy9x7ty5OvOl/3eVekxMDD4+PqSkpNSJeeeddzhy5AgTJkyo94xOnToxbdo05s2bR3V1dZ35cePGYW9vzxtvvMH69et56KGH6nTTyMvLa/T12muvXcHTtxwfHx/GjBnDjh07SExMtOneHh4exMfHs2bNGjIyMti0aRMnT54ETAUYubm5bN++3VxsMXDgQN58800OHz7cYAcMMBVU9O3b11ww4+TkxObNm22ae0N0BYmIiIiIiIiIiIiIiIiIiNjMxS4Y778Pvr5gMPzU/WLkSNt3v7ho1apVREdHc+edd/LMM8/QvXt3qqqq2LZtG6tXr+bgwYO4ubnxyiuvMH78eB5++GGmTZuGh4cHWVlZPP7448TFxTFu3LgGz5g7dy5r1qyhsLCQ+Ph4izl3d3fi4+OZN28ep06dwmg01llv7RUkeXl5AJw9e5YTJ06Ql5eHk5MTERERVu1Tn6+++qrOWH37pqens2rVKnx9fZt95kV//OMf8ff3JyoqCjs7O9566y06duyIl5cXAAMGDODMmTO8++67PPvss4CpKOOBBx6gffv2DT7/nj17yMrKIiYmBj8/P/bs2cOJEycIDw+3We6NUQGGiIiIiIiIiIiIiIiIiIjY1NCh8Omnpq4X7dq1bPeLizp37sy+fftYvHgxs2bNoqioiPbt29O7d29Wr15tjouLiyM7O5slS5YwYMAAysvLCQ4O5sknn+Sxxx6r07XiUj4+PsyZM4d58+bVO5+UlMTatWuJiYkhICCg2c/Us2dP89eff/45b7zxBoGBgRw9erTZe48fP77O2KVXdVzk4uKCi4tLs8+7lLu7O3/4wx84cuQI9vb29OnTh/fffx87O9MlHp6envTs2ZNjx46Ziy369+9PTU1No90vPDw8+Pjjj1m+fDmnT58mMDCQlJSUq3a1i6G29vKbd+T06dN4enpy6tQpPDw8WjsdEZFrW1ERzJ1rKmFt27bp686cMf3b1tKl4O/fcvmJiIiIiIiIiIiIiIiFxj4PraiooLCwkM6dO+Ps7Nysc157zdQFIzISDhwwdb+YPLlZW0oTDBo0CKPRWG8HjqvFaDQSFBREcnJyq+VgK9b8nbC7SjmJiIiIiIiIiIiIiIiIiMhNZOhQU9eLw4dbvvuFyLVABRgiIiIiIiIiIiIiIiIiImJzQUHQty+cOmX6NSiotTMSaVkOrZ2AiIiIiIiIiIiIiIiIiIjcmIYNg2+/Nf0qV4fRaCQqKqpVc4iNjcXLy6tVc2gNKsAQEREREREREREREREREZEWERgITz0Fjo6tncnNw2g0tnYKxMbGtnYKrUJXkIiIiIiIiIiIiIiIiIiISItR8YXcLFSAISIiIiIiIiIiIiIiIiIiItJMuoJERERso6ysZeNFRERERERERERERERErmEqwBARkeZxcgIvLygthfJy69Z6eZnWi4iIiIiIiIiIiIiIiFznVIAhIiLN4+sL8+dDZaX1a52cTOtFREREREREREREROSGVVEBzs6tnYVIy1MBhoiINJ+KKERERERERERERERE5DIXLsDWraZXTAwMHw4O+oS6xQ0aNAij0YjRaGzVPIxGI0FBQSQnJ1+Vs0pLS9myZYtNY61lZ/MdRURERERERERERERERETkpnbiBLz0EqxbB8ePQ3o6rFxpGm9JxcXFTJ8+ndtuu402bdrQqVMnRo8eTVZWlkXczp07GTlyJN7e3jg7OxMZGUlKSgrV1dUWcQaDAWdnZ7755huL8djYWHOBw+jRoxk6dGi9+ezatQuDwcC+ffusfpa3336bYcOG0b59ezw8PLjnnnv429/+ZvU+9dm0aRN33XUXnp6etG3blm7dujFr1izzfHp6OgaDgfDw8DprMzMzMRgMBAUF2SQXW1ixYgXp6emtnYYKMERERERERERERERERERExDZqa+Hzz+H3v4cdOyAoCLp2hcBA0/vf/940X1tr+7OPHj1K79692b59O8uWLePAgQN8+OGHDB48mKlTp5rjNm/ezMCBA7n11lvJzs7m0KFDzJgxg8WLFzN+/HhqL0vOYDCwYMGCBs9NSkpi+/btdYo0ANLS0oiKiqJXr15WP8/HH3/MsGHDeP/99/n8888ZPHgwo0ePZv/+/VbvdamPPvqI8ePHExcXx969e/n8889ZvHgxlZddN+/m5sbx48fZtWtXnWcKCAhoVg625unpiZeXV2unoQIMERERERERERERERERERFpvvJyyMiA5cuhqAhuvx3c3U1z7u6m90VFpvmMDFO8LT3yyCMYDAb27t1LXFwcoaGhdOvWjZkzZ7J7924Azp07x5QpUxgzZgyvvvoqUVFRBAUFMXnyZNavX8/GjRvJzMy02Hf69Om8/vrrHDhwoN5zR40ahZ+fX50ODGVlZWRkZJCUlHRFz7N8+XKeeOIJ+vTpQ0hICEuWLCEkJIR33333iva76K9//Sv9+vXj8ccfp2vXroSGhhIbG8uLL75oEefg4MDEiRNJS0szj3333Xfk5OQwceLEZuUAUFlZybRp0/D398fZ2ZmgoCCWLl0KwKxZsxg9erQ5dvny5RgMBt577z3zWNeuXXnllVcA07UisbGx5rmNGzcSGRmJi4sLvr6+DB06lHPnzlmc//zzz+Pv74+vry9Tp07lwoULzX4mFWCIiIiIiIiIiIiIiIiIiEizfPcdpKZCZiZ4eEBoKNjbW8bY25vGPTxMcamppnW2cPLkST788EOmTp2Km5tbnfmL3RG2bt1KSUkJs2fPrhMzevRoQkND2bBhg8V4dHQ0o0aNYu7cufWe7eDgQEJCAunp6RbdM9566y0qKyuZNGmSeczd3b3R17333tvgM9bU1HDmzBl8fHwa/V78nI4dO/Kvf/2Lf/7znz8bm5SUREZGBmVlZYDpapIRI0bQoUOHZuUA8MILL/DOO++QmZnJV199xeuvv26+1mTQoEHk5uZSU1MDwI4dO2jXrh07duwATFfNHD58mIEDB9bZt6ioiAkTJpCYmMjBgwfJyclh7NixFr832dnZFBQUkJ2dzfr160lPT7fJFSYOzd5BRERERERERERERERERERuah99BLt2mbpcODs3Htu+PbRta4q/5RYwGpt/fn5+PrW1tYSFhTUad/jwYQDCw8PrnQ8LCzPHXGrp0qV0796d3Nxc+vfvX2c+MTGR5557jpycHAYPHgyYruoYO3Ys3t7e5ri8vLxG83NxcWlwLiUlhXPnzjFu3LhG9/g506dPJzc3l8jISAIDA7n77ruJiYlh0qRJtGnTxiI2KiqKLl26sHHjRh588EHS09NJTU3l66+/blYOAMeOHSMkJIR+/fphMBgIDAw0zw0YMIAzZ86wf/9+evXqRW5uLrNnz+btt98GTAUUHTp0qPf3u6ioiKqqKsaOHWveMzIy0iLG29ublStXYm9vT1hYGPfddx9ZWVlMmTKlWc+kDhgiIiIiIiIiIiIiIiIiItIswcGmwgtHx6bFOzqa4oODbXP+xe4GBoPBqvj6xuvbIyIigoSEBObMmVPvurCwMKKjo83XdRQUFJCbm0tiYqJFXHBwcKOvW265pd79N2zYQHJyMhkZGfj5+TXpGRvi5ubGe++9R35+Pk899RTu7u7MmjWLO++809zp4lKJiYmsW7eOHTt2cPbsWUaOHNms8y8yGo3k5eXRtWtXHn30UbZu3Wqe8/T0JCoqipycHA4cOICdnR2//vWv+eKLLzhz5gw5OTn1dr8A6NGjB0OGDCEyMpJf/epXrFmzhh9//NEiplu3bthf0qLF39+f48ePN/uZVIAhIiIiIiIiIiIiIiIiIiLNEh4O3t5w8mTT4k+eBB8fiIiwzfkhISEYDAYOHjzYaFxoaChAg3GHDh0iJCSk3rmFCxeyf/9+tmzZUu98UlISmzZt4vTp06xbt47AwECGDBliEXMlV5BkZGSQlJREZmYmQ4cObfT5rNGlSxcmT57Ma6+9xr59+/jyyy/JyMioEzdp0iR2795NcnIyCQkJODjY5qKNXr16UVhYyKJFiygvL2fcuHHExcWZ5wcNGkROTg47duxg4MCBeHt7061bNz799FNycnIYNGhQvfva29uzbds2PvjgAyIiInjxxRfp2rUrhYWF5hjHyyqFDAaD+bqT5lABhoiIiIiIiIiIiIiIiIiINIuvL3TrBj/80LT4H34wxfv42OZ8Hx8fhg8fzksvvcS5c+fqzJeWlgIQExODj48PKSkpdWLeeecdjhw5woQJE+o9o1OnTkybNo158+ZRXV1dZ37cuHHY29vzxhtvsH79eh566KE63TTy8vIafb322msW8Rs2bMBoNPLGG29w3333NfXbYbWgoCBcXV3r/d75+PgwZswYduzYUaejR3N5eHgQHx/PmjVryMjIYNOmTZz8vyqeQYMGkZuby/bt283FFgMHDuTNN9/k8OHDDXbAAFNBRd++fc1FM05OTmzevNmmudfHNqUpIiIiIiIiIiIiIiIiIiJyU+vZEz75BGpqwK6RVgA1NVBVBVFRtj1/1apVREdHc+edd/LMM8/QvXt3qqqq2LZtG6tXr+bgwYO4ubnxyiuvMH78eB5++GGmTZuGh4cHWVlZPP7448TFxTFu3LgGz5g7dy5r1qyhsLCQ+Ph4izl3d3fi4+OZN28ep06dwmg01lkfbMWdKxs2bCAhIYEVK1Zw9913U1xcDICLiwuenp5N3udyycnJlJWVMXLkSAIDAyktLeWFF17gwoULDBs2rN416enprFq1Cl9f3ys+93J//OMf8ff3JyoqCjs7O9566y06duyIl5cXAAMGDODMmTO8++67PPvss4CpKOOBBx6gffv2RDTQPmXPnj1kZWURExODn58fe/bs4cSJE4SHh9ss94aoA4aIiIiIiIiIiIiIiIiIiDRbeDh4ecGPPzYed/Kk6boSW10/clHnzp3Zt28fgwcPZtasWdx+++0MGzaMrKwsVq9ebY6Li4sjOzubb7/9lgEDBtC1a1dSU1N58sknefPNN+t0rbiUj48Pc+bMoaKiot75pKQkfvzxR4YOHUpAQECznueVV16hqqqKqVOn4u/vb37NmDGjWfsOHDiQr7/+moSEBMLCwrj33nspLi5m69atdO3atd41Li4uNi2+AFPByh/+8AfuuOMO+vTpw9GjR3n//fex+7/qHU9PT3r27ImPj4+52KJ///7U1NQ02v3Cw8ODjz/+mJEjRxIaGspTTz1FSkpKvde72Jqhtra2tsVPuc6cPn0aT09PTp06hYeHR2unIyIiIiIiIiIiIiIiIiJiM419HlpRUUFhYSGdO3fG2dnZqn1ra+H55+HzzyEsrOG4Q4egTx+YNetKspefM2jQIIxGY70dOK4mo9FIUFAQycnJrZpHc1nzd0JXkIiIiIiIiIiIiIiIiIiISLMZDNC7N+zeDUeONBxXWWm6rkTkRmN1AcbFyg4REREREREREREREREREZFLRUXBf/2XqciiIU5OpjiRG43VBRjBwcEMGDCApKQk4uLirG47IyIiIiIiIiIiIiIiIiIiNyYfH5g6tbWzuLkZjUairoEKl9jYWLy8vFo7javKztoFX3zxBT179mTWrFl07NiRX//61+zdu7clchMRERERERERERERERERERErXEsFGIMGDWrtNK4qqwswbr/9dlJTU/n+++9Zt24dxcXF9OvXj27dupGamsqJEydaIk8RERERERERERERERERERGRa5bVBRgXOTg4cP/995OZmckf/vAHCgoKmD17NrfeeisJCQkUFRXZMk8RERERERERERERERERERGRa9YVF2D8/e9/55FHHsHf35/U1FRmz55NQUEB27dv5/vvv+eXv/ylLfMUERERERERERERERERERERuWY5WLsgNTWVdevW8dVXXzFy5Ej+9Kc/MXLkSOzsTLUcnTt35pVXXiEsLMzmyYqIiIiIiIiIiIiIiIiIiIhci6wuwFi9ejWJiYk89NBDdOzYsd6YgIAA1q5d2+zkRERERERERERERERERETk+nb+PLRp09pZiLQ8qwswjhw58rMxTk5O/L//9/+uKCEREREREREREREREREREbkxFBXByy/Db34D/v6tnY1Iy7KzdsG6det466236oy/9dZbrF+/3iZJiYiIiIiIiIiIiIiIiIjI9e+f/4SvvjL9KlfHoEGDSE9Pb9UcjEYjycnJV+2s2NhYm8deCasLMH7/+9/Trl27OuN+fn4sWbLE6gRWrVpF586dcXZ2pnfv3uTm5jYa/9JLLxEeHo6Liwtdu3blT3/6k8X8mjVr6N+/P97e3nh7ezN06FD27t1rdV4iIiIiIiIiIiIiIiIiInLlamvh73+H4mL4/HPT+5ZWXFzM9OnTue2222jTpg2dOnVi9OjRZGVlWcTt3LmTkSNH4u3tjbOzM5GRkaSkpFBdXW0RZzAYcHZ25ptvvrEYj42NxWg0AjB69GiGDh1abz67du3CYDCwb98+q5/l7bffZtiwYbRv3x4PDw/uuece/va3v1m9z+WOHj2KwWAgLy+v3vn09HQMBgPh4eF15jIzMzEYDAQFBTU7D1tZsWJFqxecXGR1AcY333xD586d64wHBgZy7Ngxq/bKyMjgscce48knn2T//v3079+fe++9t8F9Vq9ezdy5c0lOTuZf//oXCxcuZOrUqbz77rvmmJycHCZMmEB2dja7du0iICCAmJgYvv/+e+seVERERERERERERERERERErlhxMRw5Ap06mX79z39a9ryjR4/Su3dvtm/fzrJlyzhw4AAffvghgwcPZurUqea4zZs3M3DgQG699Vays7M5dOgQM2bMYPHixYwfP57ayypFDAYDCxYsaPDcpKQktm/fXqdIAyAtLY2oqCh69epl9fN8/PHHDBs2jPfff5/PP/+cwYMHM3r0aPbv32/1XtZyc3Pj+PHj7Nq1y2I8LS2NgICAFj/fGp6ennh5ebV2GsAVFGD4+fnxj3/8o874F198ga+vr1V7paamkpSUxOTJkwkPD2f58uV06tSJ1atX1xv/v//7v/z6178mPj6e2267jfHjx5OUlMQf/vAHc8yf//xnHnnkEaKioggLC2PNmjXU1NTUqWgSERERERERERERERERERHb+OEH0zUjl7527oRTpyAoCEpLTe8vj/nhB9vl8Mgjj2AwGNi7dy9xcXGEhobSrVs3Zs6cye7duwE4d+4cU6ZMYcyYMbz66qtERUURFBTE5MmTWb9+PRs3biQzM9Ni3+nTp/P6669z4MCBes8dNWoUfn5+dbowlJWVkZGRQVJS0hU9z/Lly3niiSfo06cPISEhLFmyhJCQEIsGBS3FwcGBiRMnkpaWZh777rvvyMnJYeLEic3ev7KykmnTpuHv74+zszNBQUEsXboUgFmzZjF69Ghz7PLlyzEYDLz33nvmsa5du/LKK68Ada8V2bhxI5GRkbi4uODr68vQoUM5d+6cxfnPP/88/v7++Pr6MnXqVC5cuNDsZwJwsHbB+PHjefTRR2nbti0DBgwAYMeOHcyYMYPx48c3eZ/Kyko+//xzfve731mMx8TEsHPnznrXnD9/HmdnZ4sxFxcX9u7dy4ULF3B0dKyzpqysjAsXLuDj49NgLufPn+f8+fPm96dPn27yc4iIiIiIiIiIiIiIiIiI3OyysuCjj0wFF5dydgZ7e9Ovb75pOefpCUOHQnx8888/efIkH374IYsXL8bNza3O/MUOCVu3bqWkpITZs2fXiRk9ejShoaFs2LCB+EuSio6O5quvvmLu3Ln89a9/rbPOwcGBhIQE0tPTWbBgAQaDAYC33nqLyspKJk2aZI51d3dv9Dn69+/PBx98UO9cTU0NZ86cafSzb1tKSkpiwIABrFixAldXV9LT0xkxYgQdOnRo9t4vvPAC77zzDpmZmQQEBPDtt9/y7bffAjBo0CDWrl1LTU0NdnZ27Nixg3bt2rFjxw7uu+8+iouLOXz4MAMHDqyzb1FRERMmTGDZsmXcf//9nDlzhtzcXIuuJtnZ2fj7+5OdnU1+fj7x8fFERUUxZcqUZj+X1QUYzz77LN988w1DhgzBwcG0vKamhoSEBJYsWdLkfX744Qeqq6vr/OZ06NCB4uLietcMHz6c1157jdjYWHr16sXnn39OWloaFy5c4IcffsDf37/Omt/97nfccsstDd65A7B06VIWLlzY5NxFREREREREREREREREROQnv/wlGAzw179CRQUEB5sKL+z+706G4GCoqYHqasjPNxVkxMTAJY0OmiU/P5/a2lrCwsIajTt8+DAA4eHh9c6HhYWZYy61dOlSunfvTm5uLv37968zn5iYyHPPPUdOTg6DBw8GTNd1jB07Fm9vb3NcXl5eo/m5uLg0OJeSksK5c+cYN25co3vYSlRUFF26dGHjxo08+OCDpKenk5qaytdff93svY8dO0ZISAj9+vXDYDAQGBhonhswYABnzpxh//799OrVi9zcXGbPns3bb78NmAooOnToUO/vdVFREVVVVYwdO9a8Z2RkpEWMt7c3K1euxN7enrCwMO677z6ysrJapwDDycmJjIwMFi1axBdffIGLiwuRkZEW3xBrXKz+uai2trbO2EXz58+nuLiYu+++m9raWjp06IDRaGTZsmXY29vXiV+2bBkbNmwgJyenTueMS82dO5eZM2ea358+fZpOnTpd0fOIiIiIiIiIiIiIiIiIiNxsnJ3hV7+CkBD485/h4EFT0cXFZhQGg6kwo6AAbrsN/vu/oUcP07gtXOxw0NBnzQ3F1zde3x4REREkJCQwZ86cem90CAsLIzo6mrS0NAYPHkxBQQG5ubls3brVIi44OLhJ+V1uw4YNJCcn85e//AU/P78r2uNKJCYmsm7dOgICAjh79iwjR45k5cqVzd7XaDQybNgwunbtyogRIxg1ahQxMTEAeHp6EhUVRU5ODo6OjtjZ2fHrX/+ap59+mjNnzpCTk1Nv9wuAHj16MGTIECIjIxk+fDgxMTHExcVZFMF069bNor7A39+/wetlrGV3pQtDQ0P51a9+xahRo66o+KJdu3bY29vX6XZx/PjxBluWuLi4kJaWRllZGUePHuXYsWMEBQXRtm1b2rVrZxH7/PPPs2TJErZu3Ur37t0bzaVNmzZ4eHhYvEREREREREREREREREREpOkMBujZE373OwgPh+++s5z/7jvT+O9+B1FRtiu+AAgJCcFgMHDw4MFG40JDQwEajDt06BAhISH1zi1cuJD9+/ezZcuWeueTkpLYtGkTp0+fZt26dQQGBjJkyBCLGHd390Zf9957b519MzIySEpKIjMzs9GbH1rCpEmT2L17N8nJySQkJJhvyWiuXr16UVhYyKJFiygvL2fcuHHExcWZ5wcNGkROTg47duxg4MCBeHt7061bNz799FNycnIYNGhQvfva29uzbds2PvjgAyIiInjxxRfp2rUrhYWF5hhHR0eLNQaDgZqaGps81xV9d7777jveeecdjh07RmVlpcVcampqk/ZwcnKid+/ebNu2jfvvv988vm3bNn75y182utbR0ZFbb70VgDfffJNRo0ZhZ/dTLclzzz3Hs88+y9/+9jfuuOOOpj6WiIiIiIiIiIiIiIiIiIg0k50d/PAD+PhAZSUUF0PHjqb3J078dC2JLfn4+DB8+HBeeuklHn30Udwutt74P6WlpXh5eRETE4OPjw8pKSlER0dbxLzzzjscOXKERYsW1XtGp06dmDZtGvPmzaNLly515seNG8eMGTN44403WL9+PVOmTKnTTcPaK0g2bNhAYmIiGzZs4L777mt0bUvw8fFhzJgxZGZm8vLLL9t0bw8PD+Lj44mPjycuLo4RI0Zw8uRJfHx8GDRoEGvXrsXBwcFcdDJw4EDefPNNDh8+3GAHDDAVVPTt25e+ffuyYMECAgMD2bx5s8WtGC3F6gKMrKwsxowZQ+fOnfnqq6+4/fbbOXr0KLW1tfTq1cuqvWbOnMmDDz7IHXfcwT333MOrr77KsWPH+M1vfgOYrgb5/vvv+dOf/gSY7uPZu3cvd911Fz/++COpqan885//ZP369eY9ly1bxvz583njjTcICgoyd9i4WDEkIiIiIiIiIiIiIiIiIiIt58sv4eRJU9HFwYPg7w+HDsEvfgE//mga69vX9ueuWrWK6Oho7rzzTp555hm6d+9OVVUV27ZtY/Xq1Rw8eBA3NzdeeeUVxo8fz8MPP8y0adPw8PAgKyuLxx9/nLi4OMaNG9fgGXPnzmXNmjUUFhYSHx9vMefu7k58fDzz5s3j1KlTGI3GOuutuYJkw4YNJCQksGLFCu6++27zZ98uLi54eno2eZ+GfPXVV3XGIiIi6oylp6ezatUqfH19m33mRX/84x/x9/cnKioKOzs73nrrLTp27IiXlxcAAwYM4MyZM7z77rs8++yzgKkrxgMPPED79u3rzRNgz549ZGVlERMTg5+fH3v27OHEiROEh4fbLPfGWF1bNHfuXGbNmsU///lPnJ2d2bRpE99++y0DBw7kV7/6lVV7xcfHs3z5cp555hmioqL4+OOPef/9981XmhQVFXHs2DFzfHV1NSkpKfTo0YNhw4ZRUVHBzp07CQoKMsesWrWKyspK4uLi8Pf3N7+ef/55ax9VRERERERERERERERERESs9MUXpkKLkydh9GiYPx9GjTK9P3kSfqYJxBXr3Lkz+/btY/DgwcyaNYvbb7+dYcOGkZWVxerVq81xcXFxZGdn8+233zJgwAC6du1KamoqTz75JG+++WadrhWX8vHxYc6cOVRUVNQ7n5SUxI8//sjQoUMJCAho1vO88sorVFVVMXXqVIvPvmfMmNGsfS8aP348PXv2tHj9+9//rhPn4uJi0+ILMBWr/OEPf+COO+6gT58+HD16lPfff99884Wnpyc9e/bEx8fHXGzRv39/ampqGu1+4eHhwccff8zIkSMJDQ3lqaeeIiUlpd6rXVqCoba2ttaaBW3btiUvL48uXbrg7e3NJ598Qrdu3fjiiy/45S9/ydGjR1so1avn9OnTeHp6curUKTw8PFo7HRERERERERERERERERERm2ns89CKigoKCwvp3Lkzzs7OVu996hTMmwcGA0yYANHRpq9ra+HTT+HNN01fL1kCNmjiIPUYNGgQRqOx3g4cV4vRaCQoKIjk5ORWy8FWrPk7YfUVJG5ubpw/fx6AX/ziFxQUFNCtWzcAfvjhhytIV0REREREREREREREREREbgQGA9xzDwweDLfcYjnerx8EBUFOjum9yI3G6gKMu+++m08//ZSIiAjuu+8+Zs2axYEDB3j77be5++67WyJHERERERERERERERERERG5Dnh4wH//d8Pzt97a+LzI9czqAozU1FTOnj0LQHJyMmfPniUjI4Pg4GD++Mc/2jxBERERERERERERERERERERaRqj0UhUVFSr5hAbG4uXl1er5tAarCrAqK6u5ttvv6V79+4AuLq6smrVqhZJTERERERERERERERERERERKxjNBpbOwViY2NbO4VWYWdNsL29PcOHD6e0tLSF0hERERERERERERERERERERG5/lhVgAEQGRnJ119/3RK5iIiIiIiIiIiIiIiIiIiIiFyXrLqCBGDx4sXMnj2bRYsW0bt3b9zc3CzmPTw8bJaciIjIDe/cOTh/3vp1bdrAZf8MFhERERERERERERERkdZjdQHGiBEjABgzZgwGg8E8Xltbi8FgoLq62nbZiYiI3MgqK+HZZ+H4cevX+vnBM8+Ao6Pt8xIRERERERERERERERGrWV2AkZ2d3RJ5iIiI3HycnKBLFzhwwPTrJYWNDaqthYICuOceFV+IiIiIiIiIiIiIyDWttraW8qpyKqsrcbJ3wsXBxeKH/EVuNFYXYAwcOLAl8hAREbk5DR0Kn34K1dXQrt3Px//wA7Rvb1onIiIiIiIiIiIiInINqqiqIK84j0+OfUL+yXyqaqpwsHMg2CeYfgH9iOoYhbODc2unecMaNGgQRqMRo9HYqnkYjUaCgoJITk62+b6lpaVs2bLFprG2YGftgo8//rjRl4iIiFghKAj69oV//9vU3aIxtbWmuL59TetERERERERERERERK4x+SfzWbhjIam7Uvns+8+wM9jh5uiGncGOz77/jNRdqSzcsZD8k/ktcn5xcTHTp0/ntttuo02bNnTq1InRo0eTlZVlEbdz505GjhyJt7c3zs7OREZGkpKSQnV1tUWcwWDA2dmZb775xmI8NjbWXOAwevRohjbwg5O7du3CYDCwb9++K3qeHTt20Lt3b5ydnbntttt4+eWXr2ify23atIm77roLT09P2rZtS7du3Zg1a5Z5Pj09HYPBQHh4eJ21mZmZGAwGglrps4oVK1aQnp7eKmf/HKs7YAwaNKjO2KVtYi7/AykiIiI/42IXjJKSxrtglJSAp6e6X4iIiIiIiIiIiIjINSn/ZD4rdq+g+FwxIT4hONk7Wcz7uflRWV3JkZNHWLFnBTPumkGwT7DNzj969Ch9+/bFy8uLZcuW0b17dy5cuMDf/vY3pk6dyqFDhwDYvHkz48aN46GHHiI7OxsvLy8++ugjnnjiCXbv3m0uMLjIYDCwYMEC1q9fX++5SUlJjB07lm+++YbAwECLubS0NKKioujVq5fVz1NYWMjIkSOZMmUKr7/+Op9++imPPPII7du354EHHrB6v4s++ugjxo8fz5IlSxgzZgwGg4Evv/yyTpGKm5sbx48fZ9euXdxzzz0WzxQQEHDF5zeXp6dnq539c6zugPHjjz9avI4fP86HH35Inz592Lp1a0vkKCIicmNrShcMdb8QERERERERERERkWtYRVUFa/evpfhcMRHtIuoUX1zkZO9ERLsIis8Ws3b/WiqqKmyWwyOPPILBYGDv3r3ExcURGhpKt27dmDlzJrt37wbg3LlzTJkyhTFjxvDqq68SFRVFUFAQkydPZv369WzcuJHMzEyLfadPn87rr7/OgQMH6j131KhR+Pn51enKUFZWRkZGBklJSVf0PC+//DIBAQEsX76c8PBwJk+eTGJiIs8///wV7XfRX//6V/r168fjjz9O165dCQ0NJTY2lhdffNEizsHBgYkTJ5KWlmYe++6778jJyWHixInNygFMBTMGg4HMzEz69++Pi4sLffr04fDhw3z22WfccccduLu7M2LECE6cOGFeZzQaiY2NNb/fuHEjkZGRuLi44Ovry9ChQzl37pzFWc8//zz+/v74+voydepULly40Oz862N1AYanp6fFq127dgwbNoxly5bxxBNPtESOIiIiN76hQ03dLUpK6p+/AbtflJSUUFRUZH6VNPTsN4iSshKKzhSZXyVlN/bzNuh8CZQX/fQ6f5N+H0RERERERERERG4wecV5FJwsIMQnxKJ7RH0MBgPB3sEUnCzgi+IvbHL+yZMn+fDDD5k6dSpubm515r28vADYunUrJSUlzJ49u07M6NGjCQ0NZcOGDRbj0dHRjBo1irlz59Z7toODAwkJCaSnp1N7yQ9avvXWW1RWVjJp0iTzmLu7e6Ove++91xy7a9cuYmJiLM4aPnw4f//735tVQNCxY0f+9a9/8c9//vNnY5OSksjIyKCsrAwwXU0yYsQIOnTocMXnX+7pp5/mqaeeYt++fTg4ODBhwgSeeOIJVqxYQW5uLgUFBSxYsKDetUVFRUyYMIHExEQOHjxITk4OY8eOtfh9yM7OpqCggOzsbNavX096enqLXWFi9RUkDWnfvj1fffWVrbYTERG5uVzsgvH+++DrC5f+y+nF7hcjR94w3S9KSkpYtGgRpaWl5jEvLy/mz5+Pr69v6yXWQkrKSlj08SJKK0rNY17OXswfMB9f1xvveRt0vgQOLIILpT+NOXpB5HxocxN9H0RERERERERERG4wtbW1fHLsEwwYGux8cbk2Dm0wYCD3WC533nLnzxZt/Jz8/Hxqa2sJCwtrNO7w4cMAhIeH1zsfFhZmjrnU0qVL6d69O7m5ufTv37/OfGJiIs899xw5OTkMHjwYMF3VMXbsWLy9vc1xeXl5jebn4uJi/rq4uLhOoUOHDh2oqqrihx9+wN/fv9G9GjJ9+nRyc3OJjIwkMDCQu+++m5iYGCZNmkSbNm0sYqOioujSpQsbN27kwQcfJD09ndTUVL7++usrOrs+s2fPZvjw4QDMmDGDCRMmkJWVRd++fQFTEUhDBRNFRUVUVVUxduxY8/UvkZGRFjHe3t6sXLkSe3t7wsLCuO+++8jKymLKlCk2e4aLrC7A+Mc//mHxvra2lqKiIn7/+9/To0cPmyUmIiJy0xk6FD791NTtol27n8ZvwO4XlZWVlJaW4uLigqurK2VlZZSWllJZWdnaqbWIyupKSitKcXFwwdXRlbILZZRWlFJZfWM+b4NqKk3FF/YuYO8K1WWm9zU32fdBRERERERERETkBlNeVU7+yXyrf+DM19WX/JP5lFeV4+ro2qwcLnY8aGohR20DV4LX1tbWu0dERAQJCQnMmTOHnTt31pkPCwsjOjqatLQ0Bg8eTEFBAbm5uWzdutUiLjg4uEn5XXR5LtY+Z33c3Nx47733zF0hdu/ezaxZs1ixYgW7du3C1dXy9yIxMZF169YREBDA2bNnGTlyJCtXrrzi8y/XvXt389cXC04uLaLo0KEDx48fr3dtjx49GDJkCJGRkQwfPpyYmBji4uIsil66deuGvb29+b2/v3+D18k0l9VXkERFRdGzZ0+ioqLMX48cOZLKykrWrl3bEjmKiIjcHC52wfj3v01dL+Cn7hd9+94w3S8u5erqStu2bev8y9yNytXRlbZt2jb7/0hc9+xdwbGt6VcRERERERERERG57lVWV1JVU4WjnaNV6xzsHKiqqbLJD6uFhJiuPjl48GCjcaGhoQANxh06dIiQkJB65xYuXMj+/fvZsmVLvfNJSUls2rSJ06dPs27dOgIDAxkyZIhFjDVXkHTs2JHi4mKL9cePH8fBwcEm3aS7dOnC5MmTee2119i3bx9ffvklGRkZdeImTZrE7t27SU5OJiEhAQcHm120AYCj409/bi4Wllw+VlNTU+9ae3t7tm3bxgcffEBERAQvvvgiXbt2pbCwsN79f26/5rL6O3NpogB2dna0b98eZ2dnmyUlIiJy07q8C8YN2P1CRERERERERERERG4sTvZOONg5cKHmglXrqmqqcLBzaPK1JY3x8fFh+PDhvPTSSzz66KO4ublZzJeWluLl5UVMTAw+Pj6kpKQQHR1tEfPOO+9w5MgRFi1aVO8ZnTp1Ytq0acybN48uXbrUmR83bhwzZszgjTfeYP369UyZMqVOpwprriC55557ePfddy3mt27dyh133FGnqKC5goKCcHV15dy5c3XmfHx8GDNmDJmZmbz88ss2PdcWDAYDffv2pW/fvixYsIDAwEA2b97MzJkzr3ouVhdgXLw3RURERFrAxS4Y778Pvr6m7hcjR1733S9KSkosrhc5ceJEvXGXjzs5OdmkivdqKykrsajYPlHWwPNeNu5k72R1i75r2vkSy+tFKur/PtQZt3OCNjfQ90FEREREREREROQG5+LgQrBPMJ99/xl+bn5NXldSVkKfW/rg4uDy88FNsGrVKqKjo7nzzjt55pln6N69O1VVVWzbto3Vq1dz8OBB3NzceOWVVxg/fjwPP/ww06ZNw8PDg6ysLB5//HHi4uIYN25cg2fMnTuXNWvWUFhYSHx8vMWcu7s78fHxzJs3j1OnTmE0Guust+YKkt/85jesXLmSmTNnMmXKFHbt2sXatWvZsGFDk/eoT3JyMmVlZYwcOZLAwEBKS0t54YUXuHDhAsOGDat3TXp6OqtWrbrm/pv9nj17yMrKIiYmBj8/P/bs2cOJEycIDw9vlXysLsB49NFHCQ4O5tFHH7UYX7lyJfn5+SxfvtxWuYmIiNycLnbBOHz4huh+UVJSwqJFiygtLbUYLy0txc/P9C/i9vb2lJaWkpqaahHj5eXF/Pnzr7l/oWtMSVkJiz5eRGlFqcV4aUWp+f942NvZU1pRSuquy57X2Yv5A+bfGEUY50vgwCK4UGo5XlkKbf7v/4AZ7E3vD1l+H3D0gsj5KsIQERERERERERG5ThgMBvoF9GPv93uprK5sUkeL81XnqaWW/gH963SJuFKdO3dm3759LF68mFmzZlFUVET79u3p3bs3q1evNsfFxcWRnZ3NkiVLGDBgAOXl5QQHB/Pkk0/y2GOPNZqPj48Pc+bMYd68efXOJyUlsXbtWmJiYggICGj287z//vv89re/5aWXXuIXv/gFL7zwAg888ECz9h04cCAvvfQSCQkJ/Oc//8Hb25uePXuydetWunbtWu8aFxcXi+4c1woPDw8+/vhjli9fzunTpwkMDCQlJcXiKperyVBbe/GS+aa55ZZbeOedd+jdu7fF+L59+xgzZgzfffedTRNsDadPn8bT05NTp07h4eHR2umIiMjN6LXX4O23YexYmDy5tbNplqKiIubOnYuLiwuurq7mcXt7e4v3ZWVlVFdXW7wvLy9n6dKl+Pv7X9Wcm6PoTBFzs+bi4uCCq+Mlz2tnb/G+7EIZ1TXVFu/Lq8pZOmQp/m2vn+dtUHkR5M0Fexew/+m5MdiDwyXvq8qg9qfvA9VlUF0OUUvB5Qb4PoiIiIiIiIiIiFyDGvs8tKKigsLCQjp37oyzs3OT96yoqmDhjoXkn8wnol1Eo0UMtbW1fPnDlwT7BPP0wKdxdmj6OfLzBg0ahNForLcDx9VkNBoJCgoiOTm5VfNoLmv+TljdAaOkpARPT8864x4eHvzwww/WbiciIiL1GTYMvv3W9OsNwtXVlbZt2zY6f7ny8vKWTKlFuTq60rZNI8/rWM/zVl2/z9sge1dwbPj7YFGMcVH1Dfh9EBERERERERERucE5OziT1DOJFXtWmIorvINp49CmTtz5qvPk/5hPR/eOTO45WcUXckOxugAjODiYDz/8kGnTplmMf/DBB9x22202S0xEROSmFhgITz0Fjo6tnYnNlJWVWbxvSgeM61nZhcuetwkdMG5I1Zc9V1M6YIiIiIiIiIiIiMh1KdgnmBl3zWDt/rUUnCzAgAFfV18c7ByoqqmipKyEWmoJ9glmcs/JdPHp0topi9iU1QUYM2fOZNq0aZw4cYL/+q//AiArK4uUlBSWL19u6/xERERuXjdI8YWTkxNeXl6UlpZadLQoLS0lNDQUV1dXysrKOHz4MF5eXhZrvby8cHL6+bsCryVO9k54OXtRWlFq0dGitKKUUN9QXB1dKbtQxuGSw3g5e1ms9XL2atLdiNcFOydw9IILpZYdLSpLoW2oqQijqgzOHAYnL8u1jl6m9SIiIiIiIiIiInLduXityBfFX5B7LJf8k/mUXSjDwc6BPrf0oX9Af3p07KHOFy3IaDQSFRXV2mkQGxtb57/73+gMtbW1tdYuWr16NYsXL+bf//43gPneloSEBJsn2Boau/NIRERErFdSUkJlZaX5/YkTJ0hNTcXX15e2bdty5swZSkpKmDlzJu3btzfHOTk54evr2xopN0tJWQmV1Zc8b9kJUnel4uviS9s2bTlz/gwl5SXMvGcm7V0veV57J3xdr7/nbdD5Eqj56ftAxQk4lApOvqZrSS6cgcoSCJsJzj99H7BzgjY30PdBRERERERERETkGtPY56EVFRUUFhbSuXNnnJ2bVyRRW1tLeVU5ldWVONk74eLggsFgaNaeIlebNX8nrO6AAfA///M//M///A8nTpzAxcUFd3f3K0pUREREbg5NLaJo3749/v7+LZxNy2tqEUV71/b4t73+n7dBTS2icG4PLjfw90FEREREREREROQmZTAYcHV0tbieWeRGZnUBRmFhIVVVVYSEhFj8hOqRI0dwdHQkKCjIlvmJiIiIiIiIiIiIiIiIiIiIXPPsrF1gNBrZuXNnnfE9e/ZgNBptkZOIiIjcJMrKyjhz5gxlZWWtncpVUXahjDPnz1B24eZ43gZVl5muH6m+yb8PIiIiIiIiIiIiInJDsboAY//+/fTt27fO+N13301eXp4tchIREZEbnJOTE15eXpSXl1NSUkJ5eTleXl44OTm1dmotwsneCS9nL8qryikpL6G8qhwvZy+c7G/M522QnRM4ekF1OVSWmH519DKNi4iIiIiIiIiIiIhc56y+gsRgMHDmzJk646dOnaK6utomSYmIiMiNzdfXl/nz51NZWWkec3JywtfXtxWzajm+rr7MHzCfyupLntfeCV/XG/N5G9TGFyLnQ81P3wfsnEzjIiIiIiIiIiIicsMpKSvRfxeVm4rVBRj9+/dn6dKlbNiwAXt7ewCqq6tZunQp/fr1s3mCIiIicmO6UYstGqL/U/F/VGwhIiIiIiIiIiJyUygpK2HRx4sorSg1j3k5ezF/wHz991K5YVldgLFs2TIGDBhA165d6d+/PwC5ubmcPn2a7du32zxBERERERERERERERERERG5vlRWV1JaUYqLgwuujq6UXSijtKLUoiOGtIxBgwZhNBoxGo2tloPRaCQoKIjk5GSb71taWsqWLVtsGmsrdtYuiIiI4B//+Afjxo3j+PHjnDlzhoSEBA4dOsTtt9/eEjmKiIiIiIiIiIiIiIiIiMh1yNXRlbZt2uLq6HpVzisuLmb69OncdttttGnThk6dOjF69GiysrIs4nbu3MnIkSPx9vbG2dmZyMhIUlJSqK6utogzGAw4OzvzzTffWIzHxsaaCxxGjx7N0KFD681n165dGAwG9u3bd0XPs2PHDnr37o2zszO33XYbL7/88hXtc6mjR49iMBjIy8urdz49PR2DwUB4eHiduczMTAwGA0FBQc3O40qsWLGC9PT0Vjm7KawuwAD4xS9+wZIlS3jvvffYuHEjCxYsoLa2luXLl9s4PRERERERERERERERERERkZ939OhRevfuzfbt21m2bBkHDhzgww8/ZPDgwUydOtUct3nzZgYOHMitt95KdnY2hw4dYsaMGSxevJjx48dTW1trsa/BYGDBggUNnpuUlMT27dvrFGkApKWlERUVRa9evax+nsLCQkaOHEn//v3Zv38/8+bN49FHH2XTpk1W72UtNzc3jh8/zq5duyzG09LSCAgIaPHzG+Lp6YmXl1ernf9zrqgA46La2lr+9re/MW7cOH7xi1+wePFiW+UlIiIiIiIiIiIiIiIiIiLXiZKyEorOFJlfJ8pO1Bt3ouyERVxJWYnNcnjkkUcwGAzs3buXuLg4QkND6datGzNnzmT37t0AnDt3jilTpjBmzBheffVVoqKiCAoKYvLkyaxfv56NGzeSmZlpse/06dN5/fXXOXDgQL3njho1Cj8/vzqdGcrKysjIyCApKemKnufll18mICCA5cuXEx4ezuTJk0lMTOT555+/ov2s4eDgwMSJE0lLSzOPfffdd+Tk5DBx4sRm73+xC0dmZib9+/fHxcWFPn36cPjwYT777DPuuOMO3N3dGTFiBCdO/PRnyWg0Ehsba36/ceNGIiMjcXFxwdfXl6FDh3Lu3DmLs55//nn8/f3x9fVl6tSpXLhwodn5N8ThShYdPXqUtLQ00tPT+f7775k4cSLvvfcegwcPtnV+IiIiIiIiIiIiIiIiIiJyDSspK2HRx4sorSi1GC+tKMXPzQ8Aezt7SitKSd2VahHj5ezF/AHz8XX1bVYOJ0+e5MMPP2Tx4sW4ubnVmb/YNWHr1q2UlJQwe/bsOjGjR48mNDSUDRs2EB8fbx6Pjo7mq6++Yu7cufz1r3+ts87BwYGEhATS09NZsGABBoMBgLfeeovKykomTZpkjnV3d2/0Ofr3788HH3wAmK4viYmJsZgfPnw4a9eu5cKFCzg6Oja6V3MlJSUxYMAAVqxYgaurK+np6YwYMYIOHTrY7Iynn36a5cuXExAQQGJiIhMmTMDDw8N85rhx41iwYAGrV6+us7aoqIgJEyawbNky7r//fs6cOUNubq5FB5Ps7Gz8/f3Jzs4mPz+f+Ph4oqKimDJlis2e4VJNLsA4f/48b7/9Nq+99ho7d+7k3nvvJTU1lQkTJjB37lwiIiJaJEEREREREREREREREREREbl2VVZXUlpRiouDC66OruZxPzc/83tXR1dCfUOprqk2z5ddKKO0opTK6spm55Cfn09tbS1hYWGNxh0+fBiA8PDweufDwsLMMZdaunQp3bt3Jzc3l/79+9eZT0xM5LnnniMnJ8fcuCAtLY2xY8fi7e1tjsvLy2s0PxcXF/PXxcXFdYodOnToQFVVFT/88AP+/v6N7tVcUVFRdOnShY0bN/Lggw+Snp5OamoqX3/9tc3OmD17NsOHDwdgxowZTJgwgaysLPr27QuYikAu7yxyUVFREVVVVYwdO5bAwEAAIiMjLWK8vb1ZuXIl9vb2hIWFcd9995GVldX6BRi33HILERER/Pd//zcbN240/yGZMGFCiyQmIiIiIiIiIiIiIiIiIiLXD1dHV9q2advo/OXKq8ptcvbFrgcXu080Nb6+8fr2iIiIICEhgTlz5rBz584682FhYURHR5OWlsbgwYMpKCggNzeXrVu3WsQFBwc3Kb+LLs/F2udsrsTERNatW0dAQABnz55l5MiRrFy50mb7d+/e3fz1xWKTS4soOnTowPHjx+td26NHD4YMGUJkZCTDhw8nJiaGuLg4i4KXbt26YW9vb37v7+/f4FUytmDX1MDq6moMBgMGg8EiQRERERERERERERERERERkbILZZw5f8b8KrtQZtV8c4SEhGAwGDh48GCjcaGhoQANxh06dIiQkJB65xYuXMj+/fvZsmVLvfNJSUls2rSJ06dPs27dOgIDAxkyZIhFjLu7e6Ove++91xzbsWNHiouLLdYfP34cBwcHfH2bd2VLU02aNIndu3eTnJxMQkICDg5N7vHQJJdeo3KxqOTysZqamnrX2tvbs23bNj744AMiIiJ48cUX6dq1K4WFhfXu/3P72UKTCzCKiop4+OGH2bBhAx07duSBBx5g8+bNV62yRkRERERERERERERERERErj1O9k54OXtRXlVOSXmJ+XW45LC5yKLsQhmHSw5bzJdXlePl7IWTvVOzc/Dx8WH48OG89NJLnDt3rs58aWkpADExMfj4+JCSklIn5p133uHIkSMN3gLRqVMnpk2bxrx586iurq4zP27cOOzt7XnjjTdYv349Dz30UJ3P0/Py8hp9vfbaa+bYe+65h23btlms37p1K3fccUedwoKW4uPjw5gxY9ixYweJiYlX5UxrGAwG+vbtay6OcXJyYvPmza2WT5PLU5ydnZk0aRKTJk2ioKCAdevW8eijj1JVVcXixYsxGo3813/9l7pjiIiIiIiIiIiIiIiIiIjcRHxdfZk/YD6V1ZXmsRNlJ0jdlUp1jalQobqmGi9nL2beM5P2ru3NcU72Tvi62qabw6pVq4iOjubOO+/kmWeeoXv37lRVVbFt2zZWr17NwYMHcXNz45VXXmH8+PE8/PDDTJs2DQ8PD7Kysnj88ceJi4tj3LhxDZ4xd+5c1qxZQ2FhIfHx8RZz7u7uxMfHM2/ePE6dOoXRaKyz3porSH7zm9+wcuVKZs6cyZQpU9i1axdr165lw4YNTd6jMV999VWdsYiIiDpj6enprFq16qp13WiqPXv2kJWVRUxMDH5+fuzZs4cTJ04QHh7eajldUX+QLl268Oyzz/LMM8/wt7/9jbVr1zJq1Cjatm3LDz/8YOscRURERERERERERERERETkGtbUIor2ru3xb+vfIjl07tyZffv2sXjxYmbNmkVRURHt27end+/erF692hwXFxdHdnY2S5YsYcCAAZSXlxMcHMyTTz7JY4891ugtED4+PsyZM4d58+bVO5+UlMTatWuJiYkhICCg2c/z/vvv89vf/paXXnqJX/ziF7zwwgs88MADzdr3ovHjx9cZu/T6jotcXFxwcXGxyZm25OHhwccff8zy5cs5ffo0gYGBpKSkWFzjcrUZamtra22x0YkTJ/jf//1fZs6caYvtWtXp06fx9PTk1KlTeHh4tHY6IiIiIiIiIiIiIiIiIiI209jnoRUVFRQWFtK5c2ecnZ2v+IyiM0XMzZqLr4svbdu05cz5M5SUl7B0yNIWK8AQk0GDBmE0GuvtwHG1GI1GgoKCSE5ObrUcbMWavxNX1AGjPu3bt78hii9ERERERERERERERERERMQ2yi6UWfwqciOzWQGGiIiIiIiIiIiIiIiIiIgIgJO9E17OXpRWlFJeVQ6Al7MXTvZOrZyZSMtRAYaIiIiIiIiIiIiIiIiIiNiUr6sv8wfMp7K60jzmZO+Er6tvK2Z1czAajURFRbVqDrGxsXh5ebVqDq1BBRgiIiIiIiIiIiIiIiIiImJzKrZoHUajsbVTIDY2trVTaBUqwBARuV7V1kJ1OdRUgp0T2LuAwdDaWYmIiIiIiIiIiIiIiIjclKwuwKiuriY9PZ2srCyOHz9OTU2Nxfz27dttlpyIiNSjugJ+zIMTn8CZfKipAjsHaBsM7fuBdxTYO7d2liIiIiIiIiIiIiIiIiI3FasLMGbMmEF6ejr33Xcft99+Owb9tLWIyNVzJh8K1sKZAlO3CydfcHCDmgtQ8hn8sBfadoEuSaaCDBERERERERERERERERG5KqwuwHjzzTfJzMxk5MiRLZGPiIg05Ew+fLUCyovBPQTsnSznnf2guhLOHDHFdZ2hIgwRERERERERERERERGRq8TqAgwnJyeCg/WBnojIVVVdYep8UV4MHhGm7hf1sXcyzZ/+0hQf+bSuIxERERERERERERERkRZXUlZCZXWl1euc7J3wdfVtgYxErj6rCzBmzZrFihUrWLlypa4fERG5Wn7MM1074h7ScPHFRQYDuAeb4n/8AtrddVVSFBERERERERERERGRm1NJWQmLPl5EaUWp1Wu9nL2YP2C+ijBsaNCgQRiNRoxGY6vmYTQaCQoKIjk52eb7lpaWsmXLFpvG2oKdtQs++eQT/vznP9OlSxdGjx7N2LFjLV4iImJjtbVw4hNTYcXl1440xL6NKf5Ermm9iIiIiIiIiIiIiIhIC6msrqS0ohQXBxd8XXyb/HJxcKG0ovSKOmc0pLi4mOnTp3PbbbfRpk0bOnXqxOjRo8nKyrKI27lzJyNHjsTb2xtnZ2ciIyNJSUmhurraIs5gMODs7Mw333xjMR4bG2sucBg9ejRDhw6tN59du3ZhMBjYt2+f1c9SVFTExIkT6dq1K3Z2djz22GNW79GQTZs2cdddd+Hp6Unbtm3p1q0bs2bNMs+np6djMBgIDw+vszYzMxODwUBQUJDN8rHGihUrSE9Pb5Wzf47VBRheXl7cf//9DBw4kHbt2uHp6WnxEhERG6suhzP54GRl5aeTr2lddXnL5CUiIiIiIiIiIiIiInIJV0dX2rZp2+SXq6OrTc8/evQovXv3Zvv27SxbtowDBw7w4YcfMnjwYKZOnWqO27x5MwMHDuTWW28lOzubQ4cOMWPGDBYvXsz48eOpveyHWw0GAwsWLGjw3KSkJLZv316nSAMgLS2NqKgoevXqZfXznD9/nvbt2/Pkk0/So0cPq9c35KOPPmL8+PHExcWxd+9ePv/8cxYvXkxlpWUhjJubG8ePH2fXrl0W42lpaQQEBNgsH2t5enri5eXVauc3xuoCjHXr1jX6EhERG6uphJoqsHO0bp3BwbSuxnZVoyIiIiIiIiIiIiIiIteqRx55BIPBwN69e4mLiyM0NJRu3boxc+ZMdu/eDcC5c+eYMmUKY8aM4dVXXyUqKoqgoCAmT57M+vXr2bhxI5mZmRb7Tp8+nddff50DBw7Ue+6oUaPw8/Or05WhrKyMjIwMkpKSruh5goKCWLFiBQkJCTZthvDXv/6Vfv368fjjj9O1a1dCQ0OJjY3lxRdftIhzcHBg4sSJpKWlmce+++47cnJymDhxYrPzOHr0KAaDgczMTPr374+Liwt9+vTh8OHDfPbZZ9xxxx24u7szYsQITpw4YV5nNBqJjY01v9+4cSORkZG4uLjg6+vL0KFDOXfunMVZzz//PP7+/vj6+jJ16lQuXLjQ7PzrY3UBxkUnTpzgk08+4dNPP7V4WBERsTE7J7BzgBor/0FQW2VaZ9fEa0tERERERERERERERESuUydPnuTDDz9k6tSpuLm51Zm/2DFh69atlJSUMHv27Doxo0ePJjQ0lA0bNliMR0dHM2rUKObOnVvv2Q4ODiQkJJCenm7RPeOtt96isrKSSZMmmcfc3d0bfd17771X8vhW6dixI//617/45z//+bOxSUlJZGRkUFZWBpiuJhkxYgQdOnSwWT5PP/00Tz31FPv27cPBwYEJEybwxBNPsGLFCnJzcykoKGiwA0lRURETJkwgMTGRgwcPkpOTw9ixYy1+H7KzsykoKCA7O5v169eTnp7eYleYOFi74Ny5c0yfPp0//elP1NTUAGBvb09CQgIvvvgirq62bRMjInLTs3eBtsFQ8hk4+zV9XWUJ+PYxrRcREREREREREREREbmB5efnU1tbS1hYWKNxhw8fBiA8PLze+bCwMHPMpZYuXUr37t3Jzc2lf//+deYTExN57rnnyMnJYfDgwYDpqo6xY8fi7e1tjsvLy2s0PxeXlv9cZ/r06eTm5hIZGUlgYCB33303MTExTJo0iTZt2ljERkVF0aVLFzZu3MiDDz5Ieno6qampfP311zbLZ/bs2QwfPhyAGTNmMGHCBLKysujbty9gKgJpqGCiqKiIqqoqxo4dS2BgIACRkZEWMd7e3qxcuRJ7e3vCwsK47777yMrKYsqUKTZ7hous7oAxc+ZMduzYwbvvvktpaSmlpaX85S9/YceOHcyaNcvmCYqI3PQMBmjfD2probqJ14lUnzfFt+9vWi8iIiIiIiIiIiIiInIDu9jxwNDEz0Uu7ZBw+Xh9e0RERJCQkMCcOXPqXRcWFkZ0dLT5uo6CggJyc3NJTEy0iAsODm70dcsttzQp/+Zwc3PjvffeIz8/n6eeegp3d3dmzZrFnXfeae50canExETWrVvHjh07OHv2LCNHjrRpPt27dzd/fbGzxqVFFB06dOD48eP1ru3RowdDhgwhMjKSX/3qV6xZs4Yff/zRIqZbt27Y29ub3/v7+ze4X3NZXYCxadMm1q5dy7333ouHhwceHh6MHDmSNWvWsHHjxpbIUUREvKOgbRc4e8RUWNGY2lo4m2+K9+5xVdITERERERERERERERFpTSEhIRgMBg4ePNhoXGhoKECDcYcOHSIkJKTeuYULF7J//362bNlS73xSUhKbNm3i9OnTrFu3jsDAQIYMGWIRcy1cQXJRly5dmDx5Mq+99hr79u3jyy+/JCMjo07cpEmT2L17N8nJySQkJODgYPVFG41ydHQ0f32x+OXysYu3c1zO3t6ebdu28cEHHxAREcGLL75I165dKSwsrHf/n9uvuawuwCgrK6v3Phc/P796q2FERKTpSkqgqOinV0nJ/03YO0OXJHDpCKe/NHW4qE/1edO8S0foMtm0TkRERERERERERERE5Abn4+PD8OHDeemllzh37lyd+dLSUgBiYmLw8fEhJSWlTsw777zDkSNHmDBhQr1ndOrUiWnTpjFv3jyqq6vrzI8bNw57e3veeOMN1q9fz0MPPVSnm0ZeXl6jr9dee+0Knr75goKCcHV1rfd75+Pjw5gxY9ixY0edjh7XAoPBQN++fc0FMk5OTmzevLlVcrG6NOWee+7h6aef5k9/+hPOzqYP9srLy1m4cCH33HOPzRMUEblZlJTAokXwf//8B8DLC+bPB19foG0wdJ0BBWvhTIHpahEnXzA4QG0VVJaYul+0DTYVX7Tt0kpPIiIiIiIiIiIiIiIicvWtWrWK6Oho7rzzTp555hm6d+9OVVUV27ZtY/Xq1Rw8eBA3NzdeeeUVxo8fz8MPP8y0adPw8PAgKyuLxx9/nLi4OMaNG9fgGXPnzmXNmjUUFhYSHx9vMefu7k58fDzz5s3j1KlTGI3GOuuDg4Oteqa8vDwAzp49y4kTJ8jLy8PJyYmIiAir9rlUcnIyZWVljBw5ksDAQEpLS3nhhRe4cOECw4YNq3dNeno6q1atwtfX94rPbQl79uwhKyuLmJgY/Pz82LNnDydOnCA8PLxV8rG6AGPFihWMGDGCW2+9lR49emAwGMjLy8PZ2Zm//e1vLZGjiMhNobLSVHzh4gKurlBWZnpfWXlJUNtgiHwafvwCTuTCmXyoLgM7B/DtA+37m64dUecLERERERERERERERG5yXTu3Jl9+/axePFiZs2aRVFREe3bt6d3796sXr3aHBcXF0d2djZLlixhwIABlJeXExwczJNPPsljjz1Wp2vFpXx8fJgzZw7z5s2rdz4pKYm1a9cSExNDQEBAs5+pZ8+e5q8///xz3njjDQIDAzl69OgV7zlw4EBeeuklEhIS+M9//oO3tzc9e/Zk69atdO3atd41Li4uuLi4XPGZLcXDw4OPP/6Y5cuXc/r0aQIDA0lJSbmqV7lcylBbW1tr7aLy8nJef/11Dh06RG1tLREREUyaNOma/IZfidOnT+Pp6cmpU6fw8PBo7XRE5CZRVARz55q6XbRtC2fOmLpiLF0K/v71LKithepyqKkEOyewdzF1xRAREREREREREREREWlEY5+HVlRUUFhYSOfOnc03IvycojNFzM2ai6+LL23btG1yHmfOn6GkvISlQ5bi37a+D0PkSgwaNAij0VhvB46ryWg0EhQURHJycqvm0VzW/J2wugMGmKpbpkyZckXJiYiIjRgM4OAKuLZ2JiIiIiIiIiIiIiIiIiI3vSYVYLzzzjvce++9ODo68s477zQaO2bMGJskJiJyoyspsbxe5MSJ+uMuH3dyMnXJEBERERERERERERERuZaUXShr0XiRa12TCjBiY2MpLi7Gz8+P2NjYBuMMBgPV1dW2yk1E5IZVUgKLFkFpqeV4aSn4+Zm+trc3vU9NtYzx8oL581WEISIiIiIiIiIiIiIi1wYneye8nL0orSilvKrcqrVezl442Tu1UGY3J6PRSFRUVGunQWxsLF5eXq2dxlXVpAKMmpqaer+2hVWrVvHcc89RVFREt27dWL58Of37928w/qWXXmLlypUcPXqUgIAAnnzySRISEixiNm3axPz58ykoKKBLly4sXryY+++/36Z5i4g0R2WlqbjCxQVcL7lBxM/vp/eurhAaCpfWtZWVmdZd2jlDRERERERERERERESkNfm6+jJ/wHwqq63/AMPJ3glfV/3UqS0ZjcbWTgGg0eYON6omFWBc6k9/+hPx8fG0adPGYryyspI333yzTjFEYzIyMnjsscdYtWoVffv25ZVXXuHee+/lyy+/JCAgoE786tWrmTt3LmvWrKFPnz7s3buXKVOm4O3tzejRowHYtWsX8fHxLFq0iPvvv5/Nmzczbtw4PvnkE+666y5rH1dErkO1tbWUV5VTWV2Jk70TLg4uGAyG1k6rXq6u0LZt4/OXK7eucFRERERERERERERERKTFqYhCBAy1tbW11iywt7enqKgIv4s98v9PSUkJfn5+Vl1Bctddd9GrVy9Wr15tHgsPDyc2NpalS5fWiY+OjqZv374899xz5rHHHnuMv//973zyyScAxMfHc/r0aT744ANzzIgRI/D29mbDhg1Nyuv06dN4enpy6tQpPDw8mvw8ItK6KqoqyCvO45Njn5B/Mp+qmioc7BwI9gmmX0A/ojpG4ezg3NppAlBUBHPn1u2AYW9v+b6srG4HjPJyWLoU/P2vXr4iIiIiIiIiIiIiInLjaOzz0IqKCgoLC+ncuTPOztfG5yoircmavxNWd8Cora2t9yfJv/vuOzw9PZu8T2VlJZ9//jm/+93vLMZjYmLYuXNnvWvOnz9f54FcXFzYu3cvFy5cwNHRkV27dvHb3/7WImb48OEsX768wVzOnz/P+fPnze9Pnz7d5OcQkWtD/sl81u5fS8HJAgwY8HX1xc3RjQs1F/js+8/Y+/1euvh0IalnEsE+wa2dLk5O4OVluk7k0o4WpaWma0dcXU3FFocPm+Iu5eVlWi8iIiIiIiIiIiIiIiIi144mF2D07NkTg8GAwWBgyJAhODj8tLS6uprCwkJGjBjR5IN/+OEHqqur6dChg8V4hw4dKC4urnfN8OHDee2114iNjaVXr158/vnnpKWlceHCBX744Qf8/f0pLi62ak+ApUuXsnDhwibnLiLXlvyT+azYvYLic8WE+ITgZG9ZneDn5kdldSVHTh5hxZ4VzLhrRqsXYfj6wvz5UHnJVWgnTkBq6k8dL6qrTcUWM2dC+/Y/xTk5mdaLiIiIiIiIiIiIiIiIyLWjyQUYsbGxAOTl5TF8+HDc3d3Nc05OTgQFBfHAAw9YncDl3TQa6rABMH/+fIqLi7n77rupra2lQ4cOGI1Gli1bhr29/RXtCTB37lxmzpxpfn/69Gk6depk9bOIyNVXUVXB2v1rKT5XTES7iAb/rjvZOxHRLoIvf/iStfvX8vTAp1v9OpKmFlG0b6/rRkRERERERERERERE5Np2rvIc56vP/3zgZdrYt8HNya0FMhK5+ppcgPH0008DEBQURHx8fLPv+2nXrh329vZ1OlMcP368TgeLi1xcXEhLS+OVV17hP//5D/7+/rz66qu0bduWdu3aAdCxY0er9gRo06YNbdq0adbziEjryCvOo+BkASE+IY0WWoGpOCvYO5iCkwV8UfwFd91611XKUkRERERERERERERE5MZVWV3Jsx8/y/Fzx61e6+fmxzODn8HR3rEFMhO5uuysXfD//t//a3bxBZi6ZvTu3Ztt27ZZjG/bto3o6OhG1zo6OnLrrbdib2/Pm2++yahRo7CzMz3KPffcU2fPrVu3/uyeInL9qa2t5ZNjn2DAUOfakYa0cWiDAQO5x3Kpra1t4QyvTFkZnDlj+lVERERERERERERERORa52TvRBefLvzn3H9wd3KnbZu2P/tyd3LnP+f+QxefLiq+sLFBgwaRnp7eqjkYjUaSk5NbZN+Lt3fYMtZWrC7AqK6u5vnnn+fOO++kY8eO+Pj4WLysMXPmTF577TXS0tI4ePAgv/3tbzl27Bi/+c1vANPVIAkJCeb4w4cP8/rrr3PkyBH27t3L+PHj+ec//8mSJUvMMTNmzGDr1q384Q9/4NChQ/zhD3/go48+4rHHHrP2UUXkGldeVU7+yXx8XZt4l8f/8XX1Jf9kPuVV5S2U2ZVxcgIvLygvh5IS069eXqZxERERERERERERERGRa9nQ24bS3rU91bXVeDl7/eyruraa9q7tGXrbUJvmUVxczPTp07ntttto06YNnTp1YvTo0WRlZVnE7dy5k5EjR+Lt7Y2zszORkZGkpKRQXV1tEWcwGHB2duabb76xGI+NjcVoNAIwevRohg6t/zl27dqFwWBg3759Vj9LUVEREydOpGvXrtjZ2dnsM++jR49iMBjIy8urdz49PR2DwUB4eHiduczMTAwGA0FBQTbJxVorVqxo9eKSxlhdgLFw4UJSU1MZN24cp06dYubMmYwdOxY7OzurK1ji4+NZvnw5zzzzDFFRUXz88ce8//77BAYGAqY/UMeOHTPHV1dXk5KSQo8ePRg2bBgVFRXs3LnT4jc3OjqaN998k3Xr1tG9e3fS09PJyMjgrrt01YDIjaayupKqmioc7ayrinSwc6CqporK6soWyuzK+PrC/PmwdOlPr/nzTeMiIiIiIiIiIiIiIiLXsiCvIPoG9OXfZ/79s13Ia2tr+feZf9M3oC9BXkE2y+Ho0aP07t2b7du3s2zZMg4cOMCHH37I4MGDmTp1qjlu8+bNDBw4kFtvvZXs7GwOHTrEjBkzWLx4MePHj6+Tv8FgYMGCBQ2em5SUxPbt2+sUaQCkpaURFRVFr169rH6e8+fP0759e5588kl69Ohh9frmcHNz4/jx4+zatctiPC0tjYCAgKuay6U8PT3x8vJqtfN/jtUFGH/+859Zs2YNs2fPxsHBgQkTJvDaa6+xYMECdu/ebXUCjzzyCEePHuX8+fN8/vnnDBgwwDyXnp5OTk6O+X14eDj79++nrKyMU6dOsWXLFrp27Vpnz7i4OA4dOkRlZSUHDx5k7NixVuclItc+J3snHOwcuFBzwap1VTVVONg5NPnakqvJ1xf8/X96qfhCRERERERERERERESuF0NvG4pnG09KyksajSspL8GzjafNu1888sgjGAwG9u7dS1xcHKGhoXTr1o2ZM2eaP8s+d+4cU6ZMYcyYMbz66qtERUURFBTE5MmTWb9+PRs3biQzM9Ni3+nTp/P6669z4MCBes8dNWoUfn5+dTozlJWVkZGRQVJS0hU9T1BQECtWrCAhIQFPT88r2uNKOTg4MHHiRNLS0sxj3333HTk5OUycOLHZ+1/swpGZmUn//v1xcXGhT58+HD58mM8++4w77rgDd3d3RowYwYkTJ8zrLr9WZOPGjURGRuLi4oKvry9Dhw7l3LlzFmc9//zz+Pv74+vry9SpU7lwwbrPFq1hdQFGcXExkZGRALi7u3Pq1CnA9Ifqvffes212IiKNcHFwIdgnmJKyxv8hfrmSshKCfYJxcXBpocxERERERERERERERERuPk3pgtFS3S9OnjzJhx9+yNSpU3Fzc6szf7FrwtatWykpKWH27Nl1YkaPHk1oaCgbNmywGI+OjmbUqFHMnTu33rMdHBxISEggPT3d4rnfeustKisrmTRpknnM3d290de99957JY/fIpKSksjIyKCsrAwwNVAYMWIEHTp0sNkZTz/9NE899RT79u0zN4B44oknWLFiBbm5uRQUFDTYfaSoqIgJEyaQmJjIwYMHycnJYezYsRa/B9nZ2RQUFJCdnc369etJT09v0StMHKxdcOutt1JUVERAQADBwcFs3bqVXr168dlnn9GmTZuWyFGkVdXWQnk5VFaCkxO4uIDB0NpZCZjaPfUL6Mfe7/dSWV3ZpI4W56vOU0st/QP6Y9BvpIiIiIiIiIiIiIiIiE0NvW0onx77lJLyEtq5tqsz31LdL/Lz86mtrSUsLKzRuMOHDwOm2xfqExYWZo651NKlS+nevTu5ubn079+/znxiYiLPPfccOTk5DB48GDBd1zF27Fi8vb3NcXl5eY3m5+Jy7fwAcVRUFF26dGHjxo08+OCDpKenk5qaytdff22zM2bPns3w4cMBmDFjBhMmTCArK4u+ffsCpiKQhgomioqKqKqqYuzYsQQGBgKYm0lc5O3tzcqVK7G3tycsLIz77ruPrKwspkyZYrNnuJTVBRj3338/WVlZ3HXXXeZvwNq1azl27Bi//e1vWyJHkVZRUQF5efDJJ5CfD1VV4OAAwcHQrx9ERYGzc2tnKVEdo+ji04UjJ48Q0S6i0aKK2tpa8n/MJ9gnmB4dr+49WSIiIiIiIiIiIiIiIjeDi10w3j/yPr4uvhaf3VzsfjEyZKRNu19c3Bto8g/gNtaho749IiIiSEhIYM6cOezcubPOfFhYGNHR0aSlpTF48GAKCgrIzc1l69atFnHBwcFNyu9akZiYyLp16wgICODs2bOMHDmSlStX2mz/7t27m7++2Fnj0iKKDh06cPz48XrX9ujRgyFDhhAZGcnw4cOJiYkhLi7OouClW7du2Nvbm9/7+/s3eJWMLVh9Bcnvf/975s2bB0BcXBy5ubn8z//8D2+99Ra///3vbZ6gSGvIz4eFCyE1FT77DOzswM3N9Otnn5nGFy40xUnrcnZwJqlnEh3dO/LlD19yvup8vXHnq87z5Q9f0tG9I5N7TsbZQdUzIiIiIiIiIiIiIiIiLWHobUPxbONJSbnlNfIt1f0CICQkBIPBwMGDBxuNCw0NBWgw7tChQ4SEhNQ7t3DhQvbv38+WLVvqnU9KSmLTpk2cPn2adevWERgYyJAhQyxirqcrSAAmTZrE7t27SU5OJiEhAQcHq3s8NMrR0dH89cXCl8vHampq6l1rb2/Ptm3b+OCDD4iIiODFF1+ka9euFBYW1rv/z+1nC83+7tx9993cfffdtshF5JqQnw8rVkBxMYSEmK4duZSfn+k6kiNHTHEzZpi6YkjrCfYJZsZdM1i7fy0FJwswYMDX1RcHOweqaqooKSuhllqCfYKZ3HMyXXy6tHbKIiIiIiIiIiIiIiIiN6z6umC0ZPcLAB8fH4YPH85LL73Eo48+ipubm8V8aWkpXl5exMTE4OPjQ0pKCtHR0RYx77zzDkeOHGHRokX1ntGpUyemTZvGvHnz6NKl7udN48aNY8aMGbzxxhusX7+eKVOm1OmmcT1dQQKm7+uYMWPIzMzk5Zdfbu106jAYDPTt25e+ffuyYMECAgMD2bx5MzNnzmyVfJpUgPHOO+80ecMxY8ZccTIira2iAtauNRVfRERAQx2KnJxM819+aYp/+mldR9Lagn2CeXrg03xR/AW5x3LJP5lP2YUyHOwc6HNLH/oH9KdHxx7qfCEiIiIiIiIiIiIiInIVDL1tKJ8e+5SS8hLaubZr0e4XF61atYro6GjuvPNOnnnmGbp3705VVRXbtm1j9erVHDx4EDc3N1555RXGjx/Pww8/zLRp0/Dw8CArK4vHH3+cuLg4xo0b1+AZc+fOZc2aNRQWFhIfH28x5+7uTnx8PPPmzePUqVMYjcY66629guRiwcbZs2c5ceIEeXl5ODk5ERERYdU+9fnqq6/qjNW3b3p6OqtWrcLX17fZZ9rSnj17yMrKIiYmBj8/P/bs2cOJEycIDw9vtZyaVIARGxtr8f5ihdLlYwDV1dW2yUykFeTlQUGBqfPFz10PZTCYOl8UFMAXX8Bdd12VFKURzg7O3HXrXdx5y52UV5VTWV2Jk70TLg4uTb7vS0RERERERERERERERJrv8i4YLdn94qLOnTuzb98+Fi9ezKxZsygqKqJ9+/b07t2b1atXm+Pi4uLIzs5myZIlDBgwgPLycoKDg3nyySd57LHHGv1cycfHhzlz5jBv3rx655OSkli7di0xMTEEBAQ0+5l69uxp/vrzzz/njTfeIDAwkKNHjzZ77/Hjx9cZu/T6jotcXFyuuc4cAB4eHnz88ccsX76c06dPExgYSEpKSqt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      "text/plain": [
       "<Figure size 2400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Create a figure and an axes\n",
    "fig, ax = plt.subplots(figsize=(24, 8))\n",
    "\n",
    "markers = ['o', 'P', 's', 'v', 'p', '*']\n",
    "\n",
    "for i in range(6):\n",
    "    plt.scatter(training_times[i], validation_accuracy[i], c='red', label=architectures[i], alpha=0.5, marker=markers[i], s=100)\n",
    "for i in range(6):\n",
    "    plt.scatter(training_times[i+6], validation_accuracy[i+6], c='blue', label=architectures[i+6], alpha=0.5, marker=markers[i], s=100)\n",
    "for i in range(6):\n",
    "    plt.scatter(training_times[i+12], validation_accuracy[i+12], c='green', label=architectures[i+12], alpha=0.5, marker=markers[i], s=100)\n",
    "for i in range(6):\n",
    "    plt.scatter(training_times[i+18], validation_accuracy[i+18], c='orange', label=architectures[i+18], alpha=0.5, marker=markers[i], s=100)\n",
    "for i in range(6):\n",
    "    plt.scatter(training_times[i+24], validation_accuracy[i+24], c='black', label=architectures[i+24], alpha=0.5, marker=markers[i], s=100)\n",
    "\n",
    "\n",
    "# Add legend outside the plot\n",
    "legend = ax.legend(loc='upper left', bbox_to_anchor=(1, 1))\n",
    "\n",
    "# Add axis labels\n",
    "ax.set_xlabel('time [s]')\n",
    "ax.set_ylabel('Validation Accuracy')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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