Day76-optimizer_HW.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業重點:\n",
    "\n",
    "(1)以, Adam, 為例, 調整 batch_size, epoch , 觀察accurancy, loss 的變化\n",
    "\n",
    "(2)以同一模型, 分別驗證 SGD, Adam, Rmsprop 的 accurancy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 作業目標:\n",
    "    \n",
    "    取得各種優化器的運算結果"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from __future__ import print_function\n",
    "import keras\n",
    "from keras.datasets import cifar10\n",
    "from keras.preprocessing.image import ImageDataGenerator\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense, Dropout, Activation, Flatten\n",
    "from keras.layers import Conv2D, MaxPooling2D\n",
    "import os\n",
    "from keras import optimizers\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Blas GEMM launch failed , 避免動態分配GPU / CPU, 出現問題\n",
    "import tensorflow as tf\n",
    "gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)\n",
    "sess = tf.Session(config=tf.ConfigProto(gpu_options=gpu_options))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "'''\n",
    "   宣告並設定\n",
    "   batch_size：對總的樣本數進行分組，每組包含的樣本數量\n",
    "   epochs ：訓練次數\n",
    "   \n",
    "''' \n",
    "\n",
    "#batch_size = 32\n",
    "num_classes = 10\n",
    "#epochs = 20\n",
    "data_augmentation = True\n",
    "num_predictions = 20\n",
    "save_dir = os.path.join(os.getcwd(), 'saved_models')\n",
    "model_name = 'keras_cifar10_trained_model.h5'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x_train shape: (50000, 32, 32, 3)\n",
      "50000 train samples\n",
      "10000 test samples\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# The data, split between train and test sets:\n",
    "(x_train, y_train), (x_test, y_test) = cifar10.load_data()\n",
    "print('x_train shape:', x_train.shape)\n",
    "print(x_train.shape[0], 'train samples')\n",
    "print(x_test.shape[0], 'test samples')\n",
    "\n",
    "# Convert class vectors to binary class matrices.\n",
    "y_train = keras.utils.to_categorical(y_train, num_classes)\n",
    "y_test = keras.utils.to_categorical(y_test, num_classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\Jeffrey.yc Chen\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Colocations handled automatically by placer.\n",
      "WARNING:tensorflow:From C:\\Users\\Jeffrey.yc Chen\\Anaconda3\\lib\\site-packages\\keras\\backend\\tensorflow_backend.py:3445: calling dropout (from tensorflow.python.ops.nn_ops) with keep_prob is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use `rate` instead of `keep_prob`. Rate should be set to `rate = 1 - keep_prob`.\n"
     ]
    }
   ],
   "source": [
    "#    第一步：選擇模型, 順序模型是多個網絡層的線性堆疊\n",
    " \n",
    "model = Sequential()\n",
    "\n",
    "#   第二步：構建網絡層\n",
    "model.add(Conv2D(32, (3, 3), padding='same',\n",
    "                 input_shape=x_train.shape[1:]))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(32, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Conv2D(64, (3, 3), padding='same'))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Conv2D(64, (3, 3)))\n",
    "model.add(Activation('relu'))\n",
    "model.add(MaxPooling2D(pool_size=(2, 2)))\n",
    "model.add(Dropout(0.25))\n",
    "\n",
    "model.add(Flatten())\n",
    "model.add(Dense(512))\n",
    "model.add(Activation('relu'))\n",
    "model.add(Dropout(0.5))\n",
    "\n",
    "model.add(Dense( 10)) # 輸出結果是10個類別，所以維度是10   \n",
    "model.add(Activation('softmax')) # 最後一層用softmax作為激活函數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Parameters：1250858\n"
     ]
    }
   ],
   "source": [
    "# 模型建立完成後，統計參數總量\n",
    "print(\"Total Parameters：%d\" % model.count_params())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "conv2d_1 (Conv2D)            (None, 32, 32, 32)        896       \n",
      "_________________________________________________________________\n",
      "activation_1 (Activation)    (None, 32, 32, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_2 (Conv2D)            (None, 30, 30, 32)        9248      \n",
      "_________________________________________________________________\n",
      "activation_2 (Activation)    (None, 30, 30, 32)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2 (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "dropout_1 (Dropout)          (None, 15, 15, 32)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_3 (Conv2D)            (None, 15, 15, 64)        18496     \n",
      "_________________________________________________________________\n",
      "activation_3 (Activation)    (None, 15, 15, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv2d_4 (Conv2D)            (None, 13, 13, 64)        36928     \n",
      "_________________________________________________________________\n",
      "activation_4 (Activation)    (None, 13, 13, 64)        0         \n",
      "_________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2 (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "dropout_2 (Dropout)          (None, 6, 6, 64)          0         \n",
      "_________________________________________________________________\n",
      "flatten_1 (Flatten)          (None, 2304)              0         \n",
      "_________________________________________________________________\n",
      "dense_1 (Dense)              (None, 512)               1180160   \n",
      "_________________________________________________________________\n",
      "activation_5 (Activation)    (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dropout_3 (Dropout)          (None, 512)               0         \n",
      "_________________________________________________________________\n",
      "dense_2 (Dense)              (None, 10)                5130      \n",
      "_________________________________________________________________\n",
      "activation_6 (Activation)    (None, 10)                0         \n",
      "=================================================================\n",
      "Total params: 1,250,858\n",
      "Trainable params: 1,250,858\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# 輸出模型摘要資訊\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#第三步編譯\n",
    " '''\n",
    " SGD(隨機梯度下降) - Arguments\n",
    "lr: float >= 0. Learning rate.\n",
    "momentum: float >= 0. Parameter that accelerates SGD in the relevant direction and dampens oscillations.\n",
    "decay: float >= 0. Learning rate decay over each update.\n",
    "nesterov: boolean. Whether to apply Nesterov momentum.\n",
    "'''\n",
    "\n",
    "'''\n",
    "RMSprop- Arguments\n",
    "lr: float >= 0. Learning rate.\n",
    "rho: float >= 0.\n",
    "epsilon: float >= 0. Fuzz factor. If None, defaults to K.epsilon().\n",
    "decay: float >= 0. Learning rate decay over each update.\n",
    "'''\n",
    "\n",
    "'''\n",
    "Example:\n",
    "opt = optimizers.SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)\n",
    "\n",
    "'''\n",
    "\n",
    "model.compile(optimizer = opt, loss = 'categorical_crossentropy', metrics = ['accuracy'])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 資料正規化\n",
    "x_train = x_train.astype('float32')\n",
    "x_test = x_test.astype('float32')\n",
    "x_train /= 255\n",
    "x_test /= 255\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using real-time data augmentation.\n",
      "\n",
      "WARNING:tensorflow:From C:\\Users\\Jeffrey.yc Chen\\Anaconda3\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py:3066: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Use tf.cast instead.\n",
      "Train on 50000 samples, validate on 10000 samples\n",
      "Epoch 1/20\n",
      "50000/50000 [==============================] - 267s 5ms/step - loss: 1.5363 - acc: 0.4370 - val_loss: 1.1807 - val_acc: 0.5734\n",
      "Epoch 2/20\n",
      "50000/50000 [==============================] - 294s 6ms/step - loss: 1.1569 - acc: 0.5878 - val_loss: 1.0264 - val_acc: 0.6432\n",
      "Epoch 3/20\n",
      "50000/50000 [==============================] - 318s 6ms/step - loss: 1.0124 - acc: 0.6402 - val_loss: 0.8865 - val_acc: 0.6837\n",
      "Epoch 4/20\n",
      "50000/50000 [==============================] - 335s 7ms/step - loss: 0.9158 - acc: 0.6756 - val_loss: 0.8725 - val_acc: 0.7007\n",
      "Epoch 5/20\n",
      "50000/50000 [==============================] - 311s 6ms/step - loss: 0.8622 - acc: 0.6966 - val_loss: 0.7604 - val_acc: 0.7365\n",
      "Epoch 6/20\n",
      "50000/50000 [==============================] - 338s 7ms/step - loss: 0.8161 - acc: 0.7131 - val_loss: 0.7739 - val_acc: 0.7353\n",
      "Epoch 7/20\n",
      "50000/50000 [==============================] - 335s 7ms/step - loss: 0.7814 - acc: 0.7250 - val_loss: 0.7316 - val_acc: 0.7465\n",
      "Epoch 8/20\n",
      "50000/50000 [==============================] - 328s 7ms/step - loss: 0.7445 - acc: 0.7401 - val_loss: 0.7629 - val_acc: 0.7361\n",
      "Epoch 9/20\n",
      "50000/50000 [==============================] - 320s 6ms/step - loss: 0.7213 - acc: 0.7471 - val_loss: 0.6916 - val_acc: 0.7653\n",
      "Epoch 10/20\n",
      "50000/50000 [==============================] - 319s 6ms/step - loss: 0.6983 - acc: 0.7559 - val_loss: 0.7136 - val_acc: 0.7550\n",
      "Epoch 11/20\n",
      "50000/50000 [==============================] - 311s 6ms/step - loss: 0.6774 - acc: 0.7630 - val_loss: 0.6747 - val_acc: 0.7657\n",
      "Epoch 12/20\n",
      "50000/50000 [==============================] - 322s 6ms/step - loss: 0.6587 - acc: 0.7695 - val_loss: 0.6972 - val_acc: 0.7630\n",
      "Epoch 13/20\n",
      "50000/50000 [==============================] - 317s 6ms/step - loss: 0.6422 - acc: 0.7749 - val_loss: 0.6841 - val_acc: 0.7685\n",
      "Epoch 14/20\n",
      "50000/50000 [==============================] - 310s 6ms/step - loss: 0.6272 - acc: 0.7780 - val_loss: 0.6595 - val_acc: 0.7810\n",
      "Epoch 15/20\n",
      "50000/50000 [==============================] - 297s 6ms/step - loss: 0.6168 - acc: 0.7841 - val_loss: 0.6626 - val_acc: 0.7746\n",
      "Epoch 16/20\n",
      "50000/50000 [==============================] - 312s 6ms/step - loss: 0.5972 - acc: 0.7883 - val_loss: 0.6600 - val_acc: 0.7772\n",
      "Epoch 17/20\n",
      "50000/50000 [==============================] - 316s 6ms/step - loss: 0.5854 - acc: 0.7952 - val_loss: 0.6822 - val_acc: 0.7715\n",
      "Epoch 18/20\n",
      "50000/50000 [==============================] - 312s 6ms/step - loss: 0.5779 - acc: 0.7962 - val_loss: 0.7067 - val_acc: 0.7686\n",
      "Epoch 19/20\n",
      "50000/50000 [==============================] - 327s 7ms/step - loss: 0.5650 - acc: 0.8021 - val_loss: 0.6999 - val_acc: 0.7738\n",
      "Epoch 20/20\n",
      "50000/50000 [==============================] - 291s 6ms/step - loss: 0.5575 - acc: 0.8040 - val_loss: 0.6804 - val_acc: 0.7766\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\\n   第四步：訓練\\n   .fit的一些參數\\n   batch_size：對總的樣本數進行分組，每組包含的樣本數量\\n   epochs ：訓練次數\\n   shuffle：是否把數據隨機打亂之後再進行訓練\\n   validation_split：拿出百分之多少用來做交叉驗證\\n   verbose：屏顯模式 - 0：不輸出, 1：輸出進度, 2：輸出每次的訓練結果\\n'"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 是否要做資料處理\n",
    "if not data_augmentation:\n",
    "    print('Not using data augmentation.')\n",
    "    history=model.fit(x_train, y_train,\n",
    "              batch_size=batch_size,\n",
    "              epochs=epochs,\n",
    "              validation_data=(x_test, y_test),\n",
    "              shuffle=True)\n",
    "else:\n",
    "    print('Using real-time data augmentation.')\n",
    "    print('')\n",
    "        \n",
    "    # This will do preprocessing and realtime data augmentation:\n",
    "    datagen = ImageDataGenerator(\n",
    "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
    "        samplewise_center=False,  # set each sample mean to 0\n",
    "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
    "        samplewise_std_normalization=False,  # divide each input by its std\n",
    "        zca_whitening=False,  # apply ZCA whitening\n",
    "        zca_epsilon=1e-06,  # epsilon for ZCA whitening\n",
    "        rotation_range=0,  # randomly rotate images in the range (degrees, 0 to 180)\n",
    "        # randomly shift images horizontally (fraction of total width)\n",
    "        width_shift_range=0.1,\n",
    "        # randomly shift images vertically (fraction of total height)\n",
    "        height_shift_range=0.1,\n",
    "        shear_range=0.,  # set range for random shear\n",
    "        zoom_range=0.,  # set range for random zoom\n",
    "        channel_shift_range=0.,  # set range for random channel shifts\n",
    "        # set mode for filling points outside the input boundaries\n",
    "        fill_mode='nearest',\n",
    "        cval=0.,  # value used for fill_mode = \"constant\"\n",
    "        horizontal_flip=True,  # randomly flip images\n",
    "        vertical_flip=False,  # randomly flip images\n",
    "        # set rescaling factor (applied before any other transformation)\n",
    "        rescale=None,\n",
    "        # set function that will be applied on each input\n",
    "        preprocessing_function=None,\n",
    "        # image data format, either \"channels_first\" or \"channels_last\"\n",
    "        data_format=None,\n",
    "        # fraction of images reserved for validation (strictly between 0 and 1)\n",
    "        validation_split=0.0)\n",
    "\n",
    "    # Compute quantities required for feature-wise normalization\n",
    "    # (std, mean, and principal components if ZCA whitening is applied).\n",
    "    datagen.fit(x_train)\n",
    "    history=model.fit(x_train, y_train,\n",
    "              batch_size=batch_size,\n",
    "              epochs=epochs,\n",
    "              validation_data=(x_test, y_test),\n",
    "              shuffle=True)   \n",
    "\n",
    "'''\n",
    "   第四步：訓練\n",
    "   .fit的一些參數\n",
    "   batch_size：對總的樣本數進行分組，每組包含的樣本數量\n",
    "   epochs ：訓練次數\n",
    "   shuffle：是否把數據隨機打亂之後再進行訓練\n",
    "   validation_split：拿出百分之多少用來做交叉驗證\n",
    "   verbose：屏顯模式 - 0：不輸出, 1：輸出進度, 2：輸出每次的訓練結果\n",
    "''' \n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Saved trained model at C:\\Users\\Jeffrey.yc Chen\\Downloads\\ML100\\Temp\\Day76\\saved_models\\keras_cifar10_trained_model.h5 \n",
      "10000/10000 [==============================] - 14s 1ms/step\n",
      "Test loss: 0.6803855864524841\n",
      "Test accuracy: 0.7766\n"
     ]
    }
   ],
   "source": [
    "# Save model and weights\n",
    "if not os.path.isdir(save_dir):\n",
    "    os.makedirs(save_dir)\n",
    "model_path = os.path.join(save_dir, model_name)\n",
    "model.save(model_path)\n",
    "print('Saved trained model at %s ' % model_path)\n",
    "\n",
    "# Score trained model.\n",
    "scores = model.evaluate(x_test, y_test, verbose=1)\n",
    "print('Test loss:', scores[0])\n",
    "print('Test accuracy:', scores[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " test set \n",
      "\n",
      " The test loss is %f  [0.680385582447052, 0.7765999960899354]\n"
     ]
    }
   ],
   "source": [
    "#    第六步：輸出\n",
    "import numpy \n",
    "\n",
    "print ( \" test set \" )\n",
    "scores = model.evaluate(x_test,y_test,batch_size=200,verbose= 0)\n",
    "print ( \"\" )\n",
    "#print ( \" The test loss is %f \" % scores)\n",
    "print ( \" The test loss is %f \", scores)\n",
    "\n",
    "\n",
    "result = model.predict(x_test,batch_size=200,verbose= 0)\n",
    "\n",
    "result_max = numpy.argmax(result, axis = 1 )\n",
    "test_max = numpy.argmax(y_test, axis = 1 )\n",
    "\n",
    "result_bool = numpy.equal(result_max, test_max)\n",
    "true_num = numpy.sum(result_bool)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kJ0ZxsraOnUdOeDsUpZTyCE0KLUirn55TG5uVUj5Ck0ILEqODiQ0NYLM2NiulfITbkoKIvCgieSLybTPrrxORzY7HChFJd1csbSUipCVGsklLCkopH+HOksLLwKwW1u8Fphhj0oD/BZ5zYyxtlp4Uxe68UkqrarwdilJKuZ3bkoIxZhlQ1ML6FcaYY463q4BEd8XSHumJURgD3x7SW1OVUt1fZ2lTuBlY2NxKEZknImtFZG1+fr4HwzrV2Kyd2JRSvsDrSUFEpmElhV80t40x5jljTKYxJjM+Pt5zwQGxYYEkRgezWTuxKaV8gJ83Ty4iacALwEXGmEJvxtKS9KQoNh7QkoJSqvvzWklBRJKBd4HrjTG73H7C/SvhpYuhsqTVu6YnRnLoeAUFpVVuCEwppToPd96S+gawEhgkIjkicrOI3C4itzs2eQCIBf4qIhtFZK27YgHAPwj2fw2rn231rumJOmKqUso3uK36yBgz5yzrbwFucdf5z9B7FAy+FFb8CcbcAiExLu86vE8kNoFNB4uZPriHG4NUSinv8npDs0dNux+qTsDKP7dqt9BAPwYmhGsnNqVUt+dbSaHHMBh+Bax6FsoKWrVrelIkm3OKMUan51RKdV++lRQApv4Saipg+f+1are0xCiKyk6yJ7/UTYEppZT3+V5SiBsIadfAmhfgxBGXdzt/aA+C/G386cssNwanlFLe5XtJAWDKz6GuBr560uVdekQEccvkfry/MVfvQlJKdVu+mRRiUmHUXFj3Mhw/6PJut03pR2xoAI98vF3bFpRS3ZJvJgWA8+6znpf93uVdwoP8uXvmQFZlF7F4Z56bAlNKKe/x3aQQmQijb4INr0FRtsu7zRmbTGpcKI9+vIOaWp27WSnVvfhuUgA49x6w+8PSJ1zexd9u4xezBrE7r5S31+W4MTillPI8304K4T1h7K2w+U3Id334pQuH9WR032j++Nkuyk/q5DtKqe7Dt5MCwKSfgn8ILHnU5V1EhPsvHkzeiSpe+GqvG4NTSinP0qQQGgfjboet78KRJqeTbtLovjHMGtaTvy/dQ/4JHT1VKdU9aFIAmPgTCIxsVWkB4OezBlFVU8fTX7h/5G+llPIETQoAwdFWYtjxERxa7/Ju/eLDuHZcMm98c1CHv1BKdQuaFOqNux2CY2DxI63a7a4ZAwn2t/P4wh1uCkwppTxHk0K9oAiYdDdkfQYHVrm8W1xYILdP6ceibUdZs6/IjQEqpZT7aVJwNvZWCE2AL3/bqt1untyPHhGBOvyFUqrLcykpiEh/EQl0vJ4qIneJSJR7Q/OCgFCrQ9u+ryB7qcu7BQfY+Z/zB7HhwHEWfuv6yKtKKdXZuFpSeAeoFZEBwD+AVOBfbovKm0bfBOG9YfHvoBW/+q8cncigHuE8/skOTtbo8BdKqa7J1aRQZ4ypAb4HPGWM+RnQy31heZF/EEy5Dw6uhqwvXN7NbhPmXzyY/YXl/Gv1fjcGqJRS7uNqUqgWkTnAjcBHjmX+7gmpExg5F6KSYfFvW1VamHpOPJMGxPL0F7spqax2Y4BKKeUeriaFm4AJwO+MMXtFJBV4zX1heZlfAEyZD7kbYOfHLu8mIvzyoiEcK6/mb0v2uDFApZRyD5eSgjFmmzHmLmPMGyISDYQbYx5zc2zelXY1xA6AL38Hda63EQzvE8n3RvXhxeV7yT1e4cYAlVKq47l699ESEYkQkRhgE/CSiPzRvaF5md0Ppv4S8rbCtvdatev/XHAOBnhykQ5/oZTqWlytPoo0xpQAVwAvGWNGAzPdF1YnMewKiB8Cix+FWteHyE6MDuGmiSm8uyGHbbklbgxQKaU6lqtJwU9EegFXcaqhufuz2WDa/VC4G7a81apd75g6gIggfx5duN1NwSmlVMdzNSk8DHwK7DHGrBGRfsBu94XViQz5DvRMg6WPQa3rdxRFhvhz5/QBfLW7gGW78t0YoFJKdRxXG5rfMsakGWN+5HifbYy50r2hdRIiMP3/wbF9sPH1Vu16/YS+JMUE8+jCHdTW6fAXSqnOz9WG5kQReU9E8kTkqIi8IyKJ7g6u0xh4AfTJhKW/hxrXJ9QJ9LNz34WD2X64hP9sOOTGAJVSqmO4Wn30EvAB0BvoA3zoWOYbRGDGr6EkB1Y/26pdLx3Ri7TESJ5ctJPK6lo3BaiUUh3D1aQQb4x5yRhT43i8DMS7Ma7Op99UGHghLPsDlLreRmCzWR3acosreenrfe6KTimlOoSrSaFAROaKiN3xmAsUtrSDiLzoqG5qcuJjsTwjIlkisllEMlobvMdd8FuoLoclrZuIZ0L/WGYMTuCvi7PIK6l0U3BKKdV+riaFH2LdjnoEOAzMxhr6oiUvA7NaWH8RMNDxmAf8zcVYvCf+HMi8Gda9DEe3tWrXX148hJo6w22vraOqRquRlFKdk6t3Hx0wxnzXGBNvjEkwxlyO1ZGtpX2WAS1NRXYZ8KqxrAKiHH0hOrep8yEwAj69v1WD5Q1ICOPJq9LZcOA4v3rvW52MRynVKbVn5rV72nnuPsBBp/c5jmWdW0iMlRiyF8PuRa3a9eIRvbh7xkDeXpfDP5bvdVOASinVdu1JCtLOcze1f5M/n0VknoisFZG1+fmdoCPYmFuswfI+/VWrOrQB3D1jILOG9eSRj7ezVDu1KaU6mfYkhfbWf+QASU7vE4HcJk9kzHPGmExjTGZ8fCe46cnubzU6F+6GtS+2alebTXjyqnTO6RHOT/61nj35pW4KUimlWq/FpCAiJ0SkpInHCaw+C+3xAXCD4y6k8UCxMeZwO4/pOefMsm5TXfwIlLfUdHKm0EA/nr8hE3+7jVtfWUtxhU7Io5TqHFpMCsaYcGNMRBOPcGOMX0v7isgbwEpgkIjkiMjNInK7iNzu2ORjIBvIAp4H7uiAz+M5InDhI1BVAkufaPXuSTEh/O26DA4UlXPnGxt0GAylVKcgXe0umMzMTLN27Vpvh3HKh3fDhtfgjlUQN7DVu/9r9QHuf28Lt56byq8uGeqGAJVSCkRknTEm82zbtadNQQFM+xX4BcOiX7dp92vHJXPDhL48/9Ve3l6X08HBKaVU62hSaK+wBDjvXti1EPYsbtMhfn3pUCb2j+X+d7ew/sCxDg5QKaVcp0mhI4z/EUT1tW5RrWt9b2V/u42/XJtBz8ggbvvnOg4X69zOSinv0KTQEfwC4fyHrfmc17/apkNEhwbwwo2ZlFfVMO/VdVSc1KEwlFKep0mhowy9DJInwuLfQWXb5mU+p0c4T18zim9zi/n5O5t1KAyllMdpUugoIjDrESgrgK+ebPNhZg7twb0XDOLDTbn8dcmeDgxQKaXOTpNCR+o9CtLnwKq/QlHbxza6Y2p/vpPemz8s2sln2452YIBKKdUyTQodbcYDYPODzx9s8yFEhCeuTGN470h+umADu46e6MAAlVKqeZoUOlpEL5j0U9j2Puxf0ebDBAfYee6G0YQE+nHLK2s5VnayA4NUSqmmaVJwh4l3QkQf+OSXUFfX5sP0igzm79eP5khxJXe8vp7q2rYfSymlXKFJwR0CQmDmQ3B4I2xe0K5DZSRH88gVI1iZXcj/ftS62d6UUqq1NCm4y/DZ0Gc0fPEwnCxr16Fmj07k1nNTeXXlfn7yr/UUaVWSUspNNCm4i80GFz4KJw7D10+3+3DzLxrCvRecw6dbj3DB/y1j0dYjHRCkUkqdTpOCOyWPg+FXwtfPQHH7Bruz24SfTB/I+z+eTHx4IPP+uY573txIcbnOxaCU6jiaFNxt5kOAgc9/0yGHG9o7gvd/PIm7pg/g/U25XPDUUpbszOuQYyullCYFd4tKhgk/hi3/hpx1HXLIAD8b91wwiPfumEhEkD8/eGkN89/ZzIlKLTUopdpHk4InTP4ZhPWAT34Bxw9AB41plJYYxYd3Tua2Kf3499qDzHrqK1ZkFXTIsZVSvklnXvOUDa/D+44ZRwMjIGEo9BjqeB5uvQ6KbPPh1+0/xr1vbWJvQRk3TOjL/IsGExLQ4oypSikf4urMa5oUPOnQesjdAHnb4OhWOLoNqopPrY9MOpUsegy3XscNBLu/S4evOFnL7z/dyUsr9pIcE8Ifvp/OmJQYN30YpVRXokmhKzAGSg45EsTWU8miYBfU1Vjb2PwhfpCVIBIzYfRN4BfQ4mFXZxdy79ubyDlWwc2TUrn3wkEE+ds98IGUUp2VJoWurOYkFO62ShJHv3Uki21QkgOT7rYm9DmLsqoaHl24nddWHaB/fCh/+H46o5KjPRC8Uqoz0qTQHX34U1j3Mtz4AaSe59Iuy3cX8PO3N3GkpJLbp/TnrhkDtdSglA9yNSno3UddyYW/g9j+8N7tUHHMpV0mD4zjk5+dx+zRifx1yR5m/nEpC7cc1lndlFJN0qTQlQSEwpUvQOlR+OhnLt/aGhHkzxOz0/nXreMIC/TjR6+v59rnV7PjSNumDVVKdV+aFLqa3qNg2q9g63uwqXUjsE7sH8dHd07mfy8bxvYjJVz89Fc88P63OleDUqqBJoWuaNLd0HcSfHxvq6f99LPbuH5CCkvuncr14/vy+uoDTHtyCa+u3EeNzteglM/TpNAV2ezwvb+D2OHdeVBb0+pDRIUE8JvLhvPxXecytFcED7y/lUv/tJwVe7RHtFK+TJNCVxWVBJf+EXK+ga+ebPNhBvUM5/VbxvHs3AxKq2q49vnV/Oi1dRwsKu/AYJVSXYUmha5sxGxIuxqWPg4H17T5MCLCrOG9+PyeKdx7wTks2ZnPjD8u5clFOyk/2fpSiFKq69Kk0NVd/HtrPuh3b4GqE+06VJC/nZ9MH8iX907houE9+dOXWcx4cinvbzykt7Aq5SM0KXR1QZFwxXPW6KsL53fIIXtFBvP0NaN4+/YJxIYFcPeCjXz/2ZV8e6j47Dsrpbo0TQrdQd8JMPke2PgabP1Phx02MyWG9388mceuGMHegjK+8+fl3PLKGlZkFWjJQaluyq1JQURmichOEckSkTN+xopIsogsFpENIrJZRC52Zzzd2tT50DsDPrwbig912GHtNuGascksvm8qd04fyIYDx7n2hdVc9PRX/HvNQSqrazvsXEop73Pb2EciYgd2AecDOcAaYI4xZpvTNs8BG4wxfxORocDHxpiUlo7r02MfnU3hHnh2MiSOgev/A7aOz/mV1bV8sCmXF5fvZceRE8SEBjB3XDJzx/clISKow8+nlOoYnWHso7FAljEm2xhzElgAXNZoGwNEOF5HArlujKf7i+0Psx6DvUth1V/ccoogfztXZSax8O5zeePW8YzuG82fFmcx6fEv+dmbG9mSo+0OSnVl7pyaqw9w0Ol9DjCu0TYPAYtE5E4gFJjZ1IFEZB4wDyA5ObnDA+1WMm6A3Yvgi4chdQr0SnPLaUSECf1jmdA/lv2FZby8Yh//XnOQ9zYcYkxKND+clMr5Q3vgZ9dmK6W6Enf+j5UmljWuq5oDvGyMSQQuBv4pImfEZIx5zhiTaYzJjI+Pd0Oo3YgIfOcZCI6Bd26B6gq3n7JvbCgPfmcYK++fwa8vHcqRkkp+9Pp6pvx+Cc8vy6a4otrtMSilOoY7k0IOkOT0PpEzq4duBv4NYIxZCQQBcW6MyTeExsLlf4WCnfDZAx47bUSQPzdPTmXJvdP4+/WjSYwO5ncfb2fCo1/w4PvfsregzGOxKKXaxp1JYQ0wUERSRSQAuAb4oNE2B4AZACIyBCsp5LsxJt8xYAaMvwO+eQ52LfLoqe024cJhPXnztgl8dOdkLhreize+Ocj0J5cw79W1rD/g2lwQSinPc+vMa45bTJ8C7MCLxpjficjDwFpjzAeOO46eB8KwqpZ+boxp8RtM7z5qhepKeH46lOXBj1ZCWBuq3qpKIX8n5G+HksMw4kqI6dfqw+SdqOS1lft5ZeV+iiuqGZsaw4+m9GfqoHhEmqppVEp1JJ2OU1mOboXnpkH/aTBngdXm0JTqCseX/w5rTui8HVYiOH7g9O1sfjD6JjjvPgjv0epwyqpqWLDmIP/4Kpvc4koG9wzntin9uDStN/7aKK2U22hSUKes/Ct8+ku45I8wai4U7D7zy79oLw33Adj8Ie4cSBgM8UOs54Sh4Bdojci67hXr9YRgdO0bAAAWeUlEQVQfw8Q7raE2Wqm6to4PNuby7NI97M4rpU9UMLecm8rVY5IICWjiprjqCshZA8kTwO7fvuuhlA/SpKBOqauD166Avcus98bRC1nsEDvA6cvf8Yjp1/IXb+Ee+PK3sPVd6y6nc/8HxtwC/q3vvFZXZ/hyRx7PLt3D2v3HiA7x54YJKdw4MYWY0AA48i2sfwU2vwmVxTDgfLjqFWtqUqWUyzQpqNOdOGr1XQjveerLP3aA9Yu/rXI3whe/gT1fQkQiTPslpF0D9rZ1f1m7r4hnl+5hxfYDXBGwmtvDlpNYvhXsATDku1bpZelj0GskXPcWhOqNakq5SpOC8pzspVZyOLQO4gbBjAdg8CXNt180xRjI3QDrX6Fu81vYqsvIMn1YUDudisFXMnfGaIb0ioAdH8PbN1nDhV//LkSnuO1jKdWdaFJQnmUMbP/QKo0U7rbGX5r5EKRMbnm/ymLY/G+riujIFvALhmHfg9E3khuexj++3scb3xyg/GQtUwfF89303swM3UvEe3OtUs51b0GvdE98QqW6NE0Kyjtqa2DTv2Dxo3AiFwbMhBkPnj7chjFwcLXVYL31PaipgJ4jIONGGPF9CI467ZDHy0/y6sr9/HPVfvJPVCECl/Yq4ZGyBwmpK8N2zetI/6me/ZxKdTGaFJR3VVfAN89bdytVHofhs607lfavsEoF+TsgIMyaUjTjRug96qzVTXV1hi2HivlyRx6Ld+aRl5PNywFP0N92mLeSfkXs+GuZPCCO0EB3DumlVNekSUF1DhXHYcUzsOpvUF1uLeszGkb/AIZdAYFhbT503olKVmzJYtiy2xlYuYWHq6/nNS5hXL8Ypg5KYPrgBFLj9C4lpUCTgupsThyx2hySJ0DP4R177OpK6t65BduOD1nRcy4PlF5JVoE1EGBqXCjTHAlibGoMAX7aQU75Jk0KyrfU1cLH98Haf0DaNRyY/ARf7i7iy535rMou5GRNHaEBdiYPjGPaoASmDkqgZ6ROCqR8h6tJQStfVfdgs8MlT0JEL/jytySX5fODq17lB5NSKT9Zw4qsQr7cmceSHXl8uvUoAEN6RTBtUDzTBicwKimq5bkfSg5D7nqrpBMS46EPpZTnaUlBdT/rX4UPf2rd8XTtW6cNBGiMYdfRUhbvzGPxjjzW7j9GbZ0hMtif886JZ9qgeM47J564QAMHVkLW51bnvDzHLLIB4TD2VpjwE2uIcqW6CK0+Ur5t5yfw1g+sksPcdyEmtcnNiiuq+TqrgMXbj5K9cyNplWs5z7aZifYdBFJFnS0A6TsBGTDDum12/T+t22j9Q2DMD2HiXRCW4NnPpjznZJn1o2DHx9YPBAyExlu96UPjm3jt9D4grHUdON1Mk4JSB9fAv75vjex63dvQe+Tp6yuOW/NZZ31h/ccvtmaPPRaczNeM5J2SQayqHUJIaARTBsUzbVACkwbEEVO+F5b9Ab59G+yBkHmTlRwiern389RWW5+lE33RdEulebBzIez8GLKXQE2lNejjgPMhMBzK8qGs4NRzVTPzkvsFnZk0egyHlEnWs83u0Y+lSUEpgPxd8NqVUFFkDaQXFA17vrASQc4aa3DAwAhIPc+amKj/DIjuC0BR2UmW7cpn8c48lu7K53h5NSIwtFcEkwfEMSOhlIwD/8Bvy7+tL+uMG2DyTyEysWNir6uDI5tg9+eQ9ZkVb1CUNW5V/CCIH3zqEZagyaI98nfBzv9aJYKcNYCByGRruJbBF7c8Om9N1elJoizf6eH0/sQRKD1i7RMYCX0nQN9JVpLomd7mMcNcpUlBqXolh63EkLfVsUCsUkP/GVYiSBxz1uG4a+sMm3KO8/XuAr7eU8C6/ceorjUE2G3M6lPBbbb3GXr0IxBBRs2FyT9rSC6tUlYI2Yth92dW8ipzTETYa6SVuCqLT016VOn0CzUoykoOCfWJYpA18m14T00WTamrtb78d/zXKhEUZlnLe6XDIEci6DG8469dcY7VgXPfctj/9anzBoRD8jjoOxH6TrY6c/oFdOipNSko5ayyGNa8AFF9od+0djcSl5+sYc2+Y6zIKmB5VgFbc0voQz53Bv6XK2UxdjGUDrqS8PN/gcT2b/5AdbXWQIBZn1uJ4NA6wFhDkvefDgPPt5JX41nzjIHSo1bP8IbJkRxzY1Q4TXcaGGkliPrh0VPPgx7DfDNRnCy3qoN2/tdqcyovsEp4KedaJYJBF3VcKc9VJ45YyWHf11ayyN9uLfcPsX6spEy2ShN9RrdpaHpnmhSU8qCispOs3FPI8qwCdu3awaWlbzHH/iX+Usv6yJkUZdxF+sgxVt+I0nxHFdbnVjVWRREg1n/8ATOtRNB7VNvqnI2xqizydzg9dkLedutLEKzEOPgSGFRfLdJN70w3xvolnvW59di33GofCIywrvGgi63nNkwS5TZlBVZyqE8UR78FjNV2lZgJmT+0hoZpA00KSnnRwaJy1n27nfANf2PSsfcJMNV8UZdBkn8x59TtwYahNjgW28CZyIDzrVKBu29xPXEEdn1qVZfsWQy1VRAcDQMvtJJE/+ntGnakU6gssSaTqk+4xY7pZGMHWCWuQbOs6pkOrppxm4pjsH+lI0ksh7SrrBkP20CTglKdRN2JPIo+/z9Ct73BIXtvFlWNYGHlcL41KcSEBpGZEs2YlBgyU2IY1jvCM3NVV5Vad1zt/Bh2fWJ9+dgDod/UU1UpXeFW27o6OLrFSgBZX8DBVVBXY90OmjrFajMaMEPn3UCTglKdljGG7IIy1u4r4pu9x1i7v4j9hdZggcH+dkYmRTEmNYYxKdGMSo4mzN2jvtbWWB31dn5sNbwe3w+IVac9+GIYfCnEDWz78Y2x7tCpqbDq8P2C21dlVd8YX18aKMuzlvccYVW/DZgJiWO7TmnAQzQpKNWF5JVUsnb/Mb7ZW8Ta/UVsyy2hzoDdJgztFdFQmhiZFEWvyCDEXQ3FxsDRracSxOGN1vLYgVbVS1CkNSx6dYU16u1pz00sO1nuGB230feMzc+6j98vCPyDrQmT/IKtxtSG5UGnbyM2646hQ+s5rTF+wEzrObyHe65JN6FJQakurLSqhvX7j7F2XxFr9h1jw8FjVFbXARAXFsjIpEjSEqNIS4wkPTGK6FA3/SouzrE6cu34L+z7yqqaETsEhFpf1P7B1p0ypz03tSzE+nKvqzlVaqiutBp+ayqtBFK/vKbK8b5+XaVj+UlrhN0BM60qoV4jPd4BrCvTpKBUN1JdW8fW3BI2HTzOppzjbM4pZk9+KfX/fZNigklPjCLdkSiG94ns+MmGaqqsX+tn6dOhOicdJVWpbsTfbmNkUhQjk05NVXqispoth4rZnFPM5pzjbDhwnI82HwbAJjAwIZy0xEjSkqJIT4xkcM+I9s0n4RfY3o+hugBNCkp1UeFB/kzsH8fE/nENywpKq9icc5xNB61E8cWOPN5alwNAgN3GoJ7hDO0VwdDeEQzrHcHgXhHub8hWXYpWHynVjRljyDlW0VCa2Ha4hK25JRSVnQSsjs0psaENiaI+WSSE6wRE3Y1WHymlEBGSYkJIignhkjRrFFdjDEdLqtiaW8y2XCtJbDlUzH+3HG7YLy4skGFOSWJorwhSYkOx2XxweAwfo0lBKR8jIvSMDKJnZBAzhpy6jbOksprtjiRRX6L4elk2NXVWbUJIgJ2+saEkxwSTFB1CcqyVbJKiQ0iMDibIX+8E6g40KSilAIgI8mdcv1jG9Ts13EZVTS27j5ay7XAJ23JLOFBUTnZ+GUt25lNVU3fa/j0iAkl2JImkmBDrteM5ITxQSxldhCYFpVSzAv3sDO9j3eLqzBhD/okqDh4r50BROQcKKxper8ou5L2Nh3Burgzws5EYbd02O75fDONSY+kbG+K+TniqzdyaFERkFvA0YAdeMMY81sQ2VwEPYXV53GSMudadMSml2k9ESIgIIiEiiNF9Y85YX1VTy6FjFRw8VsGBonJyisrJLihj2a583ttwCLBKFuP7xTIuNZZx/WLoFxeqSaITcFtSEBE78BfgfCAHWCMiHxhjtjltMxD4JTDJGHNMRLrACFxKqbMJ9LPTLz6MfvGnj7pqjCErr5RVe4tYnV3Iij2FvL8xF4D48EDGpcYwrl8s41NjGJAQpknCC9xZUhgLZBljsgFEZAFwGbDNaZtbgb8YY44BGGPy3BiPUsrLRISBPcIZ2COc68f3bRgccHV2Eav3FrI6u6ihA15cWABjU62qpvH9YhmYEKbtEh7gzqTQBzjo9D4HGNdom3MARORrrCqmh4wxnzQ+kIjMA+YBJCcnuyVYpZTniQj948PoHx/GteOSMcawv7C8IUGsyi7k4y3WvMbRIf4M7hlBanwo/eJCSXU8kmJCPDPcuI9wZ1JoKqU37innBwwEpgKJwFciMtwYc/y0nYx5DngOrM5rHR+qUqozEBFS4kJJiQvl6jHJDZ3vVmUXsmZfEVl5pSzccphj5dUN+9htQlJ0sCNJhJ2WNHpGBGnpopXcmRRygCSn94lAbhPbrDLGVAN7RWQnVpJY48a4lFJdhHPnu+9nnvo6OVZ2kr2FZezNL2NfYRnZBdbrVdlFVFTXNmwX5G8jJTaUfvGhpMSG0isqmPiwQBIiAokPCyQ+PFD7VzTizqSwBhgoIqnAIeAaoPGdRf8B5gAvi0gcVnVSthtjUkp1A9GhAUSHBpCRHH3a8vre2tkFpex1JIq9BWXsOHyCRVuPNnTEcxYe5EdCuJUg4sODTr12JI36BBIdEuATpQ63JQVjTI2I/AT4FKu94EVjzFYReRhYa4z5wLHuAhHZBtQC9xljCt0Vk1Kqe3Pure08UCBATW0dRWUnyTtRRX79o7SKvJJK8kut95tzjpNXUnVaaaOen01Ijgmhf0IYAxPCGNgjjAHx4fRPCCUkoPt0+dIB8ZRSqpGyqprTkkfeiUqOllSxr6CM3Xkn2F9Yflqpo09UsCNJOJJFQjgDEsKIDO48c0/ogHhKKdVGoYF+pAb6kRoX2uT6kzV17C8sIyuvlN15pQ3PK/cUnjb8R0J4IAMcJYsBCWH0iQ4mPiyIhIhAYkMD8OuEd01pUlBKqVYK8LM19Le4yGl5bZ0h51j5GcninfWHKK2qOe0YIhATEuBoywgkITzI6fXpz2GBfh7ryKdJQSmlOojdJvSNDaVvbOhpI9DWN4AfLq5wVEc5t2lYz3vyCsgvraK69swq/SB/GwnhQdwwoS+3nNvPrZ9Bk4JSSrmZcwN4S4wxFFdUn9GeUf86Ptz9U6JqUlBKqU5CRIgKCSAqJIBzeoR7JYbO18qhlFLKazQpKKWUaqBJQSmlVANNCkoppRpoUlBKKdVAk4JSSqkGmhSUUko10KSglFKqQZcbJVVE8oH9bdw9DijowHA6WmePDzp/jBpf+2h87dOZ4+trjIk/20ZdLim0h4isdWXoWG/p7PFB549R42sfja99Ont8rtDqI6WUUg00KSillGrga0nhOW8HcBadPT7o/DFqfO2j8bVPZ4/vrHyqTUEppVTLfK2koJRSqgWaFJRSSjXolklBRGaJyE4RyRKR+U2sDxSRNx3rV4tIigdjSxKRxSKyXUS2isjdTWwzVUSKRWSj4/GAp+JznH+fiGxxnHttE+tFRJ5xXL/NIpLhwdgGOV2XjSJSIiI/bbSNx6+fiLwoInki8q3TshgR+UxEdjueo5vZ90bHNrtF5EYPxvd7Ednh+Dd8T0Simtm3xb8HN8b3kIgccvp3vLiZfVv8/+7G+N50im2fiGxsZl+3X78OZYzpVg/ADuwB+gEBwCZgaKNt7gCedby+BnjTg/H1AjIcr8OBXU3ENxX4yIvXcB8Q18L6i4GFgADjgdVe/Lc+gtUpx6vXDzgPyAC+dVr2BDDf8Xo+8HgT+8UA2Y7naMfraA/FdwHg53j9eFPxufL34Mb4HgLudeFvoMX/7+6Kr9H6J4EHvHX9OvLRHUsKY4EsY0y2MeYksAC4rNE2lwGvOF6/DcwQEfFEcMaYw8aY9Y7XJ4DtQB9PnLsDXQa8aiyrgCgR6eWFOGYAe4wxbe3h3mGMMcuAokaLnf/OXgEub2LXC4HPjDFFxphjwGfALE/EZ4xZZIypcbxdBSR29Hld1cz1c4Ur/9/braX4HN8dVwFvdPR5vaE7JoU+wEGn9zmc+aXbsI3jP0UxEOuR6Jw4qq1GAaubWD1BRDaJyEIRGebRwMAAi0RknYjMa2K9K9fYE66h+f+I3rx+9XoYYw6D9WMASGhim85yLX+IVfprytn+HtzpJ47qrRebqX7rDNfvXOCoMWZ3M+u9ef1arTsmhaZ+8Te+79aVbdxKRMKAd4CfGmNKGq1ej1Ulkg78CfiPJ2MDJhljMoCLgB+LyHmN1neG6xcAfBd4q4nV3r5+rdEZruWvgBrg9WY2Odvfg7v8DegPjAQOY1XRNOb16wfMoeVSgreuX5t0x6SQAyQ5vU8EcpvbRkT8gEjaVnRtExHxx0oIrxtj3m283hhTYowpdbz+GPAXkThPxWeMyXU85wHvYRXRnblyjd3tImC9MeZo4xXevn5OjtZXqzme85rYxqvX0tGwfSlwnXFUgDfmwt+DWxhjjhpjao0xdcDzzZzX29fPD7gCeLO5bbx1/dqqOyaFNcBAEUl1/Jq8Bvig0TYfAPV3ecwGvmzuP0RHc9Q//gPYboz5YzPb9Kxv4xCRsVj/ToUeii9URMLrX2M1Rn7baLMPgBscdyGNB4rrq0k8qNlfZ968fo04/53dCLzfxDafAheISLSjeuQCxzK3E5FZwC+A7xpjypvZxpW/B3fF59xO9b1mzuvK/3d3mgnsMMbkNLXSm9evzbzd0u2OB9bdMbuw7kr4lWPZw1h//ABBWNUOWcA3QD8PxjYZq3i7GdjoeFwM3A7c7tjmJ8BWrDspVgETPRhfP8d5NzliqL9+zvEJ8BfH9d0CZHr43zcE60s+0mmZV68fVoI6DFRj/Xq9Gaud6gtgt+M5xrFtJvCC074/dPwtZgE3eTC+LKz6+Pq/w/o78noDH7f09+Ch+P7p+PvajPVF36txfI73Z/x/90R8juUv1//dOW3r8evXkQ8d5kIppVSD7lh9pJRSqo00KSillGqgSUEppVQDTQpKKaUaaFJQSinVQJOCUo2ISG2jkVg7bORNEUlxHmlTqc7Gz9sBKNUJVRhjRno7CKW8QUsKSrnIMS7+4yLyjeMxwLG8r4h84Ri47QsRSXYs7+GYp2CT4zHRcSi7iDwv1nwai0Qk2GsfSqlGNCkodabgRtVHVzutKzHGjAX+DDzlWPZnrKHE07AGlXvGsfwZYKmxBubLwOrRCjAQ+IsxZhhwHLjSzZ9HKZdpj2alGhGRUmNMWBPL9wHTjTHZjkENjxhjYkWkAGsIhmrH8sPGmDgRyQcSjTFVTsdIwZo/YaDj/S8Af2PMb93/yZQ6Oy0pKNU6ppnXzW3TlCqn17Vo257qRDQpKNU6Vzs9r3S8XoE1OifAdcByx+svgB8BiIhdRCI8FaRSbaW/UJQ6U3CjSdg/McbU35YaKCKrsX5QzXEsuwt4UUTuA/KBmxzL7waeE5GbsUoEP8IaaVOpTkvbFJRykaNNIdMYU+DtWJRyF60+Ukop1UBLCkoppRpoSUEppVQDTQpKKaUaaFJQSinVQJOCUkqpBpoUlFJKNfj/V6+/L3FyBH0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# Plot training & validation accuracy values\n",
    "plt.plot(history.history['acc'])\n",
    "plt.plot(history.history['val_acc'])\n",
    "plt.title('Model accuracy')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valiidation'], loc='upper left')\n",
    "plt.show()\n",
    "\n",
    "# Plot training & validation loss values\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Valiidation'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}