Create Model_Pneumonia_detection.ipynb

{
 "nbformat": 4,
 "nbformat_minor": 0,
 "metadata": {
  "colab": {
   "name": "Pneumonia.ipynb",
   "provenance": [],
   "collapsed_sections": []
  },
  "kernelspec": {
   "name": "python3",
   "display_name": "Python 3"
  },
  "accelerator": "GPU",
  "pycharm": {
   "stem_cell": {
    "cell_type": "raw",
    "source": [],
    "metadata": {
     "collapsed": false
    }
   }
  }
 },
 "cells": [
  {
   "cell_type": "code",
   "metadata": {
    "id": "HXEE_1mvZ1Kd",
    "colab_type": "code",
    "outputId": "1c19538b-9195-4be4-e61a-c337b61818d5",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    }
   },
   "source": [
    "history# Dataset:  https://www.kaggle.com/paultimothymooney/chest-xray-pneumonia\n",
    "\n",
    "from google.colab import drive\n",
    "drive.mount('/drive')"
   ],
   "execution_count": 212,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Mounted at /drive\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "uEAAti5tZ4bx",
    "colab_type": "code",
    "outputId": "b0cec6d7-6da6-4836-cf22-d437cea52ca6",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    }
   },
   "source": [
    "import os\n",
    "os.listdir('/drive/My Drive/Colab Notebooks/chest_xray')"
   ],
   "execution_count": 213,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['val', 'train', 'test', '.DS_Store']"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 213
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "pqjbKz12aArx",
    "colab_type": "code",
    "outputId": "6dd17c73-3882-4040-8ee1-d9785ca68e29",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    }
   },
   "source": [
    "loc = '/drive/My Drive/Colab Notebooks/chest_xray'\n",
    "loc"
   ],
   "execution_count": 214,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "'/drive/My Drive/Colab Notebooks/chest_xray'"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 214
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "1DsvlfFraRG6",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "from keras.models import Model  \n",
    "from keras.layers import Flatten, Dense\n",
    "from keras.applications.vgg16 import VGG16\n",
    "from glob import glob\n",
    "import numpy as np \n",
    "import matplotlib.pyplot as plt"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "MKB_I-xCa8SL",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "image_size = [224, 224]"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "y05EZd8ubQKP",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "train_data = loc + '/train'\n",
    "test_data = loc + '/test'"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "3-XOLjI8bX5H",
    "colab_type": "code",
    "outputId": "777be7a4-91c9-4dd6-e14e-b07898f58671",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    }
   },
   "source": [
    "vgg = VGG16(input_shape = image_size+[3], weights = 'imagenet', include_top = False)\n",
    "vgg"
   ],
   "execution_count": 218,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<keras.engine.training.Model at 0x7ff24da92f60>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 218
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "CeRAmXZYcPEw",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "for layers in vgg.layers:\n",
    "  layers.trainable = False"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "I-tLkFyVcm9F",
    "colab_type": "code",
    "outputId": "b65afb30-7275-4513-8d4e-9b8d8a8e72df",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    }
   },
   "source": [
    "folder = glob(train_data+'/*')\n",
    "folder"
   ],
   "execution_count": 220,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['/drive/My Drive/Colab Notebooks/chest_xray/train/NORMAL',\n",
       " '/drive/My Drive/Colab Notebooks/chest_xray/train/PNEUMONIA']"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 220
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RI4Nv3XhrKAo",
    "colab_type": "text"
   },
   "source": [
    "# Flatten Model"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "ruFu4ZvIiz25",
    "colab_type": "code",
    "outputId": "dabc9a9c-5975-4712-9b24-f8bdccac965b",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    }
   },
   "source": [
    "X = Flatten()(vgg.output)\n",
    "X"
   ],
   "execution_count": 221,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'flatten_5/Reshape:0' shape=(?, ?) dtype=float32>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 221
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "U8sm7Tw-o-ts",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "# n-2 layer\n",
    "x = Dense(1024, activation='relu')(X)\n",
    "\n",
    "# n-1 layer\n",
    "X = Dense(256, activation = 'relu')(X)"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "m4umxkukpc9u",
    "colab_type": "code",
    "outputId": "5b40bc54-7543-4e8b-b3c1-a24fea5c0446",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    }
   },
   "source": [
    "prediction = Dense(len(folder), activation = 'softmax')(X)\n",
    "prediction"
   ],
   "execution_count": 223,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'dense_33/Softmax:0' shape=(?, 2) dtype=float32>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 223
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "dmgV-iThpowO",
    "colab_type": "code",
    "outputId": "71f252b9-dfdd-4ea0-aa93-e3b12c7b5919",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 88
    }
   },
   "source": [
    "model = Model(input = vgg.input, output = prediction)\n",
    "model"
   ],
   "execution_count": 224,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ],
     "name": "stderr"
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<keras.engine.training.Model at 0x7ff24da73710>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 224
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "WsQ2KsxpqCFY",
    "colab_type": "code",
    "outputId": "3c1f0ae5-b9bb-4e71-8e03-5945366a4cb9",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 901
    }
   },
   "source": [
    "model.summary()"
   ],
   "execution_count": 225,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Model: \"model_18\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_17 (InputLayer)        (None, 224, 224, 3)       0         \n",
      "_________________________________________________________________\n",
      "block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      \n",
      "_________________________________________________________________\n",
      "block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     \n",
      "_________________________________________________________________\n",
      "block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     \n",
      "_________________________________________________________________\n",
      "block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    \n",
      "_________________________________________________________________\n",
      "block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    \n",
      "_________________________________________________________________\n",
      "block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    \n",
      "_________________________________________________________________\n",
      "block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   \n",
      "_________________________________________________________________\n",
      "block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   \n",
      "_________________________________________________________________\n",
      "block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "flatten_5 (Flatten)          (None, 25088)             0         \n",
      "_________________________________________________________________\n",
      "dense_32 (Dense)             (None, 256)               6422784   \n",
      "_________________________________________________________________\n",
      "dense_33 (Dense)             (None, 2)                 514       \n",
      "=================================================================\n",
      "Total params: 21,137,986\n",
      "Trainable params: 6,423,298\n",
      "Non-trainable params: 14,714,688\n",
      "_________________________________________________________________\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "eA-YiaSnqje7",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "model.compile(loss = 'categorical_crossentropy',\n",
    "             optimizer = 'adam', \n",
    "              metrics = ['accuracy'])"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "4m_szSpUwh4R",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "1ERfMqRexXfy",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip=True)\n",
    "                                   \n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "vo-MMjcaxvIA",
    "colab_type": "code",
    "outputId": "ae4a6712-faf7-4ff8-9276-0b9ebea6b706",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    }
   },
   "source": [
    "training_set = train_datagen.flow_from_directory(loc+'/train',\n",
    "                                                target_size=(224, 224),\n",
    "                                                batch_size=32,\n",
    "                                                class_mode='categorical')\n",
    "\n",
    "testing_set = test_datagen.flow_from_directory(loc+'/test',\n",
    "                                               target_size=(224, 224),\n",
    "                                               batch_size=32,\n",
    "                                               class_mode='categorical')"
   ],
   "execution_count": 229,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Found 5229 images belonging to 2 classes.\n",
      "Found 624 images belonging to 2 classes.\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "jVEFTD9-ynwe",
    "colab_type": "code",
    "outputId": "d3a25997-93e8-4f82-cb9f-098922aeb22b",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 901
    }
   },
   "source": [
    "f = model.fit_generator(training_set,\n",
    "              validation_data = testing_set,\n",
    "              epochs=40,\n",
    "              steps_per_epoch = len(training_set),\n",
    "              validation_steps = len(testing_set)\n",
    "             )"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "164/164 [==============================] - 146s 888ms/step - loss: 0.0386 - acc: 0.9869 - val_loss: 0.3555 - val_acc: 0.9135\n",
      "Epoch 2/40\n",
      "164/164 [==============================] - 143s 872ms/step - loss: 0.0571 - acc: 0.9779 - val_loss: 0.3397 - val_acc: 0.9183\n",
      "Epoch 3/40\n",
      "164/164 [==============================] - 142s 867ms/step - loss: 0.0485 - acc: 0.9817 - val_loss: 0.4066 - val_acc: 0.9119\n",
      "Epoch 4/40\n",
      "164/164 [==============================] - 141s 860ms/step - loss: 0.0384 - acc: 0.9843 - val_loss: 0.2837 - val_acc: 0.9231\n",
      "Epoch 5/40\n",
      "164/164 [==============================] - 138s 844ms/step - loss: 0.0406 - acc: 0.9851 - val_loss: 0.3920 - val_acc: 0.9151\n",
      "Epoch 6/40\n",
      "164/164 [==============================] - 137s 834ms/step - loss: 0.0477 - acc: 0.9813 - val_loss: 0.4040 - val_acc: 0.9022\n",
      "Epoch 7/40\n",
      "164/164 [==============================] - 132s 803ms/step - loss: 0.0368 - acc: 0.9845 - val_loss: 0.4710 - val_acc: 0.9135\n",
      "Epoch 8/40\n",
      "164/164 [==============================] - 132s 807ms/step - loss: 0.0376 - acc: 0.9872 - val_loss: 0.3185 - val_acc: 0.9167\n",
      "Epoch 9/40\n",
      "164/164 [==============================] - 139s 848ms/step - loss: 0.0318 - acc: 0.9895 - val_loss: 0.3682 - val_acc: 0.9151\n",
      "Epoch 10/40\n",
      "164/164 [==============================] - 144s 875ms/step - loss: 0.0365 - acc: 0.9851 - val_loss: 0.2845 - val_acc: 0.9263\n",
      "Epoch 11/40\n",
      "164/164 [==============================] - 141s 859ms/step - loss: 0.0363 - acc: 0.9869 - val_loss: 0.3389 - val_acc: 0.9151\n",
      "Epoch 12/40\n",
      "164/164 [==============================] - 142s 866ms/step - loss: 0.0385 - acc: 0.9863 - val_loss: 0.4566 - val_acc: 0.8974\n",
      "Epoch 13/40\n",
      "164/164 [==============================] - 143s 872ms/step - loss: 0.0338 - acc: 0.9873 - val_loss: 0.4142 - val_acc: 0.9054\n",
      "Epoch 14/40\n",
      "164/164 [==============================] - 143s 874ms/step - loss: 0.0322 - acc: 0.9878 - val_loss: 0.5958 - val_acc: 0.8830\n",
      "Epoch 15/40\n",
      "164/164 [==============================] - 140s 854ms/step - loss: 0.0474 - acc: 0.9827 - val_loss: 0.4969 - val_acc: 0.8878\n",
      "Epoch 16/40\n",
      "164/164 [==============================] - 132s 803ms/step - loss: 0.0361 - acc: 0.9870 - val_loss: 0.4616 - val_acc: 0.9151\n",
      "Epoch 17/40\n",
      "164/164 [==============================] - 129s 784ms/step - loss: 0.0320 - acc: 0.9884 - val_loss: 0.3564 - val_acc: 0.9199\n",
      "Epoch 18/40\n",
      "164/164 [==============================] - 128s 780ms/step - loss: 0.0331 - acc: 0.9877 - val_loss: 0.3345 - val_acc: 0.9263\n",
      "Epoch 19/40\n",
      "164/164 [==============================] - 127s 774ms/step - loss: 0.0293 - acc: 0.9874 - val_loss: 0.4065 - val_acc: 0.9311\n",
      "Epoch 20/40\n",
      "164/164 [==============================] - 128s 778ms/step - loss: 0.0320 - acc: 0.9893 - val_loss: 0.2497 - val_acc: 0.9263\n",
      "Epoch 21/40\n",
      "164/164 [==============================] - 126s 769ms/step - loss: 0.0282 - acc: 0.9896 - val_loss: 0.3088 - val_acc: 0.9247\n",
      "Epoch 22/40\n",
      "164/164 [==============================] - 125s 760ms/step - loss: 0.0358 - acc: 0.9882 - val_loss: 0.3849 - val_acc: 0.9135\n",
      "Epoch 23/40\n",
      "164/164 [==============================] - 133s 809ms/step - loss: 0.0311 - acc: 0.9889 - val_loss: 0.5543 - val_acc: 0.9119\n",
      "Epoch 24/40\n",
      "164/164 [==============================] - 136s 831ms/step - loss: 0.0307 - acc: 0.9889 - val_loss: 0.5613 - val_acc: 0.9006\n",
      "Epoch 25/40\n",
      "164/164 [==============================] - 137s 835ms/step - loss: 0.0246 - acc: 0.9903 - val_loss: 0.3255 - val_acc: 0.9231\n",
      "Epoch 26/40\n",
      "164/164 [==============================] - 138s 844ms/step - loss: 0.0292 - acc: 0.9889 - val_loss: 0.3641 - val_acc: 0.9263\n",
      "Epoch 27/40\n",
      "164/164 [==============================] - 138s 839ms/step - loss: 0.0318 - acc: 0.9892 - val_loss: 0.3983 - val_acc: 0.9247\n",
      "Epoch 28/40\n",
      "164/164 [==============================] - 137s 837ms/step - loss: 0.0338 - acc: 0.9889 - val_loss: 0.4146 - val_acc: 0.8926\n",
      "Epoch 29/40\n",
      "164/164 [==============================] - 134s 816ms/step - loss: 0.0311 - acc: 0.9886 - val_loss: 0.3667 - val_acc: 0.9247\n",
      "Epoch 30/40\n",
      "164/164 [==============================] - 133s 810ms/step - loss: 0.0289 - acc: 0.9897 - val_loss: 0.3707 - val_acc: 0.9183\n",
      "Epoch 31/40\n",
      "164/164 [==============================] - 132s 806ms/step - loss: 0.0193 - acc: 0.9933 - val_loss: 0.3295 - val_acc: 0.9231\n",
      "Epoch 32/40\n",
      "164/164 [==============================] - 132s 806ms/step - loss: 0.0274 - acc: 0.9920 - val_loss: 0.4899 - val_acc: 0.9215\n",
      "Epoch 33/40\n",
      "164/164 [==============================] - 131s 797ms/step - loss: 0.0266 - acc: 0.9912 - val_loss: 0.4306 - val_acc: 0.9071\n",
      "Epoch 34/40\n",
      "164/164 [==============================] - 133s 810ms/step - loss: 0.0206 - acc: 0.9918 - val_loss: 0.4366 - val_acc: 0.9215\n",
      "Epoch 35/40\n",
      "164/164 [==============================] - 133s 812ms/step - loss: 0.0207 - acc: 0.9929 - val_loss: 0.4157 - val_acc: 0.9279\n",
      "Epoch 36/40\n",
      "164/164 [==============================] - 133s 813ms/step - loss: 0.0236 - acc: 0.9912 - val_loss: 0.4734 - val_acc: 0.9231\n",
      "Epoch 37/40\n",
      "164/164 [==============================] - 133s 809ms/step - loss: 0.0216 - acc: 0.9917 - val_loss: 0.7750 - val_acc: 0.8750\n",
      "Epoch 38/40\n",
      "164/164 [==============================] - 133s 814ms/step - loss: 0.0319 - acc: 0.9878 - val_loss: 0.3535 - val_acc: 0.9295\n",
      "Epoch 39/40\n",
      "164/164 [==============================] - 134s 814ms/step - loss: 0.0252 - acc: 0.9914 - val_loss: 0.3363 - val_acc: 0.9151\n",
      "Epoch 40/40\n",
      "164/164 [==============================] - 132s 805ms/step - loss: 0.0284 - acc: 0.9897 - val_loss: 0.4022 - val_acc: 0.9087\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "UHxlWADo0FPc",
    "colab_type": "code",
    "outputId": "8eec62bd-b718-448e-e267-dccc11ed00e2",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 612
    }
   },
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(f.history['loss'], label='training loss')\n",
    "plt.plot(f.history['val_loss'], label='val loss')\n",
    "plt.legend()\n"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdb71145978>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 168
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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ISZIkSZJUaFJ1UN4HKkfu/ZzKajuEtFcGQpIkSZIkFZpUXVguVraPt/WVI2H9ypyVpMJi\nICRJkiRJUqFJ1cOQvcwPSqussUNIe2UgJEmSJElSIYnjjg6h8fs+L71krL09F1WpwBgISZIkSZJU\nSDalYMu6LgRCNdC+DTY356QsFRYDIUmSJEmSCklnO4ylVVaHozuNaQ8MhCRJkiRJKiTN9eHYlQ4h\nMBDSHnUpEIqi6KQoil6IoujlKIou38PPfxhF0ZMd/70YRZH9aJIkSZIkZcP2DqFOhkoP7NiSvsVA\nSLvr1dkJURSVA1cDJwCNQG0URXfEcfxs+pw4jr+w0/mfBWZmoVZJkiRJkpSqg/7Doc/AfZ/nkjHt\nQ1c6hOYCL8dx/Gocx1uBG4DT93H++cB/Z6I4SZIkSZK0i67sMAbQuxIq+hsIaY+6EgiNBpbv9H1j\nx2O7iaJoHDAB+GvPS5MkSZIkSbtJ1XctEIqiHVvPS7vI9FDpDwE3xXHctqcfRlH0iSiKFkVRtKip\nqSnDt5YkSZIkqci1tcLa5TCkk/lBaZU1dghpj7oSCK0Axu70/ZiOx/bkQ+xjuVgcx9fEcTwnjuM5\nVVVVXa9SkiRJkiTBuhXQ3tq1DiGwQ0h71ZVAqBaYGEXRhCiKehNCnzt2PSmKoknAUGBBZkuUJEmS\nJEnATjuMje/a+ZU1sH5ltqpRAes0EIrjuBX4R+Be4DlgXhzHy6Io+kYURaftdOqHgBviOI6zU6ok\nSZIkSSWuuT4cuxwIjYRNKWjdkrWSVJg63XYeII7ju4C7dnnsyl2+/1rmypIkSZIkSbtJ1UFUDoP2\nuNfT7tJbz29ogsFjslaWCk+mh0pLkiRJkqRsSdXBkLFQ3qX+jrBkDBwsrd0YCEmSJEmSVChSdV1f\nLgY7OoQcLK1dGAhJkiRJklQoUvX7GQjZIaQ9MxCSJEmSJKkQbGmBjathyLiuP2dAVTjaIaRdGAhJ\nkiRJklQIUvu5wxhAr97Qfzi0uPW83s5ASJIkSZKkQpCqC8f9CYQgLBtzyZh2YSAkSZIkSVIh6HYg\nVO2SMe3GQEiSJEmSpELQXA99BkG/ofv3PDuEtAcGQpIkSZIkFYJUHQwdB1G0f89LdwjFcVbKUmEy\nEJIkSZIkqRCk6vZ/uRiEDqHWTWGXMqmDgZAkSZIkSfmuvT3sMtbdQAhcNqa3MRCSJEmSJCnfrX8T\n2rbAkHH7/1wDIe2BgZAkSZIkSflu+w5jE/b/uQZC2gMDIUmSJEmS8l13t5yHMFQa3Hpeb2MgJEmS\nJElSvkvVAREMGbv/z+03FMoq7BDS2xgISZIkSZKU75rrYdBo6NVn/58bRWHZmB1C2omBkCRJkiRJ\n+S5VB0O7MVA6rbLaDiG9jYGQJEmSJEn5LlXXvflBaQNHQouBkHYwEJIkSZIkKZ9t2wQtb/QsELJD\nSLswEJIkSZIkKZ81Lw/HHgVCNbBxNbS3ZaQkFT4DIUmSJEmS8ll6y/khPZwhFLfDhtUZKUmFz0BI\nkiRJkqR8lg6EetohBC4b03YGQpIkSZIk5bNUHfTqF7p8umt7IOTW8woMhCRJkiRJymfN9aE7KIq6\nf43tgdDKjJSkwmcgJEmSJElSPkvVwdAezA+CHd1FLhlTBwMhSZIkSZLyVRx3BELje3adin7QZ7BL\nxrSdgZAkSZIkSflq4xrYur7ngRCELiE7hNTBQEiSJEmSpHyVqg/HjARCNXYIaTsDIUmSJEmS8lXq\ntXAc0sMZQmCHkN7GQEiSJEmSpHyVqgvHng6VhtAh1GIgpMBASJIkSZKkfJWqgwHV0HtAz681sAa2\ntsDWDT2/lgqegZAkSZIkSfkqEzuMpVXWhKNzhISBkCRJkiRJ+au5PjPLxSDMEAIDIQEGQpIkSZIk\n5ae2bbC2MQsdQs4RkoGQJEmSJEn5ae1yiNsNhJQVBkKSJEmSJOWj7TuMjc/M9foPh6jMJWMCDIQk\nSZIkScpPqfpwzFQgVFYedixbvzIz11NBMxCSJEmSJCkfpeqgrAIGjsrcNSur7RASYCAkSZIkSVJ+\nStXBkANDZ0+mVNY4Q0iAgZAkSZIkSfkpVZe55WJplTV2CAkwEJIkSZIkKT8112chEOpYMtbentnr\nquAYCEmSJEmSlG82NcOmFAwdl9nrVtZA+zbY3JzZ66rgGAhJkiRJkpRvmjO8w1haZXU4trjTWKkz\nEJIkSZIkKd+k6sIx04HQwJHh6GDpkmcgJEmSJElSvkllq0OoJhwdLF3yDIQkSZIkSco3qTroOwT6\nDs7sddNLxuwQKnkGQpIkSZIk5ZtsbDkP0LsSKvobCMlASJIkSZKkvJOtQCiKdmw9r5JmICRJkiRJ\nUj5pb4O1y7MTCEGYI2SHUMkzEJIkSZIkKZ+0vAFtW2HouOxc30BIGAhJkiRJkpRfsrXlfJqBkDAQ\nkiRJkiQpv+QiENqUgtYt2bm+CoKBkCRJkiRJ+SRVB1EZDB6bneunt57f0JSd66sgGAhJkiRJkpRP\nUvUwaAyUV2Tn+pU14eiysZJmICRJkiRJUj5J1WVvoDTs6BBy6/mSZiAkSZIkSVI+SdVlb34Q7OgQ\nalmZvXso7xkISZIkSZKUL7ZugA2rshwI2SGkLgZCURSdFEXRC1EUvRxF0eV7OefcKIqejaJoWRRF\n12e2TEmSJEmSSkBzQzhmMxAqr4D+w50hVOJ6dXZCFEXlwNXACUAjUBtF0R1xHD+70zkTgX8Fjo7j\nOBVFUXW2CpYkSZIkqWhle8v5tMoaA6ES15UOobnAy3EcvxrH8VbgBuD0Xc75OHB1HMcpgDiO7TuT\nJEmSJGl/5SwQqnbJWInrSiA0Gli+0/eNHY/t7GDg4CiKHo2iaGEURSft6UJRFH0iiqJFURQtampq\n6l7FkiRJkiQVq1Qd9K4MS7qyyQ6hkpepodK9gInA8cD5wC+jKBqy60lxHF8Tx/GcOI7nVFVVZejW\nkiRJkiQViVR96A6Kouzep7I6BEJxnN37KG91JRBaAYzd6fsxHY/trBG4I47jbXEcvwa8SAiIJEna\ntxfugRfvS7oKSZKk/JCqgyHjsn+fypHQuhm2rMv+vZSXuhII1QIToyiaEEVRb+BDwB27nHMboTuI\nKIpGEJaQvZrBOiVJxeqeL8Ptn4bWrUlXIkmSlKw4DoFQtucHQVgyBs4RKmGdBkJxHLcC/wjcCzwH\nzIvjeFkURd+Ioui0jtPuBdZEUfQs8CBwWRzHa7JVtCSpSKxvCi96NjTB839OuhpJkqRkrV8FrZty\nFAh1bA7uHKGS1em28wBxHN8F3LXLY1fu9HUMfLHjP0mSuqaxNhzLe8Pia2HaWcnWI0mSlKTm+nDM\naYeQgVCpytRQaUmS9l9jLZT1gqM+B689DGteSboiSZKk5Gzfcj4XM4TSHUIuGStVBkKSpOQ01kLN\nNJj78RAMLb426YokSZKSkw6EhhyY/Xv1Gxq6tO0QKlkGQpKkZLS3wYolMOZwGDgSDjkZnrweWrck\nXZkkSVIyUnUwcBRU9Mv+vaIoLBtrMRAqVQZCkqRkrHoOtm0IgRDA7Ith4xp47k/J1iVJkpSUVH1u\n5gelVVbbIVTCDIQkSclID5Qe2xEIHfSe8AJokcvGJElSiUrVwZAczA9Kq6xxhlAJMxCSJCWjcRH0\nHw5DJ4Tvy8pg1j9A/XxoejHZ2iRJknKtdQusW2GHkHLGQEiSlIzG2rBcLIp2PDbzwo7h0r9NrCxJ\nkqRENC8H4hwHQjWwcXWY7aiSYyAkScq9Tc2w+gUYM+ftj1dWw6S/g6euh22bkqlNkiQpCdu3nB+f\nu3tWVkPcDhtW5+6eyhsGQpKk3FuxOBzTA6V3Nudi2JSCZ2/PbU2SJElJaq4Lx6E5niEEsH5l7u6p\nvGEgJEnKvcZFQAQHzNr9Z+OPhWEHOVxakiSVllQdlPeBypG5u2f6Xg6WLkkGQpKk3GusherJ0HfQ\n7j8rK4PZF8HyhWFrekmSpFKQqgvdQWU5fJteWR2ODpYuSQZCkqTciuOOgdJz9n7OjAugvLddQpIk\nqXSk6nI7PwgMhEqcgZAkKbfWvAKbm/c8PyhtwAiYfCo8dQNs3Zi72iRJkpIQx5CqhyE5nB8EUNEP\n+gx2yViJMhCSJOVW4+PhuK9ACGD2xbBlLSy7Nfs1SZIkJWlTCrasy32HEIQuITuESpKBkCQptxpr\noc8gGHHIvs8b/24YPhEWu2xMkiQVuSS2nE+rrIEWA6FSZCAkScqtxloYPavzgYlRFLagb6yFlUtz\nU5skSVISkgyEBtbYIVSiDIQkSbmzdQO8uazz5WJph50ftl+1S0iSJBWz5vpwHJrjGUIQOoScIVSS\nDIQkSbnz+hMQt3c9EOo/DKaeAU/PC2GSJElSMUrVQf/h0Gdg7u9dWQ1bW3ytVYIMhCRJudNYG46j\n97Hl/K5mXxyGLC69OTs1ScofcRz+k6RSk8SW82mVNeFol1DJ6ZV0AZKkEtK4CIa9AwYM7/pzDjwS\nqibBomth1kezV5uk3NrUDKueg1XLwvHNZ2HVszByOlz056Srk6TcStXB6NnJ3LuyOhzXr4JhE5Kp\nQYkwEJIk5UYchw6hg96zf8+LotAldM+X4Y2nYNRh2alPUna0boGmF3aEP28+G75e17jjnD6DoHpK\nmJ1R/yhs2wwVfZOrWZJyqa0V1jbCtLOTuf/2DiEHS5caAyFJUm6sXR5eaIzZj+ViaYedBw9cFbqE\nTv1R5muT1HPt7dBct6PT582Ozp81L0PcFs4pq4CqQ2DcUVAzBaqnQvVkGDwmhL9Lb4abLoE1L4VO\nIUkqBetWQHsrDElgoDQYCJUwAyFJUm6k5wd1daD0zvoNhalnwTP/Ax/4ZjIDFyXtsH5VR+CTDn+e\nhabnYdvGHecMHR8CnymnhdCneioMfweUV+z9utVTwnHV8wZCkkpHklvOQxhmHZUbCJUgAyFJUm40\nLoJe/aBmaveeP+dieOp6eOam8LWk3Fm5FJ74w46un42rd/ys/4jQ7TPrH3Z0/VQdAn0q9/8+w94B\nZb2g6bnM1S5J+S7pQKisHAZUGQiVIAMhSVJuNNbCATP33R2wL2MOD280F19rICTl2v1XQt0joWvn\nkJPCn8V0+FNZlbn79OoNw98ZQidJKhXN9SEMHzQ6uRoqq91lrAQZCEmSsq91SxgIfcSnun+NKApB\n0F1fghVLYPSszNUnae/a22D54zDzQvi7H2b/flWTwt8XklQqUnVhllp5gm/PK2vsECpBZUkXIEkq\nASufgbat3ZsftLNDz4WK/qFLSFJuvLkMtrbAge/Kzf2qJ4c3R1s3dnqqJBWFVF1yy8XSKmvsECpB\nBkKSpOzryUDpnfUdDNPOgmduhs3rel6XpM41LAzHA4/Mzf2qJwMxrH4xN/eTpKTlRSBUHTqE2tuT\nrUM5ZSAkScq+xloYNAYGjer5tWZfAts2wDPzen4tSZ1rWBDmWgwem5v7VU0Ox6bnc3M/SUrSlhbY\nuCb5QGjgSGhvhU2pZOtQThkISZKyb3ktjJmTmWuNnhUG2y76LcRxZq4pac/iOARCB74rzPHKhWEH\nQXnvsJ29JBW7VH04DhmXbB2V1eHoHKGSYiAkScqulpWwtqHny8XSoghmXwxvPgMrFmfmmpL2rLkB\nWt7I3XIxCENVh0+EVXYISSoBSW85n1ZZE44GQiXFQEiSlF2Ni8IxU4EQwPRzoGIALHK4tJRV2+cH\n5WigdFr1JGhy63lJJSDvAiEHS5cSAyFJUnY11kJZBYw6NHPX7DsIpv89LL0ZNjVn7rqS3q5hAfQZ\n3DHoOYeqJofupC3rc3tfScq1VF34e7bf0GTrcMlYSTIQkiRlV+OiMPOnol9mrzvnYmjdBE87XFrK\nmoYFMHYulJXn9r7pAGr1C7m9ryTlWnM9DD0wd3Pa9qZ3JVT0NxAqMQZCkqTsaWuF15eEN5SZdsBM\nGDUDFl/rcGkpGza+FXb6yuX8oLR0ILTKZWOSilw+bDkPIZCqrDEQKjEGQpKk7Fn1LGzbmNn5QTub\nc3G4x/LHs3N9qZQtfywccz0/CMKbo159DYQkFbf29rDLWD4EQmAgVIIMhCRJ2dNYG46Z2nJ+V9P+\nHnoPDF1CkjKrYUGY/zV6Vu7vXVYOIyaGDiVJKlbrV0LbljwKhKodKl1iDIQkSdnTuAgGVMGQcdm5\nfp9KOPQcWHYrbEpl5x5SqWpYGJZmZnr+V1dVTXbreUnFLVUfjkPGJ1rGdnYIlRwDIUlS9jTWhuVi\n2RyUOPtiaN0MT92QvXtIpWbKHiXoAAAgAElEQVTbJlixBMYlsFwsrXoyrGuEzeuSq0GSsilftpxP\nq6wJH7C1bkm6EuWIgZAkKTs2vgVrXsrecrG0UYfC6NmwyOHSUsa8/gS0b0tmflBaerC0y8YkFatU\nHRDBkLFJVxKkt57f0JRsHcoZAyFJUnasWBKO2RoovbPZF4ftqRsWZP9eUilI/1kae0RyNVRNCkcH\nS0sqVqk6GDQaevVJupKgsiYcW1w2VioMhCRJ2dFYC1FZmEGSbdPOgj6DQpeQpJ5rWBgCmf7Dkqth\nyDio6G+HkKTi1ZxHO4wBDOwIhJwjVDIMhCRJ2dFYC9VToM/A7N+r9wA49Dx49jbYsCb795OKWXs7\nNDwGBx6ZbB1lZTDiYDuEJBWvVB0MzdLGG91RaSBUagyEJEmZ194OKxZlf37QzuZcDG1b4anrc3dP\nqRiteha2rE12flBa9RQ7hCQVp22boOWN/OoQGlAVjm49XzIMhCRJmbfmJdi8Njfzg9JqpsKYubD4\ntw6XlnoiPT8o6Q4hgOpJ4Q3TplTSlUhSZjU3hGM+BULlFdB/uB1CJcRASJKUeY214ZjLQAhCl9Ca\nl6HukdzeVyomDQth4KgwwydpVR07ja2yS0hSkUnVh2M+BUIQlo0ZCJUMAyFJUuY11kKfwTB8Ym7v\nO/VM6DvY4dJSTzQsDN1BUZR0JaFDCKDJOUKSikyqLhzzIXzfWWW1gVAJMRCSJGVe4yIYMzsMhc2l\nin5w2Pnw3J9gfVNu7y0Vg+blsK4xP+YHAQweC70r7RCSVHxSddCrXwhg8knlSAOhEmIgJEnKrC0t\nYSjtmLnJ3H/2xdC+DZ78YzL3lwpZw8JwzJdAKIqg6hA7hCQVn1RdWC6WD92YO6usDkOlncdYEgyE\nJEmZ9foTELfnfn5QWvWk8GZ28W/DbmeSuq5hAfQeGIa054vqyXYISSo+zfX5Nz8Iwgyh1s2wZV3S\nlSgHDIQkSZmVHig9elZyNcy+GFKvwWv/m1wNUiFqWAhj50JZedKV7FA1GTasgg1rkq5EkjIjjjs6\nhPJsfhCEQAjcer5EGAhJkjKrcVEYJt1/WHI1TDkd+g2FxQ6XlrpsUyos98yX5WJpDpaWVGw2roGt\n6/O0Q6hjppFzhEqCgZAkKXPiOHQIJbVcLK2iLxz2YXj+TmjxBY3UJctrgTjsMJZPtm89byAkqUik\ndxjLy0Ao3SHk66dSYCAkScqc5nrY0ARj5iRdCcy+CNpb4cnrkq5EKgwNf4OyXjB6dtKVvN2gA6DP\nIGhyjpCkIpHXgVBHh5AfqJUEAyFJUuY0LgrHpDuEAKoOhnHvhsW/c7i01BUNC2HUDOjdP+lK3i6K\nHCwtqbikA6EhByZaxh71Gwrlve0QKhEGQpKkzGmshYr+UD0l6UqCOReHrqVX/5p0JVJ+27YZVizO\nv+ViaVWTwnwjt0GWVAxSdTCgGnoPSLqS3UVRWDbmUOmS0KVAKIqik6IoeiGKopejKLp8Dz+/KIqi\npiiKnuz479LMlypJynuNtXDALCjvlXQlweRTof9wWORwaWmf3ngS2rbm30DptOrJsOmtsCRVkgpd\nqi4/l4ulVVbbIVQiOg2EoigqB64GTgamAOdHUbSnj35vjON4Rsd/v8pwnZKkfLdtM7zxdH7MD0rr\n1QdmXAAv3A3r3ki6Gil/NSwIx3zuEAIHS0sqDqn6PA+E7BAqFV3pEJoLvBzH8atxHG8FbgBOz25Z\nkqSCs/JpaN+WH/ODdjb7Iojb4AmHS0t71bAQRhwMA0YkXcmeVXfsNOZgaUmFrm0brGuEoeOSrmTv\n7BAqGV0JhEYDy3f6vrHjsV2dHUXR01EU3RRF0diMVCdJKhzLHw/HfOoQAhj+DphwHCz5HbS3JV2N\nlH/a20MglK/dQRA+re431A4hSYVv7XKI2/O/Q2hDE7S1Jl2JsixTQ6X/BIyP4/hQ4H7gd3s6KYqi\nT0RRtCiKokVNTa4Bl6Si0lgLgw+EgSOTrmR3cy4OL8Be/kvSlUj5Z/ULsLk5f+cHQRhyWjXZQEhS\n4cvnLefTKmuAGDauTroSZVlXAqEVwM4dP2M6HtsujuM1cRxv6fj2V8DsPV0ojuNr4jieE8fxnKqq\nqu7UK0nKV42L8q87KO2QU2BAFTz0bdjUnHQ1Un7J9/lBadWToOk5dxqTVNgKJhDCZWMloCuBUC0w\nMYqiCVEU9QY+BNyx8wlRFI3a6dvTAD++kaRSsu71sB4+3+YHpfXqDR/8Pqx8Bq49Gdau6Pw5Uqlo\nWBhe/A+dkHQl+1Y1GTavhZaVSVciSd2XqoeyChg4qvNzk7I9EHKwdLHrNBCK47gV+EfgXkLQMy+O\n42VRFH0jiqLTOk77XBRFy6Ioegr4HHBRtgqWJOWhxkXhOHZusnXsy9Qz4IL/gebl8OsT4M1nk65I\nyg8NC0J3UBQlXcm+VXfsNNbk546SCliqDoYcCGXlSVeyd5XV4WiHUNHr0gyhOI7viuP44DiO3xHH\n8bc6HrsyjuM7Or7+1ziOp8ZxfFgcx++J49gtICSplDTWQnlvGDk96Ur27R3vgYvvCsOlf3MS1M1P\nuiIpWWtXQHNDfs8PSqvq2GlslS8zJRWwVF1+LxcDA6ESkqmh0pKkUta4CEYdBr36JF1J50YdCpfe\nH4Zf/+FMWHpL0hVJySmU+UEAlVXQfwSssrtP2qMNa6D+b0lXoc4UQiBU0Q/6DHbJWAkwEJIk9Uzb\nNnj9ifydH7QnQw6ES+6B0bPhpothwdVJVyQlo2EhVAyAmjzv7kurngxNdghJe/Twf4Q5eXWPJl2J\n9mZTc9jVMd8DIQhdQs5sK3oGQpKknnlzGbRuyt8dxvam/zD4yG0w+TS49wq49yvQ3p50VVJuNSyE\nsYdDea+kK+maqknQ9II7jUl78trD4Xj7p2HrhmRr0Z4114fj0HHJ1tEVA0faIVQCDIQkST3TWBuO\nhdQhlFbRF875Lcz9JCz4Cdz8MWjdknRVUm5sXgtvLoUDj0q6kq6rngRb1sE6dwqU3mbDmrCccuKJ\nYUnSA19LuiLtSSFsOZ9WWe0MoRJgICRJ6pnGRWF70sFjk66ke8rK4eTvwgnfgGW3wHVnh5Zuqdgt\nrwXiwpgflOZgaWnPGjpmBx3zz3DEp+Dxa+C1R5KtSbsrqECoxg6hEmAgJEnqmcba0B2U71tW70sU\nwdH/BGf9KiyhufbksPuSVMwaFkBUXljLPavTgZCDpaW3qZsPvfrBATPhfVfCsIPC0rEt65OuTDtL\n1UO/odB3cNKVdK6yGra2uPywyBkISZK6b+Nb8NYrhfWGcl8OPQcuvAmal8OvT4A3fdOpItawMOwO\n2HtA0pV0Xf9h4VNrB0tLb1f3KBx4BPTqHf5Mn/7T8G/ZA1clXZl2lqqDIQUwPwjC37Vgl1CRMxCS\nJHVf46JwLMT5QXtz0PFwyd3Q3ga/OcmWexWn1q2wYhEc+K6kK9l/VZNg1XNJVyHlj41vhXlg4969\n47Fx74IjPw21v4JXH0qsNO2iELacT6usDkfnCBU1AyFJUvc11kJUFlrUi8nI6XDp/WGHjevOgqU3\nJ12RlFlvPAWtmwtrflBa9eSw05i7AkpBwwIghvHvfvvj7/0qDHsH3P5Z2NKSSGnaSXsbNDcUUCA0\nMhwNhIqagZAkqfsaH4eaqYW15KSrhhwIl9wDo2fDTZfA336SdEVS5qQH0BZiIFQ1CbZtgLXLk65E\nyg9186FXXxg96+2P9+4PZ/ws/Fm57/8mU5t2WPc6tG8roEDIJWOlwEBIktQ97W3QuLi4lovtqv8w\n+MhtMPk0uO8rcM8VdiWoODQsDJ0D6SUBhaR6Sjg6R0gK6ubD2LnQq8/uPzvwCHjXZ2DxtfDKX3Nf\nm3Zorg/HoQUyQ6j/sLDxgB1CRc1ASJLUPatfDLtPFHMgBFDRF875Lcz9JCy8Gm6+BFq3JF2V1H3t\n7SEQKsT5QQBVh4SjO41JsCkFK595+/ygXb33qzB8Ylg6tnld7mrT2731WjgWSodQWTkMqDIQKnIG\nQpKk7mmsDcdiD4QgvCg6+btwwjdg2a3wh7NgU3PSVUnds+Yl2PRWYS4XA+g3BAYeAKvsEJJoWEiY\nH3T03s+p6BeWjrW8HrpdlXutW2Dhz8JcnsFjk66m6yqrXTJW5AyEpELRtg1+9X74678lXYkUNNZC\n3yEw/J1JV5IbUQRH/xOc9StY/ljYgWxtY9JVSfuvYUE4jjsq2Tp6onoSNLnTmETdfCjvA6Pn7Pu8\nsYfDUZ+FJb+Hlx/ITW3a4cFvwaplcNqPobwi6Wq6rrIGWlYmXYWyyEBIKhSPXxPegD9zU9KVSEHj\notAdFEVJV5Jbh54DF94UwqBfnQBvLku6Imn/NCwMywCGHZR0Jd1XNRmaXnSml1Q3P/xbXNG383OP\nvwJGHBKWjtnlmjv1C+DRH8Osf4CDT0y6mv0zsMYOoSJnICQVgpY34cFvQ8UASL0WtqyUkrR5Hax6\nrjSWi+3JQcfDJXdD3A6/ORleeyTpiqSua1gQlosVcphbPQlaN0FzXdKVSMnZvBZWPr37dvN7U9E3\nLB1bvxLudelYTmxpgVs/GQZJn/jvSVez/yprYMMqw/ciZiAkFYIHroK2LXDWNeH7V/832Xqk15cA\nMYzppEW9mI2cDpfeDwNHwnVnwdKbk65I6ty6NyBVV7gDpdPSO42tctmYSljDwvDBxL7mB+1qzGw4\n+vPw5HXw4n3Zq03BvV8JH+Se8XPoU5l0NfuvsgbaW8PwchUlAyEp3zUshKf+O6z7nnRKaPN/zUBI\nCUsPlB49O9k6kjbkQLjknvD7cNMl8LefJF2RtG/LF4ZjoQ6UTtu+05iBkEpY3Xwo773/3brHXx6W\nXf7pc77Rz6YX7oElvwvzB8cVaAhfWR2O7jRWtAyEpHzW3gZ3fQkGjYZj/jm09084Fl57GOI46epU\nyhoXhTkE/YYkXUny+g+Dj9wGk08Lu7fcc4Wt1cpfDQuhoj+MPDTpSnqmz8CwU0+TO42phNXND8Ok\nK/rt3/N69YEzfhpmw9xzRXZqK3Ub1sAdn4WaafCeAv49rqwJRwOhomUgJOWzxdfCymfgxG9B7wHh\nsQnHhb+Um15ItjaVrjgOHUKlOj9oTyr6wjm/hbmfhIVXwz1fTroiac8aFoSlnoW0y83eVE1y63mV\nrs3r4I2nuj4/aFejZ8G7vwBPXQ8v3J3Z2kpdHMOfPx+6r878RQjgCtX2QMjB0sXKQEjKVxvWwF++\nGTqCppyx4/GDjgtHl40pKanXYOOa0p4ftCdl5XDyd2HmhbD4t7DxraQrkt5u87rwIUOhzw9Kq54E\nq1+EttakK5Fyb/ljELft3/ygXR33L1A9Ff70ef/NyqSn58Fzd8B7vwIjpyVdTc9sXzLm1vPFykBI\nyld/+TpsXQ8nf+/tO8EMHQ9DxjlYWslpXBSOdgjtLopg7iegbatDppV/GmvDANpCnx+UVj0lbLiQ\nei3pSqTcq5sPZRUwZm73r5FeOrZxNdxzeeZqK2VrG+Guy2DskXDU55Kupuf6DAy7HNshVLQMhKR8\ntGIxLPk9HPGp8AnoriYcG14ItLflvjYl74V74KUHkrt/Y214cVA9Obka8tmow6BmOjxxXdKVSG/X\nsBCisuIJc6s6/n10sLRKUd38sKFB7/49u84BM8KcyqdvhOfvzExtpaq9HW77dNiV68yfhc7hYlBZ\n7QyhImYgJOWb9vbwycKAKjhuL3NIDjoetqyFN57MZWXKB61b4dZPwPXnwLN3JFNDY22YPVAsL3Sy\nYeYF4c/nm8uSrkTaoWFBGCbdZ2DSlWRGeqcxB0ur1GxZD68/0f35Qbs65kvhgwyXjvXM49eEkQ4n\n/TsMOyjpajKnssZAqIgZCEn55sk/hg6hD3wT+g7a8zkTjg1Hl42VnlcfhM1roXJk2Ob85b/k9v7b\nNoUZJMXSYZAt08+Bsl7w5PVJVyIFbdvCcs9imR8EYbOFIePsEFLpWb6w5/ODdtard1g6tumt8KGk\n9l/Ti/DAVTDxRJj1D0lXk1mV1S4ZK2IGQlI+2ZSCB74W1h0fet7ez6usDrMTHCxdepbdCn0Hwycf\nDsslbrwQGh7L3f1ffzK0QhsI7duAEXDwSaEFv21b0tVI8MbT0LqpeOYHpVVPtkNIpafu0fChw9gj\nMnfNUYfCsf8CS29KrgO5ULVtC93bFf3htP96++zPYmCHUFEzEJLyyYPfDp/OfPB7nf9jMuHYMA+i\ndUtualPytm0O6/snnQqVVfCRW2DgqLB8bOUzuamhsTYc3WGsczMvhA1N8NJ9SVciheViUJyB0OqX\nDF5VWurmwwGzQpdcJh3zxbCs9M4vht1u1TUPfz8s4Tv1RzCwJulqMq+yJnxo7XuOomQgJOWLlUuh\n9pcw55LwKU1nJhwHrZth+ePZr0354ZW/wpZ1MO3M8H1lNXz0duhdCX84E9a8kv0aGmvDEo30NqTa\nu3e+HwZUu2xM+aFhAQydAANHJl1JZlVNhvZtufn7T8oHWzfA60syt1xsZ+UVcMbPYFMz3PWlzF+/\nGK1YDA9/L3T2Tzk96WqyIx1yuWysKBkISfkgjsOa7b5D4D1f6dpzxh8ddotx2VjpWHYL9BsWwsC0\nIWPhI7eF/w/9/vSw3Wk2NS6CsT3Y4raUlFfAoefCi/fA+qakq1Epi+PQUVpM84PS0jtxNjlHSCVi\n+WNh6XamBkrvauQ0OP7L4TXHstuyc49isXUj3PLJELSf/B9JV5M9lQZCxcxASMoHz9wEDX+D918F\n/Yd17Tl9B4d2YQdLl4Ztm+CFu2HyqSFo2FnVwWH52Oa18Pszshc+rF0BLa87P2h/zLwwvHB/Zl7S\nlaiUrXkFNq4uvuViACMODh+OrHKOkEpE3aMQlWd2ftCujv4CjJoRlo75gcbe/eXrsOalMJC735Ck\nq8medFe4c4SKkoGQlLQtLXDfV+GAmTDzI/v33AnHhlbVLS3ZqU3546X7Yet6mHrmnn8+6jD48LzQ\nIXTdmaHdO9OcH7T/qieH4PaJP4YuDSkJDX8Lx2LsEKroB0PH2yGk0lH/aHjN2Gdg9u5R3issHdvS\nEkIh//3a3SsPwmM/hyM+BQcdn3Q12bW9Q8hAqBgZCElJ+9//gPUr4YPfh7Ly/XvuQceFbUfr/5ad\n2pQ/lt0K/UfA+GP2fs64d8F514VPyq8/L7QyZ1JjLZT3gZrpmb1usZt5AaxaBm88mXQlKlUNC8Ny\n0xETk64kO6qnuPW8SsPWjWHpdjbmB+2qZgocfzk8d0dYPqYdNjXD7Z+B4RPhfVclXU32DagKR5eM\nFSUDISlJTS/Cwp+GZSXd6boYe0R4g+6yseK2dUOYQzPltPCp3b5MfD+c/UtofDxsSd+6NXN1NC6C\nA2ZAr96Zu2YpmHZ2+HPqcGklpWFB6A4qtq2Q06omhWVx7oCjYtdYG4ao7+vDoUw66p9Cl+udXzIM\n2Nnd/wItK+GsX0Dv/klXk33lFdB/uB1CRcpASEpKHMPdl4UtQ9//9e5do6IfHHiEg6WL3Uv3wbaN\nMPWsrp0/9Uw49cfwyl/glkuhva3nNbRuDR0uzg/af/2GwqRT4Jn/8Q2rcq/lTXjr1dBBWKyqJ4du\n2TUvJ12JlF1188PMrGzOD9pZeunY1vXw5y+4dAzCoO2nb4RjL4PRs5OuJncqawyEipSBkJSU5/4E\nrz4E7/kqDBjR/etMOBbeXAobVmesNOWZpbeEf4jHHdX158z6CHzgW/Ds7fCnz/X8RdybS6F1s/OD\numvmBbApBS/clXQlKjXLF4ZjMc4PSqvq2GnMZWMqdvWPhmHPfQfl7p7Vk8IOuM//GZbenLv75qOW\nlSEYO2AmHPulpKvJLQOhomUgJCVh60a49wqomQZzLunZtSYcH46vPdzjspSHtqwPHUJTTt//GVNH\n/SMc+y/wxHVw71d6Fgo1LgpHO4S656D3wKDRYbi0lEsNC6FXPxh5aNKVZM+IiWHXpSZ3GlMR27Yp\nLBnLxfygXR31WRg9B+76Uug6zKb29rBBxisPwuO/hMd+AZvXZfeeXRHHcMfnQsf2mdfsvuNrsTMQ\nKlqdDKOQlBXzfwhrl8NZ13Q+E6YzB8yEPoPCsrFpXVxSpMLx4j2hM2dvu4t15j1XhO3oF14dtkQ9\n7l+6d53GWhg4KoQa2n9l5XDYh8Kf/XVvwKBRSVekUtGwIHT2FfPsr159YNhBdgipuDUugratMO7d\nub93WXlYOvbzd4cOmQ/9seczyTavC8s817wMq1/q+PqlMA9s2y6bYjz0HTjmi3D4pWFcQhKW/A5e\nuhdO+i5UHZxMDUmqrA5zpOK4eOfRlSgDISnX3noVHv1PmH7u/i0B2pvyXjDuaAdLF6tlt4YgZuyR\n3Xt+FMFJ34Et6+DBb0HfwXDEJ/f/Oo214U2lLwK677APwyP/D56+Ad79haSrUSnYsh7eeDq8kSp2\n1ZPhzWVJVyFlT3p+0IHdfD3QU1UHw/v+L9z3VXh6Hhx2XufPaWuF5vqdQp+XYHVHCLR+5Y7zojIY\nMg6GvzMMzB7+ztD5N3witLwOf/lmuO+Cn8LxX4YZF+S2Q+et1+CeK2DCcTD3E7m7bz6prAkfUG5Z\nF15LqmgYCEm5ds8V4R+xE76RuWtOOBZevBuaG2DIgZm7rpK1eR28dH9YVljWgxW+ZWVw2k9gS0vY\nGaPPIJhxftefv2E1pF6DORd3vwbBiHeGYO+JP8LRnzdcU/atWBSGLSf1BjKXqieHGSfbNkNF36Sr\nkTKv/lEYOT10+yblyE+HGZh3XxZee6a7XTes6Qh7dgl93no17IqW1m9YCHre+X4Y/o4doc+wCaHT\nb08GjYKP3hZGI/zlG/Cnf4JHfxw6oKee1bPXR13R3ga3fgrKesEZP83+/fJVZU04rl9lIFRkDISk\nXHrx3hDcnPDNzC4ZOei4cHzt4bCFvYrDC3dD25bMLAUs7wV//xu4/ly4/dPQpxImn9q15zbWhqPz\ng3pu5gVwx2fD7+nYuUlXo2LXsDB88j6mBP6/VjUJ4nZY/SKMKuJ5SSpN2zbD8sdh7seTraOsHE7/\nKfz8aPjDmeG1xJqXw6YJaeW9wxLOERNh0gdD4JPu+Ok/rPv3nnAsfOz+8Nror9+Emz8G838UupYm\nfiB7H7L87b/CcP4zfwGDx2TnHoWgsjocW1aG/y1VNAyEpFzZthnu/jKMOBiO+FRmr109BQZUhWVj\nBkLFY9ktMGhMGOSYCb36wHl/hD+cATddAh+eB+94T+fPa6wNA1tHzchMHaVs6pnh74EnrjMQUvbV\n/w1qpuZ2R6KkVE8Ox6bnDYRUfFYsDh8QjUtgoPSuRrwzLEV/5Adhl9ypZ4bQZ0RH8DPkwP3fBKOr\noiiETAefGHY8e/Bb4YOusUfC+67M/MDtlUvDPSafBod2YYlcMRs4MhwdLF10SrTnTUrAgv8Ky25O\n/m7mh3tGUfjk5LX/7fn24soPm5rh5b/A1DMy257cpxIu+J/w4u2GC8Injp1prIWR06B3/8zVUar6\nDAw7xi27New2KGVL27YwhLaYt5vf2bB3hCUdDpZWMaqbD0QwLk/+PM+5GL7wDFz0Z/i7H8K7Pg0T\nTwhLv7IVBu2srBwOPRc+Uwun/ABSdfDbD8J1Z8PrT2bmHq1b4NZPQt8h8Hc/cpl3ukNo/apk61DG\nGQhJudC8HB7+f+EThne8Nzv3mHBsSO1Xv5id6yu3nr8zrLufmoWd4/oNhY/cCgNr4I9/Hz4B25v2\nNlixpDSWnOTKjA+HoYzP/znpSlTMVj4D2zaUxvwgCB+0DJ9oIKTiVD8/fDDTb2jSleSXXr3h8I/B\n556A9389hODXHAfz/iHMM+qJB/8d3lwKp/0XDBiemXoLWd8hYTmgHUJFx0BIyoX7vhKOJ34re/eY\n0DFHyN3GisOyW0Pb9ehZ2bn+wBr4yG1QMSDMAVjzyp7Pa3oetq53flAmjXt3+N/2ieuSrkTFrGFh\nOJZKhxBA9SRoMhBSkWndAstrw+5b2rPe/eHdn4fPPw3HXhY25Lh6Ltz+mfCh7P6qXxB2BJ71UTjk\npMzXW4iiKAyWtkOo6BgISdn2yoPw7O1wzD9ndwewYRPC9V8zECp4G9+CVx8M6/Kz2aI8dFzYuSNu\ng9+fDmsbdz9n+0DpDM0xUlgCOOOCMAS+uSHpalSsGhaEbZwHHZB0JblTNRlS9S7HVHFZsQRaN+XH\n/KB813cwvPer8E9PwdxPwtPz4L9mwd2Xw/qmrl1jSwvc9qnwmvrEf89uvYWmstoOoSJkICRlU+vW\nsM330PFw1Gezf78Jx0HdI2GZjwrX83+G9tbsLBfbVdUhcOEtsHkt/P6MsMX8zhprwzaxww7Kfi2l\n5LDzgRieuiHpSlSM4jh0CJVSdxCEDiFiWP1C0pVImVOfnh90VNKVFI7KKjj5O/DZJWHW0OO/gP88\nDP76b+H1zr7c99UQLJ/58zD3TztU1hgIFSEDISmbHv9FmOlz0nehom/27zfhuPAP3RtPZf9eyp6l\nt8DQCTDqsNzc74AZ8OEbYe3ysHxs5xdLjYvCcrFSH6aYaUPHhfb/J/8I7e1JV6Ni89arsGFV6cwP\nSqvq2Gls1fPJ1iFlUt38sFtgT7ZsL1VDxsLpV8OnHwtDrx/+XgiGHv3PPXcSvngvLP4tHP05A7g9\nMRAqSgZCUra0rISHvgMTT8zd+uMJx4ajy8YK14bVYSnRtLNyG8KMOwrOuw5WPQvXnxdeKG1qDjOE\nnB+UHTMvDDujNPwt6UpUbEpxfhCETsby3uHvMakYtG4Nu4GOf3fSlRS2qoPh3N/BJx6C0bPh/ivh\nxzOh9tdhR0aADWvg9n+E6qnwnq8kWW3+qqwJr1PbWpOuRBlkICRly/1XQttWOOnbubvnwJrwCamD\npQvXc3eEmT5Tz8z9vSeeAGf9MryZnPdRWP5YeNz5Qdkx+TToPRCe+GPSlajYNCwIuxGNODjpSnKr\nvFf4NTfZIaQi8foTsJD5R0AAACAASURBVG2j84My5YCZcOHNcNFdoVP3zi/CT+aEWUN3fgE2peCs\nX0CvPklXmp8qq4EYNq7u9FQVDgMhKRvq/wZP3whHfQ6GvyO39z7ouPCGvnVLbu+rzFh2a9g6uWZa\nMvefdhac+p/w8v1w6yeBKHs7nZW63v1h2pnw7G1hiKWUKQ0LYeyRYYB5qama5JKxbGrb5t9XuVQ/\nPxwNhDJr/NFwyb3w4Xnhg5lbPh42gHnPFTByetLV5a/KmnB02VhRKcFXClKWtbXCXZfB4LFhZ7Fc\nm3Bs2I0ivTuUCsf6VWFWQLZ3F+vM7H+AE74ZPimrmhR27VB2zLgwfPr77O1JV6JisWE1rHmp9OYH\npVVPgrUNsGV90pUUl7WNYSDvD6bAjw6FNa8kXVFpqJsP1VNgwPCkKyk+UQQHnwiffBjO/jUc8yU4\n+p+Sriq/bQ+E3Hq+mPRKugCp6Cy+Ft5cCuf+PnQA5Nq4oyEqC8vGXHNeWJ69HeL20KWTtKM/BxX9\nYEBV0pUUt7FzYfg7w7KxmRcmXY2KQcOC/8/efYdHVWB9HP/dJISW0BN6SOihCCICAqEJCJZgVyyr\na1t17bq7upZd66uubS3r6tobqNgQsStIVekdpITQCb0GUu77x0ko0lJm5s7M/X6eZ58xyeTew0Im\nc889xR79Nj+oWPFg6ZyFUqMTvI0l0hUWSkt/sDkri76y7XUtBtgNp2FDpau+5YZBMBXkSdk/Sx0v\n8jqS6BYTI7U/1+soIkNCsj1SIRRVqBACAmnnBumHB6WmfWw+iBcq17AeaQZLR565n1hFTnK615GY\nLldLbc/0Ooro5jj2Zj97InfcERjZk6XYirY90I+KXz8ZLF12uzZJE56VnuskvXOODTXucYt080zp\n4g+l89+WNi2RRlwpFRZ4HW30Wj1DytvJzT2Ej+IKoe1rvY0DAUVCCAik7++X9u6UBj/ubctPWm9p\n1VT6/CPJtjU2e6ptGFQHIbQ6DLWqvhnveR0JokH2JBsE79ehqDVTpbhKDJYuLde1xM/Hf5KebC19\ne6+UWN9aaW6bJ/X/hw3hlaS0DOnUJ2zW3Df3eht3NGN+EMJNhUpWFUjLWFQhIQQEysqp0rS3pW7X\nSUmtvI0lrZdUmC8tn+RtHCi5eZ9Jcr3ZLgZvVWsgNesnzRzG3XaUz96d0pqZ/p0fJEkxsbZpbP18\nryOJDHt2SFNel17KkF4dIC34Qup0qXTdJOmKL62V5nDJxc5/lLpeK01+QZr2Vujj9oOs8VY1nEDr\nNsJIQl1axqIMCSHsV5AvvXchv9jLorBQGn2HvUj2+qvX0djFQGxF2sYiydxPbLNYks/WRMN0vFja\ntsrfP7OTX5TGPu51FJFt1VS7GeDX+UHFktOpEDqW9QtsAcZT6dKoW6xC6PSnpdvnS6c9KdVtc+xj\nDHzYktmjbpOyJgQ/Zj8pyLf2T6qDEG4S6lIhFGVICGG/X1+RFn1pb8pROtPekFZPkwY+KFWq5nU0\nNgy4cRcbLI3wt3WltGIy83r8rNWpVoY9/V2vI/HGpqXWejL+GRukirLJnizJkRqd6HUk3kpqbQnW\n3K1eRxJe8vdKcz6SXj9V+k9XaeobUqvB0hXfSNeOlzpfIVVMLPnxYuOkc1+3VrIPLpU2ZwUrcv9Z\nM1Pau4P5QQg/CclUCEWZEiWEHMcZ5DjOQsdxFjuOc+dRnneO4ziu4zidAxciQmLHeunHh6X4BBvE\nuGGx1xGFt8IC2/zw7T+k57tIo26VUrpL7c/zOrL9mvaW1s22QdcIb8Urx5kf5F8VKtnrx4JR0u4t\nXkcTet/dLxXm2QDV1dO9jiZyZU+S6ra15QJ+tm+wNFVCkqQt2dL3D0hPt5VGXGE3IfrfL902Xzr7\nZSmla9nnHlauIQ193yrThg1ldmGgMD8I4SqaK4Rc14bq+8wxE0KO48RKekHSYEltJA11HOeQOlLH\ncRIl3Szp50AHiRD47n4pb7d0YdHd6fmfeRtPONqzQ5r/ufTp9dITLaTXBkqTnpeq1bch0kPf83aQ\n9O+l9bbHrHHexoFjm/OxVO84qXYzryOBlzpeLOXn2h18P1nxizTvU+nEq+3jZT95G0+kKsi3/y/9\nPD+oWHFCKMfHc4QKC6XfvrVRAP/uII1/Wmp4gnTxCOmmGVLPW6SqdQJzrjrNpfPelHIWSh9dxSy0\nQMiaYLOwEut6HQlwsIRkae92m1kXbWYOk57tKOUs8jqSkIorwXO6SFrsuu5SSXIcZ7ikIZJ+v8/z\nQUmPSfpLQCNE8K34VZrxjtTjZluX3rCzNG+klHG715F5b9tqaeGX0qKvrP2qYI+1dbQYaGXWzfvb\nx+GoQScpPtHiZlBx+Nq8XFo1Rer/T68jgdcaHC8lt7FtYyde6XU0oeG60jf32B3H/v+Ulk+wJHav\nO7yOLPKsn2stJn6fHyRJ1VOkClX8WSG0c6M0/W1p6uvWwlU1Sep5m3TC5VKNxsE7b7O+0uDHbJ7i\n9/dLAx4I3rmiXWGBVfu1O8frSIBDJdSzxx3rpFpNvY0lkHZvsdb1Oq2k2s29jiakSpIQaihpxQEf\nr5TU9cAnOI7TSVJj13W/cBzniAkhx3GukXSNJKWkpJQ+WgReYYH98k6sL/Uq+qtrkyl9e59dqBav\nGPUL15XWzpIWfiUtHC2tmWGfr5kmnXiVJYFSukmxFbyNsyRi46TUHv4eUhsJ5n1qj22YH+R7jiN1\nvMgSJDkLvd9WGArzP5dW/Cyd8W+pYoKUmmGLDfL3+HdtelllT7ZHKoSkmBj7+fFThdCG36Sf/mUL\nCgr2Sk16SiffJ7U+Q4qLD00MXa627W4T/m1znDpeFJrzRpu1s6Q925gfhPCUkGyPO9ZHV0Loh4ek\n3Zuk0z623yE+Uu4/reM4MZKeknTMchLXdV92Xbez67qdk5JYoRgWpr1lSY+BD+0fJJieaY/zP/cu\nrlDK3yMt/k764nbp6XbSS72kMf9nFyP9/yld/7N003Rp0CNSWkZkJIOKpfW2Ya1bVhz7ufDG3E+s\nmqtWmteRIBwcd4HkxErT3/E6kuDL3yt99w8pKV3qeIl9Li1Dyt9t27JQOssnStUbS9UbeR1JeEhK\n90+F0JIfpf/1kxaMtkqg6ydLf/zCKkxClQwqNvgxKa2X9PnNNmsRpZdVND+IhBDCUUJRG2M0DZZe\nPUOa8qrd/K/fwetoQq4kCaFVkg6sMW1U9LliiZLaSRrjOE6WpG6SRjJYOgLs2mRlvU16HFyWWitN\nqtdemj/Su9iCbedGacYw6f1LpcebSu+cY20aDTpKQ16Q7vhNuvIbqeetUnLr8JoNVBppveyRmRzh\nadNSG6BLSx+KJSRLLU+RZr1vM2Gi2dTX7Wdg4INW0SgVDVB1pGXMPisV17UKIaqD9ktuLe1YG/0D\nQqe+Kb17riUDr58knfqv/TOUvBBbweYJVW8kvX+xDbRG6WRNkGo1kxLreR0JcKh9CaEoGSxdWGhF\nAVVqS33v9joaT5QkIfSrpBaO46Q5jhMv6UJJ+zIFrutudV23juu6qa7rpkqaLCnTdd0pQYkYgfPD\nQ1LuNhuI/PuER/oQK+Pfvtab2IJhw29WxvzaIOmJ5tKn10orf7XNPhd9KP11mQ3VPv4SKSFKKtiS\n20hV6tA2Fq7mFrWLsW4eB+p4sd15W/K915EEz+4t0phHrYqxef/9n69SS6rXjmH4pbU5y5IfzA/a\nL7lo/0lOlFYJFRZK3/1T+vwm+zm64qvgzggqjSq1bPNY/t6izWM7vI4ochQWWLUf1UEIV1VqWSVz\ntFQIzXjHZnkOeNC3GzqPmRByXTdf0g2SvpY0X9IHruvOdRznAcdxMoMdIIJk9QxpymvW712v3aFf\nTz/DHiO9bWz9ApvH8dwJ0vOdbTbS3h02L+nqH6Vb50lnPCO1HGhrn6NNTIxVCS0da3eQEV7mfiw1\nOlGqwUw1HKDlKZbIjea2sfFPS7s3W7vy729IpPaybVl5ud7EFon2zQ8iIbRPUmt7XB+Fc4Tydksj\n/mg/Ryf8UbroA6lSNa+jOlhSS+m816T186RP/mQJLBzbujnSnq0khBC+YmJtWH00FA3s2iR9+w/7\n3dnhQq+j8UyJZgi5rjvadd2Wrus2c1334aLP3ee67iE9Ra7r9qE6KMwVFkqj/2KlcX3uOvxzklvb\nustIbhvLy5VeHyRN/q9Uo4l06hPSLXOka8dLff8uNezkj6FhTXvbneMN/lqhGPY2LJbWzpbanu11\nJAg3sRWk4863DYc7N3odTeBtyZYmv2hvvuofd+jX0zJso+PKX0IfW6TKnmQbL4uTILCWpfjE6KsQ\n2pEjvXmGNO8zS6ie/vT+lstw07y/dMoj0oJR0o8PeR1NZCieH9Skh7dxAEeTkBwdLWPfPyDlbpVO\nezJyx4MEgA+uhnGIWcPtjfaA+49eGpeeaX3MkXpBsnC03YG+6H3p0o+tGipcyqlDKa23PTJHKLzM\n/cQe2wzxNg6Ep44XS4V50uwPvY4k8H4oqgrqd8/hv96ku+TEMEeoNLInS427+eMmR0k5jm0ai6YK\noZyF0isnS2vnSOe/JXW/MfwvYrpeK3W6TBr3pDTrA6+jCX9ZE2yzbfWGXkcCHFlivchvGVs1VZr6\nhr1G1W3rdTSe4p2D3+Rutbaphp2lDsdYB9omU3ILpIVfhCa2QJs5XEpsIDXt43Uk3qqZKlVPkZaO\n8ToSHGjuJ1aiyps+HE69drbpYsa7XkcSWKun28DsbtcfeRtWper2Z2eOUMns3ChtWMhA6cNJbh09\nFULLfpJeHSDl7ZIu/8Leo0UCx7EK7SY9pc9ukFbSRHBEhYXS8gm0iyH8RXqFUGGBNOo2G5Dd506v\no/EcCSG/GfOotHODbaE41p3EesdZq9W8CGwb27HeVskfd771uvqZ40hNe9nFVWGB19FAsru86+ey\nXQxH1/ESae0say2MBq4rfXOvzUfqeevRn5uaYReOe3eFJrZItqJotTfzgw6VlC7tzLH3PZFs+rvS\n22dJifWlq76XGp3gdUSlExdvFU3V6kvDL5K2rvQ6ovC0fq6Uu4WEEMJfQl1p5/rInQ029Q1pzQzp\nlIfDb/6aB0gI+cm6edLPL0knXG7zc47FcewO1NIxthEmksweYdVNPh4QdpC0PlYdtmam15FAKmoX\nc2gXw9G1P1eKjbeLwWiw6GtLTPe589hvwNJ6WcvcismhiS2SLZ9g/04aHO91JOGneP16pLaNua61\nWH52vSUJrvhaqtnE66jKpmptaehwS/IOGyrt3el1ROGH+UGIFAl1pcJ8G80RaXZusNlBqRlSu3O8\njiYskBDyC9eVvvyrvQk/+b6Sf196pr0p/+2b4MUWDDOHSfU77n8z6HdpveyROULec11pzsf2hi+x\nntfRIJxVqSW1GizN/sDWN0eygnzp23ul2s3tpsSxpHSztbbMETq63G3SjPesNToaN2WWV/F7gEhs\nG8vLlT66SvrpX9Lxl0oXj4j8lcjJ6dK5r1rV46fXRW51QbBkjbfKfD/Ou0RkSUi2x0icI/TdP2zj\n9KlPhP8MthAhIeQXcz+2O7P97rWLjJJq2NlKlOd9FrzYAm3dXGuz6DDU60jCR2Jd2z6zbKzXkWD9\nPJv30Y52MZRAx0ukXRulRV95HUn5TH/LNh32v9+2qB1LxUSrZC2+Y47Dm/yitHvTkTeG+l1ifali\n9cirENq5UXr7TGnOCKn/P6XM50r2cxMJWp4iDXzQ3leOfdTraMJHYaG0fKJVLQDhLqGuPe6IsNXz\n2T9L09+RTvqzzZiDJBJC/rBnh/T1PTYTqCR3Zg8UEyOlnyEt/j5yyntnDpdi4qzdAvul9ZaWT5Ly\n93gdib/N/cQ2KKXTLoYSaNZPSqhnVSCRas926cdHpJTuUuvTSv59qRnS6mn2OwyH2rVJmvic/Y4u\nSRu4HzlO5A2W3rBYerW/tGqadN4bNm8r2u5in3SDJbvHPibN+cjraMJDznxL7qbSLoYIsC8hFEGD\npQvypdG3S9UaSr3+6nU0YYWEkB+Me0LavtpK48oyYDk9U8rfLf32beBjC7TCAltr2mKgVLWO19GE\nl6a97e+RDR/eKW4XS82QEpK8jgaRIDbOZqH99o20PQJLsyVpwrM22HfgQ6W7sE3LsBkF2cwROqzx\nT1vZe997vI4kvCW1tspM1/U6kmPLmmDJoNxt0uWjonfxgONIpz9lg9A/vd6SX36XNcEemR+ESLAv\nIRRB70umFLWrnvKIVDHB62jCCgmhaLdhsTTxeVsxn9K1bMdo0t22wsyPgG1jS8dY+SLDpA/VpIdV\nptA25p21s6VNS6R2Z3sdCSJJx4ttSP6s972OpPS2rbYqlnbnlH4zUuNuUkwFKYvZZ4fYtkb65WX7\nXUfZ+9Elt7HBp+F+J3vm+9JbQ6SqSdJV30mNu3gdUXDFVZTOf1uqmmybx7at8Toib2WNk6qnRO7Q\ncPhLxQSpQtXwf10ttmO9Dehv2peFLodBQiiaFQ+SrlDZetDLKibWyvwXfW1DDsPZzOFSpepSy0Fe\nRxJ+KtewQdtLSQh5Zu7HNii39RleR4JIktRSanSiNOPdyKhyONCPD1syqzTLDIrFV5EadWaw9OH8\n9C+rnur9N68jCX/FCbOcMJ0j5LrSmEelT66xYepXfiPVSvM6qtBISJKGDrOKqOEXSXm7vY7IG65r\n2wJpF0MkSUiOnAqhb+611xcGSR8WCaFotnC0tOR7GzaZWLd8x0rPtNL0pT8GJrZg2LNdmv+53YmO\nq+h1NOGpaW9p1RRmcnjBdW1+UNM+tn4XKI2OF9sclNUR1Fqxdo40/V2pyzVSzdSyHSM1Q1ozQ8rd\nGtDQItqmZdK0N6VOl/kncVAeScWr58NwjlD+HumTa6Ux/2c/45d8LFWu6XVUoVWvnXTOK9Lq6dJn\nf468pHcg5Cyw5QGpPb2OBCi5hLqRkRDKmiDNGi71uEmq09zraMISCaFolbdb+upOeyPU5eryHy+t\nl23qmBfGbWPzRtqMHLaLHVla76KZHJO8jsR/Vk+XNmdF70wIBFe7s6W4SpZgiRTf3mcVm73uKPsx\n0jIkt9AG4sOMedQWJ/T6i9eRRIaEZEuyhFuF0K5N0ttn24VKv3ukIS9IcfFeR+WN1qdaFeGcj6Sf\nnvA6mtAr3qbI/CBEkoTk8J9tWJAnjb7D2jEzyvFeJMqREIpWE/4tbcmWTn08MKtK4+KlVoOt6qgg\nr/zHC4aZw6RaTa21AoeX0k2KjbdZSwituZ/YPJT0072OBJGoUnXbJjVnRPi37kq2mXLJ91Lvv5av\n4qFRFym2os3XgK1Pn/W+VV1Vq+91NJHBcezmWDitnt+0VHp1gLTyF+mcVy255/c2hp63SsddKP34\nkK2kD6a8XGnzcltBPe8z7wfXZ42XqjUqeyUl4IVIqBD6+SVbKjD4UWtDx2HFeR0AgmBzlm0faXu2\nVfYESptMu5OVNc5WIYeTLdkWV9+7eVN1NBUqS427Mlg61FxXmvup1Kyv/9oBEDgdL5ZmfygtGCW1\nP9fraI6ssMCqg2o0kU68qnzHqlDJhusuY7C0JBuKGZ9gF88oueR0afYIey32+j1C9mSbl+O60h9G\nSk1O8jaecOE40hn/tsULn1xryZH6HUp3jL27bLHI9qL/7VgnbV9jVQw71trj9jVS7pZDv/eMZ6UT\nLgvIH6VUiucHNevn/b9NoDQS69rPUv6e8BzVsW2NteO2GCi1OtXraMIaCaFo9NXfbXDtwIcCe9xm\n/Wyi/LyR4ZcQmvWBPR53vrdxRIK03nYHbudGb2fZFA/SrNMivC9uA2HVVGlrttT3715HgkiW1luq\n3tiGS4fzz8zMYdK6OdK5rwfmTWJqhr2p27VJqlKr/MeLVKumWjKwz9/9/f9DWSSnS3u2WjKgWgPv\n4pg9wtasV28kXfyhVLuZd7GEowqVpAvelf7XTxo2VLr6R7vo3LN9fzJnx7qihM8B/138uGfboceM\nqSAl1rNqhtrNbHBzQj37XGI9a3v5/kHp85utLbfDBaH9M29YJO3MYX4QIs++1fPrpRqNvY3lcL65\n27paBj9GsvUYSAhFm9++lRZ+YVvFqjcM7LErVJZaDrQ3pKc9advHwoHr2gVIkx6U25ZE06KEUNY4\nqe2Z3sUx8Tlp7KNFW+FOkSomehdLsM352Fr1WnOHAuUQE2Mz0n76l7R1VeBf4wNh7y6rYmnYOXDz\nstIypDGPSMsn+rvl8oeHpCq1pZOu9zqSyJNUtGls/XxvEkKuK417wv4OU7pLF75LUu9IEutKQ9+T\nXhskvXCiVJAv5e089HmxFfcndZJa2zrpxLq/S/bUs/+fj3UxeMHb0nvnS59eayMSQjnrj/lBiFTh\nnBBaOtZmkvW+08aJ4KhICEWT/D3Sl3+TajeXugXpDWN6ps1CyZ4cPusxV02VNi6WetzsdSSRoUEn\nKT7R2sa8Sggt+UH67h82H2TlL9KU16L376+wUJr3qdS8vyW/gPLoOFT66XFLgpdnWHOwTHrB7tyf\n90bg7sg1PEGKq2xJbL8mhJaNs9fNgQ9Hd/I8WJKLNo3lLJCanxzac+fvlUbdKs14R2p/vjTk+fBs\nrwgn9TvYOvrp70hV6hyQ4KkrJda3xE+lGoF7jalQWRo6XHrnHOmjqyzZFKobOFnjpcQGXLQi8iQk\n22O4zRHK32uDpGumSj1v8TqaiMBQ6Wgy6QXrvR78WPDebLQYYL8o54fRtrGZw6zMt80QryOJDLFx\nUpPulj33wuYsacQVdkfv0qI17JNeiIxBuWWx8hdp2yq2iyEwajW1O8kz3g2/9cw71ksTnrHh1ynd\nAnfcuIpSSldLiviR60o/PGgXjSde6XU0kalqHUssrJ8X2vPm7ZaGD7VkUO87pbNfJhlUUk372Dr6\nwY/aRV2HC20OX3Jrm8UX6BaQ+KrSRR9YMurDy6TF3wX2+IdTPD8otQctLYg8+yqEwiwhNPk/1oo5\n+HFL9uKYSAhFi62rrI2g9elWiRAsFRPt7tr8z63ywWv5e6wksPVpVF+URtPeljzcujK05927Uxp+\nsa2RvvBdqWKClHG7/TKZEUHrtEtj7ieWRG012OtIEC06XmxbirzejPN7Y/5Pys+V+t8f+GOnZkjr\n50o7NwT+2OHut2+kFT/bxjbe3JZdcrq0fkHozrd3p7UhLf5eynxO6nsXF/3hrlI16ZKPpKRW9l4l\n2EnojUvs/Q/zgxCJqiZJcsIrIbR1pTT2ManVaTaOAiVCQihafHOPXWSf8nDwz5WeaRUPq6cH/1zH\n8ts30u7NNlcDJZfW2x5DubnHdaWRN0rr5krnvLa/PDo1w+aNTHjGZgVEk8IC2y7WYgBtHgicNkNs\nwH84JVFzFkpT35Q6XxmcQbnFGzOL5234RWGhDbytmSYdf4nX0US25HT7dxqKyrrcbdZ+lDVeOusl\nqdMfgn9OBEblmtKln1q7yXsXBDfxnlWUcGpCQggRKLaCzbULp4TQV3fZa/yg//M6kohCQigaLPtJ\nmvuxraENxVDlVoOkmDhp/mfBP9exzBxuJYtN+3odSWRJbmPl86FsG5v4nFVznXyv1OKAKjbHsSqh\nLdn29WiSPdlW3bY72+tIEE0qJlgL4txPrAohHHz7D2u56P234By/wfGWBMvyWdvYvE+kdbNtQ2Fs\nBa+jiWxJraW924NfGbt7i/T2WdLKX6VzXwv91iqUX9U60h9GStXqS++eJ62aFpzzLJ9gg6/ZNodI\nlVDX2sXDweLvbKRJr9ulmk28jiaikBCKdAV50ui/SjVSQjeUt3JNqzCZN9LbGRY7N0qLvpban2dz\ncVByMTG2uWfZ2ND8HRYPkW4zROp526FfbznIklTjnw6PVsRAmfuxDcNtQdkqAqzjRdLeHfY67LVl\n46RFX0oZt0lVawfnHLEVpCYn+WuOUEG+9MPD9trY7hyvo4l8Bw6WDpZdm6S3MqU1M6Xz32J2XCRL\nrGtJoco1LcG3dnZgj++6VkHG/CBEsoTk8KgQyt9j18O1mkndb/I6mohDQqi8xj1p/eFeXcT+8j8p\nZ7406NHQzhZokyltXiatmxO6c/7e3I+lwjzaxcoqrbdtA9rwW3DPc+AQ6SH/Ofwbn5gYq3DLmW8X\nltGgsECa95n1MFdM8DoaRJsm3a2N6IcHrVLIq+R8YaG1LFdrJHW9NrjnSs2QNiyUtofBm89QmPme\nzXrrd48UE+t1NJHvwNXzwbAjR3rjdJtTNHSYzTZEZKveULrsc6t+fOvMwM6g2rTU3oOxbh6RLKFu\neCSEJj5rvy9P/ReD+8uAhFB57NkhTf6v9M7Z0rMdpZ+ekLavDd35t6+zIZ7N+0utQrQes1ir0yQn\nxtu70zOHSXXbS/XaeRdDJGtaPEcoiG1jhxsifSRtz5ZqNLEka7htTyqLrPHSzhzuECM4HEc6+382\nTP/Dy6VX+kvLJ4U+jjkjpDUzpJPvC/5NidQMe/RD21j+HmnMY1LDE0L/+z1aVallFy/BSAhtWyO9\ncapd5F/8gc2NQ3So2cSSQjGxVv21cUlgjls8D634dQ2IRAnJ1jLm5fv2zculn560LoTmJ3sXRwQj\nIVQeFROkW+dYj3jNJnan9qk2dgG8+LvgVw19909baTrosdCXmyYkSSndvVs/n7NIWjXV1pCibGqm\nSdVTgpcQOtIQ6SOJjbPVsqumhnbYdbDM/cRmnrQY6HUkiFaNT5SuHS9lPm+D/l8fJA27KPhVf8Xy\ncqXvH7A1ze3PC/756neQ4hP9kRCa8rq0baUl2mgnCZzkdKtEDaStKy0ZtG21bahq2iewx4f3ajez\n9rHCfOnNTLsALa/lE6SqyVKdFuU/FuCVmk1su+i759m1rxeJoa/usiKFUx4J/bmjBAmh8oqraL39\nl30u3ThN6n6DDZJ95xzp2Q62Cn7bmsCfN/tnKyfvfqNUp3ngj18SbTKtFz9nUejPPWu4/fCH4iIk\nWjmObe5ZNs7apKXGrwAAIABJREFUmwJt3xDp+w4eIn00HS6yAYvjngx8PKFUkG/J0laDpPgqXkeD\naBYTK3W6VLpxqrUWLRsrvdBVGnVb8Ac9/vxfaesKacCD1vYZbLFx1ioX7XOE9uyQxj1hr89N+3gd\nTXRJKto0FqgbdpuzpNcH20zDSz+1eTCITsmtpT98ZrPb3jxD2rqq7MdifhCixfF/kPr8XVo7y659\nX+gi/fqK/R4LhUVfSwu/kHr/VareKDTnjEIkhAKpdjNpwAPSbfOkc18vmu/wkPR0W6sa+u27wFx4\nFxZIo2+XqjWUet1R/uOVVfoZ9hjqKqHCQmnm+1Kzk23oH8quaW8pd4u9kAfSQUOkby3591WoJJ30\nZ7uoXTk1sDGFUtZP0q6N1gYHhEJ8VanXX6SbZkidr5CmvSk9e7w09vHgbCLbuVEa95QNTC9uPw2F\ntAybE7BtdejOGWo//9faTfvd53Uk0Se5tZS3S9qaXf5jbVwivX6arZi/7DOr2EN0q9deuvRjafdm\nax8r6zyzzVlW1cn8IES6uHipz9+kW+ZIZ71s70W+uN06Zr6+OzDVdEeSt1sa/RepTiup2/XBO48P\nkBAKhriKtmb6spEHVw29e470747S2HJWDU193bYdDHzIfvC8Uq2B1OjE0CeElo+3UnraxcovrZc9\nBrJFqyRDpI+m8x+lSjWk8U8FLqZQm/OxtbY0L2FlFBAoCUnSaU9I1/8sNesr/fiw9Gwnaeqbga0E\n/OlxW+E94IHAHbMkiudtRGuV0O7N0oRnbW4QCYbASyraNFbe4cA5C6XXT5Xyd0uXj5IaHF/+2BAZ\nGp4gXfyhvY9/a4glx0uL+UGINnHxUocLpKt/lK781mb5TH7RZuwOv9h+Zwe6nWz8M9KW5faeJy4+\nsMf2GRJCwbavami+dN4bUu2m0o9FVUPDLpJ++7Z0b9J3bpS+f9B+iYTDsNr0ovWqm7NCd86Zw6WK\n1djgEQiJ9SxxszRAc4RKM0T6SCom2raiBaOCtw0mmArypPmfS61PtYonwAt1mksXvCNd8bVUI0X6\n/CbpxR5WXl3eN2Ubl1hJeKc/WMVFKNVrb4O0s6JgztjhTHhW2rNN6nu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Kefj46nvVc/A/EIMyurXPFeM8urzKxU2vv8D1UACAcNY+kYe9JuYDTuC4xayzudmF3uLuWaw2Kh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\niYiIiIiIiIj0mP8PsqwwiSGnwksAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "eEBUKzThDFA0",
    "colab_type": "code",
    "outputId": "439d9739-604d-4622-bf2f-fa2687f654b6",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 612
    }
   },
   "source": [
    "plt.figure(figsize=(20, 10))\n",
    "plt.plot(f.history['acc'], label='training accuracy')\n",
    "plt.plot(f.history['val_loss'], label = 'validation accuracy')\n",
    "plt.legend()"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fdb7112d048>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 169
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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56w6MFQAAAJibSQ7cnQyPXf+88VYgZMoYV9tOhdDbkjxbluUXy7JcSvLBJN92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\n838bqnfmnDwd2QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "TgcS7BgLNNFB",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "import torch\n",
    "name  = 'Pneumonia_model.h5'\n",
    "\n",
    "path = loc+name\n"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "sl3XO2jfPxKT",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "model.save(path)"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "jtEZr1KBP0On",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "from keras.models import load_model\n",
    "from keras.preprocessing import image\n",
    "from keras.applications.vgg16 import preprocess_input\n",
    "import numpy as np"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "byRJ1IrBP2aF",
    "colab_type": "code",
    "outputId": "789749ec-8924-4c4a-ac1d-a57bf5abcb87",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    }
   },
   "source": [
    "os.listdir(loc)"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['val', 'train', 'test', '.DS_Store']"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 172
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "CspdXju9QQsB",
    "colab_type": "code",
    "outputId": "4f3a1c42-e2a1-4ccf-80d1-c52377cd3475",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    }
   },
   "source": [
    "img = image.load_img(loc+'/val/PNEUMONIA/person1954_bacteria_4886.jpeg', target_size = (224, 224))\n",
    "img"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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LwGi5+fV6TRwt44gqxXuRNIQQktW/Q24KIfC89G2M1hZBix8NiRTOpWonIoS1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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FDB710C4FD0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 173
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "SbeeLg4fRC1l",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "#  converting to array\n",
    "\n",
    "\n",
    "#  convert to array\n",
    "\n",
    "x = image.img_to_array(img)\n",
    "\n",
    "x = np.expand_dims(x, axis=0)"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "JGhv2aFyRVQE",
    "colab_type": "code",
    "outputId": "ef824e26-c993-49cd-cba2-e5e29e463951",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    }
   },
   "source": [
    "image_data = preprocess_input(x)\n",
    "classes = model.predict(image_data)\n",
    "\n",
    "if classes[0][1] ==0.0:\n",
    "  print('Congratulation Free from Pneumonia')\n",
    "else:\n",
    "  print('Sorry , You have Pneumonia. \\nPlease consult Doctor as soon as')\n"
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Sorry , You have Pneumonia. \n",
      "Please consult Doctor as soon as\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "x8qUT20gRunX",
    "colab_type": "code",
    "outputId": "6a806c83-c1a5-4b25-dbd9-bc5165b8cda6",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    }
   },
   "source": [
    "import os\n",
    "\n",
    "for i in os.listdir(loc+'/val'):\n",
    "  if i != '.DS_Store':\n",
    "    \n",
    "    for j in os.listdir(loc+'/val/'+i):\n",
    "      if j != '.DS_Store':\n",
    "        \n",
    "        img1 = image.load_img(loc+'/val/'+i+'/'+j, target_size=(224, 224))\n",
    "        \n",
    "        x = image.img_to_array(img1)\n",
    "        \n",
    "        x = np.expand_dims(x, axis=0)\n",
    "        \n",
    "        image_data = preprocess_input(x)\n",
    "        classes = model.predict(image_data)\n",
    "        print(i,classes[0][1])\n",
    "        \n",
    "#  0: No pneumonia\n",
    "#  1: Pneumonia "
   ],
   "execution_count": 0,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "PNEUMONIA 1.0\n",
      "PNEUMONIA 1.0\n",
      "PNEUMONIA 1.0\n",
      "PNEUMONIA 1.0\n",
      "PNEUMONIA 1.0\n",
      "PNEUMONIA 1.0\n",
      "PNEUMONIA 1.0\n",
      "PNEUMONIA 1.0\n",
      "NORMAL 3.5563516e-20\n",
      "NORMAL 0.0\n",
      "NORMAL 1.0\n",
      "NORMAL 0.0\n",
      "NORMAL 1.0\n",
      "NORMAL 1.0\n",
      "NORMAL 1.0\n",
      "NORMAL 1.0\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Xa209BEtTRXf",
    "colab_type": "text"
   },
   "source": [
    "# InceptionV3\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "sqBRDEVClIj-",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    ""
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "Rm9mtm6lkFw1",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "from keras.models import Sequential, Model\n",
    "from keras.layers import Dense, Activation\n",
    "from keras.layers import Dropout, Flatten,GlobalAveragePooling2D\n",
    "from keras.layers import Conv2D,BatchNormalization, MaxPooling2D\n",
    "from keras.applications.inception_v3 import InceptionV3"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "zGujMwusqwHa",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "inc_model = InceptionV3(input_shape = (224, 224, 3), weights = 'imagenet', include_top=False)"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "l8xDw6YXr3Ou",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "outputId": "d9dd76e5-9a83-4bee-d5ce-fecc9fe01ac4"
   },
   "source": [
    "x = inc_model.output\n",
    "x"
   ],
   "execution_count": 232,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'mixed10_7/concat:0' shape=(?, 5, 5, 2048) dtype=float32>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 232
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "0p8Fsc1vtIHi",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "outputId": "9985cd95-f2ad-4446-e335-d490bdfe83a9"
   },
   "source": [
    "x = Dropout(.5)(x)\n",
    "x"
   ],
   "execution_count": 233,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'dropout_13/cond/Merge:0' shape=(?, 5, 5, 2048) dtype=float32>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 233
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "sACAUHI6uT0_",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "outputId": "be81a752-4c53-4fc5-cf9b-4401d4ebb235"
   },
   "source": [
    "x = GlobalAveragePooling2D()(x)\n",
    "x"
   ],
   "execution_count": 234,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'global_average_pooling2d_12/Mean:0' shape=(?, 2048) dtype=float32>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 234
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "SIJTt__duXAW",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "outputId": "01c508ea-781f-46f4-b4dd-9a6c3ee48923"
   },
   "source": [
    "# densely-connected NN layer.\n",
    "x = Dense(128, activation = 'relu')(x)\n",
    "x"
   ],
   "execution_count": 235,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'dense_34/Relu:0' shape=(?, 128) dtype=float32>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 235
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "emRSM4YRuY7G",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "outputId": "33499579-5a9f-4cd7-a2a1-62134cea1dd5"
   },
   "source": [
    "# Normalize the activations of the previous layer al every batch\n",
    "x  = BatchNormalization()(x)\n",
    "x"
   ],
   "execution_count": 236,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'batch_normalization_763/cond/Merge:0' shape=(?, 128) dtype=float32>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 236
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "zbKI63xCub_P",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "outputId": "7f10796b-ea58-45b6-bce2-f9da8b174da7"
   },
   "source": [
    "#  Last Layer\n",
    "prediction = Dense(2, activation='sigmoid')(x)\n",
    "prediction"
   ],
   "execution_count": 237,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<tf.Tensor 'dense_35/Sigmoid:0' shape=(?, 2) dtype=float32>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 237
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "tFMgPx23ufGm",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 71
    },
    "outputId": "687e45e9-4ae1-4825-b287-9122a57bedd9"
   },
   "source": [
    "model = Model(input = inc_model.input, output = prediction)"
   ],
   "execution_count": 238,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:1: UserWarning: Update your `Model` call to the Keras 2 API: `Model(inputs=Tensor(\"in..., outputs=Tensor(\"de...)`\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ],
     "name": "stderr"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "7mrlyFIv54dt",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "model.compile(loss = 'categorical_crossentropy', \n",
    "             optimizer = 'adam',\n",
    "             metrics = ['accuracy'])"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "YppOXPdcvVii",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "outputId": "b3db802c-3ea2-4e73-fbc2-40950f99773c"
   },
   "source": [
    "model.summary()"
   ],
   "execution_count": 240,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Model: \"model_19\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_18 (InputLayer)           (None, 224, 224, 3)  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_659 (Conv2D)             (None, 111, 111, 32) 864         input_18[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_669 (BatchN (None, 111, 111, 32) 96          conv2d_659[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_659 (Activation)     (None, 111, 111, 32) 0           batch_normalization_669[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_660 (Conv2D)             (None, 109, 109, 32) 9216        activation_659[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_670 (BatchN (None, 109, 109, 32) 96          conv2d_660[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_660 (Activation)     (None, 109, 109, 32) 0           batch_normalization_670[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_661 (Conv2D)             (None, 109, 109, 64) 18432       activation_660[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_671 (BatchN (None, 109, 109, 64) 192         conv2d_661[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_661 (Activation)     (None, 109, 109, 64) 0           batch_normalization_671[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_29 (MaxPooling2D) (None, 54, 54, 64)   0           activation_661[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_662 (Conv2D)             (None, 54, 54, 80)   5120        max_pooling2d_29[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_672 (BatchN (None, 54, 54, 80)   240         conv2d_662[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_662 (Activation)     (None, 54, 54, 80)   0           batch_normalization_672[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_663 (Conv2D)             (None, 52, 52, 192)  138240      activation_662[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_673 (BatchN (None, 52, 52, 192)  576         conv2d_663[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_663 (Activation)     (None, 52, 52, 192)  0           batch_normalization_673[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_30 (MaxPooling2D) (None, 25, 25, 192)  0           activation_663[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_667 (Conv2D)             (None, 25, 25, 64)   12288       max_pooling2d_30[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_677 (BatchN (None, 25, 25, 64)   192         conv2d_667[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_667 (Activation)     (None, 25, 25, 64)   0           batch_normalization_677[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_665 (Conv2D)             (None, 25, 25, 48)   9216        max_pooling2d_30[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_668 (Conv2D)             (None, 25, 25, 96)   55296       activation_667[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_675 (BatchN (None, 25, 25, 48)   144         conv2d_665[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_678 (BatchN (None, 25, 25, 96)   288         conv2d_668[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_665 (Activation)     (None, 25, 25, 48)   0           batch_normalization_675[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_668 (Activation)     (None, 25, 25, 96)   0           batch_normalization_678[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_64 (AveragePo (None, 25, 25, 192)  0           max_pooling2d_30[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_664 (Conv2D)             (None, 25, 25, 64)   12288       max_pooling2d_30[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_666 (Conv2D)             (None, 25, 25, 64)   76800       activation_665[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_669 (Conv2D)             (None, 25, 25, 96)   82944       activation_668[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_670 (Conv2D)             (None, 25, 25, 32)   6144        average_pooling2d_64[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_674 (BatchN (None, 25, 25, 64)   192         conv2d_664[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_676 (BatchN (None, 25, 25, 64)   192         conv2d_666[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_679 (BatchN (None, 25, 25, 96)   288         conv2d_669[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_680 (BatchN (None, 25, 25, 32)   96          conv2d_670[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_664 (Activation)     (None, 25, 25, 64)   0           batch_normalization_674[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_666 (Activation)     (None, 25, 25, 64)   0           batch_normalization_676[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_669 (Activation)     (None, 25, 25, 96)   0           batch_normalization_679[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_670 (Activation)     (None, 25, 25, 32)   0           batch_normalization_680[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 25, 25, 256)  0           activation_664[0][0]             \n",
      "                                                                 activation_666[0][0]             \n",
      "                                                                 activation_669[0][0]             \n",
      "                                                                 activation_670[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_674 (Conv2D)             (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_684 (BatchN (None, 25, 25, 64)   192         conv2d_674[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_674 (Activation)     (None, 25, 25, 64)   0           batch_normalization_684[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_672 (Conv2D)             (None, 25, 25, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_675 (Conv2D)             (None, 25, 25, 96)   55296       activation_674[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_682 (BatchN (None, 25, 25, 48)   144         conv2d_672[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_685 (BatchN (None, 25, 25, 96)   288         conv2d_675[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_672 (Activation)     (None, 25, 25, 48)   0           batch_normalization_682[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_675 (Activation)     (None, 25, 25, 96)   0           batch_normalization_685[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_65 (AveragePo (None, 25, 25, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_671 (Conv2D)             (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_673 (Conv2D)             (None, 25, 25, 64)   76800       activation_672[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_676 (Conv2D)             (None, 25, 25, 96)   82944       activation_675[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_677 (Conv2D)             (None, 25, 25, 64)   16384       average_pooling2d_65[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_681 (BatchN (None, 25, 25, 64)   192         conv2d_671[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_683 (BatchN (None, 25, 25, 64)   192         conv2d_673[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_686 (BatchN (None, 25, 25, 96)   288         conv2d_676[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_687 (BatchN (None, 25, 25, 64)   192         conv2d_677[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_671 (Activation)     (None, 25, 25, 64)   0           batch_normalization_681[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_673 (Activation)     (None, 25, 25, 64)   0           batch_normalization_683[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_676 (Activation)     (None, 25, 25, 96)   0           batch_normalization_686[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_677 (Activation)     (None, 25, 25, 64)   0           batch_normalization_687[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 25, 25, 288)  0           activation_671[0][0]             \n",
      "                                                                 activation_673[0][0]             \n",
      "                                                                 activation_676[0][0]             \n",
      "                                                                 activation_677[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_681 (Conv2D)             (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_691 (BatchN (None, 25, 25, 64)   192         conv2d_681[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_681 (Activation)     (None, 25, 25, 64)   0           batch_normalization_691[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_679 (Conv2D)             (None, 25, 25, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_682 (Conv2D)             (None, 25, 25, 96)   55296       activation_681[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_689 (BatchN (None, 25, 25, 48)   144         conv2d_679[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_692 (BatchN (None, 25, 25, 96)   288         conv2d_682[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_679 (Activation)     (None, 25, 25, 48)   0           batch_normalization_689[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_682 (Activation)     (None, 25, 25, 96)   0           batch_normalization_692[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_66 (AveragePo (None, 25, 25, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_678 (Conv2D)             (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_680 (Conv2D)             (None, 25, 25, 64)   76800       activation_679[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_683 (Conv2D)             (None, 25, 25, 96)   82944       activation_682[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_684 (Conv2D)             (None, 25, 25, 64)   18432       average_pooling2d_66[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_688 (BatchN (None, 25, 25, 64)   192         conv2d_678[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_690 (BatchN (None, 25, 25, 64)   192         conv2d_680[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_693 (BatchN (None, 25, 25, 96)   288         conv2d_683[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_694 (BatchN (None, 25, 25, 64)   192         conv2d_684[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_678 (Activation)     (None, 25, 25, 64)   0           batch_normalization_688[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_680 (Activation)     (None, 25, 25, 64)   0           batch_normalization_690[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_683 (Activation)     (None, 25, 25, 96)   0           batch_normalization_693[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_684 (Activation)     (None, 25, 25, 64)   0           batch_normalization_694[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 25, 25, 288)  0           activation_678[0][0]             \n",
      "                                                                 activation_680[0][0]             \n",
      "                                                                 activation_683[0][0]             \n",
      "                                                                 activation_684[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_686 (Conv2D)             (None, 25, 25, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_696 (BatchN (None, 25, 25, 64)   192         conv2d_686[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_686 (Activation)     (None, 25, 25, 64)   0           batch_normalization_696[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_687 (Conv2D)             (None, 25, 25, 96)   55296       activation_686[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_697 (BatchN (None, 25, 25, 96)   288         conv2d_687[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_687 (Activation)     (None, 25, 25, 96)   0           batch_normalization_697[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_685 (Conv2D)             (None, 12, 12, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_688 (Conv2D)             (None, 12, 12, 96)   82944       activation_687[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_695 (BatchN (None, 12, 12, 384)  1152        conv2d_685[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_698 (BatchN (None, 12, 12, 96)   288         conv2d_688[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_685 (Activation)     (None, 12, 12, 384)  0           batch_normalization_695[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_688 (Activation)     (None, 12, 12, 96)   0           batch_normalization_698[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_31 (MaxPooling2D) (None, 12, 12, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 12, 12, 768)  0           activation_685[0][0]             \n",
      "                                                                 activation_688[0][0]             \n",
      "                                                                 max_pooling2d_31[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_693 (Conv2D)             (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_703 (BatchN (None, 12, 12, 128)  384         conv2d_693[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_693 (Activation)     (None, 12, 12, 128)  0           batch_normalization_703[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_694 (Conv2D)             (None, 12, 12, 128)  114688      activation_693[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_704 (BatchN (None, 12, 12, 128)  384         conv2d_694[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_694 (Activation)     (None, 12, 12, 128)  0           batch_normalization_704[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_690 (Conv2D)             (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_695 (Conv2D)             (None, 12, 12, 128)  114688      activation_694[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_700 (BatchN (None, 12, 12, 128)  384         conv2d_690[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_705 (BatchN (None, 12, 12, 128)  384         conv2d_695[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_690 (Activation)     (None, 12, 12, 128)  0           batch_normalization_700[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_695 (Activation)     (None, 12, 12, 128)  0           batch_normalization_705[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_691 (Conv2D)             (None, 12, 12, 128)  114688      activation_690[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_696 (Conv2D)             (None, 12, 12, 128)  114688      activation_695[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_701 (BatchN (None, 12, 12, 128)  384         conv2d_691[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_706 (BatchN (None, 12, 12, 128)  384         conv2d_696[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_691 (Activation)     (None, 12, 12, 128)  0           batch_normalization_701[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_696 (Activation)     (None, 12, 12, 128)  0           batch_normalization_706[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_67 (AveragePo (None, 12, 12, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_689 (Conv2D)             (None, 12, 12, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_692 (Conv2D)             (None, 12, 12, 192)  172032      activation_691[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_697 (Conv2D)             (None, 12, 12, 192)  172032      activation_696[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_698 (Conv2D)             (None, 12, 12, 192)  147456      average_pooling2d_67[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_699 (BatchN (None, 12, 12, 192)  576         conv2d_689[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_702 (BatchN (None, 12, 12, 192)  576         conv2d_692[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_707 (BatchN (None, 12, 12, 192)  576         conv2d_697[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_708 (BatchN (None, 12, 12, 192)  576         conv2d_698[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_689 (Activation)     (None, 12, 12, 192)  0           batch_normalization_699[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_692 (Activation)     (None, 12, 12, 192)  0           batch_normalization_702[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_697 (Activation)     (None, 12, 12, 192)  0           batch_normalization_707[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_698 (Activation)     (None, 12, 12, 192)  0           batch_normalization_708[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 12, 12, 768)  0           activation_689[0][0]             \n",
      "                                                                 activation_692[0][0]             \n",
      "                                                                 activation_697[0][0]             \n",
      "                                                                 activation_698[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_703 (Conv2D)             (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_713 (BatchN (None, 12, 12, 160)  480         conv2d_703[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_703 (Activation)     (None, 12, 12, 160)  0           batch_normalization_713[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_704 (Conv2D)             (None, 12, 12, 160)  179200      activation_703[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_714 (BatchN (None, 12, 12, 160)  480         conv2d_704[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_704 (Activation)     (None, 12, 12, 160)  0           batch_normalization_714[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_700 (Conv2D)             (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_705 (Conv2D)             (None, 12, 12, 160)  179200      activation_704[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_710 (BatchN (None, 12, 12, 160)  480         conv2d_700[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_715 (BatchN (None, 12, 12, 160)  480         conv2d_705[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_700 (Activation)     (None, 12, 12, 160)  0           batch_normalization_710[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_705 (Activation)     (None, 12, 12, 160)  0           batch_normalization_715[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_701 (Conv2D)             (None, 12, 12, 160)  179200      activation_700[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_706 (Conv2D)             (None, 12, 12, 160)  179200      activation_705[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_711 (BatchN (None, 12, 12, 160)  480         conv2d_701[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_716 (BatchN (None, 12, 12, 160)  480         conv2d_706[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_701 (Activation)     (None, 12, 12, 160)  0           batch_normalization_711[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_706 (Activation)     (None, 12, 12, 160)  0           batch_normalization_716[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_68 (AveragePo (None, 12, 12, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_699 (Conv2D)             (None, 12, 12, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_702 (Conv2D)             (None, 12, 12, 192)  215040      activation_701[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_707 (Conv2D)             (None, 12, 12, 192)  215040      activation_706[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_708 (Conv2D)             (None, 12, 12, 192)  147456      average_pooling2d_68[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_709 (BatchN (None, 12, 12, 192)  576         conv2d_699[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_712 (BatchN (None, 12, 12, 192)  576         conv2d_702[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_717 (BatchN (None, 12, 12, 192)  576         conv2d_707[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_718 (BatchN (None, 12, 12, 192)  576         conv2d_708[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_699 (Activation)     (None, 12, 12, 192)  0           batch_normalization_709[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_702 (Activation)     (None, 12, 12, 192)  0           batch_normalization_712[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_707 (Activation)     (None, 12, 12, 192)  0           batch_normalization_717[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_708 (Activation)     (None, 12, 12, 192)  0           batch_normalization_718[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 12, 12, 768)  0           activation_699[0][0]             \n",
      "                                                                 activation_702[0][0]             \n",
      "                                                                 activation_707[0][0]             \n",
      "                                                                 activation_708[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_713 (Conv2D)             (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_723 (BatchN (None, 12, 12, 160)  480         conv2d_713[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_713 (Activation)     (None, 12, 12, 160)  0           batch_normalization_723[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_714 (Conv2D)             (None, 12, 12, 160)  179200      activation_713[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_724 (BatchN (None, 12, 12, 160)  480         conv2d_714[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_714 (Activation)     (None, 12, 12, 160)  0           batch_normalization_724[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_710 (Conv2D)             (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_715 (Conv2D)             (None, 12, 12, 160)  179200      activation_714[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_720 (BatchN (None, 12, 12, 160)  480         conv2d_710[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_725 (BatchN (None, 12, 12, 160)  480         conv2d_715[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_710 (Activation)     (None, 12, 12, 160)  0           batch_normalization_720[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_715 (Activation)     (None, 12, 12, 160)  0           batch_normalization_725[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_711 (Conv2D)             (None, 12, 12, 160)  179200      activation_710[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_716 (Conv2D)             (None, 12, 12, 160)  179200      activation_715[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_721 (BatchN (None, 12, 12, 160)  480         conv2d_711[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_726 (BatchN (None, 12, 12, 160)  480         conv2d_716[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_711 (Activation)     (None, 12, 12, 160)  0           batch_normalization_721[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_716 (Activation)     (None, 12, 12, 160)  0           batch_normalization_726[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_69 (AveragePo (None, 12, 12, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_709 (Conv2D)             (None, 12, 12, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_712 (Conv2D)             (None, 12, 12, 192)  215040      activation_711[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_717 (Conv2D)             (None, 12, 12, 192)  215040      activation_716[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_718 (Conv2D)             (None, 12, 12, 192)  147456      average_pooling2d_69[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_719 (BatchN (None, 12, 12, 192)  576         conv2d_709[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_722 (BatchN (None, 12, 12, 192)  576         conv2d_712[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_727 (BatchN (None, 12, 12, 192)  576         conv2d_717[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_728 (BatchN (None, 12, 12, 192)  576         conv2d_718[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_709 (Activation)     (None, 12, 12, 192)  0           batch_normalization_719[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_712 (Activation)     (None, 12, 12, 192)  0           batch_normalization_722[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_717 (Activation)     (None, 12, 12, 192)  0           batch_normalization_727[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_718 (Activation)     (None, 12, 12, 192)  0           batch_normalization_728[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 12, 12, 768)  0           activation_709[0][0]             \n",
      "                                                                 activation_712[0][0]             \n",
      "                                                                 activation_717[0][0]             \n",
      "                                                                 activation_718[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_723 (Conv2D)             (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_733 (BatchN (None, 12, 12, 192)  576         conv2d_723[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_723 (Activation)     (None, 12, 12, 192)  0           batch_normalization_733[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_724 (Conv2D)             (None, 12, 12, 192)  258048      activation_723[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_734 (BatchN (None, 12, 12, 192)  576         conv2d_724[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_724 (Activation)     (None, 12, 12, 192)  0           batch_normalization_734[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_720 (Conv2D)             (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_725 (Conv2D)             (None, 12, 12, 192)  258048      activation_724[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_730 (BatchN (None, 12, 12, 192)  576         conv2d_720[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_735 (BatchN (None, 12, 12, 192)  576         conv2d_725[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_720 (Activation)     (None, 12, 12, 192)  0           batch_normalization_730[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_725 (Activation)     (None, 12, 12, 192)  0           batch_normalization_735[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_721 (Conv2D)             (None, 12, 12, 192)  258048      activation_720[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_726 (Conv2D)             (None, 12, 12, 192)  258048      activation_725[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_731 (BatchN (None, 12, 12, 192)  576         conv2d_721[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_736 (BatchN (None, 12, 12, 192)  576         conv2d_726[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_721 (Activation)     (None, 12, 12, 192)  0           batch_normalization_731[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_726 (Activation)     (None, 12, 12, 192)  0           batch_normalization_736[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_70 (AveragePo (None, 12, 12, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_719 (Conv2D)             (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_722 (Conv2D)             (None, 12, 12, 192)  258048      activation_721[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_727 (Conv2D)             (None, 12, 12, 192)  258048      activation_726[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_728 (Conv2D)             (None, 12, 12, 192)  147456      average_pooling2d_70[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_729 (BatchN (None, 12, 12, 192)  576         conv2d_719[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_732 (BatchN (None, 12, 12, 192)  576         conv2d_722[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_737 (BatchN (None, 12, 12, 192)  576         conv2d_727[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_738 (BatchN (None, 12, 12, 192)  576         conv2d_728[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_719 (Activation)     (None, 12, 12, 192)  0           batch_normalization_729[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_722 (Activation)     (None, 12, 12, 192)  0           batch_normalization_732[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_727 (Activation)     (None, 12, 12, 192)  0           batch_normalization_737[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_728 (Activation)     (None, 12, 12, 192)  0           batch_normalization_738[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 12, 12, 768)  0           activation_719[0][0]             \n",
      "                                                                 activation_722[0][0]             \n",
      "                                                                 activation_727[0][0]             \n",
      "                                                                 activation_728[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_731 (Conv2D)             (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_741 (BatchN (None, 12, 12, 192)  576         conv2d_731[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_731 (Activation)     (None, 12, 12, 192)  0           batch_normalization_741[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_732 (Conv2D)             (None, 12, 12, 192)  258048      activation_731[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_742 (BatchN (None, 12, 12, 192)  576         conv2d_732[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_732 (Activation)     (None, 12, 12, 192)  0           batch_normalization_742[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_729 (Conv2D)             (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_733 (Conv2D)             (None, 12, 12, 192)  258048      activation_732[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_739 (BatchN (None, 12, 12, 192)  576         conv2d_729[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_743 (BatchN (None, 12, 12, 192)  576         conv2d_733[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_729 (Activation)     (None, 12, 12, 192)  0           batch_normalization_739[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_733 (Activation)     (None, 12, 12, 192)  0           batch_normalization_743[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_730 (Conv2D)             (None, 5, 5, 320)    552960      activation_729[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_734 (Conv2D)             (None, 5, 5, 192)    331776      activation_733[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_740 (BatchN (None, 5, 5, 320)    960         conv2d_730[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_744 (BatchN (None, 5, 5, 192)    576         conv2d_734[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_730 (Activation)     (None, 5, 5, 320)    0           batch_normalization_740[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_734 (Activation)     (None, 5, 5, 192)    0           batch_normalization_744[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_32 (MaxPooling2D) (None, 5, 5, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 5, 5, 1280)   0           activation_730[0][0]             \n",
      "                                                                 activation_734[0][0]             \n",
      "                                                                 max_pooling2d_32[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_739 (Conv2D)             (None, 5, 5, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_749 (BatchN (None, 5, 5, 448)    1344        conv2d_739[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_739 (Activation)     (None, 5, 5, 448)    0           batch_normalization_749[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_736 (Conv2D)             (None, 5, 5, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_740 (Conv2D)             (None, 5, 5, 384)    1548288     activation_739[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_746 (BatchN (None, 5, 5, 384)    1152        conv2d_736[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_750 (BatchN (None, 5, 5, 384)    1152        conv2d_740[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_736 (Activation)     (None, 5, 5, 384)    0           batch_normalization_746[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_740 (Activation)     (None, 5, 5, 384)    0           batch_normalization_750[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_737 (Conv2D)             (None, 5, 5, 384)    442368      activation_736[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_738 (Conv2D)             (None, 5, 5, 384)    442368      activation_736[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_741 (Conv2D)             (None, 5, 5, 384)    442368      activation_740[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_742 (Conv2D)             (None, 5, 5, 384)    442368      activation_740[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_71 (AveragePo (None, 5, 5, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_735 (Conv2D)             (None, 5, 5, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_747 (BatchN (None, 5, 5, 384)    1152        conv2d_737[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_748 (BatchN (None, 5, 5, 384)    1152        conv2d_738[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_751 (BatchN (None, 5, 5, 384)    1152        conv2d_741[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_752 (BatchN (None, 5, 5, 384)    1152        conv2d_742[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_743 (Conv2D)             (None, 5, 5, 192)    245760      average_pooling2d_71[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_745 (BatchN (None, 5, 5, 320)    960         conv2d_735[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_737 (Activation)     (None, 5, 5, 384)    0           batch_normalization_747[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_738 (Activation)     (None, 5, 5, 384)    0           batch_normalization_748[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_741 (Activation)     (None, 5, 5, 384)    0           batch_normalization_751[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_742 (Activation)     (None, 5, 5, 384)    0           batch_normalization_752[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_753 (BatchN (None, 5, 5, 192)    576         conv2d_743[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_735 (Activation)     (None, 5, 5, 320)    0           batch_normalization_745[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 5, 5, 768)    0           activation_737[0][0]             \n",
      "                                                                 activation_738[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_15 (Concatenate)    (None, 5, 5, 768)    0           activation_741[0][0]             \n",
      "                                                                 activation_742[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_743 (Activation)     (None, 5, 5, 192)    0           batch_normalization_753[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 5, 5, 2048)   0           activation_735[0][0]             \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate_15[0][0]             \n",
      "                                                                 activation_743[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_748 (Conv2D)             (None, 5, 5, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_758 (BatchN (None, 5, 5, 448)    1344        conv2d_748[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_748 (Activation)     (None, 5, 5, 448)    0           batch_normalization_758[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_745 (Conv2D)             (None, 5, 5, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_749 (Conv2D)             (None, 5, 5, 384)    1548288     activation_748[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_755 (BatchN (None, 5, 5, 384)    1152        conv2d_745[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_759 (BatchN (None, 5, 5, 384)    1152        conv2d_749[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_745 (Activation)     (None, 5, 5, 384)    0           batch_normalization_755[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_749 (Activation)     (None, 5, 5, 384)    0           batch_normalization_759[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_746 (Conv2D)             (None, 5, 5, 384)    442368      activation_745[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_747 (Conv2D)             (None, 5, 5, 384)    442368      activation_745[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_750 (Conv2D)             (None, 5, 5, 384)    442368      activation_749[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_751 (Conv2D)             (None, 5, 5, 384)    442368      activation_749[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_72 (AveragePo (None, 5, 5, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_744 (Conv2D)             (None, 5, 5, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_756 (BatchN (None, 5, 5, 384)    1152        conv2d_746[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_757 (BatchN (None, 5, 5, 384)    1152        conv2d_747[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_760 (BatchN (None, 5, 5, 384)    1152        conv2d_750[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_761 (BatchN (None, 5, 5, 384)    1152        conv2d_751[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_752 (Conv2D)             (None, 5, 5, 192)    393216      average_pooling2d_72[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_754 (BatchN (None, 5, 5, 320)    960         conv2d_744[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_746 (Activation)     (None, 5, 5, 384)    0           batch_normalization_756[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_747 (Activation)     (None, 5, 5, 384)    0           batch_normalization_757[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_750 (Activation)     (None, 5, 5, 384)    0           batch_normalization_760[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "activation_751 (Activation)     (None, 5, 5, 384)    0           batch_normalization_761[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_762 (BatchN (None, 5, 5, 192)    576         conv2d_752[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "activation_744 (Activation)     (None, 5, 5, 320)    0           batch_normalization_754[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 5, 5, 768)    0           activation_746[0][0]             \n",
      "                                                                 activation_747[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_16 (Concatenate)    (None, 5, 5, 768)    0           activation_750[0][0]             \n",
      "                                                                 activation_751[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "activation_752 (Activation)     (None, 5, 5, 192)    0           batch_normalization_762[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 5, 5, 2048)   0           activation_744[0][0]             \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_16[0][0]             \n",
      "                                                                 activation_752[0][0]             \n",
      "__________________________________________________________________________________________________\n",
      "dropout_13 (Dropout)            (None, 5, 5, 2048)   0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d_12 (Gl (None, 2048)         0           dropout_13[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "dense_34 (Dense)                (None, 128)          262272      global_average_pooling2d_12[0][0]\n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_763 (BatchN (None, 128)          512         dense_34[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "dense_35 (Dense)                (None, 2)            258         batch_normalization_763[0][0]    \n",
      "==================================================================================================\n",
      "Total params: 22,065,826\n",
      "Trainable params: 22,031,138\n",
      "Non-trainable params: 34,688\n",
      "__________________________________________________________________________________________________\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "kK8W2SkHvbT_",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "from keras.preprocessing.image import ImageDataGenerator"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "wRC78sWe2DRF",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "traindatagen = ImageDataGenerator(rescale=1./255,\n",
    "                                  zoom_range = 0.2,\n",
    "                                  horizontal_flip=True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale=1./255)\n",
    "\n",
    "\n",
    "                                 "
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "9Xf4lBj53aWF",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "outputId": "75edac07-a990-42ca-821b-451ff023a71a"
   },
   "source": [
    "training_set = train_datagen.flow_from_directory(loc+'/train',\n",
    "                                                target_size=(224, 224),\n",
    "                                                batch_size=32,\n",
    "                                                class_mode='categorical')\n",
    "\n",
    "testing_set = test_datagen.flow_from_directory(loc+'/test',\n",
    "                                               target_size=(224, 224),\n",
    "                                               batch_size=32,\n",
    "                                               class_mode='categorical')\n"
   ],
   "execution_count": 243,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Found 5229 images belonging to 2 classes.\n",
      "Found 624 images belonging to 2 classes.\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "cZ5eZ3NR5Vbb",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 867
    },
    "outputId": "2759d163-de7a-47aa-8f3d-920afa52ae23"
   },
   "source": [
    "f = model.fit_generator(training_set,\n",
    "              validation_data = testing_set,\n",
    "              epochs=25,\n",
    "              steps_per_epoch = len(training_set),\n",
    "              validation_steps = len(testing_set)\n",
    "             )"
   ],
   "execution_count": 248,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Epoch 1/25\n",
      "164/164 [==============================] - 178s 1s/step - loss: 0.0414 - acc: 0.9857 - val_loss: 0.1939 - val_acc: 0.9247\n",
      "Epoch 2/25\n",
      "164/164 [==============================] - 175s 1s/step - loss: 0.0338 - acc: 0.9882 - val_loss: 0.3341 - val_acc: 0.8670\n",
      "Epoch 3/25\n",
      "164/164 [==============================] - 181s 1s/step - loss: 0.0376 - acc: 0.9866 - val_loss: 0.3172 - val_acc: 0.9087\n",
      "Epoch 4/25\n",
      "164/164 [==============================] - 180s 1s/step - loss: 0.0306 - acc: 0.9888 - val_loss: 0.2379 - val_acc: 0.9263\n",
      "Epoch 5/25\n",
      "164/164 [==============================] - 179s 1s/step - loss: 0.0257 - acc: 0.9910 - val_loss: 0.2215 - val_acc: 0.9215\n",
      "Epoch 6/25\n",
      "164/164 [==============================] - 173s 1s/step - loss: 0.0328 - acc: 0.9878 - val_loss: 0.2937 - val_acc: 0.9071\n",
      "Epoch 7/25\n",
      "164/164 [==============================] - 172s 1s/step - loss: 0.0262 - acc: 0.9916 - val_loss: 0.3706 - val_acc: 0.8686\n",
      "Epoch 8/25\n",
      "164/164 [==============================] - 175s 1s/step - loss: 0.0233 - acc: 0.9916 - val_loss: 0.2358 - val_acc: 0.9247\n",
      "Epoch 9/25\n",
      "164/164 [==============================] - 173s 1s/step - loss: 0.0307 - acc: 0.9889 - val_loss: 0.3309 - val_acc: 0.8846\n",
      "Epoch 10/25\n",
      "164/164 [==============================] - 174s 1s/step - loss: 0.0435 - acc: 0.9859 - val_loss: 0.1829 - val_acc: 0.9135\n",
      "Epoch 11/25\n",
      "164/164 [==============================] - 174s 1s/step - loss: 0.0225 - acc: 0.9918 - val_loss: 0.4384 - val_acc: 0.8718\n",
      "Epoch 12/25\n",
      "164/164 [==============================] - 172s 1s/step - loss: 0.0252 - acc: 0.9909 - val_loss: 0.1816 - val_acc: 0.9295\n",
      "Epoch 13/25\n",
      "164/164 [==============================] - 169s 1s/step - loss: 0.0184 - acc: 0.9933 - val_loss: 0.4338 - val_acc: 0.8846\n",
      "Epoch 14/25\n",
      "164/164 [==============================] - 170s 1s/step - loss: 0.0271 - acc: 0.9923 - val_loss: 0.1295 - val_acc: 0.9535\n",
      "Epoch 15/25\n",
      "164/164 [==============================] - 170s 1s/step - loss: 0.0284 - acc: 0.9893 - val_loss: 0.2493 - val_acc: 0.8958\n",
      "Epoch 16/25\n",
      "164/164 [==============================] - 176s 1s/step - loss: 0.0444 - acc: 0.9838 - val_loss: 0.4567 - val_acc: 0.8429\n",
      "Epoch 17/25\n",
      "164/164 [==============================] - 182s 1s/step - loss: 0.0252 - acc: 0.9914 - val_loss: 0.4830 - val_acc: 0.8317\n",
      "Epoch 18/25\n",
      "164/164 [==============================] - 181s 1s/step - loss: 0.0197 - acc: 0.9941 - val_loss: 0.2197 - val_acc: 0.9215\n",
      "Epoch 19/25\n",
      "164/164 [==============================] - 178s 1s/step - loss: 0.0237 - acc: 0.9910 - val_loss: 0.2502 - val_acc: 0.9151\n",
      "Epoch 20/25\n",
      "164/164 [==============================] - 174s 1s/step - loss: 0.0455 - acc: 0.9877 - val_loss: 0.3116 - val_acc: 0.9087\n",
      "Epoch 21/25\n",
      "164/164 [==============================] - 173s 1s/step - loss: 0.0267 - acc: 0.9910 - val_loss: 0.3760 - val_acc: 0.8734\n",
      "Epoch 22/25\n",
      "164/164 [==============================] - 174s 1s/step - loss: 0.0299 - acc: 0.9889 - val_loss: 0.2806 - val_acc: 0.8750\n",
      "Epoch 23/25\n",
      "164/164 [==============================] - 177s 1s/step - loss: 0.0186 - acc: 0.9933 - val_loss: 0.1727 - val_acc: 0.9503\n",
      "Epoch 24/25\n",
      "164/164 [==============================] - 176s 1s/step - loss: 0.0229 - acc: 0.9922 - val_loss: 0.6880 - val_acc: 0.8045\n",
      "Epoch 25/25\n",
      "164/164 [==============================] - 177s 1s/step - loss: 0.0203 - acc: 0.9939 - val_loss: 0.3984 - val_acc: 0.8670\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "_pMN5iES79cd",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 503
    },
    "outputId": "399f7b7b-d7c1-4fcc-dc28-7d22b95134a5"
   },
   "source": [
    "plt.figure(figsize = (20,8))\n",
    "plt.plot(f.history['loss'], label = 'Data loss')\n",
    "plt.plot(f.history['val_loss'] , label = 'Validation loss')\n",
    "plt.legend()"
   ],
   "execution_count": 249,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff244b3f630>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 249
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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Ly6FhF6yZ36Fh6Z1MCEmSJEmSpPSorTzw/KCU4klAZNtYFjAhJEmSJEmS2q+xATatPPD8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\nlqQ0m7O0dYklzMJNxW4o2RS5rU0nUzDfqgEbqnruMUs2Q+X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meV7kZM79MWxZZFvH/ud1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      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "F2RuWrHeQ1qA",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 503
    },
    "outputId": "e50ba7e1-612f-470d-b13d-f16091ccf6df"
   },
   "source": [
    "plt.figure(figsize = (20,8))\n",
    "plt.plot(f.history['acc'], label = ' Data Accuracy')\n",
    "plt.plot(f.history['val_acc'] , label = 'Validation accuracy')\n",
    "plt.legend()"
   ],
   "execution_count": 251,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7ff24d1560b8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 251
    },
    {
     "output_type": "display_data",
     "data": {
      "image/png": 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IiIiISGRMJ0RaBDQFfd/s3xfsNeA2/9fvADKNMfmTjrkDuHfSvi/5p8D9tzEm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\nrkJggg==\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "tags": []
     }
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "MZozoSJFiaK7",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "from keras.preprocessing.image import image\n",
    "from keras.applications.inception_v3 import preprocess_input\n",
    "import math"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "RwfhJXgiirDv",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 241
    },
    "outputId": "55d361cd-5a45-408e-e569-7060113e9a8a"
   },
   "source": [
    "img = image.load_img(loc+'/val/PNEUMONIA/person1950_bacteria_4881.jpeg', target_size=(224, 224))\n",
    "img"
   ],
   "execution_count": 260,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "image/png": 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GxgYUO78fxYNieHh4uLq6goPiTA/3D3qHJTIKxpIZ5em6jh2Fpk4i2/cMs4RO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\nTBhNwRXxcfrXvv4/1JfgjfayxyMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FF24D11A748>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 260
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "WmPryA_SjzuZ",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "img = image.img_to_array(img)\n",
    "img = np.expand_dims(img, axis=0)"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "4v7CBMPLkxCn",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "outputId": "6b70507c-7967-47c9-ee4d-6bee5a4e9f3c"
   },
   "source": [
    "img = preprocess_input(img)\n",
    "pred = model.predict(img)\n",
    "if pred[0][0]> pred[0][1]:\n",
    "  print('Congratulation Free from Pneumonia')\n",
    "else:\n",
    "  print('Sorry , You have Pneumonia. \\nPlease consult Doctor as soon as')\n"
   ],
   "execution_count": 281,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "Sorry , You have Pneumonia. \n",
      "Please consult Doctor as soon as\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "qaKqBNCYk7gG",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 289
    },
    "outputId": "d4d68f79-9315-4a7a-94be-de600dcbb298"
   },
   "source": [
    "for i in os.listdir(loc+'/val'):\n",
    "  if i != '.DS_Store':\n",
    "    \n",
    "    for j in os.listdir(loc+'/val/'+i):\n",
    "      if j != '.DS_Store':\n",
    "        \n",
    "        img1 = image.load_img(loc+'/val/'+i+'/'+j, target_size=(224, 224))\n",
    "        \n",
    "        x = image.img_to_array(img1)\n",
    "        \n",
    "        x = np.expand_dims(x, axis=0)\n",
    "        \n",
    "        image_data = preprocess_input(x)\n",
    "        classes = model.predict(image_data)\n",
    "        print(i,classes[0])"
   ],
   "execution_count": 290,
   "outputs": [
    {
     "output_type": "stream",
     "text": [
      "PNEUMONIA [0.001736   0.86667186]\n",
      "PNEUMONIA [0.04754859 0.6666887 ]\n",
      "PNEUMONIA [0.03778194 0.6519393 ]\n",
      "PNEUMONIA [3.2441696e-04 9.2275167e-01]\n",
      "PNEUMONIA [0.02942887 0.6968192 ]\n",
      "PNEUMONIA [0.26043382 0.47089902]\n",
      "PNEUMONIA [2.8591702e-04 9.2470688e-01]\n",
      "PNEUMONIA [0.05474186 0.61111605]\n",
      "NORMAL [0.98859555 0.00310914]\n",
      "NORMAL [0.0107008  0.78003716]\n",
      "NORMAL [0.9022903  0.02579189]\n",
      "NORMAL [0.69669676 0.15849957]\n",
      "NORMAL [2.879556e-04 9.257846e-01]\n",
      "NORMAL [0.9556075  0.00625437]\n",
      "NORMAL [1.3843561e-04 9.4005299e-01]\n",
      "NORMAL [0.9730374  0.00333784]\n"
     ],
     "name": "stdout"
    }
   ]
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "KthLW0pmlkCr",
    "colab_type": "code",
    "colab": {}
   },
   "source": [
    "name = 'InceptionV3'\n",
    "\n",
    "model.save(loc+name)"
   ],
   "execution_count": 0,
   "outputs": []
  },
  {
   "cell_type": "code",
   "metadata": {
    "id": "9YSuZYIqn_r2",
    "colab_type": "code",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 85
    },
    "outputId": "d4846294-7f95-41f2-e8f4-ab70709761b1"
   },
   "source": [
    "import os \n",
    "os.listdir('/drive/My Drive/Colab Notebooks')"
   ],
   "execution_count": 297,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "['chest_xray',\n",
       " 'chest_xrayPneumonia_model.h5',\n",
       " 'Pneumonia.ipynb',\n",
       " 'chest_xrayInceptionV3']"
      ]
     },
     "metadata": {
      "tags": []
     },
     "execution_count": 297
    }
   ]
  }
 ]
}