Transfer Learning Inception V3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Transfer Learning Inception V3 using Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please download the dataset from the below url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "\n",
    "config = ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.5\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import the libraries as shown below\n",
    "\n",
    "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "#from keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications.inception_v3 import preprocess_input\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
    "from tensorflow.keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "#import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# re-size all the images to this\n",
    "IMAGE_SIZE = [224, 224]\n",
    "\n",
    "train_path = 'Datasets/train'\n",
    "valid_path = 'Datasets/test'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/inception_v3/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "87916544/87910968 [==============================] - 11s 0us/step\n"
     ]
    }
   ],
   "source": [
    "# Import the Vgg 16 library as shown below and add preprocessing layer to the front of VGG\n",
    "# Here we will be using imagenet weights\n",
    "\n",
    "inception = InceptionV3(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# don't train existing weights\n",
    "for layer in inception.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "  # useful for getting number of output classes\n",
    "folders = glob('Datasets/train/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# our layers - you can add more if you want\n",
    "x = Flatten()(inception.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "prediction = Dense(len(folders), activation='softmax')(x)\n",
    "\n",
    "# create a model object\n",
    "model = Model(inputs=inception.input, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 111, 111, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 111, 111, 32) 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 111, 111, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 109, 109, 32) 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 109, 109, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 109, 109, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 109, 109, 64) 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 109, 109, 64) 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 109, 109, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 54, 54, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 54, 54, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 54, 54, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 54, 54, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 52, 52, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 52, 52, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 52, 52, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 25, 25, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 25, 25, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 25, 25, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 25, 25, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 25, 25, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 25, 25, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 25, 25, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 25, 25, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 25, 25, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 25, 25, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 25, 25, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 25, 25, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 25, 25, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 25, 25, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 25, 25, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 25, 25, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 25, 25, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 25, 25, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 25, 25, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 25, 25, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 25, 25, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 25, 25, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 25, 25, 256)  0           activation_5[0][0]               \n",
      "                                                                 activation_7[0][0]               \n",
      "                                                                 activation_10[0][0]              \n",
      "                                                                 activation_11[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_15 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 25, 25, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 25, 25, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 25, 25, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 25, 25, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 25, 25, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 25, 25, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 25, 25, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 25, 25, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 25, 25, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 25, 25, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 25, 25, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 25, 25, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 25, 25, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 25, 25, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 25, 25, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 25, 25, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 25, 25, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 25, 25, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 25, 25, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 25, 25, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 25, 25, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 25, 25, 288)  0           activation_12[0][0]              \n",
      "                                                                 activation_14[0][0]              \n",
      "                                                                 activation_17[0][0]              \n",
      "                                                                 activation_18[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_22 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 25, 25, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 25, 25, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 25, 25, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 25, 25, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 25, 25, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 25, 25, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 25, 25, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 25, 25, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 25, 25, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 25, 25, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 25, 25, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 25, 25, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 25, 25, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 25, 25, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 25, 25, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 25, 25, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 25, 25, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 25, 25, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 25, 25, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 25, 25, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 25, 25, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 25, 25, 288)  0           activation_19[0][0]              \n",
      "                                                                 activation_21[0][0]              \n",
      "                                                                 activation_24[0][0]              \n",
      "                                                                 activation_25[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_27 (Conv2D)              (None, 25, 25, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 25, 25, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 25, 25, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 25, 25, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 25, 25, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 25, 25, 96)   0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, 12, 12, 384)  995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, 12, 12, 96)   82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, 12, 12, 384)  1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, 12, 12, 96)   288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, 12, 12, 384)  0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, 12, 12, 96)   0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, 12, 12, 288)  0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, 12, 12, 768)  0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, 12, 12, 128)  384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, 12, 12, 128)  0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, 12, 12, 128)  114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, 12, 12, 128)  384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, 12, 12, 128)  0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, 12, 12, 128)  98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, 12, 12, 128)  114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, 12, 12, 128)  384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, 12, 12, 128)  384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, 12, 12, 128)  0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, 12, 12, 128)  0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, 12, 12, 128)  114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, 12, 12, 128)  114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, 12, 12, 128)  384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, 12, 12, 128)  384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, 12, 12, 128)  0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, 12, 12, 128)  0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, 12, 12, 768)  0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, 12, 12, 192)  147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, 12, 12, 192)  172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, 12, 12, 192)  172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, 12, 12, 192)  576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, 12, 12, 192)  576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, 12, 12, 192)  576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, 12, 12, 192)  576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, 12, 12, 192)  0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, 12, 12, 192)  0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, 12, 12, 192)  0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, 12, 12, 192)  0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, 12, 12, 768)  0           activation_30[0][0]              \n",
      "                                                                 activation_33[0][0]              \n",
      "                                                                 activation_38[0][0]              \n",
      "                                                                 activation_39[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_44 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, 12, 12, 160)  480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, 12, 12, 160)  0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, 12, 12, 160)  179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, 12, 12, 160)  480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, 12, 12, 160)  0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, 12, 12, 160)  122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, 12, 12, 160)  179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, 12, 12, 160)  480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, 12, 12, 160)  480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, 12, 12, 160)  0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, 12, 12, 160)  0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, 12, 12, 160)  179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, 12, 12, 160)  179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, 12, 12, 160)  480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, 12, 12, 160)  480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, 12, 12, 160)  0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, 12, 12, 160)  0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, 12, 12, 768)  0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, 12, 12, 192)  147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, 12, 12, 192)  215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, 12, 12, 192)  215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, 12, 12, 192)  576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, 12, 12, 192)  576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, 12, 12, 192)  576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, 12, 12, 192)  576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, 12, 12, 192)  0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, 12, 12, 192)  0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, 12, 12, 192)  0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, 12, 12, 192)  0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, 12, 12, 768)  0           activation_40[0][0]              \n",
      "                                                                 activation_43[0][0]              \n",
      "                                                                 activation_48[0][0]              \n",
      "                                                                 activation_49[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_54 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, 12, 12, 160)  480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, 12, 12, 160)  0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, 12, 12, 160)  179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, 12, 12, 160)  480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, 12, 12, 160)  0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, 12, 12, 160)  122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, 12, 12, 160)  179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, 12, 12, 160)  480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, 12, 12, 160)  480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, 12, 12, 160)  0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, 12, 12, 160)  0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, 12, 12, 160)  179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, 12, 12, 160)  179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, 12, 12, 160)  480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, 12, 12, 160)  480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, 12, 12, 160)  0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, 12, 12, 160)  0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, 12, 12, 768)  0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, 12, 12, 192)  147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, 12, 12, 192)  215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, 12, 12, 192)  215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, 12, 12, 192)  576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, 12, 12, 192)  576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, 12, 12, 192)  576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, 12, 12, 192)  576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, 12, 12, 192)  0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, 12, 12, 192)  0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, 12, 12, 192)  0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, 12, 12, 192)  0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, 12, 12, 768)  0           activation_50[0][0]              \n",
      "                                                                 activation_53[0][0]              \n",
      "                                                                 activation_58[0][0]              \n",
      "                                                                 activation_59[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_64 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, 12, 12, 192)  576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, 12, 12, 192)  0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, 12, 12, 192)  258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, 12, 12, 192)  576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, 12, 12, 192)  0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, 12, 12, 192)  258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, 12, 12, 192)  576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, 12, 12, 192)  576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, 12, 12, 192)  0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, 12, 12, 192)  0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, 12, 12, 192)  258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, 12, 12, 192)  258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, 12, 12, 192)  576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, 12, 12, 192)  576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, 12, 12, 192)  0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, 12, 12, 192)  0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, 12, 12, 768)  0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, 12, 12, 192)  147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, 12, 12, 192)  258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, 12, 12, 192)  258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, 12, 12, 192)  147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, 12, 12, 192)  576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, 12, 12, 192)  576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, 12, 12, 192)  576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, 12, 12, 192)  576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, 12, 12, 192)  0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, 12, 12, 192)  0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, 12, 12, 192)  0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, 12, 12, 192)  0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, 12, 12, 768)  0           activation_60[0][0]              \n",
      "                                                                 activation_63[0][0]              \n",
      "                                                                 activation_68[0][0]              \n",
      "                                                                 activation_69[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_72 (Conv2D)              (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, 12, 12, 192)  576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, 12, 12, 192)  0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, 12, 12, 192)  258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, 12, 12, 192)  576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, 12, 12, 192)  0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, 12, 12, 192)  147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, 12, 12, 192)  258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, 12, 12, 192)  576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, 12, 12, 192)  576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, 12, 12, 192)  0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, 12, 12, 192)  0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, 5, 5, 320)    552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, 5, 5, 192)    331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, 5, 5, 320)    960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, 5, 5, 192)    576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, 5, 5, 320)    0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, 5, 5, 192)    0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, 5, 5, 768)    0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, 5, 5, 1280)   0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, 5, 5, 448)    573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, 5, 5, 448)    1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, 5, 5, 448)    0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, 5, 5, 384)    491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, 5, 5, 384)    1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, 5, 5, 384)    1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, 5, 5, 384)    1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, 5, 5, 384)    0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, 5, 5, 384)    0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, 5, 5, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, 5, 5, 384)    442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, 5, 5, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, 5, 5, 384)    442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, 5, 5, 1280)   0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, 5, 5, 320)    409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, 5, 5, 384)    1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, 5, 5, 384)    1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, 5, 5, 384)    1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, 5, 5, 384)    1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, 5, 5, 192)    245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, 5, 5, 320)    960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, 5, 5, 384)    0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, 5, 5, 384)    0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, 5, 5, 384)    0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, 5, 5, 384)    0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, 5, 5, 192)    576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, 5, 5, 320)    0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, 5, 5, 768)    0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 5, 5, 768)    0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, 5, 5, 192)    0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, 5, 5, 2048)   0           activation_76[0][0]              \n",
      "                                                                 mixed9_0[0][0]                   \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 activation_84[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_89 (Conv2D)              (None, 5, 5, 448)    917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, 5, 5, 448)    1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, 5, 5, 448)    0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, 5, 5, 384)    786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, 5, 5, 384)    1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, 5, 5, 384)    1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, 5, 5, 384)    1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, 5, 5, 384)    0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, 5, 5, 384)    0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, 5, 5, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, 5, 5, 384)    442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, 5, 5, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, 5, 5, 384)    442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, 5, 5, 2048)   0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, 5, 5, 320)    655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, 5, 5, 384)    1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, 5, 5, 384)    1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, 5, 5, 384)    1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, 5, 5, 384)    1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, 5, 5, 192)    393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, 5, 5, 320)    960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, 5, 5, 384)    0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, 5, 5, 384)    0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, 5, 5, 384)    0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, 5, 5, 384)    0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, 5, 5, 192)    576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, 5, 5, 320)    0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, 5, 5, 768)    0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 5, 5, 768)    0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, 5, 5, 192)    0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, 5, 5, 2048)   0           activation_85[0][0]              \n",
      "                                                                 mixed9_1[0][0]                   \n",
      "                                                                 concatenate_1[0][0]              \n",
      "                                                                 activation_93[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 51200)        0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 4)            204804      flatten[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 22,007,588\n",
      "Trainable params: 204,804\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# view the structure of the model\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# tell the model what cost and optimization method to use\n",
    "model.compile(\n",
    "  loss='categorical_crossentropy',\n",
    "  optimizer='adam',\n",
    "  metrics=['accuracy']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Use the Image Data Generator to import the images from the dataset\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "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)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1951 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "# Make sure you provide the same target size as initialied for the image size\n",
    "training_set = train_datagen.flow_from_directory('Datasets/train',\n",
    "                                                 target_size = (224, 224),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 18 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "test_set = test_datagen.flow_from_directory('Datasets/test',\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-16-2d02736eff38>:8: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "Please use Model.fit, which supports generators.\n",
      "Epoch 1/20\n",
      "61/61 [==============================] - 20s 333ms/step - loss: 2.0140 - accuracy: 0.7401 - val_loss: 1.0249 - val_accuracy: 0.7222\n",
      "Epoch 2/20\n",
      "61/61 [==============================] - 18s 300ms/step - loss: 0.7547 - accuracy: 0.8785 - val_loss: 0.2171 - val_accuracy: 0.9444\n",
      "Epoch 3/20\n",
      "61/61 [==============================] - 18s 299ms/step - loss: 0.4645 - accuracy: 0.9149 - val_loss: 0.1363 - val_accuracy: 0.9444\n",
      "Epoch 4/20\n",
      "61/61 [==============================] - 18s 298ms/step - loss: 0.6390 - accuracy: 0.9021 - val_loss: 0.1251 - val_accuracy: 0.9444\n",
      "Epoch 5/20\n",
      "61/61 [==============================] - 18s 300ms/step - loss: 0.6016 - accuracy: 0.9190 - val_loss: 0.0513 - val_accuracy: 0.9444\n",
      "Epoch 6/20\n",
      "61/61 [==============================] - 18s 300ms/step - loss: 0.4437 - accuracy: 0.9329 - val_loss: 0.0368 - val_accuracy: 1.0000\n",
      "Epoch 7/20\n",
      "61/61 [==============================] - 18s 299ms/step - loss: 0.8518 - accuracy: 0.9021 - val_loss: 0.6182 - val_accuracy: 0.9444\n",
      "Epoch 8/20\n",
      "61/61 [==============================] - 18s 299ms/step - loss: 0.4498 - accuracy: 0.9416 - val_loss: 0.1105 - val_accuracy: 0.9444\n",
      "Epoch 9/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.5562 - accuracy: 0.9426 - val_loss: 0.0010 - val_accuracy: 1.0000\n",
      "Epoch 10/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.3613 - accuracy: 0.9508 - val_loss: 0.0884 - val_accuracy: 0.9444\n",
      "Epoch 11/20\n",
      "61/61 [==============================] - 18s 299ms/step - loss: 0.4488 - accuracy: 0.9467 - val_loss: 1.9574e-05 - val_accuracy: 1.0000\n",
      "Epoch 12/20\n",
      "61/61 [==============================] - 18s 298ms/step - loss: 0.4369 - accuracy: 0.9421 - val_loss: 0.0121 - val_accuracy: 1.0000\n",
      "Epoch 13/20\n",
      "61/61 [==============================] - 18s 299ms/step - loss: 0.3984 - accuracy: 0.9457 - val_loss: 2.0362e-05 - val_accuracy: 1.0000\n",
      "Epoch 14/20\n",
      "61/61 [==============================] - 18s 299ms/step - loss: 0.3902 - accuracy: 0.9569 - val_loss: 0.0014 - val_accuracy: 1.0000\n",
      "Epoch 15/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.4971 - accuracy: 0.9446 - val_loss: 2.3416e-05 - val_accuracy: 1.0000\n",
      "Epoch 16/20\n",
      "61/61 [==============================] - 18s 298ms/step - loss: 0.3149 - accuracy: 0.9621 - val_loss: 0.0120 - val_accuracy: 1.0000\n",
      "Epoch 17/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.2741 - accuracy: 0.9646 - val_loss: 2.8941e-06 - val_accuracy: 1.0000\n",
      "Epoch 18/20\n",
      "61/61 [==============================] - 18s 297ms/step - loss: 0.4285 - accuracy: 0.9616 - val_loss: 0.0100 - val_accuracy: 1.0000\n",
      "Epoch 19/20\n",
      "61/61 [==============================] - 18s 298ms/step - loss: 0.4299 - accuracy: 0.9549 - val_loss: 0.1313 - val_accuracy: 0.9444\n",
      "Epoch 20/20\n",
      "61/61 [==============================] - 18s 298ms/step - loss: 0.5233 - accuracy: 0.9487 - val_loss: 0.0012 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "# fit the model\n",
    "# Run the cell. It will take some time to execute\n",
    "r = model.fit_generator(\n",
    "  training_set,\n",
    "  validation_data=test_set,\n",
    "  epochs=20,\n",
    "  steps_per_epoch=len(training_set),\n",
    "  validation_steps=len(test_set)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the loss\n",
    "plt.plot(r.history['loss'], label='train loss')\n",
    "plt.plot(r.history['val_loss'], label='val loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LossVal_loss')\n",
    "\n",
    "# plot the accuracy\n",
    "plt.plot(r.history['accuracy'], label='train acc')\n",
    "plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('AccVal_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# save it as a h5 file\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model.save('model_inception.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "y_pred = model.predict(test_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9.8109645e-01, 1.8903527e-02, 3.2149321e-13, 3.6633790e-15],\n",
       "       [0.0000000e+00, 1.0000000e+00, 6.7508175e-31, 9.6766906e-34],\n",
       "       [1.0000000e+00, 0.0000000e+00, 5.1786687e-12, 4.6748233e-21],\n",
       "       [5.8609026e-21, 3.5736174e-38, 1.0000000e+00, 6.6462439e-35],\n",
       "       [7.0799731e-33, 1.0000000e+00, 2.7118872e-18, 1.2365503e-16],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 1.6401240e-34],\n",
       "       [7.8406102e-38, 1.5452786e-03, 0.0000000e+00, 9.9845469e-01],\n",
       "       [1.1753855e-34, 6.4181074e-30, 2.3469242e-26, 1.0000000e+00],\n",
       "       [1.6965836e-25, 7.5243352e-23, 1.0000000e+00, 1.5732070e-17],\n",
       "       [2.4483354e-23, 9.3621990e-09, 2.4969464e-17, 1.0000000e+00],\n",
       "       [2.9495364e-14, 9.7012167e-14, 1.4914777e-04, 9.9985087e-01],\n",
       "       [1.0000000e+00, 2.5258806e-23, 2.3514068e-17, 3.6277342e-38],\n",
       "       [0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
       "       [1.6495063e-24, 9.0913505e-33, 1.0000000e+00, 1.4732772e-30],\n",
       "       [7.5178525e-30, 3.0929136e-20, 1.0000000e+00, 4.5963940e-38],\n",
       "       [2.4522337e-15, 5.0726370e-04, 3.0320957e-09, 9.9949276e-01],\n",
       "       [3.3097366e-23, 0.0000000e+00, 1.0000000e+00, 0.0000000e+00],\n",
       "       [8.4912202e-33, 1.0000000e+00, 3.7951684e-15, 4.5114293e-16]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "y_pred = np.argmax(y_pred, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 0, 2, 1, 1, 3, 3, 2, 3, 3, 0, 1, 2, 2, 3, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.preprocessing import image"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=load_model('model_resnet50.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ 6.7060997e+01,  5.4221001e+01,  4.7320000e+01],\n",
       "         [ 6.9060997e+01,  5.6221001e+01,  4.9320000e+01],\n",
       "         [ 7.3060997e+01,  6.0221001e+01,  5.3320000e+01],\n",
       "         ...,\n",
       "         [ 7.4060997e+01,  5.6221001e+01,  4.6320000e+01],\n",
       "         [ 5.5060997e+01,  3.7221001e+01,  2.7320000e+01],\n",
       "         [ 4.1060997e+01,  2.3221001e+01,  1.3320000e+01]],\n",
       "\n",
       "        [[ 7.5060997e+01,  6.2221001e+01,  5.5320000e+01],\n",
       "         [ 7.8060997e+01,  6.5221001e+01,  5.8320000e+01],\n",
       "         [ 8.1060997e+01,  6.8221001e+01,  6.1320000e+01],\n",
       "         ...,\n",
       "         [ 9.7060997e+01,  7.9221001e+01,  6.9320000e+01],\n",
       "         [ 7.3060997e+01,  5.5221001e+01,  4.5320000e+01],\n",
       "         [ 4.9060997e+01,  3.1221001e+01,  2.1320000e+01]],\n",
       "\n",
       "        [[ 8.7060997e+01,  7.4221001e+01,  6.7320000e+01],\n",
       "         [ 9.0060997e+01,  7.7221001e+01,  7.0320000e+01],\n",
       "         [ 9.3060997e+01,  8.0221001e+01,  7.3320000e+01],\n",
       "         ...,\n",
       "         [ 1.0106100e+02,  8.3221001e+01,  7.3320000e+01],\n",
       "         [ 7.5060997e+01,  5.7221001e+01,  4.7320000e+01],\n",
       "         [ 5.0060997e+01,  3.2221001e+01,  2.2320000e+01]],\n",
       "\n",
       "        ...,\n",
       "\n",
       "        [[ 1.0406100e+02,  8.9221001e+01,  9.4320000e+01],\n",
       "         [ 1.0206100e+02,  8.7221001e+01,  9.2320000e+01],\n",
       "         [ 9.9060997e+01,  8.4221001e+01,  8.9320000e+01],\n",
       "         ...,\n",
       "         [-1.0939003e+01, -1.6778999e+01, -1.4680000e+01],\n",
       "         [-1.0939003e+01, -1.6778999e+01, -1.4680000e+01],\n",
       "         [-1.0939003e+01, -1.6778999e+01, -1.4680000e+01]],\n",
       "\n",
       "        [[ 1.0606100e+02,  9.1221001e+01,  9.6320000e+01],\n",
       "         [ 1.0406100e+02,  8.9221001e+01,  9.4320000e+01],\n",
       "         [ 1.0006100e+02,  8.5221001e+01,  9.0320000e+01],\n",
       "         ...,\n",
       "         [-5.9390030e+00, -1.1778999e+01, -9.6800003e+00],\n",
       "         [-5.9390030e+00, -1.1778999e+01, -9.6800003e+00],\n",
       "         [-5.9390030e+00, -1.1778999e+01, -9.6800003e+00]],\n",
       "\n",
       "        [[ 1.0806100e+02,  9.4221001e+01,  9.6320000e+01],\n",
       "         [ 1.0606100e+02,  9.2221001e+01,  9.4320000e+01],\n",
       "         [ 1.0206100e+02,  8.8221001e+01,  9.0320000e+01],\n",
       "         ...,\n",
       "         [ 6.0997009e-02, -5.7789993e+00, -3.6800003e+00],\n",
       "         [ 6.0997009e-02, -5.7789993e+00, -3.6800003e+00],\n",
       "         [ 6.0997009e-02, -5.7789993e+00, -3.6800003e+00]]]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "img_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "img=image.load_img('Datasets/Test/Coffee/download (2).jpg',target_size=(224,224))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        ...,\n",
       "        [254., 254., 254.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       [[254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        ...,\n",
       "        [254., 254., 254.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       [[254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        [254., 254., 254.],\n",
       "        ...,\n",
       "        [254., 254., 254.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       ...,\n",
       "\n",
       "       [[255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        ...,\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       [[255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        ...,\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]],\n",
       "\n",
       "       [[255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        ...,\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.],\n",
       "        [255., 255., 255.]]], dtype=float32)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=image.img_to_array(img)\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(224, 224, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=x/255"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 224, 224, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x=np.expand_dims(x,axis=0)\n",
    "img_data=preprocess_input(x)\n",
    "img_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9745471, 0.0254529]], dtype=float32)"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(img_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.argmax(model.predict(img_data), axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a==1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.2.0'"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
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