cotton_prediction_InceptionV3.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "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": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n",
    "from tensorflow.keras.models import Model\n",
    "\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
    "\n",
    "from tensorflow.keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob\n",
    "import matplotlib.pyplot as plt\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "path_train = 'D:\\ML_workspace\\cotton_prediction\\datasets\\train'\n",
    "path_test = 'D:\\ML_workspace\\cotton_prediction\\datasets\\test'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "image_size = [224,224]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "inception = InceptionV3(input_shape = image_size + [3], weights = 'imagenet', include_top = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.engine.functional.Functional at 0x2cf71d77e20>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inception"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "folders = glob(r'D:\\ML_workspace\\cotton_prediction\\datasets\\train\\*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['D:\\\\ML_workspace\\\\cotton_prediction\\\\datasets\\\\train\\\\diseased cotton leaf',\n",
       " 'D:\\\\ML_workspace\\\\cotton_prediction\\\\datasets\\\\train\\\\diseased cotton plant',\n",
       " 'D:\\\\ML_workspace\\\\cotton_prediction\\\\datasets\\\\train\\\\fresh cotton leaf',\n",
       " 'D:\\\\ML_workspace\\\\cotton_prediction\\\\datasets\\\\train\\\\fresh cotton plant']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "folders"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "for layer in inception.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = Flatten()(inception.output)\n",
    "prediction = Dense(len(folders), activation='softmax')(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"functional_1\"\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": [
    "model = Model(inputs = inception.input, outputs = prediction)\n",
    "model.summary()\n",
    "\n",
    "# 23 million params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(\n",
    "  loss='categorical_crossentropy',\n",
    "  optimizer='adam',\n",
    "  metrics=['accuracy']\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1951 images belonging to 4 classes.\n",
      "Found 18 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   shear_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
    "\n",
    "train_set = train_datagen.flow_from_directory(r'D:\\ML_workspace\\cotton_prediction\\datasets\\train',\n",
    "                                              target_size=(224, 224),\n",
    "                                              batch_size = 32,\n",
    "                                              class_mode = 'categorical')\n",
    "                                          \n",
    "test_set = test_datagen.flow_from_directory(r'D:\\ML_workspace\\cotton_prediction\\datasets\\test',\n",
    "                                            target_size = (224,224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From <ipython-input-29-b4cfc27b4946>:1: 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 [==============================] - 241s 4s/step - loss: 2.8390 - accuracy: 0.7335 - val_loss: 0.8361 - val_accuracy: 0.7222\n",
      "Epoch 2/20\n",
      "61/61 [==============================] - 225s 4s/step - loss: 0.5644 - accuracy: 0.8913 - val_loss: 0.1053 - val_accuracy: 0.9444\n",
      "Epoch 3/20\n",
      "61/61 [==============================] - 223s 4s/step - loss: 0.6160 - accuracy: 0.8934 - val_loss: 0.0155 - val_accuracy: 1.0000\n",
      "Epoch 4/20\n",
      "61/61 [==============================] - 215s 4s/step - loss: 0.5764 - accuracy: 0.9052 - val_loss: 0.2009 - val_accuracy: 0.9444\n",
      "Epoch 5/20\n",
      "61/61 [==============================] - 212s 3s/step - loss: 0.3376 - accuracy: 0.9395 - val_loss: 0.0965 - val_accuracy: 0.9444\n",
      "Epoch 6/20\n",
      "61/61 [==============================] - 216s 4s/step - loss: 0.4589 - accuracy: 0.9308 - val_loss: 0.0019 - val_accuracy: 1.0000\n",
      "Epoch 7/20\n",
      "61/61 [==============================] - 215s 4s/step - loss: 0.3801 - accuracy: 0.9313 - val_loss: 0.0025 - val_accuracy: 1.0000\n",
      "Epoch 8/20\n",
      "61/61 [==============================] - 228s 4s/step - loss: 0.3989 - accuracy: 0.9405 - val_loss: 0.0457 - val_accuracy: 1.0000\n",
      "Epoch 9/20\n",
      "61/61 [==============================] - 226s 4s/step - loss: 0.3419 - accuracy: 0.9457 - val_loss: 0.0017 - val_accuracy: 1.0000\n",
      "Epoch 10/20\n",
      "61/61 [==============================] - 249s 4s/step - loss: 0.4531 - accuracy: 0.9329 - val_loss: 0.1118 - val_accuracy: 0.9444\n",
      "Epoch 11/20\n",
      "61/61 [==============================] - 235s 4s/step - loss: 0.4530 - accuracy: 0.9400 - val_loss: 0.0171 - val_accuracy: 1.0000\n",
      "Epoch 12/20\n",
      "61/61 [==============================] - 230s 4s/step - loss: 0.4736 - accuracy: 0.9411 - val_loss: 0.4007 - val_accuracy: 0.8889\n",
      "Epoch 13/20\n",
      "61/61 [==============================] - 230s 4s/step - loss: 0.4424 - accuracy: 0.9549 - val_loss: 0.0372 - val_accuracy: 1.0000\n",
      "Epoch 14/20\n",
      "61/61 [==============================] - 234s 4s/step - loss: 0.6910 - accuracy: 0.9298 - val_loss: 3.3770e-05 - val_accuracy: 1.0000\n",
      "Epoch 15/20\n",
      "61/61 [==============================] - 210s 3s/step - loss: 0.3440 - accuracy: 0.9569 - val_loss: 3.0108e-04 - val_accuracy: 1.0000\n",
      "Epoch 16/20\n",
      "61/61 [==============================] - 103s 2s/step - loss: 0.3266 - accuracy: 0.9646 - val_loss: 0.3197 - val_accuracy: 0.9444\n",
      "Epoch 17/20\n",
      "61/61 [==============================] - 110s 2s/step - loss: 0.4161 - accuracy: 0.9498 - val_loss: 0.0024 - val_accuracy: 1.0000\n",
      "Epoch 18/20\n",
      "61/61 [==============================] - 102s 2s/step - loss: 0.6448 - accuracy: 0.9370 - val_loss: 0.7210 - val_accuracy: 0.9444\n",
      "Epoch 19/20\n",
      "61/61 [==============================] - 102s 2s/step - loss: 0.4879 - accuracy: 0.9518 - val_loss: 0.0817 - val_accuracy: 0.9444\n",
      "Epoch 20/20\n",
      "61/61 [==============================] - 102s 2s/step - loss: 0.3502 - accuracy: 0.9667 - val_loss: 2.3113e-06 - val_accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "r = model.fit_generator(train_set, \n",
    "                        validation_data= test_set, \n",
    "                        epochs=20, \n",
    "                        steps_per_epoch= len(train_set), \n",
    "                        validation_steps = len(test_set))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "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": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r.history['loss'], label = 'loss')\n",
    "plt.plot(r.history['val_loss'], label = 'val_loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('lossVal_loss_inceptionv3')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(r.history['accuracy'], label = 'accuracy')\n",
    "plt.plot(r.history['val_accuracy'], label='val_accuracy')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('acc_val_acc_inceptionv3')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.0000000e+00, 1.0000000e+00, 0.0000000e+00, 2.8170223e-27],\n",
       "       [0.0000000e+00, 0.0000000e+00, 1.1884088e-37, 1.0000000e+00],\n",
       "       [1.4661520e-14, 5.3854612e-14, 1.0000000e+00, 2.9326289e-26],\n",
       "       [1.7732269e-35, 1.0000000e+00, 1.3788003e-28, 1.5966858e-22],\n",
       "       [1.0000000e+00, 8.1695225e-30, 6.2469322e-26, 1.4616835e-38],\n",
       "       [1.6556238e-21, 0.0000000e+00, 1.0000000e+00, 2.0857744e-33],\n",
       "       [1.5335033e-38, 1.0000000e+00, 0.0000000e+00, 0.0000000e+00],\n",
       "       [4.6016399e-07, 6.3476506e-19, 9.9999952e-01, 8.5680096e-17],\n",
       "       [4.2738547e-12, 5.1347747e-15, 1.0000000e+00, 2.2046094e-11],\n",
       "       [4.0750187e-05, 1.4950799e-25, 9.9995923e-01, 1.4644285e-19],\n",
       "       [3.7890813e-21, 1.0000000e+00, 1.8500962e-16, 1.5134924e-10],\n",
       "       [1.0000000e+00, 0.0000000e+00, 5.9465667e-30, 4.5445409e-24],\n",
       "       [9.9999976e-01, 2.9245012e-07, 1.6093148e-27, 1.2027939e-21],\n",
       "       [9.9454869e-23, 2.5283825e-19, 1.6758040e-14, 1.0000000e+00],\n",
       "       [8.6299998e-08, 1.5469940e-11, 4.6825350e-20, 9.9999988e-01],\n",
       "       [4.4574838e-35, 5.7068098e-13, 1.8442624e-38, 1.0000000e+00],\n",
       "       [1.1872126e-22, 2.9633570e-15, 1.7106242e-21, 1.0000000e+00],\n",
       "       [4.7244477e-28, 1.0000000e+00, 7.4986632e-30, 3.2543503e-30]],\n",
       "      dtype=float32)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(test_set)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 4)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 2, 1, 0, 2, 1, 2, 2, 2, 1, 0, 0, 3, 3, 3, 3, 1], dtype=int64)"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final_pred = np.argmax(y_pred, axis=1)\n",
    "final_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(18, 4)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('model_inceptionv3.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "### Tommorrow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model=load_model('model_inceptionv3.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(test_set)\n",
    "len(y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 2, 2, 2, 2, 1, 0, 0, 2, 2, 1, 2, 2, 0, 1, 1, 2, 1], dtype=int64)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(test_set)\n",
    "y_pred_fin = np.argmax(y_pred, axis=1)\n",
    "y_pred_fin"
   ]
  },
  {
   "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": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}