Final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d3e99410",
   "metadata": {},
   "source": [
    "### Inception v3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "0726d1fc",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pathlib import Path\n",
    "import os.path\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.preprocessing import image\n",
    "import tensorflow as tf\n",
    "\n",
    "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "from tensorflow.keras.optimizers import RMSprop\n",
    "\n",
    "\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from tensorflow.keras.applications.vgg16 import VGG16\n",
    "from tensorflow.keras.applications import ResNet50\n",
    "from tensorflow.keras.layers import Dense, Dropout, BatchNormalization\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "import time"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d190291f",
   "metadata": {},
   "outputs": [],
   "source": [
    "image_dir = Path(r'/Users/rafay/Documents/FYP/abc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b6945b7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "filepaths = list(image_dir.glob(r'**/*.jpg'))\n",
    "labels = list(map(lambda x: os.path.split(os.path.split(x)[0])[1], filepaths))\n",
    "\n",
    "filepaths = pd.Series(filepaths, name='Filepath').astype(str)\n",
    "labels = pd.Series(labels, name='Label')\n",
    "\n",
    "images = pd.concat([filepaths, labels], axis=1)\n",
    "\n",
    "category_samples = []\n",
    "for category in images['Label'].unique():\n",
    "    category_slice = images.query(\"Label == @category\")\n",
    "    category_samples.append(category_slice.sample(100, random_state=1))\n",
    "image_df = pd.concat(category_samples, axis=0).sample(frac=1.0, random_state=1).reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "2776c146",
   "metadata": {},
   "outputs": [],
   "source": [
    "abc = list(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b84c974a",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_c = int(len(abc)/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "905d508f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "num_c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9bf1c42e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c53722f1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "steak             100\n",
       "pizza             100\n",
       "apple_pie         100\n",
       "burger            100\n",
       "cup_cakes         100\n",
       "french_fries      100\n",
       "club_sandwich     100\n",
       "cheesecake        100\n",
       "french_toast      100\n",
       "samosa            100\n",
       "baklava           100\n",
       "ice_cream         100\n",
       "fried_rice        100\n",
       "soup              100\n",
       "waffles           100\n",
       "sushi             100\n",
       "donuts            100\n",
       "chocolate_cake    100\n",
       "grilled_salmon    100\n",
       "pancakes          100\n",
       "Name: Label, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_df['Label'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "bd577ad7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                steak\n",
       "1                pizza\n",
       "2       grilled_salmon\n",
       "3       chocolate_cake\n",
       "4       grilled_salmon\n",
       "             ...      \n",
       "1995      french_fries\n",
       "1996            burger\n",
       "1997           baklava\n",
       "1998         ice_cream\n",
       "1999            burger\n",
       "Name: Label, Length: 2000, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "image_df['Label']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d0c1e68",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_df, test_df = train_test_split(image_df, train_size=0.7, shuffle=True, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "7e5a44d2",
   "metadata": {},
   "outputs": [],
   "source": [
    "train_generator = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input,\n",
    "    validation_split=0.2\n",
    ")\n",
    "\n",
    "test_generator = tf.keras.preprocessing.image.ImageDataGenerator(\n",
    "    preprocessing_function=tf.keras.applications.mobilenet_v2.preprocess_input\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "3a4f49b4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 1120 validated image filenames belonging to 20 classes.\n",
      "Found 280 validated image filenames belonging to 20 classes.\n",
      "Found 600 validated image filenames belonging to 20 classes.\n"
     ]
    }
   ],
   "source": [
    "train_images = train_generator.flow_from_dataframe(\n",
    "    dataframe=train_df,\n",
    "    x_col='Filepath',\n",
    "    y_col='Label',\n",
    "    target_size=(224, 224),\n",
    "    color_mode='rgb',\n",
    "    class_mode='categorical',\n",
    "    batch_size=32,\n",
    "    shuffle=True,\n",
    "    seed=42,\n",
    "    subset='training'\n",
    ")\n",
    "\n",
    "val_images = train_generator.flow_from_dataframe(\n",
    "    dataframe=train_df,\n",
    "    x_col='Filepath',\n",
    "    y_col='Label',\n",
    "    target_size=(224, 224),\n",
    "    color_mode='rgb',\n",
    "    class_mode='categorical',\n",
    "    batch_size=32,\n",
    "    shuffle=True,\n",
    "    seed=42,\n",
    "    subset='validation'\n",
    ")\n",
    "\n",
    "test_images = test_generator.flow_from_dataframe(\n",
    "    dataframe=test_df,\n",
    "    x_col='Filepath',\n",
    "    y_col='Label',\n",
    "    target_size=(224, 224),\n",
    "    color_mode='rgb',\n",
    "    class_mode='categorical',\n",
    "    batch_size=32,\n",
    "    shuffle=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a122adb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "45f09ca3",
   "metadata": {},
   "outputs": [],
   "source": [
    "batch_size = 32\n",
    "img_height = 228\n",
    "img_width = 228\n",
    "channels = 3\n",
    "img_shape = (img_height, img_width, channels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a5402494",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-05 13:31:06.743391: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-07-05 13:31:06.743702: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "pre_trained = InceptionV3(weights='imagenet', include_top=False, input_shape=img_shape, pooling='avg')\n",
    "\n",
    "for layer in pre_trained.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "595a0d30",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = pre_trained.output\n",
    "x = BatchNormalization(axis=-1, momentum=0.99, epsilon=0.001)(x)\n",
    "x = Dropout(0.2)(x)\n",
    "x = Dense(1024, activation='relu')(x)\n",
    "x = Dropout(0.2)(x)\n",
    "predictions = Dense(num_c, activation='sigmoid')(x)\n",
    "\n",
    "model = Model(inputs = pre_trained.input, outputs = predictions)\n",
    "model.compile(optimizer = RMSprop(learning_rate=0.0001), loss='categorical_crossentropy', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c82fa2c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model_1\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 228, 228, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 113, 113, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 113, 113, 32) 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, 113, 113, 32) 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 111, 111, 32) 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 111, 111, 32) 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, 111, 111, 32) 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 111, 111, 64) 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 111, 111, 64) 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, 111, 111, 64) 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, 55, 55, 64)   0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 55, 55, 80)   5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 55, 55, 80)   240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, 55, 55, 80)   0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, 53, 53, 192)  138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, 53, 53, 192)  576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, 53, 53, 192)  0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, 26, 26, 192)  0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, 26, 26, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, 26, 26, 64)   192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, 26, 26, 64)   0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, 26, 26, 48)   9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, 26, 26, 96)   55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, 26, 26, 48)   144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, 26, 26, 96)   288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, 26, 26, 48)   0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, 26, 26, 96)   0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, 26, 26, 192)  0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, 26, 26, 64)   12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, 26, 26, 64)   76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, 26, 26, 96)   82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, 26, 26, 32)   6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, 26, 26, 64)   192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, 26, 26, 64)   192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, 26, 26, 96)   288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, 26, 26, 32)   96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, 26, 26, 64)   0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, 26, 26, 64)   0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, 26, 26, 96)   0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, 26, 26, 32)   0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, 26, 26, 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, 26, 26, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, 26, 26, 64)   192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, 26, 26, 64)   0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, 26, 26, 48)   12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, 26, 26, 96)   55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, 26, 26, 48)   144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, 26, 26, 96)   288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, 26, 26, 48)   0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, 26, 26, 96)   0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, 26, 26, 256)  0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, 26, 26, 64)   16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, 26, 26, 64)   76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, 26, 26, 96)   82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, 26, 26, 64)   16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, 26, 26, 64)   192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, 26, 26, 64)   192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, 26, 26, 96)   288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, 26, 26, 64)   192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, 26, 26, 64)   0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, 26, 26, 64)   0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, 26, 26, 96)   0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, 26, 26, 64)   0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, 26, 26, 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, 26, 26, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, 26, 26, 64)   192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, 26, 26, 64)   0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, 26, 26, 48)   13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, 26, 26, 96)   55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, 26, 26, 48)   144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, 26, 26, 96)   288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, 26, 26, 48)   0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, 26, 26, 96)   0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, 26, 26, 288)  0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, 26, 26, 64)   18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, 26, 26, 64)   76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, 26, 26, 96)   82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, 26, 26, 64)   18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, 26, 26, 64)   192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, 26, 26, 64)   192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, 26, 26, 96)   288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, 26, 26, 64)   192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, 26, 26, 64)   0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, 26, 26, 64)   0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, 26, 26, 96)   0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, 26, 26, 64)   0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, 26, 26, 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, 26, 26, 64)   18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, 26, 26, 64)   192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, 26, 26, 64)   0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, 26, 26, 96)   55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, 26, 26, 96)   288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, 26, 26, 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",
      "global_average_pooling2d (Globa (None, 2048)         0           mixed10[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_95 (BatchNo (None, 2048)         8192        global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dropout_2 (Dropout)             (None, 2048)         0           batch_normalization_95[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 1024)         2098176     dropout_2[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "dropout_3 (Dropout)             (None, 1024)         0           dense_2[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_3 (Dense)                 (None, 20)           20500       dropout_3[0][0]                  \n",
      "==================================================================================================\n",
      "Total params: 23,929,652\n",
      "Trainable params: 2,122,772\n",
      "Non-trainable params: 21,806,880\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1c916b36",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-05 13:36:43.956294: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - ETA: 0s - loss: 2.5205 - accuracy: 0.2955"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-05 13:36:51.920728: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "35/35 [==============================] - 11s 264ms/step - loss: 2.5205 - accuracy: 0.2955 - val_loss: 2.1072 - val_accuracy: 0.4297\n",
      "Epoch 2/20\n",
      "35/35 [==============================] - 9s 247ms/step - loss: 1.3404 - accuracy: 0.6009 - val_loss: 1.7067 - val_accuracy: 0.5430\n",
      "Epoch 3/20\n",
      "35/35 [==============================] - 9s 249ms/step - loss: 0.9474 - accuracy: 0.7196 - val_loss: 1.5107 - val_accuracy: 0.5664\n",
      "Epoch 4/20\n",
      "35/35 [==============================] - 9s 246ms/step - loss: 0.6501 - accuracy: 0.8339 - val_loss: 1.4505 - val_accuracy: 0.5586\n",
      "Epoch 5/20\n",
      "35/35 [==============================] - 9s 247ms/step - loss: 0.5219 - accuracy: 0.8598 - val_loss: 1.3754 - val_accuracy: 0.5820\n",
      "Epoch 6/20\n",
      "35/35 [==============================] - 9s 245ms/step - loss: 0.3727 - accuracy: 0.9098 - val_loss: 1.3646 - val_accuracy: 0.5977\n",
      "Epoch 7/20\n",
      "35/35 [==============================] - 9s 247ms/step - loss: 0.2757 - accuracy: 0.9411 - val_loss: 1.3419 - val_accuracy: 0.5938\n",
      "Epoch 8/20\n",
      "35/35 [==============================] - 9s 247ms/step - loss: 0.2146 - accuracy: 0.9545 - val_loss: 1.3517 - val_accuracy: 0.5781\n",
      "Epoch 9/20\n",
      "35/35 [==============================] - 9s 248ms/step - loss: 0.1438 - accuracy: 0.9830 - val_loss: 1.3265 - val_accuracy: 0.6133\n",
      "Epoch 10/20\n",
      "35/35 [==============================] - 9s 247ms/step - loss: 0.1185 - accuracy: 0.9839 - val_loss: 1.3808 - val_accuracy: 0.6094\n",
      "Epoch 11/20\n",
      "35/35 [==============================] - 9s 248ms/step - loss: 0.0896 - accuracy: 0.9920 - val_loss: 1.3620 - val_accuracy: 0.6094\n",
      "Epoch 12/20\n",
      "35/35 [==============================] - 9s 251ms/step - loss: 0.0608 - accuracy: 0.9964 - val_loss: 1.4543 - val_accuracy: 0.6016\n",
      "Epoch 13/20\n",
      "35/35 [==============================] - 9s 251ms/step - loss: 0.0567 - accuracy: 0.9964 - val_loss: 1.4356 - val_accuracy: 0.6133\n",
      "Epoch 14/20\n",
      "35/35 [==============================] - 9s 257ms/step - loss: 0.0389 - accuracy: 0.9973 - val_loss: 1.4469 - val_accuracy: 0.6250\n",
      "Epoch 15/20\n",
      "35/35 [==============================] - 9s 262ms/step - loss: 0.0326 - accuracy: 0.9982 - val_loss: 1.4251 - val_accuracy: 0.6094\n",
      "Epoch 16/20\n",
      "35/35 [==============================] - 9s 261ms/step - loss: 0.0266 - accuracy: 1.0000 - val_loss: 1.5902 - val_accuracy: 0.6055\n",
      "Epoch 17/20\n",
      "35/35 [==============================] - 9s 267ms/step - loss: 0.0225 - accuracy: 0.9991 - val_loss: 1.5572 - val_accuracy: 0.6055\n",
      "Epoch 18/20\n",
      "35/35 [==============================] - 10s 271ms/step - loss: 0.0178 - accuracy: 0.9991 - val_loss: 1.6095 - val_accuracy: 0.6055\n",
      "Epoch 19/20\n",
      "35/35 [==============================] - 10s 275ms/step - loss: 0.0152 - accuracy: 0.9991 - val_loss: 1.6382 - val_accuracy: 0.6133\n",
      "Epoch 20/20\n",
      "35/35 [==============================] - 10s 279ms/step - loss: 0.0166 - accuracy: 0.9973 - val_loss: 1.5285 - val_accuracy: 0.6445\n"
     ]
    }
   ],
   "source": [
    "STEP_SIZE_TRAIN = train_images.n // train_images.batch_size\n",
    "STEP_SIZE_VALID = val_images.n // val_images.batch_size\n",
    "\n",
    "history = model.fit_generator(train_images,\n",
    "                    steps_per_epoch = STEP_SIZE_TRAIN,\n",
    "                    validation_data = val_images,\n",
    "                    validation_steps = STEP_SIZE_VALID,\n",
    "                    epochs = 20,\n",
    "                    verbose = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "66dba596",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test Accuracy: 65.33%\n"
     ]
    }
   ],
   "source": [
    "results = model.evaluate(test_images, verbose=0)\n",
    "print(\"Test Accuracy: {:.2f}%\".format(results[1] * 100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "b86f2864",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ab116520>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Epoch Number')\n",
    "plt.ylabel('Loss')\n",
    "plt.plot(history.history['loss'], label='training set')\n",
    "plt.plot(history.history['val_loss'], label='test set')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8289e40b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x2ab0fde20>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.xlabel('Epoch Number')\n",
    "plt.ylabel('Accuracy')\n",
    "plt.plot(history.history['accuracy'], label='training set')\n",
    "plt.plot(history.history['val_accuracy'], label='test set')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "a6be57f8",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-05 13:40:05.126797: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "predictions = np.argmax(model.predict(test_images), axis=1)\n",
    "\n",
    "cm = confusion_matrix(test_images.labels, predictions)\n",
    "clr = classification_report(test_images.labels, predictions, \n",
    "                            target_names=test_images.class_indices, \n",
    "                            zero_division=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "b3740cd9",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 2160x2160 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(30, 30))\n",
    "sns.heatmap(cm, annot=True, fmt='g', vmin=0, cmap='Blues', cbar=False)\n",
    "plt.xticks(ticks=np.arange(num_c) + 0.5, labels=test_images.class_indices, rotation=90)\n",
    "plt.yticks(ticks=np.arange(num_c) + 0.5, labels=test_images.class_indices, rotation=0)\n",
    "plt.xlabel(\"Predicted\")\n",
    "plt.ylabel(\"Actual\")\n",
    "plt.title(\"Confusion Matrix\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "2d312b23",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Classification Report:\n",
      "----------------------\n",
      "                 precision    recall  f1-score   support\n",
      "\n",
      "     apple_pie       0.32      0.37      0.34        30\n",
      "       baklava       0.48      0.48      0.48        23\n",
      "        burger       0.68      0.61      0.64        31\n",
      "    cheesecake       0.46      0.50      0.48        24\n",
      "chocolate_cake       0.55      0.72      0.62        32\n",
      " club_sandwich       0.70      0.76      0.73        34\n",
      "     cup_cakes       0.82      0.78      0.80        41\n",
      "        donuts       0.70      0.66      0.68        32\n",
      "  french_fries       0.78      0.75      0.76        28\n",
      "  french_toast       0.53      0.68      0.59        28\n",
      "    fried_rice       0.86      0.88      0.87        34\n",
      "grilled_salmon       0.50      0.36      0.42        22\n",
      "     ice_cream       0.64      0.58      0.61        31\n",
      "      pancakes       0.68      0.50      0.58        30\n",
      "         pizza       0.83      0.96      0.89        25\n",
      "        samosa       0.83      0.57      0.68        35\n",
      "          soup       1.00      0.92      0.96        25\n",
      "         steak       0.60      0.60      0.60        35\n",
      "         sushi       0.62      0.65      0.63        31\n",
      "       waffles       0.53      0.62      0.57        29\n",
      "\n",
      "      accuracy                           0.65       600\n",
      "     macro avg       0.66      0.65      0.65       600\n",
      "  weighted avg       0.66      0.65      0.65       600\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(\"Classification Report:\\n----------------------\\n\", clr)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "c96ef3ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save('gfgModel123.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "8bf9366f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-05 13:40:58.349551: W tensorflow/python/util/util.cc:348] Sets are not currently considered sequences, but this may change in the future, so consider avoiding using them.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: /var/folders/l2/njhk9drs1s9dq5m_mwdjpdc00000gn/T/tmp4u99mvdc/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-07-05 13:41:06.313915: I tensorflow/core/grappler/devices.cc:78] Number of eligible GPUs (core count >= 8, compute capability >= 0.0): 0 (Note: TensorFlow was not compiled with CUDA or ROCm support)\n",
      "2022-07-05 13:41:06.314075: I tensorflow/core/grappler/clusters/single_machine.cc:357] Starting new session\n",
      "2022-07-05 13:41:06.314633: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-07-05 13:41:06.314650: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
      "2022-07-05 13:41:06.322428: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:112] Plugin optimizer for device_type GPU is enabled.\n",
      "2022-07-05 13:41:06.322461: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:605] User's config has been changed based on plugin's config.\n",
      "2022-07-05 13:41:06.322464: W tensorflow/core/grappler/optimizers/meta_optimizer.cc:606] \n",
      "Config of optimizers\t\tUser's config\tPlugin's config\tFinal config(User & Plugin)\n",
      "disable_model_pruning\t\t208\t\t0\t\t208\n",
      "function_optimization           1\t\t1\t\t1\n",
      "\n",
      "2022-07-05 13:41:06.326915: I tensorflow/core/grappler/optimizers/meta_optimizer.cc:1144] Optimization results for grappler item: graph_to_optimize\n",
      "  function_optimizer: function_optimizer did nothing. time = 0.001ms.\n",
      "  PluggableGraphOptimizer: Graph size after: 1207 nodes (0), 1626 edges (0), time = 0.921ms.\n",
      "  function_optimizer: function_optimizer did nothing. time = 0.401ms.\n",
      "  PluggableGraphOptimizer: Graph size after: 1207 nodes (0), 1626 edges (0), time = 0.905ms.\n",
      "\n",
      "2022-07-05 13:41:09.741215: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:345] Ignored output_format.\n",
      "2022-07-05 13:41:09.741249: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:348] Ignored drop_control_dependency.\n",
      "2022-07-05 13:41:09.849434: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:210] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
      "2022-07-05 13:41:09.870408: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2022-07-05 13:41:09.870427: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    }
   ],
   "source": [
    "converter = tf.lite.TFLiteConverter.from_keras_model(model)\n",
    "tflite_model = converter.convert()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3ffbd17a",
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('modelnew.tflite', 'wb') as f:\n",
    "  f.write(tflite_model)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e82acb3",
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