licenta.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.image as mpimg\n",
    "import os\n",
    "import cv2\n",
    "\n",
    "from PIL import Image\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras.models import load_model\n",
    "from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping\n",
    "from tensorflow.keras.models import Model \n",
    "from tensorflow.keras.applications import inception_v3\n",
    "from tensorflow.keras.layers import GlobalAveragePooling2D, Dense\n",
    "from tensorflow.keras import optimizers\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "\n",
    "DATASET_LOCATION = 'D:\\\\dataset256\\\\UECFOOD256'\n",
    "DATASET_SIZE = 256"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "      id                     name\n",
      "0      1                     rice\n",
      "1      2             eels on rice\n",
      "2      3                    pilaf\n",
      "3      4  chicken-'n'-egg on rice\n",
      "4      5      pork cutlet on rice\n",
      "..   ...                      ...\n",
      "251  252        winter melon soup\n",
      "252  253        steamed spareribs\n",
      "253  254      chinese pumpkin pie\n",
      "254  255      eight treasure rice\n",
      "255  256          hot & sour soup\n",
      "\n",
      "[256 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "labels = pd.read_csv(DATASET_LOCATION + '\\\\category.txt', sep='\\t')\n",
    "print(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['D:\\\\dataset256\\\\UECFOOD256\\\\1\\\\1.jpg', 1, 42]\n"
     ]
    }
   ],
   "source": [
    "data = os.walk(DATASET_LOCATION)\n",
    "x = {}\n",
    "for (dirpath, dirnames, filenames) in data:\n",
    "    for file in filenames:\n",
    "        if file.endswith('jpg'):\n",
    "            if file in x:\n",
    "                x[file].append(int(dirpath.split('\\\\')[-1]))\n",
    "            else:\n",
    "                x[file] = [dirpath + \"\\\\\" + file, int(dirpath.split('\\\\')[-1])]\n",
    "print(x[list(x.keys())[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "elem = x[list(x.keys())[26243]]\n",
    "img = mpimg.imread(elem[0])\n",
    "imgplot = plt.imshow(img)\n",
    "rez = ''\n",
    "for i in range(1, len(elem)):\n",
    "    rez += labels['name'][elem[i] - 1] + ' '\n",
    "plt.title(rez)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "SHOULD_RESIZE = False\n",
    "if SHOULD_RESIZE:\n",
    "    for el in x.keys():\n",
    "        ouput = cv2.resize(cv2.imread(x[el][0]), (224, 224))\n",
    "        cv2.imwrite(x[el][0], ouput)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "lista = []\n",
    "lista_y = []\n",
    "categs = []\n",
    "counter = {}\n",
    "for i in range(20):\n",
    "    counter[i] = 60\n",
    "for el in x.keys():\n",
    "    if sum(counter.values()) == 0:\n",
    "        break\n",
    "    if x[el][1] in counter and counter[x[el][1]] > 0:\n",
    "        lista.append(cv2.imread(x[el][0]))\n",
    "        rez = []\n",
    "        categs.append(x[el][1])\n",
    "        for i in range(20):\n",
    "            if i in x[el][1:]:\n",
    "                rez.append(1)\n",
    "            else:\n",
    "                rez.append(0)\n",
    "        lista_y.append(rez)\n",
    "        counter[x[el][1]] -= 1\n",
    "lista = np.array(lista)\n",
    "lista_y = np.array(lista_y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1140\n"
     ]
    }
   ],
   "source": [
    "print(len(lista))\n",
    "lista, val_lista, lista_y, val_lista_y = train_test_split(lista, lista_y, train_size=0.8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from collections import Counter\n",
    "vals = Counter(categs)\n",
    "\n",
    "fig = plt.figure(figsize = (10, 5))\n",
    " \n",
    "# creating the bar plot\n",
    "plt.bar(vals.keys(), vals.values(), color ='red',\n",
    "        width = 1)\n",
    " \n",
    "plt.xlabel(\"Food categories\")\n",
    "plt.ylabel(\"No. of food categories\")\n",
    "plt.title(\"Food categories \")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, None, None,  0                                            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, None, None, 3 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, None, None, 3 96          conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "activation (Activation)         (None, None, None, 3 0           batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, None, None, 3 9216        activation[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, None, None, 3 96          conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_1 (Activation)       (None, None, None, 3 0           batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, None, None, 6 18432       activation_1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, None, None, 6 192         conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_2 (Activation)       (None, None, None, 6 0           batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d (MaxPooling2D)    (None, None, None, 6 0           activation_2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, None, None, 8 5120        max_pooling2d[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, None, None, 8 240         conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_3 (Activation)       (None, None, None, 8 0           batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_4 (Conv2D)               (None, None, None, 1 138240      activation_3[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_4 (BatchNor (None, None, None, 1 576         conv2d_4[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_4 (Activation)       (None, None, None, 1 0           batch_normalization_4[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_1 (MaxPooling2D)  (None, None, None, 1 0           activation_4[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_8 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_8 (BatchNor (None, None, None, 6 192         conv2d_8[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_8 (Activation)       (None, None, None, 6 0           batch_normalization_8[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_6 (Conv2D)               (None, None, None, 4 9216        max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_9 (Conv2D)               (None, None, None, 9 55296       activation_8[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_6 (BatchNor (None, None, None, 4 144         conv2d_6[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_9 (BatchNor (None, None, None, 9 288         conv2d_9[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "activation_6 (Activation)       (None, None, None, 4 0           batch_normalization_6[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_9 (Activation)       (None, None, None, 9 0           batch_normalization_9[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d (AveragePooli (None, None, None, 1 0           max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_5 (Conv2D)               (None, None, None, 6 12288       max_pooling2d_1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_7 (Conv2D)               (None, None, None, 6 76800       activation_6[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_10 (Conv2D)              (None, None, None, 9 82944       activation_9[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_11 (Conv2D)              (None, None, None, 3 6144        average_pooling2d[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_5 (BatchNor (None, None, None, 6 192         conv2d_5[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_7 (BatchNor (None, None, None, 6 192         conv2d_7[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_10 (BatchNo (None, None, None, 9 288         conv2d_10[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_11 (BatchNo (None, None, None, 3 96          conv2d_11[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_5 (Activation)       (None, None, None, 6 0           batch_normalization_5[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_7 (Activation)       (None, None, None, 6 0           batch_normalization_7[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "activation_10 (Activation)      (None, None, None, 9 0           batch_normalization_10[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_11 (Activation)      (None, None, None, 3 0           batch_normalization_11[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed0 (Concatenate)            (None, None, None, 2 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, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_15 (BatchNo (None, None, None, 6 192         conv2d_15[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_15 (Activation)      (None, None, None, 6 0           batch_normalization_15[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_13 (Conv2D)              (None, None, None, 4 12288       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_16 (Conv2D)              (None, None, None, 9 55296       activation_15[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_13 (BatchNo (None, None, None, 4 144         conv2d_13[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_16 (BatchNo (None, None, None, 9 288         conv2d_16[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_13 (Activation)      (None, None, None, 4 0           batch_normalization_13[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_16 (Activation)      (None, None, None, 9 0           batch_normalization_16[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_1 (AveragePoo (None, None, None, 2 0           mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_12 (Conv2D)              (None, None, None, 6 16384       mixed0[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_14 (Conv2D)              (None, None, None, 6 76800       activation_13[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_17 (Conv2D)              (None, None, None, 9 82944       activation_16[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_18 (Conv2D)              (None, None, None, 6 16384       average_pooling2d_1[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_12 (BatchNo (None, None, None, 6 192         conv2d_12[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_14 (BatchNo (None, None, None, 6 192         conv2d_14[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_17 (BatchNo (None, None, None, 9 288         conv2d_17[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_18 (BatchNo (None, None, None, 6 192         conv2d_18[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_12 (Activation)      (None, None, None, 6 0           batch_normalization_12[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_14 (Activation)      (None, None, None, 6 0           batch_normalization_14[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_17 (Activation)      (None, None, None, 9 0           batch_normalization_17[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_18 (Activation)      (None, None, None, 6 0           batch_normalization_18[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed1 (Concatenate)            (None, None, None, 2 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, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_22 (BatchNo (None, None, None, 6 192         conv2d_22[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_22 (Activation)      (None, None, None, 6 0           batch_normalization_22[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_20 (Conv2D)              (None, None, None, 4 13824       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_23 (Conv2D)              (None, None, None, 9 55296       activation_22[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_20 (BatchNo (None, None, None, 4 144         conv2d_20[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_23 (BatchNo (None, None, None, 9 288         conv2d_23[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_20 (Activation)      (None, None, None, 4 0           batch_normalization_20[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_23 (Activation)      (None, None, None, 9 0           batch_normalization_23[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_2 (AveragePoo (None, None, None, 2 0           mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_19 (Conv2D)              (None, None, None, 6 18432       mixed1[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_21 (Conv2D)              (None, None, None, 6 76800       activation_20[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_24 (Conv2D)              (None, None, None, 9 82944       activation_23[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_25 (Conv2D)              (None, None, None, 6 18432       average_pooling2d_2[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_19 (BatchNo (None, None, None, 6 192         conv2d_19[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_21 (BatchNo (None, None, None, 6 192         conv2d_21[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_24 (BatchNo (None, None, None, 9 288         conv2d_24[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_25 (BatchNo (None, None, None, 6 192         conv2d_25[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_19 (Activation)      (None, None, None, 6 0           batch_normalization_19[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_21 (Activation)      (None, None, None, 6 0           batch_normalization_21[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_24 (Activation)      (None, None, None, 9 0           batch_normalization_24[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_25 (Activation)      (None, None, None, 6 0           batch_normalization_25[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed2 (Concatenate)            (None, None, None, 2 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, None, None, 6 18432       mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_27 (BatchNo (None, None, None, 6 192         conv2d_27[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_27 (Activation)      (None, None, None, 6 0           batch_normalization_27[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_28 (Conv2D)              (None, None, None, 9 55296       activation_27[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_28 (BatchNo (None, None, None, 9 288         conv2d_28[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_28 (Activation)      (None, None, None, 9 0           batch_normalization_28[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_26 (Conv2D)              (None, None, None, 3 995328      mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_29 (Conv2D)              (None, None, None, 9 82944       activation_28[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_26 (BatchNo (None, None, None, 3 1152        conv2d_26[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_29 (BatchNo (None, None, None, 9 288         conv2d_29[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_26 (Activation)      (None, None, None, 3 0           batch_normalization_26[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_29 (Activation)      (None, None, None, 9 0           batch_normalization_29[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_2 (MaxPooling2D)  (None, None, None, 2 0           mixed2[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed3 (Concatenate)            (None, None, None, 7 0           activation_26[0][0]              \n",
      "                                                                 activation_29[0][0]              \n",
      "                                                                 max_pooling2d_2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_34 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_34 (BatchNo (None, None, None, 1 384         conv2d_34[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_34 (Activation)      (None, None, None, 1 0           batch_normalization_34[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_35 (Conv2D)              (None, None, None, 1 114688      activation_34[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_35 (BatchNo (None, None, None, 1 384         conv2d_35[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_35 (Activation)      (None, None, None, 1 0           batch_normalization_35[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_31 (Conv2D)              (None, None, None, 1 98304       mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_36 (Conv2D)              (None, None, None, 1 114688      activation_35[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_31 (BatchNo (None, None, None, 1 384         conv2d_31[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_36 (BatchNo (None, None, None, 1 384         conv2d_36[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_31 (Activation)      (None, None, None, 1 0           batch_normalization_31[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_36 (Activation)      (None, None, None, 1 0           batch_normalization_36[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_32 (Conv2D)              (None, None, None, 1 114688      activation_31[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_37 (Conv2D)              (None, None, None, 1 114688      activation_36[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_32 (BatchNo (None, None, None, 1 384         conv2d_32[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_37 (BatchNo (None, None, None, 1 384         conv2d_37[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_32 (Activation)      (None, None, None, 1 0           batch_normalization_32[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_37 (Activation)      (None, None, None, 1 0           batch_normalization_37[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_3 (AveragePoo (None, None, None, 7 0           mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_30 (Conv2D)              (None, None, None, 1 147456      mixed3[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_33 (Conv2D)              (None, None, None, 1 172032      activation_32[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_38 (Conv2D)              (None, None, None, 1 172032      activation_37[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_39 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_3[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_30 (BatchNo (None, None, None, 1 576         conv2d_30[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_33 (BatchNo (None, None, None, 1 576         conv2d_33[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_38 (BatchNo (None, None, None, 1 576         conv2d_38[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_39 (BatchNo (None, None, None, 1 576         conv2d_39[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_30 (Activation)      (None, None, None, 1 0           batch_normalization_30[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_33 (Activation)      (None, None, None, 1 0           batch_normalization_33[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_38 (Activation)      (None, None, None, 1 0           batch_normalization_38[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_39 (Activation)      (None, None, None, 1 0           batch_normalization_39[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed4 (Concatenate)            (None, None, None, 7 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, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_44 (BatchNo (None, None, None, 1 480         conv2d_44[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_44 (Activation)      (None, None, None, 1 0           batch_normalization_44[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_45 (Conv2D)              (None, None, None, 1 179200      activation_44[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_45 (BatchNo (None, None, None, 1 480         conv2d_45[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_45 (Activation)      (None, None, None, 1 0           batch_normalization_45[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_41 (Conv2D)              (None, None, None, 1 122880      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_46 (Conv2D)              (None, None, None, 1 179200      activation_45[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_41 (BatchNo (None, None, None, 1 480         conv2d_41[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_46 (BatchNo (None, None, None, 1 480         conv2d_46[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_41 (Activation)      (None, None, None, 1 0           batch_normalization_41[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_46 (Activation)      (None, None, None, 1 0           batch_normalization_46[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_42 (Conv2D)              (None, None, None, 1 179200      activation_41[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_47 (Conv2D)              (None, None, None, 1 179200      activation_46[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_42 (BatchNo (None, None, None, 1 480         conv2d_42[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_47 (BatchNo (None, None, None, 1 480         conv2d_47[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_42 (Activation)      (None, None, None, 1 0           batch_normalization_42[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_47 (Activation)      (None, None, None, 1 0           batch_normalization_47[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_4 (AveragePoo (None, None, None, 7 0           mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_40 (Conv2D)              (None, None, None, 1 147456      mixed4[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_43 (Conv2D)              (None, None, None, 1 215040      activation_42[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_48 (Conv2D)              (None, None, None, 1 215040      activation_47[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_49 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_4[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_40 (BatchNo (None, None, None, 1 576         conv2d_40[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_43 (BatchNo (None, None, None, 1 576         conv2d_43[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_48 (BatchNo (None, None, None, 1 576         conv2d_48[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_49 (BatchNo (None, None, None, 1 576         conv2d_49[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_40 (Activation)      (None, None, None, 1 0           batch_normalization_40[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_43 (Activation)      (None, None, None, 1 0           batch_normalization_43[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_48 (Activation)      (None, None, None, 1 0           batch_normalization_48[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_49 (Activation)      (None, None, None, 1 0           batch_normalization_49[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed5 (Concatenate)            (None, None, None, 7 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, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_54 (BatchNo (None, None, None, 1 480         conv2d_54[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_54 (Activation)      (None, None, None, 1 0           batch_normalization_54[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_55 (Conv2D)              (None, None, None, 1 179200      activation_54[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_55 (BatchNo (None, None, None, 1 480         conv2d_55[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_55 (Activation)      (None, None, None, 1 0           batch_normalization_55[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_51 (Conv2D)              (None, None, None, 1 122880      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_56 (Conv2D)              (None, None, None, 1 179200      activation_55[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_51 (BatchNo (None, None, None, 1 480         conv2d_51[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_56 (BatchNo (None, None, None, 1 480         conv2d_56[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_51 (Activation)      (None, None, None, 1 0           batch_normalization_51[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_56 (Activation)      (None, None, None, 1 0           batch_normalization_56[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_52 (Conv2D)              (None, None, None, 1 179200      activation_51[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_57 (Conv2D)              (None, None, None, 1 179200      activation_56[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_52 (BatchNo (None, None, None, 1 480         conv2d_52[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_57 (BatchNo (None, None, None, 1 480         conv2d_57[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_52 (Activation)      (None, None, None, 1 0           batch_normalization_52[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_57 (Activation)      (None, None, None, 1 0           batch_normalization_57[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_5 (AveragePoo (None, None, None, 7 0           mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_50 (Conv2D)              (None, None, None, 1 147456      mixed5[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_53 (Conv2D)              (None, None, None, 1 215040      activation_52[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_58 (Conv2D)              (None, None, None, 1 215040      activation_57[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_59 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_5[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_50 (BatchNo (None, None, None, 1 576         conv2d_50[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_53 (BatchNo (None, None, None, 1 576         conv2d_53[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_58 (BatchNo (None, None, None, 1 576         conv2d_58[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_59 (BatchNo (None, None, None, 1 576         conv2d_59[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_50 (Activation)      (None, None, None, 1 0           batch_normalization_50[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_53 (Activation)      (None, None, None, 1 0           batch_normalization_53[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_58 (Activation)      (None, None, None, 1 0           batch_normalization_58[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_59 (Activation)      (None, None, None, 1 0           batch_normalization_59[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed6 (Concatenate)            (None, None, None, 7 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, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_64 (BatchNo (None, None, None, 1 576         conv2d_64[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_64 (Activation)      (None, None, None, 1 0           batch_normalization_64[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_65 (Conv2D)              (None, None, None, 1 258048      activation_64[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_65 (BatchNo (None, None, None, 1 576         conv2d_65[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_65 (Activation)      (None, None, None, 1 0           batch_normalization_65[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_61 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_66 (Conv2D)              (None, None, None, 1 258048      activation_65[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_61 (BatchNo (None, None, None, 1 576         conv2d_61[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_66 (BatchNo (None, None, None, 1 576         conv2d_66[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_61 (Activation)      (None, None, None, 1 0           batch_normalization_61[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_66 (Activation)      (None, None, None, 1 0           batch_normalization_66[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_62 (Conv2D)              (None, None, None, 1 258048      activation_61[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_67 (Conv2D)              (None, None, None, 1 258048      activation_66[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_62 (BatchNo (None, None, None, 1 576         conv2d_62[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_67 (BatchNo (None, None, None, 1 576         conv2d_67[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_62 (Activation)      (None, None, None, 1 0           batch_normalization_62[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_67 (Activation)      (None, None, None, 1 0           batch_normalization_67[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_6 (AveragePoo (None, None, None, 7 0           mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_60 (Conv2D)              (None, None, None, 1 147456      mixed6[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_63 (Conv2D)              (None, None, None, 1 258048      activation_62[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_68 (Conv2D)              (None, None, None, 1 258048      activation_67[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_69 (Conv2D)              (None, None, None, 1 147456      average_pooling2d_6[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_60 (BatchNo (None, None, None, 1 576         conv2d_60[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_63 (BatchNo (None, None, None, 1 576         conv2d_63[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_68 (BatchNo (None, None, None, 1 576         conv2d_68[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_69 (BatchNo (None, None, None, 1 576         conv2d_69[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_60 (Activation)      (None, None, None, 1 0           batch_normalization_60[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_63 (Activation)      (None, None, None, 1 0           batch_normalization_63[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_68 (Activation)      (None, None, None, 1 0           batch_normalization_68[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_69 (Activation)      (None, None, None, 1 0           batch_normalization_69[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed7 (Concatenate)            (None, None, None, 7 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, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_72 (BatchNo (None, None, None, 1 576         conv2d_72[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_72 (Activation)      (None, None, None, 1 0           batch_normalization_72[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_73 (Conv2D)              (None, None, None, 1 258048      activation_72[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_73 (BatchNo (None, None, None, 1 576         conv2d_73[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_73 (Activation)      (None, None, None, 1 0           batch_normalization_73[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_70 (Conv2D)              (None, None, None, 1 147456      mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_74 (Conv2D)              (None, None, None, 1 258048      activation_73[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_70 (BatchNo (None, None, None, 1 576         conv2d_70[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_74 (BatchNo (None, None, None, 1 576         conv2d_74[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_70 (Activation)      (None, None, None, 1 0           batch_normalization_70[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_74 (Activation)      (None, None, None, 1 0           batch_normalization_74[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_71 (Conv2D)              (None, None, None, 3 552960      activation_70[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_75 (Conv2D)              (None, None, None, 1 331776      activation_74[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_71 (BatchNo (None, None, None, 3 960         conv2d_71[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_75 (BatchNo (None, None, None, 1 576         conv2d_75[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_71 (Activation)      (None, None, None, 3 0           batch_normalization_71[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_75 (Activation)      (None, None, None, 1 0           batch_normalization_75[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "max_pooling2d_3 (MaxPooling2D)  (None, None, None, 7 0           mixed7[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "mixed8 (Concatenate)            (None, None, None, 1 0           activation_71[0][0]              \n",
      "                                                                 activation_75[0][0]              \n",
      "                                                                 max_pooling2d_3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_80 (Conv2D)              (None, None, None, 4 573440      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_80 (BatchNo (None, None, None, 4 1344        conv2d_80[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_80 (Activation)      (None, None, None, 4 0           batch_normalization_80[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_77 (Conv2D)              (None, None, None, 3 491520      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_81 (Conv2D)              (None, None, None, 3 1548288     activation_80[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_77 (BatchNo (None, None, None, 3 1152        conv2d_77[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_81 (BatchNo (None, None, None, 3 1152        conv2d_81[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_77 (Activation)      (None, None, None, 3 0           batch_normalization_77[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_81 (Activation)      (None, None, None, 3 0           batch_normalization_81[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_78 (Conv2D)              (None, None, None, 3 442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_79 (Conv2D)              (None, None, None, 3 442368      activation_77[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_82 (Conv2D)              (None, None, None, 3 442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_83 (Conv2D)              (None, None, None, 3 442368      activation_81[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_7 (AveragePoo (None, None, None, 1 0           mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_76 (Conv2D)              (None, None, None, 3 409600      mixed8[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_78 (BatchNo (None, None, None, 3 1152        conv2d_78[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_79 (BatchNo (None, None, None, 3 1152        conv2d_79[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_82 (BatchNo (None, None, None, 3 1152        conv2d_82[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_83 (BatchNo (None, None, None, 3 1152        conv2d_83[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_84 (Conv2D)              (None, None, None, 1 245760      average_pooling2d_7[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_76 (BatchNo (None, None, None, 3 960         conv2d_76[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_78 (Activation)      (None, None, None, 3 0           batch_normalization_78[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_79 (Activation)      (None, None, None, 3 0           batch_normalization_79[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_82 (Activation)      (None, None, None, 3 0           batch_normalization_82[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_83 (Activation)      (None, None, None, 3 0           batch_normalization_83[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_84 (BatchNo (None, None, None, 1 576         conv2d_84[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_76 (Activation)      (None, None, None, 3 0           batch_normalization_76[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_0 (Concatenate)          (None, None, None, 7 0           activation_78[0][0]              \n",
      "                                                                 activation_79[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, None, None, 7 0           activation_82[0][0]              \n",
      "                                                                 activation_83[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_84 (Activation)      (None, None, None, 1 0           batch_normalization_84[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9 (Concatenate)            (None, None, None, 2 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, None, None, 4 917504      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_89 (BatchNo (None, None, None, 4 1344        conv2d_89[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_89 (Activation)      (None, None, None, 4 0           batch_normalization_89[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_86 (Conv2D)              (None, None, None, 3 786432      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_90 (Conv2D)              (None, None, None, 3 1548288     activation_89[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_86 (BatchNo (None, None, None, 3 1152        conv2d_86[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_90 (BatchNo (None, None, None, 3 1152        conv2d_90[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_86 (Activation)      (None, None, None, 3 0           batch_normalization_86[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_90 (Activation)      (None, None, None, 3 0           batch_normalization_90[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_87 (Conv2D)              (None, None, None, 3 442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_88 (Conv2D)              (None, None, None, 3 442368      activation_86[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_91 (Conv2D)              (None, None, None, 3 442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_92 (Conv2D)              (None, None, None, 3 442368      activation_90[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "average_pooling2d_8 (AveragePoo (None, None, None, 2 0           mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_85 (Conv2D)              (None, None, None, 3 655360      mixed9[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_87 (BatchNo (None, None, None, 3 1152        conv2d_87[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_88 (BatchNo (None, None, None, 3 1152        conv2d_88[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_91 (BatchNo (None, None, None, 3 1152        conv2d_91[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_92 (BatchNo (None, None, None, 3 1152        conv2d_92[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_93 (Conv2D)              (None, None, None, 1 393216      average_pooling2d_8[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_85 (BatchNo (None, None, None, 3 960         conv2d_85[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_87 (Activation)      (None, None, None, 3 0           batch_normalization_87[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_88 (Activation)      (None, None, None, 3 0           batch_normalization_88[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_91 (Activation)      (None, None, None, 3 0           batch_normalization_91[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "activation_92 (Activation)      (None, None, None, 3 0           batch_normalization_92[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_93 (BatchNo (None, None, None, 1 576         conv2d_93[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "activation_85 (Activation)      (None, None, None, 3 0           batch_normalization_85[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed9_1 (Concatenate)          (None, None, None, 7 0           activation_87[0][0]              \n",
      "                                                                 activation_88[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, None, None, 7 0           activation_91[0][0]              \n",
      "                                                                 activation_92[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "activation_93 (Activation)      (None, None, None, 1 0           batch_normalization_93[0][0]     \n",
      "__________________________________________________________________________________________________\n",
      "mixed10 (Concatenate)           (None, None, None, 2 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",
      "dense (Dense)                   (None, 256)          524544      global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 64)           16448       dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_2 (Dense)                 (None, 20)           1300        dense_1[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 22,345,076\n",
      "Trainable params: 542,292\n",
      "Non-trainable params: 21,802,784\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model = inception_v3.InceptionV3(weights='imagenet', include_top=False)\n",
    "\n",
    "# add a global spatial average pooling layer\n",
    "x = base_model.output\n",
    "base_model.trainable = False\n",
    "x = GlobalAveragePooling2D()(x)\n",
    "\n",
    "# let's add a fully-connected layer\n",
    "x = Dense(256, activation='relu')(x)\n",
    "x = Dense(64, activation='relu')(x)\n",
    "\n",
    "# and a logistic layer\n",
    "predictions = Dense(20, activation='sigmoid')(x)\n",
    "model = Model(inputs=base_model.input, outputs=predictions)\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer=optimizers.Adam(learning_rate=0.01), loss='binary_crossentropy', metrics=['accuracy'])\n",
    "early_stopping = EarlyStopping(patience=15)\n",
    "\n",
    "checkpointer = ModelCheckpoint(os.path.join('models', 'inceptionv3_' + 'multi-label' + '_best.h5'), verbose=1, save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "29/29 [==============================] - 65s 2s/step - loss: 9.3921 - accuracy: 0.0526\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 2/30\n",
      "29/29 [==============================] - 54s 2s/step - loss: 1.1961 - accuracy: 0.0844\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 3/30\n",
      "29/29 [==============================] - 50s 2s/step - loss: 0.4027 - accuracy: 0.0954\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 4/30\n",
      "29/29 [==============================] - 52s 2s/step - loss: 0.2511 - accuracy: 0.0954\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 5/30\n",
      "29/29 [==============================] - 52s 2s/step - loss: 0.2209 - accuracy: 0.1206\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 6/30\n",
      "29/29 [==============================] - 51s 2s/step - loss: 0.2025 - accuracy: 0.1360\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 7/30\n",
      "29/29 [==============================] - 51s 2s/step - loss: 0.2037 - accuracy: 0.1414\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 8/30\n",
      "29/29 [==============================] - 51s 2s/step - loss: 0.1972 - accuracy: 0.1787\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 9/30\n",
      "29/29 [==============================] - 54s 2s/step - loss: 0.1915 - accuracy: 0.1930\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 10/30\n",
      "29/29 [==============================] - 50s 2s/step - loss: 0.2176 - accuracy: 0.1535\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 11/30\n",
      "29/29 [==============================] - 50s 2s/step - loss: 0.1973 - accuracy: 0.1798\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 12/30\n",
      "29/29 [==============================] - 50s 2s/step - loss: 0.1807 - accuracy: 0.2204\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 13/30\n",
      "29/29 [==============================] - 48s 2s/step - loss: 0.1837 - accuracy: 0.2149\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 14/30\n",
      "29/29 [==============================] - 51s 2s/step - loss: 0.1751 - accuracy: 0.2566\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 15/30\n",
      "29/29 [==============================] - 49s 2s/step - loss: 0.1794 - accuracy: 0.2281\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 16/30\n",
      "29/29 [==============================] - 49s 2s/step - loss: 0.1685 - accuracy: 0.2654\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 17/30\n",
      "29/29 [==============================] - 52s 2s/step - loss: 0.1600 - accuracy: 0.3224\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 18/30\n",
      "29/29 [==============================] - 51s 2s/step - loss: 0.1652 - accuracy: 0.2862\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 19/30\n",
      "29/29 [==============================] - 52s 2s/step - loss: 0.1591 - accuracy: 0.3333\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 20/30\n",
      "29/29 [==============================] - 31s 1s/step - loss: 0.1678 - accuracy: 0.2807\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 21/30\n",
      "29/29 [==============================] - 22s 744ms/step - loss: 0.1575 - accuracy: 0.3202\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 22/30\n",
      "29/29 [==============================] - 21s 712ms/step - loss: 0.1555 - accuracy: 0.3235\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 23/30\n",
      "29/29 [==============================] - 21s 711ms/step - loss: 0.1533 - accuracy: 0.3498\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 24/30\n",
      "29/29 [==============================] - 21s 726ms/step - loss: 0.1486 - accuracy: 0.3728\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 25/30\n",
      "29/29 [==============================] - 21s 720ms/step - loss: 0.1511 - accuracy: 0.3673\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 26/30\n",
      "29/29 [==============================] - 21s 721ms/step - loss: 0.1459 - accuracy: 0.4112\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 27/30\n",
      "29/29 [==============================] - 21s 722ms/step - loss: 0.1526 - accuracy: 0.3410\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 28/30\n",
      "29/29 [==============================] - 21s 716ms/step - loss: 0.1613 - accuracy: 0.3366\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 29/30\n",
      "29/29 [==============================] - 21s 712ms/step - loss: 0.1464 - accuracy: 0.3728\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "Epoch 30/30\n",
      "29/29 [==============================] - 21s 724ms/step - loss: 0.1409 - accuracy: 0.4046\n",
      "WARNING:tensorflow:Early stopping conditioned on metric `val_loss` which is not available. Available metrics are: loss,accuracy\n",
      "8/8 [==============================] - 6s 624ms/step - loss: 0.2145 - accuracy: 0.1623\n",
      "Test_loss_value = 0.21450378000736237\n",
      "test_accuracy = 0.16228070855140686\n"
     ]
    }
   ],
   "source": [
    "model.fit(lista, lista_y, epochs=30, callbacks=[early_stopping])\n",
    "loss_value , accuracy = model.evaluate(val_lista, val_lista_y)\n",
    "\n",
    "print('Test_loss_value = ' +str(loss_value))\n",
    "print('test_accuracy = ' + str(accuracy))"
   ]
  }
 ],
 "metadata": {
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   "language": "python",
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  "language_info": {
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    "name": "ipython",
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   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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