Xception.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fine Tuning Xception for ICB"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:26:00] Library functions loaded.\n"
     ]
    }
   ],
   "source": [
    "import gc\n",
    "import lib\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "\n",
    "from matplotlib import pyplot as plt\n",
    "from tensorflow import keras as kr\n",
    "from tensorflow.keras.utils import Sequence\n",
    "from sklearn.metrics import confusion_matrix\n",
    "from sklearn.metrics import classification_report\n",
    "from sklearn.utils.multiclass import unique_labels\n",
    "import itertools\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[01:26:01] Data preparation started.\n",
      "[01:26:01] Loaded custom functions.\n",
      "(SKIPPED) Found a non image file:  ./dataset/validation/non_biomass/metal282 - Shortcut.lnk\n",
      "[Non Processed Classifier Input Data]\n",
      "Raw pixels matrix: 242.01MB\n",
      "Raw features matrix: 1.85MB\n",
      "[01:26:11] Loaded raw congregates of images, features and labels.\n",
      "[Non Augmented Generators]\n",
      "Found 248 images belonging to 2 classes.\n",
      "Found 144 images belonging to 2 classes.\n",
      "Found 70 images belonging to 2 classes.\n",
      "[Augmented Generators]\n",
      "Found 248 images belonging to 2 classes.\n",
      "Found 144 images belonging to 2 classes.\n",
      "Found 70 images belonging to 2 classes.\n",
      "[01:26:11] Loaded all generators.\n",
      "[Total Info]\n",
      "Number of images: 924\n",
      "Size of images: 0.01MB\n",
      "[01:26:11] Data preperation completed!\n",
      "Time taken: 10.725600242614746 seconds.\n"
     ]
    }
   ],
   "source": [
    "# Implementation Dependant Globals\n",
    "REQUIRED_DIMENSIONS = (299, 299)\n",
    "TRAIN_BATCH_SIZE = 10\n",
    "VALIDATION_BATCH_SIZE = 10\n",
    "TEST_BATCH_SIZE = 10\n",
    "\n",
    "# Import dataset\n",
    "%run DataPreparation.ipynb "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fetch & Download Xception model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"xception\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "input_1 (InputLayer)            [(None, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 149, 149, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_bn (BatchNormaliza (None, 149, 149, 32) 128         block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_act (Activation)   (None, 149, 149, 32) 0           block1_conv1_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 147, 147, 64) 18432       block1_conv1_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_bn (BatchNormaliza (None, 147, 147, 64) 256         block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_act (Activation)   (None, 147, 147, 64) 0           block1_conv2_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1 (SeparableConv2 (None, 147, 147, 128 8768        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1_bn (BatchNormal (None, 147, 147, 128 512         block2_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_act (Activation (None, 147, 147, 128 0           block2_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2 (SeparableConv2 (None, 147, 147, 128 17536       block2_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_bn (BatchNormal (None, 147, 147, 128 512         block2_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 74, 74, 128)  8192        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 74, 74, 128)  0           block2_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 74, 74, 128)  512         conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 74, 74, 128)  0           block2_pool[0][0]                \n",
      "                                                                 batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_act (Activation (None, 74, 74, 128)  0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1 (SeparableConv2 (None, 74, 74, 256)  33920       block3_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_bn (BatchNormal (None, 74, 74, 256)  1024        block3_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_act (Activation (None, 74, 74, 256)  0           block3_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2 (SeparableConv2 (None, 74, 74, 256)  67840       block3_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_bn (BatchNormal (None, 74, 74, 256)  1024        block3_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 37, 37, 256)  32768       add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 37, 37, 256)  0           block3_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 37, 37, 256)  1024        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 37, 37, 256)  0           block3_pool[0][0]                \n",
      "                                                                 batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_act (Activation (None, 37, 37, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1 (SeparableConv2 (None, 37, 37, 728)  188672      block4_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_bn (BatchNormal (None, 37, 37, 728)  2912        block4_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_act (Activation (None, 37, 37, 728)  0           block4_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2 (SeparableConv2 (None, 37, 37, 728)  536536      block4_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_bn (BatchNormal (None, 37, 37, 728)  2912        block4_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 19, 19, 728)  186368      add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 19, 19, 728)  0           block4_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 19, 19, 728)  2912        conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 19, 19, 728)  0           block4_pool[0][0]                \n",
      "                                                                 batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_act (Activation (None, 19, 19, 728)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_act (Activation (None, 19, 19, 728)  0           block5_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_act (Activation (None, 19, 19, 728)  0           block5_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 19, 19, 728)  0           block5_sepconv3_bn[0][0]         \n",
      "                                                                 add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_act (Activation (None, 19, 19, 728)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_act (Activation (None, 19, 19, 728)  0           block6_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_act (Activation (None, 19, 19, 728)  0           block6_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 19, 19, 728)  0           block6_sepconv3_bn[0][0]         \n",
      "                                                                 add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_act (Activation (None, 19, 19, 728)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_act (Activation (None, 19, 19, 728)  0           block7_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_act (Activation (None, 19, 19, 728)  0           block7_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 19, 19, 728)  0           block7_sepconv3_bn[0][0]         \n",
      "                                                                 add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_act (Activation (None, 19, 19, 728)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_act (Activation (None, 19, 19, 728)  0           block8_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_act (Activation (None, 19, 19, 728)  0           block8_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 19, 19, 728)  0           block8_sepconv3_bn[0][0]         \n",
      "                                                                 add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_act (Activation (None, 19, 19, 728)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_act (Activation (None, 19, 19, 728)  0           block9_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_act (Activation (None, 19, 19, 728)  0           block9_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 19, 19, 728)  0           block9_sepconv3_bn[0][0]         \n",
      "                                                                 add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_act (Activatio (None, 19, 19, 728)  0           block10_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_act (Activatio (None, 19, 19, 728)  0           block10_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 19, 19, 728)  0           block10_sepconv3_bn[0][0]        \n",
      "                                                                 add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_act (Activatio (None, 19, 19, 728)  0           block11_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_act (Activatio (None, 19, 19, 728)  0           block11_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 19, 19, 728)  0           block11_sepconv3_bn[0][0]        \n",
      "                                                                 add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_act (Activatio (None, 19, 19, 728)  0           block12_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_act (Activatio (None, 19, 19, 728)  0           block12_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 19, 19, 728)  0           block12_sepconv3_bn[0][0]        \n",
      "                                                                 add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block13_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block13_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_act (Activatio (None, 19, 19, 728)  0           block13_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2 (SeparableConv (None, 19, 19, 1024) 752024      block13_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_bn (BatchNorma (None, 19, 19, 1024) 4096        block13_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 10, 10, 1024) 745472      add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_pool (MaxPooling2D)     (None, 10, 10, 1024) 0           block13_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 10, 10, 1024) 4096        conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 10, 10, 1024) 0           block13_pool[0][0]               \n",
      "                                                                 batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1 (SeparableConv (None, 10, 10, 1536) 1582080     add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_bn (BatchNorma (None, 10, 10, 1536) 6144        block14_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_act (Activatio (None, 10, 10, 1536) 0           block14_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2 (SeparableConv (None, 10, 10, 2048) 3159552     block14_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_bn (BatchNorma (None, 10, 10, 2048) 8192        block14_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_act (Activatio (None, 10, 10, 2048) 0           block14_sepconv2_bn[0][0]        \n",
      "==================================================================================================\n",
      "Total params: 20,861,480\n",
      "Trainable params: 20,806,952\n",
      "Non-trainable params: 54,528\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "xception = kr.applications.xception\n",
    "xception_model = xception.Xception(\n",
    "    include_top=False, \n",
    "    weights='imagenet', \n",
    "    input_shape=REQUIRED_DIMENSIONS+(3,)\n",
    ")\n",
    "\n",
    "xception_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Building and Compiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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, 299, 299, 3) 0                                            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1 (Conv2D)           (None, 149, 149, 32) 864         input_1[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_bn (BatchNormaliza (None, 149, 149, 32) 128         block1_conv1[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv1_act (Activation)   (None, 149, 149, 32) 0           block1_conv1_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2 (Conv2D)           (None, 147, 147, 64) 18432       block1_conv1_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_bn (BatchNormaliza (None, 147, 147, 64) 256         block1_conv2[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "block1_conv2_act (Activation)   (None, 147, 147, 64) 0           block1_conv2_bn[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1 (SeparableConv2 (None, 147, 147, 128 8768        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv1_bn (BatchNormal (None, 147, 147, 128 512         block2_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_act (Activation (None, 147, 147, 128 0           block2_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2 (SeparableConv2 (None, 147, 147, 128 17536       block2_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block2_sepconv2_bn (BatchNormal (None, 147, 147, 128 512         block2_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d (Conv2D)                 (None, 74, 74, 128)  8192        block1_conv2_act[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block2_pool (MaxPooling2D)      (None, 74, 74, 128)  0           block2_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization (BatchNorma (None, 74, 74, 128)  512         conv2d[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "add (Add)                       (None, 74, 74, 128)  0           block2_pool[0][0]                \n",
      "                                                                 batch_normalization[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_act (Activation (None, 74, 74, 128)  0           add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1 (SeparableConv2 (None, 74, 74, 256)  33920       block3_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv1_bn (BatchNormal (None, 74, 74, 256)  1024        block3_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_act (Activation (None, 74, 74, 256)  0           block3_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2 (SeparableConv2 (None, 74, 74, 256)  67840       block3_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block3_sepconv2_bn (BatchNormal (None, 74, 74, 256)  1024        block3_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_1 (Conv2D)               (None, 37, 37, 256)  32768       add[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "block3_pool (MaxPooling2D)      (None, 37, 37, 256)  0           block3_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_1 (BatchNor (None, 37, 37, 256)  1024        conv2d_1[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_1 (Add)                     (None, 37, 37, 256)  0           block3_pool[0][0]                \n",
      "                                                                 batch_normalization_1[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_act (Activation (None, 37, 37, 256)  0           add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1 (SeparableConv2 (None, 37, 37, 728)  188672      block4_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv1_bn (BatchNormal (None, 37, 37, 728)  2912        block4_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_act (Activation (None, 37, 37, 728)  0           block4_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2 (SeparableConv2 (None, 37, 37, 728)  536536      block4_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block4_sepconv2_bn (BatchNormal (None, 37, 37, 728)  2912        block4_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_2 (Conv2D)               (None, 19, 19, 728)  186368      add_1[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block4_pool (MaxPooling2D)      (None, 19, 19, 728)  0           block4_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_2 (BatchNor (None, 19, 19, 728)  2912        conv2d_2[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_2 (Add)                     (None, 19, 19, 728)  0           block4_pool[0][0]                \n",
      "                                                                 batch_normalization_2[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_act (Activation (None, 19, 19, 728)  0           add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_act (Activation (None, 19, 19, 728)  0           block5_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_act (Activation (None, 19, 19, 728)  0           block5_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block5_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block5_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block5_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_3 (Add)                     (None, 19, 19, 728)  0           block5_sepconv3_bn[0][0]         \n",
      "                                                                 add_2[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_act (Activation (None, 19, 19, 728)  0           add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "block6_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_act (Activation (None, 19, 19, 728)  0           block6_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_act (Activation (None, 19, 19, 728)  0           block6_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block6_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block6_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block6_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_4 (Add)                     (None, 19, 19, 728)  0           block6_sepconv3_bn[0][0]         \n",
      "                                                                 add_3[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_act (Activation (None, 19, 19, 728)  0           add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_act (Activation (None, 19, 19, 728)  0           block7_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_act (Activation (None, 19, 19, 728)  0           block7_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block7_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block7_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block7_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_5 (Add)                     (None, 19, 19, 728)  0           block7_sepconv3_bn[0][0]         \n",
      "                                                                 add_4[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_act (Activation (None, 19, 19, 728)  0           add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_act (Activation (None, 19, 19, 728)  0           block8_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_act (Activation (None, 19, 19, 728)  0           block8_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block8_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block8_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block8_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_6 (Add)                     (None, 19, 19, 728)  0           block8_sepconv3_bn[0][0]         \n",
      "                                                                 add_5[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_act (Activation (None, 19, 19, 728)  0           add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv1_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv1_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv1[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_act (Activation (None, 19, 19, 728)  0           block9_sepconv1_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv2_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv2_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv2[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_act (Activation (None, 19, 19, 728)  0           block9_sepconv2_bn[0][0]         \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3 (SeparableConv2 (None, 19, 19, 728)  536536      block9_sepconv3_act[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block9_sepconv3_bn (BatchNormal (None, 19, 19, 728)  2912        block9_sepconv3[0][0]            \n",
      "__________________________________________________________________________________________________\n",
      "add_7 (Add)                     (None, 19, 19, 728)  0           block9_sepconv3_bn[0][0]         \n",
      "                                                                 add_6[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_act (Activatio (None, 19, 19, 728)  0           block10_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_act (Activatio (None, 19, 19, 728)  0           block10_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block10_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block10_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block10_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_8 (Add)                     (None, 19, 19, 728)  0           block10_sepconv3_bn[0][0]        \n",
      "                                                                 add_7[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_act (Activatio (None, 19, 19, 728)  0           block11_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_act (Activatio (None, 19, 19, 728)  0           block11_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block11_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block11_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block11_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_9 (Add)                     (None, 19, 19, 728)  0           block11_sepconv3_bn[0][0]        \n",
      "                                                                 add_8[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_act (Activatio (None, 19, 19, 728)  0           block12_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv2_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_act (Activatio (None, 19, 19, 728)  0           block12_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3 (SeparableConv (None, 19, 19, 728)  536536      block12_sepconv3_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block12_sepconv3_bn (BatchNorma (None, 19, 19, 728)  2912        block12_sepconv3[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "add_10 (Add)                    (None, 19, 19, 728)  0           block12_sepconv3_bn[0][0]        \n",
      "                                                                 add_9[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_act (Activatio (None, 19, 19, 728)  0           add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1 (SeparableConv (None, 19, 19, 728)  536536      block13_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv1_bn (BatchNorma (None, 19, 19, 728)  2912        block13_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_act (Activatio (None, 19, 19, 728)  0           block13_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2 (SeparableConv (None, 19, 19, 1024) 752024      block13_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block13_sepconv2_bn (BatchNorma (None, 19, 19, 1024) 4096        block13_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "conv2d_3 (Conv2D)               (None, 10, 10, 1024) 745472      add_10[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block13_pool (MaxPooling2D)     (None, 10, 10, 1024) 0           block13_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "batch_normalization_3 (BatchNor (None, 10, 10, 1024) 4096        conv2d_3[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "add_11 (Add)                    (None, 10, 10, 1024) 0           block13_pool[0][0]               \n",
      "                                                                 batch_normalization_3[0][0]      \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1 (SeparableConv (None, 10, 10, 1536) 1582080     add_11[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_bn (BatchNorma (None, 10, 10, 1536) 6144        block14_sepconv1[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv1_act (Activatio (None, 10, 10, 1536) 0           block14_sepconv1_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2 (SeparableConv (None, 10, 10, 2048) 3159552     block14_sepconv1_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_bn (BatchNorma (None, 10, 10, 2048) 8192        block14_sepconv2[0][0]           \n",
      "__________________________________________________________________________________________________\n",
      "block14_sepconv2_act (Activatio (None, 10, 10, 2048) 0           block14_sepconv2_bn[0][0]        \n",
      "__________________________________________________________________________________________________\n",
      "global_average_pooling2d (Globa (None, 2048)         0           block14_sepconv2_act[0][0]       \n",
      "__________________________________________________________________________________________________\n",
      "dense (Dense)                   (None, 200)          409800      global_average_pooling2d[0][0]   \n",
      "__________________________________________________________________________________________________\n",
      "dropout (Dropout)               (None, 200)          0           dense[0][0]                      \n",
      "__________________________________________________________________________________________________\n",
      "dense_1 (Dense)                 (None, 2)            402         dropout[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 21,271,682\n",
      "Trainable params: 410,202\n",
      "Non-trainable params: 20,861,480\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "# Ease of Access\n",
    "GlobalAveragePooling2D, Model, Dropout, Flatten, Dense = kr.layers.GlobalAveragePooling2D, kr.models.Model, kr.layers.Dropout, kr.layers.Flatten, kr.layers.Dense\n",
    "\n",
    "# Construct the Head Model\n",
    "head_model = xception_model.output\n",
    "head_model = GlobalAveragePooling2D()(head_model)\n",
    "head_model = Dense(200, activation='relu')(head_model)\n",
    "head_model = Dropout(0.4)(head_model)\n",
    "head_model = Dense(2, activation=\"softmax\")(head_model)\n",
    "\n",
    "# place the head model on top of the base model (this will become the actual model we will train)\n",
    "model = Model(inputs=xception_model.input, outputs=head_model)\n",
    "\n",
    "# Freeze all the layers\n",
    "for layer in xception_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Show a summary of the model. Check the number of trainable parameters\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Warmup Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "50/49 [==============================] - 39s 784ms/step - loss: 0.7395 - accuracy: 0.4980 - val_loss: 3.8031 - val_accuracy: 0.5775\n",
      "Epoch 2/30\n",
      "50/49 [==============================] - 37s 745ms/step - loss: 0.7311 - accuracy: 0.4859 - val_loss: 6.7623 - val_accuracy: 0.4296\n",
      "Epoch 3/30\n",
      "50/49 [==============================] - 37s 743ms/step - loss: 0.7149 - accuracy: 0.5262 - val_loss: 5.0532 - val_accuracy: 0.4648\n",
      "Epoch 4/30\n",
      "50/49 [==============================] - 40s 800ms/step - loss: 0.6910 - accuracy: 0.5665 - val_loss: 6.0586 - val_accuracy: 0.4331\n",
      "Epoch 5/30\n",
      "50/49 [==============================] - 49s 982ms/step - loss: 0.6603 - accuracy: 0.6270 - val_loss: 5.5066 - val_accuracy: 0.4507\n",
      "Epoch 6/30\n",
      "50/49 [==============================] - 43s 865ms/step - loss: 0.6485 - accuracy: 0.6331 - val_loss: 4.5550 - val_accuracy: 0.4894\n",
      "Epoch 7/30\n",
      "50/49 [==============================] - 37s 737ms/step - loss: 0.6340 - accuracy: 0.6613 - val_loss: 3.8554 - val_accuracy: 0.5176\n",
      "Epoch 8/30\n",
      "50/49 [==============================] - 37s 743ms/step - loss: 0.6005 - accuracy: 0.7298 - val_loss: 3.9738 - val_accuracy: 0.5352\n",
      "Epoch 9/30\n",
      "50/49 [==============================] - 37s 732ms/step - loss: 0.5765 - accuracy: 0.7601 - val_loss: 6.5413 - val_accuracy: 0.4296\n",
      "Epoch 10/30\n",
      "50/49 [==============================] - 37s 736ms/step - loss: 0.5675 - accuracy: 0.7419 - val_loss: 3.4030 - val_accuracy: 0.6585\n",
      "Epoch 11/30\n",
      "50/49 [==============================] - 38s 756ms/step - loss: 0.5539 - accuracy: 0.7802 - val_loss: 6.5853 - val_accuracy: 0.4401\n",
      "Epoch 12/30\n",
      "50/49 [==============================] - 38s 751ms/step - loss: 0.5402 - accuracy: 0.7802 - val_loss: 5.2345 - val_accuracy: 0.4437\n",
      "Epoch 13/30\n",
      "50/49 [==============================] - 38s 755ms/step - loss: 0.5138 - accuracy: 0.8226 - val_loss: 3.8820 - val_accuracy: 0.5528\n",
      "Epoch 14/30\n",
      "50/49 [==============================] - 37s 748ms/step - loss: 0.4828 - accuracy: 0.8548 - val_loss: 5.0213 - val_accuracy: 0.4613\n",
      "Epoch 15/30\n",
      "50/49 [==============================] - 37s 737ms/step - loss: 0.4776 - accuracy: 0.8690 - val_loss: 5.9863 - val_accuracy: 0.4472\n",
      "Epoch 16/30\n",
      "50/49 [==============================] - 37s 742ms/step - loss: 0.4527 - accuracy: 0.8589 - val_loss: 8.3325 - val_accuracy: 0.4401\n",
      "Epoch 17/30\n",
      "50/49 [==============================] - 37s 748ms/step - loss: 0.4433 - accuracy: 0.8226 - val_loss: 4.1287 - val_accuracy: 0.5387\n",
      "Epoch 18/30\n",
      "50/49 [==============================] - 38s 753ms/step - loss: 0.4200 - accuracy: 0.8831 - val_loss: 3.5189 - val_accuracy: 0.6162\n",
      "Epoch 19/30\n",
      "50/49 [==============================] - 37s 742ms/step - loss: 0.4125 - accuracy: 0.8931 - val_loss: 3.4172 - val_accuracy: 0.6127\n",
      "Epoch 20/30\n",
      "50/49 [==============================] - 37s 739ms/step - loss: 0.4052 - accuracy: 0.9113 - val_loss: 3.9556 - val_accuracy: 0.5845\n",
      "Epoch 21/30\n",
      "50/49 [==============================] - 37s 744ms/step - loss: 0.3801 - accuracy: 0.9254 - val_loss: 8.2369 - val_accuracy: 0.4085\n",
      "Epoch 22/30\n",
      "50/49 [==============================] - 37s 748ms/step - loss: 0.3727 - accuracy: 0.9052 - val_loss: 5.1848 - val_accuracy: 0.4754\n",
      "Epoch 23/30\n",
      "50/49 [==============================] - 37s 744ms/step - loss: 0.3396 - accuracy: 0.9173 - val_loss: 4.6350 - val_accuracy: 0.5246\n",
      "Epoch 24/30\n",
      "50/49 [==============================] - 37s 736ms/step - loss: 0.3054 - accuracy: 0.9153 - val_loss: 4.8862 - val_accuracy: 0.5986\n",
      "Epoch 25/30\n",
      "50/49 [==============================] - 37s 737ms/step - loss: 0.3322 - accuracy: 0.9012 - val_loss: 3.7711 - val_accuracy: 0.5986\n",
      "Epoch 26/30\n",
      "50/49 [==============================] - 37s 738ms/step - loss: 0.3010 - accuracy: 0.9214 - val_loss: 7.3318 - val_accuracy: 0.4014\n",
      "Epoch 27/30\n",
      "50/49 [==============================] - 37s 742ms/step - loss: 0.3054 - accuracy: 0.9375 - val_loss: 4.4070 - val_accuracy: 0.5176\n",
      "Epoch 28/30\n",
      "50/49 [==============================] - 38s 755ms/step - loss: 0.2906 - accuracy: 0.9355 - val_loss: 4.0530 - val_accuracy: 0.5775\n",
      "Epoch 29/30\n",
      "50/49 [==============================] - 37s 749ms/step - loss: 0.2821 - accuracy: 0.9657 - val_loss: 4.9326 - val_accuracy: 0.4965\n",
      "Epoch 30/30\n",
      "50/49 [==============================] - 37s 749ms/step - loss: 0.2500 - accuracy: 0.9577 - val_loss: 5.9675 - val_accuracy: 0.4577\n"
     ]
    }
   ],
   "source": [
    "# Pls memory\n",
    "gc.collect()\n",
    "\n",
    "# Compile Model\n",
    "model.compile(\n",
    "    loss='binary_crossentropy',\n",
    "    optimizer=kr.optimizers.Nadam(lr=0.0001),\n",
    "    metrics=['accuracy']\n",
    ")\n",
    "\n",
    "# Train the head of the model(Our Layers)\n",
    "warmup_history = model.fit_generator(\n",
    "    train_set,\n",
    "    steps_per_epoch=train_set.samples/train_set.batch_size,\n",
    "    epochs=30,\n",
    "    validation_data=validation_set,\n",
    "    validation_steps=validation_set.samples/validation_set.batch_size,\n",
    "    verbose=1,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "warmup_history.plot_title = \"Warmup\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 3s 241ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     biomass       0.49      0.64      0.56        70\n",
      " non_biomass       0.48      0.33      0.39        70\n",
      "\n",
      "    accuracy                           0.49       140\n",
      "   macro avg       0.48      0.49      0.47       140\n",
      "weighted avg       0.48      0.49      0.47       140\n",
      "\n",
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14f3a767e48>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Make predictions\n",
    "predictions = model.predict_generator(test_set, steps=test_set.samples/test_set.batch_size, verbose=1)\n",
    "normalised_predictions = np.argmax(predictions, axis=1)\n",
    "\n",
    "# Print out scikit report\n",
    "print(classification_report(test_set.classes, normalised_predictions, target_names=CLASSES))\n",
    "\n",
    "# Draw confusion matrix\n",
    "lib.plot_confusion_matrix(test_set.classes, normalised_predictions, CLASSES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Final Training"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/30\n",
      "50/49 [==============================] - 57s 1s/step - loss: 0.6192 - accuracy: 0.7661 - val_loss: 1.1483 - val_accuracy: 0.6021\n",
      "Epoch 2/30\n",
      "50/49 [==============================] - 56s 1s/step - loss: 0.4311 - accuracy: 0.8367 - val_loss: 1.8156 - val_accuracy: 0.5141\n",
      "Epoch 3/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.3991 - accuracy: 0.8690 - val_loss: 2.1479 - val_accuracy: 0.4507\n",
      "Epoch 4/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.7041 - accuracy: 0.6008 - val_loss: 6.9438 - val_accuracy: 0.5423\n",
      "Epoch 5/30\n",
      "50/49 [==============================] - 56s 1s/step - loss: 0.6642 - accuracy: 0.5968 - val_loss: 1.4956 - val_accuracy: 0.5141\n",
      "Epoch 6/30\n",
      "50/49 [==============================] - 56s 1s/step - loss: 0.4757 - accuracy: 0.8690 - val_loss: 1.5537 - val_accuracy: 0.4437\n",
      "Epoch 7/30\n",
      "50/49 [==============================] - 57s 1s/step - loss: 0.3691 - accuracy: 0.8669 - val_loss: 0.6280 - val_accuracy: 0.6585\n",
      "Epoch 8/30\n",
      "50/49 [==============================] - 56s 1s/step - loss: 0.1156 - accuracy: 0.9516 - val_loss: 0.9161 - val_accuracy: 0.5634\n",
      "Epoch 9/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.5175 - accuracy: 0.7581 - val_loss: 8.4012 - val_accuracy: 0.4577\n",
      "Epoch 10/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.6872 - accuracy: 0.5645 - val_loss: 2.3740 - val_accuracy: 0.4577\n",
      "Epoch 11/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.6197 - accuracy: 0.6694 - val_loss: 0.6607 - val_accuracy: 0.5951\n",
      "Epoch 12/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.5579 - accuracy: 0.7157 - val_loss: 4.9781 - val_accuracy: 0.5423\n",
      "Epoch 13/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.6573 - accuracy: 0.6190 - val_loss: 0.6825 - val_accuracy: 0.4507\n",
      "Epoch 14/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.6025 - accuracy: 0.6774 - val_loss: 0.6592 - val_accuracy: 0.4824\n",
      "Epoch 15/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.5789 - accuracy: 0.7137 - val_loss: 0.8561 - val_accuracy: 0.4683\n",
      "Epoch 16/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.5197 - accuracy: 0.7722 - val_loss: 2.4790 - val_accuracy: 0.4930\n",
      "Epoch 17/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.4850 - accuracy: 0.7641 - val_loss: 0.9354 - val_accuracy: 0.5246\n",
      "Epoch 18/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.5268 - accuracy: 0.7359 - val_loss: 1.0895 - val_accuracy: 0.5669\n",
      "Epoch 19/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.3626 - accuracy: 0.8851 - val_loss: 1.8133 - val_accuracy: 0.4577\n",
      "Epoch 20/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.7975 - accuracy: 0.5625 - val_loss: 1.7314 - val_accuracy: 0.4577\n",
      "Epoch 21/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.7189 - accuracy: 0.5585 - val_loss: 1.5697 - val_accuracy: 0.4577\n",
      "Epoch 22/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.6971 - accuracy: 0.5363 - val_loss: 0.8133 - val_accuracy: 0.4401\n",
      "Epoch 23/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.6875 - accuracy: 0.5585 - val_loss: 0.7350 - val_accuracy: 0.4401\n",
      "Epoch 24/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.6831 - accuracy: 0.5726 - val_loss: 0.9460 - val_accuracy: 0.4542\n",
      "Epoch 25/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.6789 - accuracy: 0.5766 - val_loss: 0.7373 - val_accuracy: 0.4542\n",
      "Epoch 26/30\n",
      "50/49 [==============================] - 55s 1s/step - loss: 0.6710 - accuracy: 0.6149 - val_loss: 0.7250 - val_accuracy: 0.4930\n",
      "Epoch 27/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.6623 - accuracy: 0.6391 - val_loss: 0.7659 - val_accuracy: 0.4542\n",
      "Epoch 28/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.6613 - accuracy: 0.6310 - val_loss: 0.7429 - val_accuracy: 0.4648\n",
      "Epoch 29/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.6306 - accuracy: 0.6835 - val_loss: 0.7944 - val_accuracy: 0.4683\n",
      "Epoch 30/30\n",
      "50/49 [==============================] - 54s 1s/step - loss: 0.5198 - accuracy: 0.7984 - val_loss: 1.3965 - val_accuracy: 0.4789\n"
     ]
    }
   ],
   "source": [
    "# Reset validaiton & train set generators\n",
    "train_set.reset()\n",
    "validation_set.reset()\n",
    "\n",
    "# Now that the head FC layers have been trained/initialized, lets\n",
    "# Unfreeze the final set of CONV layers and make them trainable\n",
    "for layer in xception_model.layers:\n",
    "\tlayer.trainable = True\n",
    "\n",
    "    \n",
    "# Recompile model for changes to take effect, now using SGD with very small learning rate\n",
    "model.compile(\n",
    "    loss=\"binary_crossentropy\", \n",
    "    optimizer=\"nadam\", \n",
    "    metrics=[\"accuracy\"]\n",
    ")    \n",
    "\n",
    "# Train the whole model\n",
    "final_history = model.fit_generator(\n",
    "    train_set,\n",
    "    steps_per_epoch=train_set.samples/train_set.batch_size,\n",
    "    epochs=30,\n",
    "    validation_data=validation_set,\n",
    "    validation_steps=validation_set.samples/validation_set.batch_size,\n",
    "    verbose=1,\n",
    "    shuffle=True\n",
    ")\n",
    "\n",
    "final_history.plot_title = \"Final\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "14/14 [==============================] - 3s 224ms/step\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "     biomass       0.53      0.89      0.66        70\n",
      " non_biomass       0.64      0.20      0.30        70\n",
      "\n",
      "    accuracy                           0.54       140\n",
      "   macro avg       0.58      0.54      0.48       140\n",
      "weighted avg       0.58      0.54      0.48       140\n",
      "\n",
      "Normalized confusion matrix\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14f39d1bc08>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Reset \n",
    "test_set.reset()\n",
    "\n",
    "# Make predictions\n",
    "predictions = model.predict_generator(test_set, steps=test_set.samples/test_set.batch_size, verbose=1)\n",
    "normalised_predictions = np.argmax(predictions, axis=1)\n",
    "\n",
    "# Print out scikit report\n",
    "print(classification_report(test_set.classes, normalised_predictions, target_names=CLASSES))\n",
    "\n",
    "# Draw confusion matrix\n",
    "lib.plot_confusion_matrix(test_set.classes, normalised_predictions, CLASSES)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Visualising Training History"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "global_history = [warmup_history, final_history]\n",
    "\n",
    "for history in global_history:\n",
    "    # summarize history for accuracy\n",
    "    plt.plot(history.history['accuracy'], label='accuracy')\n",
    "    plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
    "    plt.title(\"Accuracy - \" + history.plot_title)\n",
    "    plt.ylabel('Accuracy')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.show()\n",
    "    \n",
    "    # summarize history for loss\n",
    "    plt.plot(history.history['loss'], label='loss')\n",
    "    plt.plot(history.history['val_loss'], label='val_loss')\n",
    "    plt.title(\"Loss - \" + history.plot_title)\n",
    "    plt.ylabel('Loss')\n",
    "    plt.xlabel('Epoch')\n",
    "    plt.legend(loc='upper left')\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\ProgramData\\Anaconda3\\envs\\ICB\\lib\\site-packages\\tensorflow_core\\python\\ops\\resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
      "Instructions for updating:\n",
      "If using Keras pass *_constraint arguments to layers.\n",
      "INFO:tensorflow:Assets written to: app\\trained_models\\Xception\\assets\n"
     ]
    }
   ],
   "source": [
    "model.save('app\\\\trained_models\\\\Xception')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "IBC",
   "language": "python",
   "name": "icb"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}