05c - Transfer Learning (Tensorflow).ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transfer Learning\n",
    "\n",
    "A Convolutional Neural Network (CNN) for image classification is made up of multiple layers that extract features, such as edges, corners, etc; and then use a final fully-connected layer to classify objects based on these features. You can visualize this like this:\n",
    "\n",
    "<table>\n",
    "    <tr><td rowspan=2 style='border: 1px solid black;'>&#x21d2;</td><td style='border: 1px solid black;'>Convolutional Layer</td><td style='border: 1px solid black;'>Pooling Layer</td><td style='border: 1px solid black;'>Convolutional Layer</td><td style='border: 1px solid black;'>Pooling Layer</td><td style='border: 1px solid black;'>Fully Connected Layer</td><td rowspan=2 style='border: 1px solid black;'>&#x21d2;</td></tr>\n",
    "    <tr><td colspan=4 style='border: 1px solid black; text-align:center;'>Feature Extraction</td><td style='border: 1px solid black; text-align:center;'>Classification</td></tr>\n",
    "</table>\n",
    "\n",
    "*Transfer Learning* is a technique where you can take an existing trained model and re-use its feature extraction layers, replacing its final classification layer with a fully-connected layer trained on your own custom images. With this technique, your model benefits from the feature extraction training that was performed on the base model (which may have been based on a larger training dataset than you have access to) to build a classification model for your own specific set of object classes.\n",
    "\n",
    "How does this help? Well, think of it this way. Suppose you take a professional tennis player and a complete beginner, and try to teach them both how to play raquetball. It's reasonable to assume that the professional tennis player will be easier to train, because many of the underlying skills involved in raquetball are already learned. Similarly, a pre-trained CNN model may be easier to train to classify specific set of objects because it's already learned how to identify the features of common objects, such as edges and corners. Fundamentally, a pre-trained model can be a great way to produce an effective classifier even when you have limited data with which to train it.\n",
    "\n",
    "In this notebook, we'll see how to implement transfer learning for a classification model using TensorFlow."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install and import TensorFlow libraries\n",
    "\n",
    "Let's start by ensuring that we have the latest version of the **TensorFlow** package installed and importing the Tensorflow libraries we're going to use."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!pip install --upgrade tensorflow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "TensorFlow version: 2.10.0\n",
      "Keras version: 2.10.0\n"
     ]
    }
   ],
   "source": [
    "import tensorflow\n",
    "from  tensorflow import keras\n",
    "print('TensorFlow version:',tensorflow.__version__)\n",
    "print('Keras version:',keras.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the base model\n",
    "\n",
    "To use transfer learning, we need a base model from which we can use the trained feature extraction layers. The ***resnet*** model is an CNN-based image classifier that has been pre-trained using a huge dataset of 3-color channel images of 224x224 pixels. Let's create an instance of it with some pretrained weights, excluding its final (top) prediction layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
      "94765736/94765736 [==============================] - 17s 0us/step\n",
      "Model: \"resnet50\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,587,712\n",
      "Trainable params: 23,534,592\n",
      "Non-trainable params: 53,120\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "base_model = keras.applications.resnet.ResNet50(weights='imagenet', include_top=False, input_shape=(224,224,3))\n",
    "print(base_model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Prepare the image data\n",
    "\n",
    "The pretrained model has many layers, starting with a convolutional layer that starts the feature extraction process from image data.\n",
    "\n",
    "For feature extraction to work with our own images, we  need to ensure that the image data we use to train our prediction layer has the same number of features (pixel values) as the images originally used to train the feature extraction layers, so we need data loaders for color images that are 224x224 pixels in size.\n",
    "\n",
    "Tensorflow includes functions for loading and transforming data. We'll use these to create a generator for training data, and a second generator for test data (which we'll use to validate the trained model). The loaders will transform the image data to match the format used to train the original resnet CNN model and normalize them.\n",
    "\n",
    "Run the following cell to define the data generators and list the classes for our images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Getting Data...\n",
      "Preparing training dataset...\n",
      "Found 840 images belonging to 3 classes.\n",
      "Preparing validation dataset...\n",
      "Found 360 images belonging to 3 classes.\n",
      "class names:  ['circle', 'square', 'triangle']\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "data_folder = 'data/shapes'\n",
    "pretrained_size = (224,224)\n",
    "batch_size = 30\n",
    "\n",
    "print(\"Getting Data...\")\n",
    "datagen = ImageDataGenerator(rescale=1./255, # normalize pixel values\n",
    "                             validation_split=0.3) # hold back 30% of the images for validation\n",
    "\n",
    "print(\"Preparing training dataset...\")\n",
    "train_generator = datagen.flow_from_directory(\n",
    "    data_folder,\n",
    "    target_size=pretrained_size, # resize to match model expected input\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical',\n",
    "    subset='training') # set as training data\n",
    "\n",
    "print(\"Preparing validation dataset...\")\n",
    "validation_generator = datagen.flow_from_directory(\n",
    "    data_folder,\n",
    "    target_size=pretrained_size, # resize to match model expected input\n",
    "    batch_size=batch_size,\n",
    "    class_mode='categorical',\n",
    "    subset='validation') # set as validation data\n",
    "\n",
    "classnames = list(train_generator.class_indices.keys())\n",
    "print(\"class names: \", classnames)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Create a prediction layer\n",
    "\n",
    "We downloaded the complete *resnet* model excluding its final prediction layer, so need to combine these layers with a fully-connected (*dense*) layer that takes the flattened outputs from the feature extraction layers and generates a prediction for each of our image classes.\n",
    "\n",
    "We also need to freeze the feature extraction layers to retain the trained weights. Then when we train the model using our images, only the final prediction layer will learn new weight and bias values - the pre-trained weights already learned for feature extraction will remain the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": [
     "outputPrepend"
    ]
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      " Layer (type)                   Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      " input_1 (InputLayer)           [(None, 224, 224, 3  0           []                               \n",
      "                                )]                                                                \n",
      "                                                                                                  \n",
      " conv1_pad (ZeroPadding2D)      (None, 230, 230, 3)  0           ['input_1[0][0]']                \n",
      "                                                                                                  \n",
      " conv1_conv (Conv2D)            (None, 112, 112, 64  9472        ['conv1_pad[0][0]']              \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_bn (BatchNormalization)  (None, 112, 112, 64  256         ['conv1_conv[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv1_relu (Activation)        (None, 112, 112, 64  0           ['conv1_bn[0][0]']               \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pad (ZeroPadding2D)      (None, 114, 114, 64  0           ['conv1_relu[0][0]']             \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " pool1_pool (MaxPooling2D)      (None, 56, 56, 64)   0           ['pool1_pad[0][0]']              \n",
      "                                                                                                  \n",
      " conv2_block1_1_conv (Conv2D)   (None, 56, 56, 64)   4160        ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block1_0_conv (Conv2D)   (None, 56, 56, 256)  16640       ['pool1_pool[0][0]']             \n",
      "                                                                                                  \n",
      " conv2_block1_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block1_0_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block1_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_0_bn[0][0]',      \n",
      "                                                                  'conv2_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block1_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block2_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block2_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block2_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block2_add (Add)         (None, 56, 56, 256)  0           ['conv2_block1_out[0][0]',       \n",
      "                                                                  'conv2_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block2_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_conv (Conv2D)   (None, 56, 56, 64)   16448       ['conv2_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv2_block3_1_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_1_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_2_conv (Conv2D)   (None, 56, 56, 64)   36928       ['conv2_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_2_bn (BatchNormal  (None, 56, 56, 64)  256         ['conv2_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_2_relu (Activatio  (None, 56, 56, 64)  0           ['conv2_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv2_block3_3_conv (Conv2D)   (None, 56, 56, 256)  16640       ['conv2_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv2_block3_3_bn (BatchNormal  (None, 56, 56, 256)  1024       ['conv2_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv2_block3_add (Add)         (None, 56, 56, 256)  0           ['conv2_block2_out[0][0]',       \n",
      "                                                                  'conv2_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv2_block3_out (Activation)  (None, 56, 56, 256)  0           ['conv2_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_conv (Conv2D)   (None, 28, 28, 128)  32896       ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block1_0_conv (Conv2D)   (None, 28, 28, 512)  131584      ['conv2_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block1_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block1_0_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block1_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_0_bn[0][0]',      \n",
      "                                                                  'conv3_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block1_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block2_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block2_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block2_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block2_add (Add)         (None, 28, 28, 512)  0           ['conv3_block1_out[0][0]',       \n",
      "                                                                  'conv3_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block2_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block3_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block3_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block3_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block3_add (Add)         (None, 28, 28, 512)  0           ['conv3_block2_out[0][0]',       \n",
      "                                                                  'conv3_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block3_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_conv (Conv2D)   (None, 28, 28, 128)  65664       ['conv3_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv3_block4_1_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_1_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_2_conv (Conv2D)   (None, 28, 28, 128)  147584      ['conv3_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_2_bn (BatchNormal  (None, 28, 28, 128)  512        ['conv3_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_2_relu (Activatio  (None, 28, 28, 128)  0          ['conv3_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv3_block4_3_conv (Conv2D)   (None, 28, 28, 512)  66048       ['conv3_block4_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv3_block4_3_bn (BatchNormal  (None, 28, 28, 512)  2048       ['conv3_block4_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv3_block4_add (Add)         (None, 28, 28, 512)  0           ['conv3_block3_out[0][0]',       \n",
      "                                                                  'conv3_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv3_block4_out (Activation)  (None, 28, 28, 512)  0           ['conv3_block4_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_conv (Conv2D)   (None, 14, 14, 256)  131328      ['conv3_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block1_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block1_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block1_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block1_0_conv (Conv2D)   (None, 14, 14, 1024  525312      ['conv3_block4_out[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block1_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_0_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_0_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block1_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block1_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_0_bn[0][0]',      \n",
      "                                )                                 'conv4_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block1_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block1_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block2_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block2_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block2_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block2_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block2_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block2_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block2_add (Add)         (None, 14, 14, 1024  0           ['conv4_block1_out[0][0]',       \n",
      "                                )                                 'conv4_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block2_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block2_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block3_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block3_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block3_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block3_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block3_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block3_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block3_add (Add)         (None, 14, 14, 1024  0           ['conv4_block2_out[0][0]',       \n",
      "                                )                                 'conv4_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block3_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block3_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block4_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block4_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block4_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block4_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block4_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block4_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block4_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block4_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block4_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block4_add (Add)         (None, 14, 14, 1024  0           ['conv4_block3_out[0][0]',       \n",
      "                                )                                 'conv4_block4_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block4_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block4_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block4_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block5_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block5_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block5_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block5_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block5_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block5_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block5_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block5_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block5_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block5_add (Add)         (None, 14, 14, 1024  0           ['conv4_block4_out[0][0]',       \n",
      "                                )                                 'conv4_block5_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block5_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block5_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_1_conv (Conv2D)   (None, 14, 14, 256)  262400      ['conv4_block5_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv4_block6_1_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_1_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_2_conv (Conv2D)   (None, 14, 14, 256)  590080      ['conv4_block6_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv4_block6_2_bn (BatchNormal  (None, 14, 14, 256)  1024       ['conv4_block6_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv4_block6_2_relu (Activatio  (None, 14, 14, 256)  0          ['conv4_block6_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv4_block6_3_conv (Conv2D)   (None, 14, 14, 1024  263168      ['conv4_block6_2_relu[0][0]']    \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_3_bn (BatchNormal  (None, 14, 14, 1024  4096       ['conv4_block6_3_conv[0][0]']    \n",
      " ization)                       )                                                                 \n",
      "                                                                                                  \n",
      " conv4_block6_add (Add)         (None, 14, 14, 1024  0           ['conv4_block5_out[0][0]',       \n",
      "                                )                                 'conv4_block6_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv4_block6_out (Activation)  (None, 14, 14, 1024  0           ['conv4_block6_add[0][0]']       \n",
      "                                )                                                                 \n",
      "                                                                                                  \n",
      " conv5_block1_1_conv (Conv2D)   (None, 7, 7, 512)    524800      ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block1_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block1_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block1_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block1_0_conv (Conv2D)   (None, 7, 7, 2048)   2099200     ['conv4_block6_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block1_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block1_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block1_0_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_0_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block1_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block1_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_0_bn[0][0]',      \n",
      "                                                                  'conv5_block1_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block1_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block1_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block1_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block2_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block2_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block2_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block2_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block2_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block2_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block2_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block2_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block2_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block1_out[0][0]',       \n",
      "                                                                  'conv5_block2_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block2_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block2_add[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_conv (Conv2D)   (None, 7, 7, 512)    1049088     ['conv5_block2_out[0][0]']       \n",
      "                                                                                                  \n",
      " conv5_block3_1_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_1_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_1_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_1_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_2_conv (Conv2D)   (None, 7, 7, 512)    2359808     ['conv5_block3_1_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_2_bn (BatchNormal  (None, 7, 7, 512)   2048        ['conv5_block3_2_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_2_relu (Activatio  (None, 7, 7, 512)   0           ['conv5_block3_2_bn[0][0]']      \n",
      " n)                                                                                               \n",
      "                                                                                                  \n",
      " conv5_block3_3_conv (Conv2D)   (None, 7, 7, 2048)   1050624     ['conv5_block3_2_relu[0][0]']    \n",
      "                                                                                                  \n",
      " conv5_block3_3_bn (BatchNormal  (None, 7, 7, 2048)  8192        ['conv5_block3_3_conv[0][0]']    \n",
      " ization)                                                                                         \n",
      "                                                                                                  \n",
      " conv5_block3_add (Add)         (None, 7, 7, 2048)   0           ['conv5_block2_out[0][0]',       \n",
      "                                                                  'conv5_block3_3_bn[0][0]']      \n",
      "                                                                                                  \n",
      " conv5_block3_out (Activation)  (None, 7, 7, 2048)   0           ['conv5_block3_add[0][0]']       \n",
      "                                                                                                  \n",
      " flatten (Flatten)              (None, 100352)       0           ['conv5_block3_out[0][0]']       \n",
      "                                                                                                  \n",
      " dense (Dense)                  (None, 3)            301059      ['flatten[0][0]']                \n",
      "                                                                                                  \n",
      "==================================================================================================\n",
      "Total params: 23,888,771\n",
      "Trainable params: 301,059\n",
      "Non-trainable params: 23,587,712\n",
      "__________________________________________________________________________________________________\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "from tensorflow.keras import applications\n",
    "from tensorflow.keras import Model\n",
    "from tensorflow.keras.layers import Flatten, Dense\n",
    "\n",
    "# Freeze the already-trained layers in the base model\n",
    "for layer in base_model.layers:\n",
    "    layer.trainable = False\n",
    "\n",
    "# Create prediction layer for classification of our images\n",
    "x = base_model.output\n",
    "x = Flatten()(x)\n",
    "prediction_layer = Dense(len(classnames), activation='softmax')(x) \n",
    "model = Model(inputs=base_model.input, outputs=prediction_layer)\n",
    "\n",
    "# Compile the model\n",
    "model.compile(loss='categorical_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "# Now print the full model, which will include the layers of the base model plus the dense layer we added\n",
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train the Model\n",
    "\n",
    "With the layers of the CNN defined, we're ready to train it using our image data. The weights used in the feature extraction layers from the base resnet model will not be changed by training, only the final dense layer that maps the features to our shape classes will be trained."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false,
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/3\n",
      "28/28 [==============================] - 76s 3s/step - loss: 2.4773 - accuracy: 0.5155 - val_loss: 0.3880 - val_accuracy: 0.8639\n",
      "Epoch 2/3\n",
      "28/28 [==============================] - 70s 3s/step - loss: 0.2944 - accuracy: 0.8893 - val_loss: 0.2471 - val_accuracy: 0.8889\n",
      "Epoch 3/3\n",
      "28/28 [==============================] - 82s 3s/step - loss: 0.1419 - accuracy: 0.9762 - val_loss: 0.1238 - val_accuracy: 0.9722\n"
     ]
    }
   ],
   "source": [
    "# Train the model over 3 epochs\n",
    "num_epochs = 3\n",
    "history = model.fit(\n",
    "    train_generator,\n",
    "    steps_per_epoch = train_generator.samples // batch_size,\n",
    "    validation_data = validation_generator, \n",
    "    validation_steps = validation_generator.samples // batch_size,\n",
    "    epochs = num_epochs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## View the loss history\n",
    "\n",
    "We tracked average training and validation loss for each epoch. We can plot these to verify that the loss reduced over the training process and to detect *over-fitting* (which is indicated by a continued drop in training loss after validation loss has levelled out or started to increase)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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": [
    "%matplotlib inline\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "epoch_nums = range(1,num_epochs+1)\n",
    "training_loss = history.history[\"loss\"]\n",
    "validation_loss = history.history[\"val_loss\"]\n",
    "plt.plot(epoch_nums, training_loss)\n",
    "plt.plot(epoch_nums, validation_loss)\n",
    "plt.xlabel('epoch')\n",
    "plt.ylabel('loss')\n",
    "plt.legend(['training', 'validation'], loc='upper right')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Evaluate model performance\n",
    "\n",
    "We can see the final accuracy based on the test data, but typically we'll want to explore performance metrics in a little more depth. Let's plot a confusion matrix to see how well the model is predicting each class."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating predictions from validation data...\n",
      "1/1 [==============================] - 3s 3s/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Tensorflow doesn't have a built-in confusion matrix metric, so we'll use SciKit-Learn\n",
    "import numpy as np\n",
    "from sklearn.metrics import confusion_matrix\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "print(\"Generating predictions from validation data...\")\n",
    "# Get the image and label arrays for the first batch of validation data\n",
    "x_test = validation_generator[0][0]\n",
    "y_test = validation_generator[0][1]\n",
    "\n",
    "# Use the model to predict the class\n",
    "class_probabilities = model.predict(x_test)\n",
    "\n",
    "# The model returns a probability value for each class\n",
    "# The one with the highest probability is the predicted class\n",
    "predictions = np.argmax(class_probabilities, axis=1)\n",
    "\n",
    "# The actual labels are hot encoded (e.g. [0 1 0], so get the one with the value 1\n",
    "true_labels = np.argmax(y_test, axis=1)\n",
    "\n",
    "# Plot the confusion matrix\n",
    "cm = confusion_matrix(true_labels, predictions)\n",
    "plt.imshow(cm, interpolation=\"nearest\", cmap=plt.cm.Blues)\n",
    "plt.colorbar()\n",
    "tick_marks = np.arange(len(classnames))\n",
    "plt.xticks(tick_marks, classnames, rotation=85)\n",
    "plt.yticks(tick_marks, classnames)\n",
    "plt.xlabel(\"Predicted Shape\")\n",
    "plt.ylabel(\"Actual Shape\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Use the trained model\n",
    "\n",
    "Now that we've trained the model, we can use it to predict the class of an image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 139ms/step\n",
      "circle\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from tensorflow.keras import models\n",
    "import numpy as np\n",
    "from random import randint\n",
    "import os\n",
    "%matplotlib inline\n",
    "\n",
    "# Function to predict the class of an image\n",
    "def predict_image(classifier, image):\n",
    "    from tensorflow import convert_to_tensor\n",
    "    # The model expects a batch of images as input, so we'll create an array of 1 image\n",
    "    imgfeatures = image.reshape(1, image.shape[0], image.shape[1], image.shape[2])\n",
    "\n",
    "    # We need to format the input to match the training data\n",
    "    # The generator loaded the values as floating point numbers\n",
    "    # and normalized the pixel values, so...\n",
    "    imgfeatures = imgfeatures.astype('float32')\n",
    "    imgfeatures /= 255\n",
    "    \n",
    "    # Use the model to predict the image class\n",
    "    class_probabilities = classifier.predict(imgfeatures)\n",
    "    \n",
    "    # Find the class predictions with the highest predicted probability\n",
    "    index = int(np.argmax(class_probabilities, axis=1)[0])\n",
    "    return index\n",
    "\n",
    "# Function to create a random image (of a square, circle, or triangle)\n",
    "def create_image (size, shape):\n",
    "    from random import randint\n",
    "    import numpy as np\n",
    "    from PIL import Image, ImageDraw\n",
    "    \n",
    "    xy1 = randint(10,40)\n",
    "    xy2 = randint(60,100)\n",
    "    col = (randint(0,200), randint(0,200), randint(0,200))\n",
    "\n",
    "    img = Image.new(\"RGB\", size, (255, 255, 255))\n",
    "    draw = ImageDraw.Draw(img)\n",
    "    \n",
    "    if shape == 'circle':\n",
    "        draw.ellipse([(xy1,xy1), (xy2,xy2)], fill=col)\n",
    "    elif shape == 'triangle':\n",
    "        draw.polygon([(xy1,xy1), (xy2,xy2), (xy2,xy1)], fill=col)\n",
    "    else: # square\n",
    "        draw.rectangle([(xy1,xy1), (xy2,xy2)], fill=col)\n",
    "    del draw\n",
    "    \n",
    "    return np.array(img)\n",
    "\n",
    "# Create a random test image\n",
    "classnames = os.listdir(os.path.join('data', 'shapes'))\n",
    "classnames.sort()\n",
    "img = create_image ((224,224), classnames[randint(0, len(classnames)-1)])\n",
    "plt.axis('off')\n",
    "plt.imshow(img)\n",
    "\n",
    "# Use the classifier to predict the class\n",
    "class_idx = predict_image(model, img)\n",
    "print (classnames[class_idx])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Learn More\n",
    "\n",
    "* [Tensorflow Documentation](https://www.tensorflow.org/tutorials/images/transfer_learning)"
   ]
  }
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