t81_558_class_09_3_transfer_cv.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_09_3_transfer_cv.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 9: Regularization: L1, L2 and Dropout**\n",
    "* Instructor: [Jeff Heaton](https://sites.wustl.edu/jeffheaton/), McKelvey School of Engineering, [Washington University in St. Louis](https://engineering.wustl.edu/Programs/Pages/default.aspx)\n",
    "* For more information visit the [class website](https://sites.wustl.edu/jeffheaton/t81-558/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Module 9 Material\n",
    "\n",
    "* Part 9.1: Introduction to Keras Transfer Learning [[Video]](https://www.youtube.com/watch?v=WLlP6S-Z8Xs&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_1_keras_transfer.ipynb)\n",
    "* Part 9.2: Popular Pretrained Neural Networks for Keras [[Video]](https://www.youtube.com/watch?v=ctVA1_46YEE&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_2_popular_transfer.ipynb)\n",
    "* **Part 9.3: Transfer Learning for Computer Vision and Keras** [[Video]](https://www.youtube.com/watch?v=61vMUm_XBMI&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_3_transfer_cv.ipynb)\n",
    "* Part 9.4: Transfer Learning for Languages and Keras [[Video]](https://www.youtube.com/watch?v=ajmAAg9FxXA&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_4_transfer_nlp.ipynb)\n",
    "* Part 9.5: Transfer Learning for Keras Feature Engineering [[Video]](https://www.youtube.com/watch?v=Dttxsm8zpL8&list=PLjy4p-07OYzulelvJ5KVaT2pDlxivl_BN) [[Notebook]](t81_558_class_09_5_transfer_feature_eng.ipynb)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Google CoLab Instructions\n",
    "\n",
    "The following code ensures that Google CoLab is running the correct version of TensorFlow."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Note: not using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "# Start CoLab\n",
    "try:\n",
    "    %tensorflow_version 2.x\n",
    "    COLAB = True\n",
    "    print(\"Note: using Google CoLab\")\n",
    "except:\n",
    "    print(\"Note: not using Google CoLab\")\n",
    "    COLAB = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Part 9.3: Transfer Learning for Computer Vision and Keras\n",
    "\n",
    "In this part we will make use of transfer learning to create a simple neural network that can recognize dog breeds.  To keep the example simple, we will only train for a handfull of breeds.  A much more advanced form of this model can be found at the [Microsoft Dog Breed Image Search](https://www.bing.com/visualsearch/Microsoft/WhatDog).\n",
    "\n",
    "To keep computation times to a minimum, we will make use of the MobileNet, which is built into Keras.  We will begin by loading the entire MobileNet and seeing how well it classifies with several test images.  MobileNet can classify 1,000 different images.  We will ultimatly extend it to classify image types that are not in its dataset, in this example 3 dog breeds.  However, we begin by classifying image types amoung those that it was trained on.  Even though our test images were not in its training set, the loaded neural network should be able to classify them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "import tensorflow.keras\n",
    "import matplotlib.pyplot as plt\n",
    "from tensorflow.keras.layers import Dense,GlobalAveragePooling2D\n",
    "from tensorflow.keras.applications import MobileNet\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.applications.mobilenet import preprocess_input\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We begin by downloading weights for a MobileNet trained for the imagenet dataset.  This will take some time to download the first time you train the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf.h5\n",
      "17227776/17225924 [==============================] - 1s 0us/step\n"
     ]
    }
   ],
   "source": [
    "model = MobileNet(weights='imagenet',include_top=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The loaded network is a Keras neural network, just like those that we've been working with so far.  However, this is a neural network that was trained/engineered on advanced hardware.  Simply looking at the structure of an advanced state-of-the-art neural network can be educational."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"mobilenet_1.00_224\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_1 (InputLayer)         [(None, 224, 224, 3)]     0         \n",
      "_________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)    (None, 225, 225, 3)       0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, 112, 112, 32)      864       \n",
      "_________________________________________________________________\n",
      "conv1_bn (BatchNormalization (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv1_relu (ReLU)            (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_1 (DepthwiseConv2D)  (None, 112, 112, 32)      288       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_bn (BatchNormaliza (None, 112, 112, 32)      128       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_relu (ReLU)        (None, 112, 112, 32)      0         \n",
      "_________________________________________________________________\n",
      "conv_pw_1 (Conv2D)           (None, 112, 112, 64)      2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_1_bn (BatchNormaliza (None, 112, 112, 64)      256       \n",
      "_________________________________________________________________\n",
      "conv_pw_1_relu (ReLU)        (None, 112, 112, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_pad_2 (ZeroPadding2D)   (None, 113, 113, 64)      0         \n",
      "_________________________________________________________________\n",
      "conv_dw_2 (DepthwiseConv2D)  (None, 56, 56, 64)        576       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_bn (BatchNormaliza (None, 56, 56, 64)        256       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_relu (ReLU)        (None, 56, 56, 64)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_2 (Conv2D)           (None, 56, 56, 128)       8192      \n",
      "_________________________________________________________________\n",
      "conv_pw_2_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_2_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_3 (DepthwiseConv2D)  (None, 56, 56, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_3 (Conv2D)           (None, 56, 56, 128)       16384     \n",
      "_________________________________________________________________\n",
      "conv_pw_3_bn (BatchNormaliza (None, 56, 56, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_pw_3_relu (ReLU)        (None, 56, 56, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_4 (ZeroPadding2D)   (None, 57, 57, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_4 (DepthwiseConv2D)  (None, 28, 28, 128)       1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_4_bn (BatchNormaliza (None, 28, 28, 128)       512       \n",
      "_________________________________________________________________\n",
      "conv_dw_4_relu (ReLU)        (None, 28, 28, 128)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_4 (Conv2D)           (None, 28, 28, 256)       32768     \n",
      "_________________________________________________________________\n",
      "conv_pw_4_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_4_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_5 (DepthwiseConv2D)  (None, 28, 28, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_5 (Conv2D)           (None, 28, 28, 256)       65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_5_bn (BatchNormaliza (None, 28, 28, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_5_relu (ReLU)        (None, 28, 28, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_6 (ZeroPadding2D)   (None, 29, 29, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_6 (DepthwiseConv2D)  (None, 14, 14, 256)       2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_bn (BatchNormaliza (None, 14, 14, 256)       1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_relu (ReLU)        (None, 14, 14, 256)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_6 (Conv2D)           (None, 14, 14, 512)       131072    \n",
      "_________________________________________________________________\n",
      "conv_pw_6_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_6_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_7 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_7 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_7_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_7_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_8 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_8 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_8_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_8_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_9 (DepthwiseConv2D)  (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_9 (Conv2D)           (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_9_bn (BatchNormaliza (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_9_relu (ReLU)        (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_10 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_10 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_10_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_10_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_11 (DepthwiseConv2D) (None, 14, 14, 512)       4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pw_11 (Conv2D)          (None, 14, 14, 512)       262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_11_bn (BatchNormaliz (None, 14, 14, 512)       2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_11_relu (ReLU)       (None, 14, 14, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_pad_12 (ZeroPadding2D)  (None, 15, 15, 512)       0         \n",
      "_________________________________________________________________\n",
      "conv_dw_12 (DepthwiseConv2D) (None, 7, 7, 512)         4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_bn (BatchNormaliz (None, 7, 7, 512)         2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_relu (ReLU)       (None, 7, 7, 512)         0         \n",
      "_________________________________________________________________\n",
      "conv_pw_12 (Conv2D)          (None, 7, 7, 1024)        524288    \n",
      "_________________________________________________________________\n",
      "conv_pw_12_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_12_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_dw_13 (DepthwiseConv2D) (None, 7, 7, 1024)        9216      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_pw_13 (Conv2D)          (None, 7, 7, 1024)        1048576   \n",
      "_________________________________________________________________\n",
      "conv_pw_13_bn (BatchNormaliz (None, 7, 7, 1024)        4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_13_relu (ReLU)       (None, 7, 7, 1024)        0         \n",
      "_________________________________________________________________\n",
      "global_average_pooling2d (Gl (None, 1024)              0         \n",
      "_________________________________________________________________\n",
      "reshape_1 (Reshape)          (None, 1, 1, 1024)        0         \n",
      "_________________________________________________________________\n",
      "dropout (Dropout)            (None, 1, 1, 1024)        0         \n",
      "_________________________________________________________________\n",
      "conv_preds (Conv2D)          (None, 1, 1, 1000)        1025000   \n",
      "_________________________________________________________________\n",
      "reshape_2 (Reshape)          (None, 1000)              0         \n",
      "_________________________________________________________________\n",
      "act_softmax (Activation)     (None, 1000)              0         \n",
      "=================================================================\n",
      "Total params: 4,253,864\n",
      "Trainable params: 4,231,976\n",
      "Non-trainable params: 21,888\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Just examining the above structure, several clues to neural network architecture become evident.\n",
    "\n",
    "Notice how some of the layers have zeros in their number of parameters. Items which are hyperparameters are always zero, nothing about that layer is learned.  The other layers have learnable paramaters that are adjusted as training occurs.  The layer types are all hyperparamaters, Keras will not change a convolution layer to a max pooling layer for you.  However, the layers that have parameters are trained/adjusted by the traning algorithm. Most of the parameters seen above are the weights of the neural network.\n",
    "\n",
    "Some of the parameters are maked as non-trainable.  These cannot be adjusted by the training algorithm.  When we later use transfer learning with this model we will strip off the final layers that classify 1000 items and replace with our 3 dog breed classification layer.  Only our new layers will be trainable, we will mark the existing layers as non-trainable.\n",
    "\n",
    "The Relu activation function is used throught the neural network.  Also batch and dropout normalization are used.  We cannot see the percent used for batch normalization, that might be specified in the origional paper.  Many deep neural networks are pyramid shaped, and this is the case for this one.  This neural network uses and expanding pyramid shape as you can see the neuron/filter counts expand from 32 to 64 to 128 to 256 to 512 and max out at 1,024.\n",
    "\n",
    "We will now use the MobileNet to classify several image URL's below.  You can add additional URL's of your own to see how well the MobileNet can classify."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'___________________________________________________________________________________________'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x1FF29362E88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[805]\n",
      "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n",
      "40960/35363 [==================================] - 0s 0s/step\n",
      "('n04254680', 'soccer_ball', 0.9999938)\n",
      "('n03530642', 'honeycomb', 3.8624116e-06)\n",
      "('n03255030', 'dumbbell', 4.442458e-07)\n",
      "('n02782093', 'balloon', 3.7038987e-07)\n",
      "('n04548280', 'wall_clock', 3.143911e-07)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'___________________________________________________________________________________________'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x1FF29362E88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[751]\n",
      "('n04037443', 'racer', 0.7131951)\n",
      "('n03100240', 'convertible', 0.100896776)\n",
      "('n04285008', 'sports_car', 0.0770768)\n",
      "('n03930630', 'pickup', 0.026353048)\n",
      "('n02704792', 'amphibian', 0.011636168)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'___________________________________________________________________________________________'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x1FF29362E88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[834]\n",
      "('n04350905', 'suit', 0.98860943)\n",
      "('n04591157', 'Windsor_tie', 0.004567416)\n",
      "('n10148035', 'groom', 0.0029069302)\n",
      "('n04479046', 'trench_coat', 0.0013268597)\n",
      "('n02992529', 'cellular_telephone', 0.00035402415)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'___________________________________________________________________________________________'"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x1FF29362E88>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[254]\n",
      "('n02110958', 'pug', 0.495279)\n",
      "('n02108915', 'French_bulldog', 0.39188728)\n",
      "('n02108089', 'boxer', 0.05534864)\n",
      "('n02108422', 'bull_mastiff', 0.021636527)\n",
      "('n02112706', 'Brabancon_griffon', 0.016767107)\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "from PIL import Image, ImageFile\n",
    "from matplotlib.pyplot import imshow\n",
    "import requests\n",
    "import numpy as np\n",
    "from io import BytesIO\n",
    "from IPython.display import display, HTML\n",
    "from tensorflow.keras.applications.mobilenet import decode_predictions\n",
    "\n",
    "IMAGE_WIDTH = 224\n",
    "IMAGE_HEIGHT = 224\n",
    "IMAGE_CHANNELS = 3\n",
    "\n",
    "images = [\n",
    "    \"https://cdn.shopify.com/s/files/1/0712/4751/products/SMA-01_2000x.jpg?v=1537468751\",\n",
    "    \"https://farm2.static.flickr.com/1394/967537586_87b1358ad3.jpg\",\n",
    "    \"https://sites.wustl.edu/jeffheaton/files/2016/07/jheaton_wustl1-262izm5-458x458.jpg\",\n",
    "    \"https://1.bp.blogspot.com/-0vGbvWUrSAA/XP-OurPTA4I/AAAAAAAAgtg/TGx6YiGBEGIMjnViDjvVnYzYp__DJ6I-gCLcBGAs/s320/B%252Bt%2525aMbJQkm3Z50rqput%252BA.jpg\"\n",
    "]\n",
    "\n",
    "\n",
    "def make_square(img):\n",
    "    cols,rows = img.size\n",
    "    \n",
    "    if rows>cols:\n",
    "        pad = (rows-cols)/2\n",
    "        img = img.crop((pad,0,cols,cols))\n",
    "    else:\n",
    "        pad = (cols-rows)/2\n",
    "        img = img.crop((0,pad,rows,rows))\n",
    "    \n",
    "    return img\n",
    "        \n",
    "for url in images:\n",
    "    x = []\n",
    "    ImageFile.LOAD_TRUNCATED_IMAGES = False\n",
    "    response = requests.get(url)\n",
    "    img = Image.open(BytesIO(response.content))\n",
    "    img.load()\n",
    "    img = img.resize((IMAGE_WIDTH,IMAGE_HEIGHT),Image.ANTIALIAS)\n",
    "    \n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    pred = model.predict(x)\n",
    "    \n",
    "    display(\"___________________________________________________________________________________________\")\n",
    "    display(img)\n",
    "    print(np.argmax(pred,axis=1))\n",
    "\n",
    "    lst = decode_predictions(pred, top=5)\n",
    "    for itm in lst[0]:\n",
    "        print(itm)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Overall, the neural network is doing quite well.  However, it does not classify me as a \"person\", rather I am classified as a \"suit\".  Similarly, my English Bulldog Hickory is classiified as a \"pug\".  This is likely because I am only providiing a closeup of his face."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For many applications, MobileNet might be entirely acceptable as an image classifier.  However, if you need to classify very specialized images that are not in the 1,000 image types supported by imagenet, it is necessary to use transfer learning."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Transfer\n",
    "\n",
    "It is possable to create your own image classification network from scratch.  This would take considerable time and resources.  Just creating a dog breed classifier would require many pictures of dogs, labeled by breed.  By using a pretrained neural network, you are tapping into knowldge already built into the lower layaers of the nerual network.  The transferred layers likely already have some notion of eyes, ears, feet, and fur.  These lower level concepts help to train the neural network to identify dog breeds.\n",
    "\n",
    "Next we reload the MobileNet; however, this time we set the *include_top* parameter to *False*. This instructs Keras to not load the final classification layers.  This is the common mode of operation for transfer learning.  We display a summary to see that the top classification layer is now missing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\jheaton\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\keras_applications\\mobilenet.py:207: UserWarning: `input_shape` is undefined or non-square, or `rows` is not in [128, 160, 192, 224]. Weights for input shape (224, 224) will be loaded as the default.\n",
      "  warnings.warn('`input_shape` is undefined or non-square, '\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.6/mobilenet_1_0_224_tf_no_top.h5\n",
      "17227776/17225924 [==============================] - 8s 0us/step\n",
      "Model: \"mobilenet_1.00_224\"\n",
      "_________________________________________________________________\n",
      "Layer (type)                 Output Shape              Param #   \n",
      "=================================================================\n",
      "input_2 (InputLayer)         [(None, None, None, 3)]   0         \n",
      "_________________________________________________________________\n",
      "conv1_pad (ZeroPadding2D)    (None, None, None, 3)     0         \n",
      "_________________________________________________________________\n",
      "conv1 (Conv2D)               (None, None, None, 32)    864       \n",
      "_________________________________________________________________\n",
      "conv1_bn (BatchNormalization (None, None, None, 32)    128       \n",
      "_________________________________________________________________\n",
      "conv1_relu (ReLU)            (None, None, None, 32)    0         \n",
      "_________________________________________________________________\n",
      "conv_dw_1 (DepthwiseConv2D)  (None, None, None, 32)    288       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_bn (BatchNormaliza (None, None, None, 32)    128       \n",
      "_________________________________________________________________\n",
      "conv_dw_1_relu (ReLU)        (None, None, None, 32)    0         \n",
      "_________________________________________________________________\n",
      "conv_pw_1 (Conv2D)           (None, None, None, 64)    2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_1_bn (BatchNormaliza (None, None, None, 64)    256       \n",
      "_________________________________________________________________\n",
      "conv_pw_1_relu (ReLU)        (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "conv_pad_2 (ZeroPadding2D)   (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "conv_dw_2 (DepthwiseConv2D)  (None, None, None, 64)    576       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_bn (BatchNormaliza (None, None, None, 64)    256       \n",
      "_________________________________________________________________\n",
      "conv_dw_2_relu (ReLU)        (None, None, None, 64)    0         \n",
      "_________________________________________________________________\n",
      "conv_pw_2 (Conv2D)           (None, None, None, 128)   8192      \n",
      "_________________________________________________________________\n",
      "conv_pw_2_bn (BatchNormaliza (None, None, None, 128)   512       \n",
      "_________________________________________________________________\n",
      "conv_pw_2_relu (ReLU)        (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_3 (DepthwiseConv2D)  (None, None, None, 128)   1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_3_bn (BatchNormaliza (None, None, None, 128)   512       \n",
      "_________________________________________________________________\n",
      "conv_dw_3_relu (ReLU)        (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_3 (Conv2D)           (None, None, None, 128)   16384     \n",
      "_________________________________________________________________\n",
      "conv_pw_3_bn (BatchNormaliza (None, None, None, 128)   512       \n",
      "_________________________________________________________________\n",
      "conv_pw_3_relu (ReLU)        (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "conv_pad_4 (ZeroPadding2D)   (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_4 (DepthwiseConv2D)  (None, None, None, 128)   1152      \n",
      "_________________________________________________________________\n",
      "conv_dw_4_bn (BatchNormaliza (None, None, None, 128)   512       \n",
      "_________________________________________________________________\n",
      "conv_dw_4_relu (ReLU)        (None, None, None, 128)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_4 (Conv2D)           (None, None, None, 256)   32768     \n",
      "_________________________________________________________________\n",
      "conv_pw_4_bn (BatchNormaliza (None, None, None, 256)   1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_4_relu (ReLU)        (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_5 (DepthwiseConv2D)  (None, None, None, 256)   2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_bn (BatchNormaliza (None, None, None, 256)   1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_5_relu (ReLU)        (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_5 (Conv2D)           (None, None, None, 256)   65536     \n",
      "_________________________________________________________________\n",
      "conv_pw_5_bn (BatchNormaliza (None, None, None, 256)   1024      \n",
      "_________________________________________________________________\n",
      "conv_pw_5_relu (ReLU)        (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "conv_pad_6 (ZeroPadding2D)   (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_6 (DepthwiseConv2D)  (None, None, None, 256)   2304      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_bn (BatchNormaliza (None, None, None, 256)   1024      \n",
      "_________________________________________________________________\n",
      "conv_dw_6_relu (ReLU)        (None, None, None, 256)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_6 (Conv2D)           (None, None, None, 512)   131072    \n",
      "_________________________________________________________________\n",
      "conv_pw_6_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_6_relu (ReLU)        (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_7 (DepthwiseConv2D)  (None, None, None, 512)   4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_7_relu (ReLU)        (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_7 (Conv2D)           (None, None, None, 512)   262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_7_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_7_relu (ReLU)        (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_8 (DepthwiseConv2D)  (None, None, None, 512)   4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_8_relu (ReLU)        (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_8 (Conv2D)           (None, None, None, 512)   262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_8_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_8_relu (ReLU)        (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_9 (DepthwiseConv2D)  (None, None, None, 512)   4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_9_relu (ReLU)        (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_9 (Conv2D)           (None, None, None, 512)   262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_9_bn (BatchNormaliza (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_9_relu (ReLU)        (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_10 (DepthwiseConv2D) (None, None, None, 512)   4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_10_relu (ReLU)       (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_10 (Conv2D)          (None, None, None, 512)   262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_10_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_10_relu (ReLU)       (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_11 (DepthwiseConv2D) (None, None, None, 512)   4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_11_relu (ReLU)       (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_11 (Conv2D)          (None, None, None, 512)   262144    \n",
      "_________________________________________________________________\n",
      "conv_pw_11_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_pw_11_relu (ReLU)       (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_pad_12 (ZeroPadding2D)  (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_dw_12 (DepthwiseConv2D) (None, None, None, 512)   4608      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_bn (BatchNormaliz (None, None, None, 512)   2048      \n",
      "_________________________________________________________________\n",
      "conv_dw_12_relu (ReLU)       (None, None, None, 512)   0         \n",
      "_________________________________________________________________\n",
      "conv_pw_12 (Conv2D)          (None, None, None, 1024)  524288    \n",
      "_________________________________________________________________\n",
      "conv_pw_12_bn (BatchNormaliz (None, None, None, 1024)  4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_12_relu (ReLU)       (None, None, None, 1024)  0         \n",
      "_________________________________________________________________\n",
      "conv_dw_13 (DepthwiseConv2D) (None, None, None, 1024)  9216      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_bn (BatchNormaliz (None, None, None, 1024)  4096      \n",
      "_________________________________________________________________\n",
      "conv_dw_13_relu (ReLU)       (None, None, None, 1024)  0         \n",
      "_________________________________________________________________\n",
      "conv_pw_13 (Conv2D)          (None, None, None, 1024)  1048576   \n",
      "_________________________________________________________________\n",
      "conv_pw_13_bn (BatchNormaliz (None, None, None, 1024)  4096      \n",
      "_________________________________________________________________\n",
      "conv_pw_13_relu (ReLU)       (None, None, None, 1024)  0         \n",
      "=================================================================\n",
      "Total params: 3,228,864\n",
      "Trainable params: 3,206,976\n",
      "Non-trainable params: 21,888\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "base_model=MobileNet(weights='imagenet',include_top=False) #imports the mobilenet model and discards the last 1000 neuron layer.\n",
    "\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will add new top layers to the neural network.  Our final SoftMax layer includes support for 3 classes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=base_model.output\n",
    "x=GlobalAveragePooling2D()(x)\n",
    "x=Dense(1024,activation='relu')(x) \n",
    "x=Dense(1024,activation='relu')(x) \n",
    "preds=Dense(3,activation='softmax')(x) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we mark the origional MobileNet layers as non-trainable and our new layers as trainable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Model(inputs=base_model.input,outputs=preds)\n",
    "\n",
    "for layer in model.layers[:20]:\n",
    "    layer.trainable=False\n",
    "for layer in model.layers[20:]:\n",
    "    layer.trainable=True"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To train the neural network we must create a directory structure to hold the images.  The Keras command **flow_from_directory** performs this for us.  It requires that a folder be laid out as follows:\n",
    "\n",
    "image\n",
    "\n",
    "Each class is a folder that contains images of that class.  We can also specify a target size, in this case the origional MobileNet size of 224x224 is desired."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "if COLAB:\n",
    "    PATH = \"\"\n",
    "else:\n",
    "    PATH = \"./data/transfer\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[WinError 3] The system cannot find the path specified: 'c:\\\\jth\\\\data\\\\trans'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-3b05634ea710>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m      6\u001b[0m                                                  \u001b[0mbatch_size\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m                                                  \u001b[0mclass_mode\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'categorical'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m                                                  shuffle=True)\n\u001b[0m",
      "\u001b[1;32m~\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\keras_preprocessing\\image\\image_data_generator.py\u001b[0m in \u001b[0;36mflow_from_directory\u001b[1;34m(self, directory, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation)\u001b[0m\n\u001b[0;32m    538\u001b[0m             \u001b[0mfollow_links\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mfollow_links\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    539\u001b[0m             \u001b[0msubset\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0msubset\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 540\u001b[1;33m             \u001b[0minterpolation\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0minterpolation\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    541\u001b[0m         )\n\u001b[0;32m    542\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Miniconda3\\envs\\tensorflow\\lib\\site-packages\\keras_preprocessing\\image\\directory_iterator.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, directory, image_data_generator, target_size, color_mode, classes, class_mode, batch_size, shuffle, seed, data_format, save_to_dir, save_prefix, save_format, follow_links, subset, interpolation, dtype)\u001b[0m\n\u001b[0;32m    104\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mclasses\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    105\u001b[0m             \u001b[0mclasses\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 106\u001b[1;33m             \u001b[1;32mfor\u001b[0m \u001b[0msubdir\u001b[0m \u001b[1;32min\u001b[0m \u001b[0msorted\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlistdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    107\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0misdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdirectory\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msubdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    108\u001b[0m                     \u001b[0mclasses\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msubdir\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [WinError 3] The system cannot find the path specified: 'c:\\\\jth\\\\data\\\\trans'"
     ]
    }
   ],
   "source": [
    "train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input) \n",
    "\n",
    "train_generator=train_datagen.flow_from_directory('c:\\\\jth\\\\data\\\\trans', \n",
    "                                                 target_size=(224,224),\n",
    "                                                 color_mode='rgb',\n",
    "                                                 batch_size=1,\n",
    "                                                 class_mode='categorical',\n",
    "                                                 shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to compile and fit the neural network.  Notice we are using **fit_generator** rather than **fit**.  This is because we are using the convienent **ImageDataGenerator**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy'])\n",
    "\n",
    "step_size_train=train_generator.n//train_generator.batch_size\n",
    "model.fit_generator(generator=train_generator,\n",
    "                   steps_per_epoch=step_size_train,\n",
    "                   epochs=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We are now ready to see how our new model can predict dog breeds.  The URLs in the code below provide several example dogs to look at.  Feel free to add your own."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "from PIL import Image, ImageFile\n",
    "from matplotlib.pyplot import imshow\n",
    "import requests\n",
    "import numpy as np\n",
    "from io import BytesIO\n",
    "from IPython.display import display, HTML\n",
    "from tensorflow.keras.applications.mobilenet import decode_predictions\n",
    "\n",
    "IMAGE_WIDTH = 224\n",
    "IMAGE_HEIGHT = 224\n",
    "IMAGE_CHANNELS = 3\n",
    "\n",
    "images = [\n",
    "    \"https://upload.wikimedia.org/wikipedia/commons/thumb/a/a8/02.Owczarek_niemiecki_u%C5%BCytkowy_kr%C3%B3tkow%C5%82osy_suka.jpg/2560px-02.Owczarek_niemiecki_u%C5%BCytkowy_kr%C3%B3tkow%C5%82osy_suka.jpg\",\n",
    "    \"https://upload.wikimedia.org/wikipedia/commons/5/51/DSHwiki.jpg\",\n",
    "    \"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e5/Axel%2C_the_English_Bulldog.jpg/440px-Axel%2C_the_English_Bulldog.jpg\",\n",
    "    \"https://1.bp.blogspot.com/-0vGbvWUrSAA/XP-OurPTA4I/AAAAAAAAgtg/TGx6YiGBEGIMjnViDjvVnYzYp__DJ6I-gCLcBGAs/s320/B%252Bt%2525aMbJQkm3Z50rqput%252BA.jpg\",\n",
    "    \"https://thehappypuppysite.com/wp-content/uploads/2017/12/poodle1.jpg\",\n",
    "    \"https://upload.wikimedia.org/wikipedia/commons/thumb/4/40/Pudel_Grossschwarz.jpg/440px-Pudel_Grossschwarz.jpg\"\n",
    "    \n",
    "]\n",
    "\n",
    "\n",
    "def make_square(img):\n",
    "    cols,rows = img.size\n",
    "    \n",
    "    if rows>cols:\n",
    "        pad = (rows-cols)/2\n",
    "        img = img.crop((pad,0,cols,cols))\n",
    "    else:\n",
    "        pad = (cols-rows)/2\n",
    "        img = img.crop((0,pad,rows,rows))\n",
    "    \n",
    "    return img\n",
    "        \n",
    "for url in images:\n",
    "    x = []\n",
    "    ImageFile.LOAD_TRUNCATED_IMAGES = False\n",
    "    response = requests.get(url)\n",
    "    img = Image.open(BytesIO(response.content))\n",
    "    img.load()\n",
    "    img = img.resize((IMAGE_WIDTH,IMAGE_HEIGHT),Image.ANTIALIAS)\n",
    "    \n",
    "    x = image.img_to_array(img)\n",
    "    x = np.expand_dims(x, axis=0)\n",
    "    x = preprocess_input(x)\n",
    "    pred = model.predict(x)\n",
    "    \n",
    "    display(\"___________________________________________________________________________________________\")\n",
    "    display(img)\n",
    "    print(np.argmax(pred,axis=1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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