t81_558_class_09_3_transfer_cv.ipynb

{
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   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "adgKJqiH_kqH"
   },
   "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": {
    "colab_type": "text",
    "id": "g5PUxV0G_kqI"
   },
   "source": [
    "# T81-558: Applications of Deep Neural Networks\n",
    "**Module 9: Transfer Learning**\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": {
    "colab_type": "text",
    "id": "B4w5DSec_kqI"
   },
   "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": {
    "colab_type": "text",
    "id": "0FSTWl0k_kqJ"
   },
   "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": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 51
    },
    "colab_type": "code",
    "id": "VzgSaYfg_kqJ",
    "outputId": "2328b430-e7b5-4728-ddda-7d77ee9af5a2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mounted at /content/drive\n",
      "Note: using Google CoLab\n"
     ]
    }
   ],
   "source": [
    "# Start CoLab\n",
    "try:\n",
    "  from google.colab import drive\n",
    "  %tensorflow_version 2.x\n",
    "  drive.mount('/content/drive', force_remount=True)\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": {
    "colab_type": "text",
    "id": "Fi5C1O0n_kqM"
   },
   "source": [
    "# Part 9.3: Transfer Learning for Computer Vision and Keras\n",
    "\n",
    "In this part, we will use transfer learning to create a simple neural network that can new images that are not in MobileNet.  To keep the example simple, we will only train for older storage technologies, such as floppy disks, tapes, etc.  This dataset can be downloaded from the following location: [download link](https://data.heatonresearch.com/data/t81-558/images/trans.zip).\n",
    "\n",
    "To keep computation times to a minimum, we will make use of the MobileNet included in 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 ultimately extend it to classify image types that are not in its dataset, in this example, four media types.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "qAcy9Fe0_kqM"
   },
   "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": {
    "colab_type": "text",
    "id": "hBuItb--_kqO"
   },
   "source": [
    "We begin by downloading weights for a MobileNet trained for the imagenet dataset, which will take some time to download the first time you train the network."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "mrcXOkpn_kqO"
   },
   "outputs": [],
   "source": [
    "model = MobileNet(weights='imagenet',include_top=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "5tuLNvrH_kqQ"
   },
   "source": [
    "The loaded network is a Keras neural network.  However, this is a neural network that a third party engineered on advanced hardware. Merely looking at the structure of an advanced state-of-the-art neural network can be educational."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "IarnTqeK_kqQ",
    "outputId": "985c8ee4-232c-4c17-95f6-640b1079e1c0"
   },
   "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",
      "predictions (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": {
    "colab_type": "text",
    "id": "-w5uM_BX_kqT"
   },
   "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. The summary always displays hyperparameters as zero.  The neural network fitting process does not change hyperparameters.  The other layers have learnable parameters that are adjusted as training occurs.  The layer types are all hyperparameters; Keras will not change a convolution layer to a max-pooling layer.  However, the layers that have parameters are trained/adjusted by the training algorithm. Most of the parameters seen above are the weights of the neural network.\n",
    "\n",
    "The programmer can configure some of the parameters as non-trainable.  The training algorithm cannot adjust these.  When we later use transfer learning with this model, we will strip off the final layers that classify 1000 items and replace them with our four media types.  Only our new layers will be trainable; we will mark the existing layers as non-trainable.\n",
    "\n",
    "This neural network makes extensive use of the Relu activation function.  Relu is a common choice for activation functions. Also, the neural network makes use of batch and dropout normalization.  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 grow 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": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 705
    },
    "colab_type": "code",
    "id": "WDQR4VHM_kqU",
    "outputId": "3174ad78-24ed-4eac-8800-a4ccdeeab5a1"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'_________________________________________________________'"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
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DRGYKU04BPoyq11KqdAE0uQiJ8GYmIjOzs+Vg8KY3vPG8iy956lOfeuljL9m7e/fMTFdWVvJWu64f2HrYqgQ1s35/IKKmury8/OUvf/mzn732KzfeyKZve/vbGE4gbIxRcrJ6mW42qw0/UhOmNdYxLb1NKFRb05SZGon5RDBVUyMDuNXr9mZm6v4OY7Jx81zdAKrVg2ZIddNVrWflU9SOav2q6gQaQ6vV0wGYJp2BmQf9/qWPvXRpYf6zn/vcwfvvuummyx5z2ePOP/+sc885n9nt2r2HtmaGaasStCiKsiyOHzt2+223f+GLX/zqV75y4PY7iuHwX7z65Ys794QQKPX3gFLAcrIUb4ZK0bSkAY2bWN78NANEtaohAmpWV0VPyTk0g0bL81yTc2lavXqzC9i0w9VCwshyJ3G1eh5Z8lmSMmUmI9tZPa3xRyvpKYQwO794yaWXfP2uB9b6w6/fcvPy2tp9d+87/Mgjlz32spnZuZnZ2W/9ZXjosfUImph09NjRW2+5+Ss3fvnWW2994IEHDh06PCyHsSx27d6T+WxQFs6ovobfZB1PvzARbNOhT34O6jCImRr3tBbqx8JyJjNCXFte8Vmr15tlqMHEqE64A02UlrKZlRElAjeKQhXAAdEAGNeZAkq1qlUlwOgEFY4BBqJFUwX5RzziIiv/QkJ3MBisLi/fo7a+sn7fwfuvOH7iGc/47vFbb6tgixE0XZyv3fTVT11z9T13333o8KHV1ZXjy8fX1tZDWc62/SMueqSZEQMnBeD1Aj1CXenUHLl+uApotHZYK3GqZiSNOQbJDBIRQR3Z3Pzc3NycqgKkqYoUlvzRcdIrweqSO6ttO8ZkV+9ItYmuKh81nUIlkZpLIpdCzZRgZGYa9p+/v5XnRRnaUQb9PsyEaCBhbWV5ptd98lVPPfn2m3hsJYKmL/eBg/e998///PDxY6srqxvrG2trG2trG0VZglyEo9rROil4TyFRHW188/z7pvdC42SOnMgxfR6NzWtiJlUV1XJYtLteLM2/UYzEqNojaOTXypEdyfiNNW/eyEZ/TOWjrCaAEvn6gOkYINAwhF27ds3Mzq4VZSyDmBWhXF1dZaajKn/5V3+1/9zz9uzZu7U4uvUc5w/97/99z333LK8sr62t9/v9/vqGFBFqZRnOOmv/3r1nioTUVNRc26Ya9BvRuJ6N/WxCperPTca9Npla3yqVZVVThRkUvH7ieAhl05rXEM6w2ZWoBQEzUzM2TUbezNREUwVglZU1QImVyGCVNTUwERgCGLNLCVliMpG81XYExDLGUlXNUJbFxsb6YFjef889H/rg//6WXopvB7YMQZNRueP22667/rphWQzW10Moh2VRhjKqiJnP/YP33xuKorZKm/Qdqtf6hqsNYxvBHCcJouPxTVMgMqZrVoCpRtVgpvOLS72ZWR1LODXkruPuynymStJqzg0AVHFVOqc6eosVt43NCFCDMMMxpYrR+pnpjVhiXFxaOmPv3lJEo2iIDIJZf6M/6Pe1GH76mqvvuONOGrF/C2DLEDTx6SMf+9jKylo5LMsQYgyxKGMMpkKwclhcdPFFizu2hTKMyjXq146vayNqjR25zouPHIDx1zaqJtXV9zpKjWryD41JYoxlOX58QyqMbo5PozNI5XzERq56AoFS8x5gcACn3ujqbasbBFDhRNC0ulvFciZnhsGgMO+joZQYNSYGlyrq/Mra2kc++mFUOdWtga1B0GQ+77/vnhs+fx3ByjKGGMsyxDKIqomkTp3BoDByY40ZmziaDtUwdXzNb8IgqhND1pTVWbVA64jZquklSTpNtUcgb1hfPTYYbGw+85pIQJWaRFVxBSJiTknaJl2qBoVytQpwHd+LVYpSSqyywaXIjKApG6UwkaHPMs9EsQRMRQiW4iqRIBJyx1+49m8P3nsPb14rJhlbg6AJV199zYnlFVEJsVBBCFKomjohNjIjbWWe6xzO+PKKsWT6mHY5ysg3uSUeWdKKqmKaviOqXcnmcDBTVVEAMIjAZrbtyjs9idKcc/P2VS/c2M3TRGzpRAxVwG9wZFK9ps5hEY2tAMTCXN0foNSIx8wAt1ut3Xt3Az7lJFSVU6m1QkMk5tWV5WuuuRqjt550bAGCmhkzr6+vX3fdF8A+loWIBCljLAyqMCI2OI1xx46dWavViOEjHtTHQRPf1M+o3qPxR5vGjJo1I1EpOQBmVCeKrHojNRNTi2KqVcbSzCr3spY2qym39eG5Fq0agz3mcxjAVtnLmsFVfYASLI1CMZjWhc+pfIpdZkSHDh9VIxOVWEoZWIQkmGoABRHO8ms/9/mNjX7KnD1kF+1bhq1BUAA33njjwYP3EZlEM6MYpAouVEWEmVpZduvXb1YRbmrhmsxMHR6NH7B+uF5167dLJjZ1rVut8ifCUR3ONK9NU0lEYhlCWRSHDx4c9Pt19T0171U1fFiVVq+7PVJ2KHm1dRxmRgpND9TnAwPBQZ3CA8R1EFZ9KFM2ZcCgWZbtP+ccE4tm0RBBARQU0VREQox5p/PgA/fe+OUvYYsY0S1A0HSlP/vZz4pGqEbRKKoKkzS42JiYDDHIoy6+mL0/qaxzzE5+8/Ao5TC1CWtqSqXjVD0elUReRfHjbkOMEoKUReyvrt1z93394fDIkSNFUdiYMUZavqs4nWCQ0S1E1T9px4XqvtDKaQVMjRggIRbiyqZTXamiKR6vOkZIFGLKzkhhihhDiFEBEQ0xBlGNmjG++IXrmo8/4Zh0oT4ZiaNHj958yy3e+VAWYtG0ZBMjUhVHJKAoqsCwHCbrVYci1bhN2yxNn6TA25h9bZ6PWqtqkkYAFJXrmR6OImknkH6//+CDR77w+es+/pGPXvO3f/vYyx77pCc9aX5pKW/5VquNWlVVGOoqp3HZK8lgBqrukiakqp9qo7I9S8ehdCBzsMTR5AinRMaRYRSfWcowpSJZgqmkZFPstNoHDhw4fvzY0tK2k76ZCcTWIOhXv/qV5RPHmSiqEXuVUgyqxABBxURMnfNf+epNG6trWbeTRhWPH8FO0tvHlsjxlS71yDeRyUncTYdLLBWVGEIZwvr6+nve/Z6Pf+Jjh44cGQyHN3z5xo986COXXfLYV/3wDz/y0guZmchV29GkUSJjB6dao0Uy1gSAAVIAplWPJ1XOKKWRJON3DMVUBmBIJabqHO3eNU8qahCNqlAzUSUYmFVCVInUPnZi+fbbb3viE6cE/ScjfX033ngjTJ2l2jaDmiE5Wik5bmaaZfnS0jZYmohcR+uptm3zMU9a4jfZzrEQm2pFvfEZ6gyQRZEYYzkcHj9+/I/f9Wd/8vb/1ZrpUJYBIM8bZf8TV388qDz/e5//HU97qhnmZme9H+2EZPXBNxls1Ml51IqsVTkwsxR8UdU3r0iJI1MYGVGqbIaCQgjLx5eZAIIoylKcEyJycKoaQhQpyXITvuWWW5/4xCsmnJ2YcIKm67e+vn7nbbeRc1Fj6kEyMxWBKKmZKFQdcRRZW1stiyLrddWUCKpVsb2p8pgRHT948/NYlgiqxo3YNGY+yaCmUaUcFjGGW2657W8+es0nP/GxvMNwbjgcigiZqqlv5Vdf/YnrPv/517zmNd/z7Ke3W+eaeeccN2lQAOkuwvgKPvrsm86tyg+oUdXbaQqldKoxdTIpIsPCsDh67ASISCNZBo0inpi0biwZDAYzM71ut3vgwIFhUbRbk17IvAUIetdddx05csibDmI0IYoGKJuxWmkazYxZowC448B9dx64+/Ldu/vlIFXbqcg4IcyqmotGzaxj5FEspTquOMLGOi1URVRjiIP+xg03fPHXf/U3b7v15pnFGXPtWJZVVagk64fOzMz6xvp73vMXpPqEK1YffemjGK7TaTMzo6qop1qLrW0z7KSSFFSeZeWyKhQg0uSvkim08hokRmJ/YvnwsdU15zMG1FRN2cQZW9I6zIx8GaVrevTo0Qfuv3//uedqXeo6mZjcM2tw++23DQcDqV0tgUW1IBLJxKpUosKI0C8Gt3z9diKvKjaa0QGkjE7SLCHY9BcoLOVb0q9U171XRKuzmiGEoixXVlaPHj3y3vd98A/e/u7b77pjdvt8MAoaDRCFKkVRUVWzoiy9d4cOP/Brv/7rP/1T//ajH/zo8tEjIhJCiKbaRE7VG9XxWNOO16hOI/EBBgWkkrfERE0SDWOMZejM9r7yxRsOHjrKjoOqmYmqRlNDMA0iMJhIf2NDi3K4MThw14GH/ur9UzHRBE1X6o7bb09z4NN8V1GJadSGGalRNERJJI2wz3z6M2apSSmSjodKqipJPmoutzVa0kh7oir7U+mfFUGrgD3G9Y2Vv/iL9/7nX/7VG774uW6vEwIohdJqqkFiIRLSiK+kd4LQnuncdc/dv/2W3/njd//Z3ffcE2MshgNVbdTQRrU1M6im1LoBKYmfzm3sOazKqioqUSyKRdEIco4fvPfAb/3GbxUhspGKiigAAWJdkCIxKmBq/VAEi/fefQ8w6WLT5C7xaf0tiuH9991LzldDZ1JxLqpG3WgmsBSocqTubO/LX/vSHbfcvH3XXhUjP1bWpArmuuw3GafRGwHVWttE1Q17DFCRoij6GxsH77/v7X/w7i99+fp27rI8CwKCIyIIJEoU1SgqogwyiJJjJmKJkndbB+458Nbf+p3rrr/h9a97zWWXXcLOKUXn/CaJACCiqrqqPjkyTSpsyoMmplYtyqJEGmIcDMoTx4+gDM/87u+558FDh4+ttDp5uqPER6/OjNUsmqoImQ9BWh168OADoQxZnk1yLD+5BE04euTIieVl730a4BpUVZXUoBKjxfQfS6EDtdr5V79+66c++okXv/xlqydWspzzVifvdFIRBkSompdk40OKx1LyjQKlpim+1hBiWYbhcHDrLbf+3lt/7+qrr5md63LeimbsnBlFEdWYgiczVYKopvtATJ0yMxPIO+cy++y1nxIJr/ihl/+zZ3xHu90yg/ebOJpMO2s9bCRFTmp1giAJmxEgNSuKIhaD5ePHZrq9/efsz/L2FU//ride+YSXvPTl6/3B/MxMjDGKZFHgUxOKkgRDJ5LTKEcOHTyxfGLnzp0P3+X9P2NyCZpu60OHDxfFsLIrySckUhU1TWYxbT7MzAYcu+fQf/qZn/2h172WyFqd9mBtdWN1dXVtJeNsdmGBmEWkEYtOlutHbwzUliyEMBgMVlaWP/qJq9/xv/742OEHZpfmHDuAlB2Mk3YvplGDqtYzv1Bt8EUkVKUMQFRE7XQ6N3zpi7ffetv1X3jB97/s+y+84LwYA7FzzlW6WGp1h0M9SVSrXRhjKkAmpihaDIdx2D9x7Oj8tl3nXfQYn5EZLMqhg/c9/Vnf87a3/e7P/vx/OHz4mDqNMcYsa4sQM1kWYsxU2gYzW+tvHD784M6dO6cW9B+PI0eOqJpzKRsZYVAjIRKDipqoioBAzq2eWPmVX/j3r//xNw/LIak6ptmlpdlt2war6/211WMPHlTF0u6dIAeQahqMiCRxnhTGNuwsi3D48IN//f4PvfMdf7IxXGbOHflIcHCkzkw0NT6risRUPVTr+WiCNBFRM3ZMzKbw3g/K4p3veMfnr7v21T/8I8953jNb7ZaZeedqf4QMAlMxAqQeeGZmFmIoBsON9fXh2tq+/ft3n3k2M0WJaTdRR5R5d+zI0e992Us1yktf9cOLS4uuZMmy6Nilan2mEEJRbLRzX4APHT786G/j1fxHYNIJunzsGDMDRqrOjGEaBVFNLcZoqjDizA3X1//9T/3U63/8zRvrK8zOmDUqQgRx3u125+cHa+sb6+vraxvD9dWlnXuyVg5iESFSAld6fso6qqlpWcaN/vrNN9/y1t9+y41fvsm0dJw532ZKWW5OZSpmac8Yg6Z58mhaNMeKPznlDOrJzqYW2p3sjgN3/PzP/8JNN339ta9/1a5duyPITFNZqJKYmGhqe+c46JexjGVRbKzOzi+dcda+dqsNUBQJRQwxqEaYCDt2jjL34H2Hnvm8F7z6pd/3rve9P8t8GQvyzkmWOzXKLNXbx5B7t7qy8jBf4P8TJpegadFZWTnhnFPTlENhGAEiUVSS2+dzf/zQ4Z/+yZ9488/82/W1ldQ1R6pSd2PGEEJZujzbsWd3KIrVLA/l4Oihg/Pzi935eSKnokgVJ5xqREQk9ger137m+t/6zd+65bZbW62W85nzOTlnVfmnGEwYIlrXNVd573qZ3pSmUlUGCwTVIFyOouw8s73jne84sXz89a9/zfnnX0BMBmVypholDjb6EiMRmwRiXty2vX3m2XkrE4kSo4ioaowqEtPOTAYzCIOZtIyD//iff+ULX/zyrfffm/n5GGJwUmqUyC7LmFgMRjTo9zHZgfykE7Q/GDqfSVFGTYKSiko0TdeHiNf761c84fJ/9cY3DvsbVE3q2JQlqkIgs3I4IOZtu3YD0XtfDIvl48cQpbew4LPcYCpiBhFZX19/61ve/md//heD4Uqr3fLeZ75FzptBqSpHMlEy46Q1iqilXeTNqq3hU+977eCqiYmpsnONMK6iUPUZfeLjH73l5q9913c9+1/8yCvmF2ai6HBtrYhBQtHKOzt27szbrRTFq1oxKGBJCpUopiqp9EnZp1S/wsGh3+8vbd/+khd/7y/9+m8obDAYeO9Lz20FGUQEpq0sL4oCU4L+o6GiRGh7iv3goYEoqqpExBCjMFFRlOcs7vi93/+f+cxMMeizc7VKNEIia0rPsKEshwQsbNthQCgGJw4d6a8uc5b1enNgB7K1jZX/9l//2zvf+a5Otw2wucxlLWOXWK5MWnWxKdSqeXomUYNEMENUoEibgY60AmoKQjRt4s3MIIgZqQUpD9x151t++zcI4QUvfMGO7QuIYdu27bMLC2yQGEOMVUbCTGKAwtipsVlQVSImJjKtP20BZefyE8srP/rGH7v/4L1/8lfv73TaZTHMstzByDSakfPGzG7Sd66ZbKGeeX5+vt1qZ3lO3qdLFERCjDFG51wI5Ytf/H37zrugv77eDFdq0Byn+dlqCbEsy1iWWZbv2X/e7jPPyFy2cuzYxtrKPbfd8vu/9dsf+OBHOt0cBCaX+ZzIkbEppVYkZ0SiSfq30WEVHEOMKa2VCvPSKgzAtKrvVK3cABVNUX+iOIDuTOutv/v7n/ybq3fv3nvWeefPzi+EshwUw1JiVBG1KBajqnEk1iTpp5BqrD+AiAgMMMhp1LzTffGLX4QiqtSCWYwGZKmwlGlhYeHbe0n/rzG5BE1258yzzm71ZrM8dwwixOTgK7H3h48e/YGXvvSN/+4nN9ZXneNU5DRiTFWrvqm+WJskjZmZhRCKwboobdu5a8++M7rt1vLx4x/+wIfX11bVOEYj75MApFUxKJEYRFNfZ2IY1FTEtJogYgpTqKTE/CgJpFJnsNJDUK2yWwoiFRRFcf5557/gBc9v93pFKaEIElVFTUSjiYS0m7eaQCNZ2tfLEVE6LiOVk6SiJzNE57C6vHrF0/7Zf/rFf+9ggyIMy7IsijJGcmywTqe7tLQ04S3Ik0vQ5Bft379/7949Ls+JnaXtiGMso0BtvjfzQz/4srzVilGsKuOtPk/SxlNJiGFT6r3ia8UTUzEJYTDoi6E3v3Dldz3jlT/6L33mYZpK3FE3GUdYAEqrQxKGmUmMAFKFfDKKUIKBOEm2SJ4oxoJ6s8q+Iu3TBEQTYwyG8tLvf9GjHnVxf30DIibVNk7pqJYYncrtwQomKCBpFnMquyeqbhEySdtAmbq1/vDZL3rRttkFIpYQB6WEslRTdm5paWlxcVHHEsITiMklKABVWVpauuiii3q9GeczAKYaYgTzyonlf/dTP/GkK5+0traWVFKgWssT/5qeybqk0sae0phVAymIUhtTLKMMi+c95xnPffZzhkXhPDHqifCETM2LpDjfqqxByutrEj3roCyZME31AoS0rTLXOnxFTdIqW4uqOD8uLs5d/MhHl3EYU5SuSb9KR68mOaS6O6tm4hkDDHVpYxwQLEt9fkIMIkcwCmU5mFtc/JX/8ktdYG2wrhIsBo3RZ9nZ+/b1et1xX2gCMdEENTPHfOGFF5591j7vHXE9xKMoz9q+87uf+V0b5RB1GERAU1CZSkKaUW60uYUNY9nNNKNLU/6HLJp2Z2YkxDRbqVmjoRJNzCIldd40iqgZyAyaGpYMcM6BzUgAcuxSy12MEZsjZTWLqlJ5IgazUITLH3f5xY+6qBiUolKIxGSQo4kYLBFVNXXCQWAR9SB8IwKco7Q5Xfq8ic7Vz8VgeMnll19+xRMX5uaHxSAKRGzb0tLZZ5+VefcNUeVkYaIJCsDMFhYXL7n00vm5OQJrFKhurCz/wn/8+bPPf0RZBK79QauHb4xS66NCtZoKqOhYhxSOmmljdUd8r9u74sor5uYWVUxR+awCoN6fow5xBKIQExExhWNjquvoquA9Gc7RaNymXHpTAAdmUqX9556/fccOUWmKViwlDZr6kOaDAACnT6DIQGTEWtl61MWljLRNI7gIsT03/9rXvCorCo1aSiDH55xzzp69eye5EjRhos8vrY9EdOFFF51/3nnMDEM5GDzlyiuu/I6r+oOBY64ng9UeZ3VdU8VHcznrsQtjc74NJhKTB9bEMgoLMV76uEvO2Ls7liUSRYTIHGkyTFrxVJWo3pMDRgpRNVWqGZVo1ShN1ZmkUcy1ep9OTUT37dv7lKuuVImmlX+C6myNKG0DogQBEciDXP0somqXr1RVXdVAp5oF1TSGFEQ66Pcfdcmll1z6GDU1tT179lxyyaXtdquasjPBmGiCIpWXA71u9wlXXrG4fZt5LsvyFa9+xfbde2KItLmRQ9OYubE1vU4tWmrzHXdG0TRyEFVxuiobGTA7NzszOwPATMxiWpODxQiNpqpGakoIiecVY6MnYkvzbJiIyHHyI8fLPRXJKI4CJiKI2hl79j3q4kc2pjL9W60DFY2dUlJXBWajxpGKl+mTkipB4aqtaAkQaCQVCWFmceFFL/7nxWDYnu099alP3bNnT/oSJnym7WQTtF6xAFxw/gXPfOYzvcvOO2vfd37n09bX+27zbAyqv+sUxqSZiKMn1CSuNjUE0GwVZ0ZpSBcRCKq6a/eu7/zO7yDOpJKlTJPEk0RMQvoFIKuXbCIyrYLs1KvOIEecoifH3BhO29TxDOfYoj7mksftO3tfzTYFYr0AsIEt7UpjBPNEfjQI3BSUmkA1NUqnUU31berQfGSyoigf85hLHnHWmc959rOf8ITH5608bYE34bPrJzqTlJofVNWYvXPf8dTvOHjvveXaiYWlHev1AA+MpWpQX/v0mwIQYSJw00ZRtW5Ww7+pHhJG9S7tRMwcyvIJj7987569Bx+4L/etpCily5gWUSVyYEWMpGmvTpORCJuG1pgqs0seiJoRU7olkt6Uzsc5VrWFhcUrnvQ4VanK6ap6ajKYmqRQqP50SIsyIypyUFr6q9uy0kRVUY+BUiMjFhAzlWW5tHvXm/7NTz7q0scuzM2zY7PInE9ynhMTTtDRTCMARN12+wd+8IfyzA+KktJuQ7WT2qxzwOh6prENagaRNLkD9VScqg9NjYlTYxOjMp8AmLk303PeA1ATNlcZclNK5jnJ4UpQpGb6+j2JmcFILU4GOGYYgiozXLoN6tupcUvO3LfvokdekKpOyJI5TsG1MhFSGFf5JwIyAkOVORqS6lTdj2lcGAFpA57UYGdkqgKXQYmdv/IpT82cI1YCqYLZTThBJ9q8pzBJkmdp8Hm+MDeXt9pWzTjY5HqSpfbc8XUd9YjDxMbRQC/UhrMR0muCpQWRztx31pOe+CQFoomaIHmxVOniqeWEklnlWueqlK40ZyElCIwMZPDMLo1UUuNaN0ghlIheeNGF5+zfX4tkdaxTbUczGn5becxpUa/ipPRB0ygA07TOJ0eBXCr9YmLHTiUyG0zZwEzEPuVjnfMjEzCRmHCCwjk2s5hC1notbmYib7p4lc0CxqJmqqUXqqsiGk2qoi2QnpbcOleLAp1W66qrnjA/O6/RYMQgz0zKlZVr3r0+TzNzznnvR9aRiIjETKhqbk6RtzS7hBIRkffu0ksvyXKnKk1yoXFSm0dqJH+UDayjKX1URfSNMRxF9PXLmKv7mYzgUm80gdIqMcmYeIKm/V+MtJHTx1JzJy9P1ozgHD0yEh3r3mJqaDoe9QNIvUiqIGLPnU6HyQhRLVqjqxIimZARTAj1iGVTpJCrSholL8ApmKiazoh69k59FzFzjHLGGfse/ahHahSqRzM1Zj5RdXQjEQHGhCQ5MXGt/rrkb1efPkVmOlIPkskkYosx9TPBlAwM9j5LX9O38pp9SzHxBHVpOwtRE61b2sfVzc3YpHoCaPhXy4aEmkRAUgmb7Hz1SmZ2zMz0yIsvvvDCi0XNoEzEYGMiQ27kYGZKpGRgJQYxO5hCYxo/x4BPGq3BodqeqArgas8kkfURFzziUY96lFkji25aGVAb1ERTgsICpe+hcqUZpGYmIqgWek1CQHpRFftV4oA5zsykHrRn3k0t6D8NzjtmEgWbkiFZt5GyOPbM0Vpf05crc7IJVM9CSgUlTQa9HiKXZoFDVHudzjOe/p3tvKMiogGAAzErkWNzQKqv0mgKImdmleavydFkZmJOxc1W70pXGdEUBzFD7JJLHzM3Pzc+MrI2e44xnuhKOTBEY4OrzrbaRUnqQrvqAzIRE7iu3k5lfUhhVGXCjUiJyVdL/OS6oZNOUGZmdjADGKnEou4cTyL4OFOra9SojDTqXEPtZTaOo6pRFYokz3bckhKIfZ4Nhv1QlkwwFaXKmulYptJQZcAtZWxstL9R6pOuU68Yz1cSERGryo5t2y55zKNTb9MISHVOQiy15FmXZVX/T5Vb0SBmAsA5Tt5DSlEAKbQnpuYUACBGTQ8RmIScy12WPWSX7luDSScoQM67JEmLmplyM6Wm/uJtc72DVRFVvQtC/WjKOdKIx2m80Zj1qM0vGRyRz7PzL7qwNzeX6t6hKbbOqjC9ufbg2uZVonfV0aFaTTiz2opWpU4gkGM2sfPPv+DRj3601dvLpsrlMaI2t0IVByWbnwpZzDiV9qHW9K0ae8tmDOP0dObqxjJVidGTk+pjSp75WjeYXEw+QeF9ZsaAUr07JhFUlOpZhRhf39Ov9Y7V1GThx//XjFM0Mx5RfBQEp+sm+uiLH3n5Yx+nQQCyFGVDKa2nXOUR0nqaXsWO6zFLVVp1NIyciJC2liGqgj931ZOv2rVnh4gkATU9PyljzGkgPSqBE0C1EkejlOzk2itNo8XJCNX9W/1BVUlFAWIjU2m120aAKpgV1G63vx3X75+GLUFQBxKMNtYiwGmSrDcnkMY10CoQHgs1rDJClVzfvKp6fvOq2g0gIlWJsSAyJVWIVqXGo3epwx12zjlLuXgmx6Mxi2NhuxEFhhLAZGR5u3XeIy7w7KzOp6fxE9WT0wAdMiJPVMnpliZCIE39iUqmcAAzhEzNGKRqUS3ChEBpyzoSIViMMZ08MTOUiLNWp/4+JxdbgKDO5d5lyRxa8gbTTFezuiloJJU3TE2GEWNmtdJrapo20tKYQ9lQtiph7s709l9wLnkHILUhESmxMZQBkBHDOThHROScg3eOGUSWCMqcjHEVA6UMFJFjr6rnnX/upZc9OsaYEj9W7eCt0EhoioxSS73V3c3JwVSu5M+6fHlsf5Ja+NXUG21ESqaE/mDoajFY1XyW+4l3QLElCEpEzmeJNSnqSTGBmaWRCONPHv91k02tYykbs6aNC/sNRpEAMHPm3BWXP27H4pJGSSUiVjUi1c0XY6eZSow01d9RU5haS6PpZyJm9hmb6WWPfdzuvbslRiYHJOGoziBV2SiqNFAaCUUKNngFKG2QqBFmatXAh+QMpyMlJ4OY2TkR6XS74CrTq2btdvtkFXkisQUICiBvtUbxLwCAiNN4QdScG42dqR/ctIJvijxsZECt3gSmeVrKLwJEzN6fefZZ5z3iPAMcQSVEEU1zE0KoWuJFTJVIU2KTiTKjDFWpsqtNaSKoc845x44cZ+ecvT/LsirDVC3zRswgJ1rH7M2/AACGcVWXmpzytGt3sqpJ8aVaIkV1r6VUvYgjVlS+jRk6ne5DfNG+NdgaBM2y3DlXXzETkSQQqm7i6EnY5I9+w9PS5FcCUT0ubOxpagA7JtCO7Tuf/4IXzs3NpjnioFRMZAQjwLHz3ifh0bNzzJ4dcp/SnllC/UOe5+wdiES03e485tEXM2DETVqyuaOqAeSp8w2pkNrIzMCpRYko/UwGNlNY5GpP7nrbnJQvqnac1ZXVFec4kVhEnM9a7c5DcKG+9dgaBCWiLMtlTEpMnWjNdGI02ctNNnIsMK8N8CgMstTrZpIi6/olDGKk0Z7kne92uk98/OOf+z3P8i4TsWJYFEVZhhijpHHJEktS1WAhpOGQGsrQ/LWIsYwxhKhROFpGbqbb27199/d8zzMvfOSFIQSMlTBXH6YJmkwMqbpj9AGTFqomZkQwT8ZJWDBHtbElSs4AcSo5jDY/N8cwjWX6MmZmZia/2SNhy2wqGkJYPnE8LZVm5pxTNYkxy3NL+wYZpezeSQs9Nqfsx9KMm3mcBt6NtdqZmUgIYqsnTgzL4ovX3nDLzbeWFgfDYjgoTMK999/f6bYIOHL4mPdZt9c6cuSYgXbs2B6LIKZzc3PtLO/OzezYubPbbuc+2760OLc4v7A0tzQ3d9Z5F3iXxFlF1fBUJQG08T9s0wepqkCSebQqhRujgthxJXMo1S/QNE4ZRRlz75igJo69Ee3ac2Z7i1jQSU/FNvBZluVZDKGJbJihbKrKBDLD+EzaGiMlv/4BzbDFWofiugw4uaWNApUqMaTse6YzzzrrnLPPGmz0iXljWMB4Y31ZFUyu3183CJHv9zeKYcwy353pECjzWafbIVin28nzvNobkxgmpPbAwQcHG6vzC9tEYnI+K9NeWXEA0LRHfH2PpUW/ShaZwCylAJwjI7Jql5rq0zZVewYUxaCVz5kKk1dDq91ptbaAApqwZQhKQLvVXisLVG6Vep8mfQqxs021zSPV6RvXh/FQCXVlE5qCklq0FIlmZqblcDi3sJizj6rtbs9Ufd5StdnZbiKW6k4imFqUYAaJwXnHzmHUUlINnqeKRsyOOp32kUMPLCxuSyl2Y0skpfQCI62idmJirbsAk+Ws9wCrEloEUpU0zQGUnqNp0y8CSK3bbaPK1pOY9mZmtkT8nrBllngApnr8+BEzJXJAqgZHWRTsPDnPm7Zxo8bjbMxn86dv/HlEWaqbTEwBWltdzryfW9yWkk9abxRvdeMojS2+lMqfQZUSVaV0xCzNxt9UDuKYT5w4MVg/sXvPGUpZLclW4/eNXbKDBKcqhipZD+IkB9efoXZWx1wBVFsiCQA2iyGQcyCkimn2vPfMc9zEFzE12BqecgIxt9s91WbhVgDOu9T5eVIHbWMpT2LhScZjFDyN7aeRWuOKYRlCnJmdracrVUJ6Pf+rUYfGDqUmUhUD1wcfnUm9AoPJRMrFbduczw8dPuIyD6kmKSf9MoU6qYejoR+BExvrxaK+Taq4P/V/UFr3ydgBKjGKsPPpSSoyM7uwhdiJrUVQAJ1ul8g1o41VzTlnJhpj7XZtiuLHBU7UP4z/jLE0KcakAFUdrJ2Yn5+nlMRKuw3Wf6pyO1oNp1NJDZ/a+K9j79U026VpjaoqMaoppCh27d6bMd13x63kXO1dNFvUp8qPuroKXFWmAKkSJulcbJX2aamlxaJINIlkMQ0kydotmDHITL33s3MLD/lF+pZiixGUmTvdzphErwB5nwUpqioejHLfGF+7N9vO8UcS52hklZSINtZO+Nx1uj0TjaqNEnTylI+Tcv1ESEPpR4JX9fymUikNxVO1GMuyHC7u3B3Kcv3EceczVF5Dc4bf2JBU9Tm5tAlIVXetIMDqQipG2ochRiGwM4KJYxdVe/OL3vsx4X8LYIsRFEC322XHaX57Y0QdKEqoJ4ak6ypppW0Mm40nnGhUAld7q8kKCYj6/fWNtdWFxe1puCKq5Xs0cGH8gCACMzaPekz+qNUi7Un3SVrkyWIaN77v/AuOHTl05PAD3mXViBAIgdO+ucn7YFQzHQEliqmSy5Qkzcwf1TubRoEZM0mMzqXiQIoqLsvmFxarE9462HoEZXa93oxapGpevZhq5tP2hym4ScyoNBraXOuUGGT15i6bHq6mdtqJEycWt+9il1td5HGSrwnU7XgYsbJKczWBWsqgEqVReM3z079KTjhXYwvBM+8888yV5eNl2bdqWw9TS/0tWpMv7RtaNTRX3S9WKfhOI1kwlVBad3a20+LVtXXnMiKLZAQykcWFbc65yS7+/CbYegQF0On28rwl1QRNFVViB5CIAGwxUqV7n7wWn8wza/bwtOQhgnjlxHK31ZqdX5Bq68RNy3p67cmMb4QqGhlLq41h3QE1QsqQAy7NLB8Oinanu3fvmXffcVuMQs4nvStNECWQaZpFT0DarDMVlmhVBggy86E08u2lbXNf+Ju/+bmf/plDR4/4LDOkiePSanXm5heq995S2JIEJdDszIKZEAmRqkpUYXZra32Ng3ZvLoRQSe/ftGC0/iGKWL01Ryq6CCEMh/2l7dtUQ5od23i0J7mwIyKmFFDtdY7MqmjjUY5cS1AqM2JHImWMUYhMxaJ0erOzc7OrRw+DvRAnRcmqxnoCuTRLvBKS4NXYTEwlSETG89vnVo8f+sV/+7Ov+JHXfPiTnzl66IhAzIxExGxpx87x9qYthC150gDyPO92ZiSizv4IEW1srL/lN996w6f/Ztu2beSzEAOaglEdtRJZpRUJ6p4eAGrKoJXDhxbm5/N2L42yMa0Fz29Q+JtDNY8rqo6OlONPe24n20tVv1ATAKmZ5Llj8mZgx2aI5XDX3rMkxgfvuT1nJtVKq4IZoObSiZIaRM2ixBhE8m5nbr536O473/Ebb3nTG3/yf/zPt7dnl7ZvX4yV/IQQw8z8Yqc78xBfkIcKW0kSOwkzs/NlGSyWRCSGGIZFMTx47NiP/NiPvvQF3/v6N7xx1759y6troSgy75q+OAAgNo0MMsdUx+4E9Id9I5pbWJQQDaQmqeoSVflwI/hbk83ieoCZmVGdSk+PMJKt3FS9TykTRFUKnp2ZpNqk6IhFbemMsx68+/b15aO9mXkzSTt51dMVyQgKRRSwn52foSifv/baz3zq6o995JM3fPWmmW2Lu888wxHml5a2b99OaiLqWq0d2yd6N86/H1uYoOx4bmHu+JHDABk5NVpbW29l2eKeM/7nu9519d9e+6LnPfelr/ihbWfuW11Zi6H0zZYrWs33qmoxK3rJ6tGjO/ecQc5J8hAS8SA1retGp9GoskoB0LHZjqltYzwU+buTrlXo70ijGakySma/Z+8Zd9554KxzO865KnOZnicSTPN2qzszs7G88r4/+bMvfO76T1xz9W333bdtafv2M/ZmbHnufat1zjnnzM3Ophtj1869zJO+18zfgy1MUAB53p6Zm19bWyEoEcUQRWRhdo7OPe++I4f/31/9bx+9+poXvfB5z3rhi7bt2tHvD0JREsDkKNHIqoJLMK+trHZ6s51eJ4ay2oGgqhyqNrEl42Z8RxPIV+s7gawxkFZNqR/zWVMrHCrBNPUVEXPa5I4IuWcSLU2FQXmrvX3H9oN3Htj3iAslBoCIInvfmpllRwdvv+OP/uzPP/rJq6+78WuRaGF+5owzziAm71utjDud7uzMzLn7z+12u0Hizl27O93ew3RxvjXY2gQFMDMzH8o4GG4A6HQ73W631+vFGHfYjvnZ+Zvuuvuan/25v/rz973qta++5HGXn3XOuQobDtZjWQBsSDMgJEo5WFs749xzVQUiVhX9MsGgVtfmpTesEzcnRfQAgQ0RYCMijIoBmlO1KtfP1XGQZi06qZK0pMYwLkJcWNreHwwGJ47vOWefqUrA2okTn/n0Rz758U9+7G8+devdd7dnu3PblxwTMbeyPM8yl+V5K+u02zt27dp39lmOaGZubnFp+7i934rY8gQFYX5hMRyNMRStdnt2brbT7YpoVDHCPPV63XO/fOdt//KNb969tOMl3/eiSx572eMee9mefWcIqCgCAEAP3Xf3tu3bnMtiqHdlqH1Tq0c1JgJqk+0ZH1CD6rlEDINCFak4w06mqamBK81UDVW3sVS5TYKZEHEQ2Xf2OW/5td944P7DC4vd++594MDtd95w89cCMD87u3P3LmZmtsxnma/+47O8nWdz8/Pbtu9YmF+Y6c3u3rX3G0oPth62PkEBdry4tHTsyOFWqzXTm+11ZiVYlGBp70OUi0uLM3OzK2trv/a7b7VSz9t3zpOffMUFF+y/6JLHhKJ84NDhnUuLz3jec/obQ4zmHRKgBgM7pMGcSF2/lgacYCy3iZSdIQBsEII1AXuKosbcUDLT2uimMaNEVds6DMYwmGos24tzDxw5/Dt/9Ad55gC0Wp25pW2tzKW+u8w555z37LOW81me+SzznU67NzOzsLC4uLRt9759zmebBK6tiVOBoACyLFtYWuz3N3ZuX7qn5UMrk9hhw9Bo4Hx/OCRyMzPotNsidvDEsT/8s3cz0a69ewcb/XvvvfcXfvbnnvOi792Iq0TN5IVI8HU9ncFc6kaqbeHmfpKKkQRIyhaZVaN1mhg/uaHjAVO1xUE6QjVMkVRUFezcxvLyg3fdPTcz05vpEpEZeeerpifnvXfe+cw7EGfes+NW3up2e61Wa2lp6bLLLmu1WuP3z9bFKUJQM2u3u7v27N1/7nkH7r5nMBy0Q+ZSc7BjAGVRwjSYAtbptXrdtqiura74LGv3erfefkva+IhT7V413mi0OnNd4MmcZKN04dMIr8ZeSnp2aroYr2kar+fHKMZKM04YqfGNAAObRZXZpcU/+O3f/tinPtNdmIkieZZ5753jzDlyzmXeO86dcz4DIc+8z1u9Tqeddzrd3vOe+5z5+Xmt91Te6jhFCJrq0Hu9mcsf/8S777nn+NGjbLoBa1f7XIGYjGGeXFAqOcRoRMxOos7Nz378Ex/7/Gf/9tIrn7Ry9ATV9T4jckIJZtUMpqqyBABSvhF1IRSYzAiW2NxMv9l0kiOaVnVOaf9CMyUjNVEgb2crhw6/44/+1LeyjIi8T0YyyzJ23jtOLM2dd3luRFmWtdrtVp5luXvlK1/9yIsfdcqwE1s3k/RNQDCzVrv95Kuump2fa3U7s7Pz3Xa722p1O51Ot9fr9BbavXa33e60Oq08846ZPVM7c/0i/vgbfvLQPfd3uz1EYQaq3YnAMJdio6rg0tLyrclWUlNql/KlzQhTBYE2m8xRKmq0YQ5XKXUYwTlyRByK8sff9BN3PXBfu9cl8jn7zPksy7x33vuqmzlvkfeJnXm77b1n71/7utc/+corVE4dduKUImit+Jx99v4Xv+QlLsvZ++5sr9Pr9Lrtbqvd7nbzTrvVanXa7U6n1WrlrbyV5zkTL8zPfuXOW9/0Y/9P2V/L2nmIAepM2aqadhinHZSqEIqrcjqtR88oUrKHUtdbylGKWdrusCpUric0aP0/AdTUpRFMZrEIYefSwh///tve/9GPtWfnmMhljvOKlHne8j7127ec8y7L8zzvdNreu26n/YY3/fhTnvo0VWV3al3Tb8xwbHWoCrP7+k1fe/vbfm9tfd3neX99o98fDoZFUQ5CGUIIoSyHZVkUAaJlLEMo1WjQHz7+Eef/ztt/v7WwGIZDdpw6fLlOOlVmVY3T9m/VlDnUOzIwUgFcKvkDSdrFYBRbVa4qjA0hWQcVYRCAGMvZ+fkPvvvdP/sffqlwzMze5e1WlmXO+cw757OM2Xnn095GrbzV63ZDlF07d7zpzW++5NLHpg/+8H3xDwlOQYICEFXHfN+9B972u7/74OGjrXZ7Y319faMfyrIIg2FZhI1yEENZliZaRgkhhGIYTY8cPvz9z3ver/7afw/MppHZVe0+AGnSl1J9aeqho6qFDmqJtNVOhxCA0j5aaoxIZGKkaehxUgMqzzM5Ei6Gcm5p6abrP/d93//y4HyrlWdZlrdarcxnmfM+c0zkvHMucw7MebvdyVvDQf+CCy/61//6J/fv3y8q7pRjJ05VgqKu5FhdXfmjP/xfN33tK628tbq23h8OihAHw43QH/aHIcagUogglKEoi1CWAjt26OgLnvnPfvsPfn9YSuPPVXXBAEzBaTs3AxOqll8i8mrKasbJ+yRo1anOAEwFacqNELipNUnlqFFkptu9+7Zb3/iv/tUt9z3YarWIqN1ut1qtPM+8Y+9cyzuwY++99612y/tssLHxlKc85Q1vevPCwqKINJPrTjGcsgTFqNpI3/++9374wx/yzINSVtf7ZbExHAyKoiiLoYYQRINIGUJZFmVRhqj9jf6rXvKif/Mz/w55z6TwjqMawCCDpcGLlOrpapU0PWQgp5B6y5k0ZiHJCAozNQKZMwYpTERhoBhiZ6a3dvSBV7z0FV+/5965+Rkz3+60Op229z7PMu8zYvLe+Sz3znU6HdOYOXrxS37gZT/4Q8TuVIrZvxGnMkExVtXx1a98+d3veueRo8dc1lpZWdvorw8HG2VRlEEkBhFRkRDCcFgMizKIHLz//p/+0df/3K/84vLquqmSzwCCiZqljXEARt3orI0knvKgoyQnkNSF1OWGSCCXgiqyKBqC9GZnjt1/7xt+7E3X33LzfLdDhqzd7XRbnXabncu9Z+8JcM61Wi3nXIjx3P37X/u61z3u8ic0n+5h+34fepziBE1INmZldeW97/nTz33u2sFQhsVwfX1tMBiYImrUGGOUGGNRFP3hsCxLFZGieP6zvus//tdfLUVNwa5OUFKdeLe60BN1nSksjQ6FKqWdDA1cF/WlAtGUeTcRMZ2fm7/79tte/7ofu/n2A72lOah18lan1223W3mWs3PeO+dd5rNWlqeN5L7n2c965StffSpJ8X8/TguCouYogC998fq//PP3fP2WW5URy2DDMLQYU2N7lHI43BgOBoNBGeLGYGih/PmfeNNLXvWqiLQ5rNe02whMwVVjZbVZZ1W8b6jqi42cgVilqjUxNZASsUgEkaNeu/XBv/yrX/qV//rg8eVOr8NGrpX3uu1up5uMJTtOoicRhRgvuvDCl7/8lU960pPGP84pj9OFoKi65IyYB8P+Rz/ykQ/89V/fd/99uc/S7AU1kRg1yjCEtfX1jY3+sCgd0dEH7n/NK1/5K//fr66trw+K0mcZJbaRY9N6a66KgEgxOQEgBSUFPlWI1FMZkieK2F//jV//H3/0J39KWafdaYOsl+etTrfV6XbaSVjKfOZBFGPcuXPn85//guc//wWdTqfJ6T+s3+W3D6cRQRMa23Pk8OH3vf+9H/3IR44eO9putfNWS0ViWRQhhiCD9fXltZXBYDgohrOdzrO+6+lveNOb9p519vLqGpmRqTK5lIwnroYzMFKtSd2ZRKmvSKiOpghkVsbYybPf/c23/Jff/I1tSzsYxJ5brdbMTG92dq7dahNXm4xFKRcXl77z6c944QtfsGvXbpxOhrPBaUfQhOZK33PP3X/9/vddc801Gxv9mV4XJoOykGgS46C/sb6ytry+tjEcHjp4+JlPffIrX/2KK55ylXOemcl7iLJjraeCSdXTaWxmRgpyGpS5sa4pX6oiIdjNt9326c9cc8tNXw9qM73e/Pw8e18URSiGIQQRWVhcuOqqq571rOeeeeY+1DX5p4/hbHCaEhR1MXKi6YMPPvC3n/r0566/7v4HDvbXVwaDoZnLvfNMhUi/DK0sixKfetWVL37Ri2CU55mEfqczG8ph1mlrNGYmxxpKdh5pFDy5pNkT0shjAoiYoIhmZkoxDgaDYAZwKMtjx44++OCDhw8fZeceefHFV1xxxY4dO3EaUzPh9CVowjhNB4PBTV/7+mc/e829994vhl4337Nz19LS9navN7+4uLiwuGP7trrHToeDQSv368trM/PzxaCfsjvDwaCaVZuGPTFX3zCbQVVSoFRvhg1lIMlbKgKiVndmcWnHtm3b0rmd5tRMON0JmjBOUwCqura+NuivF8NCRbKMGeSYoqXt5RRGGUhEyXEaNqZp6g57gbEIG2Il18coSkaWJuAYmZaqUEWIpZl573u9udmF+d7MXDOWe0rNBlOCjtD0GDVMLYvhcDgohsNhfyOGICZpDl5KVhI5cDW028zYALXISB0hYmoKMYGIGqKKxghVJXbsWq1Wb2Z2ZnZmZma2XU9VOB2E9/9bTAn6zXFSv4SqFkVRDvuDYb8YDsvhMIQgMUaN0QxqrNWOtiqa9uMmNTNS5pyJnPd53m3l7Xa3PTvb6/RSoujverspGkwJ+o+BmYUYJYayLFQthFJCFFVRsbTVIru0N5JzLstb3rksyzdXao5V4E2p+XdjStD/S/wT+sybr3pqLP/hmBL0n4i/8/urTeOWb/x9eDEl6BQTjdMrbzbFlsOUoFNMNKYEnWKiMSXoFBONKUGnmGicIqNvTnOcwgrr1IJOMdGYEnTLo6lxOSUxJegUE40pQbc8Tu1KqClBtzZO4cU9YUrQrY1T2wHFlKBbGs0GYg/3iTyEmOqgWxjN/t7jv55imBJ0C+OUZORJmC7xpwhOVbJOCTrFRGNK0FMBp6r5xLTlY4oJx9SCTjHRmBJ0ionG6Sgz/UO8mlPYq9taOK190ClTJx+nNUGnmHxMfdApJhpTgk4x0ZgSdIqJxpSgU0w0pgSdYqIxJegUE40pQaeYaEwJOsVEY0rQKSYaU4JOMdGYEnSKicaUoFNMNKYEnWKiMSXoFBONKUGnmGhMCTrFRGNK0CkmGlOCTjHRmBJ0ionGlKBTTDSmBJ1iojEl6BQTjSlBp5hoTAk6xURjStApJhpTgk4x0ZgSdIqJxpSgU0w0pgSdYqLx/wPkI1FlG/VRgwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FBD418928D0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[805]\n",
      "('n04254680', 'soccer_ball', 0.9999938)\n",
      "('n03530642', 'honeycomb', 3.862427e-06)\n",
      "('n03255030', 'dumbbell', 4.4424414e-07)\n",
      "('n02782093', 'balloon', 3.7039203e-07)\n",
      "('n04548280', 'wall_clock', 3.143872e-07)\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'_________________________________________________________'"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FBD418928D0>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[751]\n",
      "('n04037443', 'racer', 0.71319467)\n",
      "('n03100240', 'convertible', 0.1008973)\n",
      "('n04285008', 'sports_car', 0.07707668)\n",
      "('n03930630', 'pickup', 0.026352933)\n",
      "('n02704792', 'amphibian', 0.011636195)\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",
    "ROOT = \"https://data.heatonresearch.com/data/t81-558/images/\"\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",
    "L = \"_________________________________________________________\"\n",
    "\n",
    "def classify_array(images):\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",
    "        x = x[:,:,:,:3] # maybe an alpha channel\n",
    "        pred = model.predict(x)\n",
    "\n",
    "        display(L)\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)\n",
    "\n",
    "classify_array( [\n",
    "    ROOT+\"soccer_ball.jpg\",\n",
    "    ROOT+\"race_truck.jpg\"\n",
    "])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "zh2f29cl_kqW"
   },
   "source": [
    "Overall, the neural network is doing quite well. \n",
    "\n",
    "For many applications, MobileNet might be entirely acceptable as an image classifier.  However, if you need to classify very specialized images not in the 1,000 image types supported by imagenet, it is necessary to use transfer learning.\n",
    "\n",
    "### Transfer\n",
    "\n",
    "It is possible to create your image classification network from scratch.  This endeavor would take considerable time and resources.  Just creating an image classifier would require many labeled pictures.  Using a pretrained neural network, you are tapping into knowledge already built into the lower layers of the neural 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 these images.\n",
    "\n",
    "Next, we reload the MobileNet; however, we set the *include_top* parameter to *False*. This setting instructs Keras not to load the final classification layers.  This setting 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": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "02-d88fM_kqW",
    "outputId": "ca7b226a-2970-42ec-eadb-88fff5a1f844"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:`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",
      "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) \n",
    "#imports the mobilenet model and discards the last 1000 neuron layer.\n",
    "\n",
    "base_model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7tYJFcPh_kqY"
   },
   "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": {
    "colab": {},
    "colab_type": "code",
    "id": "skflYE-b_kqY"
   },
   "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(4,activation='softmax')(x) "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "6cTaZKtZ_kqa"
   },
   "source": [
    "Next, we mark the original MobileNet layers as non-trainable and our new layers as trainable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eraoBiUk_kqa"
   },
   "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": {
    "colab_type": "text",
    "id": "HNczbgsy_kqc"
   },
   "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. Each class is a folder that contains images of that class.  We can also specify a target size; in this case the original MobileNet size of 224x224 is desired.\n",
    "\n",
    "For this simple example I included four classes, my directories are setup as follows:\n",
    "\n",
    "* **trans** - The root directory of the dataset.\n",
    "* **trans/cd** - Pictures of CD's.\n",
    "* **trans/disk35** - Pictures of 3.5 inch disks.\n",
    "* **trans/disk525** - Pictures of 5.25 inch disks.\n",
    "* **trans/tape** - Pictures of tapes.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "NKK_x4bs_kqc",
    "outputId": "a1588c65-9578-4070-9de8-8db3a742c87a"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found 52 images belonging to 4 classes.\n"
     ]
    }
   ],
   "source": [
    "if COLAB:\n",
    "  PATH = \"/content/drive/My Drive/projects/trans/\"\n",
    "else:\n",
    "  PATH = 'c:\\\\jth\\\\data\\\\trans'\n",
    "\n",
    "train_datagen=ImageDataGenerator(preprocessing_function=preprocess_input) \n",
    "\n",
    "train_generator=train_datagen.flow_from_directory(PATH, \n",
    "                                                 target_size=(224,224),\n",
    "                                                 color_mode='rgb',\n",
    "                                                 batch_size=52,\n",
    "                                                 class_mode='categorical',\n",
    "                                                 shuffle=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZAZfnbQK_kqg"
   },
   "source": [
    "We are now ready to compile and fit the neural network.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 1000
    },
    "colab_type": "code",
    "id": "kKKlCbgi_kqg",
    "outputId": "34e041c8-3c2e-4c81-e945-444ba4f30ff3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.6215 - accuracy: 0.2885\n",
      "Epoch 2/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 2.0469 - accuracy: 0.4808\n",
      "Epoch 3/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 0.0366 - accuracy: 1.0000\n",
      "Epoch 4/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 0.9929 - accuracy: 0.6923\n",
      "Epoch 5/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0829 - accuracy: 0.9808\n",
      "Epoch 6/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 0.0026 - accuracy: 1.0000\n",
      "Epoch 7/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 8.4217e-04 - accuracy: 1.0000\n",
      "Epoch 8/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 0.0011 - accuracy: 1.0000\n",
      "Epoch 9/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000\n",
      "Epoch 10/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0024 - accuracy: 1.0000\n",
      "Epoch 11/50\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0024 - accuracy: 1.0000\n",
      "Epoch 12/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 0.0018 - accuracy: 1.0000\n",
      "Epoch 13/50\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 0.0010 - accuracy: 1.0000\n",
      "Epoch 14/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5.5196e-04 - accuracy: 1.0000\n",
      "Epoch 15/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.9253e-04 - accuracy: 1.0000\n",
      "Epoch 16/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.5706e-04 - accuracy: 1.0000\n",
      "Epoch 17/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 8.5439e-05 - accuracy: 1.0000\n",
      "Epoch 18/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.7495e-05 - accuracy: 1.0000\n",
      "Epoch 19/50\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.7115e-05 - accuracy: 1.0000\n",
      "Epoch 20/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 1.6015e-05 - accuracy: 1.0000\n",
      "Epoch 21/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 9.8780e-06 - accuracy: 1.0000\n",
      "Epoch 22/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6.5151e-06 - accuracy: 1.0000\n",
      "Epoch 23/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 4.7202e-06 - accuracy: 1.0000\n",
      "Epoch 24/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.8330e-06 - accuracy: 1.0000\n",
      "Epoch 25/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 3.5028e-06 - accuracy: 1.0000\n",
      "Epoch 26/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.5234e-06 - accuracy: 1.0000\n",
      "Epoch 27/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.7640e-06 - accuracy: 1.0000\n",
      "Epoch 28/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.1491e-06 - accuracy: 1.0000\n",
      "Epoch 29/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 4.6075e-06 - accuracy: 1.0000\n",
      "Epoch 30/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 5.0750e-06 - accuracy: 1.0000\n",
      "Epoch 31/50\n",
      "1/1 [==============================] - 0s 4ms/step - loss: 5.5081e-06 - accuracy: 1.0000\n",
      "Epoch 32/50\n",
      "1/1 [==============================] - 0s 6ms/step - loss: 5.8794e-06 - accuracy: 1.0000\n",
      "Epoch 33/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.1544e-06 - accuracy: 1.0000\n",
      "Epoch 34/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.2942e-06 - accuracy: 1.0000\n",
      "Epoch 35/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 6.3125e-06 - accuracy: 1.0000\n",
      "Epoch 36/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 6.2140e-06 - accuracy: 1.0000\n",
      "Epoch 37/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5.9986e-06 - accuracy: 1.0000\n",
      "Epoch 38/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5.7075e-06 - accuracy: 1.0000\n",
      "Epoch 39/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 5.3545e-06 - accuracy: 1.0000\n",
      "Epoch 40/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 4.9878e-06 - accuracy: 1.0000\n",
      "Epoch 41/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.6188e-06 - accuracy: 1.0000\n",
      "Epoch 42/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 4.2498e-06 - accuracy: 1.0000\n",
      "Epoch 43/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 3.8900e-06 - accuracy: 1.0000\n",
      "Epoch 44/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 3.5439e-06 - accuracy: 1.0000\n",
      "Epoch 45/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 3.2184e-06 - accuracy: 1.0000\n",
      "Epoch 46/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 2.9181e-06 - accuracy: 1.0000\n",
      "Epoch 47/50\n",
      "1/1 [==============================] - 0s 3ms/step - loss: 2.6454e-06 - accuracy: 1.0000\n",
      "Epoch 48/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 2.4001e-06 - accuracy: 1.0000\n",
      "Epoch 49/50\n",
      "1/1 [==============================] - 0s 2ms/step - loss: 2.1823e-06 - accuracy: 1.0000\n",
      "Epoch 50/50\n",
      "1/1 [==============================] - 0s 1ms/step - loss: 1.9898e-06 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<tensorflow.python.keras.callbacks.History at 0x7fbd3e91c7f0>"
      ]
     },
     "execution_count": 10,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.compile(optimizer='Adam',loss='categorical_crossentropy',\n",
    "              metrics=['accuracy'])\n",
    "\n",
    "step_size_train=train_generator.n//train_generator.batch_size\n",
    "model.fit(train_generator,\n",
    "                   steps_per_epoch=step_size_train,\n",
    "                   epochs=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "cdMArQzUC1i0"
   },
   "source": [
    "To make use of this neural network we will need to know which output neuron corrisponds to each of the training classes/directories we provided to the generator.  By calling the **class_indices** property of the generator, we are provided with this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 34
    },
    "colab_type": "code",
    "id": "Du2w8--M_kqi",
    "outputId": "1a6150a4-8599-453c-d81a-2cde126b00c2"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'cd': 0, 'disk35': 1, 'disk525': 2, 'tape': 3}\n"
     ]
    }
   ],
   "source": [
    "print(train_generator.class_indices)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "lNlmNGCS_kqk"
   },
   "source": [
    "We are now ready to see how our new model can predict our classes.  The URLs in the code some examples.  Feel free to add your own.  We did not use a large dataset, so it will not be perfect.  A larger training set will improve accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 569
    },
    "colab_type": "code",
    "id": "9UkGIYtY_kqk",
    "outputId": "ae390a63-a523-464c-a097-a46e8c007998"
   },
   "outputs": [
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'_________________________________________________________'"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FBD3E9553C8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[3.0885078e-25 1.0000000e+00 4.4679350e-28 0.0000000e+00]]\n",
      "disk35\n"
     ]
    },
    {
     "data": {
      "application/vnd.google.colaboratory.intrinsic+json": {
       "type": "string"
      },
      "text/plain": [
       "'_________________________________________________________'"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<PIL.Image.Image image mode=RGB size=224x224 at 0x7FBD3E9553C8>"
      ]
     },
     "metadata": {
      "tags": []
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1.8025676e-27 1.2575651e-34 1.0000000e+00 8.7701346e-38]]\n",
      "disk525\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "def classify_array(images,classes):\n",
    "    inv_map = {v: k for k, v in classes.items()}\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",
    "        x = x[:,:,:,:3] # maybe an alpha channel\n",
    "        \n",
    "        \n",
    "        pred = model.predict(x)\n",
    "\n",
    "        display(L)\n",
    "        display(img)\n",
    "        pred2 = int(np.argmax(pred,axis=1))\n",
    "        print(pred)\n",
    "        print(inv_map[pred2])\n",
    "        #print(classes[pred2])\n",
    "        #print(pred[0])\n",
    "\n",
    "\n",
    "classify_array( [\n",
    "    #ROOT+\"disk_35.png\",\n",
    "    #ROOT+\"disk_525.png\",\n",
    "    #ROOT+\"disk_35b.png\",\n",
    "    #ROOT+\"IMG_1563.jpg\",\n",
    "    #ROOT+\"IMG_1565.jpg\",\n",
    "    ROOT+\"IMG_1567.jpg\",\n",
    "    ROOT+\"IMG_1570.jpg\"\n",
    "],train_generator.class_indices)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "tvqMi9RG_kqm"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "SrPVdONmF5sN"
   },
   "source": [
    "This is a very simple example, with a small dataset.  The accuracy will not be great; however, it demonstrates how to expand a pretrained model with your own images."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "colab": {
   "collapsed_sections": [],
   "name": "t81_558_class_09_3_transfer_cv.ipynb",
   "provenance": []
  },
  "kernelspec": {
   "display_name": "Python 3.6 (tensorflow)",
   "language": "python",
   "name": "tensorflow"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}