Keras in Tensorflow.ipynb

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    "colab": {
      "name": "Tensorflow",
      "provenance": [],
      "authorship_tag": "ABX9TyNRIyTz5HoIihOf1jn4G5kH",
      "include_colab_link": true
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    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/jamwine/Tensorflow/blob/master/Tensorflow.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6ZWWEJXajD_r",
        "colab_type": "text"
      },
      "source": [
        "Mounting Google Drive"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbvaI0ftfpGE",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "d8a0f168-4d8e-4b67-a5a9-53cfedd02317"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('gdrive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Drive already mounted at gdrive; to attempt to forcibly remount, call drive.mount(\"gdrive\", force_remount=True).\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "deZ36K2XjjRt"
      },
      "source": [
        "Changing Directory to Google Colab Folder"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TapAWxrIfpNj",
        "colab_type": "code",
        "outputId": "74cf8778-ac5f-4867-9e11-965732985d4f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        }
      },
      "source": [
        "import os\n",
        "os.chdir('gdrive/My Drive/Colab Notebooks')\n",
        "os.listdir(os.getcwd())"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['ImageNetLabels.txt',\n",
              " 'Images',\n",
              " 'variables',\n",
              " 'assets',\n",
              " 'flags.csv',\n",
              " 'flags.gsheet',\n",
              " 'DataMining.ipynb',\n",
              " 'Week 3 Programming Assignment.ipynb',\n",
              " 'model_checkpoints',\n",
              " 'model_checkpoints_5000',\n",
              " 'results.csv',\n",
              " 'config.json',\n",
              " 'lemon.jpg',\n",
              " 'viaduct.jpg',\n",
              " 'water_tower.jpg',\n",
              " 'data',\n",
              " 'saved_model.pb',\n",
              " 'SVHN - Capstone Project',\n",
              " 'Untitled.ipynb',\n",
              " 'Tensorflow']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fiN4xe6rfpQA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# !pip install tensorflow==2"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBOKp-zEiICQ",
        "colab_type": "code",
        "outputId": "b38358da-92f4-46d5-c360-99890d83a6ad",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t3KbwOSorcj3",
        "colab_type": "text"
      },
      "source": [
        "Keras is a high-level neural network API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano. \n",
        "\n",
        "TensorFlow Documentation\n",
        " * https://www.tensorflow.org/api_docs/python/tf/keras\n",
        "\n",
        "Keras Documentation\n",
        "* https://keras.io/\n",
        "\n",
        "Jupyter Notebook's TensorFlow 1.x code to TensorFlow 2 code\n",
        " * http://tf2up.ml/\n",
        "\n",
        "\n",
        "\n",
        "### Difference between Sequential and Functional API Models:\n",
        "**Sequential models** are easy and quick. Basically, sequential models contains a sequence of layers with automatic layer interconnection.\n",
        "\n",
        "**Model Class with Functional API** are more detailed, powerful and complex neural networks. The layers are arranged into functional units having a user defined layer connection.\n",
        "\n",
        "* https://www.heatonresearch.com/2017/06/01/hidden-layers.html\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FZkgUrwLZfBd",
        "colab_type": "text"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_1\"></a>\n",
        "## Building a Sequential model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EjHq_5JpZfBh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Softmax"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Djv7NmSeZfBq",
        "colab_type": "text"
      },
      "source": [
        "#### Build a feedforward neural network model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fmBS8chmZfBt",
        "colab_type": "code",
        "outputId": "c1cd2d07-41a8-494b-c1a6-7ba6f5768608",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 870
        }
      },
      "source": [
        "# Build the Sequential feedforward neural network model by alternative methods\n",
        "\n",
        "model1 = Sequential([ # list of Keras layers\n",
        "  Flatten(input_shape=(28,28),name='Flatten_Layer'), #(784,) is the one dimensional input size\n",
        "  Dense(16, activation='relu',name='Dense_Layer_1'),\n",
        "  Dense(16, activation='relu',name='Dense_Layer_2'),\n",
        "  Dense(10, activation='softmax',name='Dense_Layer_3'),\n",
        "])\n",
        "model1.summary() # summary() outputs the layers with their shapes, number of trainable parameters\n",
        "\n",
        "model2 = Sequential([\n",
        "  Flatten(input_shape=(28,28),name='Flatten_Layer'),\n",
        "  Dense(16, activation='relu',name='Dense_Layer_1'),\n",
        "  Dense(16, activation='tanh',name='Dense_Layer_2'),\n",
        "  Dense(10,name='Dense_Layer_3'),\n",
        "  Softmax(name='Softmax_Layer') # Defining Softmax layer separately\n",
        "])\n",
        "model2.summary()\n",
        "\n",
        "model3 = Sequential()\n",
        "model3.add(Dense(16,activation='relu',input_shape=(784,),name='Dense_Layer_1')) # Adding layers separately using add()\n",
        "model3.add(Dense(16, activation='sigmoid',name='Dense_Layer_2'))\n",
        "model3.add(Dense(10,activation='softmax',name='Dense_Layer_3'))\n",
        "model3.summary()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Flatten_Layer (Flatten)      (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "Dense_Layer_1 (Dense)        (None, 16)                12560     \n",
            "_________________________________________________________________\n",
            "Dense_Layer_2 (Dense)        (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "Dense_Layer_3 (Dense)        (None, 10)                170       \n",
            "=================================================================\n",
            "Total params: 13,002\n",
            "Trainable params: 13,002\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Flatten_Layer (Flatten)      (None, 784)               0         \n",
            "_________________________________________________________________\n",
            "Dense_Layer_1 (Dense)        (None, 16)                12560     \n",
            "_________________________________________________________________\n",
            "Dense_Layer_2 (Dense)        (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "Dense_Layer_3 (Dense)        (None, 10)                170       \n",
            "_________________________________________________________________\n",
            "Softmax_Layer (Softmax)      (None, 10)                0         \n",
            "=================================================================\n",
            "Total params: 13,002\n",
            "Trainable params: 13,002\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Dense_Layer_1 (Dense)        (None, 16)                12560     \n",
            "_________________________________________________________________\n",
            "Dense_Layer_2 (Dense)        (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "Dense_Layer_3 (Dense)        (None, 10)                170       \n",
            "=================================================================\n",
            "Total params: 13,002\n",
            "Trainable params: 13,002\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UcHYAxfg2_Ny",
        "colab_type": "text"
      },
      "source": [
        "model.plot_model() illustrates the model layer hierarchy by showing the connections. The output is a graphic file with presentation quality."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "634x3fv0ZfB_",
        "colab_type": "text"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_2\"></a>\n",
        "## Convolutional and pooling layers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CErGrCo4ZfCC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LTmA4IOKZfCQ",
        "colab_type": "text"
      },
      "source": [
        "#### Build a convolutional neural network model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "q10H8-SmZfCS",
        "colab_type": "code",
        "outputId": "5be4d092-e836-4d4c-b3ba-5a4b21160edd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# Build the Sequential convolutional neural network model\n",
        "\n",
        "model1=Sequential([\n",
        "      Conv2D(16,kernel_size=(3,3),activation='relu',input_shape=(28,28,1),name='Convolutional_layer'), # output shape is (None, 26, 26, 16), 28-(3-1)=26\n",
        "      MaxPooling2D(pool_size=(3,3),name=\"MaxPooling_layer\"), # output shape is (None, 8, 8, 16), 26//3=8\n",
        "      Flatten(name='Flatten_layer'), \n",
        "      Dense(10,activation='softmax',name='Dense_layer')\n",
        "])\n",
        "model1.summary()\n",
        "\n",
        "model2=Sequential([\n",
        "      Conv2D(16,kernel_size=(3,3),activation='relu',padding='SAME',input_shape=(28,28,1),name='Convolutional_layer'), # When padding='SAME', output shape is (None, 28, 28, 16), 28-(0)=28\n",
        "      MaxPooling2D(pool_size=(3,3),name=\"MaxPooling_layer\"), # output shape is (None, 9, 9, 16), 28//3=9\n",
        "      Flatten(name='Flatten_layer'),\n",
        "      Dense(10,activation='softmax',name='Dense_layer')\n",
        "])\n",
        "model2.summary()\n",
        "\n",
        "model3=Sequential([\n",
        "      Conv2D(16,kernel_size=3,activation='relu',padding='SAME',strides=2,input_shape=(28,28,1),name='Convolutional_layer'), # When strides=2 and padding='SAME', output shape is (None, 14, 14, 16), (28-(0))/2=14\n",
        "      MaxPooling2D(pool_size=3,name=\"MaxPooling_layer\"), # output shape is (None, 4, 4, 16), 14//3=4\n",
        "      Flatten(name='Flatten_layer'),\n",
        "      Dense(10,activation='softmax',name='Dense_layer')\n",
        "])\n",
        "model3.summary()\n",
        "\n",
        "model4=Sequential([\n",
        "      Conv2D(16,kernel_size=3,activation='relu',strides=2,input_shape=(28,28,1),name='Convolutional_layer'), # When strides=2, output shape is (None, 13, 13, 16), (28-(2))/2=13\n",
        "      MaxPooling2D(pool_size=2,name=\"MaxPooling_layer\"), # output shape is (None, 6, 6, 16), 13//2=6\n",
        "      Flatten(name='Flatten_layer'),\n",
        "      Dense(10,activation='softmax',name='Dense_layer')\n",
        "])\n",
        "model4.summary()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Convolutional_layer (Conv2D) (None, 26, 26, 16)        160       \n",
            "_________________________________________________________________\n",
            "MaxPooling_layer (MaxPooling (None, 8, 8, 16)          0         \n",
            "_________________________________________________________________\n",
            "Flatten_layer (Flatten)      (None, 1024)              0         \n",
            "_________________________________________________________________\n",
            "Dense_layer (Dense)          (None, 10)                10250     \n",
            "=================================================================\n",
            "Total params: 10,410\n",
            "Trainable params: 10,410\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Convolutional_layer (Conv2D) (None, 28, 28, 16)        160       \n",
            "_________________________________________________________________\n",
            "MaxPooling_layer (MaxPooling (None, 9, 9, 16)          0         \n",
            "_________________________________________________________________\n",
            "Flatten_layer (Flatten)      (None, 1296)              0         \n",
            "_________________________________________________________________\n",
            "Dense_layer (Dense)          (None, 10)                12970     \n",
            "=================================================================\n",
            "Total params: 13,130\n",
            "Trainable params: 13,130\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Convolutional_layer (Conv2D) (None, 14, 14, 16)        160       \n",
            "_________________________________________________________________\n",
            "MaxPooling_layer (MaxPooling (None, 4, 4, 16)          0         \n",
            "_________________________________________________________________\n",
            "Flatten_layer (Flatten)      (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "Dense_layer (Dense)          (None, 10)                2570      \n",
            "=================================================================\n",
            "Total params: 2,730\n",
            "Trainable params: 2,730\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Convolutional_layer (Conv2D) (None, 13, 13, 16)        160       \n",
            "_________________________________________________________________\n",
            "MaxPooling_layer (MaxPooling (None, 6, 6, 16)          0         \n",
            "_________________________________________________________________\n",
            "Flatten_layer (Flatten)      (None, 576)               0         \n",
            "_________________________________________________________________\n",
            "Dense_layer (Dense)          (None, 10)                5770      \n",
            "=================================================================\n",
            "Total params: 5,930\n",
            "Trainable params: 5,930\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-AMNmQquGi0a",
        "colab_type": "text"
      },
      "source": [
        "### Default weights and biases\n",
        "\n",
        "In the above models, we have not specified the initial values of the weights and biases in each layer of our neural networks. The default values of the weights and biases in TensorFlow depend on the type of layers we are using. \n",
        "\n",
        "For example, in a `Dense` layer, the biases are set to zero (`zeros`) by default, while the weights are set according to `glorot_uniform`, the Glorot uniform initialiser. The Glorot uniform initialiser draws the weights uniformly at random from the closed interval $[-c,c]$, where $$c = \\sqrt{\\frac{6}{n_{input}+n_{output}}}$$\n",
        "\n",
        "and $n_{input}$ and $n_{output}$ are the number of inputs to, and outputs from the layer respectively.\n",
        "\n",
        "### Initialising own weights and biases\n",
        "\n",
        "When we construct a model in TensorFlow, each layer has optional arguments `kernel_initialiser` and `bias_initialiser`, which are used to set the weights and biases respectively.\n",
        "\n",
        "If a layer has no weights or biases (e.g. it is a max pooling layer), then trying to set either `kernel_initialiser` or `bias_initialiser` will throw an error.\n",
        "\n",
        "### Custom weight and bias initialisers\n",
        "Initializers must take in two arguments, the `shape` of the tensor to be initialised, and its `dtype`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bjkqdEKWHQQZ",
        "colab_type": "code",
        "outputId": "475314d5-3a58-4152-b1e9-7e4b58b8536a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        }
      },
      "source": [
        "#Constructing a model\n",
        "from tensorflow.keras.layers import Conv1D, MaxPooling1D\n",
        "import tensorflow.keras.backend as K\n",
        "\n",
        "model_with_weights = Sequential([\n",
        "    Conv1D(filters=16, kernel_size=3, input_shape=(128, 64), kernel_initializer='random_uniform', bias_initializer=\"zeros\", activation='relu', name='Convolution_1D_layer'),\n",
        "    MaxPooling1D(pool_size=4, name='Max_pooling_1D_layer'),\n",
        "    Flatten(name='Flatten_layer'),\n",
        "    Dense(64, kernel_initializer='he_uniform', bias_initializer='ones', activation='relu', name='Dense_layer_1'),\n",
        "])\n",
        "\n",
        "#Adding some layers to our model\n",
        "model_with_weights.add(Dense(64, \n",
        "                kernel_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.05), \n",
        "                bias_initializer=tf.keras.initializers.Constant(value=0.4), \n",
        "                activation='relu', name='Dense_layer_2'),)\n",
        "model_with_weights.add(Dense(8, \n",
        "                kernel_initializer=tf.keras.initializers.Orthogonal(gain=1.0, seed=None), \n",
        "                bias_initializer=tf.keras.initializers.Constant(value=0.4), \n",
        "                activation='relu', name='Dense_layer_3'))\n",
        "\n",
        "#Example of a custom initializer\n",
        "def my_init(shape, dtype=None):\n",
        "    return K.random_normal(shape, dtype=dtype)\n",
        "model_with_weights.add(Dense(64, kernel_initializer=my_init, name='Dense_layer_4'))\n",
        "\n",
        "model_with_weights.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "Convolution_1D_layer (Conv1D (None, 126, 16)           3088      \n",
            "_________________________________________________________________\n",
            "Max_pooling_1D_layer (MaxPoo (None, 31, 16)            0         \n",
            "_________________________________________________________________\n",
            "Flatten_layer (Flatten)      (None, 496)               0         \n",
            "_________________________________________________________________\n",
            "Dense_layer_1 (Dense)        (None, 64)                31808     \n",
            "_________________________________________________________________\n",
            "Dense_layer_2 (Dense)        (None, 64)                4160      \n",
            "_________________________________________________________________\n",
            "Dense_layer_3 (Dense)        (None, 8)                 520       \n",
            "_________________________________________________________________\n",
            "Dense_layer_4 (Dense)        (None, 64)                576       \n",
            "=================================================================\n",
            "Total params: 40,152\n",
            "Trainable params: 40,152\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HWfjWfflQEGE",
        "colab_type": "text"
      },
      "source": [
        "### Visualising the initialised weights and biases\n",
        "---\n",
        "Finally, the effect of our initialisers on the weights and biases can be visualized by plotting histograms of the resulting values. \n",
        "\n",
        "Compare these plots with the selected initialisers for each layer above."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uku9N1mHOU2_",
        "colab_type": "code",
        "outputId": "3475f140-8838-4730-d931-d11707dc9f2c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 934
        }
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, axes = plt.subplots(5, 2, figsize=(12,16))\n",
        "fig.subplots_adjust(hspace=0.5, wspace=0.5)\n",
        "\n",
        "# Filter out the pooling and flatten layers, that don't have any weights\n",
        "weight_layers = [layer for layer in model_with_weights.layers if len(layer.weights) > 0]\n",
        "\n",
        "for i, layer in enumerate(weight_layers):\n",
        "    for j in [0, 1]:\n",
        "        axes[i, j].hist(layer.weights[j].numpy().flatten(), align='left')\n",
        "        axes[i, j].set_title(layer.weights[j].name)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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4BN0egd2BPYF7AzsCfzdQ/e7AXVr5EcC72g8Sur0If9X2LtwP+DJAkgcCHwT+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\nJPVhwixJkiT1YcIsSZIk9fH/AHVhco+5aOLcAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Bsn2TBnZfCj",
        "colab_type": "text"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_3\"></a>\n",
        "## The compile method"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bIDz6nr5SgGj",
        "colab_type": "text"
      },
      "source": [
        "### Case 1 - Binary Classification with sigmoid activation function\n",
        "Suppose we are training a model for a binary classification problem with a sigmoid activation function. Given a training example with input $x^{(i)}$, the model will output a float between 0 and 1. Based on whether this float is less than or greater than our \"threshold\" (which by default is set at 0.5), we round the float to get the predicted classification $y_{pred}$ from the model.\n",
        "\n",
        "The accuracy metric compares the value of $y_{pred}$ on each training example with the true output, the one-hot coded vector $y_{true}^{(i)}$ from our training data.\n",
        "\n",
        "Let $$\\delta(y_{pred}^{(i)},y_{true}^{(i)}) = \\begin{cases} 1 & y_{pred}=y_{true}\\\\\n",
        "0 & y_{pred}\\neq y_{true} \\end{cases}$$\n",
        "\n",
        "The accuracy metric  computes the mean of $\\delta(y_{pred}^{(i)},y_{true}^{(i)})$ over all training examples.\n",
        "\n",
        "$$ accuracy = \\frac{1}{N} \\sum_{i=1}^N \\delta(y_{pred}^{(i)},y_{true}^{(i)}) $$"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Swq0EKg2TcT_",
        "colab_type": "code",
        "outputId": "9884469e-ab1b-4793-c171-ef3557629cee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Sigmoid activation function\n",
        "\n",
        "y_true = tf.constant([0.0,1.0,1.0])\n",
        "y_pred = tf.constant([0.4,0.8, 0.3])\n",
        "accuracy = K.mean(K.equal(y_true, K.round(y_pred)))\n",
        "accuracy"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=738, shape=(), dtype=float32, numpy=0.6666667>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5tMvKhqfThkm",
        "colab_type": "text"
      },
      "source": [
        "### Case 2 - Categorical Classification\n",
        "Now suppose we are training a model for a classification problem which should sort data into $m>2$ different classes using a softmax activation function in the last layer.\n",
        "\n",
        "Given a training example with input $x^{(i)}$, the model will output a tensor of probabilities $p_1, p_2, \\dots p_m$, giving the likelihood (according to the model) that $x^{(i)}$ falls into each class.\n",
        "\n",
        "The accuracy metric works by determining the largest argument in the $y_{pred}^{(i)}$ tensor, and compares its index to the index of the maximum value of $y_{true}^{(i)}$ to determine $\\delta(y_{pred}^{(i)},y_{true}^{(i)})$. It then computes the accuracy in the same way as for the binary classification case.\n",
        "\n",
        "$$ accuracy = \\frac{1}{N} \\sum_{i=1}^N \\delta(y_{pred}^{(i)},y_{true}^{(i)}) $$\n",
        "\n",
        "In the backend of Keras, the accuracy metric is implemented slightly differently depending on whether we have a binary classification problem ($m=2$) or a categorical classifcation problem. Note that the accuracy for binary classification problems is the same, no matter if we use a sigmoid or softmax activation function to obtain the output."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uKCuoxZRTkm5",
        "colab_type": "code",
        "outputId": "8d26fb39-e837-4442-e27d-eb2993bbf679",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Binary classification with softmax\n",
        "\n",
        "y_true = tf.constant([[0.0,1.0],[1.0,0.0],[1.0,0.0],[0.0,1.0]])\n",
        "y_pred = tf.constant([[0.4,0.6], [0.3,0.7], [0.05,0.95],[0.33,0.67]])\n",
        "accuracy =K.mean(K.equal(y_true, K.round(y_pred)))\n",
        "accuracy"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=745, shape=(), dtype=float32, numpy=0.5>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgQ8gP1sToKK",
        "colab_type": "code",
        "outputId": "a846fd88-c975-4e09-a48b-9d925d26c505",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Categorical classification with m>2\n",
        "\n",
        "y_true = tf.constant([[0.0,1.0,0.0,0.0],[1.0,0.0,0.0,0.0],[0.0,0.0,1.0,0.0]])\n",
        "y_pred = tf.constant([[0.4,0.6,0.0,0.0], [0.3,0.2,0.1,0.4], [0.05,0.35,0.5,0.1]])\n",
        "accuracy = K.mean(K.equal(K.argmax(y_true, axis=-1), K.argmax(y_pred, axis=-1)))\n",
        "accuracy"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=755, shape=(), dtype=float32, numpy=0.6666667>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4sQLuIjMVaAy",
        "colab_type": "text"
      },
      "source": [
        "### Sparse categorical accuracy\n",
        "\n",
        "This is a very similar metric to categorical accuracy with one major difference - the label $y_{true}$ of each training example is not expected to be a one-hot encoded vector, but to be a tensor consisting of a single integer. This integer is then compared to the index of the maximum argument of $y_{pred}$ to determine $\\delta(y_{pred}^{(i)},y_{true}^{(i)})$.\n",
        "\n",
        "### (Sparse) Top $k$-categorical accuracy \n",
        "In top $k$-categorical accuracy, instead of computing how often the model correctly predicts the label of a training example, the metric computes how often the model has $y_{true}$ in the top $k$ of its predictions. By default, $k=5$.\n",
        "\n",
        "As before, the main difference between top $k$-categorical accuracy and its sparse version is that the former assumes $y_{true}$ is a one-hot encoded vector, whereas the sparse version assumes $y_{true}$ is an integer.\n",
        "\n",
        "### Custom metrics\n",
        "A custom metric takes in (at least) two arguments called `y_true` and `y_pred` and then output a single tensor value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YVkQyrDTZfCu",
        "colab_type": "code",
        "outputId": "9c7b23dc-af20-4776-e248-b71c233f862f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "source": [
        "# Define the model optimizer, loss function and metrics\n",
        "\n",
        "model1.compile(optimizer='adam', # 'sgd', 'rmsprop', 'adadelta'\n",
        "               loss='sparse_categorical_crossentropy', # 'categorical_crossentropy', 'binary_crossentropy', 'mean_squared_error'\n",
        "               metrics=['accuracy','mae']) # 'mape'\n",
        "\n",
        "model2.compile(optimizer=tf.keras.optimizers.SGD(),\n",
        "               loss=tf.keras.losses.BinaryCrossentropy(), \n",
        "               metrics=['top_k_categorical_accuracy',tf.keras.metrics.MeanAbsoluteError()])\n",
        "\n",
        "model3.compile(optimizer=tf.keras.optimizers.SGD(learning_rate=0.001, momentum=0.9, nesterov=True),\n",
        "               loss=tf.keras.losses.BinaryCrossentropy(from_logits=True), \n",
        "               metrics=[tf.keras.metrics.BinaryAccuracy(threshold=0.7),tf.keras.metrics.MeanAbsoluteError()])\n",
        "\n",
        "# Define a custom metric\n",
        "def mean_pred(y_true, y_pred):\n",
        "    return K.mean(y_pred)\n",
        "\n",
        "# Specify k instead with the sparse top-k categorical accuracy\n",
        "model4.compile(optimizer='adam',\n",
        "              loss='sparse_categorical_crossentropy',\n",
        "              metrics=[mean_pred])\n",
        "\n",
        "print('Optimizers:\\n',model1.optimizer,model2.optimizer,model3.optimizer,model4.optimizer,'\\n')\n",
        "print('Losses:\\n',model1.loss,model2.loss,model3.loss,model4.loss,'\\n')\n",
        "print('Metrics:\\n',model1.metrics,model2.metrics,model3.metrics,model4.metrics,'\\n')"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Optimizers:\n",
            " <tensorflow.python.keras.optimizer_v2.adam.Adam object at 0x7f53732a6080> <tensorflow.python.keras.optimizer_v2.gradient_descent.SGD object at 0x7f53732a6198> <tensorflow.python.keras.optimizer_v2.gradient_descent.SGD object at 0x7f53732a6048> <tensorflow.python.keras.optimizer_v2.adam.Adam object at 0x7f53731521d0> \n",
            "\n",
            "Losses:\n",
            " sparse_categorical_crossentropy <tensorflow.python.keras.losses.BinaryCrossentropy object at 0x7f5378e3fa90> <tensorflow.python.keras.losses.BinaryCrossentropy object at 0x7f53731b05c0> sparse_categorical_crossentropy \n",
            "\n",
            "Metrics:\n",
            " [<tensorflow.python.keras.metrics.MeanMetricWrapper object at 0x7f5373230e10>, <tensorflow.python.keras.metrics.MeanMetricWrapper object at 0x7f5373230a20>] [<tensorflow.python.keras.metrics.MeanMetricWrapper object at 0x7f53731b0198>, <tensorflow.python.keras.metrics.MeanAbsoluteError object at 0x7f5378e3f470>] [<tensorflow.python.keras.metrics.BinaryAccuracy object at 0x7f53731b0cc0>, <tensorflow.python.keras.metrics.MeanAbsoluteError object at 0x7f53731316d8>] [<tensorflow.python.keras.metrics.MeanMetricWrapper object at 0x7f53730bdc50>] \n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tPLuoW4sZfDr",
        "colab_type": "text"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_4\"></a>\n",
        "## The fit method"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "y33ue-rhZfD5",
        "colab_type": "text"
      },
      "source": [
        "#### Load the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Rdz4VOkZfD9",
        "colab_type": "code",
        "outputId": "106ca5ef-6644-46af-b58c-7a4dac05f127",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "from tensorflow.keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "# Load the Fashion-MNIST dataset\n",
        "fashion_mnist_data = tf.keras.datasets.fashion_mnist\n",
        "(train_images, train_labels), (test_images, test_labels) = fashion_mnist_data.load_data()\n",
        "\n",
        "# Print the shape of the training data\n",
        "train_images.shape"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-labels-idx1-ubyte.gz\n",
            "32768/29515 [=================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/train-images-idx3-ubyte.gz\n",
            "26427392/26421880 [==============================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-labels-idx1-ubyte.gz\n",
            "8192/5148 [===============================================] - 0s 0us/step\n",
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/t10k-images-idx3-ubyte.gz\n",
            "4423680/4422102 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60000, 28, 28)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PKBlt2yaZfEZ",
        "colab_type": "code",
        "outputId": "0702bc95-e4ee-45cc-b873-61e3d5d97c06",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# Define the labels\n",
        "\n",
        "labels = [\n",
        "    'T-shirt/top',\n",
        "    'Trouser',\n",
        "    'Pullover',\n",
        "    'Dress',\n",
        "    'Coat',\n",
        "    'Sandal',\n",
        "    'Shirt',\n",
        "    'Sneaker',\n",
        "    'Bag',\n",
        "    'Ankle boot'\n",
        "]\n",
        "\n",
        "# Rescale the image values so that they lie in between 0 and 1.\n",
        "train_images=train_images/255.0\n",
        "test_images=test_images/255.0\n",
        "\n",
        "# Display one of the images\n",
        "i=0\n",
        "img=train_images[i,:,:]\n",
        "plt.imshow(img)\n",
        "print(f'label: {labels[train_labels[i]]}')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "label: Ankle boot\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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de64xbfhtd7vJjLNfAuBmAJtE5NNFyO/FWMh/IyK3AdgL4IZJXBcRlUlo2FX1LQCuQ+6V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\ngg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9GcFZncIZfE4",
        "colab_type": "code",
        "outputId": "fc5ebbc1-fd0b-4441-cc7f-bbc28c275ea1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        }
      },
      "source": [
        "# Fit the model\n",
        "history1 = model1.fit(train_images[...,np.newaxis],train_labels,epochs=8,batch_size=256)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 60000 samples\n",
            "Epoch 1/8\n",
            "60000/60000 [==============================] - 15s 253us/sample - loss: 0.8521 - accuracy: 0.7306 - mae: 4.4200\n",
            "Epoch 2/8\n",
            "60000/60000 [==============================] - 15s 242us/sample - loss: 0.4827 - accuracy: 0.8321 - mae: 4.4200\n",
            "Epoch 3/8\n",
            "60000/60000 [==============================] - 15s 243us/sample - loss: 0.4253 - accuracy: 0.8532 - mae: 4.4200\n",
            "Epoch 4/8\n",
            "60000/60000 [==============================] - 15s 243us/sample - loss: 0.3953 - accuracy: 0.8628 - mae: 4.4200\n",
            "Epoch 5/8\n",
            "60000/60000 [==============================] - 15s 243us/sample - loss: 0.3782 - accuracy: 0.8679 - mae: 4.4200\n",
            "Epoch 6/8\n",
            "60000/60000 [==============================] - 15s 242us/sample - loss: 0.3659 - accuracy: 0.8721 - mae: 4.4200\n",
            "Epoch 7/8\n",
            "60000/60000 [==============================] - 15s 242us/sample - loss: 0.3552 - accuracy: 0.8770 - mae: 4.4200\n",
            "Epoch 8/8\n",
            "60000/60000 [==============================] - 15s 244us/sample - loss: 0.3468 - accuracy: 0.8790 - mae: 4.4200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "a-HQDKFFZfFH",
        "colab_type": "text"
      },
      "source": [
        "#### Plot training history"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xHeaLZ2qZfFL",
        "colab_type": "code",
        "outputId": "11444d05-543f-4a41-d78e-54c82e9aed16",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "# Load the history into a pandas Dataframe\n",
        "\n",
        "df1=pd.DataFrame(history1.history)\n",
        "df1.head()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>accuracy</th>\n",
              "      <th>mae</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.852149</td>\n",
              "      <td>0.730567</td>\n",
              "      <td>4.420002</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.482745</td>\n",
              "      <td>0.832133</td>\n",
              "      <td>4.420000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.425349</td>\n",
              "      <td>0.853167</td>\n",
              "      <td>4.420001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.395272</td>\n",
              "      <td>0.862833</td>\n",
              "      <td>4.419998</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.378220</td>\n",
              "      <td>0.867933</td>\n",
              "      <td>4.420001</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       loss  accuracy       mae\n",
              "0  0.852149  0.730567  4.420002\n",
              "1  0.482745  0.832133  4.420000\n",
              "2  0.425349  0.853167  4.420001\n",
              "3  0.395272  0.862833  4.419998\n",
              "4  0.378220  0.867933  4.420001"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qU_70y4xZfFb",
        "colab_type": "code",
        "outputId": "e33f2e2c-c525-486d-be80-4e0605f1017c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Make a plot for the loss\n",
        "\n",
        "loss_plot=df1.plot(y='loss',title='Loss vs. Epochs',legend=False)\n",
        "loss_plot.set(xlabel='Epochs',ylabel='Loss');"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LLpo24j7uTltXP4fbujjc2s3hk+cXFkPdTgqLqU9BIDJFmRkVJflUlJxfWAy1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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "47uyJHjdZfFl",
        "colab_type": "code",
        "outputId": "48406a28-b790-43e9-b5f6-157bb87d0a87",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Make a plot for the accuracy\n",
        "\n",
        "acc_plot=df1.plot(y='accuracy',title='Accuracy vs. Epochs',legend=False)\n",
        "acc_plot.set(xlabel='Epochs',ylabel='Accuracy');"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wLa2rZ/qE0Zw1fXyuQzEz61VfEsQ+SedExHMAks4FWvvy5mmfhke6bftsxvJK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\nTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jEO2DvQ1ZfF2",
        "colab_type": "code",
        "outputId": "ce7a0b1e-952d-4359-d684-ded46109edaf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "# Make a plot for the additional metric\n",
        "\n",
        "mae_plot=df1.plot(y='mae',title='MAE vs. Epochs',legend=False)\n",
        "mae_plot.set(xlabel='Epochs',ylabel='MAE');"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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anojIa632PRv0aIxxFOV7WL37MPsO17kdSo85UtfA+7vCZ/5EW0qm5FJZU89b\nmw64HYpxUUeJwr/2Q+vfZqsLYXpMLBQzWrq1imaFGS4UKuqsWWNzyEhJYJ6tKBvTOkoU2s7ztl4b\nEzRj+6eTm5kS1c1PZd6qsO2faJGcEM+lkwby8uq91NbHbq2QWNdRovCIyFdE5Kt+z1teW9EH02NE\nhOJxHt7adIDjjdH5AVXmraRgSB9SEsOvf8Lf3IJcauqbeG2dFTSKVR0lir8C6UCa3/OW1w/0bGgm\n1hXnezhW38QSb5XboQTdYWf+xMwwHBbb2hnD+9E/I9kKGsWwgEtVqurd7e0TkWnBD8eYD8wckU1y\nQhyvr9vPuWOi6wZ26RanfyICEkV8nDBnci6PlG6l+lgDmamJbodkQqxLaxqLyHjgGudxCCjsiaCM\nAeiVFM+ZI/uxcP1+7tTxiETP+InSzZUkJcRx2pA+bofSKVeclscDb23hP6v2cPX02BgZX1vfxM9f\nWhdRI+8yUhL5+ZWTg37eDhOFiAzjg+TQAAwFClV1a9CjMaaV4nwPC59bjfdADSNz0twOJ2jKtlQy\nNQL6J1pMyM1gRE5vnlu+OyYSRVOzctsT7/Hq2n2MykkjUr6j9ElN6pHzBkwUIlIKZABPAB9T1Y0i\nssWShAmVonwPPLeahev2R02iqK5tYPXuw3zp/NFuh9JpIkLJlDx++9oG9lbXMSAzxe2Qeoyq8oPn\nV7NgzT7umjOe684a7nZIruuoM3sfvs7r/nwwyqnbw2JFJEtEFojIRufPNscHikiTiCx3Hq3raZsY\nMKhvKmP6p0XVMNl3tlShSkR0ZPubW5CLKrywMro7tf/6ppdHSrdx07kjLEk4AiYKVb0CmAQsA+4S\nkS1AXxGZ3s3r3gG8pqqjgddovxZ2raoWOI+57RxjolxRvod3tlRxpC46ihmVeStJTohjyuDI6J9o\nMTy7N1MGZfJsFK/9NG/Fbn4yfx2XTx7IHbPz3Q4nbHS4KKCqVqvqQ6p6ETAD+D7wGxHZ0Y3rlgCP\nOM8fAa7oxrlMlCse66GxWXlrY3QsI1HmrWTqkL4R0z/hb25BHqt2HWbT/qNuhxJ0Zd5KvvbUCqYP\nz+Keq6YQFxchHRMh0KXVY1V1n6r+XlXPAs7uxnX7q2pLQd69+Jq22pIiIuUiUiYiAZOJiNzkHFte\nURE71dFiwelD+5KRkhAVzU+HjtWzZs9hZobxsh2BzJk8EBHfN+9osnHfEW56tJzBWb24/1OnR2QS\n70kddWZ31C/QbnOQiLwKDGhj13f8X6iqikh7/R5DVXWXiIwAXheR91V1c1sHqur9wP0AhYWFtrxI\nFEmIj+PcMTksXF9Bc7NG9PFt2fwAABh3SURBVDe9lv6JSJg/0RZPRgpnjuzHvOW7uP2C0VExZHnf\n4Tque2gpyYnxPHz99B4bORTJOhoeOxPYATwOLKELCwGq6gXt7RORfSIyUFX3iMhAoM2viqq6y/nT\nKyJvAKcBbSYKE92K8z28sHIPq3ZXM3lQZLXt+yvzVjn9E5luh3LKSqbk8Y1/rWTlzuqI62dp7ejx\nRq5/aCkHj9Xz1OdmMjgr1e2QwlJHTU8DgG8DE4HfARcCB1R1kaou6sZ15wHXOs+vBZ5rfYCI9BWR\nZOd5NnAWsKYb1zQR7LyxHkTgtbWR3fxU6q2kcFhfkhMit2nj4okDSIqPi/glPRqamvnCY++yft8R\n/viJqUzMi9zk3dM6GvXUpKovqeq1+DqyNwFviMgt3bzuz4ALRWQjcIHzGhEpFJGWNaTGAeUisgJY\nCPxMVS1RxKis3kmcNrgPC9dHbqI4dKyedXsPM2N4ZDY7tcjslUhxvofnV+6O2Lrmqsp3nnmfxRsq\n+MlHJnLeWI/bIYW1zszMTgYuwzczexhwL/BMdy6qqpXA+W1sLwc+4zx/G9/QXGMAX/PTPa9sYP+R\nOjzpkTfha0lL/0SEdmT7KynI5aXVeynzVnLWqGy3w+mye1/bxFPlO7nt/NH877Ton2neXR2VQn0U\nKAWmAner6jRV/WFL34ExoVTkFDN6Y31kjmor3VxJSmIcUyK4j6VFUb6H9OQEnovAORVPle/gN69u\n4MrTB3H7BZEzO95NHfVRfBIYDXwJeFtEDjuPIyJyuOfDM+YD4wdmMCAjJWKr3pV5KykcmkVSQpdG\npYellMR4Lp44gP+s2ktdQ+TUC1m8oYJv//t9zhmdzU8/OikqRm2FQkd9FHGqmu48Mvwe6aqaEaog\njQHfekNF+Tm8ufEA9Y3NbofTJQdr6lm390hY18fuqpKCXI7UNUbMHd7q3dXc/H/LGN0/nT9+YiqJ\n8ZGfsEPF/qZMRCka6+Ho8UbKt0ZWMaMlWyoBInaiXVtmjuhHdlpyRDQ/7TpUy/UPLSWzVyIPXTeN\n9BSrqdEVlihMRDlrVDZJ8XERN0u7zFtFr8R4JuVFfv9Ei4T4OC6fPJDX1u3ncBivw1V9rIHr/vYO\ntQ1NPHzD9Khe+banWKIwEaV3cgJnjMji9QgbJlu62Td/Ihr6J/yVFORS39jMy6v2uh1Km443NnHT\n38vZWlnDXz51OmP6p7sdUkSKrt9aExOK8z14K2rYVlnjdiidUnn0OOv3HYnYZTsCKRjchyFZqWG5\n9lNzs/L1f65kyZYq7rlqCmeOjLxhvOHCEoWJOMXOMNlIaX56Z4uvPyUaE4WIUFKQy383HWD/kfAq\nGfqLl9czb8Vuvjk7n5KCPLfDiWiWKEzEGdqvNyNyekdMoij1VpKaFM/kQdG5RERJQS7NCi+u3NPx\nwSHyaOlW/rxoM5+cMYTPzxrhdjgRzxKFiUjn53tY4q2i5nij26F0qMxbSeGwrKgdjjnKk86E3Iyw\nWftpwZp93DVvNReM83DXnAk2VyIIovM310S9onwP9U3NvLUpvIsZHTh6nA37jkbV/Im2lBTksnzH\nIdf7jd7bfpBbH3+XSXmZ3HvNaSREaXIONftbNBFp2rAs0pMTwn6W9hKvr38i0upjd9WcKbm+gkYu\n3lVsPVDDZx4px5OewoPXTSM1qcOl7EwnWaIwESkxPo5zxmSzcP1+VMN3BdMybyW9k+KjfgnrgZm9\nmD4si2eX73Ll36Py6HGue+gdmlV55IbpZKclhzyGaGaJwkSsorEe9h0+zurd4bvsWLT3T/grKchj\nc0UNa/aE9t+jtr6Jzzxazp7qOh64dhrDs3uH9PqxIPp/e03UaqkhEK7NTxVHjrNx/9GoWrYjkEsm\nDiAxXkLaqd3UrHzpifdYvuMQv7v6NE4f2jdk144llihMxMpJT2bKoMywnaXdsr5TNM6faEvf3knM\nGpPDvOW7aQ5BQSNV5QfPr+aVNfu48/LxzJ44oMevGassUZiIVpTvYfmOQ1QePe52KB9S5q0kLTmB\nibmxs9Dy3II89h6u450QLNr41ze9PFK6jc+eM5zrzhre49eLZa4kChG5SkRWi0iziBQGOG62iKwX\nkU0ickcoYzSRoTjfgyos2hB+S12Xbq5k2rC+MTVE84JxHlKT4nu8+en5Fbv5yfx1XDZ5IN+6ZFyP\nXsu4d0exCvgosLi9A0QkHvgDcAkwHrhGRMaHJjwTKSbmZpKdlhx2s7T3H6ljc0VNzDQ7tUhNSuCi\n8f2Z//6eHqsZssRbyVefWsH0YVn86qopxMXZhLqe5kqiUNW1qrq+g8OmA5tU1auq9cATQEnPR2ci\nSVycUDQ2h8UbKmhsCp9iRifmT8RIR7a/koI8qmsbWNwDd3kb9x3hs4+WMzirF/d/+nRSEuODfg3z\nYeF8T5wH7PB7vdPZ1iYRuUlEykWkvKIi/JohTM8pzvdwuK6RZdsOuh3KCaXeStKTExg/MHb6J1qc\nPTqbrN5JPBfkFWX3H67juoeWkpwYz8PXT6dPalJQz2/a12OJQkReFZFVbTx65K5AVe9X1UJVLczJ\nyemJS5gwdfbobBLjJayan8q8lUwbnhVT/RMtEuPjuGzSQBas2Ru0tbiOHm/k+oeXcvBYPQ9dN43B\nWalBOa/pnB77LVbVC1R1YhuP5zp5il3AYL/Xg5xtxpwkPSWR6cOzwiZR7Dtch7eiJuqX7QikpCCX\nuoZmFqzZ1+1zNTQ188XH3mXd3iP84RNTo36WezgK5687S4HRIjJcRJKAq4F5LsdkwlTRWA8b9x9l\nR9Uxt0OhzBtb8yfaMnVIX/L69Op2PW1V5bvPrGLRhgp+fMVEipxJlia03Boe+xER2QnMBF4UkZed\n7bkiMh9AVRuBW4CXgbXAU6q62o14TfhrKWa0MAwm35V5q0hPSWB8DM2faC0uTphbkMvijQe6Ncfl\n3tc28WT5Dm4rHsXV04cEMULTFW6NenpGVQeparKq9lfVi53tu1X1Ur/j5qvqGFUdqao/diNWExlG\n5KQxrF9qWDQ/lXkrOWN4FvExPmyzpCCXpmZl/vunVtDon+U7+M2rG/jY1EHcfuGYIEdnuiKcm56M\n6ZKifA+lmyuprW9yLYa91XVsORB78yfakj8gg7H9009p8t3iDRV869/vc/aobH760UlWfMhllihM\n1CjO93C8sZm3N7tXzCjW1nfqyNyCXMq3HexS39Hq3dV84bF3GeVJ40+fnEpSgn1Muc3+BUzUmD48\ni9SkeFebn0o3V5KRksC4GJw/0Za5U3IBeH5l5+4qdh2q5fqHlpKeksDD108nPSWxJ8MznWSJwkSN\n5IR4zh6VzcJ17hUzKvNWMn14v5jvn2gxOCuV04f27VTlu+raBq5/6B1qG5p4+PrpDMhMCUGEpjMs\nUZioUpzvYXd1Hev3HQn5tfdU17K18ljU18fuqisKclm39wjr9rZf0Oh4YxOf+3s5Ww7U8JdPnc7Y\nAekhjNB0xBKFiSpFzjBZN5qfWuZPxOL6ToFcOmkg8XHS7l1Fc7Py9X+upMxbxT1XTeHMkdkhjtB0\nxBKFiSr9M1KYkJvhStW7ss1VZPZKZNwA65/w1y8tmXNGZ/Pc8t1tNgn+4uX1zFuxm2/MHktJQbvL\nuRkXWaIwUac438OybQc5WFMf0uuWbfHNn7Blrz+spCCXXYdqeXf7yQs3/r1sG39etJlPzhjCzbNG\nuhSd6YglChN1ivM9NCss3hi6VYR3H6plW+UxGxbbjgvHDyAlMe6kORUL1uzjzudWcX6+h7vmTLC5\nEmHMEoWJOlMG9aFf76SQ9lPY+k6BpSUncMG4/rywcg8NTc0s33GIWx9/l0l5mfz+46fF5Cq7kcT+\ndUzUiYsTZo3NYdGGCpqaQzNMtsxbSZ/URPJttE67SgryqKqp5x9LtnPjw0vxpKfw4HXTSE1KcDs0\n0wFLFCYqFed7OHSsgfe2h6aYUanX+ic6MmtMDpm9Erlz3mqaVXn4+mlkpyW7HZbpBEsUJiqdMzqH\n+LjQFDPaefAYO6pqrdmpA0kJcVxRkEtyQhwPXFvIiJw0t0MynWSJwkSlzF6JFA7tG5JEURbD9bG7\n6juXjefNbxZx+lCblBhJLFGYqFWc72Hd3iPsPlTbo9cp81bSNzWRMR7rn+hIUkIcnnRbmiPSWKIw\nUStUxYx89Sf6Wf+EiVqWKEzUGuVJY1DfXj06S3tH1TF2Hqy1ZicT1dwqhXqViKwWkWYRKQxw3FYR\neV9ElotIeShjNJFPRCjO9/DfTZXUNfRMMSObP2FigVt3FKuAjwKLO3FskaoWqGq7CcWY9hTle6ht\naDrxgR5sZd4qsnonMdpjI3hM9HKrZvZaVV3vxrVNbJk5oh8piXE9MvpJVSnzVjJjhM2fMNEt3Pso\nFHhFRJaJyE2BDhSRm0SkXETKKypCt8aPCW8pib5iRq/3QDGjnQdr2XXI5k+Y6NdjiUJEXhWRVW08\nSrpwmrNVdSpwCfBFETm3vQNV9X5VLVTVwpycnG7Hb6JHUb6HnQdr2bT/aFDPW9pSf8IShYlyPbbI\niqpeEIRz7HL+3C8izwDT6Vy/hjEnFI39oJjR6P7Bm+tQtrmSfr2TGGX9EybKhW3Tk4j0FpH0lufA\nRfg6wY3pktw+vcgfkB7UfooP+if62fLYJuq5NTz2IyKyE5gJvCgiLzvbc0VkvnNYf+AtEVkBvAO8\nqKovuRGviXzF+R7Ktx2kurYhKOfbUVXL7uo6Ztj8CRMD3Br19IyqDlLVZFXtr6oXO9t3q+qlznOv\nqk5xHhNU9cduxGqiQ3G+h6Zm5c0gFTMq9R4AYOYIW7PIRL+wbXoyJphOG9KXPqmJQWt+KvNWkZ2W\nzEhbAdXEAEsUJibExwmzxuSwaH0Fzd0sZqSqlG72zZ+w/gkTCyxRmJhRnO+hsqaeFTsPdes82yqP\nsfdwnc2fMDHDEoWJGbPG5BAndHuRQFvfycQaSxQmZvRJTWLqkL683s1lx0u9leSkJzMyp3eQIjMm\nvFmiMDGlKN/Dql2H2Xe47pTeb/MnTCyyRGFiyoliRqfY/LS18hj7Dh9nhg2LNTHEEoWJKfkD0snN\nTDnlYbKlm219JxN7LFGYmCIiFOV7eGvTAY43dr2YUZm3Ek96MsOzrX/CxA5LFCbmFOd7OFbfxDtb\nqrr0vpb+iZkjrX/CxBZLFCbmnDkym+SErhcz8h6oYf+R4zYs1sQcSxQm5vRKimfmyH5d7tC2+RMm\nVlmiMDGpON/D1spjeCs6X8yozFvFgIwUhvVL7cHIjAk/lihMTPIvZtQZtr6TiWWWKExMGpyVymhP\nGgs7OUt7c0UNB45a/4SJTZYoTMwqzvfwzpYqjh5v7PDYlv6JmVaoyMQgSxQmZhXle2hoUt7qRDGj\nUm8lAzNTGJJl/RMm9rhVCvWXIrJORFaKyDMi0qed42aLyHoR2SQid4Q6ThPdTh/al/SUhA77KVSV\nJba+k4lhbt1RLAAmqupkYAPwrdYHiEg88AfgEmA8cI2IjA9plCaqJcbHce6YHF5fF7iY0ab9Rzlw\ntN6W7TAxy62a2a+oakvDcBkwqI3DpgObnNrZ9cATQEmoYjSx4fx8DweOHmfV7up2j7H5EybWhUMf\nxQ3Af9rYngfs8Hu909nWJhG5SUTKRaS8oqLjNmdjwFfMSCTwMNkybxW5mSkMzuoVwsiMCR89lihE\n5FURWdXGo8TvmO8AjcBj3b2eqt6vqoWqWpiTk9Pd05kY0S8tmYLBfdqdpX2i/oSt72RiWEJPnVhV\nLwi0X0SuAy4HzlfVthqIdwGD/V4PcrYZE1TFYz38asEGKo4cJyc9+aR9G/cfpbKm3pqdTExza9TT\nbOAbwFxVPdbOYUuB0SIyXESSgKuBeaGK0cSOIqeY0RttTL47MX/CEoWJYW71UdwHpAMLRGS5iPwZ\nQERyRWQ+gNPZfQvwMrAWeEpVV7sUr4liE3Iz6J+R3OYs7dLNleT16cVgmz9hYliPNT0Foqqj2tm+\nG7jU7/V8YH6o4jKxSUQoGuvhxZV7aGhqJjHe9/2puVlZsqXqxLpQxsSqcBj1ZIzrivI9HDneyNKt\nHxQz2rj/KFU19bZsh4l5liiMAc4elU1SfNxJo59KNx8A4IzhWW6FZUxYsERhDNA7OYEzRmSdNJ+i\nzFvFoL7WP2GMJQpjHEVjPWyuqGFbZY3TP1Fpo52MwRKFMScU539QzGj9viMcPNZg8yeMwaVRT8aE\no2HZvRmR3fuk5qcZ1pFtjCUKY/wV53t4tHQbAEOyUsnrY+s7GWNNT8b4Kc73UN/UzJsbDzBjhI12\nMgYsURhzksJhWaQl+260rX/CGB9LFMb4SUqI45zR2YAlCmNaWB+FMa18sWgUE/MyybX+CWMASxTG\nfMjEvEwm5mW6HYYxYcOanowxxgRkicIYY0xAliiMMcYEZInCGGNMQJYojDHGBGSJwhhjTECWKIwx\nxgRkicIYY0xAoqpuxxB0IlIBbDvFt2cDB4IYTk+KpFghsuKNpFghsuKNpFghsuLtTqxDVTWnrR1R\nmSi6Q0TKVbXQ7Tg6I5JihciKN5JihciKN5JihciKt6ditaYnY4wxAVmiMMYYE5Alig+73+0AuiCS\nYoXIijeSYoXIijeSYoXIirdHYrU+CmOMMQHZHYUxxpiALFEYY4wJyBKFQ0Rmi8h6EdkkIne4HU8g\nIvI3EdkvIqvcjqUjIjJYRBaKyBoRWS0iX3I7pkBEJEVE3hGRFU68d7sdU0dEJF5E3hORF9yOpSMi\nslVE3heR5SJS7nY8gYhIHxF5WkTWichaEZnpdkztEZGxzt9py+OwiHw5aOe3PgrffzRgA3AhsBNY\nClyjqmtcDawdInIucBR4VFUnuh1PICIyEBioqu+KSDqwDLgijP9uBeitqkdFJBF4C/iSqpa5HFq7\nROQrQCGQoaqXux1PICKyFShU1bCfwCYijwBvquoDIpIEpKrqIbfj6ojzebYLOENVT3Xi8UnsjsJn\nOrBJVb2qWg88AZS4HFO7VHUxUOV2HJ2hqntU9V3n+RFgLZDnblTtU5+jzstE5xG236ZEZBBwGfCA\n27FEExHJBM4FHgRQ1fpISBKO84HNwUoSYImiRR6ww+/1TsL4wyxSicgw4DRgibuRBOY05SwH9gML\nVDWc4/0t8A2g2e1AOkmBV0RkmYjc5HYwAQwHKoCHnGa9B0Skt9tBddLVwOPBPKElChMSIpIG/Av4\nsqoedjueQFS1SVULgEHAdBEJy+Y9Ebkc2K+qy9yOpQvOVtWpwCXAF51m1HCUAEwF/qSqpwE1QFj3\nXQI4TWRzgX8G87yWKHx2AYP9Xg9ytpkgcNr6/wU8pqr/djueznKaGhYCs92OpR1nAXOddv8ngGIR\n+T93QwpMVXc5f+4HnsHX7BuOdgI7/e4mn8aXOMLdJcC7qrovmCe1ROGzFBgtIsOdjHw1MM/lmKKC\n0zn8ILBWVX/tdjwdEZEcEenjPO+Fb4DDOnejapuqfktVB6nqMHy/s6+r6iddDqtdItLbGdCA04xz\nERCWI/dUdS+wQ0TGOpvOB8JyAEYr1xDkZifw3V7FPFVtFJFbgJeBeOBvqrra5bDaJSKPA+cB2SKy\nE7hTVR90N6p2nQV8CnjfafcH+LaqzncxpkAGAo84I0figKdUNeyHnUaI/sAzvu8OJAD/UNWX3A0p\noFuBx5wvj17gepfjCchJvhcCnwv6uW14rDHGmECs6ckYY0xAliiMMcYEZInCGGNMQJYojDHGBGSJ\nwhhjTECWKIzpJBFparVCZ9Bm6orIsEhYDdjEJptHYUzn1TpLexgTU+yOwphucmos/MKps/COiIxy\ntg8TkddFZKWIvCYiQ5zt/UXkGafmxQoROdM5VbyI/NWpg/GKMzMcEbnNqeexUkSecOnHNDHMEoUx\nnderVdPT//rtq1bVScB9+FZ0Bfg98IiqTgYeA+51tt8LLFLVKfjWD2pZBWA08AdVnQAcAj7mbL8D\nOM05z+d76oczpj02M9uYThKRo6qa1sb2rUCxqnqdBRD3qmo/ETmAr2hTg7N9j6pmi0gFMEhVj/ud\nYxi+Jc1HO6+/CSSq6o9E5CV8haqeBZ71q5dhTEjYHYUxwaHtPO+K437Pm/igD/Ey4A/47j6Wioj1\nLZqQskRhTHD8r9+fpc7zt/Gt6grwCeBN5/lrwM1wokhSZnsnFZE4YLCqLgS+CWQCH7qrMaYn2TcT\nYzqvl98KuAAvqWrLENm+IrIS313BNc62W/FVSPs6vmppLauPfgm4X0RuxHfncDOwp51rxgP/5yQT\nAe6NoJKcJkpYH4Ux3eT0URSq6gG3YzGmJ1jTkzHGmIDsjsIYY0xAdkdhjDEmIEsUxhhjArJEYYwx\nJiBLFMYYYwKyRGGMMSag/wdzorJbx2kjsQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TZF3UgGRZfGC",
        "colab_type": "text"
      },
      "source": [
        "***\n",
        "<a id=\"coding_tutorial_5\"></a>\n",
        "## The evaluate and predict methods"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pTNjpo9CZfGN",
        "colab_type": "text"
      },
      "source": [
        "#### Evaluate the model on the test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B02ME8G2ZfGQ",
        "colab_type": "code",
        "outputId": "16b0b97c-b054-402d-c225-0c2b8eab78d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Evaluate the model\n",
        "\n",
        "test_loss, test_accuracy, test_mae= model1.evaluate(test_images[...,np.newaxis],test_labels, verbose=2)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "10000/1 - 2s - loss: 0.3460 - accuracy: 0.8671 - mae: 4.4200\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F29eZMavZfGb",
        "colab_type": "text"
      },
      "source": [
        "#### Make predictions from the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TYd_iY5LZfGd",
        "colab_type": "code",
        "outputId": "d97f95b2-8b91-40e2-be1a-961b8a64da86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        }
      },
      "source": [
        "# Choose a random test image\n",
        "\n",
        "random_inx = np.random.choice(test_images.shape[0])\n",
        "\n",
        "test_image = test_images[random_inx]\n",
        "plt.imshow(test_image)\n",
        "plt.show()\n",
        "print(f\"Label: {labels[test_labels[random_inx]]}\")"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPsAAAD4CAYAAAAq5pAIAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nCDuQxP8DdwLDzqO2AsgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Label: Trouser\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZR0ZvvldZfHH",
        "colab_type": "code",
        "outputId": "4a7d2e1a-3588-42eb-e338-9e71baefe31e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Get the model predictions\n",
        "\n",
        "predictions=model1.predict(test_image[np.newaxis,...,np.newaxis])\n",
        "print(f\"Model Prediction: {labels[np.argmax(predictions)]}\")"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model Prediction: Trouser\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VzXOHmrcgkt2",
        "colab_type": "text"
      },
      "source": [
        "## Model Validation and Regularization\n",
        "Regularization techniques are used into the model training as they have the effect of constraining the model capacity in preventing overfitting. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "f1qXIu_vk_TL",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the dataset\n",
        "\n",
        "from sklearn.datasets import load_diabetes\n",
        "diabetes_dataset = load_diabetes()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jkr0IMuhoJBJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Save the input and target variables\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "data = diabetes_dataset['data']\n",
        "targets = diabetes_dataset['target']"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1mclQ6odoJD7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Normalise the target data (this will make clearer training curves)\n",
        "\n",
        "targets = (targets - targets.mean(axis=0)) / (targets.std())"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KSQdsKuwoJGr",
        "colab_type": "code",
        "outputId": "01556378-60ec-4ee5-829b-e7bd88aff853",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "# Split the dataset into training and test datasets \n",
        "\n",
        "train_data, test_data, train_targets, test_targets = train_test_split(data, targets, test_size=0.1)\n",
        "\n",
        "print(train_data.shape)\n",
        "print(test_data.shape)\n",
        "print(train_targets.shape)\n",
        "print(test_targets.shape)"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(397, 10)\n",
            "(45, 10)\n",
            "(397,)\n",
            "(45,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bZ_w0LlToJJT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Flatten, Dense, Conv2D, MaxPooling2D, BatchNormalization, Dropout\n",
        "from tensorflow.keras import regularizers\n",
        "\n",
        "# Building models\n",
        "\n",
        "def get_model():\n",
        "  model = Sequential([\n",
        "    Dense(128, input_shape=[train_data.shape[1],], activation=\"relu\"),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(128, activation='relu'),\n",
        "    Dense(1)\n",
        "])\n",
        "  return model\n",
        "\n",
        "def get_regularised_model(wd,rate): # wd represents weight decay and rate represents Dropout rate\n",
        "  regularised_model = Sequential([\n",
        "    Dense(128, kernel_regularizer=regularizers.l2(wd), input_shape=[train_data.shape[1],], activation=\"relu\"),\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l2(wd), activation='relu'),\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l2(wd), activation='relu'),\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l2(wd), activation='relu'),\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l2(wd), activation='relu'),\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l2(wd), activation='relu'),\n",
        "    Dropout(rate),\n",
        "    Dense(1)\n",
        "])\n",
        "  return regularised_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tHWIo5UAlvcB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Plot the learning curves\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "def plotting_curves(history):\n",
        "  frame = pd.DataFrame(history.history)\n",
        "  epochs = np.arange(len(frame))\n",
        "\n",
        "  fig = plt.figure(figsize=(12,4))\n",
        "\n",
        "  # Loss plot\n",
        "  ax = fig.add_subplot(121)\n",
        "  ax.plot(epochs, frame['loss'], label=\"Train\")\n",
        "  ax.plot(epochs, frame['val_loss'], label=\"Validation\")\n",
        "  ax.set_xlabel(\"Epochs\")\n",
        "  ax.set_ylabel(\"Loss\")\n",
        "  ax.set_title(\"Loss vs Epochs\")\n",
        "  ax.legend()\n",
        "\n",
        "  # Accuracy plot\n",
        "  ax = fig.add_subplot(122)\n",
        "  ax.plot(epochs, frame['mae'], label=\"Train\")\n",
        "  ax.plot(epochs, frame['val_mae'], label=\"Validation\")\n",
        "  ax.set_xlabel(\"Epochs\")\n",
        "  ax.set_ylabel(\"Mean Absolute Error\")\n",
        "  ax.set_title(\"Mean Absolute Error vs Epochs\")\n",
        "  ax.legend()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PVzRGjlCoJL-",
        "colab_type": "code",
        "outputId": "7a441f2d-d563-4acf-a96a-6d402d0db1d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 704
        }
      },
      "source": [
        "model=get_model()\n",
        "model.summary() # Print the model summary\n",
        "model.compile(optimizer='adam', loss='mse', metrics=['mae']) # Compile the model\n",
        "history = model.fit(train_data, train_targets, epochs=100, validation_split=0.15, batch_size=64,verbose=False) # Train the model\n",
        "model.evaluate(test_data,test_targets,verbose=2) # Evaluate the model on test set\n",
        "plotting_curves(history)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_8\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 128)               1408      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dense_6 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 84,097\n",
            "Trainable params: 84,097\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "45/1 - 0s - loss: 0.6905 - mae: 0.7333\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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IQE5OzoEHRTwJR9/Ucj7ebg60YRjRJ9pJhM1Pkh+BtkochnHAvHoDPHaCyjVC\nWT4D+h0FQ07W7+s/q/0YM3/l6YujwK51eu6Dp0Bqp5rrRl+sDwALpsLYy1tK9BnnXP9aXg1xnhuS\nt+JzCc0l3wBIaA9xCfRKLmP19iLLXzEMo3G8/D2tshRG2p4D7Uk40kQTCQ3DaCK7N8OyGVCwHjbN\nrblux0rY8S0MPR3SukDWwNp10Ou/gM8egK8eax6bQ/n0fpA4OKKWCppp2epYDzsDzvh7i6m8ISIJ\nInKTiEzzXjeKSEIDdm1I3goiMgzoCNTxRBQBRCAlky4JJRSXV7F1t43dhmE0kNw5GggpCW/RoLbn\nQCelAyrh2G6l7Ayj6cx7BlwVSDwse73muuWecsuXPPQ5XB3o0Ej1h3/W903zIquRLtoGBbk1l+Wt\ngrlPw9hLoUOP2vc7/a9wybPQLjFytoWfh4GD0c6uD3mfH97fTg3MWwF1rKdGPF8llORMsuK0Asfq\n7daR0DCMBvLRvZDSESZcE9bDtlkHuktipUk4DKOpVFer89nvaJVpLHuj5vrlM6DbKMjso9/7Hg4l\nOzUq7ZM7B1a9B70maMWLrQsjZ+8r39N61DtW6PfKcph2tWqfj21q0YqYZYJz7krn3Pve6ypgQkN2\ndM7NcM4Ncc4NdM79wVt2u3NuetA2dzrn9qkRHXFSMklHHedV5kAbRuQoLWg5za8A8tfDW7fVHmHe\nvAC+fVNzXDz/L1y0PQfak3B0SSo3CYdh1Idz8N7vdAAKZc0syF+nEofhZ6pjvN1zjrcvhw1fqPTB\np8/h+h6sg/7wz5CSBWc/pN9z50TiKqCqUqUiJbvgmfOgcCu891tNajz7QcjoFZnzRo8qERnofxGR\nAUDL73+dnElixW5SE+NZZYmEhtE0qqth55q61y9+Fe4ZBJ/c13w2HQhVlTDtGvj8QfjswX3Xz75X\ni0cccl3YT932HGjvCSQ7sZwdJuEwjLrJX6+Dzxf/2nfd10/qlNiwM2DoqbrMl3HM/BUkpsOE7wa2\nzxoAqdkBHfTGr7Vd9hE3QvYQSO8BuV9F5jq2LYGKYjj8RijeAf8+UXXXE66F4Wfsf/+Wx63AByIy\nS0Q+REvN3RJlmw6clEykNJ/+2akWgTaMuqgohcp6goNf/BP+72BNoA7ly0fhxSlQVQ7fPNsyotCz\n74XcL6FjP71XlRYE1m1bBkumq/PsVfIJJ23PgU5IAYmjYzuLQBtGvWyer+9rQ7oIFu9QycaYyZCQ\nrBHcHuN02Yp3YOU7KotIDWoqKgJ9DlOn+fHT4N8nqwPuRwV65UTOgd7whb4fch1c+KRqobseBCf9\nPjLniyJeN8ExwGDgJuCHwAlABisAACAASURBVFDn3AdRNSwcJGdCST4Ds9NYbRFow6idqZPhpTq0\nvs5pwrarUvlcMB//HWbcqgGRk/8IeSu0Q2sss+FL+PAvWjHpoqegrAC+eETXVVVqGdKE9jVLlIaR\ntudAi0BiOh3blVkE2jDqw3eg89dpNNpn4YtQXQHjrwgsG3a6Nkt54ydacaO26bIhp6iMorwIDv8+\nTJkR0KT1mgC71kJRBBqR5n4FaV1Vjz3kJLjuA7hiujr/rQyvLvNk51yZc26B92odkYLkDCjbzYBO\n7dmYX0JJectXpRhGWKmu0lm+b9/W+vyhrP0Ydq7Sz6uCnqkrSjXRbsipcNHT6pBKHCx+uXnsbiyV\nZbBwmuaxZPSE0+6B7mP0HvP5g1C6W3sPrHwXTrpr3xKlYaLtOdAASel0kFKKyioprbBB2DBqZfM8\nlWIArP0ksHzxqxrB7TI8sGzYmfqevx5O/kPtVSvGXQ6/3grf+whOvAu6jgis65Wj7xsjoIPe8IU2\ncPHL0HUfE7EBNUb4REQeEJGjRWS8/4q2UQdMSia4aoZ6PRWtI2ErZPu3UFBX6XFjv+xaCxV7NCk7\ndOYQtA5ycgYcdAGs+VAdboDVH2hg45DvQnw7nT3sfwwsfiUg48hbpU2l/n0S3DsEnjyz+SUezsHs\nv8HfRmiUPa4dXPDE3k6lHPNTDdI8dgLMfx6O+3VNKWGYaaMOdBppUgpglTiM2Gfec83faMQ5LS03\n/AyVWqz9WJcXblGHdPhZNbfPHqpO9aATNQpQF+2Sal/efayWwwu3jKNou95UesVgB8TIMRYYCdwF\n/NV73RtVi8JBsmoYB6ZVAlaJo1Xywnfgnd9E24qWy5agSkYr3qm5bs9OWDodRl8Cw05TrfCmb3Td\n0tcgKQP6HRPYfuR5sHO1zkSWFcHzl8DS1yE+EbqOhDUf1e6kh4uda9RZLtml36ur4fWbNQG853i4\n/GX44VzodXBgn145MOA42LFcc16OuTVy9hHhVt4xS2Ia7Su1nuiOonJ6dWwfZYMMox5m/xXapcDo\ni5rvnLs3wZ4d0GO8tuv2B8plrwMORoQ40CJw9UyIT2haw5HE9tDtoPA70Llf6nssthCPAJ4Gerpz\n7u/RtiXseElAvVPKEcF00K0N53QGKyUr2pa0XLYu0kBE/2M0F8W5wHg8/3lNDjz4SkjrBgisel9n\n5Ja9odrn4JnD4WeqJG/xy3o/yFup0rf+R6vk4+8j4POH9VyR4KN7YN6zWlnjhDu1gtO8Z7Uj7KQ7\n6r7PnHW/OvdjL4t486s2G4FOcaoP2rq7NMrGGEY9VFVoBHXn6uadLvP1z93HaK1nXwe9ZDp0GgzZ\nw/bdJymt7ghzQ+g1ATZ+E5hWDAcbvoC4BI1wtwF8DXS07YgIXgQ6uXI3PTJSWL3DItCtirJClR/s\nyYu2JQeOc5rEFm4qSrXKUeGW2tdvWQSdB+vM4a616vT69nz9hI6xXUeqhK37GHWg186G0nx1mINp\nnwUDJmpli4UvwnG3qfMMmj+Scw0sf1OlHU1h1ft1JylWVeqx+x8DnQapnnneszDxtvqdZ9Bcl3GX\nN0vn2DbqQKeTWKUO9Ob8kigbYxj1sGstVFdqGbaireE9dvmemokkwWyep0kk3Q4KDJpLpquUY8RZ\nkRmcek2A8kKtI90YKsu0DuiWWhqxbPhKbxStMGGwHlqvBhqgNJ+BXdJMwtHa8J3C1uBAf/Yg/GNM\nw4IB25bBk2cFpAr1sfwNLcFZW2lR0Ai0L6WDgIxj4Ytaq//gKYFtBx6vM37zntNKFYMm7Xu8kedC\nZSkMnARHhVTCnOBpkL98ZP92h1JdDS9eBW/V0Y9p/afaeGvCtXD1W3D+v+Gcf8LEnzeLY9xQ2qYD\nnZhOfGURie3i2FxgEWgjhvE750HTn/Tr4uO/wdPn1DyHz+b50HkIJKZC9nCdVv3oHi1/FKp/Dhe9\nvGZ5vuyioWz4AhZN2/emUlUBm+a2GflGEK1aA01JPgM6p7J6ezHN3U3ciCBFngNdslMdrJbMyndg\nd279DUt8vn1TE/pCu7kunAaLXqq5bMn0wLrQf/slu6Bgg0aYO/aFzkO1bOjO1fD6T6D3oVpdw2fg\n8RqcWfgiDD5RS/yGctD5GvU9/zGIC3EX07vBQefBN8/UrL3cEHau0qj3+s9rrxay9DWVLQ6apA7z\nqAtgbOxNrLVNBzopDSkrpHtGMpvMgTZimbwg53ZnAx3ohkQ9qqu0UD6oXiyUTfMCsoe4OOh3pA54\nmX00ohsJsgZoNvXGuY3bz09wXPZGzWnTLQs0etKrQV2sWw3OueNqeR0fbbsOmJAI9J7yKraYBK/1\n4EegXbWONS2BskJ49HitR+xTXa1SNIAt8/d/jG3L9D3Yga4sgzdugdduhnJP619RohHl9B5QsD5Q\n397Hl0N0G6Xvg0+EdZ9oY5S4OHWC4xMC2/c+BBJS9XNdQZGEFI36tq9Dl37YDVq945tn9n+dwfi5\nLlXlNbvTgv5+S19X5zkxtXHHbWbaqAOdDmVFdO+QZBKO1kpVJbz0XXUEWzI7voX2nVTH25AI9Id/\ngfvH7r+e8sr3oHATIAEH1Kdwi0aDgh3lfp6MY8TZkZtCE9GGLJvqcaDnPq1JlcGs/UR/n5KdOvXn\n43c97H1o+G2NQUTkvqDPPwpZ90SzGxRuEtM0KlW4lYHZemNdtqUwykYZYaNwc+BzS5FxrP9Cu6oG\nV0nKW6kNPQA2L9j/MbYv1fdV7wec5RVv60NEWUHg2CvfUynfaffo/4PQyky+A931IH0fdII6qJvn\nw1kPaPAjmHZJ0O8oraox+KSGX3MwPcZ5SYgzGrdf7hz9/xyfCKtn1Vy36Ru9N4VqsmOQtulAJ6aB\nq6Jvh3iTcLRWdufq1NSq96NtyYGxY6VOxXXsp1Nx+2P1h5rsN+2qQDS2tADe+mXNWs7fPAXtO6tD\nvPbjmtOBfgJhj6DEuyGnaIR4zKUHfEn10mM8bF2i0ZZQqiq0hNGsu6HEi1BVlGo0Y/x39KbiT3FW\nVWpb2u5jtNB+2yA4Hf7KkHWjm9OQiCCiTkD+Osb17khyQhyzlm2LtlVGuCgMyvFoKQ60H0kNnsXz\na9knZdSelxFMdZXmfHQbrbNlK73ugPOnQmoX6DpKxzHntARdSkcYcrJWzFj8io6JPlsWarAlvZt+\n73uEHuPQ6/etmuRz4l3awS+5Q+Ov3af3Yer0Nib5O/cr6HmwBjdWh+ThLHtNtdVDTm66Tc1E23Sg\nve5nfdJ1CrCq2nR0rQ4/AttYbVaskbcCOg+CTgP370A7p4Nox36aWf3BH7QxwaOT4POHYOqlqskr\n2q4ZzmMuUR1c8baaOujN8wEJTAWCaupu+qZm85NI0HO86qy3LNp33ar3oXi7RlX86c7cr7RpwOCT\ndcpv2es6BbjoJdi1Rgvrtx2kjs+th8zeULCBlMR4jh6czTtLtpoOurXQEiPQvgO9Y3lAgpI7B5I6\naK3lLQvqr560a606zjlXq3O87A2t1/ztTBh1IRz6Pdi2WKO0y9+CoaerDGP0RTrjFhwg8hMI/RnC\ndklw8yI45e66z99lmDrjB0KvHI2Mb1vasO3L92i0vNcEGHCs3rOKd+g651T/3O8o/T1inDbtQPdM\nqaKq2rG90JqptDr8ihUt2YHes1NvJJ2HaPR3f6Xs8tfrlN+RP4LxV2qS4CMTdSrwXC9T+r/f0XJG\n1ZVa6qffUbo8uCD+pnlaOshvs92c9PCKRdQm45g/VZMZM/oEWsyu+wQQ6HOYRtMLN+tNbfa90GWE\n3nDaDnEi0lFEOgV9zhKRLCA+2saFhcw+e9vKnziiK5sKSlm8aXeUjTLCQuGWgMygJTjQ1dUabfZz\nRdZ4Y+jGOZ60Yaw+8NdVcg4CTme30dpG+9u3NEGwukKd5FEXqCP5vxt1bPcjyQMn6XJfxlFVqccK\nDnqAOtGRrlrR02tk0tAuspvnaZCk1wQY4KVm+DKOzfNUAtMC5BvQVh3oxDQAerTXKe5NBaaDbnUU\ne1O7LSUZBdQ5Do4y+zU8Ow1WB7piT/2DsT9d2G00nPoXnVrrOhKumwVjLtZSQFsXwQe/h5452oo7\na4AmpQR3Glz1vk7/RYMOPSCt676JhKUFGp056Hw46FwdcPfsVLu7j9YEs8EnqRb6tR+pdvyYW/fN\nHG/dZABfA3OADsBc7/vXQBSehiJARm+tNlBWyKRhXYgTeHtJmMs7GtGhaEtAv9sSHOi8lTou5Vyt\nFWLWfKjSs62LNSrb3VNNbalHB+3rn7OHwrDT9X71wR+0zn73MZrEN+47KklM6qB1mUEbnow4B5bP\n0HF/5yqNZHcdGckrrp2sAerM59bhQO9YoXkrvsTDj9r3ylGZYHKGjuflxdoqPDUbRpzbLKYfKG3q\n7rKXJHWguySqfmhzvumgWx1FvgPdgiLQi16C+8cHHOEd3+p758Eq4YD6K3FsWai1m7uM0LrHV78F\n330HMnrp+sEnwPG/0s8HexJZEY1C+zroD/+i0Y+jfhz+62sIIhqFDo1AL/mfSjXGTNYWs9WVGqnZ\n8GUgwTElU6cEty/Vh44R5zS//VHEOdfPOTfAOde/lteAaNsXFvwIZf4GOqUlkdM3i7cX1/NQabQM\nnNOH96wBWpPYn9JvCnOfrqmnjhS+I9j7UB1D13yk8rfqSo3K+g8D9TnQ25bqv+mkNJXTtUtRJ3r0\nxYHI8YRrAFFNcHCjqsNu0GDgI8fBzNt0mX/O5kREr7e26knVVZrM/95d2gkR1NHu2A9SO0Oc1zVx\n9SyY8TPVg5/3iDZ6aQG0TQc6UYMx2QnlAGy2CHTrw3egS1pQBHrlu4BTqQLok3tcAmT21RsL1F+J\nY8tClV4keq3pa5u6O+oWuPZ9jWr49DtKI/bfzoS5T6r8IyuK/lbP8XrtpUFT8/On6rX1HK+RmawB\n8OGf1anue2RgO78c0zG36uBsNAoROUVElovIShGptcuBiFwkIktEZLGIPNesBmb21feCDYDKOJZt\nKWTDzlpqybYUvnm28aUbWxtlu3WGLa2rJsLt2dm04+zepF3rPv5b021xrva/x6cPwDu3B75v9LTO\nnYdA/2O1W+siT1rWM0cT8zr2r78Sx7ZlWmcfdNweNAkQlW/4dOwHl72o7ayDyR4KP/hCZR0r39XE\nu+yhjb/ecNAzRwMXZSHNjeY+pbKM9p3hvd/p+tw5NUuLDpio/5/nPaM5KwNbTsXNtulAe9rO9pTQ\nPjGeTRaBbn20NA20cwEZxcIX9ck9b6U6ivHtdOo6PnH/EehQDVwocXEaLQh2rv1Og698Tx32Y392\nYNdyoPQYDzgdeAF2rVOt85hL1G4RjULv2QEI9D08sO/YS+GS52HURbUd2agHEYkHHgROBUYAk0Vk\nRMg2g4FfAkc650YCzTtVkdlb34N00NCCZRzVVfD6zdq5rqXgt5NuqpNbG37EOL271hxuqoRj11p9\nX/pa/fki9TH3KXj0uJqVNaqr1Cn/5B+BcnF+JYm4uMAYOvcpHavT9d8l3UfXHYGuqtBZxi7DA8uO\n/w2c+6/ArKHP4BP3XQb6W13wH7j4WTjj7zUj1M1Jrxyt373pm8CyPTu1alLfo+CS51SiM/OXWqKu\nhgN9nL73PQom1tGZMEZpow60SjikvIjuGckWgW6NFLewKhy71upTeL+j1flf86FGYTsP1vVx8fWX\nsivJ1+L6+3Oga6Njf+jQU6cOD7s+UAYpWvQYp+9+FOgTr7xxsFN80Hn63u2gmtna8Qma/d62tM/h\n4hBgpXNutXOuHJgKnB2yzbXAg865XQDOueatI5faBeKTNNoH9OucypCuabyzpIXKOApydRZlVwM6\n1sUK6z/TdtKLXwnfMf0KHOndvAh0Ux1o/XfB7o1Ni+pXVwceZvxoMmjTEt+mj+5Vva5fSQJUs5ya\nDZUlgaQ60HyUXWtrvw/tXK1yuWAHusswzVdpLMPPgPFXNH6/cFFbIuH7v9dZxNP+An0OVUnd3Ke8\n7XMC23UaqEGPi59ucbOGbfMu4yURUlZIj8wU60bYGtkbgc5veiSiOfGjzyf9XqcF5z2vA2ynQYFt\nsgZAXh0O9Fav7FtTHGgRjQIkZ2oFj2iT2kl1gZvmqi58zn/g8Bu1lJ5PlxGatR7putQtFBE5SkSu\n8j5ni0j/BuzWE9gQ9D3XWxbMEGCIiHwiIp+LyCl1nP86EZkjInO2b99PU5/GEBenkbj8gJknjejG\nl2t2sjWkK2FJeRXllTHeEtpPFG5Iy+dYwbfZnyEKB35ydHo3ne5vqgPtPVgR1w6W/m/f9c6pY/fg\nYfD4afDfy2FVUB3ile9qSbr2nTWK7Se+LXtDZwAnfFcfHBb8VyOuvgMtolpe0GisTzc/kbCWspzb\nluh7sAPdUmmfpYEYP5Fw3ac6bh9yXSCx8YQ79TeMT9r3PjXstLq7HcYwbduB9iPQ1o2w9VG0XRPq\nqitVWxfrrJ2tEYzuY7Qc26KXNDrhR6ABsrxa0NW1OAV+4mHXJjjQAKf8CW74NHZqb/YYrw8V02/S\nJJ0T7qy5XgQunQqHfz8a1sU0InIH8HNUagGQADSy126dtAMGAxOBycCjIpIZupFz7hHnXI5zLic7\nOztMp/YIKmUHcFGOyjr+/XHACS2rrOL0/5vNT6c1oJVyNPGd0ZKdsTlbtnl+oNW0j29zOLu8FgU7\n0AcQgc5frzKQ/sdqU6XQ4Mkn98FH93ht4QU2fAVTLwtc42cPaFWiU+5Widi6T/UYy95QB/nYX2hl\njJleMnaws9z/WG9ZkDyhvkoc25bpParzkKZda6zRK0e7MuZv0HKpWQPguF8G1mf1hxN+qw8h7RKj\nZ2cYaZsOdFycOtFlRXTPSGF7UVnsRyqMhlNWpIXd/YSjWE8k9PXP/Y5Sx3D0xVonE7SahE+nATpF\nWFTLdPWWhTq97WvvGktyh9jq2NdzvN5E2yXDhU+oNMNoKOcCZwHFAM65TTSsjN1GoHfQ917esmBy\ngenOuQrn3BrgW9Shbj68Zio+fTq158wxPXj283UU7NHKSk9/to7V24uZsXAzO4vLm9W8RuE7oxCb\nUeiXr4PXQ2Tuvs3blqgeOhwUbtF7clK6OtBlu6GyCX+3Xet03B9+pspitgZFfhdOg3fvhIMugCkz\n4Ko3tMRnYqpGotd9ptK5Q7+nEdF2KVr9Z/syPdaw0yEtW8vWVezRgEZw1HTMZNUi9z40sCy9m47L\ntSUSbl+qUduElMZfZyzSM0elOE+fo82uJk/VEnXBHP59OOWP0bEvArRNBxo8B3o3PTKTcY59pv+M\nFoxfA9p/so/FyE4wO1erZs8vx9b3SOjgJYyERqCh9kocWxY0Tb4Rqww4Tlvhnv+Y1oY2GkO50/Z8\nDkBEUhu431fAYBHpLyKJwCXA9JBtXkWjz4hIZ1TS0YAe82Eks4/mOJQHZpauP3YgxeVVPPXZWnYV\nl3P/eysY2jWdiirH9HmhzwAxRN5KSPD+PLGmgy4v1rJiWxbVjOTuWKGVrKortUteOCjcrBU4IOCU\nlnhJilUVMOfxGn/vOslfp1KvYWdodHfpa7p88Svw6g2aqHbOQ4EciQ7d4cLHdQx++lz9Wxx8pTrV\ng0/U9tn+MYZ4HfuO+KE+2Ac7yqBR1eFn7Fv9qO8RsGiadhIMZtvS1iHf8PGj8XmrtOdAdiuJrNdD\n23Wgk9I9CYc+/W02HXTrwS9h5zufse5A+10AfQc6Lg4OuVYd4uAIh19aLrQSR2W5Tge2Jge6+2j4\nxToYeFy0LWmJvCAi/wIyReRa4F3gsf3t5JyrBG4EZgJLgRecc4tF5C4R8eoDMhPIE5ElwAfAT51z\nzdv1Ym8pu9y9i4Z378Dxw7rw+Kdr+cvMZRSVVfJ/l47joJ4dmDY3t44DxQB5KwPa2ViLQG9dDDgo\nLwxoiyvLVCbhd4oLl4yjcKtKL0Aj0BCQcax8T6PgM35a/zGqKjQQkdlXI8V9jtCo89TL4MUpqsW9\n5Jl9K1X0O0olYpUl2p3Vl7GNPEdzaT57QJPkOnj2pXeDq2fCib9t2LWd8Xetz/zfyzSi7Zw+mOSt\nal0OdLdRmkx5yp9gyEnRtqZZaMMOtEo4emQmA1YLulVRFBqBjnEJx9qPNfoSHG0+6sdw/cc1t8vo\npQmGX/wLCoKiajuWq166NTnQEPkWtK0U59y9wDTgJWAocLtz7v4G7jvDOTfEOTfQOfcHb9ntzrnp\n3mfnnPuJc26Ec26Uc25qpK6jTjJqlrLzuWHiQHYWl/P8lxu4eEIfhnRN54LxvVi0cTdLN8dgu++K\nUtWL9hir+Q+xFoHeHKQf90u37VwNOH2wTelYs2zZgVC4OVD9J7WzvvsOtJ9sN+8ZdYjromCDJvb5\nycYjztJgw8r34MS74Jp36s7xOOKHWmpt0m8CywafrJHm0gKVbwTTYyykdWnYtbXPgiteVSf8xavg\nr0PhwUO8dtaHNOwYLYF2SVqX+rAbom1JsxFRBzqmi/InpkFZ4d4ItNWCbkX4FTgiJeHY/m0gae9A\ncQ7WzA7on+sjLh4uelJvuo9N0jJNi16CV7wByy//ZrRpROTPzrl3nHM/dc7d6px7R0T+HG27wsbe\nboTraiye0C+LCf06kpoYz09O1P/7Z43tSUK8MO3rGIxC+85op0GqhY21CPSWBfrAjgSqSPj6506D\ndLwJRyUOvwuh70D7EWi/G+G2pRqd7n0ovPbjun8n/4HK//cx9lJN+vv+p1pdqL48ChF1kpOCUgWS\n0mDQCfp56Om179dQkjPg8pfVpv7HwBn3wQ/ntplIbWslYg50zBflT+oA5UWkJrWjQ3I7i0C3Joq3\nAxJofx3OJMLKcnj2AnjqHNUI1kdpAbxyPTx2IlRV1lxXkKv1RB86XJMCBzRQqjDweLhmJki8Fvuf\ndrVOPZ73aOB6jbbOibUsO7XZrYgU6d20TFnBhn1WPXDpeF7+/pFkp+s0fVZqIpOGdeXVbzZSURVj\nieJ7ndGBWqHAbwISK2xeoE5yVv9AMt6OFfreaRB0H6vO7YEmEpbt1jEs1IH2I9Dbl6r84vzHVNf8\n4pWBes/B+Mt8iU9SulaBOJCuqsf8FCb+Mjwd/pLS4OwH9DpyrrLxuhUQyQh0bBflT9IINKC1oC0C\nHR3KijQCG06Ktuo0YIqnHw5nBHreMxr52rMDvvp33dut+wwePgrmPw+5XwZ0zqDJMP86Ft7/nUYm\nTv8rjL2s4TZ0HQnffRdyroGLn4EffFWz9avRJhGRG0RkITBURBYEvdYA9fQTbmHExXu1oNfvs6pr\nh2SGdkvXBg5PnAGz7ubCnF7kFZfzUqxFoX0HOmugRqALclVjHAtUVah0ovtoHW98CUfeKpWbJXdQ\nGUN1ZWBdo8/hBRX21oD2NMa+zGLPTq3DvP1b1dZm9oFz/6nfHzwEPvhTzcTC/HUaWOgQxmpCPcZq\ndzyTkxm1EEkHOmxF+SNCYsCBtm6EUWTuk/DkmYFWruGgaLvXsayd/p0PxIEOLqVUUapR414TNGL8\n6f21R6FXvgdPeN3wrnxdZzuCtXtLp6sDfvlLGk2e8N3Gd87r0B3O+Jsm81jXPUN5DjgTrZxxZtDr\nYOfc5dE0LOxk9K7RTKUGFSXw/GR9aF3xNscOyWZs70x+8fJC/jhjaexEovNWQVo3dUaz+gOu9shq\nOPjkfvjwnoZvv325liLrNkZry+9crWNd3spAaU1fMra5kTroom2a2PeX/iqFC26iAiq1SM7UCPTO\nNdqp0U+2G3Ya/HAODD0NPrwbnr0wcNxd6/TBKr5d4+wxjCYS7Ttvg4ryR6SrVVIalBcB0D0zxapw\nRIu8lYCDvBXhO2bR1kCCR3Jm05MIc+fAH7pp56rqKnX2d2+E436lUYni7dptKZjqKnj719p2+3uz\nof/RWlJp6fTAVOfcp3RaceCkJl+iYYTinCtwzq1Fm6i4oFeaiPSJpm1hJ7NvrRFoqio0UWvdJxq1\n3LGCdnHC1OsO4zuH9eWRj1Zz9gOfcMkjn3HYH9/jyLvfp6CkovntBx3z/E6jHb1GkZFIJHROH/Y/\n+D18+3bt2xRuha+fDHTe8xt/+BFonMo18lYEpAcZvXWWrzGVOBa9BA8eCiveUUd56mV6XNCHCR+/\nmcp2b11wtYqMXlp6buJtsO7jQDWW/PUB/bNhNAORdKDDVpQ/Il2tktL1CbuynJ6ZKewsLo/eQNqW\n8SMuwQ0FDpTibUEOdEbTI9Ab52qm9Ef3aKRj9l+1RvOAidDnMH3/5B81pxHnT9Wpz0m3a2QJYNQF\nqvNb8TbsWKk393HfsWlBI1K8Abzuvb+H1ml+M6oWhZvMPpo7ECx5KC+GaVfBt2/CafeoxKlsNxRt\nJTkhnt+dcxD3Tx5HaaW2+M7p15GN+SVM/bIWR7w5yFsZcEazPAc6EomEO1frw35cAky/EYpDqg5W\nlsHUS+G1m9TBBa3AkdBeHfxuB+mytbPVqfWdfhGVODTUgZ7/X83ZyBoA18+Gyf+F3Zu0uQnUbALV\nvpPO0vnOdedaNMgjz9H3b736yn4NaMNoJiLpQMd2Uf5EL9u2vIjDBmjSwqzlzSfBjghlRfDsRYFE\nj5ZAfpgdaOd0ijAcDnT+Oi1jdMbf9eZRtFWjz77je6wXhX73DtXzVZTAB3/QckUjzgkcp/+xWqZq\n0TT45mnV6Y299MCu0zDqwCsvN9p7H4zmo3wWbbvCSqZfys6TcRRshP+cAktfh5P/pHXU/bKQQePh\nWWN68P4tE3n5+0fywKXjOXxAJx7/ZG3zd6Lds7OmM5qarU08IhGBXv+5vp/zsJ73jZtrNkZ565ew\ncY7a8NE9GoXevEAjz3HxkNFH75eLX9Xtg8ttdh+rUeL9NTnZtQ5m3Ap9DtcaytlDofcEOPXPmkCY\nmF6zAoYfgd62VGcbktL2PWbnIRq5/3amjr1FWyGzX5N+IsNoCg1yoEUkVUTivM9DROQsEam3t27M\nF+X3/0OW7WZc70y64ai6/QAAIABJREFUpCfx1qJaWiS3JDbOgRUzVYPbEqiuDkzD1tZdrymUFUJl\nqWqgAVIym16Fw58SzLlaa4ie/RD0OzKwvu/hcPBV8OUj8J+TNZKyeyOc8Nua0eX4djDyPO1ENe9Z\nGHJKQO9nGBHGOTcXOHS/G7YkfMnDv46BR46DR47V6O2lL2i7YAiUsdzxbZ2HufaY/mzZXcobCzdF\nxs7SAph1tzp4wez04kTB0dysAZGJQG/4XAMJB50Px/9Km3m8egMse0MlaHP+DUfcpFH7Hd9q174t\nC6HbaN0/Lk6dab9knW8zwIBjNZHQ79ZXG9VV8Mr31Gk/9181Nco5V8Mh1wWayfi076TO/vZldTcb\nEYGhp8LqD3U7sAi00aw0VG3/EXC0iHQE3kajyxcD9ZYOcM7NAGaELLs96LMDfuK9mhf/abesiLg4\n4eSR3Zj2dS4l5VWkJMY3uzlhwZ/uqqW8U9RxTjs6DTklEMEo3KwyGghfBNpvouK3hU3OgNJFgfWL\nX9VEwO++Awkp9R8rf11AU9djrL5COfM+1Tm/frM+wAw+Sb+HMuoC+PJfGrEe/53GX5dhNBARCR5P\n44DxQIQ8xCjR+xCNqG6er+Ne5yFazSbY2erQQ6O69czITRzShUFd0nj0ozWcM7YnEm5Z1YIXYNaf\nVLc7LiiP0x/vgqO5Wf00eS/crP8ceh+mjvARN6mTvuglrRAE2gF10h1aIi57GMy8TbsPdh8TOEbX\nkeqIS7zmd/j0O0Yd6q8ehTEXB5b7HQvLi9UhX/+ZOs+hDq6IOu6hpHbSsbJom46pdTHkZPj8IdVv\ng2mgjWaloRIOcc7tAc4DHnLOXQiMjJxZzUBiIAINcMpB3SipqOKjFWFKUowGfjmh2pJrok3BBk2u\nC0668+Ub3cd42daVte/bGIp9B9rTyocmEa75CLYu1OjL/shfH6gpWh8HnQ83fAaHfR9O/Uvt2/Sa\n4LWY7QaDaivTaxhhIz3olYRqoUNLiLZs4jwZ1Kl/hiunw1Uz9o1UiqiDWk8EOi5O+O5R/VmyeTef\nrYrA5OfqWfq+4L81l+9Yoc5oZl/Oe+gTHvxgpUbVd63TmblgFr2kiZFlRYFlZYXw9HlabWThtJrr\nginO0+vvc5h+j4uHs+6Hn62BK6ZrrsaFT2pUOC5O6x77jai6jw4cp6t3u+/Yr2ZDkrg4rSKU+1VA\nC12+Bx6ZCA/k6MzAJ/fByHNhdJCDvT/ad9LgSnUFdBlR93Z9jtAqR/7v25Dx2jDCREMj0CIih6MR\n52u8ZS00TOvhJ23s+Bb6HsEh/bPIbJ/AzEVbOHlkC51ej+UIdO4cfQ+uGeo3Dhg4SSNJBesPrOg9\nBAb/4Ah02W6dRoyLD2gM5z2nUeG6KN0NJbsaHtHI6Amn/Knu9SJwwX/UDiuzZEQQ59xvo21DzNB5\nMKz/ot5NzhnXk3vfXs7t0xdz2aF9OHpwNgOzUw88Gl1dpbkT8Ula675go44ToInGmX3YUeqYuz6f\nkopqfnBkfy3ZVrhJI9agrbJfuV6dyfJibTcNMO0adc7TusDyGZqr0fUg6DpCdcajL1HndoN37b4D\n7dMuUeUXA46tuXzkuSo52bWmpuPabZS+d94nxx/GTIb37oKvHtNGIe/dpdd38h89/XK6Jl835vf0\nm6kAdBlW93btEmHQJI1yxycFxn3DaAYaeif/Mdox8BVPxzwA1Sy3XDr2h6QMfWo+GBLi4zhheFfe\nXryF8spqEttFu8JfI6muDujAYjECvfFrfd+6SOUcIl4FDoGBx8HHf1Md9AE70F4EOjUoiRDUiU7p\n6DntAqver3lDCyW0LWw46JUTvmMZRggi8hpatq5WnHNn1bWu1dJ5CCx8UaOiie1r3SQ5IZ7fnX0Q\nf5m5nN++tgSAsb0z+dXpw5nQL2vvdpVV1bSLb8R9YfN81UBPvA1m/VGTiI/8kbbFXv4mHP4DFm/S\nGdClm3dTmNKbdFB9dEYv1QC/cIWOZROuVsf0rZ9rF8YVM1WycvDVKq1Y+rqWnlv6upbJrK6E8Veo\ndCI+EXqMb5jNcfFwzkOqgW6XFFjeZbhKPGpzoFMyYdSFKlcZcjJ88bDqmg//QcN/q1B8B1riAlr2\nuhhyijrQmX2sJr7RrDTIgXbOfQh8COAlE+5wzt0UScMijohOUW0OlOA5xdNBf746j2OGhKlcXnNR\nsEHrWmf2CWjPElOjbVUAPwK9J0+d3PSuKuHo0DMQ6chbCYObIG/Ys1OnNDv21WNLPLT3bnwpXlnx\nknzN9M5fr+WPFr8CC6bC0bfUfsy9DrRNCRothnujbUDM4Tt8eSt1vC8tgNl/g0O/pxppj1NHdefU\nUd3ZsHMP7y3dyj8/XM2F//yME4Z3JSkhjvkb8tlSUMrjV03g6MENvDes+VDfD54CK99RB/PIH8E7\nt+uD/dG3sOiLnXs3n7Mnm+PiEmDq5XDQufqwv3szXP2WPnyX5Gs9Z4DDfqDSCYC+R+gLNDjx+Knw\nzh1af37DF9rwJCG54b9Z70P0FUxSOlw2TaPctTHhu1on/4UrtLPiCQc4CdK+s7537L//XJXBJ6mj\nbQmERjPT0Cocz4lIBxFJBRYBS0Tkp5E1rRnoMVYlBVVa//mowZ1pnxjPmy2xGocv3xh8sr4XxFDb\n2qoKjcZ09aYBt3kyjl1rddBr30lvKE1JJNy1TjPxHzpcp2r9Nt5xnsLIj0CXFmiFjOpK7SLY90iV\ncbg6Ana+A22DstFCcM596L/QsnV53utTb1nbI7QSx9ynVJP7zPm1VufpndWeKUf254NbJ3LLiUP4\nYnUe89bnM6ZXJr06pnDLC/PZVVy+z361suYjyB6uwYLRF+vs2yf3w6r34NifQfssFm0soGdmCikJ\n8cza1A6mvA5DT9GayatnqSzMn7k64bda23rs5XDS72o/p4hGpksLNBlw0zfQO0wFWAZNqlmrOZju\nowPnOefhOqP9DcYPgNRVgSN02yN/1DiNtWGEgYbOd4xwzu0GzkEL8vcHWn4pge5jVVvmOZ/JCfGc\nPLIbL32dy4fftrBkQt8p9TOW62pzGw22LdFan+Ov0O++DnrXOk1KEdFM7sY60AW52ga8bLcmDT57\nAWz4MlADGjSJEDSR0Nc/Z/XXBKS8lbp9beSv10YCwVo8w2gBiMhEYAXwIPAQ8K2IHFPvTq2VrIGA\nBMaW+f/VDno7VmjzkIraO9CmJMbzw0mDWXDnSXzyi+N58LLxPHjZeHbtKeeXLy/E1fXg7VNZBus+\nC5RnG3muSi/e+Y2OeV70eNGmAsb2ziSnX0c+W52nWuXzHoGfroBr3g1EmUHlCWf8Dc55MBAgqI2u\nI+HQ67XKRlW5aqKbg/Mehe+8An3C4LCnehHohjjQACfcCaMvOvDzGkYjaKgDneDVfT4Hr3Mg9Wjt\nWgw9xul7kIzjzjNHMqhLGt97es7/s3ff4VFXWQPHv3cmvfdeSUILJUDovahgQ7CAimvvbS1reV11\nrWtfxY7YFUGkqSBIR6SX0CEktBRIQiAJhPTc949fEhJISAKZNM7nefKE+dU7KDNn7px7Dmv3NU5J\n6gaRvst4Y/AtS4fIbkZ50OXpG1GXgLO/EUAX5RuLZcpTJDwj61cL+kSaETznHYdbZsOtvxvB8tE9\np/OfoeoMdPmiRfcwo9GJtSPE/VD99ctL2Em3QNHyvAtcqrUerLUeBFwG/K+Jx9Q0rO2Mb5GOxht5\nvWnbjNnKMZ8ZHUFn3n121YtKKhYSak20lzXPDA9h6Y5DzFi331gkWFMgnbzemDQoX6Tn6AWRI4w/\nj/gPWNmSfaqIpGN5RAe60DfCk/i0kxw9WdZZ0dbZaDRyvq8/Q5453Rq7oWaga+MeanRnbQh2rnD9\nN9Dr3oa5nhAWUNdFhJ8DB4AtwAqlVCiQY6lBNRr3cKMETmpcxeyoq4M139/Zi3GT1nDHN+t5eXQn\n+kV64u9aSx5WU0vfZXxad/Y3Zjqa0wx0ykZjJtc9zMh3TttxulKIe6UAeus0o+FAbTlvAEtfNRYB\n3vY7BJYtkLn1V/jmyqqzFpUD6GP7jXa2LoHGDE7na40ZqYFPnp2qkXVQ8p9FS2Wtta4oKKy1jq+t\n8VWr5llWym7LVOO1MXqsUWf4xGGjtOa6z6HP/Wefl5cFvz1qpF7kHIaiXO4E7rTD+B62vDm6rYvx\nrZZHBHS4ypht3rfcyMsNrdR4acizxreeZV1Kd6QaHVI7B7riZGu8Fa/Zl8mVXU7nZp83OxdjJvvQ\nauO5tkTRY5p6BEKcU10XEU4EJlbadFApNdQyQ2pEJpNRg7jSDDSAp5MtP97VmxsnreGJ6VsACHC1\n486BbbitXxhmUzOblSwpMt4gIkcYgaFLQPMqZZe8AQJjjdkU32hY+9np2ebyovyeEcbvY/tO1xyt\nSW6msSCn6/iqi108wuGRTcaq83KVFxEeP1C2Urvs68/Bz8DW6UYr7uu/qXqPrENG8wEhWp4NSqnJ\nQPnXKxOADU04nqbl1RYOrISTGcYakfKAsu9DcOBvY1Ff2EDwq7RArjAXptxg5BC3G2Wkxjl6gzKR\nW1DE71uSSMnMpYOfI8NDrbDJOWikg+2YCRu/htyjxjec5a8/cFYzpm0pRgAdHeCKs50VjjZmVic2\nUAAN1ZepE0I0mDoF0EopV+BFoDyPbjnwMpBtoXE1Hv+usO4LIwitVCDe18WOBY8NYtfhHDYePM6O\nret58/cT/LYllbeu60JbX+cmHPQZju0zct3Kq1m4hTafGej8bCO4L6+57NvJGGtiWbvxyikcYOQq\n1hZAb/rGaNfd+76z91UuvQRGwxxlLkvh2F+1i5ZroPF17vI3jGuV10rNyzKOl65WomW6H3gQKK+U\n9BdGLnStlFIjgQ8w6vxP1lq/ccb+24C3gZSyTR9prSc3wJgtxyvKSKc4mWd86C6nlFG3+NN+MOMu\nuGep8e1XcSFMu8VIw7j+W+hYtfqfI3DdUM0nSxN4cFE8dhlmuga5EdvRhX/YLsV77ZvG68eAx845\nrO2pOQS62ePhaHzg7xnuwZqWlDYoxEWurjnQXwEngBvKfnKAry01qEYV0M0oXl9eQ7kSa7OJLkFu\n3N62iLfT72V670QOHTvFlRNXkpB+ogkGW4PyRXnlqQuuwZarBa01zLwHVn14ztzBCqmbAQ2BPYzH\n5Tnau+dVLXzvUTYDXdtCwpIiWP8lhA8+fa1zUaqsnXfZDHR5A51y/R8x0l7mP3v6+UgFDtGCaa0L\ntNbvaa3HAncBi7XWBbWdp5QyYyw8HAV0BG5USlX3j2ya1jqm7Kd5B89wuhKHnZtRp7gyRy+j7nHG\nLmNNxZTx8MVQ4wP+VRPPCp7LmU2Kh4dHMeP+ftwQG8zJgmI+WXGAgUvaMKv/HPTQ52rN392Rkk10\ngEvF475tPEnMyCU9p/qFjUKI5qWuAXSE1vpFrfW+sp+XgAvseNFM+Hc1fqfG1XzM+i9QupSu9pnM\nfWQAxaWlzN6c2jjjq4v0XVULzrsFG/l9xXUst1Tfe22dZuQOTrvZWMR3LuULCMvzlL3aGnmIOclG\ngFpe+N7WyQhka1tIuOs3oxxddTmLNbFzNYLn/OyqM9Bg1Moe/gKkbjIaLoBlmqgI0UiUUsvKyo56\nABuBL5RSdVlE2AtIKHuNLwSm0hpagJe/LkaPOfsbKjBS30b8x6gnn51svCZcNRG6115oqluIO/+5\nOprfHh7A6meGERvqwWNzU3k4ZQTHzDXnHp/IL2Lf0Vw6B7pWbOsXYVSemL6xagnS/KISUrPyah2L\nEKJx1XURYZ5SaoDWeiWAUqo/0Dr+RXtEGA02DsdRbWW+ghMQ95Px5+wk/F3t6dPGk3nbDvPEpW0v\nvN1rQ0jfaTyP8mL5rsGANgLNM2dcL9T+FcbvgU8YNU0/HwTdbjEK+XtGGTM5h7cYC/zQkLjUSM+w\ndzfOs7I13tDSd569SM8z0igvdS5rPzMWf0Zddu7jKrNzPf0Byb2av48u42HNp7Dsv9DpWmmiIlo6\nV611jlLqLuA7rfWLSqmtdTgvEKic+5UMVFfC4dqysnjxwGNa67PyxZRS9wD3AISENPEHUSdvuOH7\nqgv6zjTgsVpTLmrj42LHd3f04tPliby3MJ4lu9O5pU8oE/qEknw8jw0HjlFQXMrdA9uw+4ixBr9T\npQC6U6ALl3b05Z0/9xDl48Sl0X4czs7j9q/XczDzFEufHIKfaz0aogghLKquAfR9wHdludAAx4Fb\nLTOkRmYyGUXga5qB3jIVCk8YqQY5RtrfqM7+PD97O/FpJ2nn1wxyodN3gl/n04/dgo3f2UmWCaDd\nw41Z27ajYN6TsPR1zqpqaOtqpE8odXausk9HY8xnpkj4d4V1k4xugpVrOadsgj3zjIVASWth5Jv1\na9lq73Z6oeiZM9BgXGvIszD1RmN2Peug8aGqPOgXomWxUkr5Y6TbPdfA1/4N+ElrXaCUuhf4Fhh2\n5kFa60nAJIDY2NimL3laQypGQzOZFA8OjeSyaF8+WpLAF3/t4/MV+4Cyl0Pg5w1JxIYZry3RgadT\nOJRSfDC+G+MnreaRqZt59ZrOvLNgDycLiikuLeXDJXt5bUzn6m4rhGgCda3CsQXoqpRyKXuco5T6\nJ1CXWY3mzz8GNnx51kJCtIb1k439fp1g70LAaPn9wpztzNt2uOkD6OwUYxFheZMSKJuBpuEXEpYU\nG0FstFGGieCecO9yI43j0FpjHD7twa/ruUsn+UbD9l/ODmZ73AarP4INX8OQp41tR7bD5OGAMlaw\nD3oKYm+v37jtTs/yVBtAg7HS3q8LrHjbWHQkNaBFy/UysAD4W2u9XinVBqOxSm1SgOBKj4M4vVgQ\nAK115VVuk4G3LnCsrVKkjzPvj+/GI8Oj+HNnGm19negR4kHS8VM8OX0L87YdwcfZFh/nqjPK9jZm\nJt/ak7Gf/s2T07fg52LH9Pv6MmXtIX5ad4h7B0UQ4nmBXf6EEA2iHtN4RuBc1pEQ4HELjKdpBPc0\nqjqcOQt94C9jcWGve4yg9GQaFBfg7WxLrzAP5m073DTjrWzPPON3u8tPb3MNAlT9Stkd3lJ7PvOR\nLVCQfbq7Vjl7d6P9bN8HIGJY7XVHfcvKRZ2ZIuEVZeQjbvjydP724peMpgJP7Ia7l8Cw56rPYzyX\n8m6Ejt5GrnV1lILBTxuVOhIWSf6zaLG01tO11l201veXPd6ntb62DqeuB6KUUuFKKRtgPPBr5QPK\nZrbLXQ3saqhxt0ZtvJ24b3AEw9r74upgTadAV359aADPjmrPk5e1q/Ycb2dbvr29Fzf3DmHWg/3o\n4O/Cw8MisTIr3l9ktCTPLyrhy5X72XDgWGM+HSFEJfUKoM/QeqbnwgYav/cvr7p93RdGcNhpbFlQ\nCuQYiwcv7+zP3vST7E1r4moce/4w8p/LF8qAEWA6+9V9Bnr7TCOX+a02MHkErHin+ha35fnPZwbQ\n9RUxFEa+cbrteGW97zc+qOycbdRo3funkZtYOaWjvspnoKvLf66s/RVGKowulQocosVSSrVRSv2m\nlMpQSqUrpeaUzUKfk9a6GHgIY/Z6F/Cz1nqHUuplpVR5DsQjSqkdSqktGGXybrPU82itbKxM3Ds4\nghtig2s8po23E6+N6VzRwMvHxY5b+4YxKy6Fr1buZ8R7y3nl952Mm7SGyX/tq721uBCiwV1IAN16\n/sU6eoFPtDHjXC4/G+LnQ9cbjdqgLoHG9mxjhfTITkab1D+2H6GopJS/E45WdJZqNPk5RlDb/vKz\n0w1cg41c3trkZsK8fxn5x4P+ZaStLHkFvrnC6L5V2f4VRv7yhQSzYKTJ9Ln/9KLHyiKGGYsJ13wK\ni/5jVOa40Hau5c0MakrfKFc+Cw2ygFC0ZFOAnwF/IACYDvxUlxO11vO01m211hFa69fKtr2gtf61\n7M/Paq2jtdZdtdZDtdZn1/8UFnHf4Agcbax4+fedONiY+fq2nozo4MOrc3fx8E+bySssaeohCnFR\nOWcOtFLqBNUHygpo5r2t6yl8IGz8FooLjBncvQuNhh8dy6o4lecVlwXQvi52xIa689Xf+5n81z5y\n8ovxcLRh1TPDsLM2N86YExZBaVHV9I1ybsFGC+3azH/a+LBw669GbvLQ/4Odv8Ks+2DSEBj3Q1mK\nSwEcXA09LLx21GQyFh3Oe9J4fNUHYHOBOX8VM9BhtR/b7goY/XH1f6dCtAwOWuvvKz3+QSn1ryYb\njWgQ7o42vD8uhiM5+YzrGYy12cSQdt58tnwfby3YjaONFW9e16Wph9lg5m8/Qo9Qd7yd65myJ0Qj\nOecMtNbaWWvtUs2Ps9a6rhU8WoawgUa3qvKgc9evRuWNoLJW0a5lM9A5p2t03twnBFsrE5d09OPJ\nS9tyLLfQsnnRu36DH28wAl4w0jfsPSC4mkpTrsHGAsMzm53kHjVqMx8/CDvnGLWPBz1Ztftfx6vh\nroXGDPE3l8PGb4xzivMuPH2jLrqOB1sXoyxezIQLv155DnRdKpKYTNBtAjh4XPh9hWhESimPstrP\nfyilnlFKhSmlQpVSTwHzmnp84sKN6OjLhD6hWJuNt26lFPcPieC+wRFM25DE/O1HmniEDSPp2Cnu\n+2EjD03ZRGlp6/myW7QurSsIvhBh/QFlpCkEdIO9i6DruNPl0qztwcGrYgYaYEy3IMZ0M3KjtdbM\n2pzCt6sOMLZ7kGXGuPJ/RoA//XYYPwX2LjBmTE3VzHi7BRuz04lLIGqEse3gavhpvNGVr5xPNAyo\nZj2obzTcvdRocfvbo2VVKUznrqXaUGyd4ZZZxsyxuQH+Fy1Pv/Fuf+HXEqL52ojxjWF5Plfl3CcN\nPNvoIxKN4rERbflrbwbPztxK9xA3fFwsVy86t6CYiYv3MiDKiwGRXhbphbAq8SgAa/cf47vVB7it\nfwOXYxWiAVxIDnTrYu9u1IPe/5fR/KMoF9pfWfUY18AqAXRlSilu7RfGluRs4pKyqj3mgmQdMoLn\noJ5Gm9kfxhoz0e1rSDVof5Uxg/vjdUad5m2/wHejjUoUN3wPV39kdN8a9z1Y2VR/DQcPuHk6DHzS\nuL9/19P5xJYWFGtU5WgIIX3g/lWnuyEK0QpprcO11m3Kflf5Aaov+SBaBRsrE++P60ZeUQlP/rKV\nopLS2k+qgy1JWRVNX8p9vsKobX3Ll+u45pNVLNmd1iD3qmxlQiZeTrYMa+/DG/N3s/9oboPfQ4gL\nJQF0ZWEDIXmdkdZg53q6Oke58rSIGoztHoSTrRXfrTrQ8GPb9Zvxe8zn0OdBOPg3mG2hzdDqj3f2\nhXuWGYsgl78JM+40Asg7/zRSNLrfYlS38Iw4931NZhj+PNw+H675tCGfUeNRqmqKihAXAWUYrpT6\nEqOroGjFIn2ceP7KjqyIz+C6T1eRkH7ygq63+0gO4yatZvykNaTlGFWZ0k/kM/mvfYzq5MfrYzpz\nLLeAO77ZwA9r6rBgvY601qxOPMqASE/+O7YzNmYTT07fQsaJgga7hxANQQLoysIHGQsHd8w0uuyd\nOTPrGmTUVq6hZJCTrRXX9Qji962HOXqygf+x75xjlFjzjIBLX4GYm6HnXTXXNQZj35hPYexk6PcI\n3DL7/HN7Q/uCT4fzO1cI0WiUUn2UUhOBg8AcYAUg+UsXgZt7h/LJzd05dOwUV0z8iy9W7CO/6HR1\njo0Hj3P9Z6uY/Ne+c17nZEExD/y4CSdba/KLSnhy+hZKSzUTF++lsLiUp0e256beISx5YgjD2/vw\nfFljsYawJ+0ER08W0i/SC18XO14e3YmNB4/T87VFDHt3Ge/9uUfK9olmQXKgKwvpC8oMugQ6XHn2\nfpdAKDxppE7UkMowoU8o36w6wK1fraNTgCtB7vZcHRNAqKfj+Y8rJ9VoYT3s38Zjkxmu+aTu53e5\n3vgRQrRaSqnXgeuBQxhl614CNmitv23SgYlGdXlnf2JD3Xlm5jZem7eLSX/t4+6B4SQdy+OHtQex\nNptYf+A4znZWjOt5dsMorTXPzNjKgaO5TLm7D3vTT/L87O28OncXP61L4ubeIYR5Ge9n1mYTH93U\nnVu+XMs/p8bham9N/0ivCxr/yr1G/nP5da7pFkikjxN/Jxxlye50Ji5JYFBbb2LDZKG3aFoyA12Z\nnYuxgNDKHiKGn72/oplKzWkckT5OPD2yPVZmE0v2pPPuwniGv7ucF+ZsP/+voMrTNzpec87DMk4U\nMPjtpayX7lRCXIzuAtKAT4Hvy9puy1TdRcjHxY4vb41lyt29ifJx4vV5u/lx7UFu6xfGuv8bzqC2\n3jw7cxt/7jhCYXEpu4/kMCcuhXcW7OG2r9fz+9bDPHFpO/q08WRC7xCGtvPmq7/3Y2tl4uFhVdem\n2NuY+fLWnoR7OfLQlE3kFhRf0NhXJWYS7uVIoNvpSrmdAl25d3AEX93WE3trM7M21/weLERjkRno\nM414EU4cqb72cOVa0OfIqb1/SAT3DzFyi9Nz8pm4ZC8/rj3ET+sOEezhQLC7A7Gh7jw4NBKTqQ4r\nmHfMNhqY1LKobuHONA5mnmLa+iR6yqdzIS42/sAlwI3A+0qppYC9UsqqrMuguIgopegX4UW/CC+2\np2RjZ20i0scZgE9v7s5Nk9fywI+bUAqKSozPWWaTItTTgbsGhHP/4IiK67x1XVeu/2wVE/qEVluX\n2dXBmjeu7cyYT1bx49qD3DOolrU1NSgqKWXtvkzGdA+sdr+jrRWXRvvy+9bDvHBVR2ytGqnnghDV\nkAD6TOeqc1w+A51dxxbZGDMBr17TmTsHtGHa+iQOZuay/2gu7y6MB+Dh4TUExaWlxkx3+k44tBqG\nPFPrvcpXQy/alUZRSWlFrVAhROuntS4B5gPzlVK2wJUYDa9SlFKLtdY3NekARZPpFOha5bGjrRVf\n39aT9xfF42hrRXs/Z9r6OtPG27HaoNTb2ZalTw45Z8m6biHuDIj0YtKK/fyjb9h5NRTbkpRFbmEJ\n/SNqTgO5plut2wr7AAAgAElEQVQgc+JSWbYng8ui/ep9DyEaigTQ9eHkAyarc1biqEm4lyPPjDLW\n8WiteWxaHO8tiqdzkCtD2pW1xi4uMOo275hlNEkpKCsfpMzQ6dpzXj+/qISVCUcJ83TgQOYp1u0/\ndsG5aEKIlklrXQDMAGYopVyAc+d/iYuOh6MNL4/uVOfj61Lv+cGhkdz4xRqmrU/i1n5h9R7T3wmZ\nKAV9IzxrPGZgpBdeTjbM2pQiAbRoUhJA14fJDC4Bp2tB5+dAxm4I7lWvyyil+O/YLuxJO8mbP/1J\nl8HZeBxZaTRxKTxpdM7reDUE9gDPSKMBiJPPOa+5OjGT/KJSnr28A/+cGsf87UckgBZCoLXOAb5r\n6nGI1q9PGw9iQ935fHkiN/YKwcaqft+C/p1wlE4Brrg51NCbALAym7iqawA/rjlE9qkiXB2sL3TY\nQpwX+Y6/vlyDjQBaa5h+G3x5CWyfUbdzc1IhdTMkLMZ+/cfMtnmeP3gQj+X/R17yVuhyA9w0HZ7c\nC6M/htg7jJSSWoJngMW703CwMTOknTdD2nmzYMcRaYEqhBCi0SileHBYJKnZ+fy8oe6pjgBZpwrZ\neOg4g9rWPvEzplsghSWlzNveMKXzhDgfFg2glVIjlVJ7lFIJSqkak3iVUtcqpbRSKtaS42kQLoGQ\nkwybfzA6Ajr6wOwH4fDWms8pLYWFL8B7HWDSEKOL4MLnsTVDas9nudXxMzpkvsWDObeQ5DWg5s6A\nNdBas2RXOgMivbC1MjOykx/pJwrYbImOiEIIIUQNhrT1pnuIGy/M2c57C+MprmNXxGV7Migp1VzS\nsfa0jM6BrkR4O0o1DtGkLJbCoZQyAx9jrApPBtYrpX7VWu884zhn4FFgraXG0qBcg2B7Ciz4P6NT\n4bWTYdJQmHoz3LMUHM/49FxcCHMeMLobdpsA7a4w2oa7BYNrEAHApMtK+Hz5Pj5aksDcrYfp08aD\nsd2D6BrkRqC7PU625/7PtPvICVKz83l0hLEgcWh7H6zNigU7jtAj1N1CfxFCiOZKKdUPCKPSa7zW\nWtI4hMUppfj2jl68OGcHExfvZeXeDD65uQd+rnbnPG/hzjS8nW3pcsaCx5rucUWXAD5aspfMkwV4\nOp1dGUQIS7NkDnQvIEFrvQ9AKTUVGA3sPOO4V4A3gX9ZcCwNxzXIaLRSWgxXfwjOfjD+R/hqJHwx\nFNpfCRHDAGVU0Nj1m9EefPgLMOBxo630GWytzDwyPIprewQxY2MyMzYl89Qvp2e0A93s+fjm7sQE\nn27eorWmpFQb9aZ3pwMwtGwxooudNQMivfhj+2GeHdW+Tos/hBCtg1LqeyACiAPK29BpJA9aNBJn\nO2veGxfD4HZGvenHpsUx5e7eNb4XFRSXsDw+g6u6+tettCtwSQdfJi7ey9I9GVzXI6ghhy9EnVgy\ngA4EKidBJQO9Kx+glOoOBGut5yqlagyglVL3APcAhISc3TmpUXm0MX6PeAk8wo0/B3aHG6fA6o9h\nw1ewplKXQOcAGPM5dB1f66UD3ex5ZHgUDw+LZEdqDvuP5pJ8PI8p6w7yjy/X8tM9fYgOcGVnag7/\nnLaZ/UdzifB2IjO3kC5Brvi4nP6Ef0WXAJ6cvoUHp2zi1Ws64+FYv7QQIUSLFQt01NLvWDSx0TGB\nnMgv5t+ztzNzUwrX1hDort13jJMFxYzo4Fvna3cKdMHXxZbFu9LqHEAXlZTy6u/GHN5L9ahAIkR1\nmqwKh1LKBLwH3FbbsVrrScAkgNjY2KZ9UwgfDHcugqAz0rUjRxg/RXlG222TNfh0AIf6NzRRStEp\n0LWidueVXfwZP2kNEyav5Za+YXy2PBFXe2tu6xdGQvpJThYUc1Ovqh8sxnYLJONEAe8t3MO6/cd5\nZHgk9tZmiko0PULdaefnfN5/BUKIZm074AfUe4WVUmok8AFgBiZrrd+o4bhrgV+AnlrrDRcwVtHK\n3dQrhFmbU3h17k6GtvepdjJn0a407K3N9aocpZRieAdfZm9OIb+o5Ky60zn5RXy6LJERHXzpEepO\nXmEJD07ZVPGN7R0Dwgn1dLywJycuapYMoFOA4EqPg8q2lXMGOgHLyr7W8QN+VUpd3axfkE0mCO5Z\n835re2gzpEFvGezhwI939WbcpNVMXLyXYe19ePu6LufM+zKZFPcPiWBwW28e/zmOF+bsqNjn52LH\n0ieHYG8jXZyEaIW8gJ1KqXVAQflGrfXV5zqp1a5bEU3KZFK8PqYzV0z8i1fn7uSlq6PJyS/GyqTw\ndbFDa82inWkMjPKqd/OVER18mLL2EGv2ZZ7up1Bm2rokPl2WyKfLEukZ5k5xqSYuKYvHRrTlg8Xx\nTFufxFMj2zfkUxUXGUsG0OuBKKVUOEbgPB6o6ISltc7GeKEHQCm1DHiyWQfPTSjMy5Ff7uvHtpRs\nRnXyq3Nec8cAF35/eADJx/MwmxTxaSe489sNfPX3fh4cGmnhUQshmsB/zvO81rluRTS5dn7O3DOo\nDZ8sS2TmptPzaFd3DeCyaD9Ss/P55yVt633dfhFe2FubWbwr/awAes6WFDr6u3B9bBCT/9pPxokC\nPr6pO5d39mdrchbTNybz2CVtpWOvOG8WC6C11sVKqYeABRhfB36ltd6hlHoZ2KC1/tVS926tgj0c\nCPZwqPd5VmYTYV6OFdcY0cGXT5clMr5nsKxeFqKV0VovP89TW+e6FdEsPDI8Cj9XOwqKSnG1t2bf\n0Vy+XXWAX7ekohQMa197v4Mz2VmbGRDlxeJdabw8OrpiYikh/STbU3L49xUduL1/OBP6hJKdV4RX\n2fvd+F4hLP5uA0t2p0s3Q3HeLJoDrbWeB8w7Y9sLNRw7xJJjEac9M6odl73/Fx8uSeA/V0dTWFzK\n7iM5RAe4Yq7jCmghRPOklOoDfAh0AGwwJjBytdYuF3jdlrluRTQLdtZm/tE3rMq2OwaE8emyRGys\nTBXBbX1d0sGXhTvT2HnYeA8D+DUuBaWMGW4Aa3PV6w9t542viy1T1x2SAFqcN2nlfRGK9HFmXM9g\nflhzkNyCYhbuSiPrVBET+oTwyuhOUvZOiJbtI4yUuekYFTn+AdTl+/HWuW5FNFs+zna8eFX0BV1j\naHsflIJ52w4THeCK1po5W1LpF+FZpTJVZVZmE9f3COaTZQmkZuUR4GZ/QWMQFydJ/rlI/XNEFPbW\nZuZuO8zgtt5c3yOIH9YcYvJf+5t6aEKIC6S1TgDMWusSrfXXwMg6nFaxbkUpZYMRhFek2mmts7XW\nXlrrMK11GLAGkOBZNClvZ1tGdPDls+X7mL/9MHFJWRzMPMXomMBznjeuZzClGqauO9RIIxWtjcxA\nX6R8nO1Y8uQQHG3NONhYUVqqOVVUwmvzdhHkbs+ozv5NPUQhxPk5VRYAxyml3sIoZ1frZImsWxEt\n1fvjYrjly7U8/NNmYoLdsLEyMbLTuVMzgj0cuCzaly9X7mdC31B8nM/dKVGIM8kM9EXM29kWBxvj\nM5TJpHj3+q50D3Hj0WlxfL/6ANKHQYgW6RaM1/aHgFyMtIxr63Ki1nqe1rqt1jpCa/1a2bYXqgue\ntdZDZPZZNAeOtlZ8fXsv2vo6s/7AcYa398HFzrrW854Z1YHCklLe+zO+EUYpWhsJoEUFO2szX97a\nkz5tPHl+zg7u/HYDGScKaj+xjrLzis4KyktLNYcyT1FaKsG6EA1Ba30QUIC/1volrfXjZSkdQrRa\nrvbWfH9nb0bHBPDAkLqVaA33cuQffcOYtiGJnak5Fh6haG1US5tljI2N1Rs2yKSHJZWWar5bfYDX\n/9iNo42ZZ0a15/oewZguoEJHQvpJrv5oJSOj/Xj3hq4VCxX/b9Y2pqw9hLOdFd1C3Lm8kx/jegY3\n6ELGoycLeHHODo6eLOBkQTGeTra8c30X+cpONDql1EatdWztR17QPa4C3gFstNbhSqkY4OXaGqlY\nirxmi+Ys+1QRQ95ZSgd/F368qzf5RaUUFpfi6lD7DLa4ONT0ui0z0OIsJpPitv7hzH14AFE+zjw9\nYxtjP13F9pTs87pecUkpT0zfQmFxKTM3p/DlSmOh4o9rDzJl7SGuiQngyi4BpGbl8czMbTw3ezvF\nJaUN9nx+jUtl7rbDaG10YVy//xg3fbGW9BP5DXYPIZqR/2A0RckC0FrHAeFNOSAhmitXB2v+OaIt\nqxIziX5xAR1emE+3V/5ka3JWUw9NNHOyiFDUKMrXmWn39mF2XAqvzd3NVR+t5KZeITx5aTvcHKxJ\nPp7HoWOn6BnmgY1VzZ/FvvhrP1uSsph4Yzfmbk3l9Xm7KCgu5f1F8Qxq6827N8RgNilKSzXv/LmH\nT5Ylcjgrj49u6o6j7YX/L7p0TzoR3o78fF9fANbuy+S2r9dz0xdrmXJ3b5mJFq1NkdY6+4xvcVrW\nV41CNKKbeoeQmpVHYUkpXk62fLYskcl/7Wfijd2aemiiGZMAWpyTUoox3YIY3sGX9xfu5dvVB/h9\n62GszSaOnjTyo7uHuPHxzd3xdz27lubetBP8b2E8ozr5cVUXf4a192HsJ3/z9oI9hHo68OH4bhXN\nW0wmxVMj2xPk7sDzc7bz79nb+d+4mAsa/6nCYtbuO8Y/+oZWbOvdxpNvbu/JbV+v577vNzLj/n5S\n+1q0JjuUUjcBZqVUFPAIsKqJxyREs2VtNvHs5R0qHh/PLeSbVQd49vL21b6vCQGSwiHqyMXOmheu\n6si8RwYyINKLQVFevDI6mv+O7cyeIye4cuJKFu5MY9fhHLanZPPbllSe+HkLN3y+Gic7K165xmjQ\n4mRrxaRbYhnVyY/J/4itNs/spt4hTOgdwtxth8nJL7qgca9OzKSwpJShZ7SJ7d3Gk/+7vD2bDmWx\n6ZB8VSdalYeBaKAA+AnIAf7ZpCMSogW5tV8YpVrz/eqDTT0U0YzJDLSol3Z+znx8c/cq23qGuXPf\nD5u4+7uqC4XcHKwZGOXNnQPCq7RRDfNy5NMJPc55nzHdg/h29UH+2HaYcT1Dznu8S/ek42BjJjbM\n/ax9Y7sH8daCPXy76gA9Qs/eL0RLpLU+BTxX9iOEqKdgDwcu6ejLT+sO8fCwKOxtzE09JNEMSQAt\nLlikjzNzHuzPX3uPAhorkwlPJxu6BLlVpGfUV9cgV9p4OTJzU8p5B9Baa5buzqB/pBe2Vme/ADra\nWnFDbDDfrjrAc1d0wLeGtq9CtARKqXM2OmmqKhxCtER39A9nwY40ZselcGOv85/EEa2XBNCiQTja\nWtXa+ak+lFKM7R7IO3/Gk3z8FEHuDvW+RmLGSVKy8nhgaESNx/yjbyhf/b2fH9ce4vFL2l7IkIVo\nan2BJIy0jbUYtaCFEOehV7gH0QEufLFiH8M7+Mhic3EWyYEWzdbomEAAZm9OOa/zl+7OAGBIO58a\njwn1dGRYOx+mrD1IQXHJed1HiGbCD/g/oBPwAXAJcFRrvVxrvbxJRyZEC6OU4snL2pGSlcel/1vB\n71tTm3pIopmRAFo0W8EeDvQK92Dm5pTzaiu+LD6dtr5OBLqdexX1rf3COHqykLlbD5/vUIVoclrr\nEq31fK31rUAfIAFYppR6qImHJkSLNLSdD3MfGUiopyMPTdnMLV+uZU5cCrkFxU09NNEMSAAtmrWx\n3QLZl5Fb70oZx3ILWbf/2Dlnn8sNjPIi1NOBOXEywyBaNqWUrVJqLPAD8CAwEZjVtKMSouWK9HFi\nxn19eXpkexLST/Lo1Dh6vLqQGRuTm3pooolJAC2atcu7+ONqb82d365nye60Op83acU+iks1N8QG\n1XqsUoqh7XxYsy+T/CJJ4xAtk1LqO2A10B14SWvdU2v9itb6/HKghBAAWJlN3D8kgr+fHsYv9/Ul\n0seJd//cQ0lp7d+MzolLYd42+XazNZIAWjRrLnbWzH6wP/6u9tzxzQZen7eLzLIGLjU5erKAb1cd\nYHTXACJ9nOt0nyHtvCkoLmXt/mN1HltBcQnPzNjKnzuO1PkcISxoAhAFPAqsUkr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HEkII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\nzMzMrA0eQJuZmZmZteH/E2BpfNt8OnQAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r6lABkuSoJOf",
        "colab_type": "code",
        "outputId": "f63ef11a-dccd-4cd7-f91c-eae9acb42d4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 917
        }
      },
      "source": [
        "regularised_model=get_regularised_model(1e-5,0.3)\n",
        "regularised_model.summary() # Print the model summary\n",
        "regularised_model.compile(optimizer='adam', loss='mse', metrics=['mae']) # Compile the model\n",
        "regularised_history = regularised_model.fit(train_data, train_targets, epochs=100, validation_split=0.15, batch_size=64,verbose=False) # Train the model\n",
        "regularised_model.evaluate(test_data,test_targets,verbose=2) # Evaluate the model on test set\n",
        "plotting_curves(regularised_history)"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_9\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_7 (Dense)              (None, 128)               1408      \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_9 (Dense)              (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_2 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_10 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_3 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_11 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_4 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_12 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_5 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_13 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 84,097\n",
            "Trainable params: 84,097\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "45/1 - 0s - loss: 0.5189 - mae: 0.6246\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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qavfWN5JnaK5Ap1LNhj22I6ExxhtWgQYG+RvYagm0MabveFtEHgD+DlQGDqrq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\nEmygJUmSpBJsoCVJkqQSbKAlSZKkEv4PG8+p9C6Y77oAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4p0tyIc_o1V9",
        "colab_type": "text"
      },
      "source": [
        "## Batch normalisation layers\n",
        "We can implement batch normalisation into our model by adding it in the same way as any other layer. There are some parameters and hyperparameters associated with batch normalisation.\n",
        "\n",
        "* The hyperparameter **momentum** is the weighting given to the previous running mean when re-computing it with an extra minibatch. By **default**, it is set to 0.99.\n",
        "\n",
        "* The hyperparameter **$\\epsilon$** (epsilon) is used for numeric stability when performing the normalisation over the minibatch. By **default** it is set to 0.001.\n",
        "\n",
        "* The parameters **$\\beta$** (beta) and **$\\gamma$** (gamma) are used to implement an affine transformation after normalisation. By **default**, $\\beta$ is an all-zeros vector, and $\\gamma$ is an all-ones vector.\n",
        "\n",
        "### Customising parameters\n",
        "These can all be changed (along with various other properties) by adding optional arguments to `tf.keras.layers.BatchNormalization()`.\n",
        "\n",
        "Also, the axis for batch normalisation can be specified which is set as -1 by default."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tEeAh7AfoJQ1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def get_model_with_normalisation(l1,l2,rate):\n",
        "  model = Sequential([\n",
        "    Dense(128, kernel_regularizer=regularizers.l1_l2(l1,l2), input_shape=[train_data.shape[1],], activation=\"relu\"),\n",
        "    BatchNormalization(),  # Batch normalisation layer\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l1_l2(l1,l2), activation='relu'),\n",
        "    BatchNormalization(),  # Batch normalisation layer\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l1_l2(l1,l2), activation='relu'),\n",
        "    BatchNormalization(momentum=0.95, epsilon=0.005, axis = -1, beta_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.05), gamma_initializer=tf.keras.initializers.Constant(value=0.9)),  # Batch normalisation layer\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l1_l2(l1,l2), activation='relu'),\n",
        "    BatchNormalization(momentum=0.95, epsilon=0.005, axis = -1, beta_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.05), gamma_initializer=tf.keras.initializers.Constant(value=0.9)),  # Batch normalisation layer\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l1_l2(l1,l2), activation='relu'),\n",
        "    BatchNormalization(momentum=0.95, epsilon=0.005, axis = -1, beta_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.05), gamma_initializer=tf.keras.initializers.Constant(value=0.9)),  # Batch normalisation layer\n",
        "    Dropout(rate),\n",
        "    Dense(128, kernel_regularizer=regularizers.l1_l2(l1,l2), activation='relu'),\n",
        "    BatchNormalization(momentum=0.95, epsilon=0.005, axis = -1, beta_initializer=tf.keras.initializers.RandomNormal(mean=0.0, stddev=0.05), gamma_initializer=tf.keras.initializers.Constant(value=0.9)),  # Batch normalisation layer\n",
        "    Dropout(rate),\n",
        "    Dense(1)\n",
        "])\n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nKRMiChfjLO9",
        "colab_type": "code",
        "outputId": "5dcb3d7c-c7a4-4eb7-ebfa-696790e73f8f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "normalised_model=get_model_with_normalisation(1e-5,1e-5,0.3)\n",
        "normalised_model.summary() # Print the model summary\n",
        "normalised_model.compile(optimizer='adam', loss='mse', metrics=['mae']) # Compile the model\n",
        "normalised_history = regularised_model.fit(train_data, train_targets, epochs=100, validation_split=0.15, batch_size=64,verbose=False) # Train the model\n",
        "normalised_model.evaluate(test_data,test_targets,verbose=2) # Evaluate the model on test set\n",
        "plotting_curves(normalised_history)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_10\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_14 (Dense)             (None, 128)               1408      \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_6 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_15 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "batch_normalization_1 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_7 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_16 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "batch_normalization_2 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_8 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_17 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "batch_normalization_3 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_9 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_18 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "batch_normalization_4 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_10 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_19 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "batch_normalization_5 (Batch (None, 128)               512       \n",
            "_________________________________________________________________\n",
            "dropout_11 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_20 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 87,169\n",
            "Trainable params: 85,633\n",
            "Non-trainable params: 1,536\n",
            "_________________________________________________________________\n",
            "45/1 - 0s - loss: 1.0402 - mae: 0.9063\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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pRyUA5/yF5SdnPg6E9eCO16rgxdpS4Mganmz0iOY8pt54oI/8zG0dcj5rxO0N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y1G9+zT++qobO3l0hWZYT1/blIst1+9nIqWHs6cn8zTXzqLT24u4KkvbSU3KZZ0WzRr9ezKeclx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\nTYng1rHJTr+2s35bFhEKIYQTw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            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zT0j3HbEBUWf",
        "colab_type": "text"
      },
      "source": [
        "## Callbacks\n",
        "* Collect information on training\n",
        "* Remotely monitor training\n",
        "* Tune parameters while training\n",
        "* Create checkpoints\n",
        "* Terminate training early\n",
        "* API for user defined callbacks\n",
        "\n",
        "### Callbacks using the logs Dictionary\n",
        "The `logs` dictionary stores the loss value, along with all of the metrics we are using at the end of a batch or epoch. We can incorporate information from the `logs` dictionary into our own custom callbacks.\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SUuEhvWJCBLH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create the custom callback\n",
        "\n",
        "class LossAndMetricCallback(tf.keras.callbacks.Callback):\n",
        "\n",
        "    # Print the loss after every second batch in the training set\n",
        "    def on_train_batch_end(self, batch, logs=None):\n",
        "        if batch %2 ==0:\n",
        "            print('\\n After batch {}, the loss is {:7.2f}.'.format(batch, logs['loss']))\n",
        "    \n",
        "    # Print the loss after each batch in the test set\n",
        "    def on_test_batch_end(self, batch, logs=None):\n",
        "        print('\\n After batch {}, the loss is {:7.2f}.'.format(batch, logs['loss']))\n",
        "\n",
        "    # Print the loss and mean absolute error after each epoch\n",
        "    def on_epoch_end(self, epoch, logs=None):\n",
        "        print('Epoch {}: Average loss is {:7.2f}, mean absolute error is {:7.2f}.'.format(epoch, logs['loss'], logs['mae']))\n",
        "    \n",
        "    # Notify the user when prediction has finished on each batch\n",
        "    def on_predict_batch_end(self,batch, logs=None):\n",
        "        print(\"Finished prediction on batch {}!\".format(batch))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m46eBlJIjLST",
        "colab_type": "code",
        "outputId": "7eb19c90-81ef-43d1-f0f6-d12efb483725",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "regularised_model1=get_regularised_model(1e-5,0.3)\n",
        "regularised_model1.summary() # Print the model summary\n",
        "regularised_model1.compile(optimizer='adam', loss='mse', metrics=['mae']) # Compile the model\n",
        "regularised_history1 = regularised_model1.fit(train_data, train_targets, epochs=20, validation_split=0.15, batch_size=100,verbose=False,callbacks=[LossAndMetricCallback()]) # Train the model\n",
        "regularised_model1.evaluate(test_data,test_targets,verbose=False,batch_size=10,callbacks=[LossAndMetricCallback()]) # Evaluate the model on test set\n",
        "regularised_model1_pred = regularised_model1.predict(test_data, batch_size=10, callbacks=[LossAndMetricCallback()], verbose=False)\n",
        "plotting_curves(regularised_history1)"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_11\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_21 (Dense)             (None, 128)               1408      \n",
            "_________________________________________________________________\n",
            "dropout_12 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_22 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_13 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_23 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_14 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_24 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_15 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_25 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_16 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_26 (Dense)             (None, 128)               16512     \n",
            "_________________________________________________________________\n",
            "dropout_17 (Dropout)         (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_27 (Dense)             (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 84,097\n",
            "Trainable params: 84,097\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "\n",
            " After batch 0, the loss is    1.02.\n",
            "\n",
            " After batch 2, the loss is    0.89.\n",
            "\n",
            " After batch 0, the loss is    1.10.\n",
            "Epoch 0: Average loss is    0.98, mean absolute error is    0.83.\n",
            "\n",
            " After batch 0, the loss is    0.99.\n",
            "\n",
            " After batch 2, the loss is    0.99.\n",
            "\n",
            " After batch 0, the loss is    1.09.\n",
            "Epoch 1: Average loss is    0.97, mean absolute error is    0.83.\n",
            "\n",
            " After batch 0, the loss is    0.86.\n",
            "\n",
            " After batch 2, the loss is    0.94.\n",
            "\n",
            " After batch 0, the loss is    1.05.\n",
            "Epoch 2: Average loss is    0.96, mean absolute error is    0.82.\n",
            "\n",
            " After batch 0, the loss is    1.04.\n",
            "\n",
            " After batch 2, the loss is    0.83.\n",
            "\n",
            " After batch 0, the loss is    0.96.\n",
            "Epoch 3: Average loss is    0.93, mean absolute error is    0.81.\n",
            "\n",
            " After batch 0, the loss is    1.00.\n",
            "\n",
            " After batch 2, the loss is    0.80.\n",
            "\n",
            " After batch 0, the loss is    0.84.\n",
            "Epoch 4: Average loss is    0.88, mean absolute error is    0.78.\n",
            "\n",
            " After batch 0, the loss is    0.75.\n",
            "\n",
            " After batch 2, the loss is    0.76.\n",
            "\n",
            " After batch 0, the loss is    0.71.\n",
            "Epoch 5: Average loss is    0.76, mean absolute error is    0.72.\n",
            "\n",
            " After batch 0, the loss is    0.57.\n",
            "\n",
            " After batch 2, the loss is    0.73.\n",
            "\n",
            " After batch 0, the loss is    0.61.\n",
            "Epoch 6: Average loss is    0.67, mean absolute error is    0.67.\n",
            "\n",
            " After batch 0, the loss is    0.57.\n",
            "\n",
            " After batch 2, the loss is    0.63.\n",
            "\n",
            " After batch 0, the loss is    0.55.\n",
            "Epoch 7: Average loss is    0.65, mean absolute error is    0.65.\n",
            "\n",
            " After batch 0, the loss is    0.57.\n",
            "\n",
            " After batch 2, the loss is    0.59.\n",
            "\n",
            " After batch 0, the loss is    0.50.\n",
            "Epoch 8: Average loss is    0.60, mean absolute error is    0.64.\n",
            "\n",
            " After batch 0, the loss is    0.55.\n",
            "\n",
            " After batch 2, the loss is    0.50.\n",
            "\n",
            " After batch 0, the loss is    0.43.\n",
            "Epoch 9: Average loss is    0.57, mean absolute error is    0.62.\n",
            "\n",
            " After batch 0, the loss is    0.53.\n",
            "\n",
            " After batch 2, the loss is    0.56.\n",
            "\n",
            " After batch 0, the loss is    0.43.\n",
            "Epoch 10: Average loss is    0.59, mean absolute error is    0.63.\n",
            "\n",
            " After batch 0, the loss is    0.60.\n",
            "\n",
            " After batch 2, the loss is    0.49.\n",
            "\n",
            " After batch 0, the loss is    0.43.\n",
            "Epoch 11: Average loss is    0.54, mean absolute error is    0.60.\n",
            "\n",
            " After batch 0, the loss is    0.68.\n",
            "\n",
            " After batch 2, the loss is    0.55.\n",
            "\n",
            " After batch 0, the loss is    0.40.\n",
            "Epoch 12: Average loss is    0.58, mean absolute error is    0.61.\n",
            "\n",
            " After batch 0, the loss is    0.54.\n",
            "\n",
            " After batch 2, the loss is    0.67.\n",
            "\n",
            " After batch 0, the loss is    0.41.\n",
            "Epoch 13: Average loss is    0.58, mean absolute error is    0.62.\n",
            "\n",
            " After batch 0, the loss is    0.49.\n",
            "\n",
            " After batch 2, the loss is    0.49.\n",
            "\n",
            " After batch 0, the loss is    0.42.\n",
            "Epoch 14: Average loss is    0.51, mean absolute error is    0.57.\n",
            "\n",
            " After batch 0, the loss is    0.49.\n",
            "\n",
            " After batch 2, the loss is    0.55.\n",
            "\n",
            " After batch 0, the loss is    0.41.\n",
            "Epoch 15: Average loss is    0.53, mean absolute error is    0.60.\n",
            "\n",
            " After batch 0, the loss is    0.52.\n",
            "\n",
            " After batch 2, the loss is    0.51.\n",
            "\n",
            " After batch 0, the loss is    0.41.\n",
            "Epoch 16: Average loss is    0.54, mean absolute error is    0.60.\n",
            "\n",
            " After batch 0, the loss is    0.50.\n",
            "\n",
            " After batch 2, the loss is    0.47.\n",
            "\n",
            " After batch 0, the loss is    0.40.\n",
            "Epoch 17: Average loss is    0.54, mean absolute error is    0.59.\n",
            "\n",
            " After batch 0, the loss is    0.52.\n",
            "\n",
            " After batch 2, the loss is    0.43.\n",
            "\n",
            " After batch 0, the loss is    0.40.\n",
            "Epoch 18: Average loss is    0.53, mean absolute error is    0.59.\n",
            "\n",
            " After batch 0, the loss is    0.55.\n",
            "\n",
            " After batch 2, the loss is    0.49.\n",
            "\n",
            " After batch 0, the loss is    0.41.\n",
            "Epoch 19: Average loss is    0.53, mean absolute error is    0.59.\n",
            "\n",
            " After batch 0, the loss is    0.82.\n",
            "\n",
            " After batch 1, the loss is    0.28.\n",
            "\n",
            " After batch 2, the loss is    0.43.\n",
            "\n",
            " After batch 3, the loss is    0.42.\n",
            "\n",
            " After batch 4, the loss is    0.81.\n",
            "Finished prediction on batch 0!\n",
            "Finished prediction on batch 1!\n",
            "Finished prediction on batch 2!\n",
            "Finished prediction on batch 3!\n",
            "Finished prediction on batch 4!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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uYPb6fRQPC+LOVpW4q3Uc0cVCM3/iV7fCtl/h8dUQlMV5xph8xQroS5M69Wja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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LyCr3NMjDf9N",
        "colab_type": "text"
      },
      "source": [
        "## Learning rate scheduler\n",
        "\n",
        "**Usage:** `tf.keras.callbacks.LearningRateScheduler(schedule, verbose=0)`\n",
        "\n",
        "This function `schedule` should take two arguments:\n",
        "* The current epoch (as an integer), and\n",
        "* The current learning rate,\n",
        "\n",
        "and return new learning rate for that epoch. \n",
        "\n",
        "The `LearningRateScheduler` also has an optional `verbose` argument, which prints information about the learning rate if it is set to 1.\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wJ_PiuoDHixA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the learning rate schedule function\n",
        "\n",
        "def lr_function(epoch, lr):\n",
        "    if epoch % 2 == 0:\n",
        "        return lr\n",
        "    else:\n",
        "        return lr + epoch/1000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aDmXGilPH3iQ",
        "colab_type": "code",
        "outputId": "37d55256-92b1-4268-81c3-69488f393b29",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        }
      },
      "source": [
        "# Using defined schedule function\n",
        "regularised_history2 = regularised_model1.fit(train_data, train_targets, epochs=10, validation_split=0.15, batch_size=100,verbose=False, callbacks=[tf.keras.callbacks.LearningRateScheduler(lr_function, verbose=1)]) # Train the model\n",
        "plotting_curves(regularised_history2)"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00001: LearningRateScheduler reducing learning rate to 0.0010000000474974513.\n",
            "\n",
            "Epoch 00002: LearningRateScheduler reducing learning rate to 0.0020000000474974513.\n",
            "\n",
            "Epoch 00003: LearningRateScheduler reducing learning rate to 0.0020000000949949026.\n",
            "\n",
            "Epoch 00004: LearningRateScheduler reducing learning rate to 0.005000000094994903.\n",
            "\n",
            "Epoch 00005: LearningRateScheduler reducing learning rate to 0.004999999888241291.\n",
            "\n",
            "Epoch 00006: LearningRateScheduler reducing learning rate to 0.009999999888241292.\n",
            "\n",
            "Epoch 00007: LearningRateScheduler reducing learning rate to 0.009999999776482582.\n",
            "\n",
            "Epoch 00008: LearningRateScheduler reducing learning rate to 0.01699999977648258.\n",
            "\n",
            "Epoch 00009: LearningRateScheduler reducing learning rate to 0.016999999061226845.\n",
            "\n",
            "Epoch 00010: LearningRateScheduler reducing learning rate to 0.025999999061226846.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3S0bSo0PzvfUYKlWtEJFfA5ZAG2McvjWANroKHKrKnxeu5clFGxnXtyMDurSm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\nz+HwrefiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yJTt6CbNI4_7",
        "colab_type": "code",
        "outputId": "fc86be4c-787f-4131-9d65-269b47f4f7d7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 651
        }
      },
      "source": [
        "# Using lambda function\n",
        "regularised_history2 = regularised_model1.fit(train_data, train_targets, epochs=10, validation_split=0.15, batch_size=100,verbose=False, callbacks=[tf.keras.callbacks.LearningRateScheduler(lambda x:1/(3+5*x), verbose=1)]) # Train the model\n",
        "plotting_curves(regularised_history2)"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00001: LearningRateScheduler reducing learning rate to 0.3333333333333333.\n",
            "\n",
            "Epoch 00002: LearningRateScheduler reducing learning rate to 0.125.\n",
            "\n",
            "Epoch 00003: LearningRateScheduler reducing learning rate to 0.07692307692307693.\n",
            "\n",
            "Epoch 00004: LearningRateScheduler reducing learning rate to 0.05555555555555555.\n",
            "\n",
            "Epoch 00005: LearningRateScheduler reducing learning rate to 0.043478260869565216.\n",
            "\n",
            "Epoch 00006: LearningRateScheduler reducing learning rate to 0.03571428571428571.\n",
            "\n",
            "Epoch 00007: LearningRateScheduler reducing learning rate to 0.030303030303030304.\n",
            "\n",
            "Epoch 00008: LearningRateScheduler reducing learning rate to 0.02631578947368421.\n",
            "\n",
            "Epoch 00009: LearningRateScheduler reducing learning rate to 0.023255813953488372.\n",
            "\n",
            "Epoch 00010: LearningRateScheduler reducing learning rate to 0.020833333333333332.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m181BU3t/yp08EraGJSI6CVFEMuSzwOXAZ8Lb5WT6xNlsiG+HDiXTIhKN5Z1M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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RoqbGeclHgnj",
        "colab_type": "text"
      },
      "source": [
        "### Custom learning rate scheduler\n",
        "\n",
        "We are going to define a callback to change the learning rate of the optimiser of a model during training. We will do this by specifying the epochs and new learning rates where we would like it to be changed.\n",
        "\n",
        "Before training the model with a custom callback, we define the auxillary function that returns the learning rate for each epoch based on our schedule."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hxA8-5CrjLdD",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the learning rate schedule. The tuples below are (start_epoch, new_learning_rate)\n",
        "\n",
        "lr_schedule = [\n",
        "    (4, 0.03), (7, 0.02), (11, 0.005), (15, 0.007)\n",
        "]\n",
        "\n",
        "def get_new_epoch_lr(epoch, lr):\n",
        "    # Checks to see if the input epoch is listed in the learning rate schedule \n",
        "    # and if so, returns index in lr_schedule\n",
        "    epoch_in_sched = [i for i in range(len(lr_schedule)) if lr_schedule[i][0]==int(epoch)]\n",
        "    if len(epoch_in_sched)>0:\n",
        "        # If it is, return the learning rate corresponding to the epoch\n",
        "        return lr_schedule[epoch_in_sched[0]][1]\n",
        "    else:\n",
        "        # Otherwise, return the existing learning rate\n",
        "        return lr"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PB-YKlFjLba",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define the custom callback\n",
        "\n",
        "class LRScheduler(tf.keras.callbacks.Callback):\n",
        "    \n",
        "    def __init__(self, new_lr):\n",
        "        super(LRScheduler, self).__init__()\n",
        "        # Add the new learning rate function to our callback\n",
        "        self.new_lr = new_lr\n",
        "\n",
        "    def on_epoch_begin(self, epoch, logs=None):\n",
        "        # Make sure that the optimizer we have chosen has a learning rate, and raise an error if not\n",
        "        if not hasattr(self.model.optimizer, 'lr'):\n",
        "              raise ValueError('Error: Optimizer does not have a learning rate.')\n",
        "                \n",
        "        # Get the current learning rate\n",
        "        curr_rate = float(tf.keras.backend.get_value(self.model.optimizer.lr))\n",
        "        \n",
        "        # Call the auxillary function to get the scheduled learning rate for the current epoch\n",
        "        scheduled_rate = self.new_lr(epoch, curr_rate)\n",
        "\n",
        "        # Set the learning rate to the scheduled learning rate\n",
        "        tf.keras.backend.set_value(self.model.optimizer.lr, scheduled_rate)\n",
        "        print('Learning rate for epoch {} is {:7.3f}'.format(epoch, scheduled_rate))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GE5u5gj5jLVH",
        "colab_type": "code",
        "outputId": "f990f428-59b0-4ec5-9db8-31a95fb73413",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 473
        }
      },
      "source": [
        "regularised_history3 = regularised_model1.fit(train_data, train_targets, epochs=10, validation_split=0.15, batch_size=100,verbose=False,callbacks=[LRScheduler(get_new_epoch_lr)]) # Train the model\n",
        "plotting_curves(regularised_history3)"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Learning rate for epoch 0 is   0.021\n",
            "Learning rate for epoch 1 is   0.021\n",
            "Learning rate for epoch 2 is   0.021\n",
            "Learning rate for epoch 3 is   0.021\n",
            "Learning rate for epoch 4 is   0.030\n",
            "Learning rate for epoch 5 is   0.030\n",
            "Learning rate for epoch 6 is   0.030\n",
            "Learning rate for epoch 7 is   0.020\n",
            "Learning rate for epoch 8 is   0.020\n",
            "Learning rate for epoch 9 is   0.020\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Q5jm45G440ywzUdFQ8RL3uPJF2LnS1c2rp8JX/3aPkhd4CXh7N4bHZq7xRShbuocDg4AR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\nxULPo0EV7zcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZzN6ueEfJA8P",
        "colab_type": "text"
      },
      "source": [
        "## CSV logger\n",
        "**Usage** `tf.keras.callbacks.CSVLogger(filename, separator=',', append=False)`\n",
        "\n",
        "This callback streams the results from each epoch into a CSV file.\n",
        "The first line of the CSV file will be the names of pieces of information recorded on each subsequent line, beginning with the epoch and loss value. The values of metrics at the end of each epoch will also be recorded.\n",
        "\n",
        "The only compulsory argument is the `filename` for the log to be streamed to. This could also be a filepath. The `separator` can be used for defining entries between each line. The `append` argument allows to append results to an existing file with the same name. This can be particularly useful if training is continuous."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uxneImpkEwfY",
        "colab_type": "code",
        "outputId": "8539aedd-ae0f-4618-b116-65d077c07f00",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        }
      },
      "source": [
        "regularised_history3 = regularised_model1.fit(train_data, train_targets, epochs=10, validation_split=0.15,verbose=False,callbacks=[tf.keras.callbacks.CSVLogger(\"results.csv\")]) # Train the model\n",
        "plotting_curves(regularised_history3)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3blsy/6PmZU2cJm3CQNHaoE5aGAzZ5ajwV/vKz0PXkTkvwqtXhqVt31DR7jUi\nVOpr0h8KVaxx48Ycf/zx/PCHP2T06NFs2rSJRo0a0axZM1atWsXLL7/MkCFDyj3/2GOPZcyYMVx5\n5ZUUFBTwwgsv8OMf/xiAzZs3065dO/Lz8/n3v/9Nhw4dAGjSpAmbN2/e7Vo9e/Zk0aJFLFiw4Os+\n2scdd1xc7ltERCKlq9iPjoKcH4ZpemtgFTvhAxoTas5L4ZHiW1Z/cyLzYg1aliTJWb2ipDlKlIuT\n5sZtqqcLgaQmM2jXNywnXBXmA53zYqhqT7opLC27RYn2qdDhsDDQM8mMHj2a008/nSeffJJevXrR\nv39/evXqRadOnRg8eHCF5w4YMICzzz6bfv360bp1aw4/vOTpYjfccAODBg0iKyuLQYMGfZ1Qn3PO\nOVx00UXcfvvtPP30018fn5GRwUMPPcRZZ5319YDGSy65JD43LSIi31S6ij3/dRh1R5gMoAYxT6L5\ndnNycrx4jttKWfYx5D7wzepybNKs7hpSk21aAXNfCon2orfDtyqN24ZuML1HhJlM0tL36y1mz55N\n7969qyhgKVbW79XMprl7ToJCSoi9brNFRIot+QDG/SQ8/TEBVeyK2uzUrlx3GBAWkdqoabvQZ//w\nC2H7epg3MfTR/vSJ8EdjRrMwHWH3odAxJ1S4k7j7iIiIpJDOR5RUsT+4Cxa8DiNrRhU7tZNrkWTR\noAX0Ozssu7aFR8nOfhHmvQyf/Tcc0zATOh4eEu1OA6H9AKjfOLFxi4iI7Kt6DWH4n0PXyOd/Co+O\nDAWnb/0poZ9vSq5Fkk29hiWzpBQVwpo5sPQjyJsalnnRw1CtDrQ+KDwVq+Ph0HEgtOq+W3Xb3TFV\nvKtMMnXFExGpEboc+c0q9vyJMOpO6HpsQsJRci2SzOqkQZuDwpLzg7Bt2zpYNi0k2ks/gs+fhtwH\nw74GLUoS7Y45ZNTryNq1a2nVqpUS7Crg7qxdu5aMDA2CFhGpUt+oYv8EHjk1YVVsJdciqaZhy/AU\nyANODK+LiuCrud+sbs+fCEDHei3IO/IG1jTtGubiTqsPdeqq7/Z+yMjIoGPHjokOQ0QkOXU5Ei55\nF968AT64OzwJedSd0PWYagtBybVIqqtTJ8yV3bo3HHZB2LZ9AyybRnpeLl3zxsGHU2HHxrAvoxl0\niPptdzw8TP/XoHni4hcREYlVryEMv7mkL/YjI+Dwi8KTm6uhiq3kWkR216A59BgaFgjV7bULIC+q\nbi+dCpNvARwwyOoZBkp2HBiS7syeSTnftoiI1CJdjgpV7Deuhw/viemLHd8qtpJrEdmzOnUg68Cw\n9D8vbNuxCZZ/HBLtvKnhocaQTfoAACAASURBVEyfPB721W8aKtpdj4Fux0O7fqH/t4iISHWq1xBO\nugX6jAzzYj8yAgZeDEOvjVsVW8m1iOybjKbQbUhYANzDEyTzpoYK95IPQ7XgjevDQMmux4ZEu9sQ\naNk1UVGLiEgq6nIUXBpTxZ73Kpx2F2QfXeVvpeRaRKqGGWT2CMuho8O2Lath4VuwcHKYe3vW82F7\ni+yQaHc/PjxJsmHLREUtIiKpol4jOOkv0HtkmFHk4VNCFftb14V9VUTJtYjET+PW0PessLjDV/ND\nkr1wcpgCcNpDgEH7/iHR7jYEOg0KM5OIiIjEQ/ZguPS9UMWe/gQM/kWVJteWTA80yMnJ8dzc3ESH\nISKVUZgf5tteOBm+mBS6k3ghpDcMX991GxKq220OSomp/8xsmrvnJDqO6qQ2W0QSbutaaNRqr0+r\nqM1W5VpEEiMtHTofEZYhvw8DJBe/GxLthZNg4tXhuEatS/p2dz8emrZPXMwiIpJc9iGx3hMl1yJS\nM2Q0hZ4nhQVg47KSvtoLJ8HnT4XtmT1LupBkHw31myQoYBERkd0puRaRmqlZB+j/vbAUFcHqmSVd\nSKY9EkZ716kbHmTTbUjoQtLhMEhTsyYiIokTt08hM3sQGAGsdveDy9jfC3gIGABc5e5jY/b9EriQ\n8ISKz4EfuPuOeMUqIjVcnTrQ9pCwHPVzyN8Rpvsr7kIy+RaYfHOYXzv76JKqdlZvPcxGRESqVTxL\nPA8DdwCPlrN/HXAZcFrsRjPrEG3v4+7bzewp4JzoeiIikJ4R5s3ueixwLWxbB19OCYn2F5Ng7oRw\nXEbzMDiyeGnbT5VtERGJq7h9yrj7FDPLrmD/amC1mZ1STlwNzCwfaAgsj0uQIpIcGraEg04LC8D6\nxbD4vTBAcvF7Jcl2vcbh8exdBoelwwBN+xfDzIYDtwFpwP3ufkup/V2AB4EsQoHkPHfPi/ZdAESj\nULnR3R+ptsBFRGqQGlfCcfdlZjYWWAJsBya6+8Tyjjezi4GLATp37lw9QYpIzdaiS1iKH2azeWWU\nbEfLmzeE7Wn1Q5/t4sp2p4FVOtdpbWJmacCdwIlAHjDVzMa7+6yYw8YCj7r7I2Z2AnAzcL6ZtQSu\nBXII3fmmReeur967EBFJvBqXXJtZC2AU0BXYAPzPzM5z98fLOt7d7wXuhTBnarUFKiK1R5O2cPAZ\nYYHQjWTJ+yXV7bfHwpSiMECy3aFRsj04TBPYoHliY68+A4EF7r4QwMyeJLTFscl1H+BX0fokYFy0\n/m3gNXdfF537GjAceKIa4hYRqVFqXHINfAv40t3XAJjZs8BRQJnJtYjIXmvYEnqdEhYIc2znfVRS\n2f7wHnjvdsCg7cFRN5KjoPNR0DgroaHHUQdgaczrPGBQqWM+Bc4gdB05HWhiZq3KObdD/EIVEam5\namJyvQQ4wswaErqFDAX0CC8RiZ+MptDjW2EByN8Oebklle3iqf8AMg8sqWx3OQqadUxc3NXvCuAO\nMxsDTAGWAYV7cwF15RORZBfPqfieAIYAmWaWR+iPlw7g7veYWVtC0twUKDKzywkzhHxoZk8DHwMF\nwCdE3T5ERKpFegPoekxYAAp2wYpPSwZIzngOpj0c9jXvUpJodzkKWnarrY9rXwZ0inndMdr2NXdf\nTqhcY2aNge+4+wYzW0Zo72PPnVzWm6grn4gkO3NPnrYtJyfHc3NV5BaROCsqhFUzo8r2O+HntrVh\nX1Yv+MkHe51gm9k0d8+JQ7SVff+6wDzCt4XLgKnAue4+M+aYTGCduxeZ2U1AobtfEw1onEZ4bgGE\n4shhxX2wy6M2W0Rqq4ra7JrYLUREpGarkwbt+obliEvAHb6aFyrbO7fUysq1uxeY2c+AVwlT8T3o\n7jPN7Hog193HE6rTN5uZE7qF/DQ6d52Z3UBIyAGu31NiLSKSrJRci4jsLzPI6hmWWszdJwATSm27\nJmb9aeDpcs59kDAHtohIStNzgUVEkoiZ1TGz7yY6DhGRVKXkWkQkibh7EfDbRMchIpKqlFyLiCSf\n183sCjPrZGYti5dEByUikgrU51pEJPmcHf38acw2B7olIBYRkZSi5FpEJMm4e9dExyAikqqUXIuI\nJBkzSwcuBY6NNk0G/uXu+QkLSkQkRSi5FhFJPncTnoh7V/T6/GjbhQmLSEQkRSi5FhFJPoe7e7+Y\n12+a2acJi0ZEJIVothARkeRTaGbdi1+YWTegMIHxiIikDFWuRUSSzxXAJDNbCBjQBfhBYkMSEUkN\nSq5FRJKImaUB/YADgOLnsc91952Ji0pEJHWoW4iISBJx90JgtLvvdPfPokWJtYhINVHlWkQk+bxr\nZncA/wW2Fm90948TF5KISGpQci0iknwOjX5eH7PNgRMSEIuISEpRci0ikkSiPtfj3f0fiY5FRCQV\nqc+1iEgSKe5zneg4RERSVdySazN70MxWm9mMcvb3MrP3zWynmV1Ral9zM3vazOaY2WwzOzJecYqI\nJKF3zewOMzvGzAYUL4kOSkQkFcSzW8jDwB3Ao+XsXwdcBpxWxr7bgFfc/Uwzqwc0jEuEIiLJSX2u\nRUQSJG7JtbtPMbPsCvavBlab2Smx282sGXAsMCY6bhewK15xiogkG3c/PtExiIikqprY57orsAZ4\nyMw+MbP7zaxReQeb2cVmlmtmuWvWrKm+KEVEahgzuzVm/Rel9j1c7QGJiKSgmphc1wUGAHe7e3/C\nHK2/L+9gd7/X3XPcPScrK6u6YhQRqYmOjVm/oNS+vtUZiIhIqqqJyXUekOfuH0avnyYk2yIiUjEr\nZ11ERKpJjZvn2t1XmtlSM+vp7nOBocCsRMclIlIL1DGzFoTCSfF6cZKdlriwRERSR9ySazN7AhgC\nZJpZHnAtkA7g7veYWVsgF2gKFJnZ5UAfd98E/Bz4dzRTyELgB/GKU0QkiTQDplGSUMc+7tyrPxwR\nkdQTz9lCKnyIgbuvBDqWs286kBOPuEREkpW7Zyc6BhGRVFcT+1yLiIiIiNRKSq5FRERERKqIkmsR\nERERkSqi5FpEJAmZ2dFm9oNoPcvMuiY6JhGRVKDkWkQkyZjZtcDvgCujTenA44mLSEQkdSi5FhFJ\nPqcDIwlPuMXdlwNN9nSSmQ03s7lmtsDMdnsyrpl1NrNJZvaJmX1mZidH29PN7BEz+9zMZpvZlbtf\nXUQkNSi5FhFJPrvc3YnmtjazRns6wczSgDuBk4A+wGgz61PqsKuBp9y9P3AOcFe0/SygvrsfAhwG\n/NjMsqvgPkREah0l1yIiyecpM/sX0NzMLgJeB+7fwzkDgQXuvtDddwFPAqNKHeOEB39BeGDN8pjt\njcysLtAA2AVs2v/bEBGpfWrc489FRGT/uPtYMzuRkOD2BK5x99f2cFoHYGnM6zxgUKljrgMmmtnP\ngUbAt6LtTxMS8RVAQ+CX7r5uv25CRKSWUuVaRCTJmNlf3P01d/+Nu1/h7q+Z2V+q4NKjgYfdvSNw\nMvCYmdUhVL0LgfZAV+DXZtatnNguNrNcM8tds2ZNFYQkIlKzKLkWEUk+J5ax7aQ9nLMM6BTzumO0\nLdaPgKcA3P19IAPIBM4FXnH3fHdfDbwL5JT1Ju5+r7vnuHtOVlbWHm9ERKS2UXItIpIkzOxSM/sc\n6BnN5lG8fAl8tofTpwIHmFlXM6tHGLA4vtQxS4Ch0Xv1JiTXa6LtJ0TbGwFHAHOq6r5ERGoT9bkW\nEUke/wFeBm4GYqfS27ynPtDuXmBmPwNeBdKAB919ppldD+S6+3jg18B9ZvZLwiDGMe7uZnYn8JCZ\nzQQMeMjd95TMi4gkJSXXIiJJwt03AhvN7HeldjU2s8buvmQP508AJpTadk3M+ixgcBnnbSFMxyci\nkvKUXIuIJJ+XCJVlI3Td6ArMBQ5KZFAiIqlAybWISJKJHubyNTMbAPwkQeGIiKSUSg1oNLNG0XRL\nmNmBZjbSzNLjG5qIiFQFd/+Y3eesFhGROKhs5XoKcIyZtQAmEkaVnw18r7wTzOxBYASw2t0PLmN/\nL+AhYABwlbuPLbU/DcgFlrn7iErGKSKS8szsVzEv6xDa2eXlHC4iIlWoslPxmbtvA84A7nL3s9hz\n372HgeEV7F8HXAaMLWf/L4DZlYxPRERKNIlZ6hP6YJd+lLmIiMRBZSvXZmZHEirVP4q2pVV0grtP\nMbPsCvavBlab2SllvFlH4BTgJuBXpfeLiEj53P1PiY5BRCRVVTa5vhy4Engumve0GzApfmFxK/Bb\nQtWlQmZ2MXAxQOfOneMYkohIzWZmLxBmCSmTu4+sxnBERFJSpZJrd38LeAsgGtj4lbtfFo+AzKy4\nn/Y0MxtSidjuBe4FyMnJKfdDRUQkBZTXzU5ERKpJpZJrM/sPcAlQSBjM2NTMbnP3v8UhpsHASDM7\nmTA/a1Mze9zdz4vDe4mIJI2oEAJA9AjzA6OXc909PzFRiYiklsoOaOzj7puA0wiP1u0KnB+PgNz9\nSnfv6O7ZwDnAm0qsRUQqL/rWbz5wJ3AXMM/Mjk1oUCIiKaKyfa7To3mtTwPucPd8M6uwC4aZPQEM\nATLNLA+4FkgHcPd7zKwtYaq9pkCRmV1OSRIvIiL77v+AYe4+F8LzCYAngMMSGpWISAqobHL9L2AR\n8Ckwxcy6ABUmwe4+eg/7VwId93DMZGByJWMUEZEgvTixBnD3eXrwl4hI9ajsgMbbgdtjNi02s+Pj\nE5KIiOynXDO7H3g8en0e4ZtCERGJs8o+/ryZmf3dzHKj5f+ARnGOTURE9s2lwCzCg7ouA2ZG20RE\nJM4qO6DxQWAz8N1o2UR4dLmIiNQw7r7T3f/u7mcAFwJvuPvORMclIpIKKtvnuru7fyfm9Z/MbHo8\nAhIRkf1jZpOBkYQ2fhrhabjvufsvExqYiEgKqGzleruZHV38wswGA9vjE5KIiOynZtHMS2cAj7r7\nIGBogmMSEUkJla1cXwI8ambNotfrgQviE5KIiOynumbWjtCN76pEByMikkoqVbl290/dvR/QF+jr\n7v2BE+IamYiI7KvrgVeBL9x9qpl1IzxURkRE4qyy3UIAcPdNMQ95+VUc4hERkf3k7v9z977ufmn0\nemGpcTMiIhIne5Vcl2JVFoWIiFQZM+tmZi+Y2RozW21mz0fVaxERibP9Sa4rfPy5iIgkzH+Ap4B2\nQHvgf4THn4uISJxVOKDRzDZTdhJtQIO4RCQiIvurobs/FvP6cTP7TcKiERFJIRUm1+7epLoCERGR\n/WNmLaPVl83s98CThALJ2cCEhAUmIpJCKjsVn4iI1HzTCMl08ZiYH8fsc+DKao9IRCTFKLkWEUkS\n7t61vH1mll6dsYiIpKr9GdAoIiI1mAVDzewBIC/R8YiIpAIl1yIiScbMjjCz24HFwPPAFKBXYqMS\nEUkNSq5FRJKEmf3ZzOYDNwGfAf2BNe7+iLuvT2x0IiKpQX2uRUSSx4XAPOBu4AV332lmeiaBiEg1\nilvl2swejJ4MNqOc/b3M7H0z22lmV8Rs72Rmk8xslpnNNLNfxCtGEZEk0w64ETgV+MLMHgMamFml\nCilmNtzM5prZgmgqv9L7O0ft8ydm9pmZnRyzr2/Ups80s8/NLKOqbkpEpDaJZ7eQh4HhFexfB1wG\njC21vQD4tbv3AY4AfmpmfeISoYhIEnH3Qnd/xd0vALoD44B3gWVm9p+KzjWzNOBO4CSgDzC6jLb3\nauApd+8PnAPcFZ1bF3gcuMTdDwKGAPlVdmMiIrVI3JJrd59CSKDL27/a3adSqgF29xXu/nG0vhmY\nDXSIV5wiIsnI3Xe6+zPufiZwAPDKHk4ZCCxw94XuvovwAJpRpS8LNI3WmwHLo/VhwGfu/mn03mvd\nvbAq7kNEpLap0QMazSybMCDnwwqOudjMcs0sd82aNdUVmohIreHum9z90T0c1gFYGvM6j90LG9cB\n55lZHuGJjz+Pth8IuJm9amYfm9lvqyBsEZFaqcYm12bWGHgGuNzdN5V3nLvf6+457p6TlZVVfQGK\niKSe0cDD7t4ROBl4zMzqEAbHHw18L/p5upkNLesCKoiISLKrkcl19CSxZ4B/u/uziY5HRCQFLAM6\nxbzuGG2L9SPgKQB3fx/IADIJVe4p7v6Vu28jVLUHlPUmKoiISLKrccm1mRnwADDb3f+e6HhERGoj\nMzvKzM41s+8XL3s4ZSpwgJl1NbN6hAGL40sdswQYGl2/NyG5XgO8ChxiZg2jwY3HAbOq8n5ERGqL\nuM1zbWZPEEaMZ0b9864F0gHc/R4zawvkEgbHFJnZ5YQR6n2B84HPzWx6dLk/uPuEeMUqIpJMoin4\nugPTgeKBhQ6U2+/a3QvM7GeERDkNeNDdZ5rZ9UCuu48Hfg3cZ2a/jK43xt0dWG9mfyck6A5McPeX\n4nR7IiI1WtySa3cfvYf9KwlfO5b2DmBxCUpEJDXkAH2ixLfSoiLGhFLbrolZnwUMLufcxwnT8YmI\npLQa1y1ERET22wygbaKDEBFJRXr8uYhI8skEZpnZR8DO4o3uPjJxIYmIpAYl1yIiyee6RAcgIpKq\nlFyLiCQZd38r0TGIiKQq9bkWEUkyZnaEmU01sy1mtsvMCs2s3IdxiYhI1VFyLSKSfO4gPE1xPtAA\nuBC4M6ERiYikCCXXIiJJyN0XAGnuXujuDwHDEx2TiEgqUJ9rEZHksy16yuJ0M/srsAIVU0REqoUa\nWxGR5HM+oX3/GbAV6AR8J6ERiYikCFWuRUSSjLsvNrMGQDt3/1Oi4xERSSWqXIuIJBkzOxWYDrwS\nvT7UzMYnNioRkdSgynU1c3fe/2It9729kHXb8vnJkO4M69MGM0t0aCKSPK4DBgKTAdx9upl1TWRA\nIiKpQsl1NckvLGLC5yu4d8pCZi7fRGbj+jTJqMuPH5tGTpcW/P6kXuRkt0x0mCKSHPLdfWOpP9o9\nUcGIiKQSJddxtmVnAU9+tISH3l3Esg3b6Z7ViFvOOITT+negbh3jqdw8/vH6PM68531O7NOG3w3v\nSY/WTRIdtojUbjPN7FwgzcwOAC4D3ktwTCIiKUHJdZys2rSDh95dxL8/XMzmHQUM7NqSP408iBN6\ntaZOnZJq0rmDOnNa//Y8+M6X3PPWQob9YwpnH96Jy791IG2aZiTwDkSkFvs5cBWwE3gCeBW4IaER\niYikCCXXVWzuys3c9/ZCnp++jMIi56SD23HRsd04tFPzcs9pWK8uPzvhAEYP7Mwdkxbw+AeLee6T\nZfzo6K78+LjuNM1Ir8Y7EJHazt23EZLrqxIdi4hIqlFyXQWKByne+/ZCJs9dQ4P0NM4d2JkfHd2N\nzq0aVvo6rRrX59pTD+IHR3Vl7MS53DnpC/7z4RJ+dsIBnHdEZ+rXTYvjXVSPgsIi6qZpkhqReNjT\njCDuPrK6YhERqYwFq7fw6PuLuOiYbnRqWfmcqSaLW3JtZg8CI4DV7n5wGft7AQ8BA4Cr3H1szL7h\nwG1AGnC/u98Srzj3R0FhES99voL73l7IjGWbyGxcjyuGHcj3BnWhRaN6+3zdzq0acvvo/lx8bDdu\neXkON7w4i4fe/ZLffLsnp/Zt/41uJbXBxm35vDxjBeM/Xc4HC9cytHcbrh91EO2aNUh0aCLJ5khg\nKaEryIdA7WosRCRl7Mgv5I43F/CvKV+QX+gsWbeNh38wMNFhVQlzj88AcjM7FtgCPFpOct0a6AKc\nBqwvTq7NLA2YB5wI5AFTgdHuPmtP75mTk+O5ublVdxPl2LKzgP9OXcqD73zJsg3b6ZbViIuP6cZp\n/TuQkV711eUp89Zwy8tzmLViEwe1b8rvT+rFMQdkVfn7VKXtuwp5Y84qnp++nMlzV5Nf6GS3asgR\n3Voxbvoy6tapw2+H9+S8QV1q3R8LIvFgZtPcPWc/r5FGaDtHA32Bl4An3H1mFYRY5aqrzRaRmmXS\n3NVc8/wMlq7bzhkDOtCheQP++eYCHhyTwwm92iQ6vEqpqM2OW+Xa3aeYWXYF+1cDq83slFK7BgIL\n3H0hgJk9CYwC9phcx9uqTTt4+L1F/PuDxWyqYJBiVTv2wCyO7pHJ+E+XM3biXM5/4COOOSCT3w3v\nxcEdmsXtffdWQWER7yz4ivHTl/PqzJVs3VVI6yb1+f6R2Yzs156+HZthZlw6pDtXPTeDa56fybhP\nlnHzGX3p2VYzpIjsL3cvJDw45hUzq09Isieb2Z/c/Y7ERiciqW7lxh1c/+JMJny+ku5ZjfjPRYM4\nqnsmuwpCT4AbXpzN0T2yqFe3dncfrYl9rjsQvtYslgcMKu9gM7sYuBigc+fOcQlo3qrN3DdlIeNi\nBileeExX+nduEZf3K0udOsZp/Ttw0iFtefyDJdzx5nxG/PMdRh3aniuG9UxYPyV35+MlGxg/fRkv\nfraCtVt30SSjLiP6tmfUoe0Z1K0VaaX+8OjSqhGP/Wggz32yjBtenMWIf77NJcd156fH94hL5V+q\n37ZdBbw6cyXdMht//UeVVI8oqT6FkFhnA7cDzyUyJhFJbQWFRTzy/mL+PnEuBUXOb77dk4uO6fZ1\nEl2vbh2uGdGHMQ9N5aF3v+THx3VPcMT7pyYm13vF3e8F7oXwFWMVXpf3F67lvikLmRQzSPGHR3el\nS6tGVfU2e61+3TR+dHRXzsrpyL/e+oIH3vmSCZ+v4LwjuvDzEw6g5X709d4b81Zt5vnpy3h++nLy\n1m+nft06fKt3G0Ye2p4hPbP2OPjSzDhjQEeOOzCLm16azT/fXMBLn63gz2ccwhHdWlXLPUjVKypy\nnvtkGX97dS4rN+0AoG3TDE7s04ZhB7VhUNdWtb4iUZOZ2aPAwcAE4E/uPiPBIYlIivtkyXquem4G\ns1ZsYkjPLK4feXCZkz0M6dmaob1a8883F3D6gA60blJ7pyOOW59rgKhbyItl9bmOOeY6YEtMn+sj\ngevc/dvR6ysB3P3mPb1fVfTfKygsYsKMldw3ZSGfL9tIZuN6XHBkNucdsX+DFONl5cYd3Pr6PJ7K\nXUqjenW5ZEh3fji4Kw3qVX0FOG/9Nl74dAXPT1/GnJWbqWNw9AFZjOrXnmEHtaHJfkwZOGXeGq4a\n9zlL123nnMM7ceVJvWnWUFMQ1iYfLlzLjS/N5vNlG+nbsRm/+XZP1mzeycSZq3hr3hq25xfSJKMu\nJ/RqzbA+bTmuZxaN69f6v++rTBX1uS4CtkYvYxt3A9zdm+7P9aua+lyLJK+N2/L566tz+M9HS2jd\npD7XnXoQww9uW+E3mV9+tZVh/3iLUYd2YOxZ/aox2r1XUZtdE5PruoQBjUOBZYQBjedWZkDO/jTU\nW6NBig/EDFK86JhunB6nQYpVbf6qzfz11bm8NmsVrZvU55cnHshZh3Xc72nv1m3dxUufr2D89GVM\nXbQegAGdmzPq0A6cfEg7sprUr4rwgdCV4LbX53P/O1/SomE9rhvZh1MOaacuBTXc4rVbuXnCHF6Z\nuZJ2zTL47fCejOrX4RvjEHbkF/LO/K+YOGslr89ezbqtu6iXVofBPVox7KC2fKt3myr9t1QbVUVy\nXdsouRZJPu7O89OXc+NLs1i3dRdjjurKr4YdWOliys0vz+Zfby1k3E8HV/iMkERLSHJtZk8AQ4BM\nYBVwLZAO4O73mFlbIBdoChQRZhbp4+6bzOxk4FbCVHwPuvtNlXnPfWmoV0eDFB8vHqSY3ZKLju3G\n0DgPUoyXqYvWccvLc5i2eD3dsxrx2+G9GNanzV4lqFt3FvDarFU8P30Zb8//ioIi54DWjTmtfwdO\n7dt+r+bu3hczlm3k989+xoxlmxjaqzU3nHYw7Ztr2r6aZuP2fO54cz4Pv7eI9LQ6XHpcdy48ptse\nvzUpLHKmLV7PxJkreXXWSpau244ZDOjcgmF92jDsoLZ0zUxc16tEUXItIrsKithZULhf3wQn0hdr\ntvDHcTN474u19OvUnJtOO3ivJ17YsrOA48dOpkPzBjx76VE1NhdLWOW6uu1tQ/3Cp8v59VOfUlBU\nxPCD23LRMd2qdZBivLg7E2et4i+vzGHhmq0c1qUFV57Ui5zsluWes6ugiCnz1vD8p8t5bdZKduQX\n0aF5A07tFwYm9mrbpForyAWFRTz83iL+b+I86hj85ts9Of/I7N0GR0r1yy8s4omPlvCP1+axYXs+\nZx3WkV8P60mbpnvfP87dmbtqMxNnrmLirJXMWLYJgANaN2bYQW0Y1qdtygyIVHItktoKCov4/oMf\n8dGX6xjcI5MRfdsx7KC2NGtQ8xPtHfmF3DlpAf96ayEZ6XX47fBejB7YeZ8/s5+elscV//uU/zur\nH985rGMVR1s1lFyXY9mG7fzrrS/4UYIHKcZLQWERT+Xmcevr81i9eScn9mnD74b3pEfrMO1dUZHz\n0aJ1PD99ORM+X8HG7fm0aJjOKX3bMerQDhzWuUXC/2Jcum4bV42bwZR5azi0U3Nu+c4h9Gpbo7qN\npgx3Z9Lc1dz00my+WLOVI7u14uoRvTmofdVNB5m3fhuvzVrFxJmr+GjROgqLPGUGRCq53rP8wiLe\nnr+GFg3rhaVRPZpm1E2JP74k+d3y8hzueesLRvZrz8dL1pO3fjvpacaxB2Qxol87vtV7/8Y2xctb\n89bwx3EzWLJuG6f378AfTu693938ioqc0+9+j+UbtjPpiiE1cnyOkusUt21XAQ++8yX3vLWQbbsK\n+G5OJ5o2SGf89OWs3LSDhvXSGNanDaMO7cDRB2SSXsMeT+7ujP90Ode/MIuN2/O5+NhuXDb0gFrR\nFz5ZzFm5iZtems3b87+ia2Yj/nByb/6/vfsOj6rMHjj+Pekk9F5CElroNfTeVJoiCAtBmq4NV8oq\nP9cuq7LrKu6qqCgoKojGBgiIFJEqvSYQOiQQCJ2EhPTM+/tjBoxKZyZ3kjmf5+HJzL137j03ISdn\n3nlL97rlXVrUnL+YxS97TrEk9gQr950mI9tWqAdEanF9fYnJ6bT59y+/2+bjJZQM9M1TcPtSOsiP\nkoF+lHYU4KUCfSkVjxldiwAAIABJREFU5Hge6EexAB/LGw6UymvxrhM8OnMLQ1qF8K9+DTHGsCMh\nmQU7jvNjTCKJyRn4+XjRObwcfRpXplud8gRZnP9OXsjglQWx/BidSPWyQbx2bwPa1izrtPNvO3Ke\nfh+s5bFONXimZx2nnddZtLhWgH1w4uRf9vPF+ngAOoWXp2+TynSrW55AP/cvUs5fzGLiwt18tyWB\nsDKB/Kt/Q9rWcN4vsvqz0ymZ/HfpPr7edIRiAb6M7VaLoa1D8731OD0rlzUHzrBk1wmW7SmcAyK1\nuL6+zJxcdiemcD4ti/MXszh3MYuktGzOOZ7bt9ufJ6VlkZ175b9v3l5CySK/FdwlA+0F+eVCPNDv\ntwLdcYwW5MpV4s5c5O7Ja6hWLohvHm3zp4Yjm82w7eh55u9IZGFMIqdSMgnw9aJrnfL0aVSZLrXL\nu2SGsKvJtRlmrLN328zKtTG6S00e6VT9ulPw3oqnvtnB/B3HWfL3joS52VgcLa7V75xNzcTHy6vA\nTnX364EzPDcnhvizaQyMCOb53nUpGeh+0yQWZBnZuXyy5jAfLD9AZo6N4W3CGNOtplt8nwvrgEgt\nrp3LGENqZg7nL2ZzPi0rTwGebS/MHQX4uYtZl485f42C3EugeBFffL298PESvL0kz1cv+1fvq2y/\n9Nxb8Pa60usd2//0+t+O9/ISvMX+xsDLS/ASwVsc272wP3dsv/T4trd72a9Rtqjfbc88pa4sPSuX\nfh/8yokLGSwY3Z7gUteeMMBmM2yKO8eC6ER+2pnImdQsAv286Va3An0aVaJTeDmXfqq742gSz8+N\nYeexC3QML8cr99R3adF76kIGXSatoE2NMnw8ooXLrnMrtLhWhU56Vi7vLNvPtNWHKBXoy0t31+fu\nRjpt3+0yxjA/OpH//LSHY0npdK9bged61aF6uaJWh3ZFVxsQ2aBKcaaPbFGgFiFwh+JaRHoA72Cf\nqeljY8zrf9gfAnwOlHQc84wxZuEf9sdiX6tg0vWu5245+1JBnpSWzbnfFeD2gjw5PZscmyHXZnN8\nNfavuebK2215tufan/9+n+0K57Bvt7nRn+aKxQMY0iqEwS2rFqjfKXdnjOGpb3YwZ/sxPh3Zgs61\ny9/U63Nthg2HzjI/OpFFOxM5n5ZNUX8f7qhnL7Tb1yrrtNbk5PRsJi3eyxcb4ilX1J+X765Pr4bX\nnrPaWaasOMh/Fu3h8wdb0im8nMuvd6O0uFaFVuzxCzw7O5odCcl0rl2O1+5tcN13/urKth45z6sL\nYtl2JIm6lYrzYu+6Tu0/lx8SzqexZNdJ3ly8l4ZVSjDr4VZuN4bgaqwurkXEG/saA3cACdjXGIg0\nxsTmOWYqsM0YM0VE6gELjTFhefZ/h33xmg0Fsbh2JzabIdf8voC/9NyY3x7bbJBrDDZjfvcamw1s\njuNsjqLd/vjP223GYDNcfnzpTYAxkJlrY8muE6zefwZfb6FHg0oMbxNK89BS2phxm2ZtiOf5OTsZ\n170W47qH39a5snNtrDt4lgXRx1m86yTJ6dkUC/DhrvoV6dOoEu1q3tp4qktjnl5dsJtzFzMZ3iaM\np+4Mz9eBlZk5udz1v1V4ewmLxnV0m5yuxbUq1HJtxjFt316MgafuDOeBdtV02r4blHA+jTcW7WXe\njuOUK+bP/91Zm/siggv09++H7ccYG7WdB9qF8fLd9a0O54a4QXF93dVxReQj4JAx5j+O498yxrR1\n7LsXaId9hchULa4Ll0OnU5m5Pp7vtiSQkpFD3UrFGd4mlL5NKheIMTvuZsfRJAZ+uI62NcswfUQL\np/bnz8qx8euBMyyITmTJrhOkZOZQMtCXHvUr0rtRJdpUL3ND3XwOnU7lxR928uuBszQOLsHEfg1v\nes5qZ/k59iQPzdjMi33q8df21SyJ4Y+0uFYeIeF8Gi/O3cnyvadpFFyCf/dv6NRp4gqb1MwcPlh+\ngI/XHEaARztW59FONSwfge4sr8yPZfqvh3l7UBPubVrF6nCuyw2K6wFAD2PMQ47nw4BWxpgn8hxT\nCVgClAKCgO7GmC0iUhRYir3VezxaXBdaaVk5zN12nBnr4thzIoViAT4MjKjKsDahBXKsgxXOXczi\n7slrAFgwuj2lglw3liUzJ5dV+87wY/Rxlsae5GJWLmWC/OjRwF5ot6pW5k8NKRnZuUxZcZApKw7i\n7+PF0z1qM6RVqKUNLsYYRny6iW1HzrNifGfKFLV+ALsW18pjGGNYEJ3IP+fv4nxaNg91qMa4buH5\nOpLa3eXaDN9sPspbS/ZyJjWLfk2r8H931S50q2Bm59q4/+MNRCckMXtUO+pVdu/50QtIcf0k9r8b\nbzlarj8BGgBvABuNMd+IyASuUVyLyCPAIwAhISER8fHxrrwt5SLGGDbFnWfGujgW7TxBjs3QMbwc\nw1uH0qVO+QL9yZcr5doMIz/dyIZD5/huVBsaBeff8t4Z2bms2HuKBdGJLNt9ivTsXMoV86dXg4r0\nblSZ5qGlWHPgDC/9sJO4s2n0bVKZ53vXdZt+9gdOpdLj7VUMbB7Mv/s3sjocLa6V50lKy+JfC3fz\nzeYEQkoH8q9+DWlfq2D1H3aFNfvP8NqPsew5kULz0FK80KceTarmX3LPb6dTMukzeTV+Pl7Mf6K9\nW8x2cjVuUFzfSLeQXdgL8KOO54eA1sD3QFXHYSUBG/CSMea9a11Tc3bhcOpCBl9tPMqXG+M5eSGT\n4FJFuL9VKINaVKW0C1tlC6L/Ld3HO8v2869+DRnSKsSyONKycvhlzyl+jE7klz2nyMyxUTrIj3MX\ns6hWNohX+zZwy7+Zry6wfyI5/4n2lnVRuUSLa+Wx1h48w/NzdnL4zEX6N63CuO7hhJTxvAGPB06l\n8K+Fe/hlzymqli7Csz3r0rNB/oz0ttrWI+cZ9NE62tUs6/S+jc7kBsW1D/YBjd2AY9gHNA4xxuzK\nc8xPwNfGmM9EpC6wDKhi8vwhuV7LdV6aswuX7FwbS2NP8vnaODYcPoefjxd3N6rM8DahNC7Eb+Jv\n1PK9p3jws03c1yyYNwc0cpv8m5qZw7LdJ1kae5LaFYrxcMfqbrtIW3J6Nl0nraBa2SC+fayNpd9D\nLa6VR8vIzmXyL/uZtuowOTYbvRpW4rFONSx/15sfzl3M4p2f9/HFhiME+nrzRNeajGgb5raJ01W+\nWB/PC3N3MqZbLZ684/ZG5buK1cW1I4ZewNvYp9mbboyZKCKvAJuNMfMcM4RMA4pinxXkaWPMkj+c\nYwJaXHu8vSdSmLk+jtlbj5GWlUvj4BIMaxNGn0aVPC7/ABw9l0afyWuoXLIIs0e11a6KtyFq4xGe\nmR3DO4Ob0LeJdeNptLhWCvtSrdPXHGbWhiOkZubQoVZZRnWqQZsaZdymBcFZdideYMa6eOZuO0Zm\nTi5DWoXw9+7hbjEIxArGGJ7+LppvtyTw8fDmdK9XweqQ/sQdiuv8pjm78EvJyGb21mPMWBfHwdMX\nKRXoy6AWIdzfKoSqpT3jU8SM7FwGfriOuLMXWTC6PaFldODn7ci1Gfq+v4YzKVn8Mr6TZbPVaHGt\nVB7J6dnM2hDP9DVxnEnNpFFwCUZ1qsGd9SsW6EE4WTk2Fu06wcx1cWyKO4+/jxd9m1Tm4Q7VqVWh\nmNXhWe7yH7gzF5k3ur3bzWygxbUqzIwxrDt4ls/XxbE09iQG6FanPMPahNGhZlm37a7lDM/Ojuar\njUeZNrw5d7jhG/uCaHPcOQZ8uI7RXWvy1J21LYlBi2ulriAjO5fZW48xddVB4s6mUa1sEI90rE7/\nZlWctqpVfjiRnMGXG+L5atNRTqdkElomkKGtQhnYPNitB/BZIeF8GndPXkO5Yv7MebydW007qMW1\n8hTHk9L5csMRojYd4UyqfQDd/a1CGBhRlRKB+bc4SX74ZvNRnv4umsc71+DpHnWsDqdQGRu1jZ92\nnmDZk50s+RREi2ulriHXZli08wQfrjxIzLFkyhXz58F21bi/dQjF83EVqpthjGHdobPMXBfPktiT\n2IyhS+3yDGsTSqda5Qp1K9DtWrP/DMOnb6BXw0pMjmzqNl2CtLhWniYzJ5dFO08wY108W+LPU8TX\nm3ubVmZY6zC3nzrzRuw6nkz/D9YSEVqKGQ+2vKGFW9SNS0xOp+uklXQKL8eHwyLy/fpaXCt1A4wx\nrD14lg9XHmT1/jMU8/fh/tahPNgujPLF3WOez9TMHOZsTWDGunj2n0qlZKAvg5pX5f5WoR45C8qt\nmrLiIP9ZtIcXetfloQ7VrQ4H0OL6kuzsbBISEsjIyLAoqsIlICCA4OBgfH3ds6Hgkp3Hkpm5Lp4f\ndhwjI9tG89BSDGsTSp9GlQtkd73k9GzunryGrBwbC8a0p6yHjndxtfd+2c+kJfv48qFWtK2Zv1MH\nanGt1E3aeSyZD1ceZGFMIj5eXtwXUYVHOtawrJ/u/pMpzFwfz+ytx0jNzKFhlRIMaxPKPY0re+TI\n+9tljOHxWVtZEnuSmX9tSdsa1s/nqsW13eHDhylWrBhlyhS+gcb5zRjD2bNnSUlJoVo191gy+nqS\n07L5dstRZq6PJ/5sGs1DS/G/QU0K1OBHm83wyMzNrNh7mq8fbUNEaCmrQyq0MrJzueN/Kwn09eHH\nMe3z9dOBa+Vsl0YhItNF5JSI7LzKfhGRd0XkgIhEi0izPPveEJFdIrLbcYxmWZVvGlQpwXtDmrF8\nfGcGNg/m+63H6PrWCh6ftYXohKR8iSEn18ZPMYlETl3PHf9bRdTGo9xZrwJzHm/LvCfa8ZfmVbWw\nvkUiwpsDGxNWJpDRX27jeFK61SEph4yMDC2snUREKFOmTIH6FKBEoC8PdajO8qc689+/NGbviRR6\nvbOaOdsSKCiNgVNWHuTn3ad4oXddLaxdLMDXm+d71WPvyRRmbThidTiXubrE/wzocY39PYFajn+P\nAFMARKQt0A5ohH1p3RZAJ1cGqtSVhJYJYmK/hvz6j66M6lSD1fvPcM97vzJk2npW7TvtkmR/OiWT\nycv20/4/yxk1aytHzqXxdI/arHu2K/8d1ISmIaW08HCCov4+fDSsOZk5NkbN2kpmTq7VISkH/f/t\nPAX1e+nlJfRvFszCsR2oU6kYf/96B2OitpOcnm11aNf064EzvLVkL3c3rsyItmFWh+MR7qpfgXY1\ny/Dfpfs4fzHL6nAAFxfXxphVwLlrHNIXmGHs1gMlRaQS9sUJAgA/wB/wBU66MlalrqVcMX+e7lGH\ntc905bledTh4OpXh0zfSZ/Ia5u84Tk6u7bbOb4xhc9w5xny1jbavL+OtpfuoVaEo04Y3Z9XTXXi8\nc02PnaPalWqWL8qkgY3ZcTSJCfNirQ5HKfUHVUsHEvVIG8bfGc7CmER6vbOaDYfOWh3WFSUmpzPm\nq23UKFeU1/s3LLBvbAoaEeHlu+uTmpnDW0v3Wh0OAFbPQ1UFOJrneQL2pXTXichyIBEQ4D1jzO4r\nnUBEHsHe6k1ISIiLw1WerliAL490rMGItmH8sO04H646yOivthFSOpCHO1ZnYETwTXXVSMvK4Yft\nx5mxLp7diRcoFuDD0NahDGsdSvVyRV14J+qSHg0q8njnGnyw4iCNg0swuKXmEU929uxZunXrBsCJ\nEyfw9vamXLlyAGzcuBE/v6tPb7l582ZmzJjBu+++my+xegpvL+GJrrVoX6sc46K2MXjaekZ1qsG4\n7uH4+bjHDBxZOTYen7WVjOxcpgyNcKtpPj1BeIViDGsdyox1cQxpGWr5bDMuH9AoImHAAmNMgyvs\nWwC8boxZ43i+DPgHkAS8AwxyHLoU+zK7q691LR3QqPKbzWZYuvskU1YcZPvRJMoW9eOBdtUY2ir0\nmvO1Hj5zkZnr4vl2y1FSMnKoU7EYw9uEcW/TypatNuXJcm2GkZ9uZMOhc3zzWBuaVC2Z7zHogEa7\n3bt3U7duXYsi+r0JEyZQtGhRxo8ff3lbTk4OPj4F63fUnb6nt+tiZg6vLoglatNRGlYpwduDm1DD\nDRoiJszbxWdr43h/SDN6N6pkdTgeKSktiy6TVhBeoRhRj7R2+ScH18rZVmeIY0DVPM+DHduGAuuN\nMakAIvIT0Aa4ZnGtVH7z8hLuql+RO+tVYMPhc3y48iBvLt7LB8sPMKRVCH9tX52KJezT+OXaDL/s\nOcWMdXGs3n8GHy+hZ8NKDG8TSvNQ7UdtJW8v4d3BTekzeQ2Pf7GF+aPbazccN/DP+buIPX7Bqees\nV7k4L99d/6ZeM3LkSAICAti2bRvt2rVj8ODBjB07loyMDIoUKcKnn35K7dq1WbFiBZMmTWLBggVM\nmDCBI0eOcOjQIY4cOcK4ceMYM2aMU+/FEwX5+/D6fY3oXLs8z8yOps+7a3ixTz0iW1a1LIf+sP0Y\nn62N46/tq2lhbaGSgX48eWdtXpy7k4UxJyz9WVhdXM8DnhCRKKAVkGyMSRSRI8DDIvJv7N1COgFv\nWxinUtckIrSuXobW1cuwO/ECH608yPRf4/hsbRz3NqlCWNkgvtxwhGNJ6VQo7s+Td4QzuGVVyhdz\nj/mzFZQK8uOjYRHcN2Uto7/apos+qN9JSEhg7dq1eHt7c+HCBVavXo2Pjw8///wzzz33HN9///2f\nXrNnzx6WL19OSkoKtWvXZtSoUW4/33RB0aNBRZqGlOSpb3bw3JwYlu89xev9G+b7m+J9J1N45vsY\nWoSV4pmeugKj1Ya0DGHW+nj+tXA3XeuUp4ifNTNqubS4FpGvgM5AWRFJAF7GPjgRY8yHwEKgF3AA\nSAMecLz0O6ArEIN9cOMiY8x8V8aqlLPUrVSctwc35ak7a/Px6kN8vfkoGdk2WlcvzfO963JHvQr4\natHmlhpUKcHEfg0Z/+0O3ly8l2d7FY6P0guqm21hdqWBAwfi7W3/Q52cnMyIESPYv38/IkJ29pVn\nsOjduzf+/v74+/tTvnx5Tp48SXBwcH6GXahVKB7AjAdbMv3Xw7yxaC893lnNpIGN6RReLl+un5KR\nzWMztxDk78N7Q5ppXncD3l7ChHvqM3jqeqauOsTY7rUsicOlxbUxJvI6+w3wtytszwUedVVcSuWH\nqqUD+WffBozrHk5qZk6BWgTBkw2ICGbH0SQ+WnWIRsEl9WNeBUBQ0G8LSL344ot06dKFOXPmEBcX\nR+fOna/4Gn//31pRvb29ycnJcXWYHsfLS3ioQ3Xa1SzL2KhtjJi+kZFtw3imZx2XrgNgjOEf30cT\nfy6NWQ+1ooKbrOKroHX1MvRuWIkpKw8woHkwVUoWyfcY9G2WUi5WKshPC+sC5sU+9WgWUpL/+24H\n+06mWB2OcjPJyclUqVIFgM8++8zaYBRg/8Rw3hPtGdk2jM/WxtH3vV/Zc8K5/fXz+mTNYRbGnODp\nu2rTunoZl11H3Zpne9XBGPj3witONOdyWlwrpdQf+Pl4MWVoBIF+Pjw2cwsXMtx74QqVv55++mme\nffZZmjZtqq3RbiTA15sJ99TnswdacPZiFvdM/pWPVx/CZnPurGgbD5/j3z/toUf9ijzSsbpTz62c\nI7hUII91qsGC6ERL5kV3+VR8+Umn4lNKOdPGw+cYMm09XeqU56OhEXh5uW42Ap2Kz64wTRvnLjzx\ne3o2NZN/fB/Dz7tP0qFWWSYNbOyUrhunUjLo/e4aivr78MMT7SgeoANU3VV6Vi7d3lpBiUA/Foxu\nj7eT8/e1cra2XCul1FW0rFaaF3rXZWnsST5YccDqcJRSN6hMUX+mDY9gYr8GbIo7R4+3V7Fo54nb\nOmdOro0nvtxGSkY2U4Y208LazRXx8+a53nXZnXiBqE1H8vXaWlwrpdQ1jGgbxr1NKvPW0n2s2HvK\n6nCUUjdIRLi/VSgLRnegSqkiPPbFFp75PpqLmbfWleeNxXvtXUL6N6RORWtXAFQ3pnfDSrSqVppJ\ni/eSnJZ/3fu0uFZKqWsQEf7dvxF1KhZnbNR2jpxNszokpdRNqFm+KLNHtWNU5xp8vfkofSavYcfR\npJs6x6KdiUxddYhhrUPp11SnUywoRISX765Pcno2//t5X75dV4trpZS6jiJ+3nw0NAJjDI99sYX0\nrFyrQ1JK3QQ/Hy/+0aMOXz7UmszsXO6bspb3ftlP7g0Mdjx0OpXx30bTuGpJXujjWX3XC4N6lYsT\n2TKEmevj8232Jy2ulVLqBoSUCeSdyKbsPnGB5+fEUJgGgyvlKdrUKMNPYzvSo0FFJi3ZR+TU9SSc\nv/qnUWlZOYz6Yiu+3sIH9zfD38eaFf/U7XnqztoE+XnzyvzYfMndWlwrpdQN6lK7PE92D2f2tmPM\nWBdvdThKqVtQItCXyZFN+e9fGhObeIGeb69m7rZjfzrOGMOzs2PYdyqFdyObWrIYiXKO0kF+PHlH\nOGsOnGFJ7EmXX0+La6WUugl/61KT7nXL8+qCWDbFnbM6HOVkXbp0YfHixb/b9vbbbzNq1KgrHt+5\nc2cuTSfYq1cvkpL+3Jd3woQJTJo06ZrXnTt3LrGxsZefv/TSS/z88883G766QSJC/2bB/DS2A+EV\nizHu6+2MjdpGcvpvg96+WB/PD9uP82T3cDrUyp8l1ZXrDG0dSniForz2YywZ2a7t2qfFtVJK3QQv\nL+G/g5pQtXQgj8/ayqkLGVaHpJwoMjKSqKio322LiooiMjLyuq9duHAhJUuWvKXr/rG4fuWVV+je\nvfstnUvduKqlA/n6kdY8eUc4C6IT6fXOajYePsfWI+d5ZUEsXWqX429dalodpnICH28vXr67PkfP\npfPJmsOuvZZLz66UUoVQ8QBfPhwaQb8PfuXxWVv58uHW+PloW4XT/fQMnIhx7jkrNoSer19194AB\nA3jhhRfIysrCz8+PuLg4jh8/zldffcWTTz5Jeno6AwYM4J///OefXhsWFsbmzZspW7YsEydO5PPP\nP6d8+fJUrVqViIgIAKZNm8bUqVPJysqiZs2azJw5k+3btzNv3jxWrlzJa6+9xvfff8+rr75Knz59\nGDBgAMuWLWP8+PHk5OTQokULpkyZgr+/P2FhYYwYMYL58+eTnZ3Nt99+S506dZz7/fIAPt5ejOlW\niw61yjLu6+0MnrqOYgG+VCgewP8GNXHp4lEqf7WrWZa76lfg/eUHuK9ZMBVL3P7CQleifw2UUuoW\n1K5YjDcGNGJz/Hkm/hh7/ReoAqF06dK0bNmSn376CbC3Wv/lL39h4sSJbN68mejoaFauXEl0dPRV\nz7FlyxaioqLYvn07CxcuZNOmTZf39e/fn02bNrFjxw7q1q3LJ598Qtu2bbnnnnt488032b59OzVq\n1Lh8fEZGBiNHjuTrr78mJiaGnJwcpkyZcnl/2bJl2bp1K6NGjbpu1xN1bU1DSvHjmA4MiAjGGMOH\nQyMoGehndVjKyZ7vVY8cm+H1n3a77Bracq2UUreoT6PKRCckM3XVIRoFl+S+CJ3/1qmu0cLsSpe6\nhvTt25eoqCg++eQTvvnmG6ZOnUpOTg6JiYnExsbSqFGjK75+9erV9OvXj8DAQADuueeey/t27tzJ\nCy+8QFJSEqmpqdx1113XjGXv3r1Uq1aN8PBwAEaMGMH777/PuHHjAHuxDhAREcHs2bNv+949XVF/\nH94Y0JjX+zfSFutCKqRMIA93qMb7yw8yrE0oEaGlnX4NbblWSqnb8PRdtWlTvQzPzYlh57Fkq8NR\nTtC3b1+WLVvG1q1bSUtLo3Tp0kyaNIlly5YRHR1N7969yci4tb72I0eO5L333iMmJoaXX375ls9z\nib+/PwDe3t7k5NzayoPqz7SwLtwe71yTCsX9mTAvFtsNzHV+s7S4Vkqp2+Dj7cXkIU0pHeTHY19s\nISkty+qQ1G0qWrQoXbp04cEHHyQyMpILFy4QFBREiRIlOHny5OUuI1fTsWNH5s6dS3p6OikpKcyf\nP//yvpSUFCpVqkR2djazZs26vL1YsWKkpPx5gYvatWsTFxfHgQMHAJg5cyadOnVy0p0q5ZmC/H14\ntmddYo4l8+2Wo04/vxbXSil1m8oW9WfK0AhOXchkTNT2G1r1zR2JSA8R2SsiB0TkmSvsDxGR5SKy\nTUSiRaSXY/sdIrJFRGIcX7vmf/TOFRkZyY4dO4iMjKRx48Y0bdqUOnXqMGTIENq1a3fN1zZr1oxB\ngwbRuHFjevbsSYsWLS7ve/XVV2nVqhXt2rX73eDDwYMH8+abb9K0aVMOHjx4eXtAQACffvopAwcO\npGHDhnh5efHYY485/4aV8jB9m1QmIrQUby7ey4WM7Ou/4CaIq1aqEZHpQB/glDGmwRX2C/AO0AtI\nA0YaY7Y69oUAHwNVAQP0MsbEXe+azZs3N5fmG1VKqfwWtfEI+06m8lyvOvh431zbhYhsMcY0d1Fo\nN3J9b2AfcAeQAGwCIo0xsXmOmQpsM8ZMEZF6wEJjTJiINAVOGmOOi0gDYLExpsr1rnmlnL17927q\n1tUlpp1Jv6dKXVlMQjLv/rKfifc2oHzxm5s55Fo525UDGj8D3gNmXGV/T6CW418rYIrjK47XTDTG\nLBWRooDNhXEqpZRTDG4ZYnUIt6MlcMAYcwhARKKAvkDeqVAMUNzxuARwHMAYsy3PMbuAIiLib4zJ\ndHnUSil1ixoGl2DacOe3abisuDbGrBKRsGsc0heYYexN5+tFpKSIVAJKAT7GmKWO86S6KkallFKX\nVQHydj5M4LcGj0smAEtEZDQQBFxplZP7gK1aWCulPJWVfa6vlMirAOFAkojMdvTre9PxceUVicgj\nIrJZRDafPn3axSErpZRHiwQ+M8YEY+/SN1NELv8dEZH6wH+AR692ghvJ2a7qruiJ9HupVP5zxwGN\nPkAHYDzQAqgOjLzawcaYqcaY5saY5uXKlcufCJVSqvA5hn2cyyXBjm15/RX4BsAYsw4IAMoCiEgw\nMAcYbow5yFVcL2cHBARw9uxZLQqdwBjD2bNnCQhwzSp0Sqkrs3IRmaslch9ge55+f3OB1sAn+R6h\nUkp5jk1ALREVfXfPAAAHGUlEQVSphj0XDwaG/OGYI0A34DMRqYu9uD4tIiWBH4FnjDG/3k4QwcHB\nJCQkoJ9EOkdAQADBwbq4kVL5ycrieh7whGPQTCsg2RiTKCKngJIiUs4YcxroCugUIEop5ULGmBwR\neQJYDHgD040xu0TkFWCzMWYe8BQwTUT+jn1w40hjjHG8ribwkoi85DjlncaYUzcbh6+vL9WqVXPK\nPSmllBVcVlyLyFdAZ6CsiCQALwO+AMaYD4GF2PvsHcA+Fd8Djn25IjIeWOaYrm8LMM1VcSqllLIz\nxizEnpvzbnspz+NY4E+TPBtjXgNec3mASilVALhytpDI6+w3wN+usm8p0MgVcSmllFJKKeUq7jig\nUSmllFJKqQLJZSs0WkFETgPxN/myssAZF4Tj7jzxvj3xnsEz77sg3nOoMcajpjy6xZwNBfPne7s8\n8Z7BM+/bE+8ZCt59XzVnF6ri+laIyGYrlxy2iifetyfeM3jmfXviPXsST/z5euI9g2fetyfeMxSu\n+9ZuIUoppZRSSjmJFtdKKaWUUko5iRbXMNXqACziifftifcMnnnfnnjPnsQTf76eeM/gmfftifcM\nhei+Pb7PtVJKKaWUUs6iLddKKaWUUko5iRbXSimllFJKOYlHF9ci0kNE9orIARF5xup4XE1EqorI\nchGJFZFdIjLW6pjyk4h4i8g2EVlgdSz5QURKish3IrJHRHaLSBurY8oPIvJ3x//vnSLylYgEWB2T\ncg5Py9ng2Xnb03I2eGbeLow522OLaxHxBt4HegL1gEgRqWdtVC6XAzxljKkHtAb+5gH3nNdYYLfV\nQeSjd4BFxpg6QGM84N5FpAowBmhujGkAeAODrY1KOYOH5mzw7LztaTkbPCxvF9ac7bHFNdASOGCM\nOWSMyQKigL4Wx+RSxphEY8xWx+MU7L+0VayNKn+ISDDQG/jY6ljyg4iUADoCnwAYY7KMMUnWRpVv\nfIAiIuIDBALHLY5HOYfH5Wzw3LztaTkbPDpvF7qc7cnFdRXgaJ7nCXhAwrpERMKApsAGayPJN28D\nTwM2qwPJJ9WA08Cnjo9VPxaRIKuDcjVjzDFgEnAESASSjTFLrI1KOYlH52zwuLztaTkbPDBvF9ac\n7cnFtccSkaLA98A4Y8wFq+NxNRHpA5wyxmyxOpZ85AM0A6YYY5oCF4FC30dVREphb82sBlQGgkRk\nqLVRKXX7PClve2jOBg/M24U1Z3tycX0MqJrnebBjW6EmIr7YE/QsY8xsq+PJJ+2Ae0QkDvtHyV1F\n5AtrQ3K5BCDBGHOphes77Em7sOsOHDbGnDbGZAOzgbYWx6ScwyNzNnhk3vbEnA2embcLZc725OJ6\nE1BLRKqJiB/2DvTzLI7JpUREsPfl2m2M+a/V8eQXY8yzxphgY0wY9p/zL8aYAv/O+FqMMSeAoyJS\n27GpGxBrYUj55QjQWkQCHf/fu1HIBwR5EI/L2eCZedsTczZ4bN4ulDnbx+oArGKMyRGRJ4DF2Een\nTjfG7LI4LFdrBwwDYkRku2Pbc8aYhRbGpFxnNDDLUYgcAh6wOB6XM8ZsEJHvgK3YZ1nYRiFaUteT\neWjOBs3bnsaj8nZhzdm6/LlSSimllFJO4sndQpRSSimllHIqLa6VUkoppZRyEi2ulVJKKaWUchIt\nrpVSSimllHISLa6VUkoppZRyEi2ulUcQkVwR2Z7nn9NWvRKRMBHZ6azzKaWUp9OcrQoyj53nWnmc\ndGNME6uDUEopdUM0Z6sCS1uulUcTkTgReUNEYkRko4jUdGwPE5FfRCRaRJaJSIhjewURmSMiOxz/\nLi3T6i0i00Rkl4gsEZEijuPHiEis4zxRFt2mUkoVCpqzVUGgxbXyFEX+8BHjoDz7ko0xDYH3gLcd\n2yYDnxtjGgGzgHcd298FVhpjGgPNgEsrxNUC3jfG1AeSgPsc258BmjrO85irbk4ppQoZzdmqwNIV\nGpVHEJFUY0zRK2yPA7oaYw6JiC9wwhhTRkTOAJWMMdmO7YnGmLIichoINsZk5jlHGLDUGFPL8fwf\ngK8x5jURWQSkAnOBucaYVBffqlJKFXias1VBpi3XSoG5yuObkZnncS6/jWfoDbyPvcVkk4joOAel\nlLo9mrOVW9PiWikYlOfrOsfjtcBgx+P7gdWOx8uAUQAi4i0iJa52UhHxAqoaY5YD/wBKAH9qiVFK\nKXVTNGcrt6bvyJSnKCIi2/M8X2SMuTS1UykRicbekhHp2DYa+FRE/g84DTzg2D4WmCoif8Xe2jEK\nSLzKNb2BLxzJXIB3jTFJTrsjpZQqvDRnqwJL+1wrj+bov9fcGHPG6liUUkpdm+ZsVRBotxCllFJK\nKaWcRFuulVJKKaWUchJtuVZKKaWUUspJtLhWSimllFLKSbS4VkoppZRSykm0uFZKKaWUUspJtLhW\nSimllFLKSf4f8Wv8cJ1vFacAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fgz9Bk1oEwUN",
        "colab_type": "code",
        "outputId": "6974f780-8e84-4156-dd09-fcea73fffdac",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 370
        }
      },
      "source": [
        "# Load the CSV\n",
        "\n",
        "import pandas as pd\n",
        "pd.read_csv(\"results.csv\", index_col='epoch')"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>loss</th>\n",
              "      <th>mae</th>\n",
              "      <th>val_loss</th>\n",
              "      <th>val_mae</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>epoch</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1.095893</td>\n",
              "      <td>0.843119</td>\n",
              "      <td>1.194618</td>\n",
              "      <td>0.916479</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1.106437</td>\n",
              "      <td>0.841909</td>\n",
              "      <td>1.196918</td>\n",
              "      <td>0.920462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.093881</td>\n",
              "      <td>0.841662</td>\n",
              "      <td>1.211681</td>\n",
              "      <td>0.921165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.102030</td>\n",
              "      <td>0.841489</td>\n",
              "      <td>1.205945</td>\n",
              "      <td>0.922736</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.087300</td>\n",
              "      <td>0.834888</td>\n",
              "      <td>1.197076</td>\n",
              "      <td>0.920110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.076507</td>\n",
              "      <td>0.823714</td>\n",
              "      <td>1.200152</td>\n",
              "      <td>0.918342</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.086205</td>\n",
              "      <td>0.844074</td>\n",
              "      <td>1.192807</td>\n",
              "      <td>0.919211</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1.099280</td>\n",
              "      <td>0.838737</td>\n",
              "      <td>1.187684</td>\n",
              "      <td>0.918145</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.092036</td>\n",
              "      <td>0.848451</td>\n",
              "      <td>1.181462</td>\n",
              "      <td>0.915835</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1.062624</td>\n",
              "      <td>0.821349</td>\n",
              "      <td>1.175837</td>\n",
              "      <td>0.905308</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "           loss       mae  val_loss   val_mae\n",
              "epoch                                        \n",
              "0      1.095893  0.843119  1.194618  0.916479\n",
              "1      1.106437  0.841909  1.196918  0.920462\n",
              "2      1.093881  0.841662  1.211681  0.921165\n",
              "3      1.102030  0.841489  1.205945  0.922736\n",
              "4      1.087300  0.834888  1.197076  0.920110\n",
              "5      1.076507  0.823714  1.200152  0.918342\n",
              "6      1.086205  0.844074  1.192807  0.919211\n",
              "7      1.099280  0.838737  1.187684  0.918145\n",
              "8      1.092036  0.848451  1.181462  0.915835\n",
              "9      1.062624  0.821349  1.175837  0.905308"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k5MC6EMRg7b8",
        "colab_type": "text"
      },
      "source": [
        "## Lambda callbacks\n",
        "**Usage** `tf.keras.callbacks.LambdaCallback(\n",
        "        on_epoch_begin=None, on_epoch_end=None, \n",
        "        on_batch_begin=None, on_batch_end=None, \n",
        "        on_train_begin=None, on_train_end=None)`\n",
        "\n",
        "Lambda callbacks are used to quickly define simple custom callbacks with the use of lambda functions.\n",
        "\n",
        "Each of the functions require some positional arguments.\n",
        "* `on_epoch_begin` and `on_epoch_end` expect two arguments: `epoch` and `logs`,\n",
        "* `on_batch_begin` and `on_batch_end` expect two arguments: `batch` and `logs` and\n",
        "* `on_train_begin` and `on_train_end` expect one argument: `logs`."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3SrerEdOEwPQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Print the epoch number at the beginning of each epoch\n",
        "\n",
        "epoch_callback = tf.keras.callbacks.LambdaCallback(\n",
        "    on_epoch_begin=lambda epoch,logs: print('Starting Epoch {}!'.format(epoch+1)))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U59VATOpEwFJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Print the loss at the end of each batch\n",
        "\n",
        "batch_loss_callback = tf.keras.callbacks.LambdaCallback(\n",
        "    on_batch_end=lambda batch,logs: print('\\n After batch {}, the loss is {:7.2f}.'.format(batch, logs['loss'])))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XMECzGuPhoRQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Inform that training is finished\n",
        "\n",
        "train_finish_callback = tf.keras.callbacks.LambdaCallback(\n",
        "    on_train_end=lambda logs: print('Training finished!'))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6kyvKndbhGOh",
        "colab_type": "code",
        "outputId": "1f9bd493-37ce-4170-be96-e1352be3bd7b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 835
        }
      },
      "source": [
        "# Train the model with the lambda callbacks\n",
        "\n",
        "history = model.fit(train_data, train_targets, epochs=5, batch_size=100,\n",
        "                    callbacks=[epoch_callback, batch_loss_callback,train_finish_callback], verbose=False)"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Starting Epoch 1!\n",
            "\n",
            " After batch 0, the loss is    0.13.\n",
            "\n",
            " After batch 1, the loss is    0.16.\n",
            "\n",
            " After batch 2, the loss is    0.16.\n",
            "\n",
            " After batch 3, the loss is    0.22.\n",
            "Starting Epoch 2!\n",
            "\n",
            " After batch 0, the loss is    0.15.\n",
            "\n",
            " After batch 1, the loss is    0.26.\n",
            "\n",
            " After batch 2, the loss is    0.17.\n",
            "\n",
            " After batch 3, the loss is    0.12.\n",
            "Starting Epoch 3!\n",
            "\n",
            " After batch 0, the loss is    0.10.\n",
            "\n",
            " After batch 1, the loss is    0.17.\n",
            "\n",
            " After batch 2, the loss is    0.17.\n",
            "\n",
            " After batch 3, the loss is    0.15.\n",
            "Starting Epoch 4!\n",
            "\n",
            " After batch 0, the loss is    0.09.\n",
            "\n",
            " After batch 1, the loss is    0.20.\n",
            "\n",
            " After batch 2, the loss is    0.11.\n",
            "\n",
            " After batch 3, the loss is    0.12.\n",
            "Starting Epoch 5!\n",
            "\n",
            " After batch 0, the loss is    0.11.\n",
            "\n",
            " After batch 1, the loss is    0.14.\n",
            "\n",
            " After batch 2, the loss is    0.12.\n",
            "\n",
            " After batch 3, the loss is    0.11.\n",
            "Training finished!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsgUIlfRhRnM",
        "colab_type": "text"
      },
      "source": [
        "## Reduce learning rate on plateau\n",
        "\n",
        "**Usage** `tf.keras.callbacks.ReduceLROnPlateau(\n",
        "            monitor='val_loss', \n",
        "            factor=0.1, \n",
        "            patience=10, \n",
        "            verbose=0, \n",
        "            mode='auto', \n",
        "            min_delta=0.0001, \n",
        "            cooldown=0, \n",
        "            min_lr=0)`\n",
        "\n",
        "The `ReduceLROnPlateau` callback allows reduction of the learning rate when a metric has stopped improving. \n",
        "The arguments are similar to those used in the `EarlyStopping` callback.\n",
        "* The argument `monitor` is used to specify which metric to base the callback on.\n",
        "* The `factor` is the factor by which the learning rate decreases i.e., new_lr=factor*old_lr.\n",
        "* The `patience` is the number of epochs where there is no improvement on the monitored metric before the learning rate is reduced.\n",
        "* The `verbose` argument will produce progress messages when set to 1.\n",
        "* The `mode` determines whether the learning rate will decrease when the monitored quantity stops increasing (`max`) or decreasing (`min`). The `auto` setting causes the callback to infer the mode from the monitored quantity.\n",
        "* The `min_delta` is the smallest change in the monitored quantity to be deemed an improvement.\n",
        "* The `cooldown` is the number of epochs to wait after the learning rate is changed before the callback resumes normal operation.\n",
        "* The `min_lr` is a lower bound on the learning rate that the callback will produce."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u4OkYEi3hGXD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "b8a74017-67a1-49e9-92d6-2dd7211204a0"
      },
      "source": [
        "# Train the model with the ReduceLROnPlateau callback\n",
        "\n",
        "history = model.fit(train_data, train_targets, epochs=100, batch_size=100,\n",
        "                    callbacks=[tf.keras.callbacks.ReduceLROnPlateau(\n",
        "                        monitor=\"loss\",factor=0.2, verbose=1)], verbose=False)"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00042: ReduceLROnPlateau reducing learning rate to 0.00020000000949949026.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kp-sBs_wh-7C",
        "colab_type": "text"
      },
      "source": [
        "## Early stopping and patience\n",
        "\n",
        "Early stopping is a technique that monitors the performance of the network for every epoch on a held out validation set during the training run, and terminates the training conditional on the validation performance. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fi3dBylVhGam",
        "colab_type": "code",
        "outputId": "e3514dfc-2dc9-4cc2-9712-117ec6055d08",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "unregularised_model=get_model()\n",
        "unregularised_model.compile(optimizer='adam',loss='mae')\n",
        "unreg_history=unregularised_model.fit(train_data,train_targets,epochs=100,validation_split=0.15,batch_size=64,verbose=False,callbacks=[tf.keras.callbacks.EarlyStopping(patience=10)])\n",
        "unregularised_model.evaluate(test_data,test_targets,verbose=2)"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45/1 - 0s - loss: 0.5785\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6001491533385382"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_6kVQqIiixZn",
        "colab_type": "code",
        "outputId": "fe318ed8-62eb-4cfc-8ff6-1f3a7904f5d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "regularised_model=get_regularised_model(1e-8,0.2)\n",
        "regularised_model.compile(optimizer='adam',loss='mae')\n",
        "reg_history=regularised_model.fit(train_data,train_targets,epochs=100,validation_split=0.15,batch_size=64,verbose=False,callbacks=[tf.keras.callbacks.EarlyStopping(patience=10)])\n",
        "unregularised_model.evaluate(test_data,test_targets,verbose=2)"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "45/1 - 0s - loss: 0.5785\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6001491533385382"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wx5eiQmbixfz",
        "colab_type": "code",
        "outputId": "caa7c5b6-515e-4e75-87df-efefcc091a57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        }
      },
      "source": [
        "fig=plt.figure(figsize=(12,5))\n",
        "fig.add_subplot(121)\n",
        "\n",
        "plt.plot(unreg_history.history['loss'])\n",
        "plt.plot(unreg_history.history['val_loss'])\n",
        "plt.title('Unregularised Model: Loss vs. Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Training','Validation'],loc='best')\n",
        "\n",
        "fig.add_subplot(122)\n",
        "\n",
        "plt.plot(reg_history.history['loss'])\n",
        "plt.plot(reg_history.history['val_loss'])\n",
        "plt.title('Regularised Model: Loss vs. Epoch')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(['Training','Validation'],loc='best');"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YWbKZjfB9JN4bbxqed6rbT0REgsdXitXd1zafh9d7XeEngiISXkJ5RzoJvGZ404el45Vv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\nIaBEOgCseRv2NutEi73LKSi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SjxXbD3DDM98w4cmveWNBNi98tanO566NmWtyMIN/XDuE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\nCCGEsMD/A45RxGwIrPalAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "X0BzXFqwxv1g",
        "colab_type": "text"
      },
      "source": [
        "## Saving and Loading Models\n",
        "\n",
        "### Saving and Loading Models Weights\n",
        "\n",
        "Loading and inspecting the CIFAR-10 dataset which consists of, in total, 60000 color images, each with one of 10 labels: airplane, automobile, bird, cat, deer, dog, frog, horse, ship, truck. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_KcuAbd0ixdf",
        "colab_type": "code",
        "outputId": "35b1b162-56b4-479c-8c54-cf4a480b40b8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# Import the CIFAR-10 dataset and rescale the pixel values\n",
        "\n",
        "(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()\n",
        "x_train = x_train / 255.0\n",
        "x_test = x_test / 255.0\n",
        "\n",
        "# Use smaller subset -- speeds things up\n",
        "x_train = x_train[:10000]\n",
        "y_train = y_train[:10000]\n",
        "x_test = x_test[:1000]\n",
        "y_test = y_test[:1000]"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz\n",
            "170500096/170498071 [==============================] - 11s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "96BIRDItxmQ5",
        "colab_type": "code",
        "outputId": "74f5ed19-4006-45cc-ac28-dbc8645fd6d0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 78
        }
      },
      "source": [
        "# Plot the first 10 CIFAR-10 images\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "fig, ax = plt.subplots(1, 10, figsize=(10, 1))\n",
        "for i in range(10):\n",
        "    ax[i].set_axis_off()\n",
        "    ax[i].imshow(x_train[i])"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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drSClOToua1r+jiWeuq6Qssy79XrF5BzjNBNo7wk+0DYNTVPRNDVVZei6mpyWbKTCGIMx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\nnhKep3iKp3iKp3iKp/irj/8Xu3baTSjmUpgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x72 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mTt-soZSxmWK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Introduce function to test model accuracy\n",
        "\n",
        "def get_test_accuracy(model, x_test, y_test):\n",
        "    test_loss, test_acc = model.evaluate(x=x_test, y=y_test, verbose=0)\n",
        "    print('accuracy: {acc:0.3f}'.format(acc=test_acc))\n",
        "\n",
        "\n",
        "# Introduce function that creates a new instance of a simple CNN\n",
        "\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n",
        "\n",
        "def get_new_model():\n",
        "    model = Sequential([\n",
        "        Conv2D(filters=16, input_shape=(32, 32, 3), kernel_size=(3, 3), \n",
        "               activation='relu', name='conv_1'),\n",
        "        Conv2D(filters=8, kernel_size=(3, 3), activation='relu', name='conv_2'),\n",
        "        MaxPooling2D(pool_size=(4, 4), name='pool_1'),\n",
        "        Flatten(name='flatten'),\n",
        "        Dense(units=32, activation='relu', name='dense_1'),\n",
        "        Dense(units=10, activation='softmax', name='dense_2')\n",
        "    ])\n",
        "    model.compile(optimizer='adam',\n",
        "                  loss='sparse_categorical_crossentropy',\n",
        "                  metrics=['accuracy'])\n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dqxISV7xxmbN",
        "colab_type": "code",
        "outputId": "4a7f8bbd-9600-4f1c-ffef-b6419a2f6fa6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        }
      },
      "source": [
        "# Create an instance of the model and show model summary\n",
        "\n",
        "model=get_new_model()\n",
        "model.summary()"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_14\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv_1 (Conv2D)              (None, 30, 30, 16)        448       \n",
            "_________________________________________________________________\n",
            "conv_2 (Conv2D)              (None, 28, 28, 8)         1160      \n",
            "_________________________________________________________________\n",
            "pool_1 (MaxPooling2D)        (None, 7, 7, 8)           0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 392)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 32)                12576     \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 10)                330       \n",
            "=================================================================\n",
            "Total params: 14,514\n",
            "Trainable params: 14,514\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWgrWiIhxmd8",
        "colab_type": "code",
        "outputId": "813ff559-a0dd-4b6a-b65d-f1ccd49a71f8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Test accuracy of the untrained model, around 10% (random)\n",
        "\n",
        "get_test_accuracy(model, x_test, y_test)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy: 0.103\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DfAlTzclxmg1",
        "colab_type": "code",
        "outputId": "124b9af9-9d7c-4da2-d333-44cefdb2f023",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "# Train model with checkpoints\n",
        "\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint \n",
        "\n",
        "# Create Tensorflow checkpoint object\n",
        "checkpoint_path=\"model_checkpoints/checkpoint\"\n",
        "checkpoint=ModelCheckpoint(filepath=checkpoint_path, frequency='epoch',save_weights_only=True,verbose=1)\n",
        "\n",
        "# Fit model, with simple checkpoint which saves (and overwrites) model weights every epoch\n",
        "model.fit(x=x_train,y=y_train,epochs=3,callbacks=[checkpoint])"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 10000 samples\n",
            "Epoch 1/3\n",
            " 9984/10000 [============================>.] - ETA: 0s - loss: 1.9625 - accuracy: 0.2706\n",
            "Epoch 00001: saving model to model_checkpoints/checkpoint\n",
            "10000/10000 [==============================] - 13s 1ms/sample - loss: 1.9617 - accuracy: 0.2712\n",
            "Epoch 2/3\n",
            " 9984/10000 [============================>.] - ETA: 0s - loss: 1.6563 - accuracy: 0.3929\n",
            "Epoch 00002: saving model to model_checkpoints/checkpoint\n",
            "10000/10000 [==============================] - 12s 1ms/sample - loss: 1.6561 - accuracy: 0.3929\n",
            "Epoch 3/3\n",
            " 9952/10000 [============================>.] - ETA: 0s - loss: 1.5515 - accuracy: 0.4351\n",
            "Epoch 00003: saving model to model_checkpoints/checkpoint\n",
            "10000/10000 [==============================] - 12s 1ms/sample - loss: 1.5531 - accuracy: 0.4345\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f53570153c8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "95_yI4iSxmj_",
        "colab_type": "code",
        "outputId": "e671b436-8214-4f81-e7d2-027cc7645c53",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "source": [
        "# Have a look at what the checkpoint creates\n",
        "\n",
        "! ls -lh model_checkpoints/"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 177K\n",
            "-rw------- 1 root root   77 Apr  7 07:27 checkpoint\n",
            "-rw------- 1 root root 174K Apr  7 07:27 checkpoint.data-00000-of-00001\n",
            "-rw------- 1 root root 2.0K Apr  7 07:27 checkpoint.index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "-6xmIBEG6D4z"
      },
      "source": [
        "#### `checkpoint`\n",
        "This file is by far the smallest, at only 87 bytes. It's actually so small that we can just look at it directly. It's a human readable file with the following text:\n",
        "```\n",
        "model_checkpoint_path: \"checkpoint\"\n",
        "all_model_checkpoint_paths: \"checkpoint\"\n",
        "```\n",
        "This is metadata that indicates where the actual model data is stored.\n",
        "\n",
        "#### `checkpoint.index`\n",
        "This file tells TensorFlow which weights are stored where. When running models on distributed systems, there may be different *shards*, meaning the full model may have to be recomposed from multiple sources. The model we trained is a single model on a single machine, so there is only one *shard* and all weights are stored in the same place.\n",
        "\n",
        "#### `checkpoint.data-00000-of-00001`\n",
        "This file contains the actual weights from the model. It is by far the largest of the 3 files. Recall that the model we trained had around 14000 parameters, meaning this file is roughly 12 bytes per saved weight.\n",
        "\n",
        "* https://www.tensorflow.org/tutorials/keras/save_and_load#what_are_these_files"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BgIH5Kh8xmZV",
        "colab_type": "code",
        "outputId": "c3991b04-6922-410b-da66-4bbc5cef88fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Evaluate the performance of the trained model\n",
        "\n",
        "get_test_accuracy(model, x_test, y_test)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy: 0.455\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4o2ZwLVexmT-",
        "colab_type": "code",
        "outputId": "24f0683c-2644-4d26-d7a0-ed990fca1c4a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Create a new instance of the (initialised) model, accuracy around 10% again\n",
        "\n",
        "model=get_new_model()\n",
        "get_test_accuracy(model, x_test, y_test)"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy: 0.117\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WnCjrsRhGUv",
        "colab_type": "code",
        "outputId": "7f7de19a-7585-4c09-f612-2596e0e07ed5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "source": [
        "# Load weights -- accuracy is the same as the trained model\n",
        "\n",
        "model.load_weights(checkpoint_path)\n",
        "get_test_accuracy(model, x_test, y_test)"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "accuracy: 0.455\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fUrnOwhv1HBW",
        "colab_type": "code",
        "outputId": "a26b80bf-77fd-4176-acdf-3133f0c33944",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        }
      },
      "source": [
        "# Create more customised checkpoint\n",
        "\n",
        "# Create Tensorflow checkpoint object with epoch and batch details\n",
        "checkpoint_5000_path='model_checkpoints_5000/checkpoint_{epoch:02d}_{batch:04d}'\n",
        "checkpoint_5000=ModelCheckpoint(filepath=checkpoint_5000_path, save_weights_only=True, save_freq=5000, verbose=1)\n",
        "\n",
        "# Create and fit model with checkpoint\n",
        "model=get_new_model()\n",
        "model.fit(x=x_train,y=y_train,epochs=3,validation_data=(x_test,y_test),batch_size=10,callbacks=[checkpoint_5000])\n"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 10000 samples, validate on 1000 samples\n",
            "Epoch 1/3\n",
            " 4980/10000 [=============>................] - ETA: 7s - loss: 2.0120 - accuracy: 0.2460\n",
            "Epoch 00001: saving model to model_checkpoints_5000/checkpoint_01_0499\n",
            " 9960/10000 [============================>.] - ETA: 0s - loss: 1.8335 - accuracy: 0.3230\n",
            "Epoch 00001: saving model to model_checkpoints_5000/checkpoint_01_0999\n",
            "10000/10000 [==============================] - 17s 2ms/sample - loss: 1.8318 - accuracy: 0.3239 - val_loss: 1.5685 - val_accuracy: 0.4460\n",
            "Epoch 2/3\n",
            " 4970/10000 [=============>................] - ETA: 7s - loss: 1.5177 - accuracy: 0.4477\n",
            "Epoch 00002: saving model to model_checkpoints_5000/checkpoint_02_0499\n",
            " 9990/10000 [============================>.] - ETA: 0s - loss: 1.4862 - accuracy: 0.4600\n",
            "Epoch 00002: saving model to model_checkpoints_5000/checkpoint_02_0999\n",
            "10000/10000 [==============================] - 17s 2ms/sample - loss: 1.4864 - accuracy: 0.4599 - val_loss: 1.4200 - val_accuracy: 0.4870\n",
            "Epoch 3/3\n",
            " 4980/10000 [=============>................] - ETA: 7s - loss: 1.3737 - accuracy: 0.5110\n",
            "Epoch 00003: saving model to model_checkpoints_5000/checkpoint_03_0499\n",
            " 9970/10000 [============================>.] - ETA: 0s - loss: 1.3818 - accuracy: 0.5020\n",
            "Epoch 00003: saving model to model_checkpoints_5000/checkpoint_03_0999\n",
            "10000/10000 [==============================] - 16s 2ms/sample - loss: 1.3826 - accuracy: 0.5018 - val_loss: 1.4368 - val_accuracy: 0.5030\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f53566b4c88>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PbzMjikg1HGZ",
        "colab_type": "code",
        "outputId": "67ac24ff-ed3d-4245-8117-736d1af5d7b3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 266
        }
      },
      "source": [
        "# Have a look at what the checkpoint creates\n",
        "\n",
        "! ls -lh model_checkpoints_5000/"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "total 1.1M\n",
            "-rw------- 1 root root   93 Apr  7 07:28 checkpoint\n",
            "-rw------- 1 root root 174K Apr  7 07:27 checkpoint_01_0499.data-00000-of-00001\n",
            "-rw------- 1 root root 2.0K Apr  7 07:27 checkpoint_01_0499.index\n",
            "-rw------- 1 root root 174K Apr  7 07:27 checkpoint_01_0999.data-00000-of-00001\n",
            "-rw------- 1 root root 2.0K Apr  7 07:27 checkpoint_01_0999.index\n",
            "-rw------- 1 root root 174K Apr  7 07:28 checkpoint_02_0499.data-00000-of-00001\n",
            "-rw------- 1 root root 2.0K Apr  7 07:28 checkpoint_02_0499.index\n",
            "-rw------- 1 root root 174K Apr  7 07:28 checkpoint_02_0999.data-00000-of-00001\n",
            "-rw------- 1 root root 2.0K Apr  7 07:28 checkpoint_02_0999.index\n",
            "-rw------- 1 root root 174K Apr  7 07:28 checkpoint_03_0499.data-00000-of-00001\n",
            "-rw------- 1 root root 2.0K Apr  7 07:28 checkpoint_03_0499.index\n",
            "-rw------- 1 root root 174K Apr  7 07:28 checkpoint_03_0999.data-00000-of-00001\n",
            "-rw------- 1 root root 2.0K Apr  7 07:28 checkpoint_03_0999.index\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "1LTGig2v6DHf",
        "colab": {}
      },
      "source": [
        "# Clear directory\n",
        "\n",
        "# ! rm -r model_checkpoints\n",
        "# ! rm -r model_checkpoints_5000"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ysu9O7OGCpYK",
        "colab_type": "text"
      },
      "source": [
        "#### Common Methods for loading and saving models and weights\n",
        "\n",
        "**Saving/Loading Model Weights only (having the same architecture)**\n",
        "\n",
        "> model.save_weights(filepath='model_name')\n",
        "> \n",
        "> model.load_weights(filepath='model_name')\n",
        "\n",
        "**Saving/Loading Entire Models**\n",
        "\n",
        "> model.save(filepath='model_name')\n",
        "> \n",
        "> model.load(filepath='model_name')\n",
        "\n",
        "**Saving/Loading Model layer structural architecture without weights**\n",
        "\n",
        "> model.model_to_json(filepath='model_name')\n",
        ">\n",
        "> model.model_from_json(filepath='model_name')\n",
        "\n",
        " \n",
        "> model.model_to_yaml(filepath='model_name')\n",
        ">\n",
        ">model.model_from_yaml(filepath='model_name')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9VTVgnCub1P",
        "colab_type": "text"
      },
      "source": [
        "A model's configuration refers to its architecture. TensorFlow has a convenient way to retrieve a model's architecture as a dictionary. A TensorFlow model has an inbuilt method get_config which returns the model's architecture as a dictionary."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1pJ_xzdJ1HKX",
        "colab_type": "code",
        "outputId": "46a692ea-df5b-42d0-83f1-cd5862d61d38",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        }
      },
      "source": [
        "config_dict = model.get_config()\n",
        "print(config_dict)"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{'name': 'sequential_16', 'layers': [{'class_name': 'Conv2D', 'config': {'name': 'conv_1', 'trainable': True, 'batch_input_shape': (None, 32, 32, 3), 'dtype': 'float32', 'filters': 16, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Conv2D', 'config': {'name': 'conv_2', 'trainable': True, 'dtype': 'float32', 'filters': 8, 'kernel_size': (3, 3), 'strides': (1, 1), 'padding': 'valid', 'data_format': 'channels_last', 'dilation_rate': (1, 1), 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'MaxPooling2D', 'config': {'name': 'pool_1', 'trainable': True, 'dtype': 'float32', 'pool_size': (4, 4), 'padding': 'valid', 'strides': (4, 4), 'data_format': 'channels_last'}}, {'class_name': 'Flatten', 'config': {'name': 'flatten', 'trainable': True, 'dtype': 'float32', 'data_format': 'channels_last'}}, {'class_name': 'Dense', 'config': {'name': 'dense_1', 'trainable': True, 'dtype': 'float32', 'units': 32, 'activation': 'relu', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}, {'class_name': 'Dense', 'config': {'name': 'dense_2', 'trainable': True, 'dtype': 'float32', 'units': 10, 'activation': 'softmax', 'use_bias': True, 'kernel_initializer': {'class_name': 'GlorotUniform', 'config': {'seed': None}}, 'bias_initializer': {'class_name': 'Zeros', 'config': {}}, 'kernel_regularizer': None, 'bias_regularizer': None, 'activity_regularizer': None, 'kernel_constraint': None, 'bias_constraint': None}}]}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_O1kF--oufPD",
        "colab_type": "text"
      },
      "source": [
        "A new TensorFlow model can be created from this config dictionary. This model will have reinitialized weights, which are not the same as the original model."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NIiaHbsfteJh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# For models that are not Sequential models, use tf.keras.Model.from_config instead of tf.keras.Sequential.from_config.\n",
        "\n",
        "model_same_config = tf.keras.Sequential.from_config(config_dict)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PclFKQVUteP5",
        "colab_type": "code",
        "outputId": "c386ecd6-b307-4ac8-dc43-d5dea035f66f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "# check explicitly that the config of both models is the same, but the weights are not\n",
        "\n",
        "print('Same config:', \n",
        "      model.get_config() == model_same_config.get_config())\n",
        "print('Same value for first weight matrix:', \n",
        "      np.allclose(model.weights[0].numpy(), model_same_config.weights[0].numpy()))"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Same config: True\n",
            "Same value for first weight matrix: False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6tI-6DI9xDnT",
        "colab_type": "text"
      },
      "source": [
        "It is also possible to obtain a model's config in JSON or YAML formats. This follows the same pattern:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i74N4OAuteNz",
        "colab_type": "code",
        "outputId": "b8386fa1-e926-4613-b375-6a79a3b567ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 90
        }
      },
      "source": [
        "import json\n",
        "\n",
        "json_string = model.to_json()\n",
        "print(json_string)\n",
        "\n",
        "\n",
        "# Write out JSON config file\n",
        "\n",
        "with open('config.json', 'w') as f:\n",
        "    json.dump(json_string, f)\n",
        "del json_string\n",
        "\n",
        "\n",
        "# Read in JSON config file again\n",
        "\n",
        "with open('config.json', 'r') as f:\n",
        "    json_string = json.load(f)\n",
        "\n",
        "\n",
        "# Reinitialize model\n",
        "\n",
        "model_same_config = tf.keras.models.model_from_json(json_string)\n",
        "\n",
        "\n",
        "# Same config, new weights\n",
        "\n",
        "print('Same config:', \n",
        "      model.get_config() == model_same_config.get_config())\n",
        "print('Same value for first weight matrix:', \n",
        "      np.allclose(model.weights[0].numpy(), model_same_config.weights[0].numpy()))"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{\"class_name\": \"Sequential\", \"config\": {\"name\": \"sequential_16\", \"layers\": [{\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv_1\", \"trainable\": true, \"batch_input_shape\": [null, 32, 32, 3], \"dtype\": \"float32\", \"filters\": 16, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Conv2D\", \"config\": {\"name\": \"conv_2\", \"trainable\": true, \"dtype\": \"float32\", \"filters\": 8, \"kernel_size\": [3, 3], \"strides\": [1, 1], \"padding\": \"valid\", \"data_format\": \"channels_last\", \"dilation_rate\": [1, 1], \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"MaxPooling2D\", \"config\": {\"name\": \"pool_1\", \"trainable\": true, \"dtype\": \"float32\", \"pool_size\": [4, 4], \"padding\": \"valid\", \"strides\": [4, 4], \"data_format\": \"channels_last\"}}, {\"class_name\": \"Flatten\", \"config\": {\"name\": \"flatten\", \"trainable\": true, \"dtype\": \"float32\", \"data_format\": \"channels_last\"}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_1\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 32, \"activation\": \"relu\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}, {\"class_name\": \"Dense\", \"config\": {\"name\": \"dense_2\", \"trainable\": true, \"dtype\": \"float32\", \"units\": 10, \"activation\": \"softmax\", \"use_bias\": true, \"kernel_initializer\": {\"class_name\": \"GlorotUniform\", \"config\": {\"seed\": null}}, \"bias_initializer\": {\"class_name\": \"Zeros\", \"config\": {}}, \"kernel_regularizer\": null, \"bias_regularizer\": null, \"activity_regularizer\": null, \"kernel_constraint\": null, \"bias_constraint\": null}}]}, \"keras_version\": \"2.2.4-tf\", \"backend\": \"tensorflow\"}\n",
            "Same config: True\n",
            "Same value for first weight matrix: False\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JAgKHCCFyBkJ",
        "colab_type": "text"
      },
      "source": [
        "The YAML format is similar. The details of writing out YAML files, loading them and using them to create a new model are similar as for the JSON files. Writing out, reading in and using YAML files to create models is similar to JSON files."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AfYB4ph6teHE",
        "colab_type": "code",
        "outputId": "d25aeb77-9195-4837-d4a7-e50e04bbdf12",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "yaml_string = model.to_yaml()\n",
        "print(yaml_string)"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "backend: tensorflow\n",
            "class_name: Sequential\n",
            "config:\n",
            "  layers:\n",
            "  - class_name: Conv2D\n",
            "    config:\n",
            "      activation: relu\n",
            "      activity_regularizer: null\n",
            "      batch_input_shape: !!python/tuple [null, 32, 32, 3]\n",
            "      bias_constraint: null\n",
            "      bias_initializer:\n",
            "        class_name: Zeros\n",
            "        config: {}\n",
            "      bias_regularizer: null\n",
            "      data_format: channels_last\n",
            "      dilation_rate: &id001 !!python/tuple [1, 1]\n",
            "      dtype: float32\n",
            "      filters: 16\n",
            "      kernel_constraint: null\n",
            "      kernel_initializer:\n",
            "        class_name: GlorotUniform\n",
            "        config: {seed: null}\n",
            "      kernel_regularizer: null\n",
            "      kernel_size: !!python/tuple [3, 3]\n",
            "      name: conv_1\n",
            "      padding: valid\n",
            "      strides: &id002 !!python/tuple [1, 1]\n",
            "      trainable: true\n",
            "      use_bias: true\n",
            "  - class_name: Conv2D\n",
            "    config:\n",
            "      activation: relu\n",
            "      activity_regularizer: null\n",
            "      bias_constraint: null\n",
            "      bias_initializer:\n",
            "        class_name: Zeros\n",
            "        config: {}\n",
            "      bias_regularizer: null\n",
            "      data_format: channels_last\n",
            "      dilation_rate: *id001\n",
            "      dtype: float32\n",
            "      filters: 8\n",
            "      kernel_constraint: null\n",
            "      kernel_initializer:\n",
            "        class_name: GlorotUniform\n",
            "        config: {seed: null}\n",
            "      kernel_regularizer: null\n",
            "      kernel_size: !!python/tuple [3, 3]\n",
            "      name: conv_2\n",
            "      padding: valid\n",
            "      strides: *id002\n",
            "      trainable: true\n",
            "      use_bias: true\n",
            "  - class_name: MaxPooling2D\n",
            "    config:\n",
            "      data_format: channels_last\n",
            "      dtype: float32\n",
            "      name: pool_1\n",
            "      padding: valid\n",
            "      pool_size: &id003 !!python/tuple [4, 4]\n",
            "      strides: *id003\n",
            "      trainable: true\n",
            "  - class_name: Flatten\n",
            "    config: {data_format: channels_last, dtype: float32, name: flatten, trainable: true}\n",
            "  - class_name: Dense\n",
            "    config:\n",
            "      activation: relu\n",
            "      activity_regularizer: null\n",
            "      bias_constraint: null\n",
            "      bias_initializer:\n",
            "        class_name: Zeros\n",
            "        config: {}\n",
            "      bias_regularizer: null\n",
            "      dtype: float32\n",
            "      kernel_constraint: null\n",
            "      kernel_initializer:\n",
            "        class_name: GlorotUniform\n",
            "        config: {seed: null}\n",
            "      kernel_regularizer: null\n",
            "      name: dense_1\n",
            "      trainable: true\n",
            "      units: 32\n",
            "      use_bias: true\n",
            "  - class_name: Dense\n",
            "    config:\n",
            "      activation: softmax\n",
            "      activity_regularizer: null\n",
            "      bias_constraint: null\n",
            "      bias_initializer:\n",
            "        class_name: Zeros\n",
            "        config: {}\n",
            "      bias_regularizer: null\n",
            "      dtype: float32\n",
            "      kernel_constraint: null\n",
            "      kernel_initializer:\n",
            "        class_name: GlorotUniform\n",
            "        config: {seed: null}\n",
            "      kernel_regularizer: null\n",
            "      name: dense_2\n",
            "      trainable: true\n",
            "      units: 10\n",
            "      use_bias: true\n",
            "  name: sequential_16\n",
            "keras_version: 2.2.4-tf\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Fy92-3pZ1c4f",
        "colab_type": "text"
      },
      "source": [
        "## Loading pre-trained Keras models\n",
        "Import and build Keras ResNet50 model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SHVNYMkZteEg",
        "colab_type": "code",
        "outputId": "3c83c0a5-e2c7-4c65-a0bc-9e75092ada20",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        }
      },
      "source": [
        "from tensorflow.keras.applications import ResNet50\n",
        "model = ResNet50(weights='imagenet')"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.iter\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_1\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.beta_2\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.decay\n",
            "WARNING:tensorflow:Unresolved object in checkpoint: (root).optimizer.learning_rate\n",
            "WARNING:tensorflow:A checkpoint was restored (e.g. tf.train.Checkpoint.restore or tf.keras.Model.load_weights) but not all checkpointed values were used. See above for specific issues. Use expect_partial() on the load status object, e.g. tf.train.Checkpoint.restore(...).expect_partial(), to silence these warnings, or use assert_consumed() to make the check explicit. See https://www.tensorflow.org/alpha/guide/checkpoints#loading_mechanics for details.\n",
            "Downloading data from https://github.com/keras-team/keras-applications/releases/download/resnet/resnet50_weights_tf_dim_ordering_tf_kernels.h5\n",
            "102973440/102967424 [==============================] - 8s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "oGB6w9ja1HEU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retrieve the image files\n",
        "\n",
        "!wget -q -O lemon.jpg --no-check-certificate \"https://docs.google.com/uc?export=download&id=1JSgQ9qgi9nO9t2aGEk-zA6lzYNUT9vZJ\"\n",
        "!wget -q -O viaduct.jpg --no-check-certificate \"https://docs.google.com/uc?export=download&id=1sQzMKmyCR5Tur19lP3n1IIlEMG_o6Mct\"\n",
        "!wget -q -O water_tower.jpg --no-check-certificate \"https://docs.google.com/uc?export=download&id=1cPAQD1O6mAiMbg0fmG5HIk8OuO_BSC6J\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2vQOU2331G-7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import 3 sample ImageNet images\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "\n",
        "lemon_img = load_img('lemon.jpg', target_size=(224, 224))\n",
        "viaduct_img = load_img('viaduct.jpg', target_size=(224, 224))\n",
        "water_tower_img = load_img('water_tower.jpg', target_size=(224, 224))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6u9nOm0q1G5l",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Use ResNet50 model to classify images\n",
        "# Useful function: presents top 5 predictions and probabilities\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import img_to_array\n",
        "from tensorflow.keras.applications.resnet50 import preprocess_input, decode_predictions\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "def get_top_5_predictions(img):\n",
        "    x = img_to_array(img)[np.newaxis, ...]\n",
        "    x = preprocess_input(x)\n",
        "    preds = decode_predictions(model.predict(x), top=5)\n",
        "    top_preds = pd.DataFrame(columns=['prediction', 'probability'],\n",
        "                             index=np.arange(5)+1)\n",
        "    for i in range(5):\n",
        "        top_preds.loc[i+1, 'prediction'] = preds[0][i][1]\n",
        "        top_preds.loc[i+1, 'probability'] = preds[0][i][2] \n",
        "    return top_preds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0D7BQHgE1xyr",
        "colab_type": "code",
        "outputId": "d0975f31-ffab-4462-aea0-8c79a67569eb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "# Display image\n",
        "\n",
        "lemon_img"
      ],
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224 at 0x7F53544EF1D0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azZQbT1S1x39",
        "colab_type": "code",
        "outputId": "44a75687-fef9-4880-d7aa-1673ff79560d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 229
        }
      },
      "source": [
        "# Display top 5 predictions\n",
        "\n",
        "get_top_5_predictions(lemon_img)"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/download.tensorflow.org/data/imagenet_class_index.json\n",
            "40960/35363 [==================================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction</th>\n",
              "      <th>probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>consomme</td>\n",
              "      <td>0.227801</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>lemon</td>\n",
              "      <td>0.221758</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>eggnog</td>\n",
              "      <td>0.151335</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ladle</td>\n",
              "      <td>0.0400882</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>spotlight</td>\n",
              "      <td>0.0291972</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "  prediction probability\n",
              "1   consomme    0.227801\n",
              "2      lemon    0.221758\n",
              "3     eggnog    0.151335\n",
              "4      ladle   0.0400882\n",
              "5  spotlight   0.0291972"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wT6hjxm71yB_",
        "colab_type": "code",
        "outputId": "80db5ad1-4d2a-460d-d227-36864faf5aee",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "# Display image\n",
        "\n",
        "viaduct_img"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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ezaNHtAAWHsiXvvQlTP7qv/936aBAG2MwshzXv0E9cg2/QT1yjXCVPB/od7a4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\nmgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224 at 0x7F53505588D0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7ww6EhkZ1yFc",
        "colab_type": "code",
        "outputId": "66aa968c-3e75-4414-ce4e-0e4bf98068e2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "# Display top 5 predictions\n",
        "\n",
        "get_top_5_predictions(viaduct_img)"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction</th>\n",
              "      <th>probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>vault</td>\n",
              "      <td>0.367951</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>prison</td>\n",
              "      <td>0.111742</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>viaduct</td>\n",
              "      <td>0.110738</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>window_screen</td>\n",
              "      <td>0.0882308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>fire_screen</td>\n",
              "      <td>0.0206753</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      prediction probability\n",
              "1          vault    0.367951\n",
              "2         prison    0.111742\n",
              "3        viaduct    0.110738\n",
              "4  window_screen   0.0882308\n",
              "5    fire_screen   0.0206753"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "82IRlOe72SwI",
        "colab_type": "code",
        "outputId": "7e99e615-d590-4d55-d94c-bba80dc7aabb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        }
      },
      "source": [
        "# Display image\n",
        "\n",
        "water_tower_img"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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T/Gk+xr8/0RiEmjo7GZO4VFrliIZNTVi83eScLMNkH019stuznMm3yomoaJJD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\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=224x224 at 0x7F535051DDA0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TGUVVFep1yAF",
        "colab_type": "code",
        "outputId": "0fa41e6a-9309-4838-e17a-70a2307e77a0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "# Display top 5 predictions\n",
        "\n",
        "get_top_5_predictions(water_tower_img)"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction</th>\n",
              "      <th>probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>solar_dish</td>\n",
              "      <td>0.384682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ladle</td>\n",
              "      <td>0.196178</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>planetarium</td>\n",
              "      <td>0.116683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>strainer</td>\n",
              "      <td>0.04999</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>jigsaw_puzzle</td>\n",
              "      <td>0.0219474</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      prediction probability\n",
              "1     solar_dish    0.384682\n",
              "2          ladle    0.196178\n",
              "3    planetarium    0.116683\n",
              "4       strainer     0.04999\n",
              "5  jigsaw_puzzle   0.0219474"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4kejqHWA2k3f",
        "colab_type": "text"
      },
      "source": [
        "## Tensorflow Hub modules\n",
        "[Tensorflow Hub page](https://tfhub.dev/google/imagenet/mobilenet_v1_050_160/classification/4)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ra1mstC1x9C",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow_hub as hub"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ttHAI9RJ1x7U",
        "colab_type": "code",
        "outputId": "f190b816-8206-4062-ce8b-7f227eeed4dd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        }
      },
      "source": [
        "# Build Google's Mobilenet v1 model\n",
        "# Use MobileNet model to classify images\n",
        "\n",
        "module_url = \"https://tfhub.dev/google/imagenet/mobilenet_v1_050_160/classification/4\"\n",
        "model = Sequential([hub.KerasLayer(module_url)])\n",
        "model.build(input_shape=[None, 160, 160, 3])\n",
        "model.summary()"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_17\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "keras_layer (KerasLayer)     multiple                  1343049   \n",
            "=================================================================\n",
            "Total params: 1,343,049\n",
            "Trainable params: 0\n",
            "Non-trainable params: 1,343,049\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JRHKZyTT1x11",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Retrieve the image files\n",
        "\n",
        "!wget -q -O lemon.jpg --no-check-certificate \"https://docs.google.com/uc?export=download&id=1JSgQ9qgi9nO9t2aGEk-zA6lzYNUT9vZJ\"\n",
        "!wget -q -O viaduct.jpg --no-check-certificate \"https://docs.google.com/uc?export=download&id=1sQzMKmyCR5Tur19lP3n1IIlEMG_o6Mct\"\n",
        "!wget -q -O water_tower.jpg --no-check-certificate \"https://docs.google.com/uc?export=download&id=1cPAQD1O6mAiMbg0fmG5HIk8OuO_BSC6J\""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4-RBR5U91G22",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Import and preprocess 3 sample ImageNet images\n",
        "\n",
        "from tensorflow.keras.preprocessing.image import load_img\n",
        "\n",
        "lemon_img = load_img(\"lemon.jpg\", target_size=(160, 160))\n",
        "viaduct_img = load_img(\"viaduct.jpg\", target_size=(160, 160))\n",
        "water_tower_img = load_img(\"water_tower.jpg\", target_size=(160, 160))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eMI_MJ8zBoao",
        "colab_type": "code",
        "outputId": "4be6cca9-b1b0-4364-e1b1-b067949a2cc1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 212
        }
      },
      "source": [
        "! wget \"https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt\""
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2020-04-07 07:29:05--  https://storage.googleapis.com/download.tensorflow.org/data/ImageNetLabels.txt\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 64.233.189.128, 2404:6800:4008:c07::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|64.233.189.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10484 (10K) [text/plain]\n",
            "Saving to: ‘ImageNetLabels.txt.1’\n",
            "\n",
            "\rImageNetLabels.txt.   0%[                    ]       0  --.-KB/s               \rImageNetLabels.txt. 100%[===================>]  10.24K  --.-KB/s    in 0.001s  \n",
            "\n",
            "2020-04-07 07:29:06 (8.99 MB/s) - ‘ImageNetLabels.txt.1’ saved [10484/10484]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eTgQ8tNNhGSE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Read in categories text file\n",
        "\n",
        "with open('ImageNetLabels.txt') as txt_file:\n",
        "    categories = txt_file.read().splitlines()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "psi2-kZK4G64",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Useful function: presents top 5 predictions\n",
        "\n",
        "import pandas as pd\n",
        "\n",
        "def get_top_5_predictions(img):\n",
        "    x = img_to_array(img)[np.newaxis, ...] / 255.0\n",
        "    preds = model.predict(x)\n",
        "    top_preds = pd.DataFrame(columns=['prediction'],\n",
        "                             index=np.arange(5)+1)\n",
        "    sorted_index = np.argsort(-preds[0])\n",
        "    for i in range(5):\n",
        "        ith_pred = categories[sorted_index[i]]\n",
        "        top_preds.loc[i+1, 'prediction'] = ith_pred\n",
        "            \n",
        "    return top_preds"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "927809cc-8e3d-4fc9-be39-33173444d2c0",
        "id": "TlXh5GczCEfm",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "# Display image\n",
        "\n",
        "lemon_img"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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9OTn+PaA0wt6zIx/odIW9D8dNgEBDulN12+0BTKYSOxHaHLBT8dMdtq8DibLo\ni8dvA+9+63cdWgv2gIffFlEo4hXgr/yLf+7Qp4+PgFsvizSHKw40FyGL6AQoLt9x2Hh8DBirxa5H\nEXDlcz8mJ6cJcHZfjGJVMACC2kDmXJsDwWMVwbrXgVGmftK7XwAenoj5r3vjLrCoxH2y2/4y4KHl\nH4ZPgcSXG1189B+AOJU3fHrvfaA4kRXs+IdsKHjtwfT/1Q6Qae/CTI3ShS2Q8sGYQCMjfQPk2nLZ\nFcCrbYmc0to/AKKemF1srQFUkVZQiOuAFwk9LU7rwHwhwsV8PgZyTXtBwyhdgdNeW0zTO60OEC0r\nxYxyID3Twp6Ph8DpAxHcLh5fAsVEZht6MXDvdfnAv/KVvwzsXREWsnXjNYBK4v6nYx9obGujrsEC\naDY1rbIsAIvEVWWLE2B4KX1W59NnQKcl993e7QL/+1zdhWkBJJ5Q8H5vH9iui24ZuWQeDTrDnwOo\nrMf4Y4BTkSInT98BBqfCFGejM2BZEse1LN1Q8JrDZoHXHILxxRANHAA07YjA84AiK4FCW0a41CGt\nkICryFNYMTONcw8wy/J/jRYQtYTpNbe2gXpDFO5GqwdYrc5eFA1W+thm2kLR2BzwKmF309EcONXQ\nratb11mW84HJ9Bx4ovWq0nHGSscagw8k74lIcjRfAGONBmn8mT8HxAdSIzR2SVMtEcHa9Qgg1NRy\nA5CqmzJs1IG97pv6LK+ysuuNihJ465qEcPiuOoWmT0qxMe+SVZjLI+BsZxcPHZYPHgL9I/HEjM4e\nAelQT3YjLbtlu2g7NrDWELAoAV+TMpZtb4yrnZAXrOT6uUzBJZU7eqsmQrJ5PwNyLYniClTVWl25\n084M8JWgq14JNDRovhaHQH1ZnV39dK4sizZLpJgXgNXOCuenU+DZAzGW3X/nY6B/olXjTBMotCzE\nKMuAK2Mt1HX5LeBER75/9D6w/5I0WqhaNWD7Ugh6YgIg7Ilp2msdAlFLTvZqh6yENoTePlB5Ik5m\nrkz++Jtyp2YTQNNeOHoCWDWnG1fIRmNbTx9/B/AqmXM6OgXGWnw1m+Q8D0rAdwUeNBO0KDw2FLz2\nsFngNYfA6ZTLbqd5ps2fixTNq9FGHdKXUItZUc0B1LyDZwrAapuunDmwCLVAztEIWNQkira/9Qmw\npYmBzXYLMMpOw7qcVm+0Aas+x+mkAC5OhGXde+c+8OF7YufKxxVQ97XNDBUwXHZarACSuTJ/LwbG\nWrvwna89BR4/FkvW9k0fmFyKTn8yuQTufPFNOXrwMuDtqRUsPgGMJwzcBFeAuhEHSd3laH34P+Xk\nJAHQkNvps0+A2UT8B4GfAjOtxnh69AQIZMqyV2p8jaR9mSVPzhIgWwjRuj4fGwpecwhmuQFKLS9S\nLguauC5SFkTeBhXBlyKP28+XhVQcnXtK7p6tgMqoaWxSAQvtSzWengKeuu7TpA1kOnJltNKdVwNG\nU5nexcUUeP/eQ4dOhzlgCxFnGnEXWGhVhpHLOtRU41otAd6thCgbNgKUnsnnU6ClJqND3wJ5KbOt\nygr44Eg6LTaSbwK1WEM/wybQ1Pp7rcYOUBYy9Gy2AEyoGcDGsKJEpfMZkGV6I5sBWa79ojMAdS3i\nOc1H33BVAMxTTUtYxEA2Fwm03AhZnwXYLPCag/naP24Axmo/U63YHHgGbThoSuHb6cIZtvTiP2xE\nu/zlAQShMNuoVgf8UNjpRTQE4rrY3D2/BsyX0RbqvhyOS+DsQkSh07MpMBjIaZ12B6jXug51YkWh\n3aR9E7GSA+gatR21tQB8ngO+PlrNVECkPLlepsB+S06+0o2AlhbOaUQZkPhaXsh1gFNicTYFLf8o\nxoMz1fBd1yE1zclpahNj8eJRZ/Va9jxyAm2hImeeh0CWak39vAZgtN6iH7Gh4LWH4GzhoZZnlHCB\nwDmbrCsmpY2x8pLnTdKlutPzQljuX9WiAj9kpTBwSgQEqlRVqQHGEyHNRTYBZppTu6Tg2dSdpt/w\n3Adu7mkzHhsB6Vy+/3KRA76WtquHAWC0BlVWZkBUU6GPEogDTZbJY8Cm2i1+XgfO+tpL/jwArmx1\n5dp4ARR1kRC94JwVOnNm5kSzT1zpral68Vw7bX3NVEUE5IWmIs4ssMhlOTw/BmapyJiFs1UtRVGX\niq0BXIEfA0ZbSXrGZ0PBaw+bBV5zCMZzn5W+zcYslawSbVa4ZAqB78QW+Sxcj45A+9YEUQQEanfJ\njQ9UqiYXeQVUym0SAmAw0IDcPsBcjU5atpncqePKhVpRC7AzGXM8HgG+kflsdTqrz+KCyAsVo1r1\nBFicShWtJIqBeiCcdDosgLoRdGfrAChVXptPF8DxmbyH8WwM5J64VTp7daC1p++wNls+L8AAoK1P\n5Oa63NfKLAJmM20XNzbAZKoxM3kGhBpNXDoroZZyrCUxUGuIkBXGERptgirxGwpecwjSgQfMdNnL\nSr40ay1SD+p5PLpXjwCjJDuej1kh2cBGgKeWo6wogZmWNRlNZkCq9XrLC1j5kN1HqWUOngtKiZ8A\nRl1vXhGyovnsJiFgtbuWGngFbXjOVquUlI+AQ6WksACohXLyjU4LiLWrl++urcujlVttYJwKh6mS\nHnAyltnfe3oJDB9pk6/QvSu5kZv7DTUguzedK5ovJkCWTlaPLsUoJ7m2tIl7XA+AlvZ/9E0IhNq9\nK/J8Vnp71cONmvQZgM0CrzkE2942sFBj92g0cD8GozEwGgNMlrLPiwxkZx9Au8kwWwDMVOFzp2kn\nNZwJa2lWutaGVTeGb4BAdbiVHzUg9ERbdaVNa6HwKGeA89X6Hro8Pl9YpWfK1aNOfmx4b8hRD54X\nrCFwfHXpNzUFUCnqpJuGull6tgbsl+IfnORXgLkamNLKA1IlHidmVscSjm8DHzBq4COJAaO1KzzP\nibEvyLwNjVxzF9V046jXQlZKpHrLHc7dyBo2FLz2YO7//F8CikL7NC3dWPkCyIocyFXyqrBAqXQ3\nnU+ATGWctCxWUSdBLYPmXYvO562vTMRKPXjPhECg9B6qVOUa7YT6dw/DauinnKPRjV4FhL6QbBBU\ngKdHfWOAsvqyTq8EjNGG9Dg2pfVdmAC2Ut7ljup9Pc9n2VUILDFQWO3ZkLsS7+p4LwGiuvbvse5f\n5St29T98UwKexll5XgYYjYP3vYqVCha+8QGDtu8uA1YqMbsoiQ0FrzlsFnjNIQjSCgh0pWtqMyr9\nCI3WKJ/LQmYVle4cSyYc+SgrBqzvsdLbrHCmMUXT0DWmXs7EhR9oyw6dj5FjysmFwS0dghbwWTLh\nkhVByenS/jJmCQvMFnLUac9L96iz/huzFFhqgNH7BjhRaCmCWcBTI511zYeWAppvAbMUeawHjMyW\n3tey0lW7KjPAWg30Zw5Y3SmMq7JTt8/f0Ypk6lc1wFSeTsMDPKslK9jowZ8BCLrdFit7/jKjwmkE\nTqoqllTowqyWTn3PW0XLtAQytTY7AWppWIk8D/DUChYqq3gBKvtdf/CoWHH5eabiDzgoWbaVdjKg\n/rm0FiiXj4QBGoH2bZZLlgft6myNXwM8L9FnsaywH2eor5bBabZkhc24B/fVI+jGTHjh0TwlLes6\nW6hQKfkmS9nTC4HFYqDXVYBnl+ytWn185/Nd4RwbIeszAJsFXnMIzrMhK/rocz3VC1AxyotV8SIA\ngiWLBFbZe1WxyrhgdWTHqZauvSLN/uBpnnnB6gS4041nVv9eqgdwybgV9QDrvWDQqV6cbcdKHl+F\nExj1EzcxUOlQpfGBQvVva0IgV+HO+Aa1vqHMeVmfy3HsrFxKc5aVGq1y92WXRuOxdI8gYu1zQcka\nIE60KiIlKy/cqyLAVrLrSYsgvdaw6Xz2GYCg6garePG853rJUgHQT8Y6gUW/Pte9OVCid9T/PD/R\naTQaqpTnKbBQo1hS3169ryNN81ww0QOeZYWgHTnmy8PGANVzKvRfRD1W4vJdfvBMO9Bb47PSiqtw\nAqPmzhQmBColgFK8mRqOb1zIqQayOfuaFXI3RQFYtaCZqgLirlKwdWSnVrDSByqN3Xd2KI2BE2LN\niqUU6ZKDnhM8anMGrIioS9ZYsKHgtYfNAq85BGlQfcohZxZ5wXhirAGMsug4cdUIdM93QZ0aQx84\neSHSk+MY8NFohMrxz6V26EJwtXXgkguVLsFJVW1Jb1R7utsxlo45x7F1ti6qtKpeFAl92RqsZ1iR\nqirPA6y/FLLcCDoyFojVMedmVajftMorINQXGRkfiAI52cmVE6u1SUUUktAR46LAlvEf1Flh4OpP\nURbtOcFt6c4p4Hl64R+ah7Ch4DWH/wPCby5VSgIzAAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=160x160 at 0x7F534E82D320>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "b4a4d86b-46c1-4955-98d7-0ec408c4741d",
        "id": "TwPjmnSPCEf0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "# Display top 5 predictions\n",
        "\n",
        "get_top_5_predictions(lemon_img)"
      ],
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>shower cap</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>tray</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>candle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>brassiere</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>African chameleon</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          prediction\n",
              "1         shower cap\n",
              "2               tray\n",
              "3             candle\n",
              "4          brassiere\n",
              "5  African chameleon"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 90
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "1fa94bf3-8d3d-4610-8112-4f46babc402d",
        "id": "prBSHHv0CEf4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "# Display image\n",
        "\n",
        "viaduct_img"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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KeZTE+eyGSgaF1DBpTBdaBpmIslsLR0S5dFsik8zSfZAm7uSH3Has\np/vYKRYBJHb5kuDg7kYUFQDy1Bd9X9D0AU84lWuTDtXVTkOWvNulxV5LgMU/5OCxITkHvghp6pwy\nLgusCuOyHwt+FIKMWVbx51P6KLgULW0a2NLqaV0g2oHKNMGaprtBKpYssbvbZZzZzvx8Ha/9g7QP\nc9CvPzPAC1RokMNWBpJpOPgmg4ZOu4axf5ekGkqwlbuitFhv4DSzavLQHToFWfcB/R+zzsmckN3O\nvwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=160x160 at 0x7F53502F4630>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "19497d7f-5700-47cc-f841-0e158e815104",
        "id": "kWiXsQRrCEf8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "# Display top 5 predictions\n",
        "\n",
        "get_top_5_predictions(viaduct_img)"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>viaduct</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>pier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>dam</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>prison</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>solar dish</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   prediction\n",
              "1     viaduct\n",
              "2        pier\n",
              "3         dam\n",
              "4      prison\n",
              "5  solar dish"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "f831b37f-967c-4fc8-c50a-2a458aa85b4a",
        "id": "EVGiPCnACEf_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 177
        }
      },
      "source": [
        "# Display image\n",
        "\n",
        "water_tower_img"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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POacDq0UFRXzoxk8BdkR92XLZBa644pXCulYEsE3zz4WFY8ComdDqxAaBeErt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\n/wsJqCEMdC57lAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<PIL.Image.Image image mode=RGB size=160x160 at 0x7F53502F4A90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "outputId": "c45eb8b4-0dbe-48bf-94ed-6f0841b5f961",
        "id": "e6ZT4RmCCEgB",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "# Display top 5 predictions\n",
        "\n",
        "get_top_5_predictions(water_tower_img)"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>prediction</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>solar dish</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>water tower</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>aircraft carrier</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>jigsaw puzzle</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>oxygen mask</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         prediction\n",
              "1        solar dish\n",
              "2       water tower\n",
              "3  aircraft carrier\n",
              "4     jigsaw puzzle\n",
              "5       oxygen mask"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yA4oCg8sRVEj",
        "colab_type": "text"
      },
      "source": [
        "### Notes\n",
        "* The spatial dimension of the output decreases when we increase the stride in a convolutional layer.\n",
        "* The effect of using pooling layers in convolutional neural networks helps to make the model invariantto small translations of the input. It also helps to reduce the spatial dimensions of the input layer.\n",
        "* A flatten layer unrolls the input into a one-dimensional tensor.\n",
        "\n",
        "\n",
        "### Other Readings\n",
        "* https://medium.com/@ageitgey/machine-learning-is-fun-part-3-deep-learning-and-convolutional-neural-networks-f40359318721\n",
        "* https://medium.com/the-theory-of-everything/understanding-activation-functions-in-neural-networks-9491262884e0\n",
        "* https://towardsdatascience.com/complete-guide-of-activation-functions-34076e95d044\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zGAA_gWQ4HR1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "b1swxogH4HfI",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NonvxlBT4HjC",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qWhF6GyC4Hc0",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hoy6XthV4Hai",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ci-Iwjal4HVS",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pYLUI4fD4HPx",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "spkIKypj4HKr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
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