DevFestIkorodu2019.ipynb

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      "name": "DevFestIkorodu2019.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P0hH-5_VZXt8",
        "colab_type": "text"
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      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dwZR7RPvZXt9",
        "colab_type": "text"
      },
      "source": [
        "# Introduction to Tensorflow and Tensorflow lite Workshop"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QPBtJ-zcZXt-",
        "colab_type": "text"
      },
      "source": [
        "## What is Tensorflow?\n",
        "\n",
        "**TensorFlow is an end-to-end open source platform for machine learning with a comprehensive, flexible ecosystem of tools, libraries and community resources.**\n",
        "\n",
        "\n",
        " \n",
        " "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2R0J12DuZXt_",
        "colab_type": "text"
      },
      "source": [
        "# TensorFlow basics"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pg6o4U_6a-nt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "try:\n",
        "    %tensorflow_version 2.x\n",
        "except:\n",
        "    pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xAloUA79bTGM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "0777a806-b393-4456-f001-cd9920ce58a6"
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "# TensorFlow and tf.keras\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "# Helper libraries\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pathlib\n",
        "\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WQJQweN8ZXuB",
        "colab_type": "text"
      },
      "source": [
        "Tensorflow has different variation of tensors\n",
        ".constant,\n",
        ".random .\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GfA93ZqUZXuC",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "85eb60fb-3d5f-436a-8235-fa51708ba85c"
      },
      "source": [
        "#constant tensor\n",
        "x = tf.constant([[1, 2], [3, 4]])\n",
        "print(x)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[1 2]\n",
            " [3 4]], shape=(2, 2), dtype=int32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2IZR9I73ZXuH",
        "colab_type": "text"
      },
      "source": [
        "Another way constant tensors are created using `tf.ones` and `tf.zeroes`"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_LBjKd8ZXuI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 121
        },
        "outputId": "b755bb01-6c5d-4809-d0b0-c6e59244ef82"
      },
      "source": [
        "print(tf.ones(shape=(2, 1)))\n",
        "print(tf.zeros(shape=(2, 1)))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[1.]\n",
            " [1.]], shape=(2, 1), dtype=float32)\n",
            "tf.Tensor(\n",
            "[[0.]\n",
            " [0.]], shape=(2, 1), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vOrZD6aAZXuL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 87
        },
        "outputId": "2433b06e-327e-4e87-f7ed-af762b75fc69"
      },
      "source": [
        "#You can declare a random tensor by:\n",
        "\n",
        "tf.random.normal(shape=(3, 3), mean=0., stddev=1.)\n"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=12, shape=(3, 3), dtype=float32, numpy=\n",
              "array([[ 0.92151266, -1.6761822 ,  0.06845541],\n",
              "       [ 1.0399439 ,  2.407184  ,  0.26581106],\n",
              "       [ 0.93184406, -0.11953061, -0.70880646]], dtype=float32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VJHk9wYCZXuP",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "e3232ce5-196e-4295-910f-4fc55ff5a8de"
      },
      "source": [
        "#random uniform tensor\n",
        "\n",
        "tf.random.uniform(shape=(2, 2), minval=0, maxval=10, dtype='int32')"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tf.Tensor: id=16, shape=(2, 2), dtype=int32, numpy=\n",
              "array([[3, 5],\n",
              "       [4, 5]], dtype=int32)>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4EFS0XJlZXuS",
        "colab_type": "text"
      },
      "source": [
        "## Variables\n",
        "\n",
        "Variables are special tensors used to store mutable state (like the weights of a neural network). You create a Variable using some initial value."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dwEMnHOMZXuT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "55942fa9-dd27-4fe2-bc70-283a4ea11752"
      },
      "source": [
        "initial_value = tf.random.normal(shape=(2, 2))\n",
        "v = tf.Variable(initial_value)\n",
        "print(v)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<tf.Variable 'Variable:0' shape=(2, 2) dtype=float32, numpy=\n",
            "array([[ 1.3115387 , -0.2584394 ],\n",
            "       [ 0.18615824,  1.382142  ]], dtype=float32)>\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WSEd7ISeZXuW",
        "colab_type": "text"
      },
      "source": [
        "You update the value of a Variable by using the methods `assign(value)`, or `assign_add(increment)` or `assign_sub(decrement)`:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "m7mx-QkTZXuX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "new_value = tf.random.normal(shape=(2, 2))\n",
        "v.assign(new_value)\n",
        "for i in range(2):\n",
        "  for j in range(2):\n",
        "    assert v[i, j] == new_value[i, j]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "32_6XU8UZXub",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "added_value = tf.random.normal(shape=(2, 2))\n",
        "v.assign_add(added_value)\n",
        "for i in range(2):\n",
        "  for j in range(2):\n",
        "    assert v[i, j] == new_value[i, j] + added_value[i, j]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YHZPi9AeZXuh",
        "colab_type": "text"
      },
      "source": [
        "## MAT 101: Tensorflow Arthmetics "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "IGqjxva6ZXuj",
        "colab_type": "text"
      },
      "source": [
        "Simple math, as well as complex math can be done using tensorflow.\n",
        "Let's see a few"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ov7Qey2pZXuk",
        "colab_type": "text"
      },
      "source": [
        "### Addition and Subtraction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YcpHe-BvZXul",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "a = tf.random.normal(shape=(2, 2))\n",
        "b = tf.random.normal(shape=(2, 2))\n",
        "\n",
        "c = a + b\n",
        "d = tf.square(c)\n",
        "e = tf.exp(d)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B82kR7-uZXuo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 277
        },
        "outputId": "ca1922e6-d5ae-4fd1-8ee4-174741714bd9"
      },
      "source": [
        "print(\"a\", a)\n",
        "print(\"b\", b)\n",
        "print(\"c\", c)\n",
        "print(\"d\", d)\n",
        "print(\"e\", e)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a tf.Tensor(\n",
            "[[ 1.0364131e-03 -9.6974921e-01]\n",
            " [-2.0420582e+00 -4.9199268e-01]], shape=(2, 2), dtype=float32)\n",
            "b tf.Tensor(\n",
            "[[-0.15349092 -0.13893835]\n",
            " [-0.807484   -0.4876446 ]], shape=(2, 2), dtype=float32)\n",
            "c tf.Tensor(\n",
            "[[-0.1524545  -1.1086875 ]\n",
            " [-2.8495421  -0.97963727]], shape=(2, 2), dtype=float32)\n",
            "d tf.Tensor(\n",
            "[[0.02324237 1.229188  ]\n",
            " [8.11989    0.9596892 ]], shape=(2, 2), dtype=float32)\n",
            "e tf.Tensor(\n",
            "[[1.0235146e+00 3.4184525e+00]\n",
            " [3.3606519e+03 2.6108849e+00]], shape=(2, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "J8q62Yg9ZXur",
        "colab_type": "text"
      },
      "source": [
        "### Basic model of form y = mx + c"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eEs-RO1PZXus",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "118f843c-20de-4625-a739-14a3fae313b9"
      },
      "source": [
        "# Create a simple Keras model.\n",
        "x = [-1, 0, 1, 2, 3, 4]\n",
        "y = [-3, -1, 1, 3, 5, 7]\n",
        "\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Dense(units=1, input_shape=[1])\n",
        "])\n",
        "model.compile(optimizer='sgd', loss='mean_squared_error')\n",
        "model.fit(x, y, epochs=200, verbose=1)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 6 samples\n",
            "Epoch 1/200\n",
            "6/6 [==============================] - 1s 125ms/sample - loss: 23.0307\n",
            "Epoch 2/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 18.4005\n",
            "Epoch 3/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 14.7519\n",
            "Epoch 4/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 11.8757\n",
            "Epoch 5/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 9.6074\n",
            "Epoch 6/200\n",
            "6/6 [==============================] - 0s 1ms/sample - loss: 7.8173\n",
            "Epoch 7/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 6.4036\n",
            "Epoch 8/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 5.2862\n",
            "Epoch 9/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 4.4020\n",
            "Epoch 10/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 3.7014\n",
            "Epoch 11/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 3.1452\n",
            "Epoch 12/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 2.7029\n",
            "Epoch 13/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 2.3502\n",
            "Epoch 14/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 2.0681\n",
            "Epoch 15/200\n",
            "6/6 [==============================] - 0s 1ms/sample - loss: 1.8417\n",
            "Epoch 16/200\n",
            "6/6 [==============================] - 0s 1ms/sample - loss: 1.6592\n",
            "Epoch 17/200\n",
            "6/6 [==============================] - 0s 1ms/sample - loss: 1.5112\n",
            "Epoch 18/200\n",
            "6/6 [==============================] - 0s 1ms/sample - loss: 1.3906\n",
            "Epoch 19/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 1.2916\n",
            "Epoch 20/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 1.2096\n",
            "Epoch 21/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 1.1411\n",
            "Epoch 22/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 1.0834\n",
            "Epoch 23/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 1.0341\n",
            "Epoch 24/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.9916\n",
            "Epoch 25/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.9546\n",
            "Epoch 26/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.9218\n",
            "Epoch 27/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.8925\n",
            "Epoch 28/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.8660\n",
            "Epoch 29/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.8419\n",
            "Epoch 30/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.8195\n",
            "Epoch 31/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.7987\n",
            "Epoch 32/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.7792\n",
            "Epoch 33/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.7607\n",
            "Epoch 34/200\n",
            "6/6 [==============================] - 0s 1ms/sample - loss: 0.7432\n",
            "Epoch 35/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.7264\n",
            "Epoch 36/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.7103\n",
            "Epoch 37/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.6948\n",
            "Epoch 38/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.6797\n",
            "Epoch 39/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.6652\n",
            "Epoch 40/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.6511\n",
            "Epoch 41/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.6373\n",
            "Epoch 42/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.6240\n",
            "Epoch 43/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.6109\n",
            "Epoch 44/200\n",
            "6/6 [==============================] - 0s 1ms/sample - loss: 0.5982\n",
            "Epoch 45/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.5858\n",
            "Epoch 46/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.5736\n",
            "Epoch 47/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.5618\n",
            "Epoch 48/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.5502\n",
            "Epoch 49/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.5388\n",
            "Epoch 50/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.5277\n",
            "Epoch 51/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.5168\n",
            "Epoch 52/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.5062\n",
            "Epoch 53/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4958\n",
            "Epoch 54/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4856\n",
            "Epoch 55/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4756\n",
            "Epoch 56/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4658\n",
            "Epoch 57/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4562\n",
            "Epoch 58/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4469\n",
            "Epoch 59/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4377\n",
            "Epoch 60/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4287\n",
            "Epoch 61/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4199\n",
            "Epoch 62/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4112\n",
            "Epoch 63/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.4028\n",
            "Epoch 64/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3945\n",
            "Epoch 65/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3864\n",
            "Epoch 66/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3785\n",
            "Epoch 67/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3707\n",
            "Epoch 68/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3631\n",
            "Epoch 69/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3556\n",
            "Epoch 70/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3483\n",
            "Epoch 71/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3412\n",
            "Epoch 72/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3342\n",
            "Epoch 73/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3273\n",
            "Epoch 74/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3206\n",
            "Epoch 75/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3140\n",
            "Epoch 76/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3075\n",
            "Epoch 77/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.3012\n",
            "Epoch 78/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2950\n",
            "Epoch 79/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2890\n",
            "Epoch 80/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2830\n",
            "Epoch 81/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2772\n",
            "Epoch 82/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2715\n",
            "Epoch 83/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2660\n",
            "Epoch 84/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2605\n",
            "Epoch 85/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2551\n",
            "Epoch 86/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2499\n",
            "Epoch 87/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2448\n",
            "Epoch 88/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2397\n",
            "Epoch 89/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2348\n",
            "Epoch 90/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2300\n",
            "Epoch 91/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2253\n",
            "Epoch 92/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2206\n",
            "Epoch 93/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2161\n",
            "Epoch 94/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2117\n",
            "Epoch 95/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2073\n",
            "Epoch 96/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.2031\n",
            "Epoch 97/200\n",
            "6/6 [==============================] - 0s 3ms/sample - loss: 0.1989\n",
            "Epoch 98/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1948\n",
            "Epoch 99/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1908\n",
            "Epoch 100/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1869\n",
            "Epoch 101/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1830\n",
            "Epoch 102/200\n",
            "6/6 [==============================] - 0s 3ms/sample - loss: 0.1793\n",
            "Epoch 103/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1756\n",
            "Epoch 104/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1720\n",
            "Epoch 105/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1685\n",
            "Epoch 106/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1650\n",
            "Epoch 107/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1616\n",
            "Epoch 108/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1583\n",
            "Epoch 109/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1550\n",
            "Epoch 110/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1519\n",
            "Epoch 111/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1487\n",
            "Epoch 112/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1457\n",
            "Epoch 113/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1427\n",
            "Epoch 114/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1398\n",
            "Epoch 115/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1369\n",
            "Epoch 116/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1341\n",
            "Epoch 117/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1313\n",
            "Epoch 118/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1286\n",
            "Epoch 119/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1260\n",
            "Epoch 120/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1234\n",
            "Epoch 121/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1209\n",
            "Epoch 122/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1184\n",
            "Epoch 123/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1159\n",
            "Epoch 124/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1136\n",
            "Epoch 125/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1112\n",
            "Epoch 126/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1089\n",
            "Epoch 127/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1067\n",
            "Epoch 128/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1045\n",
            "Epoch 129/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1024\n",
            "Epoch 130/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.1003\n",
            "Epoch 131/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0982\n",
            "Epoch 132/200\n",
            "6/6 [==============================] - 0s 3ms/sample - loss: 0.0962\n",
            "Epoch 133/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0942\n",
            "Epoch 134/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0923\n",
            "Epoch 135/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0904\n",
            "Epoch 136/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0885\n",
            "Epoch 137/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0867\n",
            "Epoch 138/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0849\n",
            "Epoch 139/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0832\n",
            "Epoch 140/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0815\n",
            "Epoch 141/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0798\n",
            "Epoch 142/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0782\n",
            "Epoch 143/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0766\n",
            "Epoch 144/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0750\n",
            "Epoch 145/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0734\n",
            "Epoch 146/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0719\n",
            "Epoch 147/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0705\n",
            "Epoch 148/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0690\n",
            "Epoch 149/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0676\n",
            "Epoch 150/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0662\n",
            "Epoch 151/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0648\n",
            "Epoch 152/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0635\n",
            "Epoch 153/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0622\n",
            "Epoch 154/200\n",
            "6/6 [==============================] - 0s 4ms/sample - loss: 0.0609\n",
            "Epoch 155/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0597\n",
            "Epoch 156/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0585\n",
            "Epoch 157/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0573\n",
            "Epoch 158/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0561\n",
            "Epoch 159/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0549\n",
            "Epoch 160/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0538\n",
            "Epoch 161/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0527\n",
            "Epoch 162/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0516\n",
            "Epoch 163/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0505\n",
            "Epoch 164/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0495\n",
            "Epoch 165/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0485\n",
            "Epoch 166/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0475\n",
            "Epoch 167/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0465\n",
            "Epoch 168/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0456\n",
            "Epoch 169/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0446\n",
            "Epoch 170/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0437\n",
            "Epoch 171/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0428\n",
            "Epoch 172/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0419\n",
            "Epoch 173/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0411\n",
            "Epoch 174/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0402\n",
            "Epoch 175/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0394\n",
            "Epoch 176/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0386\n",
            "Epoch 177/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0378\n",
            "Epoch 178/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0370\n",
            "Epoch 179/200\n",
            "6/6 [==============================] - 0s 3ms/sample - loss: 0.0363\n",
            "Epoch 180/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0355\n",
            "Epoch 181/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0348\n",
            "Epoch 182/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0341\n",
            "Epoch 183/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0334\n",
            "Epoch 184/200\n",
            "6/6 [==============================] - 0s 3ms/sample - loss: 0.0327\n",
            "Epoch 185/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0320\n",
            "Epoch 186/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0314\n",
            "Epoch 187/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0307\n",
            "Epoch 188/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0301\n",
            "Epoch 189/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0295\n",
            "Epoch 190/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0289\n",
            "Epoch 191/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0283\n",
            "Epoch 192/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0277\n",
            "Epoch 193/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0271\n",
            "Epoch 194/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0266\n",
            "Epoch 195/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0260\n",
            "Epoch 196/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0255\n",
            "Epoch 197/200\n",
            "6/6 [==============================] - 0s 3ms/sample - loss: 0.0250\n",
            "Epoch 198/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0244\n",
            "Epoch 199/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0239\n",
            "Epoch 200/200\n",
            "6/6 [==============================] - 0s 2ms/sample - loss: 0.0235\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7fbbf6b1c080>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3oNhW3jNZXuv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 107
        },
        "outputId": "ca3f9319-0340-49f8-ccfc-e50bc0086388"
      },
      "source": [
        "export_dir = 'saved_model/1'\n",
        "tf.saved_model.save(model, export_dir)"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING:tensorflow:From /tensorflow-2.0.0/python3.6/tensorflow_core/python/ops/resource_variable_ops.py:1781: calling BaseResourceVariable.__init__ (from tensorflow.python.ops.resource_variable_ops) with constraint is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "If using Keras pass *_constraint arguments to layers.\n",
            "INFO:tensorflow:Assets written to: saved_model/1/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5-Se4hUqc8Qo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Convert the model.\n",
        "converter = tf.lite.TFLiteConverter.from_saved_model(export_dir)\n",
        "tflite_model = converter.convert()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "suoqi9xhyKUO",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "79e8b3e6-4930-4b53-d9cc-457d5f489604"
      },
      "source": [
        "tflite_model_file = pathlib.Path('model.tflite')\n",
        "tflite_model_file.write_bytes(tflite_model)"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "780"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_-oMJckTybkO",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load TFLite model and allocate tensors.\n",
        "interpreter = tf.lite.Interpreter(model_content=tflite_model)\n",
        "interpreter.allocate_tensors()\n",
        "\n",
        "# Get input and output tensors.\n",
        "input_details = interpreter.get_input_details()\n",
        "output_details = interpreter.get_output_details()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hX8Foj12yife",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Test the TensorFlow Lite model on random input data.\n",
        "input_shape = input_details[0]['shape']\n",
        "inputs, outputs = [], []\n",
        "for _ in range(100):\n",
        "  input_data = np.array(np.random.random_sample(input_shape), dtype=np.float32)\n",
        "  interpreter.set_tensor(input_details[0]['index'], input_data)\n",
        "\n",
        "  interpreter.invoke()\n",
        "  tflite_results = interpreter.get_tensor(output_details[0]['index'])\n",
        "\n",
        "  # Test the TensorFlow model on random input data.\n",
        "  tf_results = model(tf.constant(input_data))\n",
        "  output_data = np.array(tf_results)\n",
        "  \n",
        "  inputs.append(input_data[0][0])\n",
        "  outputs.append(output_data[0][0])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v9zRCyNhynz7",
        "colab_type": "text"
      },
      "source": [
        "###Let's see our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0O_ApRtOytAb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "outputId": "f9acdcaf-d927-40b5-e56b-a9923e17bf61"
      },
      "source": [
        "plt.plot(inputs, outputs, 'r')\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ee/vbG0Pgt791CJgVyIQdgaR7gd9rsuma2pWICEktf/olzQRuB1ZGxLF0+GqSADkFWEPS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\nM7OScxCYmZXc/wGYmd4AfUOYgQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RHbyEVoey2cc",
        "colab_type": "text"
      },
      "source": [
        "Download the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i-mjuJL1zP9u",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "try:\n",
        "  from google.colab import files\n",
        "  files.download(tflite_model_file)\n",
        "except:\n",
        "  pass"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "s4gtirs0yJ_w",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w3tQAv_8ZXuz",
        "colab_type": "text"
      },
      "source": [
        "## Computing gradients with GradientTape"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P74m_hlGZXu1",
        "colab_type": "text"
      },
      "source": [
        "Oh, and there's another big difference with Numpy: you can automatically retrieve the gradient of any differentiable expression.\n",
        "Just open a GradientTape, start \"watching\" a tensor via tape.watch(), and compose a differentiable expression using this tensor as input:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cFYQJ2bdZXu2",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "a3b7b5cc-3d94-4a22-df02-3ccc863f2562"
      },
      "source": [
        "a = tf.random.normal(shape=(2, 2))\n",
        "b = tf.random.normal(shape=(2, 2))\n",
        "\n",
        "with tf.GradientTape() as tape:\n",
        "    tape.watch(a)  # Start recording the history of operations applied to `a`\n",
        "    c = tf.sqrt(tf.square(a) + tf.square(b))  # Do some math using `a`\n",
        "  # What's the gradient of `c` with respect to `a`?\n",
        "    dc_da = tape.gradient(c, a)\n",
        "print(dc_da)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[-0.08683333  0.5939565 ]\n",
            " [ 0.49717274 -0.9993805 ]], shape=(2, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sMP1OI42ZXu6",
        "colab_type": "text"
      },
      "source": [
        "By default, variables are watched automatically, so you don't need to manually watch them:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ju-BxTt8ZXu7",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "119f285b-1215-4391-8cc0-6d3bf98e51fd"
      },
      "source": [
        "a = tf.Variable(a)\n",
        "\n",
        "with tf.GradientTape() as tape:\n",
        "    c = tf.sqrt(tf.square(a) + tf.square(b))\n",
        "    dc_da = tape.gradient(c, a)\n",
        "print(dc_da)"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[-0.08683333  0.5939565 ]\n",
            " [ 0.49717274 -0.9993805 ]], shape=(2, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fp2TCYN3ZXu-",
        "colab_type": "text"
      },
      "source": [
        "You can also compute higher-order derivatives by nesting tapes:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eZV0L0DjZXu_",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 69
        },
        "outputId": "f9f8a189-f32a-4739-e860-1d9602102d06"
      },
      "source": [
        "with tf.GradientTape() as outer_tape:\n",
        "    with tf.GradientTape() as tape:\n",
        "      c = tf.sqrt(tf.square(a) + tf.square(b))\n",
        "      dc_da = tape.gradient(c, a)\n",
        "      d2c_da2 = outer_tape.gradient(dc_da, a)\n",
        "print(d2c_da2)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "tf.Tensor(\n",
            "[[8.0265689e-01 5.5767441e-01]\n",
            " [1.1933215e+00 6.3484907e-04]], shape=(2, 2), dtype=float32)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bkFchV2WZXvD",
        "colab_type": "text"
      },
      "source": [
        "## An end-to-end example: linear regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "h1TtPw6tZXvF",
        "colab_type": "text"
      },
      "source": [
        "TensorFlow is a Numpy-like library that is GPU or TPU accelerated, with automatic differentiation. Time for an end-to-end example: let's implement a linear regression, the FizzBuzz of Machine Learning. \n",
        "\n",
        "For the sake of demonstration, we won't use any of the higher-level Keras components like `Layer` or `MeanSquaredError`. Just basic ops."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yuT94FZ1ZXvH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "input_dim = 2\n",
        "output_dim = 1\n",
        "learning_rate = 0.01\n",
        "\n",
        "# This is our weight matrix\n",
        "w = tf.Variable(tf.random.uniform(shape=(input_dim, output_dim)))\n",
        "# This is our bias vector\n",
        "b = tf.Variable(tf.zeros(shape=(output_dim,)))\n",
        "\n",
        "def compute_predictions(features):\n",
        "    return tf.matmul(features, w) + b\n",
        "\n",
        "def compute_loss(labels, predictions):\n",
        "    return tf.reduce_mean(tf.square(labels - predictions))\n",
        "\n",
        "def train_on_batch(x, y):\n",
        "    with tf.GradientTape() as tape:\n",
        "      predictions = compute_predictions(x)\n",
        "      loss = compute_loss(y, predictions)\n",
        "    # Note that `tape.gradient` works with a list as well (w, b).\n",
        "      dloss_dw, dloss_db = tape.gradient(loss, [w, b])\n",
        "      w.assign_sub(learning_rate * dloss_dw)\n",
        "      b.assign_sub(learning_rate * dloss_db)\n",
        "    return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fgrS18m8ZXvT",
        "colab_type": "text"
      },
      "source": [
        "Let's generate some artificial data to demonstrate our model:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PFSNa3UNZXvV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "c130f02e-950e-48cd-f762-c367baab3b88"
      },
      "source": [
        "import numpy as np\n",
        "import random\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "# Prepare a dataset.\n",
        "num_samples = 10000\n",
        "negative_samples = np.random.multivariate_normal(\n",
        "    mean=[0, 3], cov=[[1, 0.5],[0.5, 1]], size=num_samples)\n",
        "positive_samples = np.random.multivariate_normal(\n",
        "    mean=[3, 0], cov=[[1, 0.5],[0.5, 1]], size=num_samples)\n",
        "features = np.vstack((negative_samples, positive_samples)).astype(np.float32)\n",
        "labels = np.vstack((np.zeros((num_samples, 1), dtype='float32'),\n",
        "                    np.ones((num_samples, 1), dtype='float32')))\n",
        "\n",
        "plt.scatter(features[:, 0], features[:, 1], c=labels[:, 0])"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbbf54bde48>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JiHrfQ+IIMHcCax9EylhE8hjQ2gBH9EIVGjguKdP11EZUaGKc0XWdxb+u4O/v\n/yEhxcnZN5xJy04too795LGJfPvGzyqSRVGEyWLCJEwE/cGw7fYEGw9+Nop+l54acYy+rzPhfuSD\nCESjNRDabVju0gf4AAsIC6S8itBzwZQCttONB0D+i+D9BaQOtrMQyY8htAbo+/uCnhHlHGZEw4UI\nk7EwKoO7wPcnsuDVQsUfDTOG71sYFnXdbzCZrKX6+Ug9H5l9m+G+KcIG9oGQ/ComU+2OLS8uNFEp\n8ypi+9pdjOzx0DFZGElRSJRIu+Loe1E3Ro9vA97JgEA4LgPHJcj9pxl+6Yj5nYiGy5A5I8D3J5GL\nisJwXyAAi5GcE9rLoQeDBqaGUHciHBhM9AeGGdHwH4QpCb3gfSh4u3C+YOGfUmDphane+FINlXoB\ncn8fYoYtilRIGI5IuKlWRrSoOPNqyJzJCwgGlGvlWGDwDWfSslMzfvloBvk5LlLqJbJvawZ+b1ka\nhkiuHPE75GdyMJJEBtaD7w9wXgeu9whXcGawDTIiUPzziR4dIsOt51Au4U+XEOjZkHk+0RU5oLVF\nmJKQgTVQMA7D8j8SO4ay9RL16RVYiAwdQGj1o5/jcPzzQZijTFO4QWZDwX+RsgCRdFfJ89UilDKv\nZHRd5737PufHcdNV+n4tJ6luIne8fTNnXmk0i7ji/qEAvDb8PXasLZvPutsZftp2yiBcWXrANx+0\n1mDtCf55HPKbB8E7HT03CXAQHkoYi2ivCd7i3x7M7dEDWyHnESKtZYxzJ90Ntv5woJgWdcFVoJ1R\nChlLo7KC4HoPmTgCIUrnvjkcKWWNtOqVMq8A0rdn8PNHf7B/xwG6DehK///0wWozFm0mPv8tP73/\nu1LktRyT2USX0zpw8oATIvY1aFEPi81MwBfbDaGZNSxWM8FAEM2iccFNTjRtfZSRPiP7ESuHFPlh\n+zyTwH4eeH8jurItJ77p4JsWe78AIZJAa43ECbiiDCp055QGW29K55sKIQNrENboPXGPREo/Mv91\n4+cl3UhLV0TykwhL7Ci06obymceZpTNW8uTQlwgGQwT9QewJNhq1bMCb85/HmeTgwpRri62MqKg9\nmC0aTdo25qNVr7F99U7WzN9AvaZ1adWlBbeecG9E27/DsSfaueSu80hIdtJtUFfaHvcPMu8FSmdh\nH4FoDtbjwPe3sfAp3VRelxMbosFMYxHV/SPk3R85RDsOUX9qqa1h6ZuHzP4/DNdNMT+PlHGYHKUr\nCaxn3wW+WYQ98IQDUW8qwlx9mukon3kloes6L177Jt7DolO8Lh97t6Qz5fVpDHviMqXIjyGCgRAH\ndmVy3xlPsnHpFgA0TcORZJp3WFYAACAASURBVOeud2/l/fv+R25mflFTisPxFnj586t5fLb+TcPv\nrdeD/JeOrhCb3AW+A+C4FKx9If9l0LeX8+pKgxmc1yIxQXATwj4QKV6G3Ccp8sFbTkTUebP0ijyw\nFpn/AsbiqgScQLSoGQ1haV26OUP7wDeTCH+/DCBdHyNSno56XHVDxZnHkZ3r90QtqOT3Bvjzq78J\nBkq5uq+oNXgKvKyetx6f24/P7ced7yFrXw6TX5vGV3s/5L1lr5DauE7UYzN3Z5G+vTAcUCRCuWqG\ne8HzHeg7QMYolhV3dHB/Bhm9kQcuQu4/1SjI1XCJYYk3mIWp3pcIrXSdx6Sejcy6BoLrMZR5iOiu\nIxOYuyHM7UonZnArRPWtByG4pnRzVAOUMj9K0rdnMPe7f9iwZHNRk2Wbwxoz+cfmtGHSTFHbfSlq\nN0da3lKX7Fy3m62rduBz+wnEiGrRdR2TZtyi0vUBeGeUUxIPFHwElq5HcezRLAjqHApP9ANecL0L\n+09Fuj6lrK8Z0v0tyCMNIh2wG2UFMAMWsJ+PqPt+6Sc2tzYaXUTuAHPN8ZkrN0sZ0XWd1259j5lf\nzsViNaOHdJq1b8KLvz6O1W5BRukEZHNaufD2c9A0jQHX9OP38bNVx6BjnGAgxKhTHsFqt+DOi+73\ntdgsNGxRGK7n+oC4LGDKrKOsmR+vtTUJ5IFnCtL7O9T/KSIkUUod/IuMuunWbgitsbEjtI3oPwMd\nkTgC7ENAWMscwSK0xkj7QPD+QZirRVgRCVVQavgoUcq8jEx991f+/GoeAW+gyKLavnonL133Nhab\nGT3KgrLNaSM7PYfNK7Yx8s2b2LZqB+sXb6ls0RXViIMtAI/M9DycxFSjBITumW40K44LEgJVVEI3\njBBIF9L9GSLp0KKoDO5AZl0HMrdwQwDpvAqR9CiYGsWYy480t8VkilGetxSIlJeRpjfA86WxQGw5\nEZE8ulotfpaEUuZl5Pu3p0ek3wcDIZbPXImuy6gWd96BfD4f/RVfPDuZs67phxSizNl/imOPdie1\nRvdMg9yHq1qUCsIPvgXIRA/SNdFoDRfchGEdH3ZzeL4Ga3cIHYgxj4bQo2TAlgEhLIjk+yE5SrRN\nDUEp8zLiyY/+SixMAoopdRoKhggFQ0z/eEbMMrmKYxFJ51NcdDjZTWa6hXnTU/B7TdicVq4bfVph\nKF8tLvkgg8iMc0E/QMzrlB6jYUVMK9laWHfm2EYp8zJy6pAeTP90JqEjUvFTG6fSqnNzFv+6olh/\nuFLkNYu09l5ufGQvnXq6yMkwM+mtRsz6LrVUx9ocOs7EELlZZvTQoQVEYRJIXWK26Dw7fisdu7sx\nW3QCAcGdL+7ix8+P48Kb80lw3lb7lVSolNEiod2IxOFI7y9RCniFkJZTjMqO7q+Mn5n9LETivaWO\nlKkNqKShMpK1L5sR3R/ClePC5/GjWTTMFjNjpj5M03aNubP3o7jzPOXu+aioepq18fH29A3YHToH\ni/F53IKJrzfi63Gx/LdgsemMem43Z12SjQRceRrvP5XGwpkNSanrYeCwvnwzdjFDrt3Ftfftxe48\ndA9KVGX76GhQ91twvQP+OYUK3QRYIek+o2aLbx6HFkg1MNVD1J9eVNGxNqCqJsaZghwXP3/4Byv+\nWk2zdk0YOuocQkGdb8dOY92iTaRvz6AgK1rasqIm8cCb2znjohzMR7y/elwmrjihM35v9Mjeh8dt\np885udgch+4tv9+EFI2wWjIRCIJ6AwpycqlTN78iL6EWIcBxJSL5KfDPQXp/NapCOi4GLMjMS4mM\ndHFA0gOYEoZFTldDURmgcSaxTgJXPDCUKx4wCictn7WKxy94kYAvoEIOaxGdergjFDkYpb6bpPnZ\nviGytVly3SB9z83Fag83kiwWHSH2Fn02m3ZRp66ywUuPNLooAcJ2OsJ2+qE9nu9BmKIEFHggsASo\nPcq8OFTSUDGEgqESszallPz3lnfxuX1Kkdcy9m6PHq+sWSRZ+yO1fFKqmStG7kczR77tRs9WN6Fu\nwTIQWInMuRMpdaMwln8JMrAGaWoe4wBrYWciAxncgXR/ifT8iNRrXwN1ZZlHIXt/Lm+M+IAFU5cg\npeSEfh2554PbaNauScTYnIw8MveULyxKUT358o1GdO7pCvNp+zyCuT+lkJ8TfutYrDrjpv9Lw+aB\nqIpbymgKPYTykJcFP/hmI/NfMaobIjAyQOuAqQGEdhPWEENYwHEF0r8C6foMfL8DJqPNHKOhznsI\nW2TnppqKMguOIBQKce/pT7Bg6hJCwRB6SOff2Wu4s89juPIiC/rYE2xljhc321RKf01g5YJE/ntP\nC7L2m/F5BT6vYOa3qbz+QGQbwCvvSo+pyCGWZQ4q2aCseMD9qdGDVBYYC6FyL+h5YO2D0R/UDFp7\nSH4esv6DzLoWfD9RVFJAuowytzm3I2tRtFC5LXMhRAvgf0AjjG/mB1LKN8o7b1Wx9I+VZO7NDqux\nInWJ3+Nn5sS5XPB/g8PGOxLsnHpBdxZMXUKgmGy+w2lxfDO2/rsjrnIrKobZU1OZM60OdeoHceVr\nMRc9B1+RXYzCVsSXI92ZEvAjEq6DOm8CARCJyIwzQN9PsQ9M//xyFjCrPsTDMg8C90kpOwGnAiOF\nEJ3iMG+VsHvD3ogYcjBK2a74c3XUY+77aAQderXH5rRiT7SXWM5z28qdcZFVUTlIKcjOsMRU5AA2\nh2oBWKXIINL9JXinApqx8CkLKPHNR5aldV/1ptyWuZRyL7C38P/5Qoi1QDOg5tSOPIzWJ6ShmTWi\n9Tz865t5zJ48H00zcdolvbhj3C0k100iISWB1/56hh3rdpO+PYPWJ6RRv2ld/vhiNq/e+A66roOU\nRQlDVREOqig/nXq6OO+aTJxJIeZMq8PsqXUIBQ8+uJVZXrX4wDcD6ZsH+a9A4ihK/J3IIFh7V4p0\nlUFc48yFEK2A2UAXKWXeEfuGA8MB0tLSum/fXhnF8cuOlJJRvR5m8/JtJbZ2q9+8HhO2jkPTYvvA\nXbkuXr5hHAt/WVZsUSVF9eby29MZdm86VrvEZDJizTevTuDxa4/Hkx/gp+3/Yraoh3S1wdQa9N0U\nXwrBCrbTEEkPI8ytKkmw8lFcnHncFkCFEInAFODuIxU5gJTyAyllDylljwYNqm+KrRCCV2Y8RaOW\nJct4YFcmc7/9p9gxzmQny2euUoq8BpNSL8C196djdxqKHMCRoNP5lBCv/XoaVz96CX5/6VL8FZWE\nvgcc12H0Rj2IpfDzQePLD75ZyMzLkKH9lS5ivImLMhdCWDAU+RdSym/jMWdV4kxy0PWMzkbxrBKY\nM2VBsft3rNuNO0ZxLkXN4MQ+LoKByO+CwE2b4xZxw+ONcTasOXWvjw1CEPgHw9VSGM8vEg/tK0KC\n9CLdX1S6hPGm3MpcGKt9HwNrpZSvlV+k6sG5Nw/AareUOC4pNXbdh/zsAu49/Yl4iqWoAjwuLbb3\n1T8fmXMvuN6MPYGoPbVBag7WwoxRH0b0iw4yj3BFfhA/+JdVqnQVQTyShvoC1wIrhRDLC7c9KqX8\nOQ5zVxmdTj2O65/+D589MQkJ0Vt7CbjsvguKPkopWTR9OVPf+xVPgZfkeonFdmBXVH869j6OW8fe\njNl2KRCt3o4eY/thSJWbV/lIIho0R1XkAGawHFfB8lQ88YhmmUstXcq//L4LGXRdf5bNWMkP435l\n7YINRSn7QhN07NWeae//zqlDutP19E588uhEvn/7lyIFbtJMKsW/htOui5eWje+GYHmqYFZWA2WF\ngSNGrZaDWAiLVhMWhPO6SpCrYlFVE0uJlJKlf/zLrElz2bd1P2vmbzA6CwVD2Jw2ThpwAkt+WxGz\nOa+i5lG/qeTzBRswm1U54xpF4qMQXG10Ljo8vR8B5q5gaQueacY+83GI5GcR1hOrSNiyoUrgxhFX\nrosrmg7H7wkPebLYzAiTKWK7omZRt1GA6x/YS6+B+ZhtkJSiopBqHIn3gpYGuQ9iKHMdsBsNmut9\nhTC3RcogEESIyMqX1ZlaUQI3FAzxz89LSd+WQfturenct0OJmZYVwdI/VmK2aPiPCFAJ+IKFyUaK\nykHS7gQPyakh1i934so7+p99ct0gesj497/fbSY5NYi55LVvRXXFvwz873IoxtwEwgb1vkeYmwEg\nhJkapP5KRY24mv07Mri73xMU5LgJ+oNoZhPtu7XhhemPYXPYKlUWzRJbaajMzsqhUXM/z03cQv0m\nAUIhsFgkn7/cmCnvNyzTPO27unngzR00b+PDpB2qbKhqrNRkTOD/m/BkIR2kF7zTIPG2oq1ShhCi\n9hhgNaJq4kvXv03mnmw8+R4CvgBel4/1izbx5fOVH9LefVBXZIzGzWqxszKQPDthC01b+XAk6CQm\n69gckuvu38eJfUvftSe1QYCXv9lMy+N8aGZDgZtMSpHXeLSWhhUegc9IEJIS3TUBfX9vZHpH9P39\n0T1TK13MiqDaK3NXrstYbDxCUfq9AX797M9Kl8fmsPHUtw9gT7DhSLRjdVhLlVykiA+tO3pp2CyA\ndsQ7pdUhuejmAwBoZhOOJDua+dDX2+bQGXhZFlfdlc5Ft+znuYmbsTvVw7fWobsLC2xFwdQA6R4P\nBa+Anlk4fi/kPma0oavhVHs3i67LmC3tDy9TW5l0G9iVSbve58+v57Fz7W5+fPdXAj61UFYZJKaE\nCEX5tZtMkFLX+B2YzBoJKQmAj96DsjmxTz79h+aCBJtTL7K+lRVeC5HpsfdpraFgHMgjM7K9yPzX\nEfazK1S0iqbaK/Ok1ERadUlj8/KtYTrdYjXT//I+lSqLp8DDhw99wR8T/sLv8RsPGgEypHzllcWG\nFQ7MUdqyeT2Cv39JAYwEr6B3H+//sZGk1BDORD1Gpx/FMYX7U2IW3grtrlRRKoJq72YBePDzUSSk\nJGBzGr4wR6Kdhi0bcN3TV1SaDFJKHjp7DNM/mYkn30soqCN1qRR5JePzaLz3ZFO8HoFe6CXxegQZ\nu638PKFe0bjbntpDvcYBnImFSV5KkSvQQCRE32VuWbmiVADV3jIHaN0ljfFbxjFj4hz2bNpHh1Pa\nc9olp2CxVl782LqFm9j673YCPpUUVNWYNDCZJHoIdN34/0djmuBxHYpM6H12rgovVIQjBNjPMxpY\nhLla7Iik+6tMrHhRI5Q5QGKdBIbefk6VnX/76p0q9LAakNbey21P7sF6eMCCGR4et52rT+6Mu8BQ\n6FIK4tFfU7lnqiGmRqAX4xuPhQwhkh5EWnqC6w0I7QOtJSLpQUQtaB1XY5R5ZeD3Bfjpg9/5Y/xs\nLDYz5986iAHD+mEymWh+fNOjVg0Wu0Wl+ceJAZdloUXxmUtd0GtQHrO+M+qK//l9CmdflU0xfUNK\nhVLk1QxRr7CvZ2kwYag4EyAh+QXQsxD2MxDOoRUnYxWhlHkhoVCIBwc8zablW/G5jUWSzcu3sfj3\nFTwy/k469zmepq0bsW112ft3KkUeP+wOHVMUBS1MEpvjUKjhgb3WCEVcGitbWeLVHJlH6d+4LJA4\nEmFKRup+yB+NJGT0C7WdiUh5EWGK4UOvgdSIBdDK4J+flrLl3+1FihyMJs5/f/sPW1duRwjB63Oe\nxZnsqEIpay8mTdDu5NYljpv3awo+T+TX1qTB4llJRZ/PuTqrqCvQQUqjpJUir+6UwTASGsJ+jpFI\nVPAayHyQboo6DOU+WGFSVgVKmReybMZKPAWR1fEkkn9nrwUMv/37y18loY6zssWr9WgWjc0rtpU4\nbsXficybnozHZULXIRQEr1sw4bVGHNh7qEWYM7FqchAUlUEp1ZYMgtYI6foAOPLe9oPvL6SeVS5J\npPSh57+Jvr8fevqp6LlPlnvOo0W5WQqp1zQVi80SEa2imTXqNEwp+ty4VUOmZHzC3G//YcYXc1n4\n85ISGz8rSibgLW3SleDlO9Lo1r+A04fk4PcKfv+mLhv/PfSAdSaGsFilcpnUWnSMPp4mjIYTMe4/\nrQ1COJChPdH3CwuEDoCp7lFJIaVEZg8H/1KKGmF4vkH650D9nyu9IqNS5oUMvLY/E56dErHdbDHT\n+4LuYds0TaP/5X3of3kf9m5N5/u3fmHOlAVk7cshFDAsQrPNTFBlhZYJkybp1i+fuo0DrF2cwM5N\nUW4GAUjB0r+SWPpXUuR+YOBlWUikUuS1mhBgAeuF4P+JSMsbsHQx/rWeAp5dRHYakuWLLw+uBP9y\nwjsaBUHPAs/P4Lzk6Oc+CpQyL6R+07o888NDPH/1WPxeP1KX1GmYwtPfPYjVbo15XJPWjWjcqiH5\nmQVFihwACY1bNWDftoxKkL7m0zjNx6vfbsaZFMJkkggTzP81hZdHpaHrgk49XFz7QAadempsXm1l\n9g8aOzda+XdBIgGf8drdpgsMuDSTIdfuwa6WNo4BvGBOAX80y1wD71T0fdPA2huwAx4OWfEOSLwH\nEbUoVykJrCHqYqx0IwPLEShlXmV0G3ACX+35gC0rtmO2mmnVuUWJNdOllEwYMxmvO7zfYNAfJKRL\n1TqulDzx4TbqNgqEhRL2HpTLOVdnkpnu4LH3t2C1GXVVOnWHTt3A6zGhh+D5Ee1o2CzAHS/sBvzK\nIj+WcH8CwgkyhOF2Ofg2fFgPUP8cMNUDywAILAatISLhNoR9QPnOrTUDoUXR53Zj0fUwpDR89OgZ\nYOmGsHQo37mjoJT5EWiaRvtubUo9PhQMkZ8ZvfRq1t5sLFYzvnJ2HzKZTei12C/foKmfFu19ETHh\n9gTJiOdtaJoXTRy6flH4l6Ow6uHTn24tLGOrQkCPSaQbsIFtECDBN4Nwt0sQZL4RX576avzOa+1j\n+NtDXsJcOMKMcF58SLzgZmTWMKOmujTGSduZiDqvxbWeuopmKSdmi7mwQl8kAjjv1gHYnOFuGovd\ngs1hxZ5gw2QS2BNsdO57fMxz6EG9lrbMNrDYJDLGs8pq1dFE8UWQzBZRJV2nFNUJHwRXIyydCO/7\nWYh0I4Mb4npGITRE3YmGTx4zYAFzB0TdLxCHLarK7FGGH126MB4yXvD9iXRPjqs8SpnHgaR6iVG3\nC5PgnJsGcO7NA7A6rNgcVhJSnPzfq9fx1d4PGTryHOo3N4pDufM8NEyrF3UeIB6Z6dWWPVut5OdE\ne0m0GrU0REmhoCoMUYGRnm9uHb05hXCCqSnS8zPSMw2p58bllEJrhKnu54iGCxEN52Gq/yPC0rFo\nvwzuKKzIeOQN7AHPpLjIcBDlZokDiSnRlY2maUgpGfnGTdzy4jXkZRaQ2igFs8XMyjlr+f6tX4pc\nMFtX7sBiM2O2mgn6a3YUjGYxrrv0riHBS6PSeHb8VjRNYrXLoptPJN6CFAJcn2AsYEVDfY0VgKU9\n2M4odH34OGShF6b15z+HFIXfFRlEJo/BFKe0fmGKbtCB34iPjWaMyfg2f1d3QRw465p+bF25g2Ag\n3EL0efxF3W5sDhuu3P18+cK3pG/PYNvqnRG+9IAvSGJqIn7Nj7+cfvaqRAhB/8t78/d3C0u9XrBy\nQSLDz+jIKz91oXFqAGHtDfZzEcIKiaOQsgDcX2JY4Yf/nC2AHUwJxuISgpg1qxW1GDOIOuD7HVIn\nQP5z4JsJSLD2Av8SwAfysECFvMeRth4IrVnEbFLq4Psd6fkRMCOcl4D19LK787Q2IJKiNMSwgePC\nMl5j8YiqqATYo0cPuXjx4ko/b7wJBUN88thEvn3jJ4L+6K/6Xft34r+znmbeD4t4/pqxBHzB4qNb\nhKEMY/UZLZFSFgu0OW0k10skY2fm0Z2nBFLqJzFi7I28Pvx9pJQxH05Wp5VOpx7HqUO6c96tA3Ek\nhMeWy+AmZN7LRhSCSAb7RWBpB97p4JsHHNkizEKZUr4V1RPRAKyngy8y9+MQKRhvawGKEoiEE8zt\nEXUnYHwXAM9XyLwXiIxFtyAS70Yk3hq2VUqJzLnLiD4peht0gONSTCmjy3wp0r8ImX2rkZGK35BR\na42oNxEhyhZDK4RYIqXsEW2f8pmXg/fv/x/fv/1LTEUOsHLOWgL+AK/e/A4+t7/kMEXJ0SvywuNL\nQ8vOzRm/eRzH92yHSQv/GljtlnIvKOZnuxhwdT++Sf+IRybcidkauWovTIJe53XjlT+e5NK7h0RR\n5DuQmZeD/y+jr6O+B9zvQv5r4JtDpCIHpchrCclPQWhLCYO8GG9hkqK3NemGwHqk+xuEMCGEyYgi\niZolGkJGWMwYhoP/cEWO8X/PZGRwU5kvRVh7Iur/Bol3gONqo8BXva/LrMhLQinzo8Tj8vLTh3/g\n9xSvPMwWjZ3r91Q7P3j6tgwQ8MwPD9K4dUM0i4YwCUwmQZd+Hbn03iHlmr9Z+yYAOBLsnHZxL4aO\nPLeoU9RBrHYrwx6/rOizlBIZWI30/oYM7kK6Piy8EQ9/QknQdxHbf66o8YhkI/IjuK6Egb4Y272Q\n/6xRL8X1KdLaj+jhYFaE/cyIrdI3O4pbBEAH39wSZIqO0BpiSrwNU8pTCPs5CBH/zilxUeZCiHOE\nEOuFEJuEEA/HY87qzu6Ne0seJOCsq04jMcVZZc2nY+Fz+1g1Zx0+t5/cjDyjBZ4u0XXJ6r/X0aVv\nBwYOO73oHjBpJsw2c4QVH4szr+wb9nn4K9dy/VNXkNooBc2i0aFXe17+YzRtuhrJFVLPRmZeisy6\nGpn7MPLAueD9BRWpciyhAQ5EysuG77tcD2xpNLDIH2tEjTiuBw4PEbaB42KE5YTIQ0XSEWMPYi7c\nVz0p9wKoMKLexwGDgF3AIiHEj1LKNeWduzridft49eZ3mPf9QgIl1F5p3LoRt79xE84kB627tmTj\nki1hbhZ7go1QMFTiPNHQzBqhUOioQxZ9Hj+uPDefjZ6EJ99jNKc+uM/t543bP2TSrve5+cVrWDx9\nOVa7hV5DunPv6aPZ8u/2Yuc228yc8R+j2baUkh/GTWfSi9+Rm5FH664teeLr+zihX8ewY2TOw4WW\n2GE/C6lcJscWDrC0R+Y+bbjV4oIH3JOMKJcw61waHYuiIBwXIgvejrJDgn1wnOSKP/GwzE8BNkkp\nt0gp/cAkoPa18Sjk5RveZt4Pi4pVwJpF47xbB/C/jW/hTDL8Yk9Ovp9m7RtjT7TjTHZgsVk4f/hA\nBl13BhZb+DPVbDXTuE1DzFYzmkWjSZtGpDRIxpFk+JQdSXZSG6XgTCyFzy2G61vqkoZp9Vk2Y1WY\nIj+IK9dN5p5s6jetyzk3ncVZV/cjIdnJfR+PwJFoj5D5IDaHlVPP706L440Igf89/Q0fPfwFmXuy\nCQZCbFyyhUfOHcP6RYd8j1IvAP9cIpM9am/WqyIaBRBYBnIvED2rOjYWYmfWmQoXMw93y/jB9Q4y\nFPmGLbTGkPJfY6FSJBb9EXXeR5hqsWUONAMOb7+zC+h15CAhxHBgOEBaWlocTlv55B7IY8HUxVEV\nuUkTdOjZngtuH0zfi07BcYSibdC8Hh+vHsu6hZvI3pfDcT3bUr9pXdz5Hrau3MHWldsL41ElLTu3\n4KXfnsBiNeP3Bkisk4DfF+Dv7xaya/0eWp2QRuc+x3Ftm5HFypuYmoDFZiF7X07EPovNjNlixmKP\n/hXQQzoJUeLnj+velk/WjmXae7+xfe1uWnVuwa6Ne1n6+7/YnFaG/N9grrjfCLnyeXxM/u+P+I6o\nW+Nz+/n8ya94/ufHjA0ylu8TSh2eozjGKPxeCCeIhpB0l5GLEFxF5PfFT3TDQDMW0p1XROwxOQYh\n7fPBvxAwg7WnESZbjam0OHMp5QfAB2CEJlbWeeNJzv5czFZzVGXeuFVD3pj3XLHHCyHo2Kt92DZn\nkoM3/h7DuoWb2LF2Fy06NKNjr/ZF0SQHKzZabZYiP/TGpVt4+YZxRfW6D48uNWkmLrrjXC6/70Lq\nNU1l4vNTmPjct/iPaF2XWCeBxq0bkJMePRPu+J5ti94qjqR+s3rc8OxVxV4rQOae7Jj7tvy7AwDp\nX4F0/0hshV0jvyqKCscEtsEIe3+wn48QNqS5NTLzSsJDEK1GkS09yhqXMEExESVCOMDWP+6SVxTx\nUOa7gRaHfW5euK3W0aRNI6KF5Zs0Eyec3ils25xv/+GbV38gOz2XHmefyDWPXUr9ZtHT9Q8q+SMV\nfTQ2LdvKPaePjrB2TZqJLqd1YMTrN9DupEPt1y6563z++no+e7ek43X5sFjNaGaNh8ffyaq56zEX\nWv9Hklin/L0R6zZJjRlmmdaxKXr+a+D6jNiWk0IRCysi6V7E4fXIzR3BcoIRWlhkBARjN4CWOtgi\no1lqKvHwmS8C2gshWgvjPeRK4Mc4zFvtsNqt3PTcVWEhdiaTwJFo55rHLi3aNvH5Kbx8/VusXbCR\nfVv388tHM7nt5AfI2hfbUi0tnzz2ZYQiB8PCf/n30WGKHMCR6GDcohe5+73bGHR9f/7z0EV8vOZ1\nug3sWmypWLO1/M95u9PGhSPPjghJtDms3PJcX3B9imFFKUWuKCPCapSgPRz/bAiuJvxtTidmRJTz\nymLS8Gse5b5jpZRBIcQo4FeM2KJPpJSryy1ZNeXiO86jUVoDvnzxOzL3ZNG1f2eue/JymrQxVsbd\n+R4mPvdtWBp7KBjCnedh8mtTGf7ydeU6/4Ylm6Nu9/sCZO/PpX7TyBZYFquFAdf0Y8A1/cK2n3B6\nJ0SURSN7go2zb4iPxXLLi8NwJjuZ/NpU3LlumrVvwu1jb6R956XIfKXEFUeDA5IeR4hw9SW90wvL\n4ZYGC0KLHs1SU4mLz1xK+TPwczzmqgn0GdqTPkN7Rt23bdUONIsWESIb9AdZ+sfKcp+7UVp9cjPy\nou5Lrls2K8Nqs/DEN/fy1CWvIKUho9mq0f+KPpw6pHvJE5QCk8nEsMcv45rHLkUP6WhmIxNUulZj\nPPtV+KGiFIiGYEoBrSkicTjCGu3+c2A4G0pjJGhg7VvysBqEKrQVZ+o2SY2a7SkENGrZoNzzDxt9\nOc9d9To+9yHL3+a0dUffHwAAGQZJREFUct4tA4ttbxeL7oNO5Itt7zJ78gIKclz0GHwi7U5uXfKB\nZUQIUaTIAbCfDfkvxv08itqICRznYEp+vNhRwnkJ0jOZyBosGkYSUKGFJRxgvwBhid1DoCai0vnj\nTONWDenU+/gIn7PVYeOKB8offt/7gh6MeutmkusnYS1scnH+rQO57dWjd98k10tiyG2DuPKhiypE\nkUdDaA2MolkKRYno4PkeqWcVO0pYukDinYC1MGQxwfi3zkeIOi+D7SywDUKkjEUkP1s5olciqmpi\nBVCQ4+L5q99g+axVmC0amllj1Ns3M+DqfiUfXEpCoRC5GXkkpiZitcW/zkNlIL2zkDn3AKX1cyqO\nXeyQOAJT4ogSR8rQfqOGiskB1v4IU0nNTWoOxVVNVG6WCiCxTgLP//wo2ftzyc8qoGnbRpgt8f1R\na5pG3capcZ2zsjCa285ChjJA2GMsWpkxMkIPPqhMhX8CRG0LpqjleCGwqlQjhdYQnJdUsDzVD6XM\nK5DUhimkNkypajGqFTKwHpl1LRAobG4bq5CWBEsvCPyLobyLyxJV1EwKOwChUXJRLSuY49/Rvjah\nfOaKSkNKicz+P5A5hzW3jRXNIiGwgkPNBxS1CztYe0K9qeAsKZtYgLAinFdWimQ1FaXMFZVHcD3I\n0iZO6URGJShqFg6o8y6YT4qyzwuBlZAzwki3xx5lDIAASzdE3a+MRfMKQgbWI10fI92TSlxora4o\nN4uiEgkQu7KdotYhEhBaC6S5eWEBrCPWOqQbQjshuAWE+YgyPCYQdaHBDEym+HbkCRNBSmTes+CZ\njOHyM0Pe85D6FqIG1WUBZZkrKhNzRw4taCpqPfIAMvMi8E4j9qJ1AHx/GD07tbYY8eBWMHdG1JtU\noYocAP888E7hkMvPA3iROXfx/+3de5TcZX3H8ffnN/fZa24QIUAiIC2KEYhRpOVSEAJSoNBWuViC\nUo+iohQFBE8vVquARaS2CIqiFesFyAFvgBGO2hYRCKRIvdRqRRCSkIRssrfZmd+3fzyTbDY7Mzu7\nO7uzO/t9nbMnuzO/y/eXnf3OM8/veZ6v2ez6ZOgtczdtpCR0X49tvRjvQpkr6hl5JJQ6FC36Dlba\nACRRovKidI1m/WuqlIiLYPAhqFBWbqbylnkLKhVLbHpmM4P9M3AESPJASL682VG4GSMJ2eF6s0rs\nPW2JPKg19X92rR3kLfMW863PfJfPXnk7QwNDGHDyhcdz8SdWN3yc+3hZvAXb+q7yCBUfneIAIkgs\ng8Qy4k0nQbw53OzseD9KvWxaIlDudGzwgQpzHUqQPmpaYmgUT+Yt5D/veYSbLv3CiCVy77/tQSR4\n9z9d1LS4zAzbfC6Ufo0Xm5gLEtQuxC1IHBCm3hd/CTs+zq5x5oUfYFsegQVrUHIalpZIHxvWCeq/\nlzCXIQlE0HndrJs56t0sLeRLf39HxRJt937uwaZ1uZgZtuV8KP0KT+RzQRpSryKsYFjp6WOg41o0\n/zaUOT6UehsxYcjABrAd/zINsYYF4NT5MTT/i9D2zlDwYtH9RLmZW7i5Gm+Zt5BNv32h4uMCejbv\nYNGSTMXnp5L13wlDj0z7eV0zJEPx49IGSC6D4v8SutTKbUbtBYVHYOhxbPsgZP4AlKjwHh+Xu+Om\nhyRIL0fp5dN2zqngybyFHPLqg/jxt9eNKm2XyqaYv7h7WmOxoaew/m9B31en9byuGRKQWgnFJ8C2\nhK84BVE3ZM9CUSdWeAgKDxOWcSjvNvgfVB3tkjig8uOuKu9maSEXfjiUtNu9HFwmn+Evrzl/5Fri\nUyzefj22+Rzo+xywfdrO65ok/XoYemyPIX5DEO+AxELInzecyEcYJIwr33P2Zxa1XzyVEbckT+Yt\n5MDlS7nh3z/MyjccSfdenRx85Eu56svv4dSLTpy2GGzof8pFmr2255xRuJfKI5T6w6ScmnMKMpA7\nM/xLCqLFqPsTKH34VETa0rybpcUcuHwpH77nymk7n5WexXquhcIPQwWXxEH40MO5qNLN7SQkloDm\nQeIlUHp6j+cTkD2OqOtDWOcHw+Jr6g592G7cPJm7CbP4ReyFs8C2ATHYDoi3Ur1FrvKXt9jnhiTK\nnxeSc9dHsS0XMbwefSas3dJxKQBSGjT+sodumCdzN2HW99VyP+nuybnW9O0cUCC04nyYYuvKEpat\nFfbCGVjmdajjarTwHqzvX8PCWukVKH8OimZngZWZyJP5HGNWCglYbZP/ODv0BJX7Q1OEpL5nwt45\ny26sSSWu+US4OVlkXL+raB9I7BuKilh5bsPg97HCOrTovjGLMruJ8xugc4RZTLzjU9jGFdjGldim\no4n71kzuoMmDCH/wo85G7dURPZHPfEYYbTKe31UW8heEdcpHVIaKw0Sgvq80NEI3kifzOcJ6/xl2\nfKZc4acI8QvQ8zfYwNoJH1P588I61COkCa26wiSidbORuj+OkosrvCYABstJ3k0VT+ZzgFmxwrRp\ngAFsxycnfFwlFqP5/1pepzwBpCBzQijS7FpUla651OEoexIkloJVum+ShuQhUxnYnOd95nOB7QCr\n0lIu/W5Sh1bqMLTwbizuBaWQ0sRbL4bB71H5JqcI9R9XlieS+Lrms0cEtBFGpBQIN74ToT5n59UA\nKPX7WOrl5Vb4bq85pVH+3GmPeC6ZVMtc0nWSfibpvyStkTS9c8ZdfdQZ1syoJHnwuA9npWeJt76X\neMMK4o3HEO+4GZQJw8sAdby/fL6dbYUISEC0P2RPgXk3Q9vF0P52iJZQud/dzQzl3x1JSC1HC+9E\nC+6E7B+HlnbudLTgLpR65a49NO8zkDuN8HuNIPUqNP/LKLFXk65hbpDtuZDHeHaWTgIeMLOipGsA\nzOyKsfZbsWKFPfrooxM+rxu/uO+r0PMRRraEs2j+rSj96rqPY/EWbNMpw2PLy8cheyJR9/XD25We\nw3pvhcLjkHwparsIkgdhPX8H/XeFMcVWCIUJCush/t9GXKZrtNy5qON9ACiq0iCowiwG4lBhyjWE\npMfMbEWl5yb1v2xm9+/244+AP53M8dzUifJvxNSB7bgR4ucheTDquHxciRzA+r5cXsh/97HlAzBw\nP1Z8BiWXAKDES9Aew9DiHTdB/91AYbjbZ+DbhJafm3nSKLnfuJP4TlKE35abPo18y3wLUHWJPElv\nA94GsP/++zfwtK5eyp2KcqdO7iCFdYwcdrbrCaz/HtRRY4Gk3tuodBO26k0112QG2Tc0OwhXpzHf\nNiWtlfSTCl9n7LbN1YTZBbdXO46Z3WJmK8xsxaJFixoTvZt+yYOo2gbo/TQWb6u+r1VbQdFng85I\nykHk/dyzxZgtczOrueSepNXAacAJNpkOeDcrKP9mrK/Ke7YEA/dC/o0AmA0RXmJDEG8KhZyLlYoO\nlIc1+siWGaYIxZ9B6tBmB+LqMNnRLKuAy4HTzUZVRHUtSMn9IHt65SdtCOIe4r57iDceg214ObZh\nObbhcGzTqSExVJNaSe1Zo67xUtB5I6irxjbeBTZbTPbuxKeADuC7kp6Q9OkGxORmOOX/jMo1HpMY\nEfR8MNxkBUJre4jQV16tDmkJhn4MmSpvEq4OyTq7RFKgPJCFrhuI8qug4/1U/H2qA5K/1+A43VSZ\n7GiWgxoViJtFUodD5lgY/D67bmgqB9lV0P91JtZdMgCDdxNagt5bN27KoK7rsG0fgfgXNTaMoP1K\nlFuFojAtRLmzsMEHYPAhwhtvGhSh7k/52uKziA8AdeMmCbpvgMH7sL414Q8/dxZkXo9teOXYB6iq\nCNEhEP8Pvub5eAnSR0LHxbDtvTU2S6HkPrsSORDGgXffBEOPh4LL0QLIrprwkETXHJ7M3YRIEWRP\nQdlTRjxuyQOgWKtlOAbbiBauhcG1WKkH+j5N7TXSHeRQ941Y8Vew7bI6th/9Rhkq1B8Rvtys5CP6\nXUOp4/2MLtA7DlE3Si5BbauJOi+BaO+GxdZ6kpA+Du31YFjrZsv5jL1kbQzp10xHcG6aeTJ3DaXM\nsWjejeVaoKlwUy5xMEQLIbmcMUestL975M+Z19d34sQB4Yad8uVzZGjtl3c7tF+C5t2MovkwsLa8\nvHEtCdT1j0iVbl672c67WVzDKXMcWnRcxefi3ttg+z8SbrTt8XE/ezZR7rRdP1rpd5Dcn/AyrdXV\nkoT0UdB2IdgAFJ+DbZeMPv6sJyADXdeg7Mnl6fKBFX9B7Va5wjor2ROmOkjXJJ7MXUOZ9cPA9yB+\nEdKvQamRqzJGbauxzHEwcG+YLWoGUQfkziRK7ls+hmE9fwP9a0Apxm5hF6H/a+V1X4zh5Vlnsyzh\nukuQejXEv4PkUtT2dpR+1aitlVyGkWP0cgk7ZVDbBVMYr2s2T+auYWzoSWzLakKZsCIgLPcG1PkP\nI4a4KbkU2t9efTrKwBoYuBsYHK4jOaaY6olsNklA599C4bGwJrz1Q/EpaH/3cKX7SrInw/ZrIR5k\n9BtZFrquRUlfE6mVtXKnoptGZjG29R1h/RXrJUwQGoD+b8PAd+o6Rjz4MPELZ2DbrgxJbNrVc+N2\nKsZdJ4E0tF2K9l4P5GDgvvJaNga2FXZch/X9W/WolEULvg7pownLIyQgdQR0fRLt/QhRbtUUxO1m\nEm+Zu8Yo/qTKDbh+rP9rNVdrNCuGBD5wz9TFB4QboylC3/Lgbt8nQRloewfsuBmosVjYqAlNGSCG\naDHEGwh9++Po4kkcAtkTQqs7ERags95PMupThvVD7z9BW/VqPUrsg+bfilnoO5d8aeG5xJO5a4xy\nt0rl52oXd7aej8DANxsf0wgZyJ0NbW8N9VDjrWHki5UguSzcaO37GrUTeQWJpWj+Z1BicSjcMfgj\n2HYF1Zcu2H3fA4kWfQMAKz5NvPUSKDxULvxRQbwZs9KYSdqT+Nzkydw1RuowKvfa5VDuT6ruZnEv\n9N9B7dbsZKf4JyB/DqgbXjiV8LLvLccbw+BYo2VqHXoBSiwOUUbzUe5ULLE3tv26sLCYuiB+gTB6\nZ0+hXJ6VNmKbzwq1Wmv9P0SLPVG7qrzP3DWElELd1xP6ncs1PZWH1HLInVl9x3gztV+GEaReR+WF\nvSpGAolDIfUqSP8BdH0c9lqP0q+B3psJLead3UE7E+dEZ5hWfqNS+kiiBV8h2vuJUA+zWtm0xEIA\nrO8L5XsEtd7QstD+vgnG6eYCb5m7hlHmGFh0P9Z/D8SbUeZ1kP7DEeOhR0ksBkVVGt4R5M9H7Rdj\nG4+tcMIcaEF4Q5DCMMfUoWjerSjKj9g03nEDjRntUv6UoHwY2549rfbmyYNDf3rpN4xI1sqh/Pnh\n+8JjVG65C0hAYj/U8VcoezIAZgUg4a10N4Inc9dQSixG7W+rf3ulsbZ3wY4bGZVsc3+OOj6AlMA6\nLoPt17NrDLnykDoSum9GxSeh+GtIHYxSh406h8XbJ7dezMijAQnInIi6rhtzVUFJMO8WbMsF5b5w\nhXsI+dUoe3zYKPlSGFrP6Ek/GbTwGyh5QDhzYT3W89dQ/DmQwnJnoM6rfUanAzyZuxkgan8rcbQA\nem/aVZFInVeg1CuGt2lbjaWPxPruANseWqmZE0PrNH14+Kpm4D7CcL3xdKdkwyqEhXWMbtGXQiHq\njveFTxZjUPIAWPQADD0WbrymjkDlLhYAtb0F6//WHudJQ/qI4URefBrbekG5mDbAIPTfjZWeR/M/\nO47rcq3Kk7mbEaL8mZCv0bcOKHUY6hrd8h5TtdEh4cxhWKIZocWfDv92fIAo/0bibVeEmaijFLH+\nu1B7jQLWu8euCEseFKoxRQtGPpc8COZ9Guv5IJSeBwTZk1Dnh4Yvofe2CqOCBqHwMFZ82icEOU/m\nbg5IH0Xl9V1SkH8zyv5RmDJf+i3Yi5B8GVJ5ApHV+BMZ/E+oI5lb6Xnsxb8qd6UIEvtA13UovXzX\nNsocBQvXhvMrN3z+nYo/rxA/4c2n9JvyGjZuLvPRLK7lKXUoZE8BdrspqhxkjkEdV6D0SiSh5P4o\n9cqRiTR9ZPUD29gVlcxibMu5ofADQ0ABSv+Hbb0AK20cGaeEonmjEzlAusqKk1aApBf8cp7M3Ryh\nro+h7msgfSykj0adH66rLJqyx1H5A2wS0ivGPnHhodBPvufNTSti/XfUGT0o/xegLCMnZmUh+3qU\neEndx3Gty7tZ3JwgCbIn7xreV/d+0Xwsfx70fZXh2qYRKI/aLhz7AKXnwCqNHy9A6en640gshgVf\nw3o+CoUfh9E8+fNQ+zvqPoZrbZ7MnRuDOq7CEi+Fvs9DvA0yR6P2S1GijipIqcOoOIheeZSqo2W/\n+y7JA33kiqvKk7lzY5CE2s6BtnPGv2/qECxzNAz+B8Mt+1QY0ZIbY8KRc+PgfeZuypgZcd/dxJtW\nEW/8Q+JtH8HinmaHNe3UfSO0XxJK20WLIX8uWnBn5Rudzk2Qt8zdlLFtl5eLTJT1fwEbWAOLfjBq\nun0rk1Ko/SJov6jZobgW5i1zNyXi4nMjE/lO1oNtv6biPlZ8hvjFy4g3vJZ400nEvV/GKt48dM7t\nyZO5mxoD36r+3OB9ox6y0iZs85+E/WwLlP4Ptl+D9fz91MXoXAvxZO6mRs3Fn0av9md9X6ywDGw/\n9H8dK73Q6OicazkNSeaSLpNkkhaOvbWbE3JnU7XyUPbs0Y8VHiGsiLgHpctT2Z1ztUw6mUvaDzgJ\nqH8GhGt5UZSFjg8yKqEnlqCOCuuZJJdR8eVoRUjsOxUhjskK64i3vJV44/HEW9+JDf20KXE4V49G\ntMw/AVzO5Op6uRYUtb0ZLfgGZM8M1YI6P4oW3ltxSJ7yF7KrQtEuaUgtR8ml0xHuCDb4fWzLaij8\nEOJnYXAttvlNWGH9tMfiXD0mlcwlnQE8a2ZjvsIlvU3So5Ie3bRp02RO62YRpV5G1H0t0YLbiPJn\nI+2ZsIe307ybINqHkNRTkDk+PNYE1vMhhif5QGir9GPbP9aUeJwby5jjzCWtBSqtwH81cBWhi2VM\nZnYLcAvAihUrvBXvRlHmaFj0YLkMXA5FbU2Jw2wQSs9WfnLoqekNxrk6jZnMzezESo9LOgxYBqwv\nrzy3BFgnaaWZPd/QKN2cIWlXoePmSYUVCndV9dlNNG/6w3GuDhPuZjGzJ81sLzNbamZLgWeAIzyR\nu9lOiiB3HrBn334O2uqvb+rcdPLp/M5VoI5LMeuF/jtBSbAStL0F5c9tdmjOVdSwZF5unTvXEqQk\n6vpbrON9ED8PiX1RzYlQzjWXt8ydq0FRO0Rels3NfD6d3znnWoAnc+ecawGezJ1zrgV4MnfOuRbg\nydw551qAzKZ/Zr2kTcBvpul0C4FWWRDbr2Vm8muZeVrlOmDktRxgZosqbdSUZD6dJD1qZiuaHUcj\n+LXMTH4tM0+rXAfUfy3ezeKccy3Ak7lzzrWAuZDMb2l2AA3k1zIz+bXMPK1yHVDntbR8n7lzzs0F\nc6Fl7pxzLc+TuXPOtYA5lcwlXSbJJDW7lM2ESbpO0s8k/ZekNZK6mx3TeEhaJennkn4p6cpmxzNR\nkvaT9KCk/5b0lKT3NDumyZKUkPS4pG82O5bJkNQt6Y7y38lPJR3V7JgmStKl5dfXTyT9mypVQy+b\nM8lc0n6EeqVPNzuWSfou8AozeyXwC+ADTY6nbpISwD8DpwCHAudIOrS5UU1YEbjMzA4FXgu8cxZf\ny07vAX7a7CAa4JPAvWb2e8ByZuk1SdoXuARYYWavABLAm6ptP2eSOfAJ4HJCmfVZy8zuN7Ni+ccf\nEWqvzhYrgV+a2a/MrAB8BTijyTFNiJk9Z2bryt9vJySMfZsb1cRJWgK8Afhss2OZDEldwDHArQBm\nVjCzF5sb1aQkgZykJJAHfldtwzmRzCWdATxrZuubHUuDvQX4TrODGId9gd/u9vMzzOIEuJOkpcDh\nwMPNjWRSbiA0duJmBzJJy4BNwOfLXUafldTW7KAmwsyeBT5O6E14DthmZvdX275lkrmkteV+pT2/\nzgCuAv662THWa4xr2bnN1YSP+rc3L1InqR24E3ivmfU0O56JkHQasNHMHmt2LA2QBI4AbjKzw4Fe\nYFbem5E0j/DJdRmwD9Am6fxq27dM2TgzO7HS45IOI/xnrJcEoVtinaSVZvb8NIZYt2rXspOk1cBp\nwAk2uyYKPAvst9vPS8qPzUqSUoREfruZ3dXseCbhaOB0SacCWaBT0pfMrGrimMGeAZ4xs52fku5g\nliZz4ETg12a2CUDSXcDrgC9V2rhlWubVmNmTZraXmS0tF51+BjhipibysUhaRfg4fLqZ9TU7nnF6\nBDhY0jJJacLNnHuaHNOEKLQMbgV+ambXNzueyTCzD5jZkvLfx5uAB2ZpIqf8d/1bSYeUHzoB+O8m\nhjQZTwOvlZQvv95OoMbN3JZpmc8hnwIywHfLnzR+ZGZvb25I9TGzoqR3AfcR7sx/zsyeanJYE3U0\n8GbgSUlPlB+7ysy+3cSYXPBu4PZyg+FXwIVNjmdCzOxhSXcA6whdqo9TY2q/T+d3zrkW0PLdLM45\nNxd4MnfOuRbgydw551qAJ3PnnGsBnsydc64FeDJ3zrkW4MncOedawP8Dsglan9272UsAAAAASUVO\nRK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5fTGDF4dZXvb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Now let's train our linear regression by iterating over batch-by-batch over the data and repeatedly calling train_on_batch:"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tOxv9nwtZXvd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "aee13cdf-57ee-4e9d-f8bb-edd7a5f3ea20"
      },
      "source": [
        "# Shuffle the data.\n",
        "indices = np.random.permutation(len(features))\n",
        "features = features[indices]\n",
        "labels = labels[indices]\n",
        "\n",
        "# Create a tf.data.Dataset object for easy batched iteration\n",
        "dataset = tf.data.Dataset.from_tensor_slices((features, labels))\n",
        "dataset = dataset.shuffle(buffer_size=1024).batch(256)\n",
        "\n",
        "for epoch in range(10):\n",
        "    for step, (x, y) in enumerate(dataset):\n",
        "      loss = train_on_batch(x, y)\n",
        "print('Epoch %d: last batch loss = %.4f' % (epoch, float(loss)))"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 9: last batch loss = 0.0180\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UbNEZRGRvSC",
        "colab_type": "text"
      },
      "source": [
        "Here's how our model performs:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9o8XZjHZXvj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 283
        },
        "outputId": "6fb7fb56-60f6-4138-d403-b233a2c96030"
      },
      "source": [
        "predictions = compute_predictions(features)\n",
        "plt.scatter(features[:, 0], features[:, 1], c=predictions[:, 0] > 0.5)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.collections.PathCollection at 0x7fbbf542b0f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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FjpUSQputJwpuQHp/RuZ/hAyssBwitAREoy8hYTTYuoHjeETyy4ikCaB3INI6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\nDcCTuXPONQBP5s451wD+H3PgR/GQQgtJAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OcJZGCBOZXvo",
        "colab_type": "text"
      },
      "source": [
        "## Making it fast with `tf.function`"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xUHqygAEZXvp",
        "colab_type": "text"
      },
      "source": [
        "But how fast is our current code running?"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "of1wkZtRZXvq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "f16215a3-eb51-4865-80ac-bbf622c0072a"
      },
      "source": [
        "import time\n",
        "\n",
        "t0 = time.time()\n",
        "for epoch in range(20):\n",
        "    for step, (x, y) in enumerate(dataset):\n",
        "      loss = train_on_batch(x, y)\n",
        "      t_end = time.time() - t0\n",
        "print('Time per epoch: %.3f s' % (t_end / 20,))\n"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time per epoch: 0.134 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LNgKrD_vZXvs",
        "colab_type": "text"
      },
      "source": [
        "Let's compile the training function into a static graph. Literally all we need to do is add the tf.function decorator on it:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eV_Zb9cYZXvt",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "@tf.function\n",
        "def train_on_batch(x, y):\n",
        "    with tf.GradientTape() as tape:\n",
        "      predictions = compute_predictions(x)\n",
        "      loss = compute_loss(y, predictions)\n",
        "      dloss_dw, dloss_db = tape.gradient(loss, [w, b])\n",
        "      w.assign_sub(learning_rate * dloss_dw)\n",
        "      b.assign_sub(learning_rate * dloss_db)\n",
        "    return loss"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sYL68oxUZXvw",
        "colab_type": "text"
      },
      "source": [
        "Let's try this again:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9t_0rKm4ZXvx",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "1d7954b3-00f5-45ee-ec45-2dfe7060e8cc"
      },
      "source": [
        "t0 = time.time()\n",
        "for epoch in range(20):\n",
        "    for step, (x, y) in enumerate(dataset):\n",
        "      loss = train_on_batch(x, y)\n",
        "      t_end = time.time() - t0\n",
        "print('Time per epoch: %.3f s' % (t_end / 20,))"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Time per epoch: 0.084 s\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wdLZKg7iSnVC",
        "colab_type": "text"
      },
      "source": [
        "# Thank you!!"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ES714fHXS0h4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}