C1W1_A_new_programming_paradigm_1.ipynb

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        "<a href=\"https://colab.research.google.com/github/georgezoto/TensorFlow-in-Practice/blob/master/C1W1_A_new_programming_paradigm_1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "##### Copyright 2019 The TensorFlow Authors."
      ]
    },
    {
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      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
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    {
      "cell_type": "markdown",
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      "source": [
        "# The Hello World of Deep Learning with Neural Networks"
      ]
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        "Like every first app you should start with something super simple that shows the overall scaffolding for how your code works. \n",
        "\n",
        "In the case of creating neural networks, the sample I like to use is one where it learns the relationship between two numbers. So, for example, if you were writing code for a function like this, you already know the 'rules' — \n",
        "\n",
        "\n",
        "```\n",
        "float hw_function(float x){\n",
        "    float y = (2 * x) - 1;\n",
        "    return y;\n",
        "}\n",
        "```\n",
        "\n",
        "So how would you train a neural network to do the equivalent task? Using data! By feeding it with a set of Xs, and a set of Ys, it should be able to figure out the relationship between them. \n",
        "\n",
        "This is obviously a very different paradigm than what you might be used to, so let's step through it piece by piece.\n"
      ]
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      "source": [
        "## Imports\n",
        "\n",
        "Let's start with our imports. Here we are importing TensorFlow and calling it tf for ease of use.\n",
        "\n",
        "We then import a library called numpy, which helps us to represent our data as lists easily and quickly.\n",
        "\n",
        "The framework for defining a neural network as a set of Sequential layers is called keras, so we import that too."
      ]
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      "source": [
        "import tensorflow as tf\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime"
      ],
      "execution_count": 0,
      "outputs": []
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        "colab": {
          "base_uri": "https://localhost:8080/",
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      },
      "source": [
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.2.0-rc2\n"
          ],
          "name": "stdout"
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      "source": [
        "print(keras.__version__)"
      ],
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      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.3.0-tf\n"
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      "cell_type": "markdown",
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      "source": [
        "## Sample architecture of a 2 layer network (1 hidden + 1 output layer)\n",
        "\n",
        "It has 2 inputs/features ($x_1$ and $x_2$), 1 hidden layer of 4 nodes/neurons ($a_1$ through $a_4$) and final output layer of 1 node/neuron"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mWchGx5wRQKu",
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      "source": [
        "<img src=\"https://docs.google.com/uc?export=download&id=1M3Syemii5CvFzQJlR-SIBuHnNFNdRcBh\" style=\"width:400px;height:300px;\">"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "PBMa4xDXRZv6"
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      "source": [
        "## Define and Compile the Neural Network\n",
        "\n",
        "Next we will create the simplest possible neural network. It has 1 layer, and that layer has 1 neuron, and the input shape to it is just 1 value."
      ]
    },
    {
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      "source": [
        "model = tf.keras.Sequential([keras.layers.Dense(units=1, input_shape=[1], name='layer1')], name='single_neuron_model')"
      ],
      "execution_count": 0,
      "outputs": []
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 194
        }
      },
      "source": [
        "model.summary()"
      ],
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      "outputs": [
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          "text": [
            "Model: \"single_neuron_model\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "layer1 (Dense)               (None, 1)                 2         \n",
            "=================================================================\n",
            "Total params: 2\n",
            "Trainable params: 2\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
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      "source": [
        "## Access weigth and bias values"
      ]
    },
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          "height": 52
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      },
      "source": [
        "model.trainable_variables"
      ],
      "execution_count": 0,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<tf.Variable 'layer1_1/kernel:0' shape=(1, 1) dtype=float32, numpy=array([[-1.2352076]], dtype=float32)>,\n",
              " <tf.Variable 'layer1_1/bias:0' shape=(1,) dtype=float32, numpy=array([0.], dtype=float32)>]"
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        "model.layers"
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              "[<tensorflow.python.keras.layers.core.Dense at 0x7f77b35bfa90>]"
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      "source": [
        "w=model.layers[0].get_weights()[0][0][0]\n",
        "w"
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        "b=model.layers[0].get_weights()[1][0]\n",
        "b"
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      "source": [
        "Now we compile our Neural Network. When we do so, we have to specify 2 functions, a loss and an optimizer.\n",
        "\n",
        "If you've seen lots of math for machine learning, here's where it's usually used, but in this case it's nicely encapsulated in functions for you. But what happens here — let's explain...\n",
        "\n",
        "We know that in our function, the relationship between the numbers is y=2x-1. \n",
        "\n",
        "When the computer is trying to 'learn' that, it makes a guess...maybe y=10x+10. The LOSS function measures the guessed answers against the known correct answers and measures how well or how badly it did.\n",
        "\n",
        "It then uses the OPTIMIZER function to make another guess. Based on how the loss function went, it will try to minimize the loss. At that point maybe it will come up with somehting like y=5x+5, which, while still pretty bad, is closer to the correct result (i.e. the loss is lower)\n",
        "\n",
        "It will repeat this for the number of EPOCHS which you will see shortly. But first, here's how we tell it to use 'MEAN SQUARED ERROR' for the loss and 'STOCHASTIC GRADIENT DESCENT' for the optimizer. You don't need to understand the math for these yet, but you can see that they work! :)\n",
        "\n",
        "Over time you will learn the different and appropriate loss and optimizer functions for different scenarios. \n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "m8YQN1H41L-Y",
        "colab": {}
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      "source": [
        "model.compile(optimizer='sgd', loss='mean_squared_error')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
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      "source": [
        "## Providing the Data\n",
        "\n",
        "Next up we'll feed in some data. In this case we are taking 6 xs and 6ys. You can see that the relationship between these is that y=2x-1, so where x = -1, y=-3 etc. etc. \n",
        "\n",
        "A python library called 'Numpy' provides lots of array type data structures that are a defacto standard way of doing it. We declare that we want to use these by specifying the values as an np.array[]"
      ]
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      "source": [
        "xs = np.array([-1.0,  0.0, 1.0, 2.0, 3.0, 4.0], dtype=float)\n",
        "ys = np.array([-3.0, -1.0, 1.0, 3.0, 5.0, 7.0], dtype=float)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "YLhiHiWe7LRW"
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      "source": [
        "## Plotting our model as a line"
      ]
    },
    {
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          "height": 554
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      },
      "source": [
        "plt.rcParams[\"figure.figsize\"] = [16,9]\n",
        "plt.plot(xs, ys, 'bo', )"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f77b06da828>]"
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          "execution_count": 42
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          "data": {
            "image/png": 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AAEALAQoAAEALAQoAAEALAQoAAEALAQoAAEALAQoraD5PtreTEycWr/P51BMBAMDRnZx6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\nQmCC\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cfyMqI4vSNyW",
        "colab_type": "text"
      },
      "source": [
        "## Helpful plot function \n",
        "We will use it during training to get an idea of what is really going on inside our model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "edrJdlq18bvg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "xline = np.linspace(-1,5,10)\n",
        "ymodel = w*xline+b"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lIyikuB-8bxu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_model_function(xs, ys, xline, w, b):\n",
        "  plt.plot(xs, ys, 'bo')\n",
        "  ymodel = w*xline+b\n",
        "  plt.plot(xline, ymodel, '--g', label='y='+str(w)+'*x + '+str(b), alpha=0.7)\n",
        "  plt.title('Graph of y=w*x + b')\n",
        "  plt.legend(loc='upper left')\n",
        "  plt.grid()\n",
        "  #plt.show()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vu-zYcvWL-6R",
        "colab_type": "code",
        "outputId": "82f053f2-3302-4941-adbe-914f797ee452",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        }
      },
      "source": [
        "plot_model_function(xs, ys, xline, w, b)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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QNig7PZujBhzFUQOOor6hPn7C7nMrn+OB1x8gEBjeazixghjlheUUdC1IcsXS\noW3mzMafiop5TJkyJdnlSJIktVluMGzj0lLSCCEAcOG4C7n+hOs5u+Rsttdv5/aXb2f207OJogiA\ntVvXxp9HKkmSJEltjTOg7UgIgUHdBzGo+yDOHn0267at492t7xJCIIoivv3Et2mIGigrKKO8sJxx\nfceRnpqe7LIlSZIkCTCAtmu9s3vTO7s3AA1RA1+d8FUqqyv566q/8sTbT5Cems7F4y/mpCEnJblS\nSZIkSTKAdhipKalMHjCZyQMmU99Qz9/f+zuV1ZX079p4eu6b77/Jr1/4NeWF5cQKY/TL6ZfkiiVJ\nkiQdagygHVBaShrj+o5jXN9x8bbanbV8WP8hty26jdsW3Ub/rv2JFcQ4Y+QZdEnvksRqJUmSJB0q\nDKCHiDF5Y/jFib/gvW3vUVVdRWV1JX9+88+cPfpsAObXzCcQGNt3rPtGJUmSJB0UBtBDTJ/sPpwy\n7BROGXYKO3btiIfN+xffz5L1S8hIzWBC/gRiBTEmFUyia0bXJFcsSZIkqaMwgB7C9pzpvOaYa3j1\nvVepqq6iqqaK56ufJ1YQ47uTvwvA+tr19MrqlaxSJUmSJHUABlAB0Cm1ExPyJzAhfwJfL/06b218\nK/7a+tr1XPjHC+nftX/jIUYFMYb2HBp/PqkkSZIkHQgDqP5BCIHBPQbHrzNSM/jahK9RVV3F75f8\nnvsW30ePzj248nNXMqrPqCRWKkmSJKk9MYBqv3IycpgxbAYzhs1gy/YtLFy9kKqaKvJz8gF4+p2n\nqaquorywnNJ+peRk5CS5YkmSJEltkQFUn0pORg5TB05l6sCp8ba6+jpe3/A6f6v+GykhhVG9RxEr\niDFj2AyX6UqSJEmKM4CqxU4achInDj6RZe8viz/iZd6KeZw6/FQAnnnnGQq7FjK4x2ADqSRJknQI\nM4CqVYQQGNpzKEN7DuW8sedRV18HwM5dO/nVwl9RV19Hj849iBXEiBXEGJM3hk6pnZJctSRJkqRE\nSkl2AeqYMtMygcbTdW+bcRvfLP8mw3sO55nlz3D1vKv5vyX/BzQG1K07tiazVEmSJEkJ4gyoDrqc\njByOGXgMxww8hh27dvDK2lcoyi0C4MU1L/LD535ISe8SYoUxygvL6ZPdJ8kVS5IkSToYnAFVQqWn\nplParzQeMvvn9ueMEWewsW4jv37x11z84MX8yyP/wgd1HyS5UkmSJEmtzRlQJVW/nH6cP/Z8zh97\nPmu2rKGyupLX179O14yuANz18l1s3bGV8sJyRueNJi3FIStJkiS1V/5tXm1Gfk4+XxjxhY+1bd6+\nmWeWP8Mjbz5C57TOTMyfyKKngBoAACAASURBVNSBUykrKEtSlZIkSZI+KwOo2rTLyi7jaxO/xsvv\nvkxldSXzV88nJyOHsoIyoijisbceY0L+BPeNSpIkSe2AAVRtXnpqOpMKJjGpYBJRFPFh/YcAvLPp\nHW5YcAMAg7oNih9iNLDbQJ83KkmSJLVBHkKkdiWEQFanLAAGdR/ETSffxIXjLiQjLYO5f5/L5Y9e\nzkvvvgRA7c5a6hvqk1muJEmSpD04A6p2raBrAad3PZ3TR5zOB3UfsGD1Akr6lADwwJIHeGjpQ0zM\nn0isMMbE/Ilkp2cnuWJJkiTp0GUAVYeRm5nLsYOOjV+P7TuWDR9uYH7NfP6y8i+kpaQxMX8is4+a\n7RJdSZIkKQkMoOqwSvqUUNKnhIaogTfWv0FVTRU7d+2Mh8+fPv9T8rvkEyuMuW9UkiRJSgADqDq8\nlJDCiN4jGNF7RLxtx64dvLv1XSqWV3D33++mT1YfYoUxjh10LIO6D0pitZIkSVLH1SoBNIRwAnA9\nkAr8Joqia1ujX+lgSU9N58fTf8ymuk0sqFlAZXUlj731GMXdihnUfRCb6jbx9/f+zoT8CfFDjyRJ\nkiS1TIsDaAghFbgBmA5UAwtCCA9GUbS4pX1LB1u3zG5MP2w60w+bTl19HYHGZbiV1ZXcsOAG0lLS\nGNNnDLHCGGUFZfTK6pXkiiVJkqT2qzUew1IGvBlF0dtRFO0A5gKntkK/UkJlpmWSkZYBwHGHHce1\n067llKGn8O7Wd7lx4Y1c9MeL+KDuA6DxES9RFCWzXEmSJKndCS39S3QI4UzghCiKvtp0fR4Qi6Lo\nnz9x3yxgFkBeXt7EuXPntuhzD7atW7fSpUuXZJehNiCKItZtX8eqD1cxsftEtm7dyu/X/56129cy\nsutIRuSMoDi7mNSQmuxSlWT+e0PNcWxoXxwfao5jQ81pD2Nj6tSpL0RRVPrJ9oQdQhRF0S3ALQCl\npaXRlClTEvXRn0lFRQVtvUYlR0VFBeeNOo/nVj7HoncX8caWN+iyvQunDjuVL5d8OdnlKYn894aa\n49jQvjg+1BzHhprTnsdGawTQGqD/HteFTW1Sh3XMwGM4ZuAx1NXX8eKaF6mqroofVlRXX8d1f72O\n0n6lxApj9OjcI8nVSpIkSW1DawTQBcCQEMJAGoPnl4FzWqFfqc3LTMvkc/0/x+f6fy7e9t6291i1\neRXzV8/nVwt/xZAeQygvLOfYQccaRiVJknRIa3EAjaKoPoTwz8BjND6G5bYoil5rcWVSO1WUW8TN\nn7+ZVZtXUVldSVV1FXe9chel/Urp0bkHyzctZ8v2LYzsPZLUFPeNSpIk6dDRKntAoyh6BHikNfqS\nOoIQAkW5RRTlFvGlUV/i/Q/fp3tmdwAeXvowj731GDnpOZT2K6W8sJwJ+RPITMtMctWSJEnSwZWw\nQ4ikQ9meS2+/OuGrTMifQGV1JQtWL+CZ5c+Q3yWfmz9/MyEE6urrDKOSJEnqkAygUoLtuW90V8Mu\nFq9bzObtmwkhEEURlzx8Cb2zehMriFFeWE5h10JCCMkuW5IkSWoxA6iURKkpqYzOGx2/3tmwk5MG\nn0RVTRV3vnInd75yJ/ld8rlg7AUcUXREEiuVJEmSWs4AKrUh6anpnFVyFmeVnMWG2g3Mr5lPVU0V\n2enZALy98W0efONBYgUxxuePd6muJEmS2hUDqNRG9czqyYlDTuTEISfG29ZsWUNldSVPvfMU6anp\njM0bS3lhOVOKp5Cemp7EaiVJkqT9M4BK7cgRRUcQK4yxeN3i+CNeXln7ClOKpwCwZN0ScjJyKOxa\nmNxCJUmSpL0wgErtTFpKGmPyxjAmbwxfm/A11tWui89+3rTwJt7e9DYFOQWUFZRRXljO8F7DSQkp\nSa5akiRJAv9WKrVjIQT6ZPeJX3/v6O/x9Ylfp092Hx5a+hD//uS/87PnfxZ/fceuHckoU5IkSQKc\nAZU6lF5ZvTh56MmcPPRktu3YxotrXqRbZjcA1teu5+sPf51xfccRK4hRVlBGbmZukiuWJEnSocQA\nKnVQ2enZHDXgqPh1FEVMHzSdyppKqmqqCASG9xrOpZMupbhbcfIKlSRJ0iHDACodInpn9+aS0kuY\nNXEW72x6J36IUW5G4yzoX1f+lWXvLyNWEGN4r+GEEJJcsSRJkjoaA6h0iAkhMKj7IAZ1H8Q5o8+J\nt7+98W3+8Pof+P2S35ObkUtZQRmHFx7OpIJJSaxWkiRJHYkBVBIA5409j9NHnM4La16gsrqSv676\nK8s3LY8H0EXvLmJgt4HuG5UkSdJnZgCVFJedns3kAZOZPGAy9Q31vP/h+wDU1dfxg3k/oL6hnhG9\nRlBeWE6sMEa/nH5JrliSJEntiY9hkbRXaSlp8Ue8ZKRmcN306zhr1Fl8WP8hty26jUsevoSH3ngI\ngIaogSiKklmuJEmS2gFnQCXtVwiBw3ocxmE9DmPmmJm8t+09qqqrmJA/AYAFNQu4YcENlBWUESuI\nMbbvWNJT05NctSRJktoaA6ikT61Pdh9OGXZK/Do3M5dRvUcxb8U8HnvrMTLTMhnfdzyXxy4nOz07\niZVKkiSpLTGASmqx4b2GM/zI4ezctZNX33uVquoq3tr4FlmdsgD4w+t/ACBWECM/Jz+ZpUqSJCmJ\nDKCSWk2n1E5MyJ8QX5q72wurX2DR2kXc+tKtFHUtorywnCOKjmBQ90FJqlSSJEnJYACVdND91zH/\nxdqta6msrqSqpor7l9zPlh1buHTSpURRxKJ3F1HSp4ROqZ2SXaokSZIOIgOopITI65LHqcNP5dTh\np7Jl+xZ27NoBwLL3l3FVxVVkpmUyoe8EygvLKe1XSk5GTpIrliRJUmszgEpKuD3D5cBuA7n66Kup\nrK5k/ur5/K36b6SEFK6ddi0jeo+gIWogJfjEKEmSpI7AACopqTqldmJiv4lM7DeRS6NLWfb+MubX\nzOewHocBcM+r91BZXUmsMEasIMbgHoMJISS5akmSJH0WBlBJbUYIgaE9hzK059B4W//c/vz9vb9z\n72v38rvXfkePzj04esDRXDT+oiRWKkmSpM/CACqpTZs8YDKTB0xmy/YtLFi9gKrqKjZ+uDH++u2L\nbqe4WzGl/Urpkt4liZVKkiRpfwyg0gGaMwdmz4aVK4+mqAiuuQZmzkx2VYeOnIwcjhl4DMcMPCbe\nVruzlqffeZqNdRtJDamM6j2K8sJyDu9/OL2yeiWxWkmSJO2NAVQ6AHPmwKxZUFsLEFixovEaDKHJ\nlNUpiztOu4OlG5bGH/Fyy4u3kJmWyfTDprNl+xbWblvLYd0Pc9+oJElSG2AAlQ7A7Nm7w+dHamsb\n2w2gyRVCYFivYQzrNYwLxl3A6i2ryc3IBeDZlc9y48Ib6ZXVi7J+ZZQXljM6bzRpKf6rT5IkKRn8\nW5h0AFau/HTtSp5+Of3ifz6y6EgyUjOorK7kyXee5JE3HyG7Uza3nXobWZ2yiKLImVFJkqQEMoBK\nB6CoCFas2Hu72q6uGV2ZNmga0wZNY8euHSx6dxHvbHyHrE5ZAPzw2R9SV18Xf8RL7+zeSa5YkiSp\nY/Pp7tIBuOYayMr6eFtWVmO72of01HTKCso4q+SseNvA7gNZX7uem1+4mYsevIjL/3w5T7z1RBKr\nlCRJ6ticAZUOwO59no2n4EYUFQVPwe0Azhl9DueMPoeazTVU1VRRWV3JprpNANTV13Hny3dSVlBG\nSZ8S941KkiS1Av9GJR2gmTMbfyoq5jFlypRkl6NWVNC1gNO7ns7pI04niiIAlm9azmNvPcZDSx8i\nu1M2E/MnUl5YzqSCSWSmZSa5YkmSpPbJJbiStIfdhxIN7zWcu0+/m+8e9V0+1/9zvLz2ZX78tx+z\nestqANZuXcv62vXJLFWSJKndcQZUkpqRkZbReEBRYYyGqIFlG5YxsNtAAH732u944u0nOKz7YZQX\nlhMriMVnTyVJkrR3BlBJOgApIYVhvYbFr88YcQb9cvpRVV3F3a/ezZxX59Dlwy5MnToVwEe8SJIk\n7YUBVJI+g4KuBZw58kzOHHkmm+o2Mb9mPoteXgQ0hs/LHrmMQd0HUV5YzoT8CfFHv0iSJB3KDKCS\n1ELdMrtx3GHHkb4qHYDanbUM7TmUBasXMG/FPNJS0hjTZwxfHPVFSvqUJLlaSZKk5DGASlIry07P\n5hvl36AhamDJuiXxR7xsr98OwIpNK3i++nnKC8sZkDvApbqSJOmQYQCVpIMkJaQwqs8oRvUZxYXj\nLoy3v7buNea8Ooc5r84hLzuPWEHjQUclfUpICR5OLkmSOi4DqCQlwJ6znCcNOYnDCw9nfs18qmqq\n+PObf+axtx7j7jPuJj01nVUfrKJXVi86d+qcxIolSZJanwFUkpKge+fuHD/4eI4ffDx19XUs37Sc\n9NTGPaTX/e06Vm1exdi8sZQXllNWUEaPzj2SXLEkSVLLudZLkpIsMy2T4b2Gx69nTZzFyUNOpmZz\nDTcsuIEL/nABt710WxIrlCRJah3OgEpSG1PSp4SSPiVcPP5iVm1eRWV1JQO7DQRgfe16/vOp/6Ss\noIzywnJG9BpBakpqkiuWJEk6MAZQSWqjQggU5RZRlFsUb6vdWUu/nH78admf+OMbfyQnPYdJ/SZx\nzuhzyOuSl8RqJUmS9s8AKkntSFFuEVdPuZoPd37Ii2tepKqmigWrF/CVcV8B4IXVL7C+dj1lBWV0\n79w9ucVKkiR9ggFUktqhzp06c0TRERxRdAQNUUP88S3PrnyWp955ChbAsJ7DiBXEKC8sp39u/yRX\nLEmS5CFEktTu7fns0Mtjl/PLE3/JuaPPpSFq4M5X7uRnlT+Lv77yg5U0RA3JKFOSJMkZUEnqSEII\nFHcrprhbMWeVnMWG2g1sqtsEQF19Hd949BtkpmVSVlBGrCDG+PzxZKZlJrlqSZJ0qDCASlIH1jOr\nJz2zegKQGlL51uHfoqq6isrqSp565ynSU9O5PHY5kwdMTnKlkiTpUGAAlaRDRKfUThxZdCRHFh1J\nfUM9i9ctprK6kuJuxQDMr5nPfa/dR6ywcd9oYdfC5BYsSZI6HAOoJB2C0lLSGJM3hjF5Yz7WvrNh\nJ3e8fAd3vHwHBTkFlBWUce6Yc0lPTU9SpZIkqSMxgEqSACgrKKOsoIz1teupqq6iqqZxqe6F4y4E\n4Km3n6JLehfG9R1HRlpGkquVJEntkQFUkvQxvbJ6cfLQkzl56MnsathFCIEoirhv8X3UbKkhPTWd\n8X3HEyuIUVZQRm5mbrJLliRJ7YQBVJLUrNSUVKDxdN3/Oel/+Pt7f288xKimkqqaKk4afBL/NOmf\niKKINVvX0C+nX5IrliRJbZkBVJJ0QNJS0hjXdxzj+o5j1sRZvLPpHTJSG5fiLnt/GVc8fgWFOYWU\nF5ZTVlDG8F7DCSEkuWpJktSWpOz/FkmSPi6EwKDugyjoWgBAXnYesybMomdWTx54/QGufPJKznvg\nPFZsWpHkSiVJUlviDKgkqcVyM3M5ZdgpnDLsFLbt2MYLa15g4eqF5OfkA3D/4vt5Y/0blBeWM6lg\nEl0zuia5YkmSlAwtCqAhhOuAU4AdwFvAhVEUbWqNwiRJ7VN2ejaTB0xm8oDJ8bZA4M2Nb1JZU0kg\nMLL3SCYPmMxJQ05KYqWSJCnRWroE9wmgJIqiMcBS4D9aXpIkqaM5Y+QZ3DbjNn52/M84a9RZbNux\njZfWvBR//cE3HuSN9W8QRVESq5QkSQdbi2ZAoyh6fI/LSuDMlpUjSeqoQggM7jGYwT0GM3PMTHbu\n2gnAB3UfcNtLt7Er2kX3zO6UFZQRK4gxtu9Y0lPTk1y1JElqTa15CNFFwJ9bsT9JUgfWKbUT0Lh/\n9K4v3MUVh1/BqN6jmLdiHj/4yw+Yt3weALU7a9myfUsyS5UkSa0k7G+5UwjhSaDvXl6aHUXRH5vu\nmQ2UAqdHzXQYQpgFzALIy8ubOHfu3JbUfdBt3bqVLl26JLsMtUGODTXHsdE66hvqeXvb2xR2LiQr\nLYvKDZU8tOYhirOKGdF1BCO7jqRHeo9kl/mpODa0L44PNcexoea0h7ExderUF6IoKv1k+34D6P6E\nEL4CXAJMi6Ko9kDeU1paGi1cuLBFn3uwVVRUMGXKlGSXoTbIsaHmODYOjlUfrGLeinlUVVex/IPl\nABTnFvOT43/SbpboOja0L44PNcexoea0h7ERQthrAG3pKbgnAFcCRx9o+JQk6dPon9ufc8ecy7lj\nzuXdre9SVV3F6i2r4+Hz+srrSUtJo7ywnDF5Y+JLeyVJUtvT0ueA/g+QATwRQgCojKLo6y2uSpKk\nvejbpS+nDj81fh1FEfUN9Ty36jkefetRMtMymZg/kemDpjOx38QkVipJkvampafgDm6tQiRJ+rRC\nCFzxuSvYsWsHr6x9harqKuavns/AbgOZ2G8idfV1PPbmY5QXlpPXJS/Z5UqSdMhr6QyoJElJl56a\nTmm/Ukr7lXJpdCk7Gxof8bJ43WJ+89Jv+M1Lv6E4t5hYYYxYQYzBPQbTtHJHkiQlUGs+hkWSpKQL\nIcT3h07In8Atn7+Fi8dfTJf0Ltz72r186/FvsXzTcgA2b98cfx6pJEk6+JwBlSR1aPk5+Zw2/DRO\nG34am7dvZtG7iyjuVgzA7Ytu57mVzzExfyKxwhil/Urpkt62j7WXJKk9M4BKkg4ZXTO6MnnA5Pj1\n0QOOJiWkUFVTxXOrniM1pDJ5wGS+dfi3klilJEkdlwFUknTIGtt3LGP7juWy6DKWblhKZXUl2enZ\nQOMJu1c9cxXDew2nvLCcQd0HuW9UkqQWMoBKkg55IQSG9RrGsF7D4m2bt29mZ8NOfvfa75j72lx6\nZfUiVhDjpCEnUZRblMRqJUlqvwygkiTtRW5mLtceey0f1H3AgtULqKqu4om3n2BSv0kU5RZRs7mG\ntza+xcT8ifFZU0mStG8GUEmS9iE3M5djBx3LsYOOZceuHaSExgPkn135LHNenUNqSGV0n9HxR7z0\nzu6d5IolSWq7fAyLJEkHKD01nbSUxv93+6VRX+LHx/6Y04afxrraddz8ws1c+sil8ce6fFD3AVEU\nJbNcSZLaHGdAJUn6DFJCCiN6j2BE7xF8ZdxXqNlcw8oPVtIptRMA33vme2zdsZVYQYzUrakc2XBk\nPLxKknSo8r+EkiS1goKuBRR0LQAaT9CdMWwGldWVPP7246xZu4Yn/+9Jzi45m1OHn5rkSiVJSh4D\nqCRJrSyEEN83ur1+O7c+cis7++ykZ1ZPANbXrucXVb+gvLCcsoIyemX1SnLFkiQlhgFUkqSDKCMt\ng5FdRzKlfEq8bd22dazdupYbF97IjQtvZHD3wcQKY5w4+ERyM3OTV6wkSQeZAVSSpAQb0XsEN33+\nJqo3V1NVU0VVdRX3/P0eThh8AgBL1i1hZ8NORvYe6b5RSVKH4n/VJElKghAC/XP70z+3P2eOPJMt\n27eQk5EDwH2L72PB6gVkd8pmUr9JxApjTMyfSOdOnZNctSRJLWMAlSSpDdgdPgGuPOJKXlrzElU1\nVcyvmU/FigpG9BrBj6f/GIBtO7aRnZ6drFIlSfrMDKCSJLUxmWmZHN7/cA7vfzi7GnaxZP0SdjXs\nAqCuvo7z/3A+A3IHECuIESuMMSB3ACGEJFctSdL+GUAlSWrDUlNSKelTEr/e1bCLL4/6MlU1Vfz2\n1d/y21d/S152HpdNuozx+eOTWKkkSftnAJUkqR3JTs/mi6O+yBdHfZH3P3yf+TXzqaquonvn7gC8\nuOZFKpZXUF5YzoT8CWSmZSa5YkmSPmIAlSSpnerRuQcnDD4hfnouND5jdOHqhTyz/Bk6pXRibN5Y\nYoUxjh10rCfqSpKSzv8SSZLUgRx32HFMGziNJeuXUFldSVV1FSteW8Hxhx0PwIKaBfTJ7kNRbpH7\nRiVJCWcAlSSpg9m9b7SkTwkXj7+Yzds3E0IgiiJ+Mf//tXfn0VGWCb7Hf0+lUimyE0KSSlVCCIQA\nQjaSVIGIgKDggqLTm1tLi7aOvfe1p2l65pyeM7bemZ7brZ7reFpFW69Ly3GwtVVoRWltvCmCgLLI\nJmvCElaFhBCSPPePQF20CYgJ9VaS7+ccjvW+BZXfH8+J+eV5n+d5SIebD8uX7FO1v1qhQEgjMkco\nzhXndGwAQB/gcjoAAAC4cIwxSvOmRV4/OO1B3VN1j3JTcvXaptc0Z/EcPbnqSUmStVbNrc1OxgUA\n9HLMgAIA0Iecvm702IljWrF7hXJTciVJGw9s1JzFc1SWU6agP6hqf3VkcyMAALoDBRQAgD6qX3w/\nXZx/ceQ6JSFF04dOV01djWp31crUGg0bMEz3jrtX2cnZDiYFAPQWPIL7Bc8+KxUUSJMnX6qCgo5r\nAAD6gtyUXN0x5g49PuNxPTz9Yd00+ia5Xe7ILOhrG1/TvJXztLZhrdptu8NpAQA9ETOgp3n2WenO\nO6WmJkky2r6941qSbrrJyWQAAESPMUYF6QUqSC/QN0Z9I3K/7rM6LfxkoRasX6DUhFRV5VZpfP54\nVeZWOpgWANCTMAN6mrlzT5XP/6+pqeM+AAB93Xcrv6tnr39W/3TxP6k8p1w1dTVatHlR5P2lO5bq\ncPNhBxMCAGIdM6Cn2bHj/O4DANDXJMYnanz+eI3PH6/W9lYdbTkqSdrXuE8PLH1ARkbDM4dHjngJ\npAYcTgwAiCXMgJ4mP//87gMA0Je5XW6le9MlSZmJmXpw2oO6cfSNamlr0R8+/IPufu1uvbv9XUnS\nibYTrBsFADADerr77jt9DWiHxMSO+wAAoHPGGBX2L1Rh/0J9c9Q3tb9pv8J1YZVml0qS3tzypp5b\n/Zyq/dUK+oMqyylTgjvB4dQAgGijgJ7m1EZDc+dKO3ZY5ecb3XcfGxABAHC+MhMzddWwqyLX+Wn5\nKs0u1fs739ebW96UJ86jipwK/Xz8zxXninMwKQAgmiigX3DTTR1/liz5qyZOnOh0HAAAeoVRWaM0\nKmuUWttbtaZhjcJ1YR08djBSPp9a9ZRSE1IV9AflT/U7nBYAcKFQQAEAQNS4XW6V5ZSpLKcscs9a\nqzUNa7ThwAY9uepJBVICCgVCmjBoggb3H+xgWgBAd6OAAgAARxlj9JvLf6OGxgaF68IK14e1YP0C\ned1eDe4/WM2tzfpo70cqyymTJ87jdFwAQBdQQAEAQEzISsrSNcXX6Jria9TY0hjZNXfl7pX69d9+\nrYS4BJXnlCsUCKnKX6XUhFSHEwMAzhcFFAAAxJwkT1LkdZW/Sv868V9VU1ejcH1YNfU1MjJ69OpH\nlZuSq9b2Vrld/EgDAD0B360BAEBMc7vcKveVq9xXrrsq79Inhz7Rqj2r5Ev2SZIeqX1E6/evVygQ\nUtAf1LABw2SMcTg1AOBMKKAAAKDHMMZoaMZQDc0YGrk3cuBI7T26Vy99/JLmr5uv/t7+umLIFbqp\nhHPUACDWUEABAECPNqVwiqYUTtGR40f0we4PFK4Lq7W9VVLHDrsPhh9USXaJqnKrlJKQ4nBaAOjb\nKKAAAKBXSElI0cSCiZpYMDFyb1/TPq3cs1KLty6Wy7g0MnOkQoGQLhl0iTL6ZTgXFgD6KAooAADo\ntbKSsvTUtU9p88HNkU2MHl/5uAKpAWX0y1BDY4MONx9WUUYR60YBIAoooAAAoFczxqhoQJGKBhTp\nltJbtOfoHg3oN0CStGjzIr247kVl9MtQdW61QoGQSrJLFB8X73BqAOidKKAAAKBPyUnOiby+bvh1\nCqQGVFNXoyXbl2jhJwvV39tfT133lFzGpbb2NsW54hxMCwC9CwUUAAD0WSkJKZo0eJImDZ6klrYW\nfbT3I+1r3CeXcUmSfvqXnyopPknBQFChQEhZSVkOJwaAno0CCgAAIMkT51FlbmXkut22a4xvjGrq\navTYisf02IrHVJBWoK9d9DVNGDTBwaQA0HNRQAEAAM7AZVy6pfQW3VJ6i3Yf2a1wffhzR7zsb9qv\n+WvnKxgIqiS7RG4XP1YBwLnwnRIAAOAcfCk+XTf8Ol03/LrIva2Htmrx1sV6ffPr6ufupzG+MQoF\nQgoGgvK6vQ6mBYDYRQEFAAD4Cqr8VXruhuf04Z4PVVNXo2W7lmnpzqV6eubT8rq92nZ4mxLjE1k3\nCgCnoYACAAB8RZ44j6r8VaryV8laq+2fble6N12S9OTKJ7VizwoNTh+soL9jE6PC/oWcNwqgT3M5\nHQAAAKA3MMaoIL0gcv3dyu/qO2XfUT93P/1x7R/1o0U/0n3v3Rd531rrQEoAcBYzoAAAABdAbkqu\nZo6YqZkjZurT5k9Vu6tWifGJkqTm1mbNfmW2SrJLlHA4QVUtVUryJDmcGAAuPAooAADABZbmTdOU\nwimR66YTTar2V2tZ/TJtqd+id/77HY3OGq1bS29V0YAiB5MCwIVFAQUAAIiyjH4Z+kHwB2q37Xrm\n9Wdk/VY1dTWKj4uXJK3eu1prGtYoGAhqcPpg1o0C6DUooAAAAA5xGZcGJQ3SxLKJuq3stsi60I/3\nf6zn1zyv59Y8p6zELAUDQQX9HeeNUkYB9GRsQgQAABAjTpXLr1/0dT0982n9oPoHKkgv0KJPFunB\n8IORv7fpwCY1nWhyKiYAfGXMgAIAAMSgdG+6pg6ZqqlDpqq5tVl7ju6RMUbttl2/+uuv1HiiUaOz\nRisUCKnaX63MxEynIwPAOTEDCgAAEOO8bm/kiBcjo19c8gtdM+wa7T26V/+1/L8060+z9OLaFyV1\nHO/CES8AYhUzoAAAAD2IMUYjB47UyIEjNatsluo+q1O4PqzRWaMlSRsPbNS/L/13BQNBhQIhjRw4\nUm4XP/IBiA3d8t3IGPNTSb+RNNBau787PhMAAABnZ4xRXlqe8tLyIvdcxqVB6YO0cPNCvbrxVSV7\nklXpq9TtFbcr3ZvuYFoA6IYCaozJk3S5pB1djwMAAICuKBpQpH+59F/U3NqslbtXKlwf1uq9q5UY\nnyhJemfrO2pubVYwGXTQFgAAFRpJREFUEFRGvwyH0wLoa7pjBvS3kn4m6U/d8FkAAADoBl63V2Pz\nxmps3lhZayM77L634z3V7qrVI8sfUVFGkYL+oMbmjVV+Wr7DiQH0BaYri9SNMddKmmyt/aExZpuk\nys4ewTXG3CnpTknKzs4e88ILL3zlrxsNR48eVXJystMxEIMYG+gMYwOdYWzgbKI9Pqy1ajjeoHWf\nrdP6I+u1s2mnRqaO1M2DbpYk7WjaIX8/v+JMXNQy4cz43oHO9ISxMWnSpA+stZVfvH/OAmqMeUtS\nzhnemivpF5Iut9Z+eq4CerrKykq7fPnyLxXcKUuWLNHEiROdjoEYxNhAZxgb6AxjA2fj9Pg4eOyg\njp04Jn+qXw2NDbr9lduV4klRZW6lQoGQKnwV8rq9juXry5weG4hdPWFsGGPOWEDP+QiutXZKJx84\nWtJgSR+efKQjIGmFMabaWruni3kBAAAQBRn9MqR+Ha/TvemaM36OaupqtHzXcr2z7R3Fu+L1ywm/\nVIWv4nOP8gLAV/GV14Baa1dLyjp1fT4zoAAAAIg9njiPxuWN07i8cWprb9O6fesUrg9rSP8hkqQ3\nNr+ht7e+raA/qGAgqLzUPAopgPPCoVAAAAD4O3GuOI3OHq3R2aMj95I9yWq37Xr6o6f19EdPy5fs\nUygQ0qyyWRRRAF9KtxVQa21Bd30WAAAAYs+EQRM0YdAEHWg6oGX1yxSuD2vTgU2R8rng4wXKSc5R\nua+cdaMAzogZUAAAAJyXAYkDNL1ouqYXTdepDS1b21u1YP0CHWo+JE+cR6XZpQoFQqr2Vyvdm+5w\nYgCxggIKAACAr+zU7Kfb5da8a+d1rButC6umrka1u2p1S8kt+vpFX9fx1uPa17RPgdSAw4kBOIkC\nCgAAgG7hdrlVkl2ikuwSza6Yre2fbldqQqok6YPdH+j+v90vf4pf1f5qhQIhDc8cLpdxOZwaQDRR\nQAEAANDtjDEqSC+IXI8cOFJ3V96tcF1Yr258VQvWL1BqQqoemvaQBiQOcC4ogKiigAIAAOCCS/em\n68qiK3Vl0ZVqOtGkFbtXaG3D2o5zSCU9uvxR7WvcF1k3muZNczgxgAuBAgoAAICoSoxP1Pj88Rqf\nPz5yLzUhVcvql2nZrmUyMhqeOVxTCqfo8iGXO5gUQHejgAIAAMBxN46+Ud8a9S1tPbxVy+qXqaau\nRlsPbZUktdt2Pb/6eVX4KlScWcy6UaAHo4ACAAAgJhhjVNi/UIX9C/XNUd9Uu22XJNV/Vq/56+br\nhbUvKC0hTdX+agX9QZX7yuWJ8zicGsD54NdHAAAAiEmnZjrz0vL07PXP6t5x96o0u1RLdy7Vv733\nb1q9d7Uk6XDzYX3a/KmTUQF8ScyAAgAAIOYleZI0YdAETRg0Qa3trVrTsEYXDbxIkvTqhlc1f918\njcgcoWAgqKA/KH+q3+HEAM6EAgoAAIAexe1yqyynLHJ9acGlinPFKVwX1pOrntSTq55UUUaR/vPy\n/5QxxsGkAL6IAgoAAIAeLT8tXzeOvlE3jr5RDY0NCteF1XiiMVI+//ntf9bApIEaGxir0pxS1o0C\nDqKAAgAAoNfISsrSNcXXRK5b21uVkpCiv+34m97c8qYS4hJU4avQ1cOuVkl2iYNJgb6JAgoAAIBe\ny+1y62cX/0wn2k5odcNqhevCCteH1dDYIEk60HRA725/V6FASL4Un8Npgd6PAgoAAIBeLz4uXhW+\nClX4KnRX5V1qs22SpI/2fqR5q+Zp3qp5ykvNUygQUtAf1LABw1g/ClwAHMMCAACAPsUYI7erYx5m\n0uBJevyax3VHxR3q7+2vlz5+Sfe+ea8+Pd5xrMv+pv1qaWtxMi7QqzADCgAAgD4tOzlbM4pnaEbx\nDB1tOaoN+zco3ZsuSXo4/LDW7V+nipwKBQNBVeVWKSUhxeHEQM9FAQUAAABOSvYka0zumMj1zBEz\nlb0zW+H6sN6ve18u49L0odN1V+VdDqYEei4KKAAAANCJspwyleWU6e7Ku7X54GbV1NUoNyVXktTc\n2qw5b81Rha9CoUBIQzOGsm4UOAcKKAAAAHAOxhgVDShS0YCiyL1Dxw7J6/Zq/rr5enHdi8rol6Hq\n3GrNHDEzUlIBfB4FFAAAAPgKfCk+3T/lfh05fkS1u2oVrgtryfYlumrYVZKkDfs3aNeRXaryVynZ\nk+xwWiA2UEABAACALkhJSNHkwZM1efBktbS1KN4VL0lasm2J/rzpz3IZl0YNHKVgIKhQIKSspCyH\nEwPO4RgWAAAAoJt44jyRdaB3jrlTv5n6G90w4gYdaj6kx1Y8pjlvzZG1VlLHES+nXgN9BTOgAAAA\nwAVgjFFxZrGKM4t1a+mt2n1kt/Y17ZMxRu22XT9a+CO5XW4F/UEFA0GVZJdEzicFeitGOAAAABAF\nvhSffCk+SVK7bdesslkK14e1eOtivb75dfVz99MdFXdo6pCpDicFLhwKKAAAABBlbpdblxVepssK\nL1NLW4s+3POhaupqIgV1w/4NemLrEzriO6JgIMi6UfQaFFAAAADAQZ44j6r8VaryV0XuHW05qs9O\nfKbfr/i9fr/i9ypML1QwENS1xdcqyZPkYFqga9iECAAAAIgxY3LH6MfDfqxHr3pUs8pmKcGdoFc2\nvCJPnEeSVFtfq1V7Vqm1vdXhpMD5YQYUAAAAiFH+VL+uT71e14+4Xs2tzYqP6zji5fk1z2vTwU1K\nik/SGN8YBQNBjfGNYXYUMY8CCgAAAPQAXrc38vr+y+7Xqj2rFK4Pa1n9Mr27412NC4zTnEvmSJIO\nHjuojH4ZTkUFOkUBBQAAAHqYBHeCgoGO41vabbs27N8QOcKlobFBt79yuwrTCxUKhBQMBDU4fXDk\nfFLASawBBQAAAHowl3FpxMARKhpQJElKiEvQbaW3KcGdoOfXPK8fLvyhZr8yWxv2b3A4KcAMKAAA\nANCrpHnTdMPIG3TDyBt0uPmwautrVVNXo5zkHEnS4i2LtXLPSoUCIVX4KpQYn+hwYvQlFFAAAACg\nl0r3pmvqkKmaOmRq5N6RliNauWel/rr9r3K73BqdNVpjA2M1beg0HtPFBccjuAAAAEAfct3w6/TM\nzGf0wGUP6Jph12jv0b16e+vbkfK5ZNsSbTu8TdZah5OiN2IGFAAAAOhjXMali7Iu0kVZF2lW2Sw1\nnWiSJB1vPa6Hwg/pRPsJZSVmRTYxumjgRYpzxTmcGr0BM6AAAABAH2aMiZwfmuBO0BMzntD3qr6n\nQemD9MbmNzT37blasH6BJOlE2wkdO3HMybjo4ZgBBQAAABDRv19/XTH0Cl0x9Ao1tzZrxe4VGpox\nVJJUu6tW//H+f6g0u1RBf8cxMJw3ivPBDCgAAACAM/K6vRqXN05ZSVmSpLzUPF1ddLV2HdmlR5Y/\nom+//G39ZNFPdOT4EYeToqdgBhQAAADAl5KXlqfbK27Xd8q/o52f7VS4LqyNBzYq2ZMsSXrmw2d0\nvO24gv6gRg4cybpR/B0KKAAAAIDzYoxRflq+8tPyP3d/f9N+vbfjPf1pw5+U4klRZW6lJhZMVIWv\nwqGkiDUUUAAAAADd4sdjf6y7q+7Wit0rVFNXo9pdtUqMT1SFr0LWWr215S2NyR3DutE+jAIKAAAA\noNucWjc6Lm+c2trb1NzaLEnadHCTHlr2kCSpeEBxZBOjvNS8yBmk6P3YhAgAAADABRHniosc8VKU\nUaSHpz+sm0ffrHbbrqc/elr3vH6PVjesliQ1tzar3bY7GRdRwAwoAAAAgAvOGKOC9AIVpBfoG6O+\noQNNB1S7q1YjMkdIkuavna83Nr+han+1gv6gyn3l8rq9DqdGd6OAAgAAAIi6AYkDNG3otMj16OzR\namhsUE1djRZvXSxPnEchf0j3XnyvgynR3SigAAAAABxXllOmspwytba3at2+daqpq/nc+7+r+Z0C\nqQGFAiEFUgMOpURXUUABAAAAxAy3y62S7BKVZJdE7jW3Nmv74e1avHWx/vDhH+RP8avaX60phVP+\n7igYxDYKKAAAAICY5nV79dtpv9X+pv1aVr9MNXU1enXjqxqUNkj5afk6dOyQNh7YqLKcMiW4E5yO\ni7OggAIAAADoETITM3Vl0ZW6suhKNbY0yu3qqDPv73xfj37wqDxxHpXnlCvoD6raX600b5rDifFF\nFFAAAAAAPc6p410k6YqhV8if6le4Lqya+hqF68OKM3F69vpnleRJUnNrMzvqxggKKAAAAIAeze1y\nRzYxunPMndp6eKs2HdgUKam/fu/X2te4T8FAUEF/UMWZxXIZl8Op+yYKKAAAAIBewxijwv6FKuxf\nGLk3Lm+clu5YqpfXv6yXPn5JaQlpum74dfqHkf/gYNK+iQIKAAAAoFebNnSapg2dpsaWRn2w+wOF\n68JKiOvYrOjYiWP6Xc3vVOWvUlVuFetGLzAKKAAAAIA+IcmTpAmDJmjCoAmRe7uP7tbGgxv1ft37\nMjIakTlCoUBIkwdPpoxeABRQAAAAAH1WYf9CzZsxT1sObVFNXccGRvNWzVO5r1xp3jRtO7xNza3N\nKh5QLGOM03F7PAooAAAAgD7NGKMhGUM0JGOIbiq5Sfsa9ykzMVOS9PL6l7V462Kle9NVnVutUCCk\n0pxSeeI8DqfumSigAAAAAHCagUkDI69nV8xWeU65aupq9N6O9/SXLX9RXmqeHrnqEUlSS1sLZfQ8\ndLmAGmO+L+keSW2SXrPW/qzLqQAAAAAgBiR7knVpwaW6tOBSnWg7odUNq9V0okmS1G7bNfuV2fKn\n+CNHvPhSfA4njm1dKqDGmEmSrpVUaq09bozJ6p5YAAAAABBb4uPiVeGriFy3tLXo8iGXK1wX1hMr\nn9ATK59QXmqebiu7TdX+ageTxq6uzoDeLekBa+1xSbLWNnQ9EgAAAADEPq/bq5tLbtbNJTdr79G9\nCteHP3fEy+aDm7Vo8yIFA0GVZJfwqK66XkCHSbrEGHOfpGZJ/8NaW9v1WAAAAADQc2QnZ2tG8QzN\nKJ4RuVf3WZ2WbF+ihZ8slNftVUVOhUKBkC7Ov7jPllFjrT37XzDmLUk5Z3hrrqT7JL0j6QeSqiT9\nUVKhPcOHGmPulHSnJGVnZ4954YUXupb8Ajt69KiSk5OdjoEYxNhAZxgb6AxjA2fD+EBnGBu9Q2t7\nqz5p/ETrPlun9UfW63jbcf1yxC/ldrm1vXG7UuJTlOHJOK/P7AljY9KkSR9Yayu/eP+cBfRsjDEL\nJf1Pa+07J68/kRSy1u4727+rrKy0y5cv/8pfNxqWLFmiiRMnOh0DMYixgc4wNtAZxgbOhvGBzjA2\neh9rrfYc3RPZqOh7r39P2z/drkFpgxT0BxUKhDQ0Y+g5zxvtCWPDGHPGAurq4ue+LGnSyS8wTJJH\n0v4ufiYAAAAA9DrGmM/tkjv3krmaXT5bKZ4UzV83Xz/5y0/08LKHI++3trc6EfOC6uoa0HmS5hlj\n1khqkfTtMz1+CwAAAAD4PF+KT9cOv1bXDr9WR44f0fJdy5WZmClJ2nt0r77/xvdV4atQ0B9Ulb9K\nyZ7Yfuz2y+hSAbXWtki6uZuyAAAAAECflJKQokmDJ33u3qWDLlW4PqylO5fKZVwaNXCU7qq8y6GE\n3aOrM6AAAAAAgG6WnZyte6rv0T/af9TGAxsVrg9rWf0ypSakOh2tSyigAAAAABCjjDEqzixWcWax\nbi291ek4XdbVTYgAAAAAAPhSKKAAAAAAgKiggAIAAAAAooICCgAAAACICgooAAAAACAqKKAAAAAA\ngKiggAIAAAAAooICCgAAAACICgooAAAAACAqKKAAAAAAgKiggAIAAAAAooICCgAAAACICgooAAAA\nACAqKKAAAAAAgKiggAIAAAAAooICCgAAAACICgooAAAAACAqKKAAAAAAgKiggAIAAAAAooICCgAA\nAACICgooAAAAACAqjLU2+l/UmH2Stkf9C5+fTEn7nQ6BmMTYQGcYG+gMYwNnw/hAZxgb6ExPGBuD\nrLUDv3jTkQLaExhjlltrK53OgdjD2EBnGBvoDGMDZ8P4QGcYG+hMTx4bPIILAAAAAIgKCigAAAAA\nICoooJ37vdMBELMYG+gMYwOdYWzgbBgf6AxjA53psWODNaAAAAAAgKhgBhQAAAAAEBUU0E4YY75m\njFlrjGk3xvTIHabQvYwx04wxG4wxm40xP3c6D2KHMWaeMabBGLPG6SyILcaYPGPMO8aYdSf/n/JD\npzMhNhhjvMaYZcaYD0+OjV85nQmxxRgTZ4xZaYz5s9NZEFuMMduMMauNMauMMcudznO+KKCdWyPp\neknvOh0EzjPGxEn635KmSxop6VvGmJHOpkIMeUrSNKdDICa1SvqptXakpJCke/jegZOOS5psrS2V\nVCZpmjEm5HAmxJYfSvrY6RCIWZOstWU98SgWCmgnrLUfW2s3OJ0DMaNa0mZr7RZrbYukFyRd63Am\nxAhr7buSDjqdA7HHWrvbWrvi5Osj6vhh0u9sKsQC2+Hoycv4k3/YmAOSJGNMQNJVkh53OgvQ3Sig\nwJfjl7TztOs68UMkgPNgjCmQVC4p7GwSxIqTj1iuktQg6U1rLWMDp/xO0s8ktTsdBDHJSvqLMeYD\nY8ydToc5X26nAzjJGPOWpJwzvDXXWvunaOcBAPROxphkSS9J+pG19jOn8yA2WGvbJJUZY9IlLTDG\njLLWspa8jzPGXC2pwVr7gTFmotN5EJPGW2vrjTFZkt40xqw/+TRWj9CnC6i1dorTGdBj1EvKO+06\ncPIeAJyVMSZeHeXzWWvtfzudB7HHWnvYGPOOOtaSU0BxsaQZxpgrJXklpRpj/o+19maHcyFGWGvr\nT/63wRizQB1LxXpMAeURXODLqZVUZIwZbIzxSPqmpFcczgQgxhljjKQnJH1srf1fTudB7DDGDDw5\n8yljTD9JUyWtdzYVYoG1do61NmCtLVDHzxtvUz5xijEmyRiTcuq1pMvVw35xRQHthDFmpjGmTtJY\nSa8ZYxY5nQnOsda2SvqepEXq2ETkRWvtWmdTIVYYY56X9H8lFRtj6owxtzudCTHjYkm3SJp8crv8\nVSdnNQCfpHeMMR+p45ecb1prOW4DwLlkS/qbMeZDScskvWatXehwpvNirGXDNQAAAADAhccMKAAA\nAAAgKiigAAAAAICooIACAAAAAKKCAgoAAAAAiAoKKAAAAAAgKiigAAAAAICooIACAAAAAKKCAgoA\nAAAAiIr/B+vVzap5ybmiAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "n_YcWRElnM_b"
      },
      "source": [
        "# Define a custom callback that is called during training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ku9wQERMKsWo",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class MyCustomCallback(tf.keras.callbacks.Callback):\n",
        "\n",
        "  def on_train_batch_begin(self, batch, logs=None):\n",
        "    print('Training: batch {} begins at {}'.format(batch, datetime.datetime.now().time()))\n",
        "\n",
        "  def on_train_batch_end(self, batch, logs=None):\n",
        "    print('Training: batch {} ends at {}'.format(batch, datetime.datetime.now().time()))\n",
        "\n",
        "  def on_test_batch_begin(self, batch, logs=None):\n",
        "    print('Evaluating: batch {} begins at {}'.format(batch, datetime.datetime.now().time()))\n",
        "\n",
        "  def on_test_batch_end(self, batch, logs=None):\n",
        "    print('Evaluating: batch {} ends at {}'.format(batch, datetime.datetime.now().time()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yzP5MDEfLEFK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "class get_weight_and_bias(tf.keras.callbacks.Callback):\n",
        "\n",
        "  def on_train_batch_begin(self, batch, logs=None):\n",
        "    w=model.layers[0].get_weights()[0][0][0]\n",
        "    b=model.layers[0].get_weights()[1][0]\n",
        "    print(w,b)\n",
        "\n",
        "    plot_model_function(xs, ys, xline, w, b)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "6gC-ENn6KluA"
      },
      "source": [
        "# Training the Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "c-Jk4dG91dvD"
      },
      "source": [
        "The process of training the neural network, where it 'learns' the relationship between the Xs and Ys is in the **model.fit**  call. This is where it will go through the loop we spoke about above, making a guess, measuring how good or bad it is (aka the loss), using the opimizer to make another guess etc. It will do it for the number of epochs you specify. When you run this code, you'll see the loss on the right hand side."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "lpRrl7WK10Pq",
        "outputId": "719dbd53-091e-4637-bc55-d8f75b22f1cf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.fit(xs, ys, epochs=10, callbacks=[get_weight_and_bias()])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/10\n",
            "-1.2352076 0.0\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 45.3716\n",
            "Epoch 2/10\n",
            "-0.9309028 0.07705623\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 36.0725\n",
            "Epoch 3/10\n",
            "-0.6603545 0.14344218\n",
            "1/1 [==============================] - 0s 2ms/step - loss: 28.7486\n",
            "Epoch 4/10\n",
            "-0.41975445 0.20038398\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 22.9789\n",
            "Epoch 5/10\n",
            "-0.20572469 0.24896893\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 18.4323\n",
            "Epoch 6/10\n",
            "-0.0152688725 0.29016128\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 14.8479\n",
            "Epoch 7/10\n",
            "0.15427072 0.3248161\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 12.0208\n",
            "Epoch 8/10\n",
            "0.3052516 0.35369167\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 9.7895\n",
            "Epoch 9/10\n",
            "0.43976486 0.3774603\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 8.0273\n",
            "Epoch 10/10\n",
            "0.5596654 0.39671814\n",
            "1/1 [==============================] - 0s 1ms/step - loss: 6.6342\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7f77aee0da20>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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S41JKKaWUUkpVTfoallKKiooIDQ1lyZIl+Pr63tC+KPVHVIXfk1JKKaWU+uvS17Bcwo4d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uVkSiEQLhACmJKYQiIVbbVmvBdPS69Tnl5/Cdo77DcHiYv3z0F234kcVoIS0pLcavQIjxRVVV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\nYII=\n",
            "text/plain": [
              "<Figure size 1152x648 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kaFIr71H2OZ-"
      },
      "source": [
        "Ok, now you have a model that has been trained to learn the relationshop between X and Y. You can use the **model.predict** method to have it figure out the Y for a previously unknown X. So, for example, if X = 10, what do you think Y will be? Take a guess before you run this code:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "oxNzL4lS2Gui",
        "outputId": "4822d86a-d5f2-4e48-8bb3-db082b361759",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(model.predict([10.0]))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[7.077978]]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "btF2CSFH2iEX"
      },
      "source": [
        "You might have thought 19, right? But it ended up being a little under. Why do you think that is? \n",
        "\n",
        "Remember that neural networks deal with probabilities, so given the data that we fed the NN with, it calculated that there is a very high probability that the relationship between X and Y is Y=2X-1, but with only 6 data points we can't know for sure. As a result, the result for 10 is very close to 19, but not necessarily 19. \n",
        "\n",
        "As you work with neural networks, you'll see this pattern recurring. You will almost always deal with probabilities, not certainties, and will do a little bit of coding to figure out what the result is based on the probabilities, particularly when it comes to classification.\n"
      ]
    }
  ]
}