chapter04_getting-started-with-neural-networks.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/TomiToivio/deep-learning-with-python-notebooks/blob/master/chapter04_getting-started-with-neural-networks.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oWeR2ixCknob"
      },
      "source": [
        "This is a companion notebook for the book [Deep Learning with Python, Second Edition](https://www.manning.com/books/deep-learning-with-python-second-edition?a_aid=keras&a_bid=76564dff). For readability, it only contains runnable code blocks and section titles, and omits everything else in the book: text paragraphs, figures, and pseudocode.\n",
        "\n",
        "**If you want to be able to follow what's going on, I recommend reading the notebook side by side with your copy of the book.**\n",
        "\n",
        "This notebook was generated for TensorFlow 2.6."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7AU-7kfBknoe"
      },
      "source": [
        "# Getting started with neural networks: Classification and regression"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CsZpmYkqknof"
      },
      "source": [
        "## Classifying movie reviews: A binary classification example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GB10X90Kknog"
      },
      "source": [
        "### The IMDB dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wq4Ya_dNknog"
      },
      "source": [
        "**Loading the IMDB dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "aE91hduxknog",
        "outputId": "e629cb85-fe4a-4833-ee92-cbfedbb06666",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "(train_data, train_labels), (test_data, test_labels) = imdb.load_data(\n",
        "    num_words=10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "wjiw6QX6knoi",
        "outputId": "25aa7c7d-3921-439e-d4e1-d25c1bdc7e53",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              " 32]"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ],
      "source": [
        "train_data[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "d-DF9GMrknoi",
        "outputId": "724ddd95-b0b9-4651-e3a2-1f700a6b1781",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ],
      "source": [
        "train_labels[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "IRSwpy-oknoj",
        "outputId": "3f8bf00b-7a60-439c-e7da-e4746389db7a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9999"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "max([max(sequence) for sequence in train_data])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKe8q8P2knoj"
      },
      "source": [
        "**Decoding reviews back to text**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "qohn5wZRknok",
        "outputId": "946c69fc-5b1b-40cf-eb47-f159522c3186",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb_word_index.json\n",
            "1641221/1641221 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "word_index = imdb.get_word_index()\n",
        "reverse_word_index = dict(\n",
        "    [(value, key) for (key, value) in word_index.items()])\n",
        "decoded_review = \" \".join(\n",
        "    [reverse_word_index.get(i - 3, \"?\") for i in train_data[0]])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WJ2fBO6mknok"
      },
      "source": [
        "### Preparing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "L2mQbdaXknok"
      },
      "source": [
        "**Encoding the integer sequences via multi-hot encoding**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "hdrYsqWRknok"
      },
      "outputs": [],
      "source": [
        "import numpy as nptrain_data)\n",
        "x_test = vectorize_sequences(test_data\n",
        "def vectorize_sequences(sequences, dimension=10000):\n",
        "    results = np.zeros((len(sequences), dimension))\n",
        "    for i, sequence in enumerate(sequences):\n",
        "        for j in sequence:\n",
        "            results[i, j] = 1.\n",
        "    return results\n",
        "x_train = vectorize_sequences()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "sXWZ8G8eknol",
        "outputId": "d56392d9-a2f6-401c-bb7f-68eb4e35025b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 1., 1., ..., 0., 0., 0.])"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "x_train[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "jrSFWoXAknol"
      },
      "outputs": [],
      "source": [
        "y_train = np.asarray(train_labels).astype(\"float32\")\n",
        "y_test = np.asarray(test_labels).astype(\"float32\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mO1XnmvTknol"
      },
      "source": [
        "### Building your model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9b_BPK3xknol"
      },
      "source": [
        "**Model definition**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "Ab1F-_ppknol"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "model = keras.Sequential([\n",
        "    layers.Dense(16, activation=\"relu\"),\n",
        "    layers.Dense(16, activation=\"relu\"),\n",
        "    layers.Dense(1, activation=\"sigmoid\")\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i4TuO1moknom"
      },
      "source": [
        "**Compiling the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "4ubemLqfknom"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6EtbMnhzknom"
      },
      "source": [
        "### Validating your approach"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jru-ChdFknon"
      },
      "source": [
        "**Setting aside a validation set**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "gfWf0Qtyknon"
      },
      "outputs": [],
      "source": [
        "x_val = x_train[:10000]\n",
        "partial_x_train = x_train[10000:]\n",
        "y_val = y_train[:10000]\n",
        "partial_y_train = y_train[10000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jBENp7r1knon"
      },
      "source": [
        "**Training your model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "xmtbDIeqknon",
        "outputId": "42d32519-c65a-4a2c-e968-9c34496eaf99",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "30/30 [==============================] - 3s 69ms/step - loss: 0.5361 - accuracy: 0.7863 - val_loss: 0.4099 - val_accuracy: 0.8660\n",
            "Epoch 2/20\n",
            "30/30 [==============================] - 1s 34ms/step - loss: 0.3394 - accuracy: 0.8904 - val_loss: 0.3210 - val_accuracy: 0.8828\n",
            "Epoch 3/20\n",
            "30/30 [==============================] - 2s 55ms/step - loss: 0.2541 - accuracy: 0.9141 - val_loss: 0.2850 - val_accuracy: 0.8882\n",
            "Epoch 4/20\n",
            "30/30 [==============================] - 2s 57ms/step - loss: 0.2024 - accuracy: 0.9314 - val_loss: 0.2817 - val_accuracy: 0.8853\n",
            "Epoch 5/20\n",
            "30/30 [==============================] - 1s 46ms/step - loss: 0.1689 - accuracy: 0.9442 - val_loss: 0.2797 - val_accuracy: 0.8862\n",
            "Epoch 6/20\n",
            "30/30 [==============================] - 1s 37ms/step - loss: 0.1469 - accuracy: 0.9526 - val_loss: 0.2866 - val_accuracy: 0.8835\n",
            "Epoch 7/20\n",
            "30/30 [==============================] - 1s 35ms/step - loss: 0.1247 - accuracy: 0.9612 - val_loss: 0.2973 - val_accuracy: 0.8822\n",
            "Epoch 8/20\n",
            "30/30 [==============================] - 1s 36ms/step - loss: 0.1068 - accuracy: 0.9675 - val_loss: 0.3348 - val_accuracy: 0.8730\n",
            "Epoch 9/20\n",
            "30/30 [==============================] - 1s 34ms/step - loss: 0.0926 - accuracy: 0.9730 - val_loss: 0.3268 - val_accuracy: 0.8823\n",
            "Epoch 10/20\n",
            "30/30 [==============================] - 1s 35ms/step - loss: 0.0805 - accuracy: 0.9781 - val_loss: 0.3370 - val_accuracy: 0.8805\n",
            "Epoch 11/20\n",
            "30/30 [==============================] - 1s 34ms/step - loss: 0.0724 - accuracy: 0.9801 - val_loss: 0.3556 - val_accuracy: 0.8798\n",
            "Epoch 12/20\n",
            "30/30 [==============================] - 1s 34ms/step - loss: 0.0591 - accuracy: 0.9847 - val_loss: 0.4592 - val_accuracy: 0.8624\n",
            "Epoch 13/20\n",
            "30/30 [==============================] - 1s 31ms/step - loss: 0.0547 - accuracy: 0.9855 - val_loss: 0.4075 - val_accuracy: 0.8704\n",
            "Epoch 14/20\n",
            "30/30 [==============================] - 1s 32ms/step - loss: 0.0451 - accuracy: 0.9901 - val_loss: 0.4264 - val_accuracy: 0.8716\n",
            "Epoch 15/20\n",
            "30/30 [==============================] - 1s 34ms/step - loss: 0.0395 - accuracy: 0.9908 - val_loss: 0.4355 - val_accuracy: 0.8755\n",
            "Epoch 16/20\n",
            "30/30 [==============================] - 2s 56ms/step - loss: 0.0348 - accuracy: 0.9915 - val_loss: 0.4581 - val_accuracy: 0.8735\n",
            "Epoch 17/20\n",
            "30/30 [==============================] - 2s 53ms/step - loss: 0.0273 - accuracy: 0.9953 - val_loss: 0.4809 - val_accuracy: 0.8732\n",
            "Epoch 18/20\n",
            "30/30 [==============================] - 1s 49ms/step - loss: 0.0241 - accuracy: 0.9953 - val_loss: 0.5091 - val_accuracy: 0.8726\n",
            "Epoch 19/20\n",
            "30/30 [==============================] - 1s 34ms/step - loss: 0.0205 - accuracy: 0.9969 - val_loss: 0.5314 - val_accuracy: 0.8703\n",
            "Epoch 20/20\n",
            "30/30 [==============================] - 1s 35ms/step - loss: 0.0178 - accuracy: 0.9985 - val_loss: 0.5631 - val_accuracy: 0.8699\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "DU58r4wiknon",
        "outputId": "5cc3d3d4-1d31-4c38-83a6-2ff427b021c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'accuracy', 'val_loss', 'val_accuracy'])"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "history_dict = history.history\n",
        "history_dict.keys()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_I-EVT0Cknon"
      },
      "source": [
        "**Plotting the training and validation loss**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "wUPxOUTeknoo",
        "outputId": "e81d4ed0-70d7-481b-9dd7-0b08c236a9bd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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ISEjIN7nx9PTE8ePHMXnyZCxbtgxPnz5FkyZNsGfPHk3tiaHMnDkTlSpVwvLly/HBBx+gQoUKePfdd/HZZ59p5mEKCAhAaGgo9uzZg9u3b8PR0REBAQHYv3+/ZqRY9+7dce3aNaxduxb37t2Dh4cH2rVrh1mzZmlGWxGVhkIypT8riUgWPXv2xKVLl/LtD0JEZG7Y54aojHlxqYQrV65g3759aN++vTwBERHpGWtuiMoYb29vzXpH169fx8qVK5GVlYWzZ8+idu3acodHRFRq7HNDVMZ06tQJmzdvRlJSEuzs7BAcHIzPPvuMiQ0RWQzW3BAREZFFYZ8bIiIisihMboiIiMiilLk+NyqVCnfu3IGzs3OxpjwnIiIi+UiShMePH8PHx6fIySrLXHJz584d+Pr6yh0GERERlcDNmzdRtWrVQsuUueTG2dkZgPjhuLi4yBwNERER6SItLQ2+vr6a7/HClLnkRt0U5eLiwuSGiIjIzOjSpYQdiomIiMiiMLkhIiIii8LkhoiIiCwKkxsiIiKyKExuiIiIyKIwuSEiIiKLwuSGiIiILAqTGyIiIrIoTG6IiIjIojC5ISIiIovC5IaIiIgsCpMbIiIisihMboiIiEhvjh4FHj6UNwYmN0RERKQXX34JvPoqMGAA8OyZfHEwuSEiIqJSyckBRo4ERo8GlEqgUiV5k5ty8p2aiIiIzN29e0DfvsDPPwMKBfD558BHH4nncmFyQ0RERCVy8SLQvTuQkAA4OwObNgFvvCF3VGyWIiIiohLYtQsIDhaJTY0aQFycaSQ2AJMbIiIiKgZJAj77DOjVC0hPFx2IT54EGjaUO7LnmNwQERGRTp48AQYNAqZNE0nO6NHAwYNAxYpyR6aNfW6IiIioSLdvAz16AKdPA+XKAcuXA++9J3dU+WNyQ0RERIX67TegZ08gKUnU0mzfDrRrJ3dUBWOzFBERERVowwaRyCQlAY0aAadOmXZiAzC5ISIionwolcB//gMMGQJkZYkmqePHgerV5Y6saExuiIiISEtqqpi/Zt48sT1tGhAVJeayMQfsc0NEREQaV6+KxObyZcDeHli/HggLkzuq4mFyoydKpVgJNTER8PYG2rQBrK3ljoqIiEh3MTFAv35iVe8qVcREfYGBckdVfGyW0oOoKMDfX0xk9Oab4l9/f7GfiIjI1EkSsGwZEBoqEpugINFx2BwTG4DJTalFRYkFw27d0t5/+7bYzwSHiIhMWXa2mK9m3DjRCjFkCHDkiGiFMFdMbkpBqQTGjxcZ74vU+yZMEOWIiIhMzd27QEgIsHq1WMV73jzRx8beXu7ISofJTSkcPZq3xiY3SQJu3hTliIiITMn580CLFuI7ysUF2LsXmDRJJDnmjslNKSQm6rccERGRMezYAbRqBVy/DtSqBZw4AXTpIndU+sPkphR0bY8053ZLIiKyHFlZQEQE0Ls3kJEhmqR++w2oX1/uyPSLyU0ptGkDVK1acBWeQgH4+opyREREcpEkYPduoGFDYPZssW/sWGD/fqBCBXljMwQmN6VgbQ0sWSKev5jgqLcXL+Z8N0REJJ8//wQ6dRLLJ/zzj2hN+O47YOlSsbq3JWJyU0q9ewM//CAmO8qtalWxv3dveeIiIqKy7eFDMaK3SRPg0CHA1haYOhWIjxdzslkyC83ZjKt3b5ERc4ZiIiKS27NnYmj3J58A9++LfT17AgsWADVqyBqa0TC50RNra6B9e7mjICKisiw2VtTWXLggths2FN0jQkJkDcvo2CxFRERk5hISgD59gA4dRGLj7g4sXw6cO1f2EhuANTdERERmKz0diIwUTU5ZWaIVYeRIYOZMoGJFuaOTD5MbIiIiM6NSAZs2AZMnA3fuiH0dO4omqEaNZA3NJDC5ISIiMiMnT4p+NSdOiO0aNUTNTY8elrF0gj6wzw0REZEZSEwEhg4FgoJEYlO+vGiSunRJjIZiYvMca26IiIhM2NOnornp009FHxsACA8HPvsM8PGRNTSTxeSGiIjIBEkSsGsX8OGHwL//in1BQWJm4ZYt5Y3N1LFZioiIyMRcvAi89hrQq5dIbLy9gW+/BY4fZ2KjCyY3REREJiI7G/jPf4CmTYGYGMDODvj4Y+Dvv4HBgwErfmvrhM1SREREJuCff4ABA4DffxfbvXsD8+aVnSUT9InJDRERkcw2bwbeew94/FjMLrxmjWiSopIxiQquFStWwN/fH/b29ggKCsLJkycLLLt+/XooFAqth729vRGjJSIi0o+MDODtt8Uq3Y8fA6+8IpZMYGJTOrInN1u3bsXEiRMRERGBM2fOICAgAKGhoUhJSSnwPS4uLkhMTNQ8rl+/bsSIiYiISu+PP4DmzYF168QcNTNmiIUvq1WTOzLzJ3tys3DhQowYMQLDhg1DgwYNsGrVKjg6OmLt2rUFvkehUMDLy0vz8PT0NGLEREREJSdJYlHLoCDgr7/EXDU//QTMmgWUY2cRvZA1ucnOzsbp06cRkmvJUisrK4SEhCAuLq7A96Wnp8PPzw++vr7o0aMHLl26VGDZrKwspKWlaT2IiIjk8OCBaHIaO1YsdPnGG6IGp317uSOzLLImN/fu3YNSqcxT8+Lp6YmkpKR831O3bl2sXbsWu3btwsaNG6FSqdCqVSvcunUr3/KRkZFwdXXVPHx9ffV+HUREREU5elQM8d61C7C1FbMO794NeHjIHZnlkb1ZqriCg4MxZMgQNG3aFO3atUNUVBQqVaqEr776Kt/yU6dORWpqquZx8+ZNI0dMRERlmVIJzJkjamdu3gRq1wbi4sTil1wPyjBkbd3z8PCAtbU1kpOTtfYnJyfDy8tLp2PY2NigWbNmuHr1ar6v29nZwc7OrtSxEhERFdft28BbbwFHjojtIUNEfxtnZ1nDsniy1tzY2toiMDAQMTExmn0qlQoxMTEIDg7W6RhKpRIXLlyAt7e3ocIkIiIqtr17gYAAkdiULy+WT/jmGyY2xiB7v+yJEyciPDwczZs3R8uWLbF48WJkZGRg2LBhAIAhQ4agSpUqiIyMBADMnj0bL7/8MmrVqoVHjx5h3rx5uH79Ot555x05L4OIiAiA6Cg8eTKwZInYfuklYMsW0RxFxiF7chMWFoa7d+9ixowZSEpKQtOmTXHgwAFNJ+MbN27AKtdiGg8fPsSIESOQlJQEd3d3BAYG4vjx42jQoIFcl0BERARArAE1YABw9qzYnjAB+PxzsUYUGY9CkiRJ7iCMKS0tDa6urkhNTYWLi4vc4RARkYX49ltg1Cgx63DFiqIJqmtXuaOyHMX5/pa95oaIiMicPX4MjB4NbNggttu3BzZuBKpUkTWsMs3shoITERGZijNngMBAkdhYWYkh39HRTGzkxpobIiKiYpIkYOlS4KOPgJwcwNcX2LRJLHxJ8mNyQ0REVAxJScCIEWKoNwD07AmsWQNUqCBrWJQLm6WIiIh0kJMDLFoE1KkjEhs7O2DFCiAqiomNqWHNDRERURF++kksdvnnn2K7RQvgf/8DmjSRNy7KH2tuiIiICnDzJhAWBnTsKBIbDw+R1Jw4wcTGlDG5ISIiekFWFhAZCdSrB3z/vRgJNWaMmKRv+HCxTaaLzVJERES57NsnVuxWr8f8yitiscuAAHnjIt0x9yQiIgLw779A9+5iVuGrVwEvLzEZ3y+/MLExN0xuiIioTMvMBCIigAYNgD17gHLlgEmTgPh4YNAgQKGQO0IqLjZLERFRmSRJwI4dwAcfADduiH0hIWJyvvr15Y2NSofJDRERlTl//QWMGwccPiy2fX3FHDa9e7OmxhKwWYqIiMqMx4+B//wHaNxYJDZ2dsD06SLZ6dOHiY2lYM0NERFZPEkCNm8WfWkSE8W+bt1EbU3NmvLGRvrH5IaIiCza+fNijpqjR8V2zZrAkiViVBRZJjZLEREZiCQBKpXcUZRdjx6JfjXNmonExsEB+PRT4OJFJjaWjskNEZEB3L8P+PkBHTqI2W7JeFQqYO1ascDlsmViu18/0a/m448Be3u5IyRDY3JDRGQAGzeKdYl+/ll0YCXjOHECePllsUTC3btiSHd0tFhCoVo1uaMjY2FyQ0RkAOvXP3++dCmwc6dckZQNiYlAeDgQHAycOgU4OwPz5wN//CEWvaSyhckNEZGenT8PnDsH2NiIGgQAGDYMuHZNzqgsU1YWMHeuaIL69lux7+23gStXgA8/FPeAyh4mN0REevbNN+Lfbt2AlSuBoCDRuXXAACA7W9bQLMq+fWK+msmTgfR08XP+7TdgzRrA01Pu6EhOTG6IiPTo2TPgu+/E8/BwUXOwZQvg5ia+eD/+WNbwLMKVK8Abb4gRT1euiERm/Xrg+HGgZUu5oyNTwOSGiEiPDh4EkpOBSpWAzp3FPn9/YN068XzBAmDvXtnCM2uPH4tamoYNgR9/FInjRx8Bf/8tEkkrfqPR/+OvAhGRHqk7Eg8apN3fo2dPMecKIL6Ib940dmTmS6US/Wnq1BH9a3JyROJ48aLYdnGRO0IyNUxuiIj05MEDYPdu8Tw8PO/rc+cCgYGi3MCB4kuaCvf770Dr1uLnmZQE1Kolar727RPJDlF+mNwQEenJ1q2iw3CTJkDTpnlft7MTZVxcgGPHgIgIo4doNlJSgHfeEX1oTpwAnJyAL77g7MKkGyY3RER6oh4llV+tjVrNmsD//ieeR0aKPjr0XE6OWMyydm0x6kmSgMGDgfh4MRminZ3cEZI5YHJDRKQHf/0lRkNZW4v+NoXp1w8YOVI8HzwYuHPH8PGZg0OHgIAAYOJEIC1NNOEdPy762/j4yB0dmRMmN0REeqCutencWbc5VhYuFF/kd+8Cb74JKJWGjc+U/fuv6HAdGgpcvixGmv3vf8DJk2LGYaLiYnJDRFRKSiWwYYN4XliTVG729mK9Iycnsf7U7NmGi89UZWQA06cDDRoAu3YB5coBH3wghnYPH86h3VRy/NUhIiqln34Cbt8G3N3FrMS6qlMH+Oor8XzOHCAmxjDxmRKVSnQKXrYMqFsX+PRTsYRCSIhYB2rhQjHhIVFplJM7ACIic6dukhowoPgdXt98UyRHa9aIvjp//GFZSwdkZwOnTwNHj4rHsWPAw4fPX69eXSQ0PXoACoV8cZJlYXJDRFQKaWlAVJR4PnRoyY6xdKnojHzxIvDWW8CBA6Jjsjl6/BiIi3uezPz2G/D0qXaZ8uVFX5ouXUTHant7eWIly8XkhoioFLZtA548AerVA1q0KNkxHB3F/DctWgDR0WKI+PTp+o3TUJKTgV9/fZ7MnDsnmp5y8/AA2rQBXnlF/Nu0KVfrJsNickNEVAq557YpTbNKgwbAihXAsGFicr+2bcXDlEiSGNmkTmR+/VV0/n1R9erPE5k2bUTfGjY5kTEpJEmS5A7CmNLS0uDq6orU1FS4cEESIiqFf/8Vk/IpFMCNG0DVqqU/Znj483ldzp0Tw6LlolQCFy5oJzOJidplFAqgUaPnicwrr+jn50D0ouJ8f7PmhoiohL79VvwbEqK/L/QVK8T8Ln/9BQwZIla/NvaQ6Bs3gM8/BzZtAlJTtV+zsRHNZ+pkplUrMUqMyJQwuSEiKgGV6nmTVEk7EufHyUnMf9OypehYPH++WHbAGBISRH+f9eufL+rp7CwSGHUy06IF4OBgnHiISorNUkREJfDzz0D79uLLPylJdArWp9WrgXffFaOmfvlFJBiGcuUK8NlnYiJC9UzJHTsCH38s+v2U45/BZAKK8/3NSfyIiEpAXWvTv7/+ExtArIg9cKBINgYMAO7f1/85/vpLrG1Vr56orVEqxRIIv/4qRm116MDEhswTkxsiomLKyBBDwAHdl1soLoUCWLUKqFULuHlTjKLSVz37pUsicWrQANi4UTSxde0KnDghmsJat9bPeYjkwuSGiKiYduwA0tOBGjXE6CBDcXER/W9sbYE9e4DFi0t3vD/+ECuSN2oEbNkikqUePYDffwf27gWCgvQSNpHsmNwQERXT+vXi39LObaOLZs2ARYvE88mTxUiq4jpzBujVS0ye98MPYl/fvmKo+c6dQGCgnoIlMhFMboiIiuHmTbEWFCCGahvDyJEiGcnJAcLCgEePdHvfyZNiIc/AQJHEKBSi/86FC6JZLSDAkFETyYfJDRFRMWzYIJpz2rUD/P2Nc06FAvjf/8TMv9euAcOHF97/5vhxoFMn0cy0d6+YJ+ett0Rfm82bRbMUkSVjcqNnZWtgPVHZIknayy0Yk6ur6H9jYyMW6lyxIm+ZX34REwq2bg0cPCiGkQ8dKkZFbdgA1K9v3JiJ5MLkRk8uXhRDQseOlTsSIjKU334Tayk5OopmImNr3hyYN088//BD0ZdGkkQzWfv2ojYpJkYM337nHRHrunVA7drGj5VITpzBQE/u3xdt2A4OwOzZQIUKckdERPqm7kjcp4+YvE8O48YBsbHArl1i5JO3N3DsmHjNxkY0WU2ZAvj5yRMfkSlgzY2etG0rOuc9eSLaxonIsjx9CmzdKp4bu0kqN4UCWLsWqFZNLNx57BhgZweMGQP88w+wciUTGyKTSG5WrFgBf39/2NvbIygoCCd1HOu4ZcsWKBQK9OzZ07AB6kChAMaPF8+XLweePZM3HiLSr927xSglX1/g1VfljaVCBWD7drH+1IQJIslZtkzERkQmkNxs3boVEydOREREBM6cOYOAgACEhoYiJSWl0Pddu3YNkyZNQps2bYwUadEGDgQqVRJDRXfulDsaItIndUfiwYONv0p3fpo3F32AFi0CfHzkjobItMj+EV24cCFGjBiBYcOGoUGDBli1ahUcHR2xdu3aAt+jVCoxaNAgzJo1CzVq1DBitIWztwfee088X7JE3liISH8SE8XoI0DeJiki0o2syU12djZOnz6NkJAQzT4rKyuEhIQgLi6uwPfNnj0blStXxvDhw4s8R1ZWFtLS0rQehjRypBip8OuvYiQDEZm/774Ti0oGBwN16sgdDREVRdbk5t69e1AqlfD09NTa7+npiaSkpHzf8+uvv2LNmjVYvXq1TueIjIyEq6ur5uFr4EZpHx8xJBxg7Q2RJZBzbhsiKhnZm6WK4/Hjxxg8eDBWr14NDw8Pnd4zdepUpKamah43b940cJTPOxZv2QIkJxv8dERkQGfPinms7OzE0gdEZPpknefGw8MD1tbWSH4hA0hOToaXl1ee8v/88w+uXbuGbt26afapVCoAQLly5RAfH4+aNWtqvcfOzg52dnYGiL5gLVsCL78MnDgBrFoFREQY9fREpEfqWpsePQA3N1lDISIdyVpzY2tri8DAQMTExGj2qVQqxMTEIDg4OE/5evXq4cKFCzh37pzm0b17d7z66qs4d+6cwZucikNde7NyJZCVJW8sRFQy2dnApk3i+dChsoZCRMUg+wzFEydORHh4OJo3b46WLVti8eLFyMjIwLBhwwAAQ4YMQZUqVRAZGQl7e3s0emHFN7f//1Pqxf1y69MHqFIFuH1brAczeLDcERFRce3bB9y7B3h5Aa+9Jnc0RKQr2fvchIWFYf78+ZgxYwaaNm2Kc+fO4cCBA5pOxjdu3EBiYqLMURafjQ0wapR4vmQJF9QkMkfqJqm33hKjIInIPCgkqWx97aalpcHV1RWpqalwcXEx6Lnu3RMzhj59KoaGt25t0NMRkR7duydGP+bkABcuACZWOUxU5hTn+1v2mhtL5uEBDBoknnNYOJF52bxZJDYvvcTEhsjcMLkxMHXH4qgosSwDEZkH9Qrg7EhMZH6Y3BhY48ZikT2lElixQu5oiEgXFy+KGcZtbMSacURkXpjcGIG69ubrr4HMTHljIaKiqTsSd+0qmpeJyLwwuTGCN94AqlcHHj4ENm6UOxoiKsyzZ88/p1xugcg8MbkxAmtrYMwY8XzpUg4LJzJlhw8DSUlAxYpAly5yR0NEJcHkxkjefhsoXx64dAnINSEzEZkYdUfiQYMAW1tZQyGiEmJyYyRubs9HXXBYOJFpevgQ2LVLPGeTFJH5YnJjRGPHin9//BG4elXeWIgor++/F2vBNWoENGsmdzREVFJMboyobl2gc2fR52b5crmjIaIXqUdJhYcDCoW8sRBRyTG5MTL1sPC1a4G0NHljIaLn/v4biIsDrKyezyxOROaJyY2Rvf46UK8e8Pjx846LRCQ/da1Np06At7e8sRBR6TC5MTKFAhg3TjxftgxQqeSNh4jE53DDBvGcHYmJzB+TGxkMGSJGT129CuzbJ3c0RBQbK9Z+c3MDuneXOxoiKi0mNzIoXx545x3xnMPCieSnbpIKCwPs7eWNhYhKj8mNTMaMER0Xo6PFxH5EJI/Hj4Ht28VzNkkRWQYmNzLx8wN69hTPly6VNRSiMu2HH8SCtnXqAC+/LHc0RKQPTG5kpB4WvmED8OCBvLEQlVWc24bI8jC5kVGbNkDTpsCTJ8Dq1XJHQ1T2JCQAP/8skprBg+WOhoj0hcmNjBSK57U3K1YAz57JGw9RWaMe/t2hA+DrK28sRKQ/TG5kNmAAUKmSGIa6Y4fc0RCVDdevA6NHA599JrbZkZjIsjC5kZm9PfDee+I5h4UTGdaVK8DbbwO1agFffikWyXz9daBfP7kjIyJ9YnJjAkaOBMqVA44dA06fljsaIstz4QIwcKBY+mTdOtEE3LGjmLzvwAHObUNkaZjcmAAfH6B/f/GctTdE+nPqlJhyoUkTYMsWsczCG2+IBTKjo4H27TlCisgSMbkxEeqOxVu2AElJ8sZCZO6OHgVCQ4GWLYFdu0QC078/cO4csGcP57MhsnRMbkxEy5biP9ycHGDVKrmjITI/kgQcOgS0bSsehw4B1tZiLbc//wS2bgUCAuSOkoiMgcmNCVHX3qxcKTo65qZUAkeOAJs3i3+VSmNHR2SaVCpRO9OypaitOXoUsLUVHfWvXBGT9NWrJ3eURGRMTG5MSJ8+QJUqQEqK+CtTLSoK8PcHXn0VePNN8a+/v9hPVFYplSLZDwgQ/Wp+/x1wcAA++AD4919RA1q9utxREpEcmNyYEBsbYNQo8XzJElHNHhUF9O0L3LqlXfb2bbGfCQ6VNTk5YsRT/foi2b94EXBxAT7+WMxfs3Ch+COBiMouhSRJktxBGFNaWhpcXV2RmpoKFxcXucPJ4949MVPq06ei+emtt/ImNmoKBVC1qphC3traqGESGd3Tp8DatcAXXwA3boh9FSqImpoxYwA3N1nDIyIDK873N2tuTIyHBzBokHgeEVFwYgOImp2bN0UfAyJLlZ4OzJ8vmphGjxaJjZeX2Hf9OjB9OhMbItJWTu4AKK/x44E1a3RPWhITDRsPkbHduwf89JOYiyYqCrh/X+yvVg2YPFnMMsyJ94ioIExuTFDjxqLTcGysbuW9vQ0bD5m3AwdEstCiBVC7NmBlgvW1T54Av/4qkpnDh4GzZ7Vfr1VL9KkZNEiMhCIiKgyTGxM1frxIbhQK0fyUH3WfmzZtjBsbmY+5c0VNh5qrq0hyWrQQQ6dbthQzZBubUikSGHUyc+xY3ukPGjcGQkKA114T6z+xXxkR6YrJjYl64w3RxyAhQWy/mOSop4xfvJj/6VP+5swBZswQzxs3FnO+pKaKhCI6+nk5Hx+R5KgTnubN9d+HRZLE8Gx1MvPTT8DDh9plqlQRiUxIiFj3yctLvzEQUdnB0VImbNEiYOJEMXpKpRLDv9V8fUVi07u3bOGRiZIkkdT8979ie84c0ek2J0cMmz55Uqy5dPIkcOmS+N16UZ06z2t2WrQAmjYtfh+X3P1mDh8Grl3Tft3FRTS/hoSIR926XOeJiApWnO9vJjcmLDVV/DWbkSH6TdjZic7D3t6iKYo1NvQiSQKmTBHNUYD496OPCi6fkQGcOaOd8KhrC3MrV04sPpk74alfX/t3sKh+MzY2QHDw82SmRQtxXCIiXTC5KYQ5JTeAmL9jxQrRTLVnj9zRkCmTJDHni3pl+cWLny/pURz37j1PdNT/3r2bt1z58qIJq0kTUQNUWL+ZkBCx3pOTU/HjISICmNwUytySm/h4sS6OQgH8/bcYNUL0IpVKJMIrV4rtL78ERo7Uz7ElScwnkzvh+f13UevzIvabISJDYXJTCHNLbgCgSxdg/35g3Ljnf5UTqSmVYpHINWtEErx6NTB8uOHP+ddfItk5fx6oWZP9ZojIsJjcFMIck5uDB4FOnQBnZzFjsZmETUagVALDhgEbNoj5a9avBwYPljsqIiL94/ILFub110XT1OPHYl0dIkCMfnrrLZHYWFsDmzYxsSEiApjcmAWFQoyAAYDPPhPDestWfRu9KDsbGDAA2LJFjEL6/nsgLEzuqIiITAOTGzMRHg5ERornn34KjB2b//wkZPmysoC+fcWaS7a2wPbtnO+IiCg3JjdmZMoUMQpGoRDDw8PDRdMElR1PngA9e4ppAeztgV27gG7d5I6KiMi0MLkxMyNHAhs3ij4WGzcCffoAT5/KHRUZQ0aGSGQOHAAcHIC9e0VHcyIi0sbkxgy9+Sawc6f4y33PHjFU/PFjuaMiQ3r8WNznmBgxEd6BA2IeGSIiyovJjZl64w3xBefsLFYP79gRuH9f7qjIEFJTgdBQ4JdfxDQABw+K2X6JiCh/TG7MWLt2YmHCihXFrLFt22ovrknm7+FDMeNvXJxYqTs6GmjVSu6oiIhMW4mSm5s3b+LWrVua7ZMnT2LChAn4+uuv9RYY6aZ5c/EXfZUqwJ9/igU1//lH7qhIH+7dEzVyp06JBPann8Rik0REVLgSJTdvvvkmYmNjAQBJSUl47bXXcPLkSUybNg2zZ8/Wa4BUtAYNxGrMNWuKFZ1feQW4eFHuqKg0UlKADh3EytqVK4umx2bN5I6KiMg8lCi5uXjxIlq2bAkA+P7779GoUSMcP34c3333HdavX1/s461YsQL+/v6wt7dHUFAQTp48WWDZqKgoNG/eHG5ubihfvjyaNm2KDRs2lOQyLIq/P3D0qFiFOSlJNFH99pvcUVFJJCYC7dsDFy4A3t7AkSPivhIRkW5KlNzk5OTAzs4OABAdHY3u3bsDAOrVq4fExMRiHWvr1q2YOHEiIiIicObMGQQEBCA0NBQpKSn5lq9QoQKmTZuGuLg4nD9/HsOGDcOwYcNw8ODBklyKRVF/Eb78suir0bGjGF1D5uPWLdGX6vJloGpV4Oefgfr15Y6KiMi8lGjhzKCgILz66qvo2rUrXn/9dZw4cQIBAQE4ceIE+vbtq9UfR5djtWjRAsuXLwcAqFQq+Pr6YuzYsZiiXnOgCC+99BK6du2KOXPmFFnWHBfOLK70dKBXL9H51NYW2LpVTPxGpu36ddEU9e+/gJ+f6GNTo4bcURERmQaDL5z5xRdf4KuvvkL79u0xcOBABAQEAAB2796taa7SRXZ2Nk6fPo2QkJDnAVlZISQkBHFxcUW+X5IkxMTEID4+Hm0LGBublZWFtLQ0rYelc3ISE7z17i3WIOrbF/j2W7mjosL8+69oSvz3X5HQ/PwzExsiopIqV5I3tW/fHvfu3UNaWhrc3d01+9999104OjrqfJx79+5BqVTC09NTa7+npyf++uuvAt+XmpqKKlWqICsrC9bW1vjyyy/x2muv5Vs2MjISs2bN0jkmS2FnJ2psRowA1q8XSzWkpoo1qch0SBLw+++ipu32baBOHVFjU6WK3JEREZmvEtXcPHnyBFlZWZrE5vr161i8eDHi4+NRuXJlvQaYH2dnZ5w7dw6nTp3Cp59+iokTJ+LIkSP5lp06dSpSU1M1j5s3bxo8PlNRrhywZg0wfrzYHjcOmDOHK4rL7f59kXi+/Tbg6wu0bCkSmwYNRI0NExsiotIpUc1Njx490Lt3b7z//vt49OgRgoKCYGNjg3v37mHhwoUYOXKkTsfx8PCAtbU1kpOTtfYnJyfDy8urwPdZWVmhVq1aAICmTZvi8uXLiIyMRPv27fOUtbOz03R+LousrIBFiwB3d2DmTGDGDNHZeMECsQAnGd6zZ8DJk2JG6YMHxbw1uRNMe3ugc2dg1Sox7JuIiEqnRDU3Z86cQZs2bQAAP/zwAzw9PXH9+nV8++23WLp0qc7HsbW1RWBgIGJyDelRqVSIiYlBcHCwzsdRqVTIysrS/QLKGIUCiIgAFi8W24sWAcOHiy9dMowbN4DVq0V/Jw8PoHVrUWt28qRIbBo2BCZOFMnOgwdAVBQTGyIifSlRzU1mZiacnZ0BAIcOHULv3r1hZWWFl19+GdevXy/WsSZOnIjw8HA0b94cLVu2xOLFi5GRkYFhw4YBAIYMGYIqVaogMjISgOhD07x5c9SsWRNZWVnYt28fNmzYgJUrV5bkUsqU8ePFFP5vvw2sWwekpQHffSf651DpZGaKJqWDB8XjxS5j7u5iGYXQUOD118UwbyIiMowSJTe1atXCzp070atXLxw8eBAffPABACAlJaXYw6vDwsJw9+5dzJgxA0lJSWjatCkOHDig6WR848YNWFk9r2DKyMjAqFGjcOvWLTg4OKBevXrYuHEjwsLCSnIpZU54uFh8ccAAYPt2sdp0VBRQvrzckZkXSQIuXRKJzIEDYgLF3JWHVlZivqHQUPFo3hywtpYvXiKisqRE89z88MMPePPNN6FUKtGhQwccPnwYgKhV+eWXX7B//369B6ovZWGeG11ER4u5bzIyxEKMe/eK2gUq2P374ud28CBw6FDeRUp9fZ8nMx078udJRKRPxfn+LlFyA4g1pRITExEQEKCpWTl58iRcXFxQr169khzSKJjcPHfihOjI+ugR0KSJ+MJ+YVR+mZWeLtbnOn9ePE6dytsR2MFBzCasTmjq1WMnbSIiQzFKcqOmno24qpl0ImByo+3CBdEHJClJ1DwEB4uOrZUri0RH/Vz9cHa2rC9wpVJMnKdOYtSPf//Nv3yjRs+TmTZtxEgnIiIyvOJ8f5eoz41KpcJ///tfLFiwAOnp6QDE3DMffvghpk2bptVHhkxb48aiv8hrrwHXrgFFTQNkZ5c34SnoUamSaXVWvn9fJHO5k5hLl0Rn4Px4e4saLfWjfXt2BCYiMgclSm6mTZuGNWvW4PPPP0fr1q0BAL/++itmzpyJp0+f4tNPP9VrkGRYtWoBZ8+KviTJyUBKyvNH7u30dNFp9ubNopMgNVfX58lOxYqi5kf9cHEpeDv3cxub4l1PdjYQHy+Sl9zJzIt9ZNTs7UWNjDqJadxYPCpVKt55iYjINJSoWcrHxwerVq3SrAautmvXLowaNQq3C/oWMQFsliq5zEzg7l3t5Kewh77m0bGzKzwRcnYWo72uXRNJzOXLQE5O/sfy99eujWnSRCR3HMlERGTaDN4s9eDBg3w7DderVw8PHjwoySHJDDg6itWq/fyKLitJoqOyuuYnOVlsP34s5td5/Ljo5+qh1VlZIqm6e1f3WJ2d8yYxjRqJpIiIiCxbiZKbgIAALF++PM9sxMuXL0eTJk30EhiZN4VCDIV2dxejiEoiJ0e3JOjxY9Fk5uPzPJHx87Osjs9ERKS7EiU3c+fORdeuXREdHa1ZJiEuLg43b97Evn379BoglV02NkCFCuJBRESkqxINa2rXrh3+/vtv9OrVC48ePcKjR4/Qu3dvXLp0CRs2bNB3jEREREQ6K/U8N7n98ccfeOmll6BUKvV1SL1jh2IiIiLzU5zvb05IQ0RERBaFyQ0RERFZFCY3REREZFGKNVqqd+/ehb7+6NGj0sRCREREVGrFSm5cXV2LfH3IkCGlCoiIiIioNIqV3Kxbt85QcRARERHpRYkm8SPLolSKlcETE8VK2G3acK0lIiIyX0xuyrioKGD8eODWref7qlYFliwBiuhiRUREZJI4WqoMi4oC+vbVTmwA4PZtsT8qSp64iIiISoPJTRmlVIoam/zmp1bvmzBBlCMiIjInTG7KqKNH89bY5CZJwM2bohwREZE5YXJTRiUm6rccERGRqWByU0Z5e+u3HBERkalgclNGtWkjRkUpFPm/rlAAvr6iHBERkTlhclNGWVuL4d5A3gRHvb14Mee7ISIi88Pkpgzr3Rv44QegShXt/VWriv2c54aIiMwRJ/Er43r3Bnr04AzFRERkOZjcEKytgfbt5Y6CiIhIP9gsRURERBaFyQ0RERFZFCY3REREZFGY3BAREZFFYXJDREREFoXJDREREVkUJjdERERkUZjcEBERkUVhckNEREQWhckNERERWRQmN0RERGRRmNwQERGRRWFyQ0RERBaFyQ0RERFZFCY3REREZFGY3BAREZFFYXJDREREFoXJDREREVkUJjdERERkUZjcEBERkUUpJ3cAZPmUSuDoUSAxEfD2Btq0Aayt5Y6KiIgsFZMbMqioKGD8eODWref7qlYFliwBeveWLy4iIrJcJtEstWLFCvj7+8Pe3h5BQUE4efJkgWVXr16NNm3awN3dHe7u7ggJCSm0PMknKgro21c7sQGA27fF/qgoeeIiIiLLJntys3XrVkycOBERERE4c+YMAgICEBoaipSUlHzLHzlyBAMHDkRsbCzi4uLg6+uL119/Hbdv3zZy5FQYpVLU2EhS3tfU+yZMEOWIiIj0SSFJ+X39GE9QUBBatGiB5cuXAwBUKhV8fX0xduxYTJkypcj3K5VKuLu7Y/ny5RgyZEiR5dPS0uDq6orU1FS4uLiUOn7K35EjwKuvFl0uNhZo397Q0RARkbkrzve3rDU32dnZOH36NEJCQjT7rKysEBISgri4OJ2OkZmZiZycHFSoUMFQYVIJJCbqtxwREZGuZO1QfO/ePSiVSnh6emrt9/T0xF9//aXTMSZPngwfHx+tBCm3rKwsZGVlabbT0tJKHjDpzNtbv+WIiIh0JXufm9L4/PPPsWXLFuzYsQP29vb5lomMjISrq6vm4evra+Qoy6Y2bcSoKIUi/9cVCsDXV5QjIiLSJ1mTGw8PD1hbWyM5OVlrf3JyMry8vAp97/z58/H555/j0KFDaNKkSYHlpk6ditTUVM3j5s2beomdCmdtLYZ7A3kTHPX24sWc74aIiPRP1uTG1tYWgYGBiImJ0exTqVSIiYlBcHBwge+bO3cu5syZgwMHDqB58+aFnsPOzg4uLi5aDzKO3r2BH34AqlTR3l+1qtjPeW6IiMgQZJ/Eb+LEiQgPD0fz5s3RsmVLLF68GBkZGRg2bBgAYMiQIahSpQoiIyMBAF988QVmzJiBTZs2wd/fH0lJSQAAJycnODk5yXYdlL/evYEePThDMRERGY/syU1YWBju3r2LGTNmICkpCU2bNsWBAwc0nYxv3LgBK6vnFUwrV65EdnY2+vbtq3WciIgIzJw505ihk46srTncm4iIjEf2eW6MjfPcEBERmR+zmeeGiIiISN+Y3BAREZFFYXJDREREFoXJDREREVkUJjdERERkUZjcEBERkUVhckNEREQWhckNERERWRTZZygmKi2lkss7EBHRc0xuyKxFRQHjxwO3bj3fV7WqWJGcC3MSEZVNbJYisxUVBfTtq53YAMDt22J/VJQ8cRERkbyY3JBZUipFjU1+K6Op902YIMoREVHZwuSGzNLRo3lrbHKTJODmTVGOiIjKFiY3ZJYSE/VbjoiILAeTGzJL3t76LUdERJaDyQ2ZpTZtxKgohSL/1xUKwNdXlCMiorKFyQ2ZJWtrMdwbyJvgqLcXL+Z8N0REZRGTGzJbvXsDP/wAVKmivb9qVbGf89wQEZVNnMSPzFrv3kCPHpyhmIiInmNyQ2bP2hpo317uKIiIyFSwWYqIiIgsCmtuiArBRTmJiMwPkxuiAnBRTiIi88RmKaJ8cFFOIiLzxeSG6AVclJOIyLwxuSF6ARflJCIyb0xuiF7ARTmJiMwbkxuiF3BRTiIi88bkhugFXJSTiMi8MbkhegEX5SQiMm9MbojywUU5iYjMFyfxIyoAF+UkIjJPTG6ICsFFOYmIzA+bpYiIiMiiMLkhIiIii8JmKSIZcdVxIiL9Y3JDJBOuOk5EZBhsliKSAVcdJyIyHCY3REbGVceJiAyLyQ2RkXHVcSIiw2JyQ2RkXHWciMiwmNwQGRlXHSciMiwmN0RGxlXHiYgMi8kNkZFx1XEiIsNickMkA646TkRkOJzEj0gmXHWciMgwmNwQycjQq45zeQciKouY3BBZKC7vQERlFfvcEFkgLu9ARGUZkxsiC8PlHYiorGNyQ2RhuLwDEZV1sic3K1asgL+/P+zt7REUFISTJ08WWPbSpUvo06cP/P39oVAosHjxYuMFSmQmuLwDEZV1siY3W7duxcSJExEREYEzZ84gICAAoaGhSElJybd8ZmYmatSogc8//xxeXl5GjpbIPHB5ByIq6xSSlF/LvHEEBQWhRYsWWL58OQBApVLB19cXY8eOxZQpUwp9r7+/PyZMmIAJEyYU65xpaWlwdXVFamoqXFxcSho6kclSKgF/f9F5OL9Pt0IhRk0lJHBYOBGZj+J8f8tWc5OdnY3Tp08jJCTkeTBWVggJCUFcXJzezpOVlYW0tDStB5El4/IORFTWyZbc3Lt3D0qlEp6enlr7PT09kZSUpLfzREZGwtXVVfPw9fXV27GJTJWxlndQKoEjR4DNm8W/HIFFRKZA9g7FhjZ16lSkpqZqHjdv3pQ7JCKj6N0buHYNiI0FNm0S/yYk6C+xiYoSzV+vvgq8+ab419+fc+gQkfxkm6HYw8MD1tbWSE5O1tqfnJys187CdnZ2sLOz09vxiMyJoZZ3UE8S+GKfHvUkgVz8k4jkJFvNja2tLQIDAxETE6PZp1KpEBMTg+DgYLnCIqIicJJAIjJ1sq4tNXHiRISHh6N58+Zo2bIlFi9ejIyMDAwbNgwAMGTIEFSpUgWRkZEARCfkP//8U/P89u3bOHfuHJycnFCrVi3ZroOoLCnOJIGGXBSUiKggsiY3YWFhuHv3LmbMmIGkpCQ0bdoUBw4c0HQyvnHjBqysnlcu3blzB82aNdNsz58/H/Pnz0e7du1w5MgRY4dPVCZxkkAiMnWyznMjB85zQ1Q6R46IzsNFiY1lzQ0R6U9xvr9lrbkhIvPTpo0YUl7UJIFt2pTuPEqlaNpKTBSzKbdpw7l5iEg3Fj8UnIj0yxiTBHKYORGVBpMbIio2Q04SqB5m/mKnZfUwcyY4RFQU9rkhohLTd9ORel2sgkZjcV0sorKLfW6IyCj0PUkgh5kTkT4wuSEik2HMYebssExkuZjcEJHJ8PbWb7mCREWJWZZz1xJVrSo6SnPZCCLzxz43BVAqlcjJyTFiZGSJbGxsYM3qAJ2p+9wUNcy8NH1uCloXSz3Si+tiEZkm9rkpBUmSkJSUhEePHskdClkINzc3eHl5QfHiuGnKQz3MvG9fkWzkTkD0Mcy8qHWxFAqxLlaPHmyiIjJnTG5eoE5sKleuDEdHR34hUYlJkoTMzEykpKQAALxL25ZSRqiHmefXbLR4celqVdhhmahsYHKTi1Kp1CQ2FStWlDscsgAODg4AgJSUFFSuXJlNVDrq3VvUnui7wy/XxSIqG5jc5KLuY+Po6ChzJGRJ1L9POTk5TG6KQd/DzAHjdVgmInlxhuJ8sCmK9Im/T6ZDvS5WQbdEoQB8fUu/LhYRyYvJDRXI398fixcv1rn8kSNHoFAoDN4Ze/369XBzczPoOcgyGWNdLDWlUqygvnmz+FepLP0xiUg3TG4MxJj/sSkUikIfM2fOLNFxT506hXfffVfn8q1atUJiYiJcXV1LdD4iYzDkulhqXPiTSF7sc2MAxp4gLDFX78etW7dixowZiI+P1+xzcnLSPJckCUqlEuXKFX3rK1WqVKw4bG1t4eXlVaz3EMnBUB2WgYLn0VEv/Ml5dIgMjzU3eibHisZeXl6ah6urKxQKhWb7r7/+grOzM/bv34/AwEDY2dnh119/xT///IMePXrA09MTTk5OaNGiBaKjo7WO+2KzlEKhwP/+9z/06tULjo6OqF27Nnbv3q15/cVmKXXz0cGDB1G/fn04OTmhU6dOWsnYs2fPMG7cOLi5uaFixYqYPHkywsPD0bNnz2L9DFauXImaNWvC1tYWdevWxYYNGzSvSZKEmTNnolq1arCzs4OPjw/GjRunef3LL79E7dq1YW9vD09PT/Tt27dY5ybzpO6wPHCg+FdfTVGFzaMDiHl09FGTy2YvooIxudEjY/7HVlxTpkzB559/jsuXL6NJkyZIT09Hly5dEBMTg7Nnz6JTp07o1q0bbty4UehxZs2ahf79++P8+fPo0qULBg0ahAcPHhRYPjMzE/Pnz8eGDRvwyy+/4MaNG5g0aZLm9S+++ALfffcd1q1bh2PHjiEtLQ07d+4s1rXt2LED48ePx4cffoiLFy/ivffew7BhwxAbGwsA2L59OxYtWoSvvvoKV65cwc6dO9G4cWMAwO+//45x48Zh9uzZiI+Px4EDB9C2bdtinZ9IrTjz6JQGm72IiiCVMampqRIAKTU1Nc9rT548kf7880/pyZMnJTp2bKwkif++Cn/ExpbuGgqzbt06ydXVNVdMsRIAaefOnUW+t2HDhtKyZcs0235+ftKiRYs02wCk6dOna7bT09MlANL+/fu1zvXw4UNNLACkq1evat6zYsUKydPTU7Pt6ekpzZs3T7P97NkzqVq1alKPHj10vsZWrVpJI0aM0CrTr18/qUuXLpIkSdKCBQukOnXqSNnZ2XmOtX37dsnFxUVKS0sr8HylVdrfKzIfmzbp9n/Apk0lP8f27ZKkUOQ9pkIhHtu36+96iExJYd/fL2LNjR6Z8gRhzZs319pOT0/HpEmTUL9+fbi5ucHJyQmXL18usuamSZMmmufly5eHi4uLZgbe/Dg6OqJmzZqabW9vb0351NRUJCcno2XLlprXra2tERgYWKxru3z5Mlq3bq21r3Xr1rh8+TIAoF+/fnjy5Alq1KiBESNGYMeOHXj27BkA4LXXXoOfnx9q1KiBwYMH47vvvkNmZmaxzk+kZuh5dEy5dpjIlDC50SNTniCsfPnyWtuTJk3Cjh078Nlnn+Ho0aM4d+4cGjdujOzs7EKPY2Njo7WtUCigUqmKVV4y8lqtvr6+iI+Px5dffgkHBweMGjUKbdu2RU5ODpydnXHmzBls3rwZ3t7emDFjBgICAri2GJWIoefRMVazF5G5Y3KjR+Y0QdixY8cwdOhQ9OrVC40bN4aXlxeuXbtm1BhcXV3h6emJU6dOafYplUqcOXOmWMepX78+jh07prXv2LFjaNCggWbbwcEB3bp1w9KlS3HkyBHExcXhwoULAIBy5cohJCQEc+fOxfnz53Ht2jX89NNPpbgyKqsMPY+OKdcOE5kSDgXXI0OvaKxPtWvXRlRUFLp16waFQoFPPvmk0BoYQxk7diwiIyNRq1Yt1KtXD8uWLcPDhw+LNavvRx99hP79+6NZs2YICQnBnj17EBUVpRn9tX79eiiVSgQFBcHR0REbN26Eg4MD/Pz8sHfvXvz7779o27Yt3N3dsW/fPqhUKtStW9dQl0wWzpALfxqrdlipNMwweSJjYc2NnhljgjB9WLhwIdzd3dGqVSt069YNoaGheOmll4wex+TJkzFw4EAMGTIEwcHBcHJyQmhoKOzt7XU+Rs+ePbFkyRLMnz8fDRs2xFdffYV169ah/f8vTOTm5obVq1ejdevWaNKkCaKjo7Fnzx5UrFgRbm5uiIqKQocOHVC/fn2sWrUKmzdvRsOGDQ10xVQW9O4NXLsGxMYCmzaJfxMSSv/5N0btMEdikSVQSMbuACGztLQ0uLq6IjU1FS4uLlqvPX36FAkJCahevXqxvlzzw798SkalUqF+/fro378/5syZI3c4eqHP3ysi9VxaQP61w6X5I6qgCQj1cWyi0irs+/tFrLkxEENMEGaJrl+/jtWrV+Pvv//GhQsXMHLkSCQkJODNN9+UOzQik2So2mGOxCJLwj43JCsrKyusX78ekyZNgiRJaNSoEaKjo1G/fn25QyMyWYZYPqI4I7H+v8W3xFizTYbG5IZk5evrm2ekExEVTV07rC/GGoll7LX3qGxisxQRERllJJax1t7julvE5IaIiAw+EstYfXo42osAJjdERATDT0BojNmVjVUzRKaPyQ0REQEw7Dxdhu7Tw9FelBs7FBMRkYYhRmIBhu/TY8zRXmT6mNwQEZEWfY/EAp736bl9O//aFYVCvF7SPj3GXHeLQ9lNH5ulSKN9+/aYMGGCZtvf3x+LFy8u9D0KhQI7d+4s9bn1dZzCzJw5E02bNjXoOYgof4bu02OsdbfYYdk8MLmxAN26dUOnTp3yfe3o0aNQKBQ4f/58sY976tQpvPvuu6UNT0tBCUZiYiI6d+6s13MRkWkxZJ8eY627ZegOyxzGrh9MbizA8OHDcfjwYdzKp8F53bp1aN68OZo0aVLs41aqVAmOjo76CLFIXl5esLOzM8q5iEg+hlpU1NA1Q8bosMxaIf1hcmMB3njjDVSqVAnr16/X2p+eno5t27Zh+PDhuH//PgYOHIgqVarA0dERjRs3xubNmws97ovNUleuXEHbtm1hb2+PBg0a4PDhw3neM3nyZNSpUweOjo6oUaMGPvnkE+Tk5AAA1q9fj1mzZuGPP/6AQqGAQqHQxPxis9SFCxfQoUMHODg4oGLFinj33XeRnp6ueX3o0KHo2bMn5s+fD29vb1SsWBGjR4/WnEsXKpUKs2fPRtWqVWFnZ4emTZviwIEDmtezs7MxZswYeHt7w97eHn5+foiMjAQASJKEmTNnolq1arCzs4OPjw/GjRun87mJyjJDrb1nyJohQw9l5wSH+sUOxUWQJCAzU55zOzoWXMWaW7ly5TBkyBCsX78e06ZNg+L/37Rt2zYolUoMHDgQ6enpCAwMxOTJk+Hi4oIff/wRgwcPRs2aNdGyZcsiz6FSqdC7d294enrit99+Q2pqqlb/HDVnZ2esX78ePj4+uHDhAkaMGAFnZ2f85z//QVhYGC5evIgDBw4gOjoaAODq6prnGBkZGQgNDUVwcDBOnTqFlJQUvPPOOxgzZoxWAhcbGwtvb2/Exsbi6tWrCAsLQ9OmTTFixIiif2gAlixZggULFuCrr75Cs2bNsHbtWnTv3h2XLl1C7dq1sXTpUuzevRvff/89qlWrhps3b+LmzZsAgO3bt2PRokXYsmULGjZsiKSkJPzxxx86nZeIDMdQo70M2WG5qFohhULUCvXoUbrrKFNLX0hlTGpqqgRASk1NzfPakydPpD///FN68uSJZl96uiSJXy/jP9LTdb+uy5cvSwCk2NhYzb42bdpIb731VoHv6dq1q/Thhx9qttu1ayeNHz9es+3n5yctWrRIkiRJOnjwoFSuXDnp9u3bmtf3798vAZB27NhR4DnmzZsnBQYGarYjIiKkgICAPOVyH+frr7+W3N3dpfRcP4Aff/xRsrKykpKSkiRJkqTw8HDJz89PevbsmaZMv379pLCwsAJjefHcPj4+0qeffqpVpkWLFtKoUaMkSZKksWPHSh06dJBUKlWeYy1YsECqU6eOlJ2dXeD51PL7vSIi8xIbq9v/27n+CzaJY6tt3y5JCkXeYyoU4rF9e8mPnduzZyLOTZvEv7n+iy61wr6/X8RmKQtRr149tGrVCmvXrgUAXL16FUePHsXw4cMBAEqlEnPmzEHjxo1RoUIFODk54eDBg7hx44ZOx798+TJ8fX3h4+Oj2RccHJyn3NatW9G6dWt4eXnByckJ06dP1/kcuc8VEBCA8uXLa/a1bt0aKpUK8fHxmn0NGzaEda4/Y7y9vZGSkqLTOdLS0nDnzh20bt1aa3/r1q1x+fJlAKLp69y5c6hbty7GjRuHQ4cOacr169cPT548QY0aNTBixAjs2LEDz549K9Z1EpH5MGSHZUuZ4NCU+gwxuSmCoyOQni7Po7h9eYcPH47t27fj8ePHWLduHWrWrIl27doBAObNm4clS5Zg8uTJiI2Nxblz5xAaGors7Gy9/azi4uIwaNAgdOnSBXv37sXZs2cxbdo0vZ4jNxsbG61thUIBlUqlt+O/9NJLSEhIwJw5c/DkyRP0798fffv2BSBWM4+Pj8eXX34JBwcHjBo1Cm3bti1Wnx8iMh+G7LBsShMclpSpLX3B5KYICgVQvrw8D1362+TWv39/WFlZYdOmTfj222/x9ttva/rfHDt2DD169MBbb72FgIAA1KhRA3///bfOx65fvz5u3ryJxFx/Opw4cUKrzPHjx+Hn54dp06ahefPmqF27Nq5fv65VxtbWFsoi/jyoX78+/vjjD2RkZGj2HTt2DFZWVqhbt67OMRfGxcUFPj4+OHbsmNb+Y8eOoUGDBlrlwsLCsHr1amzduhXbt2/HgwcPAAAODg7o1q0bli5diiNHjiAuLg4XLlzQS3xEZHoM1WHZ0MPYLaVmqDjYodiCODk5ISwsDFOnTkVaWhqGDh2qea127dr44YcfcPz4cbi7u2PhwoVITk7W+iIvTEhICOrUqYPw8HDMmzcPaWlpmDZtmlaZ2rVr48aNG9iyZQtatGiBH3/8ETt27NAq4+/vj4SEBJw7dw5Vq1aFs7NzniHggwYNQkREBMLDwzFz5kzcvXsXY8eOxeDBg+Hp6VmyH04+PvroI0RERKBmzZpo2rQp1q1bh3PnzuG7774DACxcuBDe3t5o1qwZrKyssG3bNnh5ecHNzQ3r16+HUqlEUFAQHB0dsXHjRjg4OMDPz09v8RGR6TFEh2V1rVDfviKRyZ0kmMMEh6a49AVrbizM8OHD8fDhQ4SGhmr1j5k+fTpeeuklhIaGon379vDy8kLPnj11Pq6VlRV27NiBJ0+eoGXLlnjnnXfw6aefapXp3r07PvjgA4wZMwZNmzbF8ePH8cknn2iV6dOnDzp16oRXX30VlSpVync4uqOjIw4ePIgHDx6gRYsW6Nu3Lzp27Ijly5cX74dRhHHjxmHixIn48MMP0bhxYxw4cAC7d+9G7dq1AYiRX3PnzkXz5s3RokULXLt2Dfv27YOVlRXc3NywevVqtG7dGk2aNEF0dDT27NmDihUr6jVGIjI9hhjKbs4THBpz6QtdKSQpv4oky5WWlgZXV1ekpqbCxcVF67WnT58iISEB1atXh729vUwRkqXh7xUR6cpQ61ap+8QA+dcMlSaBOnJEdB4uSmxs6WpuCvv+fhFrboiIiEyEOU5waIylL4qLfW6IiIjKAENNcGjoPkMlweSGiIiojFDXDOmbumYovxmQFy82/gzITG6IiIio1AxVM1QSTG6IiIhILwxVM1RcJtGheMWKFfD394e9vT2CgoJw8uTJQstv27YN9erVg729PRo3box9+/bpNZ4yNoCMDIy/T0RExiV7crN161ZMnDgREREROHPmDAICAhAaGlrgGkHHjx/HwIEDMXz4cJw9exY9e/ZEz549cfHixVLHop7OP1OuZcDJIql/n15cLoKIiAxD9nlugoKC0KJFC80EbSqVCr6+vhg7diymTJmSp3xYWBgyMjKwd+9ezb6XX34ZTZs2xapVq4o8X1Hj5BMTE/Ho0SNUrlwZjo6OmuULiIpLkiRkZmYiJSUFbm5u8C7p9J9ERFSseW5k7XOTnZ2N06dPY+rUqZp9VlZWCAkJQVxcXL7viYuLw8SJE7X2hYaGYufOnfmWz8rKQlZWlmY7LS2t0Ji8vLwAQOfVpYmK4ubmpvm9IiIiw5M1ubl37x6USmWe9YI8PT3x119/5fuepKSkfMsnJSXlWz4yMhKzZs3SOSaFQgFvb29UrlyZKzxTqdnY2MBajqECRERlmMWPlpo6dapWTU9aWhp8fX2LfJ+1tTW/lIiIiMyQrMmNh4cHrK2tkZycrLU/OTm5wGp8Ly+vYpW3s7PLs+o0ERERWS5ZR0vZ2toiMDAQMTExmn0qlQoxMTEIDg7O9z3BwcFa5QHg8OHDBZYnIiKiskX2ZqmJEyciPDwczZs3R8uWLbF48WJkZGRg2LBhAIAhQ4agSpUqiIyMBACMHz8e7dq1w4IFC9C1a1ds2bIFv//+O77++ms5L4OIiIhMhOzJTVhYGO7evYsZM2YgKSkJTZs2xYEDBzSdhm/cuAErq+cVTK1atcKmTZswffp0fPzxx6hduzZ27tyJRo0a6XQ+9cj3okZNERERkelQf2/rMoON7PPcGNutW7d06lBMREREpufmzZuoWrVqoWXKXHKjUqlw584dODs7W/QEfepRYTdv3ixysiNLUJaul9dqucrS9fJaLZehrleSJDx+/Bg+Pj5aLTr5kb1ZytisrKyKzPgsiYuLS5n4MKmVpevltVqusnS9vFbLZYjrdXV11amc7GtLEREREekTkxsiIiKyKExuLJSdnR0iIiLKzASGZel6ea2WqyxdL6/VcpnC9Za5DsVERERk2VhzQ0RERBaFyQ0RERFZFCY3REREZFGY3BAREZFFYXJjhiIjI9GiRQs4OzujcuXK6NmzJ+Lj4wt9z/r166FQKLQe9vb2Roq4dGbOnJkn9nr16hX6nm3btqFevXqwt7dH48aNsW/fPiNFWzr+/v55rlWhUGD06NH5lje3+/rLL7+gW7du8PHxgUKhwM6dO7VelyQJM2bMgLe3NxwcHBASEoIrV64UedwVK1bA398f9vb2CAoKwsmTJw10Bbor7FpzcnIwefJkNG7cGOXLl4ePjw+GDBmCO3fuFHrMknwWjKGo+zp06NA8cXfq1KnI45rifQWKvt78PsMKhQLz5s0r8JimeG91+a55+vQpRo8ejYoVK8LJyQl9+vRBcnJyocct6ee8OJjcmKGff/4Zo0ePxokTJ3D48GHk5OTg9ddfR0ZGRqHvc3FxQWJiouZx/fp1I0Vceg0bNtSK/ddffy2w7PHjxzFw4EAMHz4cZ8+eRc+ePdGzZ09cvHjRiBGXzKlTp7Su8/DhwwCAfv36Ffgec7qvGRkZCAgIwIoVK/J9fe7cuVi6dClWrVqF3377DeXLl0doaCiePn1a4DG3bt2KiRMnIiIiAmfOnEFAQABCQ0ORkpJiqMvQSWHXmpmZiTNnzuCTTz7BmTNnEBUVhfj4eHTv3r3I4xbns2AsRd1XAOjUqZNW3Js3by70mKZ6X4Girzf3dSYmJmLt2rVQKBTo06dPocc1tXury3fNBx98gD179mDbtm34+eefcefOHfTu3bvQ45bkc15sEpm9lJQUCYD0888/F1hm3bp1kqurq/GC0qOIiAgpICBA5/L9+/eXunbtqrUvKChIeu+99/QcmeGNHz9eqlmzpqRSqfJ93ZzvKwBpx44dmm2VSiV5eXlJ8+bN0+x79OiRZGdnJ23evLnA47Rs2VIaPXq0ZlupVEo+Pj5SZGSkQeIuiRevNT8nT56UAEjXr18vsExxPwtyyO9aw8PDpR49ehTrOOZwXyVJt3vbo0cPqUOHDoWWMYd7++J3zaNHjyQbGxtp27ZtmjKXL1+WAEhxcXH5HqOkn/PiYs2NBUhNTQUAVKhQodBy6enp8PPzg6+vL3r06IFLly4ZIzy9uHLlCnx8fFCjRg0MGjQIN27cKLBsXFwcQkJCtPaFhoYiLi7O0GHqVXZ2NjZu3Ii333670EVezfm+5paQkICkpCSte+fq6oqgoKAC7112djZOnz6t9R4rKyuEhISY3f1OTU2FQqGAm5tboeWK81kwJUeOHEHlypVRt25djBw5Evfv3y+wrCXd1+TkZPz4448YPnx4kWVN/d6++F1z+vRp5OTkaN2nevXqoVq1agXep5J8zkuCyY2ZU6lUmDBhAlq3bo1GjRoVWK5u3bpYu3Ytdu3ahY0bN0KlUqFVq1a4deuWEaMtmaCgIKxfvx4HDhzAypUrkZCQgDZt2uDx48f5lk9KSoKnp6fWPk9PTyQlJRkjXL3ZuXMnHj16hKFDhxZYxpzv64vU96c49+7evXtQKpVmf7+fPn2KyZMnY+DAgYUuNFjcz4Kp6NSpE7799lvExMTgiy++wM8//4zOnTtDqVTmW95S7isAfPPNN3B2di6yqcbU721+3zVJSUmwtbXNk5AXdp9K8jkviTK3KrilGT16NC5evFhk22xwcDCCg4M1261atUL9+vXx1VdfYc6cOYYOs1Q6d+6sed6kSRMEBQXBz88P33//vU5/DZmrNWvWoHPnzvDx8SmwjDnfVxJycnLQv39/SJKElStXFlrWXD8LAwYM0Dxv3LgxmjRpgpo1a+LIkSPo2LGjjJEZ3tq1azFo0KAiO/qb+r3V9bvGVLDmxoyNGTMGe/fuRWxsLKpWrVqs99rY2KBZs2a4evWqgaIzHDc3N9SpU6fA2L28vPL01k9OToaXl5cxwtOL69evIzo6Gu+8806x3mfO91V9f4pz7zw8PGBtbW2291ud2Fy/fh2HDx8utNYmP0V9FkxVjRo14OHhUWDc5n5f1Y4ePYr4+Phif44B07q3BX3XeHl5ITs7G48ePdIqX9h9KsnnvCSY3JghSZIwZswY7NixAz/99BOqV69e7GMolUpcuHAB3t7eBojQsNLT0/HPP/8UGHtwcDBiYmK09h0+fFirhsPUrVu3DpUrV0bXrl2L9T5zvq/Vq1eHl5eX1r1LS0vDb7/9VuC9s7W1RWBgoNZ7VCoVYmJiTP5+qxObK1euIDo6GhUrViz2MYr6LJiqW7du4f79+wXGbc73Nbc1a9YgMDAQAQEBxX6vKdzbor5rAgMDYWNjo3Wf4uPjcePGjQLvU0k+5yUNnszMyJEjJVdXV+nIkSNSYmKi5pGZmakpM3jwYGnKlCma7VmzZkkHDx6U/vnnH+n06dPSgAEDJHt7e+nSpUtyXEKxfPjhh9KRI0ekhIQE6dixY1JISIjk4eEhpaSkSJKU91qPHTsmlStXTpo/f750+fJlKSIiQrKxsZEuXLgg1yUUi1KplKpVqyZNnjw5z2vmfl8fP34snT17Vjp79qwEQFq4cKF09uxZzQihzz//XHJzc5N27dolnT9/XurRo4dUvXp16cmTJ5pjdOjQQVq2bJlme8uWLZKdnZ20fv166c8//5Teffddyc3NTUpKSjL69eVW2LVmZ2dL3bt3l6pWrSqdO3dO63OclZWlOcaL11rUZ0EuhV3r48ePpUmTJklxcXFSQkKCFB0dLb300ktS7dq1padPn2qOYS73VZKK/j2WJElKTU2VHB0dpZUrV+Z7DHO4t7p817z//vtStWrVpJ9++kn6/fffpeDgYCk4OFjrOHXr1pWioqI027p8zkuLyY0ZApDvY926dZoy7dq1k8LDwzXbEyZMkKpVqybZ2tpKnp6eUpcuXaQzZ84YP/gSCAsLk7y9vSVbW1upSpUqUlhYmHT16lXN6y9eqyRJ0vfffy/VqVNHsrW1lRo2bCj9+OOPRo665A4ePCgBkOLj4/O8Zu73NTY2Nt/fXfU1qVQq6ZNPPpE8PT0lOzs7qWPHjnl+Dn5+flJERITWvmXLlml+Di1btpROnDhhpCsqWGHXmpCQUODnODY2VnOMF6+1qM+CXAq71szMTOn111+XKlWqJNnY2Eh+fn7SiBEj8iQp5nJfJano32NJkqSvvvpKcnBwkB49epTvMczh3uryXfPkyRNp1KhRkru7u+To6Cj16tVLSkxMzHOc3O/R5XNeWor/PzERERGRRWCfGyIiIrIoTG6IiIjIojC5ISIiIovC5IaIiIgsCpMbIiIisihMboiIiMiiMLkhIiIii8LkhojKJIVCgZ07d8odBhEZAJMbIjK6oUOHQqFQ5Hl06tRJ7tCIyAKUkzsAIiqbOnXqhHXr1mnts7OzkykaIrIkrLkhIlnY2dnBy8tL6+Hu7g5ANBmtXLkSnTt3hoODA2rUqIEffvhB6/0XLlxAhw4d4ODggIoVK+Ldd99Fenq6Vpm1a9eiYcOGsLOzg7e3N8aMGaP1+r1799CrVy84Ojqidu3a2L17t+a1hw8fYtCgQahUqRIcHBxQu3btPMkYEZkmJjdEZJI++eQT9OnTB3/88QcGDRqEAQMG4PLlywCAjIwMhIaGwt3dHadOncK2bdsQHR2tlbysXLkSo0ePxrvvvosLFy5g9+7dqFWrltY5Zs2ahf79++P8+fPo0qULBg0ahAcPHmjO/+eff2L//v24fPkyVq5cCQ8PD+P9AIio5PS6DCcRkQ7Cw8Mla2trqXz58lqPTz/9VJIksYrw+++/r/WeoKAgaeTIkZIkSdLXX38tubu7S+np6ZrXf/zxR8nKykqz2rSPj480bdq0AmMAIE2fPl2znZ6eLgGQ9u/fL0mSJHXr1k0aNmyYfi6YiIyKfW6ISBavvvoqVq5cqbWvQoUKmufBwcFarwUHB+PcuXMAgMuXLyMgIADly5fXvN66dWuoVCrEx8dDoVDgzp076NixY6ExNGnSRPO8fPnycHFxQUpKCgBg5MiR6NOnD86cOYPXX38dPXv2RKtWrUp0rURkXExuiEgW5cuXz9NMpC8ODg46lbOxsdHaVigUUKlUAIDOnTvj+vXr2LdvHw4fPoyOHTti9OjRmD9/vt7jJSL9Yp8bIjJJJ06cyLNdv359AED9+vXxxx9/ICMjQ/P6sWPHYGVlhbp168LZ2Rn+/v6IiYkpVQyVKlVCeHg4Nm7ciMWLF+Prr78u1fGIyDhYc0NEssjKykJSUpLWvnLlymk67W7btg3NmzfHK6+8gu+++w4nT57EmjVrAACDBg1CREQEwsPDMXPmTNy9exdjx47F4MGD4enpCQCYOXMm3n//fVSuXBmdO3fG48ePcezYMYwdO1an+GbMmIHAwEA0bNgQWVlZ2Lt3rya5IiLTxuSGiGRx4MABeHt7a+2rW7cu/vrrLwBiJNOWLVswatQoeHt7Y/PmzWjQoAEAwNHREQcPHsT48ePRokULODo6ok+fPli4cKHmWOHh4Xj69CkWLVqESZMmwcPDA3379tU5PltbW0ydOhXXrl2Dg4MD2rRpgy1btujhyonI0BSSJElyB0FElJtCocCOHTvQs2dPuUMhIjPEPjdERERkUZjcEBERkUVhnxsiMjlsLSei0mDNDREREVkUJjdERERkUZjcEBERkUVhckNEREQWhckNERERWRQmN0RERGRRmNwQERGRRWFyQ0RERBaFyQ0RERFZlP8D1O/e7aUdJqgAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "history_dict = history.history\n",
        "loss_values = history_dict[\"loss\"]\n",
        "val_loss_values = history_dict[\"val_loss\"]\n",
        "epochs = range(1, len(loss_values) + 1)\n",
        "plt.plot(epochs, loss_values, \"bo\", label=\"Training loss\")\n",
        "plt.plot(epochs, val_loss_values, \"b\", label=\"Validation loss\")\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8Quo9sPsknoo"
      },
      "source": [
        "**Plotting the training and validation accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "ca0GEH6Jknoo",
        "outputId": "63ac77cb-dcf4-4d60-cc38-36d8a8c72bc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.clf()\n",
        "acc = history_dict[\"accuracy\"]\n",
        "val_acc = history_dict[\"val_accuracy\"]\n",
        "plt.plot(epochs, acc, \"bo\", label=\"Training acc\")\n",
        "plt.plot(epochs, val_acc, \"b\", label=\"Validation acc\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4LujY0dJknoo"
      },
      "source": [
        "**Retraining a model from scratch**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "I-jd2KX8knop",
        "outputId": "900bcaac-03ef-4540-b083-c78572ff7931",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/4\n",
            "49/49 [==============================] - 2s 28ms/step - loss: 0.4444 - accuracy: 0.8140\n",
            "Epoch 2/4\n",
            "49/49 [==============================] - 2s 34ms/step - loss: 0.2592 - accuracy: 0.9056\n",
            "Epoch 3/4\n",
            "49/49 [==============================] - 2s 35ms/step - loss: 0.2066 - accuracy: 0.9246\n",
            "Epoch 4/4\n",
            "49/49 [==============================] - 2s 31ms/step - loss: 0.1778 - accuracy: 0.9358\n",
            "782/782 [==============================] - 2s 2ms/step - loss: 0.3059 - accuracy: 0.8782\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(16, activation=\"relu\"),\n",
        "    layers.Dense(16, activation=\"relu\"),\n",
        "    layers.Dense(1, activation=\"sigmoid\")\n",
        "])\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"binary_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(x_train, y_train, epochs=4, batch_size=512)\n",
        "results = model.evaluate(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "0b8vhZajknop",
        "outputId": "951cb40f-1239-4b34-b02a-e9f2d9b97219",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.30587130784988403, 0.8782399892807007]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PmQIMMqsknop"
      },
      "source": [
        "### Using a trained model to generate predictions on new data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "l-ewJzk_knop",
        "outputId": "7950b101-2a95-4405-cea0-92883add9178",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 2s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.2798951 ],\n",
              "       [0.99969786],\n",
              "       [0.96364754],\n",
              "       ...,\n",
              "       [0.15482375],\n",
              "       [0.11186522],\n",
              "       [0.75293416]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "model.predict(x_test)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CekADIoknop"
      },
      "source": [
        "### Further experiments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YsEOBN3Qknoq"
      },
      "source": [
        "### Wrapping up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Rc7rveC-knoq"
      },
      "source": [
        "## Classifying newswires: A multiclass classification example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "utSysv5Tknou"
      },
      "source": [
        "### The Reuters dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "A28U2pguknov"
      },
      "source": [
        "**Loading the Reuters dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "QGniyMT3knov",
        "outputId": "a5532a39-d6fe-49d5-d051-2ffa9b1262b0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/reuters.npz\n",
            "2110848/2110848 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import reuters\n",
        "(train_data, train_labels), (test_data, test_labels) = reuters.load_data(\n",
        "    num_words=10000)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "id": "HLznkx7fknov",
        "outputId": "ed6f433c-5e12-454b-d1a1-6a7f39139d20",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8982"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ],
      "source": [
        "len(train_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "id": "Ty70ySa0knov",
        "outputId": "e07e8b2b-23a4-4e4c-b90f-7821ee646d8f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2246"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "len(test_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "id": "UoeztyrHknow",
        "outputId": "31a22b60-e969-47e0-d162-01792e1c40d4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1,\n",
              " 245,\n",
              " 273,\n",
              " 207,\n",
              " 156,\n",
              " 53,\n",
              " 74,\n",
              " 160,\n",
              " 26,\n",
              " 14,\n",
              " 46,\n",
              " 296,\n",
              " 26,\n",
              " 39,\n",
              " 74,\n",
              " 2979,\n",
              " 3554,\n",
              " 14,\n",
              " 46,\n",
              " 4689,\n",
              " 4329,\n",
              " 86,\n",
              " 61,\n",
              " 3499,\n",
              " 4795,\n",
              " 14,\n",
              " 61,\n",
              " 451,\n",
              " 4329,\n",
              " 17,\n",
              " 12]"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ],
      "source": [
        "train_data[10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VCulLUsFknow"
      },
      "source": [
        "**Decoding newswires back to text**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "7Cosdm6lknow",
        "outputId": "c46115a7-8cfc-48e6-bca6-18380f17bd71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/reuters_word_index.json\n",
            "550378/550378 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "word_index = reuters.get_word_index()\n",
        "reverse_word_index = dict([(value, key) for (key, value) in word_index.items()])\n",
        "decoded_newswire = \" \".join([reverse_word_index.get(i - 3, \"?\") for i in\n",
        "    train_data[0]])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "id": "8DQy03esknow",
        "outputId": "0ab673c8-f088-46be-c9c0-5a4ec4150649",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ],
      "source": [
        "train_labels[10]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0y7BBdA1know"
      },
      "source": [
        "### Preparing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZRPt32eVknox"
      },
      "source": [
        "**Encoding the input data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "id": "qlXNF413knox"
      },
      "outputs": [],
      "source": [
        "x_train = vectorize_sequences(train_data)\n",
        "x_test = vectorize_sequences(test_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C4m__gQPknox"
      },
      "source": [
        "**Encoding the labels**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "id": "FlVSX0eJknox"
      },
      "outputs": [],
      "source": [
        "def to_one_hot(labels, dimension=46):\n",
        "    results = np.zeros((len(labels), dimension))\n",
        "    for i, label in enumerate(labels):\n",
        "        results[i, label] = 1.\n",
        "    return results\n",
        "y_train = to_one_hot(train_labels)\n",
        "y_test = to_one_hot(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "id": "ji0bXNU_knox"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.utils import to_categorical\n",
        "y_train = to_categorical(train_labels)\n",
        "y_test = to_categorical(test_labels)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MpA5uIKDknoy"
      },
      "source": [
        "### Building your model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kxZogcYLknoy"
      },
      "source": [
        "**Model definition**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "id": "KMRfv9i-knoy"
      },
      "outputs": [],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(64, activation=\"relu\"),\n",
        "    layers.Dense(64, activation=\"relu\"),\n",
        "    layers.Dense(46, activation=\"softmax\")\n",
        "])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gu61zCBvknoy"
      },
      "source": [
        "**Compiling the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "id": "3fZmFZ6Yknoy"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SK03fQV3knoz"
      },
      "source": [
        "### Validating your approach"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KiJ-CBkIknoz"
      },
      "source": [
        "**Setting aside a validation set**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "id": "ltUp3_R9knoz"
      },
      "outputs": [],
      "source": [
        "x_val = x_train[:1000]\n",
        "partial_x_train = x_train[1000:]\n",
        "y_val = y_train[:1000]\n",
        "partial_y_train = y_train[1000:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FFVBO02Kknoz"
      },
      "source": [
        "**Training the model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "id": "QMBac7irknoz",
        "outputId": "640ad73f-d6b3-4289-a4d2-4c59e91ab66c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "16/16 [==============================] - 2s 67ms/step - loss: 2.7366 - accuracy: 0.4910 - val_loss: 1.8242 - val_accuracy: 0.5830\n",
            "Epoch 2/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 1.5517 - accuracy: 0.6557 - val_loss: 1.3941 - val_accuracy: 0.6870\n",
            "Epoch 3/20\n",
            "16/16 [==============================] - 1s 50ms/step - loss: 1.2139 - accuracy: 0.7325 - val_loss: 1.2124 - val_accuracy: 0.7370\n",
            "Epoch 4/20\n",
            "16/16 [==============================] - 1s 48ms/step - loss: 0.9880 - accuracy: 0.7849 - val_loss: 1.0922 - val_accuracy: 0.7590\n",
            "Epoch 5/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 0.8127 - accuracy: 0.8262 - val_loss: 1.0006 - val_accuracy: 0.7810\n",
            "Epoch 6/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 0.6778 - accuracy: 0.8542 - val_loss: 0.9284 - val_accuracy: 0.8030\n",
            "Epoch 7/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 0.5610 - accuracy: 0.8819 - val_loss: 0.9014 - val_accuracy: 0.8170\n",
            "Epoch 8/20\n",
            "16/16 [==============================] - 1s 49ms/step - loss: 0.4689 - accuracy: 0.8998 - val_loss: 0.8783 - val_accuracy: 0.8160\n",
            "Epoch 9/20\n",
            "16/16 [==============================] - 1s 49ms/step - loss: 0.3977 - accuracy: 0.9137 - val_loss: 0.8625 - val_accuracy: 0.8200\n",
            "Epoch 10/20\n",
            "16/16 [==============================] - 1s 50ms/step - loss: 0.3342 - accuracy: 0.9280 - val_loss: 0.8664 - val_accuracy: 0.8170\n",
            "Epoch 11/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 0.2966 - accuracy: 0.9337 - val_loss: 0.8479 - val_accuracy: 0.8160\n",
            "Epoch 12/20\n",
            "16/16 [==============================] - 1s 48ms/step - loss: 0.2578 - accuracy: 0.9431 - val_loss: 0.8453 - val_accuracy: 0.8220\n",
            "Epoch 13/20\n",
            "16/16 [==============================] - 1s 72ms/step - loss: 0.2260 - accuracy: 0.9461 - val_loss: 0.8644 - val_accuracy: 0.8150\n",
            "Epoch 14/20\n",
            "16/16 [==============================] - 1s 87ms/step - loss: 0.2053 - accuracy: 0.9491 - val_loss: 0.8698 - val_accuracy: 0.8150\n",
            "Epoch 15/20\n",
            "16/16 [==============================] - 1s 81ms/step - loss: 0.1841 - accuracy: 0.9536 - val_loss: 0.8714 - val_accuracy: 0.8130\n",
            "Epoch 16/20\n",
            "16/16 [==============================] - 1s 82ms/step - loss: 0.1689 - accuracy: 0.9529 - val_loss: 0.9648 - val_accuracy: 0.7960\n",
            "Epoch 17/20\n",
            "16/16 [==============================] - 1s 51ms/step - loss: 0.1612 - accuracy: 0.9546 - val_loss: 0.9095 - val_accuracy: 0.8170\n",
            "Epoch 18/20\n",
            "16/16 [==============================] - 1s 50ms/step - loss: 0.1522 - accuracy: 0.9546 - val_loss: 0.9013 - val_accuracy: 0.8150\n",
            "Epoch 19/20\n",
            "16/16 [==============================] - 1s 50ms/step - loss: 0.1426 - accuracy: 0.9551 - val_loss: 1.0451 - val_accuracy: 0.7910\n",
            "Epoch 20/20\n",
            "16/16 [==============================] - 1s 48ms/step - loss: 0.1399 - accuracy: 0.9541 - val_loss: 0.9468 - val_accuracy: 0.8130\n"
          ]
        }
      ],
      "source": [
        "history = model.fit(partial_x_train,\n",
        "                    partial_y_train,\n",
        "                    epochs=20,\n",
        "                    batch_size=512,\n",
        "                    validation_data=(x_val, y_val))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sUX7Sk5ikno0"
      },
      "source": [
        "**Plotting the training and validation loss**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "id": "9EpGdJd0kno0",
        "outputId": "b230b4a3-331f-4902-9afd-617e6b7f1ea6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss = history.history[\"loss\"]\n",
        "val_loss = history.history[\"val_loss\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training loss\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation loss\")\n",
        "plt.title(\"Training and validation loss\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pJughI2Zkno1"
      },
      "source": [
        "**Plotting the training and validation accuracy**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "9pVYJqgtkno1",
        "outputId": "6a8f265b-856f-4e84-b979-5a1abcd37937",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.clf()\n",
        "acc = history.history[\"accuracy\"]\n",
        "val_acc = history.history[\"val_accuracy\"]\n",
        "plt.plot(epochs, acc, \"bo\", label=\"Training accuracy\")\n",
        "plt.plot(epochs, val_acc, \"b\", label=\"Validation accuracy\")\n",
        "plt.title(\"Training and validation accuracy\")\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Accuracy\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AG6fENODkno1"
      },
      "source": [
        "**Retraining a model from scratch**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "H-vxa8Xfkno1",
        "outputId": "d6ed0e4e-0e69-455b-caa0-1f5a3e9fe061",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/9\n",
            "18/18 [==============================] - 1s 45ms/step - loss: 2.7001 - accuracy: 0.4793\n",
            "Epoch 2/9\n",
            "18/18 [==============================] - 1s 45ms/step - loss: 1.5086 - accuracy: 0.6854\n",
            "Epoch 3/9\n",
            "18/18 [==============================] - 1s 44ms/step - loss: 1.1529 - accuracy: 0.7495\n",
            "Epoch 4/9\n",
            "18/18 [==============================] - 1s 43ms/step - loss: 0.9328 - accuracy: 0.7998\n",
            "Epoch 5/9\n",
            "18/18 [==============================] - 1s 42ms/step - loss: 0.7636 - accuracy: 0.8354\n",
            "Epoch 6/9\n",
            "18/18 [==============================] - 1s 45ms/step - loss: 0.6332 - accuracy: 0.8681\n",
            "Epoch 7/9\n",
            "18/18 [==============================] - 1s 60ms/step - loss: 0.5266 - accuracy: 0.8921\n",
            "Epoch 8/9\n",
            "18/18 [==============================] - 1s 74ms/step - loss: 0.4401 - accuracy: 0.9094\n",
            "Epoch 9/9\n",
            "18/18 [==============================] - 1s 77ms/step - loss: 0.3719 - accuracy: 0.9218\n",
            "71/71 [==============================] - 1s 6ms/step - loss: 0.9260 - accuracy: 0.7983\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "  layers.Dense(64, activation=\"relu\"),\n",
        "  layers.Dense(64, activation=\"relu\"),\n",
        "  layers.Dense(46, activation=\"softmax\")\n",
        "])\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(x_train,\n",
        "          y_train,\n",
        "          epochs=9,\n",
        "          batch_size=512)\n",
        "results = model.evaluate(x_test, y_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "id": "eb7CHNIokno2",
        "outputId": "68dec3bb-9884-4c43-e1c6-dcaa77272dbd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.9259987473487854, 0.7983080744743347]"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "results"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "id": "5W-ToBAkkno2",
        "outputId": "5f6ba67f-2dba-4348-a68e-2cd29b156aa5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.17764915405164738"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ],
      "source": [
        "import copy\n",
        "test_labels_copy = copy.copy(test_labels)\n",
        "np.random.shuffle(test_labels_copy)\n",
        "hits_array = np.array(test_labels) == np.array(test_labels_copy)\n",
        "hits_array.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PBZtGOjwkno2"
      },
      "source": [
        "### Generating predictions on new data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "iO7b-UcEkno2",
        "outputId": "48e9c6f0-996d-4e33-e700-e1925baa617a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "71/71 [==============================] - 0s 5ms/step\n"
          ]
        }
      ],
      "source": [
        "predictions = model.predict(x_test)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 38,
      "metadata": {
        "id": "SZMG7vbAkno2",
        "outputId": "35c52c76-5fca-4c22-b9ef-0123c1652e46",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(46,)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ],
      "source": [
        "predictions[0].shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 39,
      "metadata": {
        "id": "ARvu81IZkno3",
        "outputId": "e0fa4ba5-e8cf-4dbe-f906-e9936062118a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.0"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ],
      "source": [
        "np.sum(predictions[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 40,
      "metadata": {
        "id": "BfM9Bs5Ukno3",
        "outputId": "d4851b48-ca35-4e7f-c9ae-b56d9b7357bd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "3"
            ]
          },
          "metadata": {},
          "execution_count": 40
        }
      ],
      "source": [
        "np.argmax(predictions[0])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EaTTLGh8kno3"
      },
      "source": [
        "### A different way to handle the labels and the loss"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 41,
      "metadata": {
        "id": "WNauG_Ytkno4"
      },
      "outputs": [],
      "source": [
        "y_train = np.array(train_labels)\n",
        "y_test = np.array(test_labels)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 42,
      "metadata": {
        "id": "GSV0E9J1kno4"
      },
      "outputs": [],
      "source": [
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"sparse_categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BJkawFkPkno4"
      },
      "source": [
        "### The importance of having sufficiently large intermediate layers"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1sa1SRSBkno5"
      },
      "source": [
        "**A model with an information bottleneck**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 43,
      "metadata": {
        "id": "3J7-ZMvSkno5",
        "outputId": "9a493c25-fa24-4ea1-de3c-dfb476dc404f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "63/63 [==============================] - 2s 20ms/step - loss: 3.6193 - accuracy: 0.1496 - val_loss: 3.3895 - val_accuracy: 0.3140\n",
            "Epoch 2/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 3.1659 - accuracy: 0.4451 - val_loss: 3.0257 - val_accuracy: 0.5040\n",
            "Epoch 3/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 2.8656 - accuracy: 0.5100 - val_loss: 2.8499 - val_accuracy: 0.5180\n",
            "Epoch 4/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 2.6676 - accuracy: 0.3151 - val_loss: 2.6514 - val_accuracy: 0.2530\n",
            "Epoch 5/20\n",
            "63/63 [==============================] - 1s 19ms/step - loss: 2.4229 - accuracy: 0.2499 - val_loss: 2.4147 - val_accuracy: 0.2540\n",
            "Epoch 6/20\n",
            "63/63 [==============================] - 2s 24ms/step - loss: 2.0833 - accuracy: 0.2958 - val_loss: 1.9625 - val_accuracy: 0.5350\n",
            "Epoch 7/20\n",
            "63/63 [==============================] - 2s 26ms/step - loss: 1.6743 - accuracy: 0.5489 - val_loss: 1.6846 - val_accuracy: 0.5470\n",
            "Epoch 8/20\n",
            "63/63 [==============================] - 2s 27ms/step - loss: 1.5318 - accuracy: 0.5697 - val_loss: 1.6565 - val_accuracy: 0.5630\n",
            "Epoch 9/20\n",
            "63/63 [==============================] - 1s 19ms/step - loss: 1.4766 - accuracy: 0.5796 - val_loss: 1.6573 - val_accuracy: 0.5660\n",
            "Epoch 10/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.4355 - accuracy: 0.5802 - val_loss: 1.6097 - val_accuracy: 0.5730\n",
            "Epoch 11/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.3986 - accuracy: 0.5765 - val_loss: 1.6200 - val_accuracy: 0.5670\n",
            "Epoch 12/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.3653 - accuracy: 0.5783 - val_loss: 1.6106 - val_accuracy: 0.5700\n",
            "Epoch 13/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.3213 - accuracy: 0.5933 - val_loss: 1.6002 - val_accuracy: 0.5970\n",
            "Epoch 14/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.2194 - accuracy: 0.6265 - val_loss: 1.5299 - val_accuracy: 0.6130\n",
            "Epoch 15/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.1304 - accuracy: 0.6615 - val_loss: 1.5892 - val_accuracy: 0.6140\n",
            "Epoch 16/20\n",
            "63/63 [==============================] - 1s 17ms/step - loss: 1.0632 - accuracy: 0.6830 - val_loss: 1.5681 - val_accuracy: 0.6220\n",
            "Epoch 17/20\n",
            "63/63 [==============================] - 1s 18ms/step - loss: 1.0086 - accuracy: 0.6982 - val_loss: 1.5134 - val_accuracy: 0.6380\n",
            "Epoch 18/20\n",
            "63/63 [==============================] - 1s 22ms/step - loss: 0.9612 - accuracy: 0.7114 - val_loss: 1.5069 - val_accuracy: 0.6500\n",
            "Epoch 19/20\n",
            "63/63 [==============================] - 2s 27ms/step - loss: 0.9172 - accuracy: 0.7340 - val_loss: 1.5496 - val_accuracy: 0.6550\n",
            "Epoch 20/20\n",
            "63/63 [==============================] - 2s 26ms/step - loss: 0.8795 - accuracy: 0.7481 - val_loss: 1.4993 - val_accuracy: 0.6630\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7c41da4d0970>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ],
      "source": [
        "model = keras.Sequential([\n",
        "    layers.Dense(64, activation=\"relu\"),\n",
        "    layers.Dense(4, activation=\"relu\"),\n",
        "    layers.Dense(46, activation=\"softmax\")\n",
        "])\n",
        "model.compile(optimizer=\"rmsprop\",\n",
        "              loss=\"categorical_crossentropy\",\n",
        "              metrics=[\"accuracy\"])\n",
        "model.fit(partial_x_train,\n",
        "          partial_y_train,\n",
        "          epochs=20,\n",
        "          batch_size=128,\n",
        "          validation_data=(x_val, y_val))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C1BQxzqYkno5"
      },
      "source": [
        "### Further experiments"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "opM3xLJhkno5"
      },
      "source": [
        "### Wrapping up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yuko9L7akno5"
      },
      "source": [
        "## Predicting house prices: A regression example"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "RqzXH9LVkno6"
      },
      "source": [
        "### The Boston Housing Price dataset"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3PTCzKm7kno6"
      },
      "source": [
        "**Loading the Boston housing dataset**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 44,
      "metadata": {
        "id": "96KXfvTjkno6",
        "outputId": "f43ab11a-7293-4423-bb02-ba45e4ce6600",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/boston_housing.npz\n",
            "57026/57026 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import boston_housing\n",
        "(train_data, train_targets), (test_data, test_targets) = boston_housing.load_data()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 45,
      "metadata": {
        "id": "-8uXWApEkno6",
        "outputId": "ec398afe-6efc-439a-cc2c-2fdfc931913f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(404, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ],
      "source": [
        "train_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 46,
      "metadata": {
        "id": "db1jZ59Ukno6",
        "outputId": "14371feb-6499-4e23-f100-38c2d93a945e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(102, 13)"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ],
      "source": [
        "test_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 47,
      "metadata": {
        "id": "V2YZ883dkno7",
        "outputId": "4ea04dfc-8d21-4b20-8b6d-fbed082d4543",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([15.2, 42.3, 50. , 21.1, 17.7, 18.5, 11.3, 15.6, 15.6, 14.4, 12.1,\n",
              "       17.9, 23.1, 19.9, 15.7,  8.8, 50. , 22.5, 24.1, 27.5, 10.9, 30.8,\n",
              "       32.9, 24. , 18.5, 13.3, 22.9, 34.7, 16.6, 17.5, 22.3, 16.1, 14.9,\n",
              "       23.1, 34.9, 25. , 13.9, 13.1, 20.4, 20. , 15.2, 24.7, 22.2, 16.7,\n",
              "       12.7, 15.6, 18.4, 21. , 30.1, 15.1, 18.7,  9.6, 31.5, 24.8, 19.1,\n",
              "       22. , 14.5, 11. , 32. , 29.4, 20.3, 24.4, 14.6, 19.5, 14.1, 14.3,\n",
              "       15.6, 10.5,  6.3, 19.3, 19.3, 13.4, 36.4, 17.8, 13.5, 16.5,  8.3,\n",
              "       14.3, 16. , 13.4, 28.6, 43.5, 20.2, 22. , 23. , 20.7, 12.5, 48.5,\n",
              "       14.6, 13.4, 23.7, 50. , 21.7, 39.8, 38.7, 22.2, 34.9, 22.5, 31.1,\n",
              "       28.7, 46. , 41.7, 21. , 26.6, 15. , 24.4, 13.3, 21.2, 11.7, 21.7,\n",
              "       19.4, 50. , 22.8, 19.7, 24.7, 36.2, 14.2, 18.9, 18.3, 20.6, 24.6,\n",
              "       18.2,  8.7, 44. , 10.4, 13.2, 21.2, 37. , 30.7, 22.9, 20. , 19.3,\n",
              "       31.7, 32. , 23.1, 18.8, 10.9, 50. , 19.6,  5. , 14.4, 19.8, 13.8,\n",
              "       19.6, 23.9, 24.5, 25. , 19.9, 17.2, 24.6, 13.5, 26.6, 21.4, 11.9,\n",
              "       22.6, 19.6,  8.5, 23.7, 23.1, 22.4, 20.5, 23.6, 18.4, 35.2, 23.1,\n",
              "       27.9, 20.6, 23.7, 28. , 13.6, 27.1, 23.6, 20.6, 18.2, 21.7, 17.1,\n",
              "        8.4, 25.3, 13.8, 22.2, 18.4, 20.7, 31.6, 30.5, 20.3,  8.8, 19.2,\n",
              "       19.4, 23.1, 23. , 14.8, 48.8, 22.6, 33.4, 21.1, 13.6, 32.2, 13.1,\n",
              "       23.4, 18.9, 23.9, 11.8, 23.3, 22.8, 19.6, 16.7, 13.4, 22.2, 20.4,\n",
              "       21.8, 26.4, 14.9, 24.1, 23.8, 12.3, 29.1, 21. , 19.5, 23.3, 23.8,\n",
              "       17.8, 11.5, 21.7, 19.9, 25. , 33.4, 28.5, 21.4, 24.3, 27.5, 33.1,\n",
              "       16.2, 23.3, 48.3, 22.9, 22.8, 13.1, 12.7, 22.6, 15. , 15.3, 10.5,\n",
              "       24. , 18.5, 21.7, 19.5, 33.2, 23.2,  5. , 19.1, 12.7, 22.3, 10.2,\n",
              "       13.9, 16.3, 17. , 20.1, 29.9, 17.2, 37.3, 45.4, 17.8, 23.2, 29. ,\n",
              "       22. , 18. , 17.4, 34.6, 20.1, 25. , 15.6, 24.8, 28.2, 21.2, 21.4,\n",
              "       23.8, 31. , 26.2, 17.4, 37.9, 17.5, 20. ,  8.3, 23.9,  8.4, 13.8,\n",
              "        7.2, 11.7, 17.1, 21.6, 50. , 16.1, 20.4, 20.6, 21.4, 20.6, 36.5,\n",
              "        8.5, 24.8, 10.8, 21.9, 17.3, 18.9, 36.2, 14.9, 18.2, 33.3, 21.8,\n",
              "       19.7, 31.6, 24.8, 19.4, 22.8,  7.5, 44.8, 16.8, 18.7, 50. , 50. ,\n",
              "       19.5, 20.1, 50. , 17.2, 20.8, 19.3, 41.3, 20.4, 20.5, 13.8, 16.5,\n",
              "       23.9, 20.6, 31.5, 23.3, 16.8, 14. , 33.8, 36.1, 12.8, 18.3, 18.7,\n",
              "       19.1, 29. , 30.1, 50. , 50. , 22. , 11.9, 37.6, 50. , 22.7, 20.8,\n",
              "       23.5, 27.9, 50. , 19.3, 23.9, 22.6, 15.2, 21.7, 19.2, 43.8, 20.3,\n",
              "       33.2, 19.9, 22.5, 32.7, 22. , 17.1, 19. , 15. , 16.1, 25.1, 23.7,\n",
              "       28.7, 37.2, 22.6, 16.4, 25. , 29.8, 22.1, 17.4, 18.1, 30.3, 17.5,\n",
              "       24.7, 12.6, 26.5, 28.7, 13.3, 10.4, 24.4, 23. , 20. , 17.8,  7. ,\n",
              "       11.8, 24.4, 13.8, 19.4, 25.2, 19.4, 19.4, 29.1])"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ],
      "source": [
        "train_targets"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VqwyhMKkno7"
      },
      "source": [
        "### Preparing the data"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CGIy0UBkno7"
      },
      "source": [
        "**Normalizing the data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 48,
      "metadata": {
        "id": "S3gHNFv0kno7"
      },
      "outputs": [],
      "source": [
        "mean = train_data.mean(axis=0)\n",
        "train_data -= mean\n",
        "std = train_data.std(axis=0)\n",
        "train_data /= std\n",
        "test_data -= mean\n",
        "test_data /= std"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bDq6Sq7Dkno7"
      },
      "source": [
        "### Building your model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PaceGdQ5kno8"
      },
      "source": [
        "**Model definition**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 49,
      "metadata": {
        "id": "OzfiyVMZkno8"
      },
      "outputs": [],
      "source": [
        "def build_model():\n",
        "    model = keras.Sequential([\n",
        "        layers.Dense(64, activation=\"relu\"),\n",
        "        layers.Dense(64, activation=\"relu\"),\n",
        "        layers.Dense(1)\n",
        "    ])\n",
        "    model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "    return model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EREFJMcCkno8"
      },
      "source": [
        "### Validating your approach using K-fold validation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rKCIMMrVkno8"
      },
      "source": [
        "**K-fold validation**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 50,
      "metadata": {
        "id": "LigWn3M1kno8",
        "outputId": "106e1fd7-6350-4c89-c253-40e177b2b49b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing fold #0\n",
            "Processing fold #1\n",
            "Processing fold #2\n",
            "Processing fold #3\n"
          ]
        }
      ],
      "source": [
        "k = 4\n",
        "num_val_samples = len(train_data) // k\n",
        "num_epochs = 100\n",
        "all_scores = []\n",
        "for i in range(k):\n",
        "    print(f\"Processing fold #{i}\")\n",
        "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[:i * num_val_samples],\n",
        "         train_data[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    partial_train_targets = np.concatenate(\n",
        "        [train_targets[:i * num_val_samples],\n",
        "         train_targets[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    model = build_model()\n",
        "    model.fit(partial_train_data, partial_train_targets,\n",
        "              epochs=num_epochs, batch_size=16, verbose=0)\n",
        "    val_mse, val_mae = model.evaluate(val_data, val_targets, verbose=0)\n",
        "    all_scores.append(val_mae)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 51,
      "metadata": {
        "id": "PN4SAeqWkno9",
        "outputId": "f9842a28-7988-427d-9c38-35cb530b1d03",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.9075944423675537, 2.361785888671875, 2.4412543773651123, 2.3568150997161865]"
            ]
          },
          "metadata": {},
          "execution_count": 51
        }
      ],
      "source": [
        "all_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 52,
      "metadata": {
        "id": "3VgkGXwRkno9",
        "outputId": "916dda33-2b06-4130-9db8-79832689c6a9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.266862452030182"
            ]
          },
          "metadata": {},
          "execution_count": 52
        }
      ],
      "source": [
        "np.mean(all_scores)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QSDBcj9ekno9"
      },
      "source": [
        "**Saving the validation logs at each fold**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 53,
      "metadata": {
        "id": "EIWviSJcknpB",
        "outputId": "4d5f2f75-e37f-4481-d359-995746a03f15",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Processing fold #0\n",
            "Processing fold #1\n",
            "Processing fold #2\n",
            "Processing fold #3\n"
          ]
        }
      ],
      "source": [
        "num_epochs = 500\n",
        "all_mae_histories = []\n",
        "for i in range(k):\n",
        "    print(f\"Processing fold #{i}\")\n",
        "    val_data = train_data[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    val_targets = train_targets[i * num_val_samples: (i + 1) * num_val_samples]\n",
        "    partial_train_data = np.concatenate(\n",
        "        [train_data[:i * num_val_samples],\n",
        "         train_data[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    partial_train_targets = np.concatenate(\n",
        "        [train_targets[:i * num_val_samples],\n",
        "         train_targets[(i + 1) * num_val_samples:]],\n",
        "        axis=0)\n",
        "    model = build_model()\n",
        "    history = model.fit(partial_train_data, partial_train_targets,\n",
        "                        validation_data=(val_data, val_targets),\n",
        "                        epochs=num_epochs, batch_size=16, verbose=0)\n",
        "    mae_history = history.history[\"val_mae\"]\n",
        "    all_mae_histories.append(mae_history)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tVgkN26_knpB"
      },
      "source": [
        "**Building the history of successive mean K-fold validation scores**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 54,
      "metadata": {
        "id": "eA72ue3IknpC"
      },
      "outputs": [],
      "source": [
        "average_mae_history = [\n",
        "    np.mean([x[i] for x in all_mae_histories]) for i in range(num_epochs)]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4VnksEw3knpC"
      },
      "source": [
        "**Plotting validation scores**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 55,
      "metadata": {
        "id": "OJNkgFD4knpC",
        "outputId": "79561314-56e0-43df-857d-c5c635b08fc2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(range(1, len(average_mae_history) + 1), average_mae_history)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Validation MAE\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Przb44y5knpD"
      },
      "source": [
        "**Plotting validation scores, excluding the first 10 data points**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 56,
      "metadata": {
        "id": "V1vzdb3CknpD",
        "outputId": "33ffb7f2-9ee5-465b-8a49-5fac62f53e3d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "truncated_mae_history = average_mae_history[10:]\n",
        "plt.plot(range(1, len(truncated_mae_history) + 1), truncated_mae_history)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Validation MAE\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "npbt_gs3knpD"
      },
      "source": [
        "**Training the final model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 57,
      "metadata": {
        "id": "mppRgzn2knpD",
        "outputId": "76678155-47fa-40fe-819e-9b42d0659ec5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 5ms/step - loss: 15.2976 - mae: 2.4418\n"
          ]
        }
      ],
      "source": [
        "model = build_model()\n",
        "model.fit(train_data, train_targets,\n",
        "          epochs=130, batch_size=16, verbose=0)\n",
        "test_mse_score, test_mae_score = model.evaluate(test_data, test_targets)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "VzNObZarknpD",
        "outputId": "a29fe3a8-4f95-4b57-e605-0b15492cefa7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2.4417755603790283"
            ]
          },
          "metadata": {},
          "execution_count": 58
        }
      ],
      "source": [
        "test_mae_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XrIT8OboknpE"
      },
      "source": [
        "### Generating predictions on new data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "id": "Bjt4v-zcknpE",
        "outputId": "5b7f99b1-6471-4d19-8917-c22d36773b54",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "4/4 [==============================] - 0s 3ms/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([8.328578], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 59
        }
      ],
      "source": [
        "predictions = model.predict(test_data)\n",
        "predictions[0]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w9XPgrSbknpE"
      },
      "source": [
        "### Wrapping up"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dg7ygQKsknpF"
      },
      "source": [
        "## Summary"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "chapter04_getting-started-with-neural-networks.i",
      "provenance": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}