Regularization_and_drop_out.ipynb

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      "name": "Regularization and drop out.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/Qxr2427/MachineLearning-Opensource-Projects/blob/master/Regularization_and_drop_out.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
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        "id": "fTFj8ft5dlbS"
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      "source": [
        "##### Copyright 2018 The TensorFlow Authors."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "lzyBOpYMdp3F",
        "colab": {}
      },
      "source": [
        "#@title Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "# you may not use this file except in compliance with the License.\n",
        "# You may obtain a copy of the License at\n",
        "#\n",
        "# https://www.apache.org/licenses/LICENSE-2.0\n",
        "#\n",
        "# Unless required by applicable law or agreed to in writing, software\n",
        "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "# See the License for the specific language governing permissions and\n",
        "# limitations under the License."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "cellView": "form",
        "colab_type": "code",
        "id": "m_x4KfSJ7Vt7",
        "colab": {}
      },
      "source": [
        "#@title MIT License\n",
        "#\n",
        "# Copyright (c) 2017 François Chollet\n",
        "#\n",
        "# Permission is hereby granted, free of charge, to any person obtaining a\n",
        "# copy of this software and associated documentation files (the \"Software\"),\n",
        "# to deal in the Software without restriction, including without limitation\n",
        "# the rights to use, copy, modify, merge, publish, distribute, sublicense,\n",
        "# and/or sell copies of the Software, and to permit persons to whom the\n",
        "# Software is furnished to do so, subject to the following conditions:\n",
        "#\n",
        "# The above copyright notice and this permission notice shall be included in\n",
        "# all copies or substantial portions of the Software.\n",
        "#\n",
        "# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n",
        "# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n",
        "# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL\n",
        "# THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n",
        "# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n",
        "# FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER\n",
        "# DEALINGS IN THE SOFTWARE."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "C9HmC2T4ld5B"
      },
      "source": [
        "# Explore overfitting and underfitting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kRTxFhXAlnl1"
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      "source": [
        "<table class=\"tfo-notebook-buttons\" align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://www.tensorflow.org/tutorials/keras/overfit_and_underfit\"><img src=\"https://www.tensorflow.org/images/tf_logo_32px.png\" />View on TensorFlow.org</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/keras/overfit_and_underfit.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />Run in Google Colab</a>\n",
        "  </td>\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://github.com/tensorflow/docs/blob/master/site/en/tutorials/keras/overfit_and_underfit.ipynb\"><img src=\"https://www.tensorflow.org/images/GitHub-Mark-32px.png\" />View source on GitHub</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "19rPukKZsPG6"
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      "source": [
        "As always, the code in this example will use the `tf.keras` API, which you can learn more about in the TensorFlow [Keras guide](https://www.tensorflow.org/guide/keras).\n",
        "\n",
        "In both of the previous examples—classifying movie reviews, and predicting fuel efficiency—we saw that the accuracy of our model on the validation data would peak after training for a number of epochs, and would then start decreasing.\n",
        "\n",
        "In other words, our model would *overfit* to the training data. Learning how to deal with overfitting is important. Although it's often possible to achieve high accuracy on the *training set*, what we really want is to develop models that generalize well to a *testing set* (or data they haven't seen before).\n",
        "\n",
        "The opposite of overfitting is *underfitting*. Underfitting occurs when there is still room for improvement on the test data. This can happen for a number of reasons: If the model is not powerful enough, is over-regularized, or has simply not been trained long enough. This means the network has not learned the relevant patterns in the training data.\n",
        "\n",
        "If you train for too long though, the model will start to overfit and learn patterns from the training data that don't generalize to the test data. We need to strike a balance. Understanding how to train for an appropriate number of epochs as we'll explore below is a useful skill.\n",
        "\n",
        "To prevent overfitting, the best solution is to use more training data. A model trained on more data will naturally generalize better. When that is no longer possible, the next best solution is to use techniques like regularization. These place constraints on the quantity and type of information your model can store.  If a network can only afford to memorize a small number of patterns, the optimization process will force it to focus on the most prominent patterns, which have a better chance of generalizing well.\n",
        "\n",
        "In this notebook, we'll explore two common regularization techniques—weight regularization and dropout—and use them to improve our IMDB movie review classification notebook."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "5pZ8A2liqvgk",
        "outputId": "a399c648-d74c-4dd4-c4d8-3b02c1d02b09",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.14.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "1cweoTiruj8O"
      },
      "source": [
        "## Download the IMDB dataset\n",
        "\n",
        "Rather than using an embedding as in the previous notebook, here we will multi-hot encode the sentences. This model will quickly overfit to the training set. It will be used to demonstrate when overfitting occurs, and how to fight it.\n",
        "\n",
        "Multi-hot-encoding our lists means turning them into vectors of 0s and 1s. Concretely, this would mean for instance turning the sequence  `[3, 5]` into a 10,000-dimensional vector that would be all-zeros except for indices 3 and 5, which would be ones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QpzE4iqZtJly",
        "outputId": "24e09809-f33e-4fd3-8652-23e7bbbc255a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        }
      },
      "source": [
        "NUM_WORDS = 2000\n",
        "\n",
        "(train_data, train_labels), (test_data, test_labels) = keras.datasets.imdb.load_data(num_words=NUM_WORDS)\n",
        "\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 0s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "GSlfNbpbNjpu"
      },
      "source": [
        "## Explore the data\n",
        "\n",
        "Let's take a moment to understand the format of the data. The dataset comes preprocessed: each example is an array of integers representing the words of the movie review. Each label is an integer value of either 0 or 1, where 0 is a negative review, and 1 is a positive review."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "VK59c64qNi9f",
        "outputId": "c5905e32-6b1c-4a5e-9020-bba7658125dc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "print(\"Training entries: {}, labels: {}\".format(len(train_data), len(train_labels)))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training entries: 25000, labels: 25000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zvFRWyoBNn1e",
        "colab_type": "code",
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        }
      },
      "source": [
        "print(train_data[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 2, 66, 2, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 1920, 2, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2, 2, 16, 480, 66, 2, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 2, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "MzWVeXe3NBTn"
      },
      "source": [
        "##Multi-hot encoding\n",
        "Multi-hot-encoding our lists means turning them into vectors of 0s and 1s. Concretely, this would mean for instance turning the sequence  `[3, 5]` into a 10,000-dimensional vector that would be all-zeros except for indices 3 and 5, which would be ones."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VMNdiX3INyAe",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def multi_hot_sequences(sequences, dimension):\n",
        "    # Create an all-zero matrix of shape (len(sequences), dimension)\n",
        "    results = np.zeros((len(sequences), dimension))\n",
        "    for i, word_indices in enumerate(sequences):\n",
        "        results[i, word_indices] = 1.0  # set specific indices of results[i] to 1s\n",
        "    return results\n",
        "\n",
        "\n",
        "train_data = multi_hot_sequences(train_data, dimension=NUM_WORDS)\n",
        "test_data = multi_hot_sequences(test_data, dimension=NUM_WORDS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "71kr5rG4LkGM",
        "outputId": "0c3edbca-5f04-462f-bd2d-6c1be75bcb97",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        }
      },
      "source": [
        "plt.plot(train_data[0])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7fb26e69d470>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7e0e76Mzo+9wj+lJBNbFWGZO8RJmXw0JFESe57wUWhe4vDKYNmgZcDjxjZruBa4HOqKKq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\n+Jm/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "lglk41MwvU5o"
      },
      "source": [
        "## Demonstrate overfitting\n",
        "\n",
        "The simplest way to prevent overfitting is to reduce the size of the model, i.e. the number of learnable parameters in the model (which is determined by the number of layers and the number of units per layer). In deep learning, the number of learnable parameters in a model is often referred to as the model's \"capacity\". Intuitively, a model with more parameters will have more \"memorization capacity\" and therefore will be able to easily learn a perfect dictionary-like mapping between training samples and their targets, a mapping without any generalization power, but this would be useless when making predictions on previously unseen data.\n",
        "\n",
        "Always keep this in mind: deep learning models tend to be good at fitting to the training data, but the real challenge is generalization, not fitting.\n",
        "\n",
        "On the other hand, if the network has limited memorization resources, it will not be able to learn the mapping as easily. To minimize its loss, it will have to learn compressed representations that have more predictive power. At the same time, if you make your model too small, it will have difficulty fitting to the training data. There is a balance between \"too much capacity\" and \"not enough capacity\".\n",
        "\n",
        "Unfortunately, there is no magical formula to determine the right size or architecture of your model (in terms of the number of layers, or the right size for each layer). You will have to experiment using a series of different architectures.\n",
        "\n",
        "To find an appropriate model size, it's best to start with relatively few layers and parameters, then begin increasing the size of the layers or adding new layers until you see diminishing returns on the validation loss. Let's try this on our movie review classification network.\n",
        "\n",
        "We'll create a simple model using only ```Dense``` layers as a baseline, then create smaller and larger versions, and compare them."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "_ReKHdC2EgVu"
      },
      "source": [
        "### Create a baseline model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "QKgdXPx9usBa",
        "outputId": "e07fc2d9-b781-4f16-93c6-3a9e3a3d4936",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        }
      },
      "source": [
        "baseline_model = keras.Sequential([\n",
        "    # `input_shape` is only required here so that `.summary` works.\n",
        "    keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(16, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "baseline_model.compile(optimizer='adam',\n",
        "                       loss='binary_crossentropy',\n",
        "                       metrics=['accuracy', 'binary_crossentropy'])\n",
        "\n",
        "baseline_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "WARNING: Logging before flag parsing goes to stderr.\n",
            "W0706 21:55:06.324978 140404910229376 deprecation.py:506] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/init_ops.py:1251: calling VarianceScaling.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Call initializer instance with the dtype argument instead of passing it to the constructor\n",
            "W0706 21:55:06.422165 140404910229376 deprecation.py:323] From /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/nn_impl.py:180: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use tf.where in 2.0, which has the same broadcast rule as np.where\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense (Dense)                (None, 16)                32016     \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 16)                272       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 17        \n",
            "=================================================================\n",
            "Total params: 32,305\n",
            "Trainable params: 32,305\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "LqG3MXF5xSjR",
        "outputId": "da0ecbd0-8f61-4d79-8f6a-e5866ba5006c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "baseline_history = baseline_model.fit(train_data,\n",
        "                                      train_labels,\n",
        "                                      epochs=20,\n",
        "                                      batch_size=512,\n",
        "                                      validation_data=(test_data, test_labels),\n",
        "                                      verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.5317 - acc: 0.7488 - binary_crossentropy: 0.5317 - val_loss: 0.3745 - val_acc: 0.8530 - val_binary_crossentropy: 0.3745\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.3156 - acc: 0.8760 - binary_crossentropy: 0.3156 - val_loss: 0.3141 - val_acc: 0.8711 - val_binary_crossentropy: 0.3141\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.2706 - acc: 0.8913 - binary_crossentropy: 0.2706 - val_loss: 0.3021 - val_acc: 0.8744 - val_binary_crossentropy: 0.3021\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.2551 - acc: 0.8985 - binary_crossentropy: 0.2551 - val_loss: 0.3051 - val_acc: 0.8724 - val_binary_crossentropy: 0.3051\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.2459 - acc: 0.9027 - binary_crossentropy: 0.2459 - val_loss: 0.3102 - val_acc: 0.8709 - val_binary_crossentropy: 0.3102\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.2410 - acc: 0.9034 - binary_crossentropy: 0.2410 - val_loss: 0.3162 - val_acc: 0.8695 - val_binary_crossentropy: 0.3162\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.2369 - acc: 0.9061 - binary_crossentropy: 0.2369 - val_loss: 0.3192 - val_acc: 0.8682 - val_binary_crossentropy: 0.3192\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.2342 - acc: 0.9068 - binary_crossentropy: 0.2342 - val_loss: 0.3279 - val_acc: 0.8656 - val_binary_crossentropy: 0.3279\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.2335 - acc: 0.9071 - binary_crossentropy: 0.2335 - val_loss: 0.3265 - val_acc: 0.8665 - val_binary_crossentropy: 0.3265\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.2292 - acc: 0.9072 - binary_crossentropy: 0.2292 - val_loss: 0.3325 - val_acc: 0.8647 - val_binary_crossentropy: 0.3325\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.2229 - acc: 0.9122 - binary_crossentropy: 0.2229 - val_loss: 0.3355 - val_acc: 0.8650 - val_binary_crossentropy: 0.3355\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.2191 - acc: 0.9121 - binary_crossentropy: 0.2191 - val_loss: 0.3357 - val_acc: 0.8660 - val_binary_crossentropy: 0.3357\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.2144 - acc: 0.9152 - binary_crossentropy: 0.2144 - val_loss: 0.3387 - val_acc: 0.8643 - val_binary_crossentropy: 0.3387\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.2086 - acc: 0.9181 - binary_crossentropy: 0.2086 - val_loss: 0.3395 - val_acc: 0.8650 - val_binary_crossentropy: 0.3395\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.2033 - acc: 0.9188 - binary_crossentropy: 0.2033 - val_loss: 0.3442 - val_acc: 0.8625 - val_binary_crossentropy: 0.3442\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.1970 - acc: 0.9210 - binary_crossentropy: 0.1970 - val_loss: 0.3459 - val_acc: 0.8630 - val_binary_crossentropy: 0.3459\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.1891 - acc: 0.9252 - binary_crossentropy: 0.1891 - val_loss: 0.3529 - val_acc: 0.8630 - val_binary_crossentropy: 0.3529\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.1818 - acc: 0.9301 - binary_crossentropy: 0.1818 - val_loss: 0.3556 - val_acc: 0.8624 - val_binary_crossentropy: 0.3556\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.1748 - acc: 0.9337 - binary_crossentropy: 0.1748 - val_loss: 0.3602 - val_acc: 0.8625 - val_binary_crossentropy: 0.3602\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.1652 - acc: 0.9386 - binary_crossentropy: 0.1652 - val_loss: 0.3676 - val_acc: 0.8616 - val_binary_crossentropy: 0.3676\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "L-DGRBbGxI6G"
      },
      "source": [
        "### Create a smaller model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "SrfoVQheYSO5"
      },
      "source": [
        "Let's create a model with less hidden units to compare against the baseline model that we just created:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "jksi-XtaxDAh",
        "outputId": "36e0f072-6637-4ed4-f0fe-2c0f5e106541",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "smaller_model = keras.Sequential([\n",
        "    keras.layers.Dense(4, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(4, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "smaller_model.compile(optimizer='adam',\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy', 'binary_crossentropy'])\n",
        "\n",
        "smaller_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_3 (Dense)              (None, 4)                 8004      \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 4)                 20        \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 5         \n",
            "=================================================================\n",
            "Total params: 8,029\n",
            "Trainable params: 8,029\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "jbngCZliYdma"
      },
      "source": [
        "And train the model using the same data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "Ofn1AwDhx-Fe",
        "outputId": "69bb6404-0369-4121-c72c-db8c10ce8613",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "smaller_history = smaller_model.fit(train_data,\n",
        "                                    train_labels,\n",
        "                                    epochs=20,\n",
        "                                    batch_size=512,\n",
        "                                    validation_data=(test_data, test_labels),\n",
        "                                    verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.5820 - acc: 0.7167 - binary_crossentropy: 0.5820 - val_loss: 0.4637 - val_acc: 0.8247 - val_binary_crossentropy: 0.4637\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.3858 - acc: 0.8562 - binary_crossentropy: 0.3858 - val_loss: 0.3535 - val_acc: 0.8591 - val_binary_crossentropy: 0.3535\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.3086 - acc: 0.8789 - binary_crossentropy: 0.3086 - val_loss: 0.3175 - val_acc: 0.8716 - val_binary_crossentropy: 0.3175\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.2770 - acc: 0.8905 - binary_crossentropy: 0.2770 - val_loss: 0.3074 - val_acc: 0.8740 - val_binary_crossentropy: 0.3074\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.2625 - acc: 0.8964 - binary_crossentropy: 0.2625 - val_loss: 0.3078 - val_acc: 0.8715 - val_binary_crossentropy: 0.3078\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.2543 - acc: 0.8994 - binary_crossentropy: 0.2543 - val_loss: 0.3096 - val_acc: 0.8714 - val_binary_crossentropy: 0.3096\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.2468 - acc: 0.9030 - binary_crossentropy: 0.2468 - val_loss: 0.3083 - val_acc: 0.8705 - val_binary_crossentropy: 0.3083\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.2431 - acc: 0.9038 - binary_crossentropy: 0.2431 - val_loss: 0.3116 - val_acc: 0.8710 - val_binary_crossentropy: 0.3116\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.2399 - acc: 0.9057 - binary_crossentropy: 0.2399 - val_loss: 0.3193 - val_acc: 0.8676 - val_binary_crossentropy: 0.3193\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.2394 - acc: 0.9060 - binary_crossentropy: 0.2394 - val_loss: 0.3189 - val_acc: 0.8695 - val_binary_crossentropy: 0.3189\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.2362 - acc: 0.9075 - binary_crossentropy: 0.2362 - val_loss: 0.3226 - val_acc: 0.8684 - val_binary_crossentropy: 0.3226\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.2358 - acc: 0.9069 - binary_crossentropy: 0.2358 - val_loss: 0.3251 - val_acc: 0.8670 - val_binary_crossentropy: 0.3251\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.2341 - acc: 0.9086 - binary_crossentropy: 0.2341 - val_loss: 0.3343 - val_acc: 0.8645 - val_binary_crossentropy: 0.3343\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.2339 - acc: 0.9073 - binary_crossentropy: 0.2339 - val_loss: 0.3344 - val_acc: 0.8639 - val_binary_crossentropy: 0.3344\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.2341 - acc: 0.9068 - binary_crossentropy: 0.2341 - val_loss: 0.3373 - val_acc: 0.8630 - val_binary_crossentropy: 0.3373\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.2336 - acc: 0.9072 - binary_crossentropy: 0.2336 - val_loss: 0.3342 - val_acc: 0.8660 - val_binary_crossentropy: 0.3342\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.2316 - acc: 0.9084 - binary_crossentropy: 0.2316 - val_loss: 0.3354 - val_acc: 0.8659 - val_binary_crossentropy: 0.3354\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.2308 - acc: 0.9084 - binary_crossentropy: 0.2308 - val_loss: 0.3384 - val_acc: 0.8647 - val_binary_crossentropy: 0.3384\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.2309 - acc: 0.9079 - binary_crossentropy: 0.2309 - val_loss: 0.3386 - val_acc: 0.8648 - val_binary_crossentropy: 0.3386\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.2314 - acc: 0.9081 - binary_crossentropy: 0.2314 - val_loss: 0.3400 - val_acc: 0.8646 - val_binary_crossentropy: 0.3400\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "vIPuf23FFaVn"
      },
      "source": [
        "### Create a bigger model\n",
        "\n",
        "As an exercise, you can create an even larger model, and see how quickly it begins overfitting.  Next, let's add to this benchmark a network that has much more capacity, far more than the problem would warrant:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "ghQwwqwqvQM9",
        "outputId": "5bc441c0-9d2f-48a8-fde4-754d92979f72",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 252
        }
      },
      "source": [
        "bigger_model = keras.models.Sequential([\n",
        "    keras.layers.Dense(512, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(512, activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "bigger_model.compile(optimizer='adam',\n",
        "                     loss='binary_crossentropy',\n",
        "                     metrics=['accuracy','binary_crossentropy'])\n",
        "\n",
        "bigger_model.summary()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "dense_6 (Dense)              (None, 512)               1024512   \n",
            "_________________________________________________________________\n",
            "dense_7 (Dense)              (None, 512)               262656    \n",
            "_________________________________________________________________\n",
            "dense_8 (Dense)              (None, 1)                 513       \n",
            "=================================================================\n",
            "Total params: 1,287,681\n",
            "Trainable params: 1,287,681\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "D-d-i5DaYmr7"
      },
      "source": [
        "And, again, train the model using the same data:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "U1A99dhqvepf",
        "outputId": "70ce686b-83af-4f84-9f80-e19f83958b13",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "bigger_history = bigger_model.fit(train_data, train_labels,\n",
        "                                  epochs=20,\n",
        "                                  batch_size=512,\n",
        "                                  validation_data=(test_data, test_labels),\n",
        "                                  verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.3744 - acc: 0.8341 - binary_crossentropy: 0.3744 - val_loss: 0.3058 - val_acc: 0.8725 - val_binary_crossentropy: 0.3058\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.2347 - acc: 0.9072 - binary_crossentropy: 0.2347 - val_loss: 0.3072 - val_acc: 0.8689 - val_binary_crossentropy: 0.3072\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.1442 - acc: 0.9480 - binary_crossentropy: 0.1442 - val_loss: 0.3741 - val_acc: 0.8597 - val_binary_crossentropy: 0.3741\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.0416 - acc: 0.9904 - binary_crossentropy: 0.0416 - val_loss: 0.4449 - val_acc: 0.8621 - val_binary_crossentropy: 0.4449\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.0063 - acc: 0.9994 - binary_crossentropy: 0.0063 - val_loss: 0.5691 - val_acc: 0.8650 - val_binary_crossentropy: 0.5691\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.0021 - acc: 0.9999 - binary_crossentropy: 0.0021 - val_loss: 0.6137 - val_acc: 0.8652 - val_binary_crossentropy: 0.6137\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 5.6121e-04 - acc: 1.0000 - binary_crossentropy: 5.6121e-04 - val_loss: 0.6387 - val_acc: 0.8670 - val_binary_crossentropy: 0.6387\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 3.3946e-04 - acc: 1.0000 - binary_crossentropy: 3.3946e-04 - val_loss: 0.6642 - val_acc: 0.8677 - val_binary_crossentropy: 0.6642\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 2.4625e-04 - acc: 1.0000 - binary_crossentropy: 2.4625e-04 - val_loss: 0.6840 - val_acc: 0.8681 - val_binary_crossentropy: 0.6840\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 1.9016e-04 - acc: 1.0000 - binary_crossentropy: 1.9016e-04 - val_loss: 0.7009 - val_acc: 0.8680 - val_binary_crossentropy: 0.7009\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 1.5084e-04 - acc: 1.0000 - binary_crossentropy: 1.5084e-04 - val_loss: 0.7168 - val_acc: 0.8680 - val_binary_crossentropy: 0.7168\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 1.2046e-04 - acc: 1.0000 - binary_crossentropy: 1.2046e-04 - val_loss: 0.7343 - val_acc: 0.8680 - val_binary_crossentropy: 0.7343\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 9.4133e-05 - acc: 1.0000 - binary_crossentropy: 9.4133e-05 - val_loss: 0.7551 - val_acc: 0.8677 - val_binary_crossentropy: 0.7551\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 7.2126e-05 - acc: 1.0000 - binary_crossentropy: 7.2126e-05 - val_loss: 0.7779 - val_acc: 0.8677 - val_binary_crossentropy: 0.7779\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 5.4885e-05 - acc: 1.0000 - binary_crossentropy: 5.4885e-05 - val_loss: 0.8007 - val_acc: 0.8680 - val_binary_crossentropy: 0.8007\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 4.1952e-05 - acc: 1.0000 - binary_crossentropy: 4.1952e-05 - val_loss: 0.8224 - val_acc: 0.8678 - val_binary_crossentropy: 0.8224\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 3.2725e-05 - acc: 1.0000 - binary_crossentropy: 3.2725e-05 - val_loss: 0.8420 - val_acc: 0.8676 - val_binary_crossentropy: 0.8420\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 2.6002e-05 - acc: 1.0000 - binary_crossentropy: 2.6002e-05 - val_loss: 0.8605 - val_acc: 0.8676 - val_binary_crossentropy: 0.8605\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 2.1055e-05 - acc: 1.0000 - binary_crossentropy: 2.1055e-05 - val_loss: 0.8786 - val_acc: 0.8677 - val_binary_crossentropy: 0.8786\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 1.7360e-05 - acc: 1.0000 - binary_crossentropy: 1.7360e-05 - val_loss: 0.8941 - val_acc: 0.8675 - val_binary_crossentropy: 0.8941\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Fy3CMUZpzH3d"
      },
      "source": [
        "### Plot the training and validation loss\n",
        "\n",
        "<!--TODO(markdaoust): This should be a one-liner with tensorboard -->"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HSlo1F4xHuuM"
      },
      "source": [
        "The solid lines show the training loss, and the dashed lines show the validation loss (remember: a lower validation loss indicates a better model). Here, the smaller network begins overfitting later than the baseline model (after 6 epochs rather than 4) and its performance degrades much more slowly once it starts overfitting."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "0XmKDtOWzOpk",
        "outputId": "1a872076-4d39-4449-cb4e-ceb20da0e882",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "def plot_history(histories, key='binary_crossentropy'):\n",
        "  plt.figure(figsize=(16,10))\n",
        "\n",
        "  for name, history in histories:\n",
        "    val = plt.plot(history.epoch, history.history['val_'+key],\n",
        "                   '--', label=name.title()+' Val')\n",
        "    plt.plot(history.epoch, history.history[key], color=val[0].get_color(),\n",
        "             label=name.title()+' Train')\n",
        "\n",
        "  plt.xlabel('Epochs')\n",
        "  plt.ylabel(key.replace('_',' ').title())\n",
        "  plt.legend()\n",
        "\n",
        "  plt.xlim([0,max(history.epoch)])\n",
        "\n",
        "\n",
        "plot_history([('baseline', baseline_history),\n",
        "              ('smaller', smaller_history),\n",
        "              ('bigger', bigger_history)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jf/+YoqNFeFm8SAlPYUb/GQyJGuLa/ubYm90VVaRdU5kVEREREbkEjY5GCisK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\nAAAAQOT8P+DYscIyKgrtAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Bi6hBhdnSfjA"
      },
      "source": [
        "Notice that the larger network begins overfitting almost right away, after just one epoch, and overfits much more severely. The more capacity the network has, the quicker it will be able to model the training data (resulting in a low training loss), but the more susceptible it is to overfitting (resulting in a large difference between the training and validation loss)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "ASdv7nsgEFhx"
      },
      "source": [
        "## Strategies"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "4rHoVWcswFLa"
      },
      "source": [
        "### Add weight regularization\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "kRxWepNawbBK"
      },
      "source": [
        "You may be familiar with Occam's Razor principle: given two explanations for something, the explanation most likely to be correct is the \"simplest\" one, the one that makes the least amount of assumptions. This also applies to the models learned by neural networks: given some training data and a network architecture, there are multiple sets of weights values (multiple models) that could explain the data, and simpler models are less likely to overfit than complex ones.\n",
        "\n",
        "A \"simple model\" in this context is a model where the distribution of parameter values has less entropy (or a model with fewer parameters altogether, as we saw in the section above). Thus a common way to mitigate overfitting is to put constraints on the complexity of a network by forcing its weights only to take small values, which makes the distribution of weight values more \"regular\". This is called \"weight regularization\", and it is done by adding to the loss function of the network a cost associated with having large weights. This cost comes in two flavors:\n",
        "\n",
        "* [L1 regularization](https://developers.google.com/machine-learning/glossary/#L1_regularization), where the cost added is proportional to the absolute value of the weights coefficients (i.e. to what is called the \"L1 norm\" of the weights).\n",
        "\n",
        "* [L2 regularization](https://developers.google.com/machine-learning/glossary/#L2_regularization), where the cost added is proportional to the square of the value of the weights coefficients (i.e. to what is called the squared \"L2 norm\" of the weights). L2 regularization is also called weight decay in the context of neural networks. Don't let the different name confuse you: weight decay is mathematically the exact same as L2 regularization.\n",
        "\n",
        "In `tf.keras`, weight regularization is added by passing weight regularizer instances to layers as keyword arguments. Let's add L2 weight regularization now with lambda = 0.001."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "HFGmcwduwVyQ",
        "outputId": "41b04859-d38c-4504-895b-55806562f1a8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "l2_model = keras.models.Sequential([\n",
        "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),\n",
        "                       activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.001),\n",
        "                       activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "l2_model.compile(optimizer='adam',\n",
        "                 loss='binary_crossentropy',\n",
        "                 metrics=['accuracy', 'binary_crossentropy'])\n",
        "\n",
        "l2_model_history = l2_model.fit(train_data, train_labels,\n",
        "                                epochs=20,\n",
        "                                batch_size=512,\n",
        "                                validation_data=(test_data, test_labels),\n",
        "                                verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.5631 - acc: 0.7619 - binary_crossentropy: 0.5181 - val_loss: 0.4078 - val_acc: 0.8564 - val_binary_crossentropy: 0.3633\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.3548 - acc: 0.8753 - binary_crossentropy: 0.3096 - val_loss: 0.3522 - val_acc: 0.8743 - val_binary_crossentropy: 0.3069\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.3152 - acc: 0.8928 - binary_crossentropy: 0.2700 - val_loss: 0.3463 - val_acc: 0.8748 - val_binary_crossentropy: 0.3015\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.3021 - acc: 0.8984 - binary_crossentropy: 0.2577 - val_loss: 0.3508 - val_acc: 0.8712 - val_binary_crossentropy: 0.3071\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.2953 - acc: 0.8994 - binary_crossentropy: 0.2518 - val_loss: 0.3510 - val_acc: 0.8699 - val_binary_crossentropy: 0.3081\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.2886 - acc: 0.8996 - binary_crossentropy: 0.2463 - val_loss: 0.3529 - val_acc: 0.8703 - val_binary_crossentropy: 0.3111\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.2848 - acc: 0.9019 - binary_crossentropy: 0.2433 - val_loss: 0.3530 - val_acc: 0.8701 - val_binary_crossentropy: 0.3120\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.2800 - acc: 0.9064 - binary_crossentropy: 0.2392 - val_loss: 0.3541 - val_acc: 0.8690 - val_binary_crossentropy: 0.3138\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.2764 - acc: 0.9056 - binary_crossentropy: 0.2362 - val_loss: 0.3556 - val_acc: 0.8680 - val_binary_crossentropy: 0.3158\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.2762 - acc: 0.9060 - binary_crossentropy: 0.2364 - val_loss: 0.3559 - val_acc: 0.8684 - val_binary_crossentropy: 0.3163\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.2720 - acc: 0.9067 - binary_crossentropy: 0.2324 - val_loss: 0.3652 - val_acc: 0.8664 - val_binary_crossentropy: 0.3255\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.2655 - acc: 0.9110 - binary_crossentropy: 0.2257 - val_loss: 0.3573 - val_acc: 0.8676 - val_binary_crossentropy: 0.3176\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.2608 - acc: 0.9134 - binary_crossentropy: 0.2209 - val_loss: 0.3605 - val_acc: 0.8666 - val_binary_crossentropy: 0.3204\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.2536 - acc: 0.9158 - binary_crossentropy: 0.2131 - val_loss: 0.3607 - val_acc: 0.8667 - val_binary_crossentropy: 0.3201\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.2482 - acc: 0.9191 - binary_crossentropy: 0.2071 - val_loss: 0.3627 - val_acc: 0.8681 - val_binary_crossentropy: 0.3214\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.2421 - acc: 0.9214 - binary_crossentropy: 0.2002 - val_loss: 0.3682 - val_acc: 0.8663 - val_binary_crossentropy: 0.3261\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.2366 - acc: 0.9256 - binary_crossentropy: 0.1939 - val_loss: 0.3725 - val_acc: 0.8664 - val_binary_crossentropy: 0.3293\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.2324 - acc: 0.9280 - binary_crossentropy: 0.1885 - val_loss: 0.3772 - val_acc: 0.8660 - val_binary_crossentropy: 0.3328\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.2241 - acc: 0.9320 - binary_crossentropy: 0.1791 - val_loss: 0.3820 - val_acc: 0.8652 - val_binary_crossentropy: 0.3365\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.2171 - acc: 0.9379 - binary_crossentropy: 0.1709 - val_loss: 0.3860 - val_acc: 0.8648 - val_binary_crossentropy: 0.3393\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "bUUHoXb7w-_C"
      },
      "source": [
        "```l2(0.001)``` means that every coefficient in the weight matrix of the layer will add ```0.001 * weight_coefficient_value**2``` to the total loss of the network. Note that because this penalty is only added at training time, the loss for this network will be much higher at training than at test time.\n",
        "\n",
        "Here's the impact of our L2 regularization penalty:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "7wkfLyxBZdh_",
        "outputId": "2c657e1d-d661-4af5-d49b-6ad2c333d07a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plot_history([('baseline', baseline_history),\n",
        "              ('l2.001', l2_model_history)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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kycydO5c//elPvO997+PGG2884HGf+MQnuPvuu2loaKChoYEnn3wSgNtvv52ZM2fS0NDA\nzJkzuf322wEYPHgwd955J1/84hcPeu8rr7ySBx54YK9tDzzwAFdeeWU3fbvjL3dC7pY3Ydt6qpLl\nvLl5J027WrNdkSRJkiQd1nnnnUdZWRkA06dPZ9WqVfsds2bNGrZs2cL06dMJIXD11Vfz2GOPAfD4\n449zzTXXAHDNNdd0bk8mk0ybNo3CwsKD3nvmzJnU19ezZs0aALZt28YzzzzDpZdeCsCll17KlClT\nOPXUU/nBD37QfV86gwqyXUC3aVoLjQs5OXEKAMsam5g0cmCWi5IkSZLUY/3w3ftvO/VSOOPvoHk7\n/J/377+/7kMw+SrYth4eunrvfR/5xTGXdO+99x5wqvDq1asZMWJE5+cRI0awevVqANauXcuwYcMA\nOOGEE1i7dm2X75efn8/ll1/OQw89xGc+8xl+9rOfMWPGDCoqKgC47777GDx4MDt27GDatGlcfvnl\nVFZWHstXzLjcGckFaKxn3FA7LEuSJEnqfX784x8zd+5cbrjhhqO+RgiBEMIRnbPnlOV9pyrfeeed\nTJo0ienTp7Ny5UoaGhqOurbjJXdGcvPyIbWY0VPLKMwPLE0ZciVJkiQdwqFGXovKDr2/X2W3jNzu\n9swzz3Dbbbfx3HPPUVxcvN/+4cOH7zWNedWqVQwfPhyAoUOHsmbNGoYNG8aaNWtIJpNHdO+zzz6b\nNWvW8PLLL/P73/++M/D+6le/4plnnuH555+nrKyMGTNmsHPnzmP4lsdH7ozkFpRAYz0F+XmMqbTD\nsiRJkqTe4aWXXuJjH/sYc+bMOWhAHTZsGBUVFbzwwgvEGPnRj37EJZdcAsDFF1/M/fffD8D999/f\nub2rQghcccUVXHPNNVx44YWUlJQAsHnzZgYNGkRZWRn19fW88MILx/Atj5/cGcktKIXUYoiRqmQ5\n9W+5Vq4kSZKknmX79u17PVv7+c9/nieeeIKmpibe//70M8CjRo1izpw5ANTV1bFgwQIAvve973Ht\ntdeyY8cOLrzwws5nd2+66SY+8IEPcO+99zJ69GgeeughAN566y2mTp3Kli1byMvL49vf/jaLFi3q\nfN52T1deeSXf/OY3OzszA1xwwQXcdddd1NbWUl1dzfTp0zPzS+lmYffaSr3d1NrRce4Vm+CLDdzx\n+438+7NLWfyNCyguyM92aZIkSZJ6gMWLF1NbW5vtMtQFB/pbhRDmxRinHu7c3JquDNC4mJOT5bRH\nWL5ue3ZrkiRJkiQdV7kTcgs7Qm6qnqpkusNyQ6NTliVJkiSpL8mdkJtXCCUD0iO5iXJCcBkhSZIk\nSeprcifkAiRqIbWEksJ8Rg4qM+RKkiRJUh+TWyE3WbNXh2VDriRJkiT1LbkVchO1sGMjNDVSlSzn\n9XXbaGvPje7RkiRJkqTDy7GQW51+Ty2mKllOc2s7KzfYYVmSJElSz1BeXr7ftjvuuIPx48czceJE\nZs6cyYoVKw547rx585gwYQJVVVV8+tOfZvdysBs2bGDWrFmMGzeOWbNmsXHjRgDq6+s566yzKC4u\n5lvf+tYBr3nmmWdSV1fHqFGjSCQS1NXVUVdXx/Lly7v8nW655RaeffbZLh+fabkVcpMd6yg1/qXD\nslOWJUmSJPVkkydPZu7cufzpT3/ife97HzfeeOMBj/vEJz7B3XffTUNDAw0NDTz55JMA3H777cyc\nOZOGhgZmzpzJ7bffDsDgwYO58847+eIXv3jQe//hD39gwYIFfP3rX+eKK65gwYIFLFiwgDFjxux1\nXFtb20Gvcdttt3Heeecd4bfOnNwKueVDoWTgXssILU0ZciVJkiT1XOeddx5lZWUATJ8+nVWrVu13\nzJo1a9iyZQvTp08nhMDVV1/NY489BsDjjz/ONddcA8A111zTuT2ZTDJt2jQKCwuPuKbW1lYGDhzI\nZz/7WSZOnMiLL77IV7/6VaZNm8Zpp53Gxz/+8c6R5A9/+MOd9xwxYgT/+I//yOTJk5k4cSKvvfba\nkf9CjlHBcb9jJoWQHs1N1VNRUkiyfzENaw25kiRJkvbx3zfBW6907zVPmAAX3n5Ml7j33nu58MIL\n99u+evVqRowY0fl5xIgRrF69GoC1a9cybNiwdAknnMDatWuPqYbdNm/ezNvf/na+/e1vA1BdXc3X\nvvY1Yox86EMf4sknnzxgrUOHDuWll17izjvv5I477uCuu+7qlnq6KrdGcgESNdCY7rA8bmi5I7mS\nJEmSeoUf//jHzJ07lxtuuOGorxFCIITQLfUUFRVx2WWXdX7+n//5H8444wwmTZrEc889x8KFCw94\n3nvf+14ApkyZckTP9naX3BrJhXTI3bkJmtZSlSjnkfmriTF22x9akiRJUg44xhHX7vbMM89w2223\n8dxzz1FcXLzf/uHDh+81jXnVqlUMHz4cSI+crlmzhmHDhrFmzRqSyWS31FRaWtqZo7Zv387111/P\n/PnzGT58OLfeeis7d+484Hm768/Pz6e1tbVbajkSuTeSm6xJv3c8l9u0q5W3thz4ly9JkiRJ2fbS\nSy/xsY99jDlz5hw0oA4bNoyKigpeeOEFYoz86Ec/4pJLLgHg4osv5v777wfg/vvv79zenXbs2EFe\nXh5Dhgxh69atPPLII91+j+6SgyO5f+mwfHLyNCDdYXnYgNIsFiVJkiRJ6RHRPZ+t/fznP88TTzxB\nU1MT73//+wEYNWoUc+bMAaCuro4FCxYA8L3vfY9rr72WHTt2cOGFF3Y+D3vTTTfxgQ98gHvvvZfR\no0fz0EMPAfDWW28xdepUtmzZQl5eHt/+9rdZtGgRFRUVR1x3ZWUl11xzDePHj2fYsGGceeaZx/R7\nyKSwuyNWbzd16tQ4d+5ciBG+ORbGX0JqxjeZdtszfPU94/nI28Zmu0RJkiRJWbR48WJqa2uzXYa6\n4EB/qxDCvBjj1MOdm3vTlUNIj+Y21jOkvIgBpYWulStJkiRJfURGQ24I4YIQwpIQwtIQwk0H2H9t\nCCEVQljQ8bpuj33XhBAaOl7XHNGNE9WQWkwAqpLlNBhyJUmSJKlPyNgzuSGEfOC7wCxgFfDHEMKc\nGOOifQ59MMZ4/T7nDga+CkwFIjCv49yNXbp5shbmbYamtYxLlvP0ou5ZJ0qSJElS7+bKKz3fsT5S\nm8mR3DOApTHG12OMzcADQFfbfM0Gno4xbugItk8DF3T5zomODsuNi6lKlrN+WzMbtzUfSe2SJEmS\nckxJSQnr168/5hClzIkxsn79ekpKSo76GpnsrjwcWLnH51XAgVpwXR5CeDvwGvC5GOPKg5w7fN8T\nQwgfBT4K6Q5knZIdDyin6jk5eSoAS1NNTOs3+Gi/iyRJkqRebsSIEaxatYpUKpXtUnQIJSUle3Wg\nPlLZXkLoZ8BPY4y7QggfA+4H3tnVk2OMPwB+AOnuyp07+iWgdFB6JPeUcgAa1jYxbYwhV5IkSeqr\nCgsLGTvWVVdyXSanK68GRu7xeUTHtk4xxvUxxl0dH+8BpnT13EPa3WE5Vc/wgaWUFubbYVmSJEmS\n+oBMhtw/AuNCCGNDCEXAB4E5ex4QQhi2x8eLgcUdPz8FnB9CGBRCGASc37Gt65I1kKonL8DJyX4s\nTRlyJUmSJCnXZWy6coyxNYRwPelwmg/cF2NcGEL4OjA3xjgH+HQI4WKgFdgAXNtx7oYQwjdIB2WA\nr8cYNxxRAYla2LkZtr5FVaKcF/98ZKdLkiRJknqfjD6TG2N8Anhin21f2ePnm4GbD3LufcB9R33z\nZEeH5dRiqpIjeWzBm2zb1Uq/4mw/hixJkiRJypRMTlfOrs5lhOqpSvYHYJlTliVJkiQpp+VuyO2X\ngNLBkKqnKpnusGzzKUmSJEnKbbkbckNIr5ebqmd0ZRkFeYEGQ64kSZIk5bTcDbmQnrLcWE9hXmDs\nkH6O5EqSJElSjsvtkJushV2bYesaqpLlLDPkSpIkSVJOy+2Qm6hOvzcupipZzvL129jV2pbdmiRJ\nkiRJGZPjIbc2/Z5aQlWynPYIy9dtz25NkiRJkqSMye2QW56AssqOtXLtsCxJkiRJuS63Qy6kR3Mb\n6zk5UU4IhlxJkiRJymV9IORWQ6qekoI8RgwqpaFxa7YrkiRJkiRlSO6H3GQt7NoCW96kKlHuSK4k\nSZIk5bDcD7mJmvR7qp5xQ/vz+rpttLXH7NYkSZIkScqI3A+5yd0dluupSpTT3NrOqo12WJYkSZKk\nXJT7IbffECgbAo2LObmjw3LDWqcsS5IkSVIuyv2QC+kpy6n6vywjlDLkSpIkSVIu6hshN1kDqSUM\nKCkg2b/Y5lOSJEmSlKP6RshN1Pylw3KynAZDriRJkiTlpL4RcjubTy2mKlnOssYmYrTDsiRJkiTl\nmr4RcncvI9RYz7hkOU27Wlm7ZVd2a5IkSZIkdbu+EXJ3d1hO/aXDss/lSpIkSVLu6RshF9JTllNL\nOjssNzRuzXJBkiRJkqTu1ndCbiLdYTnRr4gBpYWO5EqSJElSDuo7ITeZ7rActqY7LBtyJUmSJCn3\n9J2Qu0fzqapEOctShlxJkiRJyjV9KOTuvYzQuqZmNm5rzm5NkiRJkqRu1XdCbr9K6JeAVD1VQzs6\nLDuaK0mSJEk5pe+EXEhPWe6YrgwuIyRJkiRJuaZvhdyOZYSGDyihtDCfhrWGXEmSJEnKJX0r5Caq\noXkreVtXc1Kin9OVJUmSJCnH9LGQu7v51BLGJctZ5nRlSZIkScopfSvkJvfusLx60w627WrNbk2S\nJEmSpG7Tt0Ju2WDol0w3n0qmm0+5Xq4kSZIk5Y6+FXIh/VxuajFVyf6AHZYlSZIkKZf0vZDb0WF5\n9OBSCvKCIVeSJEmSckjfC7mJGmhuorDpTcYM6WfIlSRJkqQc0vdCbmfzqXqqEuWGXEmSJEnKIX0v\n5CZq0u+Nixk3tJwVG7bT3Nqe3ZokSZIkSd2i74Xc3R2WU+kOy23tkeXrt2W7KkmSJElSN+h7IRcg\nWQOpek5OpJcRaljrlGVJkiRJygV9M+Qm0h2WTx7SjxBcRkiSJEmSckXfDLnJdIfl0h1vMmJQKUtT\nhlxJkiRJygV9M+QmOjosN9phWZIkSZJySR8NudXp99RiqpLlLEs10dYes1uTJEmSJOmY9c2QWzYY\nyodCagnjkv1pbm1n1cbt2a5KkiRJknSM+mbIhfR6uY2LOTmZ7rDslGVJkiRJ6v36bshNpjssVyXK\nAEOuJEmSJOWCvhtyE9XQso0Bu94i0b+YBkOuJEmSJPV6fTjkdnRYTtUzLmmHZUmSJEnKBX035CZr\n0u+p+nSH5cYmYrTDsiRJkiT1Zn035JYOgvIT0mvlJsvZuquVtVt2ZbsqSZIkSdIx6LshF9KjuanF\nVCXssCxJkiRJuaBvh9xETbrDcnJ3h+WtWS5IkiRJknQsDLkt20m0NVJRUsDSlCO5kiRJktSb9e2Q\nm0x3WA6pJVQly2lYa8iVJEmSpN6sb4fcRHX6vXEx45L9WeZIriRJkiT1an075O7usNyxjNC6pmY2\nbmvOdlWSJEmSpKPUt0MupDssNy6mKtnRYdnRXEmSJEnqtQy5iVpY9xpVid0dlg25kiRJktRbZTTk\nhhAuCCEsCSEsDSHcdIjjLg8hxBDC1I7PY0IIO0IICzped2WsyGS6w/JwUpQU5hlyJUmSJKkXK8jU\nhUMI+cB3gVnAKuCPIYQ5McZF+xzXH/gM8Id9LrEsxliXqfo6JdIdlvPWLeHkRLkhV5IkSZJ6sUyO\n5J4BLI0xvh5jbAYeAC45wHHfAP4V2JnBWg5ud4flVPq5XEOuJEmSJPVemQy5w4GVe3xe1bGtUwjh\ndGBkjPEXBzh/bAjhpRDCcyGEcw90gxDCR0MIc0MIc1Op1NFVWToQ+g+DxnqqEuWs3rSDbbtaj+5a\nkiRJkqSsylrjqRBCHnAH8IUD7F4DjIoxTgY+D/wkhFCx70Exxh/EGKfGGKcmEomjLyZRA6l6xg1N\nd1h+PbXt6K8lSZIkScqaTIbc1cDIPT6P6Ni2W3/gNOBXIYTlwHRgTghhaoxxV4xxPUCMcR6wDDgl\nY5Um9+mwnNqasVtJkiRJkjInkyH3j8C4EMLYEEIR8EFgzu6dMcbNMcYhMcYxMcYxwAvAxTHGuSGE\nREfjKkIIJwHjgNczVmki3WF5dP56CvICDWt9LleSJEmSeqOMdVeOMbaGEK4HngLygftijAtDCF8H\n5sYY5xzi9LcDXw8htADtwMfvcNwaAAAgAElEQVRjjBsyVSuJGgAK1y9hzJB+Np+SJEmSpF4qYyEX\nIMb4BPDEPtu+cpBjZ+zx8yPAI5msbS+dHZbrqUq8ndcana4sSZIkSb1R1hpP9SilA6H/iekOy8ly\nVqzfTnNre7arkiRJkiQdIUPubsmazrVy29ojy9fbYVmSJEmSehtD7m6JGkjt0WHZ53IlSZIkqdcx\n5O6WqIHWHVQVricEQ64kSZIk9UaG3N2StQCUbGpg+MBSGgy5kiRJktTrGHJ3291huTH9XK4juZIk\nSZLU+xhydysZABXDIVXPuGQ5r6eaaGuP2a5KkiRJknQEDLl7SlR3juTuam1n9cYd2a5IkiRJknQE\nDLl7StTCuteoGlIKQEPj1iwXJEmSJEk6EobcPSVroHUnpxRtBOywLEmSJEm9jSF3T4l0h+X+W5eS\n6F9syJUkSZKkXsaQu6fEKen31GKqEuUuIyRJkiRJvYwhd0+7Oyw31lOVLGdZYxMx2mFZkiRJknoL\nQ+6+EjXpZYSGlrN1VyuNW3dluyJJkiRJUhcZcveV7OiwXJnusOxzuZIkSZLUexhy95VId1iuLt4A\nQMNalxGSJEmSpN7CkLuvRA0Ag7cvo6KkgKUpR3IlSZIkqbcw5O4rUQ1ASKWbTzldWZIkSZJ6D0Pu\nvkoqoGIEpJZ0hNxt2a5IkiRJktRFhtwDSdak18pNlrOuaRebtjdnuyJJkiRJUhcYcg8kUQPrGhg3\npAyww7IkSZIk9RaG3APp6LBcU7IeMORKkiRJUm9hyD2QZC0AQ3ctp6QwjwZDriRJkiT1CobcA+no\nsJyXquekIXZYliRJkqTewpB7IMX9YcBISNUzbqghV5IkSZJ6C0PuwSSqobGeqkQ5qzftYHtza7Yr\nkiRJkiQdhiH3YBI1sO41qhKlACxzvVxJkiRJ6vEMuQeTrIW2XYzf3WE5tTXLBUmSJEmSDseQezCJ\ndIfl4S1vUJAXfC5XkiRJknoBQ+7BJE4BoGB9PaMrywy5kiRJktQLGHIPZneH5cZ6qpLlrpUrSZIk\nSb2AIfdQEjXpZYSS/VmxfjvNre3ZrkiSJEmSdAiG3ENJ1sC6BsYNKaGtPbJivR2WJUmSJKknM+Qe\nSiLdYbm2ZAOAU5YlSZIkqYcz5B5KsgaA0e0rAGw+JUmSJEk9nCH3UIZUA1C8oYERg0oNuZIkSZLU\nwxlyD6W4HAaMglS6w7IhV5IkSZJ6NkPu4STTHZarEuUsSzXR1h6zXZEkSZIk6SAMuYeTqIF1r3FK\nooRdre2s3rgj2xVJkiRJkg7CkHs4iRpoa2Z86XoAlqa2ZrkgSZIkSdLBGHIPp6PD8tj2VQA0rPW5\nXEmSJEnqqQy5h9PRYbnf5qUMKS+2+ZQkSZIk9WCG3MMpLoeBoyC1mHHJcpamDLmSJEmS1FMZcrsi\nUQuNf1lGKEY7LEuSJElST2TI7YpENaxvYNyQErbubKVx665sVyRJkiRJOgBDblcka6GtmdN2d1j2\nuVxJkiRJ6pEMuV2RSHdYPpl0h2VDriRJkiT1TIbcrkikOyxXbF1K/5ICQ64kSZIk9VCG3K4o6gcD\nRxFS6eZTDY1bs12RJEmSJOkADLldlaiFVH16GaHGbdmuRpIkSZJ0AIbcrkrWwLp0h+V1TbvYtL05\n2xVJkiRJkvZhyO2qRC20tzChzA7LkiRJktRTGXK7KtnRYTnaYVmSJEmSeipDblcNOQWAyu2vU1KY\nZ8iVJEmSpB7IkNtVRf1g4Gjy1tVz0pBylqYMuZIkSZLU0xhyj0SyFlJL0ssIrTXkSpIkSVJPY8g9\nEol0h+VThpSwetMOtje3ZrsiSZIkSdIeDLlHIpnusDyxo8Py6ynXy5UkSZKkniSjITeEcEEIYUkI\nYWkI4aZDHHd5CCGGEKbuse3mjvOWhBBmZ7LOLktUAzAurATssCxJkiRJPU3GQm4IIR/4LnAhMB64\nMoQw/gDH9Qc+A/xhj23jgQ8CpwIXAN/ruF52DakGAomdy8nPCzQ0bs12RZIkSZKkPWRyJPcMYGmM\n8fUYYzPwAHDJAY77BvCvwM49tl0CPBBj3BVj/DOwtON62VVUBoNGU7B+CWMqyxzJlSRJkqQeJpMh\ndziwco/Pqzq2dQohnA6MjDH+4kjPzZpELTTWU5UsN+RKkiRJUg+TtcZTIYQ84A7gC8dwjY+GEOaG\nEOamUqnuK+5QEtWwfimnDClh+frtNLe2H5/7SpIkSZIOK5MhdzUwco/PIzq27dYfOA34VQhhOTAd\nmNPRfOpw5wIQY/xBjHFqjHFqIpHo5vIPoqPD8qSydbS1R1ast8OyJEmSJPUUmQy5fwTGhRDGhhCK\nSDeSmrN7Z4xxc4xxSIxxTIxxDPACcHGMcW7HcR8MIRSHEMYC44AXM1hr1yVqADgl703ADsuSJEmS\n1JNkLOTGGFuB64GngMXAQzHGhSGEr4cQLj7MuQuBh4BFwJPAJ2OMbZmq9YgMOQUInNC8HDDkSpIk\nSVJPUpDJi8cYnwCe2GfbVw5y7Ix9Pt8G3Jax4o5WURkMGkPR+iUMHzidBkOuJEmSJPUYWWs81asl\naiBlh2VJkiRJ6mkMuUcjWQPrl1I9pJjX1zXR3h6zXZEkSZIkCUPu0UnUQnsrdf02sLOlndWbdmS7\nIkmSJEkShtyjk0x3WK7OXwVAQ+PWbFYjSZIkSepgyD0aHR2WhzevAOywLEmSJEk9hSH3aBSWwqAx\nlGx6jSHlxYZcSZIkSeohDLlHK1kLqSVUJfu5jJAkSZIk9RCG3KOV+EuH5aWNTcRoh2VJkiRJyjZD\n7tFKpjssn16+ga07W0lt3ZXtiiRJkiSpzzPkHq1ENQDV+asBm09JkiRJUk9gyD1aQ06BkMeIlnSH\nZZ/LlSRJkqTsM+QerY4Oy/22LKV/SYEjuZIkSZLUAxhyj0WiltBYT1Wy3JArSZIkST2AIfdYJGtg\nwzKqK4tZmjLkSpIkSVK2GXKPRaKmo8PyelJbd7F5e0u2K5IkSZKkPu2wITeEUHk8CumVEjUAjC/s\n6LCc2prNaiRJkiSpz+vKSO4LIYT/N4TwrhBCyHhFvUlHh+WRLSsBlxGSJEmSpGzrSsg9BfgB8NdA\nQwjhn0MIp2S2rF6isAQGjaWiaSnFBXk0rDXkSpIkSVI2HTbkxrSnY4xXAn8HXAO8GEJ4LoRwVsYr\n7OkSNYRUPSclym0+JUmSJElZ1qVnckMInwkhzAW+CHwKGAJ8AfhJhuvr+ZI1sH4ZtUOKnK4sSZIk\nSVnWlenKzwMVwKUxxnfHGP9vjLE1xjgXuCuz5fUCiVqIbZzefwOrN+1ge3NrtiuSJEmSpD6rKyG3\nOsb4DWBLCKH/njtijP+ambJ6kWS6w/KpBW8SI7ye2pblgiRJkiSp7+pKyJ0SQngF+BPwagjh5RDC\nlAzX1XtUjoOQx6i2FYAdliVJkiQpm7oScu8D/j7GOCbGOBr4JPDDzJbVi3R0WB607XXy84IhV5Ik\nSZKyqCshty3G+JvdH2KMvwV88HRPyVryUvWMriyjoXFrtquRJEmSpD6rKyH3uRDC90MIM0II7wgh\nfA/4VQjh9BDC6ZkusFdI1MCG16mxw7IkSZIkZVVBF46Z1PH+1X22TwYi8M5urag3SqY7LE/rv4H/\nb0keLW3tFOZ35d8PJEmSJEnd6bAhN8Z43vEopFdLdHRYLnyT1vbhrFi/japk/8OcJEmSJEnqbocd\nbgwhDAgh3BFCmNvx+rcQwoDjUdyR2NnSzupNO7Jz88oqCHmMaVsJQMNapyxLkiRJUjZ0tbvyVuAD\nHa8t9MDuystSTfzTzxdl5+aFJTD4JAZvXwa4jJAkSZIkZUtXQu7JMcavxhhf73h9DTgp04UdqUT/\nYv771bd4ftn6LBVQQ8H61xg+sJSlKUOuJEmSJGVDV0LujhDCObs/hBDeBmRpXvDBJcqLGT6wlK//\nfBFt7fH4F5CsTXdYTthhWZIkSZKypSsh9+PAd0MIy0MIy4F/Bz6W0aqOQgjw5XfVsnjNFh7848rj\nX0CiBmIbZ/TfwLJUE+3ZCNqSJEmS1McdsrtyCCEPqI4xTgohVADEGLccl8qOwrsmnMC544aweUfL\n8b95R4flCUVvsrNlJKs37WDk4LLjX4ckSZIk9WGHDLkxxvYQwo3AQz053O4WQuD+j5xBXl44/jcf\nMg5CPmPaVwIjWdrYZMiVJEmSpOOsK9OVnwkhfDGEMDKEMHj3K+OVHaXdAfdXSxp5/Xg2gCoohsEn\nMWTHnwFoaNx6/O4tSZIkSQIOM5Lb4YqO90/usS3SAzss77Z5Rwuf+slLTB0ziB9+5Izjd+NkDUWN\nixlSbvMpSZIkScqGrozk1sYYx+75AsZnurBjMaC0kE/NrOLZJSmeXdJ4/G6cqIENr1Nrh2VJkiRJ\nOmY7mtv44/IN3POb17t8TldGcn8PnN6FbT3KtWeP5acvruSffr6Ic6qGUJjflTx/jBI1ENs5s2I9\nP1jSjxgjIWTh+WBJkiRJ6mXa2iPLUk0MrShhQGkhjy9YzecfevmIl4g9aMgNIZwADAdKQwiTgd1p\nrQLo8R2VigryuPXdtfzt/XP5r+dX8DfnjM38TZO1AEwofIstO0eT2rqLZEVJ5u8rSZIkSb3M9uZW\nftOwjgUrN7HgjU28snozTbtaufPKyVw86UROPXEAfz/jZCaNGMikkQNJ/mvXrnuokdzZwLXACOCO\nPbZvBb58lN/juHpnTZJL605kYFnh8blhZRWEfMbGlcBoljY2GXIlSZIk9XnbdrXyyurNLFi5ieqh\n/TmvJsnG7S187L/mUZAXqB1WwWWThzNp5EDOHJvuc1yVLOcL51cf8b0OGnJjjPcD94cQLo8xPnLU\n3yaLQgh8+4OTj98NC4qh8mSSO/8MnMPSVBNnVw05fveXJEmSpB4ixsgtj73K/BUbeW3tVnbPOr72\n7DGcV5PkxAEl/N+/P5vxwyooKczvtvt25Zncn4cQPgSM2fP4GOPXu62KDGtta+enL77B1DGDqR1W\nkdmbJaopXruI/sUFNp+SJEmSlPPWbN7Byys38dLKTby8chMDS4u466+nEEJgWcfs1vPHD6Vu1EAm\njRhIZXkxkB6UPH3UoG6vpysh93FgMzAP2NXtFXSX9lbYvgHK9l/Ct2lXK//29GvUnlDBT/7uzMw2\ng0rUEup/QU2iiIa1hlxJkiRJuaNpVytLG5uoGzkQgE/+n/n84pU1ABTmB8afOIApo/t1Hv/gx846\n7jV2JeSOiDFekPFKjlWqHp66BS77j/12DSwr4vOzTuErjy/kqYVrueC0EzJXRzLdYfmsARv46RsD\nMncfSZIkScqwlRu2dzSH2siClZtoaGwiLwRe+cfzKSsq4MIJJzB1zCDqRg5k/IkVFBd037Tjo9Wl\nJYRCCBNijK9kvJpjUToYXv4JTL4Kxpyz3+4PnTGKH7+wgtueWMSM6kS3zvneSyLdYXlS8Rru3FrC\n5u0tDDheja8kSZIk6SjEGHlz804WvLGJl1dt4rpzxpKsKOGphW/xT79YzKCyQiaNHMi7JgyjbuRA\nCvLSS7ReNPHELFe+v66E3HOAa0MIfyY9XTkAMcY4MaOVHan+J8DAFvj55+Hjv4WCor12F+Tn8ZWL\nTuXD9/6Be3/7Zz55XlVm6qg8GUI+J8WVwFiWprYyZfT+U6glSZIkKVtijIQQeG3tVr755BJeXrWJ\n1Nb006lFBXnMOCVBsqKESycPZ9b4oYwaXJbZxz67UVdC7oUZr6I7hDx417/BT94Pz38Hzv3Cfoec\nM24If3fu2M754xnR0WF56M4/A29naWOTIVeSJElSVrS2tVP/1lZWbtjOGxu209DYxIKVm/ibt43l\nQ2eOoig/j9fXNXFu1RDqRg2kbuRAak6ooKggPVI7pLyYIR2NonqLw4bcGOOKEMI5wLgY4w9DCAmg\nPPOlHYVTzofai2FdA8QIB/iXhlvePT7zdSRqKF27kOKCPDssS5IkScqoN9anA+zu18oN25k6ZhAf\nedtYWtoiF33nt53HDikvYtKIgST6p4PrmCH9+OUXZmSp8sw4bMgNIXwVmApUAz8ECoEfA2/LbGlH\n6fJ795uqvK9tu1q5838aeNeEYUzKxKhuspZQ/3OqhxQZciVJkiQdk03bm/cLscMHlnL9O8cBcMl3\nf8vG7S1AusPxyEFlVJ/QH4DSonzuvWYqwwaUMnJwKf1Lcr9fUFemK18GTAbmA8QY3wwh9M9oVcdi\nd8BNLYGmtTD27fsdEoFH5q/mj8s38Mgnzu7+ueWJdIflswes5+eNvWPeuiRJkqTsaG5tZ/WmHXuF\n2IK8wI0X1ADwobv/wKI1WzqPH1JexHnVyc7P/3r5RCpKCxk1uIyhFSXk5+2dQWbWDj0+X6SH6ErI\nbY4xxhBCBAgh9DvcCVkXIzz297BlNXzyRSip2Gt3eXEBN86u5sZH/sScl9/kkrrh3Xv/RPo/xrri\nt/j+pn7saG6jtCj7rbQlSZIkHX8xRjZsa94rxG7a3sKtF6Ufpfz7/zOPZxY3dh5fVJC3Vx+hz886\nhfYYGVVZxshBZfQr3jvGnX9qBpdI7YW6EnIfCiF8HxgYQvg74G+AuzNb1jEKAS78V7jnr+DZf4YL\nb9/vkPdNGcF/vbCC2/+7nlnjh1JW1JVfRRdVVkFeAVVhJTGezLJUE6cNd81cSZIkKVftbGlj1cYd\nnQ2e3tiwnZsvrKEgP4+v/3wRP/zd8r2OH1pRzE0d+6+aPpoLThvG6MoyRg0uI1FeTN4eo7F/Nb5v\njcQeq640nvpWCGEWsIX0c7lfiTE+nfHKjtWIqTD1b+DF78OkD8KJdXvtzssLfOU943n/Xc9z16+W\n8fnzq7vv3gVFMPhkTti1AsCQK0mSJPViza3trGvaxYZtzWza3kLj1p2s3LCDD08fRWV5MT/83Z/5\n2s8W7XVOSWEe1507lmEDSjuX4Nn9GjGobK+ZnntOPdax60rjqX7AL2OMT4cQqoHqEEJhjLEl8+Ud\no5lfgcU/g59/Dq57BvL2njI8bcxgvvyuGt5Zk4H/qJI19FvzCvl5gYa1Np+SJEmSeoKdLW1s3N7M\nhm3NnDiglEH9ili5YTuPzF/Fpu0tbNjWzMbt6dfXLj6NKaMH8dTCt/jUT1/a71rnjKuksryYyaMG\n8bm/OoVRlaWMGlzGyI7R2N29f84+eQhnnzzkeH/VPqsrc3R/DZwbQhgEPAnMBa4ArspkYd2idGB6\nqvKbC6CtZb+QC/DRt5+cmXsnagiL5jBucL4dliVJkqQMiDESQmBnSxvzVmxMh9NtzWzY1sLG7c3M\nPvUEzjq5kiVvbeVv/vOPbNzezPbmts7zv31FHZdOHs6azTv59jMNVJQUMLhfEYP6FTG0fwkFHVOG\n60YO5F/eO4FBZUUM7ldEZXkRwweWUlKY37m/LhOrtuiodCXkhhjj9hDC3wL/EWP8ZghhQaYL6zan\nXZ5+HcL6pl38w+Ovcu3ZYzlj7ODuuW+iBoi8bcAGnkuVdM81JUmSpBy1q7XtLyOp25rZuL2F4YNK\nqRs5kB3Nbdz8f//Ehu0tHSE2PdL68XeczKdnjmPT9hauuucPe12vf0kB44aWc9bJlQwqK+TMkwYz\nuCwdYAf3K2JQWWHncqJTRg+i4bYLKczPO2BtIweXceUZozL+O1D36FLIDSGcRXrk9m87tvW+VsGv\nPwevPwt/9Y/77SorKuClNzaxYv1C5lx/zn4tt49KshaAyaVruf/PA2hpaz/o/2gkSZKkXNfeHnl9\n3TZeXb2ZV1ZvZmljE1NHD+JTM8cRY+S0rz5FS1vc65yrzhxF3ciBFBXk8dLKTQwsK2JIeRHjkuUM\n6lfE5FHpkFpZXsQDH53O4H5FDCwrZFBZ0V7/3ztZUcIdH9i7R8+e8vMC+bj0Z67oSsj9LHAz8GiM\ncWEI4STg2cyWlQFvPA+//d8w5lyomrnXrtKifG66sIbPPLCAh+et5Ipp3fCvNINPhrwCxoVVtLaP\nY8X6bVQle+7ywpIkSVJ3aWuP/HldE+ubmjnzpEoAZv3v51iW2gZAcUEe44aW0xbToTaEwE0X1lJS\nmMfgsiIGdkwLHlpRDKRD6HM3nHfQ+xXm5zG94z5SV7orPwc8BxBCyAPWxRg/3ZWLhxAuAP4f0iO/\n98QYb99n/8eBTwJtQBPw0RjjohDCGGAxsKTj0BdijB/vyj0P6m2fhT89BL/4Avz981BYutfuiyed\nyH89v4L/9dQS3jVhGP1LCo/pdhQUQWUVw3YtB2BpY5MhV5IkSTnrV0saee61FK+u3szCN7ewvbmN\nkYNL+c2N7wTgb885icL8wIQRA6hKlFOwzyzHvz1nbDbKVg467PzZEMJPQggVHV2WXwUWhRBu6MJ5\n+cB3gQuB8cCVIYTx+xz2kxjjhBhjHfBN4I499i2LMdZ1vI4t4AIUlsC7/w02/jk9ort/vXzlPeNZ\n19TMXc8tO+bbAZCopnzrUgCbT0mSJKnXa21r57W1W3lk3ir+cc5CrrrnBdrb06OxTy1cywMvrqQ9\nwgemjuRb75/EPVdP6zz3Q2eO4v1TR1JzQsV+AVfqTl2Zrjw+xrglhHAV8N/ATcA84H8d5rwzgKUx\nxtcBQggPAJcAnQtIxRi37HF8P2DvSfjd7eTzYML70yF30gdh8El77Z44YiB3fGASM7prnapELXmL\n5nDSgDwaDLmSJEnqRVrb2lmaamJMZT9KCvP56Ytv8LWfLWRnSzsApYX5nHpiBRu3N1NZXszN76rh\nny49rXv620jHoCshtzCEUAhcCvx7jLElhNCVMDocWLnH51XAmfseFEL4JPB5oAh45x67xoYQXgK2\nALfGGH9zgHM/CnwUYNSoLj5He/5tMOosGDj6gLvfe/oI4C/tyI9JsqPD8qCNzG8sP7ZrSZIkSRm0\nvmkXv6xv7GwMtWjNFna2tPPTv5vO/8/efYdHWWUPHP++U5OZSZ/0BoGE3iTSBARFBcWGYC+7ruti\nWdvPdXVtu+q6rrvWta91xbLWVbE3QEVAQHrvgZAO6Zl6f3/cSSYhBAICgXA+zzPPTN77zjt3EMyc\nOfeeM7xbArlJLi4YkkW/9Bj6pceQk+hqEdBG/9LtfkIcIO0Jcp8FNgGLgVmGYWSjA88DQin1JPCk\nYRgXAncAlwHbgSylVLlhGIOB/xmG0WeXzC9KqeeA5wDy8/PblwWOSoZjQ0Wig0EwtV4qsXVHHVe/\ntpA/nNKDUbmJ+/3eSNQVlgdHFvH2tjiCQYVJvtkSQgghhBAdyB8IsrakhqXbKlm2rZKTe6cwMtdN\nwY56/vDOEpw2M33SYrhwSDb9MqLJS9bJmvwu8eR3OUDtNoU4iNpTeOpx4PFmhzYbhtF2abOwbUBm\ns58zQsfa8ibwdOg1PYAn9HiBYRjrgTxgfjtet31WfwZf3Q2XfwaRcS2G3C47O+t83PPRCj69ftT+\n7xlI0BWW84ytNPh6sG1nPZnxjgMweSGEEEIIIfbOFwhS6/ET67BR3eDjkhfmsXJ7FR6/XnLstJnp\nnuRiZK6b3qnRfHXTaLq6XbLkWBzR9hrkGoYRA9wNjA4dmgncA1Tu5ak/AbmGYXRFB7fnAxfucu1c\npdTa0I+nAWtDxxOBCqVUINSyKBfY0K531F7RaVC2Br6+Bya2LEQVYTVz+2m9+N2rC3ht7hYuG9Fl\n/17DbNUVln2bAF18SoJcIYQQQghxsKzcXsWSrTtZuq2SpduqWLm9itP6pfLIeQNx2S0kOG1cPCyb\nfukx9E2PIcftbFppaLOYpBuI6BTas1z5RXRV5XNDP18CvARM2tOTlFJ+wzCuBT5HtxB6MdRn9x5g\nvlLqQ+BawzDGAT5gB3qpMuiA+h7DMHxAEJiqlKrYt7e2F6n9YehVMOcpGHAhZB7bYvjk3smM6JbA\nw1+u4YwBacQ5bfv3Ook9id62CNBB7tieB6iolRBCCCGEOCo0rxVTVNlAWY2HWo+fWq+fkioP9b4A\nvz5Ot9+5/s2fWVNcg8tuoU9aNJcOy+a47m5AdxN54VfHtvk6QnQWhlJ73spqGMaiUIufPR7raPn5\n+Wr+/H1czeyphieHQmQ8XDkDzC1j/lVFVZz62HdcO7Y7N53cY/8mNuMBmPEAx5mnMbJXFn+f3H//\nriOEEEIIIY4ISik8/iA1Hj+1Hj81Hj+9UqIxmQyWbq1kxfZKajwBHah6/DT4AvzlzL4APDVjHZ8t\nKwqN6XNsFhML7jwJgKumLeDTZUUtXi8r3sGsW/Ruwp+37CDWYSM73iG1YESnYxjGAqVU/t7Oa08m\nt94wjJFKqe9DFz4OqP+lEzws2KNgwt/hvxfD2i+g56kthnumRPPK5UMY0vUXbLBP1BWWR8VVsKYk\nbq+nCyGEEEKIQ68xW1rV4GPbjvqm4LQx0DylbwoxkVbmbCjn4yXbw+Nefc4Ll+WT4LLz5LfrePjL\nNQSCLRNJS/58MtERVj5aUshzs8K78GwWEy67hTsn9sZiNmEzm4hz2MiMc+C0m3HaLcREhqsWXzEq\nh7MHpeO0W3DaLcQ7bGTERTaND8qSz5tCtCfInQr8J7Q3F1ouKz7y9ZwIV3wDGYN3O9xYXbnBF8Bu\nMe17W6HEngAMjizmk4LkA9OaSAghhBBC7JPGz2AFFXV8vHQ7m8vrKKioY0tFHUWVDbxx5VAGZ8fz\nxfJibn57cavn90mPJiYyhg2ltUxfUojTbsFlt+CwmYmKsDQFtYMyY5l6fE7TuNNmwWk3Y7foQqa/\nG53DZSO64LJZcNjNWHcpcHrFqByuGJXT5vsYnC1BrBB7s8cg1zAME9BDKTXAMIxogF3b+BzxDCMc\n4FZug5j0VqesL63hon/P5b6z+jKud/K+XT+hG5is9DRvo6qhN6U1HpKiIg7AxIUQQgghxK521Hr5\nfHkRW0IBbOPtb2f3Y0K/VAoq6njg01XEO21kxjsYkBnLhH4RJLr057NhOfE8fdExOOwWXKFMqtNm\nITlaj184NIsLh2a1+flXmPQAACAASURBVPojursZEdoDuzsJLvuBfcNCiFb2GOQqpYKGYdwCvNXp\ngttdbZ4Nr5wB570KPSa0GMqKd+CKsHDfxysYlefGbjG3/7qhCstpvi2ALj4lQa4QQgghxP5p8AWY\nsbqkKXhtzMhePrIrlw7vws56H7e+txSLySAjLpLMeAcT+6eSGquX9A7uEte0dHh3MuIcZMRJNwwh\njmTtWa78lWEYNwP/BWobDx7wascdLeNYSOgOn9wCXUeDzdk0ZDWbuHNiby57cR4v/7CJ3x3fbd+u\nndSTmK0LAR3kjujW9rd7QgghhBBHM6UUczdW6CC2PBTIVtRxQo8krh+Xi1IwdZr+XBXrsJIV76BP\negxpMTqIzYyL5LtbxpIaE4Fll6XAAHaLed8SFkKII057gtzzQvfXNDumgLY3CxyJzFaY+DC8NAFm\nPggn/aXF8PF5iZzQM4l/fbOOScdkkBi1D0tNEnthXv4/Eu0B1pXUHOCJCyGEEEIcWdaVVLOhtLbF\ncuLuiS7umNgbwzC4+rWFVNR6MZsM0mMjyYp3kODS7RwjbWY+uW4U6XGRLQoyNbKYTWTGSyZWiKPZ\nXoNcpVTXQzGRw0L2CBh0Mfz4BPQ/D5J7txi+47RenPzILN5duJWp+5LNTeyBgWJ0XAXrSqRPrhBC\nCCE6t5LqBjaVhQPYgoo6Iqxm/japHwDXv7mI5YV6J1yU3UJWgoNeqdFNz3/hsnwSnHZSYyNaFWYC\n6J0W3eqYEEI0ajPINQzjYnQf3Vd3OX4JEFBKvX6wJ9chxt0Da7+EgrmtgtycRBfTrxtJj+Sofbtm\nUi8A8p3FPFyScaBmKoQQQgixR/5AEF9A4Q0E8QWCxDlsmE0G5TUeiqs8+AJBPebX9yO7u7GYTSzd\nWsnKoiq8fv08X+g6V4/phmEYTF9SyLyNFfgCQTx+PWY1GTx83kAA/vTeMr5aWQyAyYDUmEgGZMY0\nzesvZ/TBajaRneAgJtLaqvOEtMERQjQpWwc7NkFdebufsqdM7u+BE3dz/D1gFtA5g1xnAvx+Idhd\nux3umaK/OSyt9uB22drXDig+J1RhuZDS6r5U1vt2u7xGCCGEEEcHXyBIWY2H8hov5bVeykOPT+2f\nSnpsJEu3VvL6vC06CG0WaN52ai+6Jbr4ckUx//pmbbMxhdcf5PXfDiUn0cVLP2zk3ukr2KVVK7Nv\nPYG02Ehem7uFh79c02pei+8+mZhIE9OXFvLszA2txn83OgeL2WDRlp18uLgQa6iva2Ov10BQYTYZ\nTD0+h0uGZ5MV7yA9NhKbpWU2Nr9L/AH98xRCHOaUgoadUFuug9W6cl3s1zBg2buw5guoK9PHa8vB\n3wB/WKufO+NvsOydfXq5PQW5VqVUqw2kSqlawzA6d4TWGOBumAnJfcDZslDU8sJKJj/9I/+Y0p+J\n/dP2fj2zFdy5ZPg3A7r4lPQ4E0IIITqPQFCxo86L3WIiKsJKWY2H6YsLdQDbLIi9flwuo3ITmb2+\nnMtenNfqOjmJTtJjIympbuDLFcXYLSasZkMHkxYTHl8QgAiriQSnDavZhNViwm42YTWbiLTpgkr9\nM2K4Zmx3PW7W19Bz0x/9Tu2XQl5yFDaLgc1s1q9hMeEIPX/q6G5cMiwbW+j5Nou+N5v0l/t3TOzN\nHRN7t5p/IwlihejkAj4wTGAy6yzrtgVQVwG1ZeFgdeIjEBkH3z0E3/wVVKDlNW7bCvYoKFmlO904\nE8CRAO4eOv4KBsFkglE3wZAr9bG/dG/X9PYU5EYahuFUStU2P2gYRhRg27c/hSNQVSFMOwf6nwtn\nPdViqGdKNF3dTv72ySrG9UomwtqOCn2JPYgtWADAeglyhRBCiMOaUopqj19nWms8lNV4Ka/10Cs1\nmmOy4iipbuD6NxZRXquD14o6L0rpZbiXjehCRa2XP3+0ApMB8U47bpeNeKcNAx0k9kqJ4m+T+hHv\ntOF22Uhw2klw2XDZ9UezE3slM/+O5DbnNyo3kVG5iW2OD86OZ3B224Fm96Qouie1vf0qzmkjztn5\nP+4JIdBZVm9tKMNaFs62dhsLUSk6AJ39r1AAGzqnoRKmfg8p/fRWz09uDl8vMg4cbvBU68dpx8Bx\n1+sg1eEOBbNusOiK6Jxwu761JbnPPr+lPQW5LwDvGIYxVSm1GcAwjC7Ak6Gxzi06DUZcC98/AgMv\nhC4jm4bMJoO7T+/Nec/N4blZG7juxNy9Xy+xF9bl7xNj8bK2pPogTlwIIYQQu9PgC+DxB4mJtKKU\n4t2F23SGtdbbtHR4VK6bK0blUO8L0P/PX7S6xtTju3FMVhwRVjO+QJCubif5XeJxO20kuOwMzdGB\nZY7bycI7TyI20orJ1HprU1J0BBcMyTro71kIIajfoVeoVhVCdSFUF+tAdeRN0HUUrPsaXjun9fMu\nekcHub562LlFZ1lTB4SD1cjQF2l9ztaxksOtg1rzLiFmt7H6dgi1GeQqpf5pGEYNMMswjMYNqjXA\nA0qppw/J7Dra6Fv0GvHpN+lvKizhbzSH5iRwWr9Unp6xnin5GaSGerO1KamnvmRshbQREkIIcVhq\n8AWoqPWSFqt/p60vrWHrjnqUUigFCr3B84SeOsO4bFtleBydDDCbDMb3TQFg/qYKtu6oJxh6flAp\nIm3mpq0+M1aXULizQY+jLxAdaeXMgekAfLJ0O9srG1q8vttlZ9IxuojjWz8VUFLdELq2Hs+IczB5\nsB7/v7cWs6GspikbW+sNMLF/Kk9ceAyGYXDPR8upavCHlv7qbKsK7WF12CzcObE3cQ4rCS47CU4b\nbped+FB2MzrCyjtXjWjzz9JiNjWdK4QQB5RS4KnS95GxOqs69zkdwFZtD90Xwtg/Qf7lULkN3r5M\nP9dsh6hkHZD6PfpYUk8Y9xcdxDrd+t6RANH6/8V0P1Hf2uJ0t9re2dH22EJIKfUM8ExoiTJKqaMr\nBWlzwKkPwetT4Md/waj/azF864SefL2qmBmrS/f+bWyirrA8xFXKc6US5AohhDj4/IEgFU17Qr1N\nS2vPH5KJw2bh3QVbeW3u5qbxGo8fgBX3nILDZuG1OVt48YeNra676YHTAJg2ZzNv/lTQYsxltzQF\nuS/P3sT0JdtbjCdH25uC3Fdmb+Lb1aUtxnPczqYg9+UfNjFvU0WL8X7pMU1B7suzN7Fie1WL8WE5\n8U1Bbq3Hj9NmISvL0bQcuHezNjWf3TCaWIcVh233H4d+M/Lo6aIohDhMBPxQUwzV28Hm1F1a/F74\n8NpQJna7DmR9tToTO+5uUEH49j6dWY1Og6hUnXGND7U8defqhF1UGjjidbGn5mIyYOQNh/69HkSG\nUmrvZx0B8vPz1fz58w/OxT+4BjKOhcG/ajVUWu0hMcq+92sE/PDXFOanXsCUDeNZ8ZfxTcUhhBBC\niPYIBhU76304bGYirGY2l9cyc00pZTVeKkIBbHmNl79P7k9Xt5NXZm/i7g+Xt7rON/93PDmJLt7/\neSvvLNjaFAAmhJbcnj0onQirmYKKOkqqGzAMvZO08X5AZiwA23bWU1nnwzD0ZyYDA7OJpr2exVUN\n1HkDGIDJMDAMneltzBRX1HrxBYJ6l2ro+RaT0bQXtLrBR1A1Xlu/vtkwmn5/ev26CJPJoNkcaV/n\nAyGEONQ81c0yrdshIgZ6nqrHXp4IZWuhtkQHrQD9z4dJz+qM7ZNDddY2KjUcyGYNh8xj9bi/Aax7\nWVnaCRiGsUAplb+38/aYyRUhZz7Z5lBjgLu6qJrcJNdu990Aem26O5fMwGaU0kvA+qbH7P5cIYQQ\nRwWlFFUNfp1tDRU3qqj1MiwnnpxEF8u2VXL/JyubsrAVtV6CCl65fAjH5yWycns1d32wHMOA2Mjw\nslpfQH9AGt4tgXvP6quD11AAm+C0NbWxO3tQBmcPart/e2a8g8x4R5vj6bGRpMe2/aEqOTpij+9/\nb8t5oyL23Mxh17Y0QgjRYWpKobIglGktDGdiG1eCPjcWChe2fE6XUeEgN64LxGZDdGo4kE0I1f0x\nDLi2dTX2JoZxVAS4+0KC3PYKBmHRa3q9eY8JLYYWbN7B5Gdm84/JA5qWSO1WYk/it/wESJArhBBH\ng1qPn4+XbA+3kAkVOLpwSBYT+qWyvLCKif/6vtXzHjynPzmJLswmA48/SHaCg2OyY5syrjluJwDH\n5yXy0+3jiHNYsZhbB3x5yVHkJbddQVcIIUQ7bV8CJSt08FpdpANZgPNe1ffv/RY2fBs+3zBD5tBw\nkDvgAuh9hl4yHB26RaWEzz/ziUPzPo4Sew1yDcNYALwIvK6U2nHwp3SYUkGY9xzUlurqYfbwh4ZB\nmbEMyIjl75+tYnzflKby/60k9cK6/D1cJo8UnxJCiE5AKUVRVQOri6r1rbiaNcXVTOyfxtTju9Hg\nC3DLu0sAcNjMJLhsxDvteEOZ1sx4B3ec1qvpuM626nYyAL1So3l3D8WNIm1m2foihBD7w1sHNUUQ\n20X3Yl3/ja4yXF0Uum2H+gq4ZaPOlM57Fn6epp9rj9YBalyzffsjb4Ahvw1nYZ2Juodso6FXHtK3\nd7RrTyb3PODXwE+GYcwHXgK+UJ1lM297mS0w8VF4/kTdzHjCA01DplBLobOfms1T367jlvE9d3+N\nRH18ZEw5a4slyBVCiCPJjlovq4p0EBsTaeWsQekEFYz95wwafDpoTY62k5ccRaJLB6lxDhvf/3Es\nCU77boPRmEgrV4zKOaTvQwghOjW/N1S4qUjvfe12gk5OrZwOP/07HMA2VOrzb14HrkTYMgd+ekEH\nr42Fm6JSIeAFi113XRl5E7iSwe5q/bo5Yw7luxR7sdcgVym1DrjdMIw7gYnorG7AMIyXgMeUUhV7\nvEBnkjEYjv2N/iZnwPmQNrBpaFBWHJMGpfP8dxs5/9gsshJ2s4cpqbHCcgmvS4VlIYQ4LNV6/BRX\nNZCTqD/E3Pz2YmauKaW02tN0zqhcN2cNSsdsMvjnlAEkRUWQl+wi1tFyj6nJZJAR1/aeViGEEO0U\nDEBtWajPa1F42fCACyC+K6z8CD66Qfd/be7Kmfoze8AD3lpdabjr6HAwaw3VDhj9BxhzW+vKw43i\nsg/u+xMHVLv25BqG0R+dzT0VeBd4DRgJfAMM3MNTO58T7oQVH8L0G+GKr/XyhpBbxvdk5ppSlm6r\n3H2QG9cVzDb6WArZtK0WXyCIdTd7qIQQQhw6368tY/b6MtYU6+XGBRX1pMdG8sOtJwC6ONLxeYn0\nSI6iR4q+JTWrqt/YDkcIIcQv4KmGLXPDwWvj/bCroOso2DADpk3a5UkGpOfrIDcmA3pN1HteGwPY\nqBRw5+lT+56jb20x77nQnTiytHdP7k7gBeBWpVTjV9lzDcM47mBO7rAUGQtnPA6GqUWAC5ASE8EP\nt55AhLWN/VFmCyTkkh3Ygj+o2FxeR/ek3Sx3EEIIccAEgoqCijq9Xza0b3ZjWS0fXHMcFrOJz5Zv\n5815BeQkOhmQEcu5gzPpkRKuu/CnU3t14OyFEOIIpBR4a6CmBKwOXTG4oQp+fEIfqynRrXJqiuG4\nG/RKyapCeK1ZEBoZrwNVT7X+ObkPnPrP0J7XUAViZ5L+fA2QNkjfhGAvQa5hGCbgXaXU/bsbV0rt\n+nXK0aF5dWWlWixriLCaUUoxc00pI7u7W1e7TOpJ/Ka5AKwrqZYgVwhxSNR7A1jNxm4r8HYWSimK\nqzxNweyU/AxiHTaembmef3y+uum8rHgHeclR1Hj8xDps/OGUntw1sY+0oxFCiL3x1ISC02aBalwX\n6D4OAj54aUJ4zF+vnzPiOjj5Xv145oPgSABXki7MlDkMYrP0WFwXuPwLnX11JYeXETeKStGFnYRo\nhz0GuUqpoGEYk4DdBrlHvVn/gPL1cPYzLQ7/uKGcX730E/ed1ZeLh+2yfj+xF7Zl7+KgQSosCyEO\nmu2V9awvqWVkrhuAyc/MZnlhFTaziQirCYfNQt/0GJ6/TPdTv+uDZRTurCfSZiHSaiLSaiY3Oarp\n/2EfLi7E6w8SaTXjsJmJsJpJirbTLbRvtbzGg91qJtJqxtxWv/ADaGedF6vZhNNuYcHmHTzw6UpW\nF1VT1eBvOqdfRgzDchI4sVcSiS47eSlR5Ca5cO5SAb+xZ6wQQhyV/F6whOoJrP0Kdm5ulmktgaTe\ncMLtevyRPtCws+Xz+07WQa7ZqrOvcV10kOpM1MFsSn99nj0K7iwLZ153ZbFD1tCD8hbF0ac9e3K/\nMgzjZuC/QG3jwaOq4FRbggFY/Ab0mwLdT2w6PDwngaFd43noi9Wc3j+NGEezD1CJPfQ50WUS5Aoh\nDpiSqgZmry9nzoZyftxQzubyOhw2M4vvPhmr2cTNJ/dg6bZK6n0B6r36lhIT/pa8qt5H4c6G8Lgv\nwODsuKYg9++frmLbzvoWr3ly72Seu1QHySc9MouKWi8ANrOJSJuZswel8+cz+gBwwXNzMJsM3fIm\nFAyPzHVz+oA0gkHFiz9sJCJ0vLEtTo7bSXaCk6oGH58tK2paary6qJqSag8PnzuAScdkEGHVGdjT\nB6TRI0X3he2RHEWcU39o65kSTc+U6IP7H0AIIQ4n3tpQoFoKfo/e0wrw3cOwbUGzILZULwO+4ks9\n/uVdULJcP3Yk6OXAsc0SNifdo4NZZ5KuSOxKBoc7PH7RW23PyTDaDnCFOMDa20II4JpmxxQgPQ+O\nuwGWvAWf3AxX/di0rMIwDO46vTen/+t7Hvt6LXed3jv8nFCF5aGuEj6UCstCiP1UVNnA3I3ljOuV\njNNu4bW5W3js67VER1gYmpPApcO7MCwnHnNoO8XYnkmM7ZnU5vUePX/P+5g+uPa4puC3LhQkR0eG\nf4XcckoPqhp81HuD1PsCNPgC9EuPAfQyYrPJoM7rp7zWS73XT70vQGKUndMHQIM/wH0fr2z1mr8/\noTv/d3IPGrwBbnlnCXaLibzkKEblJtIjxUX/jFgA+qTF8PbUtnvJCiFEp9JQCTs2QcVGfd+wE8b9\nWY99dD0sfUfvh20Ukwk3LtOPi5dD+TqdYU3P10FqYl743PNe1Xtone7dF2IafNnBeU9CHGBGZ2l3\nm5+fr+bPn3/oX3j9t/DqWXD8rTD2thZDt723lLfnF/DZDaPonhQqYhLww/2pzHZP5vLCM1jxl/GY\nDsHSPiHEka2y3seM1SXM2VDOnA0VbCzTC2teuXwIx+clsm1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2aB6cekKBqNUJiaH/d81/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MiaUcgVERG5DQrBd2B2AgbOeJsXfIfPg/XWiS6tylZ808G3Yb/G+YqIyB1RyBUREVlDswtJ+iZi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\nAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aWv_MfXcTEu0",
        "colab_type": "text"
      },
      "source": [
        "Let's try a bigger lambda = 0.01"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0cL95-X9SPTx",
        "colab_type": "code",
        "outputId": "a41018e1-2c53-4b00-a1e2-d35af4e79edc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "l2_model_2 = keras.models.Sequential([\n",
        "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.01),\n",
        "                       activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.01),\n",
        "                       activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "l2_model_2.compile(optimizer='adam',\n",
        "                 loss='binary_crossentropy',\n",
        "                 metrics=['accuracy', 'binary_crossentropy'])\n",
        "\n",
        "l2_model_2_history = l2_model_2.fit(train_data, train_labels,\n",
        "                                epochs=20,\n",
        "                                batch_size=512,\n",
        "                                validation_data=(test_data, test_labels),\n",
        "                                verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.8543 - acc: 0.7319 - binary_crossentropy: 0.5712 - val_loss: 0.6311 - val_acc: 0.8290 - val_binary_crossentropy: 0.4362\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.5487 - acc: 0.8622 - binary_crossentropy: 0.3658 - val_loss: 0.5190 - val_acc: 0.8630 - val_binary_crossentropy: 0.3471\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.4802 - acc: 0.8776 - binary_crossentropy: 0.3179 - val_loss: 0.4770 - val_acc: 0.8716 - val_binary_crossentropy: 0.3251\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.4496 - acc: 0.8824 - binary_crossentropy: 0.3049 - val_loss: 0.4591 - val_acc: 0.8697 - val_binary_crossentropy: 0.3228\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.4297 - acc: 0.8834 - binary_crossentropy: 0.2985 - val_loss: 0.4408 - val_acc: 0.8722 - val_binary_crossentropy: 0.3161\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.4137 - acc: 0.8857 - binary_crossentropy: 0.2936 - val_loss: 0.4302 - val_acc: 0.8720 - val_binary_crossentropy: 0.3147\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.4021 - acc: 0.8877 - binary_crossentropy: 0.2897 - val_loss: 0.4203 - val_acc: 0.8730 - val_binary_crossentropy: 0.3122\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.3927 - acc: 0.8870 - binary_crossentropy: 0.2865 - val_loss: 0.4140 - val_acc: 0.8727 - val_binary_crossentropy: 0.3119\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.3851 - acc: 0.8892 - binary_crossentropy: 0.2848 - val_loss: 0.4095 - val_acc: 0.8722 - val_binary_crossentropy: 0.3120\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.3789 - acc: 0.8876 - binary_crossentropy: 0.2827 - val_loss: 0.4035 - val_acc: 0.8720 - val_binary_crossentropy: 0.3097\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.3747 - acc: 0.8889 - binary_crossentropy: 0.2822 - val_loss: 0.3995 - val_acc: 0.8726 - val_binary_crossentropy: 0.3090\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.3682 - acc: 0.8903 - binary_crossentropy: 0.2788 - val_loss: 0.3925 - val_acc: 0.8742 - val_binary_crossentropy: 0.3048\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.3643 - acc: 0.8926 - binary_crossentropy: 0.2770 - val_loss: 0.3904 - val_acc: 0.8736 - val_binary_crossentropy: 0.3048\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.3606 - acc: 0.8903 - binary_crossentropy: 0.2761 - val_loss: 0.3882 - val_acc: 0.8734 - val_binary_crossentropy: 0.3051\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.3585 - acc: 0.8906 - binary_crossentropy: 0.2756 - val_loss: 0.3858 - val_acc: 0.8742 - val_binary_crossentropy: 0.3047\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.3546 - acc: 0.8925 - binary_crossentropy: 0.2738 - val_loss: 0.3894 - val_acc: 0.8710 - val_binary_crossentropy: 0.3102\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.3521 - acc: 0.8920 - binary_crossentropy: 0.2728 - val_loss: 0.3846 - val_acc: 0.8726 - val_binary_crossentropy: 0.3071\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.3497 - acc: 0.8917 - binary_crossentropy: 0.2723 - val_loss: 0.3832 - val_acc: 0.8712 - val_binary_crossentropy: 0.3071\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.3464 - acc: 0.8928 - binary_crossentropy: 0.2706 - val_loss: 0.3791 - val_acc: 0.8723 - val_binary_crossentropy: 0.3047\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.3436 - acc: 0.8944 - binary_crossentropy: 0.2692 - val_loss: 0.3765 - val_acc: 0.8746 - val_binary_crossentropy: 0.3028\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sdmG-H_JSamf",
        "colab_type": "code",
        "outputId": "236f0adc-a3ba-4aa5-b6e1-229bb5445321",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plot_history([('baseline', baseline_history),\n",
        "              ('l2.001', l2_model_history),\n",
        "              ('l2.01',l2_model_2_history)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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m3t//vto62hQbGXseqgMAAAAwlDz9wdMDPsGV6cnUo5c82q9rvPjii5o/f/5p\nx6uqqpSWltbzPi0tTVVVVf26V7eioiKVlJRo3LhxkqTly5fL4/GotbVVM2fO1M0336yRI0eeMqa8\nvFy/+tWvlJ2drZtuukmrVq3S7bffPiD19FfIzcxK0q5aZ+u3fUk+ddpOVRyqCHJFAAAAAODMK6+8\noqKiIi1evPi83nf27Nk9QVaSfvjDHyo3N1ezZ89WZWWlPvroo9PGpKen98zkzpgxQ3v27Dlf5Z5T\nyM3MSlJpdZNy0kac8zyfx+c/v6FU2aOzg10WAAAAgCGmvzOoA239+vVaunSpNmzYoJiYmNM+T01N\nPWX5cWVlpVJTUwfk3sOGDTuljnfffVebNm1SXFycLr/88l6XEJ9cY0RExDn3255PITcz6zJGpdXO\nZmZTE1KVGJ3IvlkAAAAAg27Lli267777tGbNGiUnJ/d6jtfrldvt1qZNm2St1UsvvaQbbrhhwGtp\nbGyUx+NRXFycdu7cqb/85S8Dfo9gC7kwGxvlUkl1k6NzjTE0gQIAAABw3rW2tiotLa3ntWzZMi1e\nvFgtLS269dZblZeXpwULFvScn5eX1/P7s88+q3vvvVfp6emaNGlSz97alStXKi0tTRs3btS1116r\nwsLCPtd37bXXqrW1VVlZWVqyZIkuvfTSvn/ZQWK6n1kUKrzpU+yoO5ep+HtXyxhzzvP/z1/+j14r\nf02bvrxJka6QXFUNAAAAIAClpaXy+XyDXQYc6O3fyhiz2Vo781xjQ25mNi4qQs1tHao8dNTR+T6P\nT8c6j2lP457gFgYAAAAAOG9CLsx2P5Kn1OFS40xPpv/8htKg1QQAAAAAOL9CMMy6ZIwcN4GaOHyi\nYiJi2DcLAAAAXEBCbTvlhai//0YhF2Zdxmhi0jCVVDc6Oj/SFanJIyYTZgEAAIALRGxsrOrr6wm0\nQ5i1VvX19YqNje3zNUKyI5LP69b2KmdhVpIykzL15p43Za111DQKAAAAQOhKS0tTZWWl6urqBrsU\nnEVsbKzS0tL6PD5Ew2yifr+9Ws1t7UqMjTr3+R6fXt/1uvYf2a/UhIF54DAAAACAoSkqKkoTJ04c\n7DIQZCG3zFiSssa6JUllNc72zXY3gSqrZ6kxAAAAAISDkAyzPq8/zDrtaDx55GS5jIuOxgAAAAAQ\nJkIvzB5vVYo7ViPioxyH2bjIOE10T6QJFAAAAACEidALs4f3yhgjX4pbJQ4fzyP5m0AxMwsAAAAA\n4SH0wmzHMel4q7LGulVe06TOLmfttn0enw60HlBDW0OQCwQAAAAABFvohVlZqXanfF632tq79MnB\nI45G0QQKAAAAAMJHCIZZSTVTmmTvAAAgAElEQVTb5PMmSnLeBKo7zLLUGAAAAABCX+iFWVekVF2s\n9OQERbqM4zA7PGa4xg4bSxMoAAAAAAgDoRdmo+KkmmLFREYoPTlBJQ7DrOSfnSXMAgAAAEDoC80w\nW1sidbYry+t2PDMr+Tsa723aq9b21iAWCAAAAAAIthAMs/FS5zHp4C75vG7VNh1Tw5Hjjob6PD5Z\nWZUfKg9ykQAAAACAYArBMBvn/1ldLJ/XLakPTaDqaQIFAAAAAKEs9MJsZKwU6d83293RuGS/szA7\nJn6MRsaMZN8sAAAAAIS40AuzkjRmilRdrKSEGI1xxziemTXG0AQKAAAAAMJAaIZZb45Us12yVj6v\nO7COxkmZqjhcofbO9iAWCAAAAAAIptAMsyk50rFG6dAe+bxu7T7QomMdnY6G+jw+dXR16KPGj4Jc\nJAAAAAAgWEI3zEpSTbGyvG51dFntPtDiaChNoAAAAAAg9IVmmB2TJZmIz3Q0bnY0dLx7vOIi49g3\nCwAAAAAhLDTDbFScNOpiqaZYE0cNU2yUy3ETKJdxKWNkBmEWAAAAAEJYaIZZyd8EqrpYES6jjBS3\n48fzSOrpaNxlu4JYIAAAAAAgWEI3zKbkSC01UssBZXkTVVrTJGuto6G+JJ9aO1r1afOnQS4SAAAA\nABAMQQ2zxphrjDHlxpjdxpjHevn8HmNMnTFm64nXvY4v7j3RBOrEvtnDre2qaWpzNLSnCVQDTaAA\nAAAAIBQFLcwaYyIk/UTSfElZkhYaY7J6OfVVa23eidcLjm+Qku3/WbPtpCZQzpYap49IV6SJpKMx\nAAAAAISoYM7MXiJpt7X2Y2vtcUkrJN0wYFePGymNGCdVFyszJVGSHO+bjY6I1qQRk2gCBQAAAAAh\nKphhNlXSyZtSK08c+6ybjTHFxpjXjTGfC+gOKTlSTbESY6M0zhPv+PE80l+bQDndZwsAAAAAGDoG\nuwHUbyVNsNbmSHpL0vLeTjLG/IMxpsgYU1RXV/fXD7y5UsPHUluTfN5Ex8uMJX8TqIa2Bh1oPdC/\nbwAAAAAAOO+CGWarJJ0805p24lgPa229tfbYibcvSJrR24WstT+z1s601s4cPXr0Xz9IOdEEqnaH\nfF63Pqk/otbjHY6K624CxVJjAAAAAAg9wQyzf5E02Rgz0RgTLel2SWtOPsEY4z3p7QJJgXVkOqmj\ncZbXLWulshpnS43paAwAAAAAoStoYdZa2yHpAUlvyh9SX7PW7jTGfN8Ys+DEaQ8ZY3YaY7ZJekjS\nPQHdJNErxY+SaooD7mg8LGqYxrvHMzMLAAAAACEoMpgXt9aulbT2M8e+e9Lvj0t6vM83MMY/O1td\nrLSRcUqMjQxo32ymJ1M7Du7o8+0BAAAAAINjsBtA9V9KjlRXKtN5XL4Ud8AdjataqtR4rDGIBQIA\nAAAABlroh1lvjtTVIR0oVdZYt0qrm9TV5exxOz6PT5JU3lAezAoBAAAAAAMs9MNsSq7/Z02xfN5E\ntR7v1L6GVkdDaQIFAAAAAKEp9MOs5yIpOkGqDrwJVFJckpLjkmkCBQAAAAAhJvTDrMsljZkq1RTr\n4jGJchmpJJAmUEmZhFkAAAAACDGhH2Yl/77Zmh2KjZAmjU4IuKPxJ42fqK2jLYgFAgAAAAAGUniE\n2ZQcqf2I1PCxfN7AOhr7PD512k5VHKoIYoEAAAAAgIEUHmHWm+P/Wb1NPq9bVYePqrG13dFQmkAB\nAAAAQOgJjzA72ie5oqSaYmWN9TeBcrpvNjUhVYnRieybBQAAAIAQEh5hNjJaSs480dE4UZLzjsbG\nGGV6aAIFAAAAAKEkPMKs5H/ebE2xkhNiNCohOuAmULsO7VJHV0cQCwQAAAAADJTwCbPeHKm1Xmra\n728CVeM8zPo8Ph3rPKY9jXuCVx8AAAAAYMCET5hNOdEEqqZYWV63dtW0qL2zy9FQmkABAAAAQGgJ\nozA7VZKRarbL53XreGeXPq474mjoxOETFRMRw75ZAAAAAAgR4RNmYxIlz0U9j+eRnDeBinRFavKI\nyYRZAAAAAAgR4RNmJf++2ZpiXTR6mKIjXIE1gUrKVGlDqay1QSwQAAAAADAQwivMpuRIh/cp6nij\nLk5JcPysWcnfBKr5eLP2H9kfxAIBAAAAAAMhvMKst7sJ1Hb5UtwBP55HksrqWWoMAAAAAENdeIXZ\nlFz/z+pi+bxuHWw5rgPNbY6GTh45WS7joqMxAAAAAISA8AqzCaOlRK9UU9zTBKpkv7PZ2bjIOE10\nT6QJFAAAAACEgPAKs5J/32y1/1mzklRa3ex4aHcTKAAAAADA0BZ+YdabIx3cpeFRHUodERfQvlmf\nx6cDrQfU0NYQxAIBAAAAAP0VfmE2JUeynVJtiXzeRJpAAQAAAEAYCr8w29PReJt8Xrc+qmtRW3un\no6HdYZalxgAAAAAwtIVfmB0xXood3rNvtstKu2qd7ZsdHjNcY4eNpQkUAAAAAAxx4RdmjfEvNT6p\no3GgS40JswAAAAAwtIVfmJX8YbZ2p8aNiNaw6IiAOxrvbdqr1vbWIBYIAAAAAOiP8Ayz3hypo02u\nht3K9LodP2tW8nc0trIqP1QexAIBAAAAAP0RnmE25UQTqOpif0fjmiZZax0N7WkCVU8TKAAAAAAY\nqsIzzI66WIqM7dk329zWocpDRx0NHRM/RiNjRrJvFgAAAACGsPAMsxGRUnKWVL0t4CZQxhhlejJ5\nPA8AAAAADGHhGWYl/77ZmmJljkmQMVJJIB2NkzK1+/ButXe2B7FAAAAAAEBfhW+YTcmR2hoV37pf\nE5OGBfR4Hp/Hp46uDu0+vDuIBQIAAAAA+ip8w6w31//zxL7ZQB7P4/P4JIl9swAAAAAwRIVvmE3O\nkoyrp6PxvoZWNbc5WzY8zj1O8ZHx7JsFAAAAgCEqfMNsdLy/q3FNsbLG+ptAldU4m511GZcyPBnM\nzAIAAADAEBW+YVby75utLg64o7Hkf95seUO5umxXsKoDAAAAAPRReIdZb47UvF8pES0aER8VcBOo\n1o5W7WvaF8QCAQAAAAB9Ed5hNiVHkmRqiuVLcatkf2AzsxJNoAAAAABgKArzMJvt/3li32x5bbM6\nu6yjoekj0hXpiqQJFAAAAAAMQeEdZuM90vBxPftm29q79MnBI46GRkVEKX1EOjOzAAAAADAEhXeY\nlfz7Zmv8j+eRAm8CVdZQJmudzeYCAAAAAM6P8A+zKTlS/UdKHyFFuoxKAgyzDW0NOtB6IIgFAgAA\nAAACFf5h1psjySrmYKnSkxMC7mgs0QQKAAAAAIaa8A+zJzoaq7pYWV53QGE2w5MhI0MTKAAAAAAY\nYsI/zLrHSvFJUs02+bxu1TYdU33LMUdDh0UN0zj3OGZmAQAAAGCICf8wa4x/drba/3geSSqtbnY8\nvLsJFAAAAABg6Aj/MCv5980eKJUvOVZS4B2Nq1qq1HisMVjVAQAAAAACdGGE2ZQcqatdniMfa4w7\npk9NoMobyoNVHQAAAAAgQBdGmPXm+n/WFMvndQf8eB5JNIECAAAAgCHkwgiznklS1LCejsa7D7To\nWEeno6FJcUlKjktm3ywAAAAADCEXRph1uaSUqT0zsx1dVrsPtDgenplEEygAAAAAGEoujDAr+ffN\n1myXLyVBUuAdjT9p/ERtHW3Bqg4AAAAAEIALJ8x6c6TjLZroOqDYKFfATaA6bacqDlUEsUAAAAAA\ngFMXTphNyZEkRdQWKyPFrZL9NIECAAAAgFB14YTZZJ/kipRqipXlTVRpTZOstY6GpiakKjE6kX2z\nAAAAADBEXDhhNjJGGu2Tqv1NoA63tqumydkeWGOMMj00gQIAAACAoeLCCbOSf99sTbF8KYmSFPBS\n412HdqmjqyNY1QEAAAAAHLqwwmxKjnSkTr6EI5IUcBOoY53HtKdxT5CKAwAAAAA4dWGFWa+/CVTC\noVKN88QH/HgeiSZQAAAAADAUXFhhdsxU/8/qYvm8iQHNzE4cPlExETHsmwUAAACAIeDCCrOxbslz\nkVSzTT6vW5/UH1HrcWd7YCNdkZo8YjJhFgAAAACGgAsrzEr+fbPVxcryumWtVFYTwFLjpEyVNpQ6\nfqQPAAAAACA4Lrww682RDu9VlscfSANtAtV8vFn7j+wPVnUAAAAAAAdCLszWHKlRZ1dn3y+QkitJ\nSm2rUGJsZEBhtrsJVFk9S40BAAAAYDCFXJitb6vXO5Xv9P0CJzoam5rt8nndAT1rdvLIyXIZFx2N\nAQAAAGCQhVyYjXZF65WSV/p+gYRkKSGlZ99sWU2zurqc7YGNi4zTRPdEmkABAAAAwCALuTDrifWo\nqLZIpfX9mB315kg1/sfztB7v1L6GVsdDu5tAAQAAAAAGT8iF2RGxIxQXGadXSvsxO5uSI9WVK2t0\ntKTAm0AdaD2g+qP1fb8/AAAAAKBfQi7MRpgI3ZV1l8bEj+n7Rbw5ku3UxeZTRbiMSvrSBIqlxgAA\nAAAwaCIHu4C+eHDag/27QIq/CVRM3Q5dNOqiPnU0Lm0o1WWpl/WvDgAAAABAn4TczGw3a63eq3pP\nxzqPBT545AQpZviJfbNulVY3Ox46PGa4UhNSmZkFAAAAgEEUsmF2y4Et+ur6r2rtx2sDH2yMlJIt\nVfvDbNXhozrcetzx8ExPJmEWAAAAAAZRyIbZacnTdPHIi/Vy6cuy1tmjdU7hzZFqdyorZZgkBTQ7\nm+nJ1N6mvTrSfiTw+wIAAAAA+i1kw6wxRnf67lTFoQr9uebPgV8gJUfqOKqpsbWSAu9oLEnlDeWB\n3xcAAAAA0G8hG2Yl6QsXfUGeWI9eKenDY3q8/iZQSU3lGpUQ3ecmUAAAAACA8y+kw2xMRIxuy7hN\n5YfK1dreGtjgURdLETFS9Tb5vO6AHs+THJ8sT6yHfbMAAAAAMEhCOsxK0j1T7tHam9YqPio+sIER\nUdKYLKmmWFletypqW9Te2eVoqDGGJlAAAAAAMIhCPszGR8UryhWl9s52tXW0BTY4Jcff0TglUcc7\nu/RxnfOGTpmeTO0+vFvtne0BVgwAAAAA6K+QD7OS1HisUdf85hr9quxXgQ305khth5Wd6O9kHGgT\nqI6uDu0+vDuwewIAAAAA+i0swuzwmOEaP3y8fln2S3V0dTgfmJIrSRp/fLeiI1wB7ZvtbgLFUmMA\nAAAAOP/CIsxK0l2+u1RzpEbr9613PmjMFMm4FHlguy5OSQhoZnace5ziI+PpaAwAAAAAgyBswuyV\naVfqc4mfC+wxPdHxUtLkE/tm3QGFWZdxKcOTwcwsAAAAAAyCsAmzEa4I3eG7Q9vqtqmkvsT5QG+O\nVFMsn9etgy3HdaDZeROpTE+myhvK1WWddUEGAAAAAAyMsAmzknRj+o366byfyufxOR+UkiM1VSnH\n499rW7I/sCZQrR2t2te0L9BSAQAAAAD9EFZhdljUMH1+7OdljHE+yJsjSfJpjySptLrZ8VCaQAEA\nAADA4AirMCtJ1lotK1qmnxX/zNmAFH+YHdawU6kj4gLaN5s+Il2RrkiaQAEAAADAeRZ2YdYYo8qW\nSi3fuVyt7a3nHhDvkYZ/7sS+2cSAwmxURJTSR6QzMwsAAAAA51nYhVlJutN3p5qON+l3H//O2YCU\nHKm6WFletz6qa1Fbe6fje2V6MlXWUCZrbR+rBQAAAAAEKizD7LTkaZqSNEUvl7zsrNOwN0eq362p\noyPUZaVdtYHtm21oa9CB1gP9qBgAAAAAEIiwDLPGGN2Zdaf2NO3Re1XvnXtASo4kq+zISkkKaKlx\nd+dklhoDAAAAwPkTlmFWkgrHF+q2jNs0NmHsuU8+0dF4TOsuDYuOCOjxPBmeDBkZmkABAAAAwHkU\nea4TjDFJ1tr681HMQIqKiNKS/CXOTnanSnEeuWqKlemdGtDjeYZFDdM49zhmZgEAAADgPHIyM7vJ\nGPNrY8wXTEAPcB0ayhvKtWr3qrOfZIx/dra7o3FNU0ANnbqbQAEAAAAAzg8nYfZiST+TdJekCmPM\nvxljLg5uWQPnV2W/0r9u+lcdajt09hNTcqQDpcoaE6fmtg5VHjrq+B6ZnkxVtVSp8VhjP6sFAAAA\nADhxzjBr/d6y1i6U9PeS7pb0gTFmgzFmdtAr7Ke7su7Ssc5j+vWuX5/9RG+u1Hlc02JrJUklfWgC\nVd5Q3uc6AQAAAADOnTPMGmOSjDEPG2OKJH1b0oOSRkn6lqRfBrm+fps0YpIuG3uZVpStUHtn+5lP\nTPE3gbqo8yMZE1hH40xPpiTRBAoAAAAAzhMny4w3SnJLutFae6219g1rbYe1tkjSc8Etb2DcmXWn\n6o7Wad2edWc+KWmSFBWvmLqdmpg0LKAwmxSXpOS4ZPbNAgAAAMB5cs5uxpIyrLXWGOM2xiRaa3ta\n/Vprnw5ibQPmsrGXKXtU9tn3tLoipDFTpepi+by3aXtVYPtfM5NoAgUAAAAA54uTmdkZxpjtkool\n7TDGbDPGzAhyXQPKGKP//sJ/686sO89+ojdHqtkuX8ow7WtoVXPbWZYlf0amJ1OfNH6ito62flYL\nAAAAADgXJ2H255K+Zq2dYK0dL+kfJf1XcMsaeMYYWWu169CuM5+UkiMdb9YMt39WtqzG+fNmfR6f\nOm2nKg5V9LdUAAAAAMA5OAmzndbaP3a/sdb+SVJH8EoKnpdKXtKtv71Vlc2VvZ/g9TeBytQnkmgC\nBQAAAABDlZMwu8EY81NjzBxjzFXGmGclvWOMmW6MmR7sAgdS4YRCueTSL8vO0IQ5OUtyRWpEY5lG\nxEcFFGZTE1KVGJ3IvlkAAAAAOA+cNIDKPfHze585Pk2SlfQ3A1pREKUMS9G8CfP0RsUb+lru15QQ\nnXDqCZEx0uhMmZpiZXkLVbLfeZg1xijTQxMoAAAAADgfzjkza62de5ZXyATZbnf57tKR9iNa/dHq\n3k9IyTnR0dit8tpmdXZZx9fO9GRq16Fd6ugKyVXYAAAAABAyzhlmjTHDjTHLjDFFJ17/1xgz/HwU\nFwzZo7OVNzpPb+55s/cTUrKlIwc0beQxtbV36ZODRxxf2+fx6VjnMe1p3DMwxQIAAAAAeuW0m3Gz\npC+deDUpBLsZn+zpK5/WC1e/0PuHJ5pAZUfskSSV0AQKAAAAAIYcJ2F2krX2e9baj0+8npR0UbAL\nC6axCWMVHRGtzq7O0z9MyZYkpbZVKCrCBNQEauLwiYqJiCHMAgAAAECQOQmzR40xl3e/McZcJulo\n8Eo6P3Yc3KEvvPGF0xs2xQ6XRk5QZO12TRqdEFCYjXRF6uKRF9MECgAAAACCzEmY/aqknxhj9hhj\n9kj6f5Luc3JxY8w1xphyY8xuY8xjZznvZmOMNcbMdFT1ABjnHqdDxw7p5ZKXT/8wJUeqKVaW1x1Q\nmJXU09HYWueNowAAAAAAgTlrmDXGuCRlWGtzJeVIyrHWTrPWFp/rwsaYCEk/kTRfUpakhcaYrF7O\nS5T0sKQ/96H+PnNHu3Vj+o36n0/+RwePHjz1Q2+OdGiPckYb1TYdU33LMcfXzfRkqvl4s6paqga4\nYgAAAABAt7OGWWttl6RHTvzeZK0NZJryEkm7T+yzPS5phaQbejnvXyQ9LaktgGsPiDt8d6ijq0Ov\nlr966gcp/kfrzoiplCSVVjc7vqbP45MklhoDAAAAQBA5WWa83hjzbWPM54wxnu6Xg3Gpkj496X3l\niWM9jDHTJX3OWvt75yUPnPHu8boq7Sq9Vv6ajnWeNPt6oqPxRZ0fS1JAS40nj5ysCBNBEygAAAAA\nCKJIB+fcduLnP550zKqfHY1PLGFeJukeB+f+g6R/kKRx48b157an+WreV1V/tF5Rrqi/HkxMkYYl\na1h9ica4fQGF2djIWE0cPpGZWQAAAAAIIidh1metPWUJsDEm1sG4KkmfO+l92olj3RIlTZX0jjFG\nklIkrTHGLLDWFp18IWvtzyT9TJJmzpw5oJ2VpiRN6f0Dr78JlM/7lYCeNSv5981+UP3BAFQHAAAA\nAOiNk2XG7zs89ll/kTTZGDPRGBMt6XZJa7o/tNY2WmtHWWsnWGsnSNok6bQgez60HG/Rjzb/SEU1\nJ906JUeqK1P2mFjtPtCiYx29PJP2DDI9mTpw9IDqj9YHoVoAAAAAwBnDrDEmxRgzQ1KcMWaaMWb6\nidccSfHnurC1tkPSA5LelFQq6TVr7U5jzPeNMQsGqP4BERURpZW7V+oXO3/x14PeHKmrQ7Pia9XR\nZbX7QIvj69EECgAAAACC62zLjAvl38+aJv/e1m7Nkr7j5OLW2rWS1n7m2HfPcO4cJ9cMhpiIGH0p\n40t6bttz2tu0V+Pd4/0zs5Iy9YmkVJVWN2vK2OGOrpfhyZAklTaU6rLUy4JVNgAAAABcsM44M2ut\nXW6tnSvpHmvt3JNeC6y1b5zHGs+L2zJuU5QrSq+UvOI/MHKiFJ2oUS3lio1yqWS/832zw2OGKzUh\nlZlZAAAAAAgSJw2gfmeM+bKkCSefb639frCKGgyj4kbpCxO/oNUfrdYD0x7Q8JjhUkq2XDXFyki5\nKaCOxpJ/3yxhFgAAAACCw0kDqNWSbpDUIenISa+wc1fWXboq7Sod7TjqP+DNkWp3aEpKvEprmmSt\n80bKmZ5M7W3aqyPtYfmnAgAAAIBB5WRmNs1ae03QKxkCMjwZ+o+r/uOvB1JypPZWXeI+rF+2tqum\nqU3e4XGOrtXdBKq8oVzTx0wPRrkAAAAAcMFy9GgeY0x20CsZQj46/JF2HNzhn5mVlBOxV5IC2jeb\n6cmU5G8CBQAAAAAYWE7C7OWSNhtjyo0xxcaY7caY4mAXNlistXroDw/p6Q+elkZnShHRSju2W5IC\n2jebHJ8sT6yHfbMAAAAAEAROlhnPD3oVQ4gxRl/2fVlPffCUtjeUKTvZp+i67RrnKVBpdXNA16EJ\nFAAAAAAExzlnZq21eyV9TtLfnPi91cm4UHZj+o1KiErQy6Uv+/fNVhfLl5Kgkj50NN59eLfaO9uD\nVCkAAAAAXJjOGUqNMd+T9Kikx08cipL0SjCLGmzDoobppsk36a09b6lm1EXS0QZdktSmPfVH1Hq8\nw/F1fB6fOro6tPvw7iBWCwAAAAAXHiczrF+UtEAnHsdjrd0vKTGYRQ0FX/Z9WTGRMdoZGy9Jmh69\nT9ZKZTXOlxp3N4FiqTEAAAAADCwnYfa49T9g1UqSMWZYcEsaGlITUvX2l95WwdQ7JRld1PGxpMCa\nQI1zj1N8ZDwdjQEAAABggDkJs68ZY34qaYQx5u8lrZf0fHDLGhriIuNko4epYdQkuQ+XKDE2MqDH\n87iMSxmeDGZmAQAAAGCAOWkA9QNJr0v6jaQMSd+11v442IUNFd99/7u6x+2Srdkun9cd0Mys5F9q\nXN5Qri7bFaQKAQAAAODC46QB1DBJf7DWLpZ/RjbOGBMV9MqGiEu9l+oT26b3j9dpxmirsppmdXVZ\nx+N9Hp9aO1q1r2lfEKsEAAAAgAuLk2XG70qKMcakSlon6S5JvwhmUUNJ4fhCjY5262V3oi6Jq1Tr\n8U7ta2h1PJ4mUAAAAAAw8JyEWWOtbZV0k6T/tNbeKmlKcMsaOqIiorRw8pf0fnycYtq3SFJAz5tN\nH5GuSFckTaAAAAAAYAA5CrPGmNmS7pD0+xPHIoJX0tBzy9RFirFW7zT/RREuE9C+2aiIKKWPSGdm\nFgAAAAAGkJMw+3VJj0taaa3daYy5SNLbwS1raBkZO1I/jZyobx5q1EWjhvWpCVRZQ5n8TzgCAAD/\nn737Do+qSh84/r3TMiVl0jskgRAIEHoVkKKCgA1s2MXu2pa17s/eC7urYu+uYltXxRUQCyAKYqUT\neklIIQnpmSTT7u+PM0mIoQRIaL6f57lP4j1zzz0DMcx7z3veI4QQQhyq1lQz/k7X9dN1XX9C0zQD\nUKLr+k2HYWxHlX6JQ7Du2kRWrIXsgqoDurZrRFdK60opchW10+iEEEIIIYQQ4s+lNdWM39M0LTRQ\n1Xg1sFbTtNvaf2hHmbgsFtiCWM8j5JVXUe5yt/rSbhHdACkCJYQQQgghhBBtpTVpxpm6rlcCZwJz\ngVRUReM/l/gszLrOdu92TKGrDmh2NiMiAw1NikAJIYQQQgghRBtpTTBrDuwreybwua7rHuDPt/gz\nLJmhupUUzYYlYjFr8ytafanD7KBDaAeyd0kwK4QQQgghhBBtoTXB7MvANsABLNI0rSNwYBWQjgea\nhiE+i4vdBoy2HfyY9+sBXT4gbgCLdizil8Jf2mmAQgghhBBCCPHn0ZoCUM/qup6o6/p4XdkOjDoM\nYzv6xGVxWuEWjLqdldVfHNCl0/pNIykkiWkLp7Gjakc7DVAIIYQQQggh/hxaUwAqTNO0f2qa9mvg\n+AdqlvaIKKioo87jOzI3j++FzVvPyKBxVBZl4fH5W31piCWEGaNn4NN93LTgJlweVzsOVAghhBBC\nCCGOb61JM34DqALODRyVwJvtOah9Kamu559fbzgyN4/LAuBieyx1ld3YUlxzQJenhKUwfcR0Npdv\n5u8//B2/3vpgWAghhBBCCCFEk9YEs510Xb9P1/UtgeMBIK29B7Y3EQ4Lr32/hWU5ZYf/5lHpYLKR\n6t2MZqzm+eUvUOM5sIB2aOJQbu1/K9/mfMsLy19op4EKIYQQQgghxPGtNcFsraZpwxr+Q9O0E4Da\n9hvSvsWHWYkLtXLbxysPf7qxwQix3XFWZmOxlrNg57t8tumzA+7mom4XcVbns3h55ct8ue3Ldhio\nEEIIIYQQQhzfWhPMXgs8r2naNk3TtgHPAde066j2waBpPDY5i01F1by4cPPhH0B8FobC1aSHdcOh\nd2Jm9kx8/gMLqjVN4+7Bd9M7ujf3/HCPbNkjhBBCCCGEEAdon8GspmkGIEPX9V5AFpCl63ofXddX\nHpbR7cWJXaJ56MweXDiow+G/eVwW1FdwQmQN9SUnkFuVy6Idiw64G4vRwr9G/YuwoDBuWnATJbUl\n7TBYIYQQQgghhDg+7UKoO1gAACAASURBVDOY1XXdD9we+L5S1/WjZn/Ziwd3JCbUit+vH1BV4UMW\nr4pADbTuoKwkgxhbHO9kv3NQXUXZonh29LOU15Xz1wV/xe1zt+VIhRBCCCGEEOK41Zo04280TbtV\n07RkTdMiGo52H1kr1Hl8nP/qUmbM33T4bhrTHTQjGWwFjJwQcyahltCDDkQzIzN5aNhDLC9ezsNL\nH0bX9bYdrxBCCCGEEEIch1oTzJ4H/AVYBPwWOH5tz0G1ltVsJNFp44UFm1iTX3F4bmq2QnQGsTXr\nAYjTx/L0qKexGC0H3eW4lHFcnXU1n276lJnZM9tqpEIIIYQQQghx1Kp1+1ieW86ny3Y0nnv6m9Zv\nw2ra3wt0XU89uKEdHvedlsn3G0u47T8rmXXDCZiNrYnPD1FcFuYtC0l02lhXWAXAtopthAWFEW4N\nP6gu/9L7L2wq28RTvz5FWlgaQxOHtuWIhRBCCCGEEOKI0XUdTdNYtKGYj3/bwdqCSrYUV+MPJKaO\n7hpLmM1M3w6tj6f2GvlpmnaRpmkX7+H8xZqmXXAQ428XTruFh8/swdqCSl7+7jBVN47PgupCBsV4\nWVtQSbGrmDNmncH7694/6C4NmoHHhj9GJ2cnbl10K9srt7fhgIUQQgghhBCi/fn8OpuKqvl8RT6P\nz13HpW/8zIBHvmFzcQ0AuWUuftteRkqkgxtGp/PSRf1YdNsoQq1qnnVEl+hW32tfM7M3AmP2cP4T\nVMrxe62+Szsb1yOOiVnxfLosj6tHdMJiaufZ2ThVBGqYI5/PNkYTYo5geOJwPlz/IVf0vIIgY9BB\ndWs323l21LNMmT2FG+ffyMzxMwmxhLTlyIUQQgghhBCiTdTUe1lXWMnagiqGpEXSOSaYb7N3cvU7\nvwFgNmqkx4QwIj0ag6auuWBgBy4c1LFN7r+vYNas63r1H0/qul6jaZq5Te7ehh45sycmo9b+gSxA\nXE8Auhu24dej2bCzioszL+bKr65kzpY5nJV+1kF3nRSSxD9H/pOrv7qaOxbdwYzRMzAajG01ciGE\nEEIIIYQ4ILqu4/HpWEwGiqrquP/zNWQXVLFtVw0N9WvvPy2TzjHB9OsYzvRzepEZH0rnmOAW8Zmm\naW02rn0FszZN0xy6rtf84eYhwMFXOzpUteXg94Oh+R9KmF3F13UeH8tyyhnSKbL9xmBzgrMjSfWb\ngAFkF1Rybv+BdAnvwrvZ73Jm5zMP6S9pQNwA7hp0Fw8tfYhnfn+Gaf2ntd3YhRBCCCGEEGIvdF1n\nw85q1hZUkF1Qxdr8StYWVHJOvyTuGt+NUKuZtfmVdI0L5aw+iWTGh5KZEEp8mBWAyOAgzu6XdFjG\nuq9g9nXgY03TrtV1fTuApmkpwPOBtiOjbCssfQGG3rDH5sfnruO9n3OYc9NwOscEt9844rOw71yD\nw2JkbX4lmqZxUbeLeHjpw2yr3EZq2KHVzTo341w2lG3gzTVvkh6ezmmdTmujgQshhBBCCCEEVNZ5\nWFdQxdr8CoLMRqYM7ADA+a/8SJnLg8VkICM2hJO7xdI/Re3OajUbWXjbqCM57EZ7DWZ1XZ+uaVo1\nsEjTtIaosBp4XNf1Fw/L6PbE6oRvH4C0ExvTfXd3/ahOfLY8j9s+XsHH1w7FaGi7aexm4nqhZf+P\nPrFGsgtUReMJaRM4MflEIqxtsw3vHQPvYEvFFu5fcj8dQzuSFZ3VJv0KIYQQQggh/jx0Xae0xk1k\nsKrt8/AXa5m3tpDc0trG1wxICWfKwA5omsaMKX2JCQ0iLcqB6XDsFnOQNL0hyXlfL1Kpxei6XtXu\nI9qP/n17679e4AJbBFy9AMy2Fq/5dNkO/vrhCu6e0I0rh6e1z0A2zIP3zuXVzs/z7MZoVt5/SrPU\nYo/Pg9l46EuLy+rKmDJ7Cm6fm/cnvE+sI/aQ+xRCCCGEEEIcv7aV1PDr9rJAirBKF9Y0WHbPyWia\nxhNfriOn1KVShANpwjEhQW26nvVQaJr2m67r/ff3uv3uMwtHRxDbyGCCM1+AdyfDN/fDqU+0eMmZ\nvRP5YkUBT81bz5husaRGOdp+HIGKxn3MOVTVh7OjrJbkCDt+3c+VX11JZ2dn/j7o74d8m3BrODNG\nz+CiORdxy4JbeHPcm1hN1kPuVwghhBBCCHFs2lxczc9bS8kvryWvvJaC8jryK2r5343DCLWa+eCX\nXF76bjNWs4GucaFMyIonMz4Un1/HZNS4Y1zXI/0W2kSrgtmjTueTYPTd0GHIHps1TePRST259T8r\naM3M80EJiQNHNKnezUAv1hZUkhxhx6AZiHfE89mmz7ihzw2EWkIP+Vbp4ek8Nvwxbl5wM/f/eD+P\nDXvsqHlqIoQQQgghhDh0Hp8fALPRwObiauatKSS/vJb88rrA11o+vm4oXWJDWLyphHtnrcGgQVyo\nlXinjawkJ26v6uPCQR04p38SKZGO9lt2eRQ4NoNZgBG3NX2/h+rGsaFW3rliUPvdX9MgLovwinVo\nGmQXVDK2exwAF2dezOebP+fTjZ9yafdL2+R2ozuM5sY+NzJj2QzSnelc0fOKNulXCCGEEEII0b4a\n1qwGmY0EB5nI2eXi3Z+2kxcIUgvK6yiqquONywYwMiOGLcU1PPnlepx2MwlhNpLCbQxMjcBmVlt2\nnt4rgTHdYokNCdrjmtbkCPvhfotHxH6DWU3TfgPeAN7Tdb2s/Yd0gBY8CjvXwHnvqgDzD0pr3Dw6\nJ5ubRqfTIbKN/1LjszBsnUGXCAvZBZWNp7tGdGVA3ABmZs/kwm4XYjK0zTODq3pexcayjTzz+zN0\ndnbmxOQT26RfIYQQQgghxMFzub3kl9cRYjURG2qlqKqOp75cT35F08xqvdfPY5N6MmVgByrrPLy1\nZBuJThsJTivD0qNIcNpIClfxyvD0KNY8MBZH0J7jCKfdgvPPEa/uU2uirPOAy4FfNE37FXgT+Epv\nt/zdAxQUCuu+gN/fhn6XtWiu8/j4cnUheWW1vHfVoLZNz43LAr+XURG7mF1gbNZ0UbeLuHnBzczP\nmc8pKae0ye00TePBEx5ke+V27vj+DmaOn0knZ6c26VsIIYQQQgjRktfnp6iqnvzyWkKsZjLiQqh1\n+7jx/WVqVrWiljKXB4CbxqQz7eQumAwGFm0sJsFpIzM+lJO6xZDgtNG/YzgAmfGhrH9o3F5jE6vZ\nuMfzorlWVTMG0DTNAEwEXgR8qKD2GV3XS9tveC31799f//XXX5tO+P3w7lmQ+zNc8z1EdW5xzXs/\n5fD3T1fxyFk9uHBQx7YbzK7NMKMv87vcw9SV3Vh1/ymEWFUFY5/fx+ebP2dsyljs5rZ9bFJYU8j5\nX5yP3Wzn/QnvExYU1qb9CyGEEEIIcbzRdR2PT8diUmm5y3LKKK6qp6LW03gkR9g5t38yABe+tpSt\nxTXsrKrH51cx0zn9knjqnF7ous7pzy0mOiSI+DArCU4biU4bPRLD6BwTvNcxiNZpbTXj1m7Nk4Wa\nnR0PzANmAsOAi3Vd732IYz0gLYJZgMp8eHEohKfCFV/BH7bE0XWdi17/iRW5Fcz76wgSnS238zko\nfj883oEdHU9n2KoJ/OfaIQxIaZs9ZvdnedFyps6bSt/Yvrx00kttlsoshBBCCCHEsSC31MXOyjrK\nXU3BqM1iZMrADgDc//kaVuVVNAWrLg99Ojj58BpVRHb0PxaypbimsT+DBmO7x/HiRf0AuOWDZRg0\njQSnjXinClg7RQW3/dJF0UKbbc0TWDNbDrwO3Knren2g6SdN0044tGG2kdAEOO0Z+O9VkPc7dGhe\n+EnTNB6flMXYpxfx6Jxsnr+gb9vc12CAuB7EVG8AJpBdUNkimJ21aRbbKrdxc9+b2+aeAb1jenPP\n4Hu4d8m9TP91OncOvLNN+xdCCCGEEKI9VdR6KKmubxZs1nl8nB8IRt9avJUfNu2istZDea2biloP\njiAT8/82EoC7P1vNdxuKm/WZFuVoDGbrvT6CTAa6xAYTZjMTajPTKapp1vRf5/bGaNAa20KCTBh2\nq/z79Pl92vlPQByqfQazgdTi/+q6/uie2nVdn9QuozoYmWdAYn8IS9xjc3KEnRcu7EtmwqFvldNM\nXBbmZe8SYTOwNr+yRfOGsg28l/0e52ecT6wjtk1vfVb6WWws38g7a98h3ZnO5C6T27R/IYQQQggh\nDtaSTSV8uaaQnZV1gYDVS2Wth+9uG4nJaOCpeet4d2lOs2tMBo3zBiSjaRoFlXXkldcSZjORGuUg\nzGYmNtTa+NqbxqQzdVgqTpuZsMARamvK0HxsUtY+x9cr2dm2b1gcdvsMZnVd92uaNgnYYzB71GkI\nZLO/gNThYG2+lnRkRgwAfr9OjdvbuL71kMRnof1cw6iY6mYVjRtM6TqFd7Pf5YP1H7T57CzAtH7T\n2FK+hYd/epiUsBT6xfZr83sIIYQQQgjxR/VeH5uKqllfWMX6nVWsL6xiQ2EVn1x/AnFhVlbmVfDJ\n73kkOm2E2cwkBooheXw6JiNM6pvEgJQIQncLRsN2C0bvOrUbd5269/v3CxRTEn9erVlo+Y2mabcC\nHwKNSeWHu/BTq5VugY8ugZ5nw6RXWjTrus7lb/2C0aDx+qX9D726cZx64nNCcD6zNwbj8+vNNiZO\nCklidPJo/rPhP1yddTU2Uxut1w0wGUw8eeKTXDj7QqYtnMb7E94nITihTe8hhBBCCCH+vHx+nZxS\nlwpaC6s4rVc8adHBzFlVwF8/XAGA2ajRKTqYgakReHx+AKaekMo1I9L2+nm7b4dw+naQgFQcvNZu\nzQPwl93O6UBa2w+nDUSkwYm3w8LHIP0UFdTuRtM0RnSJ5qEv1vLpsjwm9U06tPtFdwWDme7aNuo8\nndlaUtOigtnFmRfzTc43/G/z/zg349xDu98ehFpCeXb0s1w4+0Jumn8T/z71321eQVkIIYQQQhzf\ndF2nuKoeo0EjMjiIzcXV3PLBcjYWVVHnUQGqpkFqtIO06GCGdoriuQv6kBEbQkqUA7PR0Ky/hqrB\nQrSX/Qazuq6nHo6BtKnht8Kmb+GLaZA8CJzJzZovG5rCnFUFPPC/tQzrHEXMbrn3B8xkgZhuJNZt\nBE5ibUFli2C2T0wfJqdPJjFYpUH7/D6MhrbdOyo1LJUnRjzBDfNv4O7FdzP9xOkYNPkFIoQQQggh\n9qze6+Pj33Y0zriu31lFucvDtJO7cNOYdMLtFsJsZi4c1JGM2BAy4kJIjw3GblEhRGyolYlZkhEo\njpzWbs3TA8gEGqM+Xdf/3Y7j2qs9bs2zJ6Vb4aVhEN8bLv2fqjy8m83F1Yx/5ntO7BLNyxf3O7R0\n41l/QV8/ly4VM7hyeCfuGNd1ry/VdZ3L511Ockgyl/e4nLSwtp3gfnvN20z/dTrX976e63pd16Z9\nCyGEEEKIY0u918fmohrW76xkfWE16wsr6ZEYxt9OycDn18m890vMRlXxNyMuhIzYEAZ3iqRrXBsX\nTRXiALTl1jz3ASNRwewc4FTgB+CIBLOtFpGqtuvR9RaBLECn6GCmndyFd3/aTmmNm8jgoIO/V1wv\ntGXvMiiyfo9FoHbn8XvICM/gk42fMGvTLMZ0GMMVPa+gR1SPg7//bi7JvIQNZRt4YfkLdHZ25uSO\nJ7dJv0IIIYQQ4ujlD6xrXVdYRb3Xxxm9VUbg2H8tYtsuF9C0rrV3slqnajRofH/7KKJDgg69jowQ\nR8B+Z2Y1TVsF9AKW6breS9O0WOBdXdePSJTU6pnZP/L74A+pvT6/Tr3X15gqcdBylsIbY3kt6VFe\nLcrgp7+ftN9LSutKmZk9k/fXvU+Vu4p/jvxnmwWe9b56ps6bysayjbxz6jtkRGS0Sb9CCCGEEOLI\n0nWdcpeHcIcFgBcXbmbu6gI27qym1uMDICncxg93jAbg02U7MBkMZMSFkLqHda1CHI3abGYWqA1s\n0ePVNC0UKAKS93fRUWX5e7D0RZg6DyxNhZGMBg27xUSdx8ecVQUHXwwqtgeg0cecw87KFHZV1+93\npjfCGsGNfW5kao+pfLLxE4YlDgPg25xv8fl9jOkw5qDX1QYZg3h65NOcP/t8bpx/I+9PeJ9IW+RB\n9SWEEEIIIdpPncdHZZ2HWrcPV+CodfsYmBqBxWRgWU4Zv20vI7fU1bj9jcvtY+2D4zAaNKrrPYRY\nTUwZ2IGMuGAy4kJJ361+y1l9DrHYqRBHsdYEs79qmuYEXgV+A6qBH9t1VG0tNAEKV8JXd8PEf7Zo\n/vCXXO77fA02s5FTe8YfeP9BwRDZiRTvZmAE2QVVDEtvXdqyw+zg4syLG//7P+v/w+L8xaSEpnB5\nj8s5Le00zMYD3w832h7Ns6Oe5dIvL2Xawmm8dsprB9WPEEIIIcSfma7r1Hv9uNw+auq91HpUwJka\n5SDMZia31MWSzSXU1PsCbV5cbh9XDk8j0WljwfoiXl20hRq3j9pAm8vt45PrhpIS5eDfP27j0Tnr\nWtx36V1jiAuz8t2GYp7+ZiMOi5EucSGM7R5HRlwIHp8fo8HIbWP3XqtFiONdqwpANb5Y01KAUF3X\nV7bXgPbnoNOM5/0f/PgcTPkAMprvvuzx+Tnz+cXsrKzjq7+eSEQgbeOA/OdyfLm/0KnoCf5vfDeu\nGnFwhZ18fh9f53zNG6veILs0mxh7DLf1v41xqeMOqr85W+Zwx/d3MDl9MvcNuU/WQwghhBDiT6XO\n42N1XgV55bVU1HpUMFnv5eTMOHomhbG5uJon5q6j1qOCVZdbBaX3n9adUV1jWLi+iMve/KVFv29P\nHciJXaL5cnUB1777e+N5gwYOi4l/XzGQPh3Cmb9uJy8s2IzNYsRhMWG3GLFZjNw4Op24MCtr8iv4\nfXsZNosJR6DNbjGRlRSG1Wykpt6L168TEmTCYJDPceLPoS3TjNE0LRHo2PB6TdNG6Lq+6NCGeJiN\nuRe2fAezboDrf4TgmMYms9HA9HN6cdqMH3jwf2t4+vw+B95/fBbGNZ+QHuJm7X6KQO2L0WBkXMo4\nxnYcy5L8Jby++nVMBvXXVOWuwuf34bQ6W93f+LTxbCzfyGurXiMjIoMpXacc9NiEEEIIIY429V4f\nizeVkFdeR355beNxbv9kzumfTF55LWe/1DKpMMFpo2dSGL5A4aSGYDMyOAi7xUioTWW0dYoO5vZx\nGdjNRuxBKhi1W4z0SFDVfkd0iWbxnaOxm1UgGmQyNJs8GN01ltFdY/c6/u4JYXRPCNtruyPoEGu7\nCHEca0014yeA84C1gC9wWgeOrWDWFASTX4VXRsHGr6DPRc2au8WHcsPozjz9zUYmZiVwUubef+ns\nUVwWACeHFzG/4NDXp2qaxgmJJ3BC4gk0zJ6/s/Yd3lrzFpPTJ3Np90uJc8S1qq8b+9zIprJNPPHz\nE6SGpTI4fvAhj08IIYQQoj3puo6maei6zmfL88gvryNvt2B1XI94pp3cBZ9fZ+pbKmvPbNSID7OR\n4LRiMalCR0nhNt66fAAJThsRDgt2ixGrydg4y9klNoQvbxmx13EkR9i5fmTnvbbbLaZDLyYqhDgo\nrfk/70wgQ9f1+vYeTLuL6QY3L4eQPQeB14/sTF5ZLR0j7Xts36f4XgAMsO7glR3J1Ht9BJkOroDT\nHzU83RubMpa86jzeX/c+H6z/gIlpE1u1V61BM/DY8Me4eO7F/G3h3/hgwgckhx5bNbyEEEIIcfzQ\ndZ06jx+bRX1W+uiXXDYXV+8WrNYxMDWCZ6f0QdM07v98LRW1HiIcFhKcVlIiHSSH2wAVTM76ywnE\nh1mJCg5qkYobZDIyMiOmxRiEEMe+1mzNMxc4R9f16sMzpH076DWzf7T9R7BHQHQbblvzj27scPZj\n2MYpzL5p2D5TRg5FfnU+b695m082fsLg+MHMGDOjVdflVuYyZc4UoqxRvDv+XYItwfu/SAghhBDi\nANV7fZS7PMSGWgF4/+ccVuSWNwtWMxNC+e91QwE49Znv2VxcTaLTRnyYlQSnjQEp4Zw3oAMAuaUu\nooKDGoNfIcTxrS3XzLqA5ZqmfQs0zs7qun7TIYzvyPLUwkeXQEgsXPmtSkHeTWWdh//7dDWT+iYy\n6kCe5MVnEV28HoC1+ZXtFswmBCdw16C7uDrralxetQl2bmUuDy19iMt7XM7g+MF7LPSUHJrMP078\nB9d8fQ13fn8nz4x65qC3/xFCCCHEn5Ou65TWuCmqqqdbvFo3+uEvOXy3obhx3WpxVT0JYVaW3DUG\ngG/W7mTFjgoSnFbSY0I4sUsMGXFND9U/umYwwUGmvRaqTI44iKw5IcRxrzXB7OeB4/hhtsFpz8AH\nU2D+w3DKQ82ag0wG1hVUctd/S/lq2ghCra3c0iYuC8vGr3CaPWQXVLXDwJuLtEUSiVqfm1udy6by\nTVz99dV0j+zOFT2vYEyHMRi05htjD4ofxB0D7+DRnx7lueXPcXPfm9t9nEIIIYTYP79fx+3z4/Xr\neLx+PH4/oVYzVrORyjoPeWW1eHx+PD4dj8+P16eTlRxGqNVMzi4Xy3LLdmvz4/bpTOqTSLjDwm/b\ny/g2eydev47b68fr9+Px6tw1vitOu4XZKwv4dFmeujbQ5vb5effKQQQHmXj5u83M/CkHj89PaY2b\neq8fo0Fj/UPjMBkNbNhZzbrCKhKdNrpmxJDgtJEUSAMGeOWS/hj3UYk3pLWftYQQxzafF2rLWh69\nzgdNgxUfqi1VW2m/wayu628f0oCPVl3HQ7/LYMkMSD8ZUpsW/geZjDx1Ti8mvbCYR2dn8/jkrNb1\nGZ+Fpvs5OXIX2YdQ0fhgDE0YypeTv+TzzZ/z5uo3mbZwGl3Cu/DhxA8bqyE3OD/jfDaUbeC1Va+R\n7kxnfNr4wzpWIYQQ4njj9fkpqqqnzOWm3OWhzOWmzOVhcGoE6bEhbNxZxSNzsilzeSh3uXG5fXh9\nfp48uxcnZ8by3YZiLn3j5xb9/nvqQEZ0ieaHjSVcP/P3Fu3/vW4o/TqG89PWXdz2ccsPgCd0jiTc\nYWF1XgWvLNqC2WjAZNQwGw2YjRo3n5SO0w5VdR7yy2sxmwyYDao9xGzCH1iOFu+00a9jOCaDhtNu\nJsFpI8Fpo2Gx2j0TM7lnYuZe/3z2FcgKIY5B3vrmwWh8b7DYIfcX2DC3eZurFC6ZpZZ4LnwMvp/e\nsr9uEyEoBEq3QG7L34V7s9c1s5qmfaTr+rmapq0CWrxI1/VWRnhtq83WzAK4a+DlESrt+LolYGu+\n5c1jc7J5edEW3rliIMPTo/ffX9l2eCaLTxP/xgMFg1l2z8lHZF/Xhr1q86vzmdpjKqD2mx2ZPBK7\nWaXpeHwervr6KlaXrOatcW/RI6rHYR+nEEIIcbTx+3Uq6zyUBQLSSIeFjpEOquu9vLRwc4tg9bKh\nHTlvQAc2FVVz0j+/a9HfQ2f24OLBHdlUVMVfP1yB024m3G7BEWTEbDRwbv9keiSGsX1XDZ8ty8dk\n1LDsFnCO7qpmOQsr6lieW4bJYNjtNQa6xYcQYjVTUeuhpLq+2bVmg4Fgq0kCSSHEnum6ioNqy1Qc\nZHFAxQ7Y9G3LmdPR90B0F1j1MXx+E3hqmvd17WKI6wE/vwpz7wBbeNNhj4DTnlVLPHN/gYLlgTZn\n02ucHWG35Y+tXTO7r2A2Xtf1Ak3TOu75vevbD+TPqq20aTALkPc7bPsehtwIhuYpuXUeH+Of+R6b\nxcgXNw7bf2Cq6/BEChuixnDKpsksuXM0CU7bvq85DDaUbWDy55MJCwrjgq4XcEHXC3BanZTWlTLl\niyl4/V4+mPgB0fZWBOxCCCHEMaLe68Pt9TemsM5bU0hpjVsFojUqGO3XMZwpAzvg8fkZ9Oi3lLvc\n+Hf7aHT1iDT+Pr4bVXUeej3wFU67pTEgDbebObtfEuN6xFNT7+XzFfmEN7Q5ml5nNhr2MkIhhGhj\nbhcUrICaInDtagpGM8+CpH6q7dNr1WxpbRn4AiWRzn8Puk6AjV/DzLPVOYNZBaK2cDjjeUjqD/nL\nYdV/AoFoRFMwmtRfzaz6vCooPcQJvUMOZvfSaRSwSz+Qi9pYmwezu/N5wdg8JXddYSURDgsxIdbW\n9fHWRGqqK+i+405ev7Q/Y7od4H617WR50XJeX/06C3MXYjPZmJw+mWt7XUthTSEXz72YdGc6b4x7\ngyBj0P47E0IIcUzbffaxzuOjS2wIRoNGYUUdJdX16Dr4dR0dVeynd7ITTdPYvquG4qp6/Lo6rwMG\nTWNgagQAG3ZWUVRZ3+xai9HA0M5RAKzILQ9c39AOdouREV3Uw9SlW3ZRUt3UP0CozdxYjHH+up2U\n1njUvXVwub2EOyyc0TsRgJs/WMbm4mrKalQqb43bx6k94njxon4A9H7wK8pdHkDVx4hwWDizTyJ3\njOsKwP2fryHEasIZCFTD7RbSoh10jHQ03vOP274IIUS78tSpwNQYpGY266vgxxfUueqdUF2kjqE3\nwoAroHgDPD+geR8mG0z4B/S5EHZthq/vbQpSG460URDeUQXDtaXqnNl+yEHpwTrkasaapg0GHgdK\ngYeAd4AowKBp2iW6rn/ZVoM9Kmz9HmZdD5d8DhGpjae7xqkqfX6/TkFlHYn7m2mN74X9l9cw4iO7\noPKoCWZ7x/RmxugZbCrbxBur32DO1jnc0OcGMiIyuH/I/dzx/R08sOQBHhn2yBFJjRZCCHFw6jw+\nzEYDRoNGbqmLVXkVTamwgdnHeydmEmY38/oPW3lu/kYqaj3NZh9X3n8KoVYzbyzeyiuLtrS4x5ZH\nx6Np8PKiLbz3U06zNqvZwLqHTgXg+QWbmLU8v1l7VLCFX+8+GYAZ8zfyTXZRs/aOkXa+u20UAM98\ns5Eft+xq1p4ZH9oYzD79zUZW7qho1t63g7MxmAWIDg6iS0xIY0DaNVBtF+Dja4dgt5gIt1v2uMXL\n/ad3b3GugaZpR+oznRDieOPzQE1xIBgNfA1Lgk6j1OTav09vClLrA7/zht4IpzwMaLDwUTUrGhyj\njqT+6noAZwe4mSd3dAAAIABJREFU6L/giAFHVCAo3S1+iewE58/c+9gsdnUcI/aVZvwr8HcgDHgF\nOFXX9aWapnUF3td1vc/hG2aTdpuZLc+BF4epfWcvn9tihnbaR8v5dVsZX94yHLtlH3WzVnwIn17N\npdZnCE7O4vkL+7b9WNuAy+PCbrbj1/1MmjUJHZ0tFVu4tf+tXNr90iM9PCGE+FOq8/goqKgLBKNu\nymrU2szxPeNJcNpYsrmE5+ZvaiwiVOZyU+fxN+5t/s7S7dzz2erG/mxmI+F2Mx9eM4TkCDsL1hXx\n7bqdhNstjcGezWzkpMxYzEYDG3ZWsa2kBkMgcNM00NAYmRGNpmls3FlFQUVdUztqpnJwmqqsv6mo\nmjKXG42Gh/lqbWfPJLVV3daSGqrrvI1BoaapGdLOMSGA2ku0zuNrvLahPSlcfbAqrKjD4/M3Xmu3\nmAi1mjBJGq8Q4kjT9aZZzG2LoSK3+cxpZGcYeYdqf7ITuEqaX99jMpz9hvr+3clgCYbgWAiOVl/j\ne6kDwOsGk+XwvK8jpC32mTXpuv5VoLMHdV1fCqDr+rrjcubO2UFNv39yJXz/j6YftoBz+yfzye95\nPPnl+n0+uSVe1cU6MbSAdwpS2nHAh6ahEJTb52Z0h9G8n/0+ANN/nU69r56rel4lM7RCCHGQ6jy+\nwLpMFXSmRDlIcNrIK6/ljR+2UuZyUxEoIlTu8nDPxExGdY3hp62le6xo2yk6WNVg0KHe6yfRaaV7\nQijhdjNOu4VIh1oicmqPOPp3DA8Eq2pLl92N6hrDqK573z+9S2wIXWJD9tqeHhtC+j7aO8cE77UN\nIDXKsc/2/e0lGhfWyiU/QgjRFnRdrSutLgKPCxIDk1Q/vQL5y3YLVndCRBpcMU+1z70ddgYeLJrt\navY0aLffjyPvVOtKHTGBgDUww9rgov/ue1zHeSB7IPYVzPp3+772D21HbM1su8o6BzbOg++egE6j\nIbkp33xwWiSXDOnI2z9uY0JWPANSIvbcR2Q6mKz0NuXw0K4sXG7vvmdyjzCrycpNfW9iao+pvJf9\nHi+seIEZy2YQZgnjvK7nHenhCSHEUafW7WPbrhq2FNewpbiarSU1jOkWy4SseHJ2uTjl6e+o8/ib\nXfPgGd25ZEgKVXUe3v85pzHYDLdbSHDaCLGqfye6xYfwz3N7NWsPt1sa24d2jmpcf7onUcFBRAVL\n7QMhhNin2jKoyFOpvjXFTcHqiber9i//Dms+UW1+rzoXmgjT1qrvt36nCiEFR0NYIiT2geiuTf1P\nehVMQSpQDdrDQ76BV7Xv+/sT2VeasQ+oQWUR2QBXQxNg1XX9iOxu3a4FoABqy+Gl4dD9jEBeepOa\nei9jn16E2Whgzk3D97jeBoBXR1PqNtI39xY+uX4ofTuEt99429j2yu2c98V5RFojeW/Ce8zPmQ/A\nxLSJmI2yobkQ4s/B59fJL69lS4kKWJPD7ZyUGUt1vZee989j9386E8KsXD+qMxcN7kh1vZdnvtkQ\nSOG1NM6cdopxtL6QoBBCiAPjdavAMyROzXju+A22LVLrUWuKVbGkmhK4eiEYzTD7b/DLa837MNvh\nrjy1u8lPr6iqv46oppnTkHhIOeFIvLs/pXapZnw0aPdgFtQPviNqj9W7Fm8q4e+fruKNywbQKXov\n6VT/uwX/6k9Iq3iBh8/syUWD97i70VHr952/c8VXVzAgdgAaGksKlmDSTMQ54kgMTmRIwhCu6HkF\nANm7som0RRJli8KgyZolIcSxpdzlZnNxDX5db8y4OfvFJazMq8DtbZpdPaN3As+cr0pFvLJoMwlO\nG2lRwaRE2Y/q7BshhDhmuV2Bir27BaTdTldVeNfPhSXPBdqLoK5cXXPLKrV08Pt/wLcPgtmhZk8d\n0Sql96yXwBoKeb+pejmOQHqvIxqsYUescq9oqS3WzP55BQf2Wy3eAJV5qrJYwAmdo/hm2on73jMu\nPgvDb2/S1VpGdkFlOw+27fWN7cs9g+/hviX3cXG3i7m0+6X8svMX8qrzyKvOo8ilKlHqus7UeVOp\n9lRjMVhICE4gITiBsSljmZQ+CYDVJauJd8QTYY2QNbhCiCOi3uujuKq+sYjQP79az+LNu9hSXE1Z\nYJuW3slOPvuLeuLeK9lJ347hpEY5SItykBYdTFRw0/qkq0d0OvxvQgghjnW6TmB/K6jZBdt/UIFo\nQ5pvTbFK843vBas/gY8vb9lHdDfoMCjQlx9iukHqiSoYDY5W+5wCDLwGBl0Llr2s00/spw5xzJNg\ndl++uAWK1sJ1SyA0ofG02WigzuPjjcVbmXpCaosCG8SpSmMnOQtZcgwGswCT0iexoWwD72S/Q3p4\nOjf3vXmPr3tyxJPkVeeRX53Pjuod5Ffns6tWbatQ5a5iyuwpANhMNhIcKtid3GUyYzqMwePzsLF8\nI4nBiYRaQiXYFUIcNF3XG3+HzF+3k+83lrCluIatJTXsKHMRHRLET38/CYCiqnpMBo1xPeLpFO0g\nNcrRrHDRPRMzj8h7EEKIY4rfr2ZEa0rAtUtV543uClHpULEDvn2o6XxNiQpWT58BWedCyXr46JJA\nR5qabXXEQF1gG5q4LBhzb/OZU0e0SiMG6DpeHXuzp3Wq4rgkwey+nPYMvDwCPrsOLvpUPUkK+H17\nGU9+uZ7qOi+3j+va/LrYTNCMDLDu4I38Kvx+/ZjcZP3W/reyuXwzDy19iKSQJAbENd+AWdM0hicN\n3+v1FqOFGaNnNM7o5lfnk1edR2W9CvAb1ucCBJuDSQhOIDE4kUsyL6F/XH+q3dXkVeeRGJxIsEV+\nKQmxL36/TrXbS6hVrW3fVFSFyWDAajZiNauvQSbDcfHQKGeXi99ySlUBphJViGlHqYvf7jkZi8nA\nog0lfPRrLqlRDnolOzmzTyKdoh2NAe/jk7OO9FsQQoijj9ulih1ZQ9U+qKs/aQpGXbvU0XUi9Dof\nKgvgX5lqdnR3Jz0Aw24Bvw+2LwFHpNoPNSpDzZxGdlavi+8F13yvAlV7VIstMYnqDMP/dnjetzim\nSTC7L1HpMPYR+OKv8NOLMOQvjU1DO0dxbv8kXl60hVN7xDfuoQeojYmjupDu34LL7WN7qWu/2xEc\njUwGE9NPnM4Fsy9g6rypZEVlMT5tPGNTxhJl23s1zQZBxiBGJo/ca3usI5anRz7dOKObX51PblUu\n9b56AJYXL+e6b64DICwojASHCnav73096eHplNaVUlpbSkJwQuNWQ0L8WZTWuFmeW8aynHKW5ZSz\nIrecMd1ieDqwrvP05xbjcvuaXTNlYDKPTcpC13WGPDYfi8nQGOhaTUbO6JPAhYM6Uufxce+s1YFA\n2IjVZCDIbGRwWiT9OoZT5/HxTfZOrCZjs2A5wWkjwmHB59dxub1YzcZ9L8nYC4/PT06pKzCzWt0Y\ntD5zfm/iw2zMWV3A43PXYdDUVi5pUQ6GpEVS7/VhMRm489Su3Hda5nERuAshxEFrmA1tCERrSlSm\nYcapqv2986CqQKX8unaBtxb6T4WJ/wI0+PQaQAfNCPZIdTTMnNojVLBpj1TBqD1C1ZsJS1bt4R3h\nr6v2PjaLo3E7SyEOhQSz+9Pvctj4NXxzP6SNUrOuAf83IZPvNhRz28cr+PyGYVhMu31oi88ietNC\nALILKo/JYBZUEPnO+HeYtWkWc7bO4fGfH+epX55icPxgxqeNZ0yHMTjMB/feQiwhjOk4Zq/tXSO6\nMv3E6c3SmDdXbEYP7Aw1P2c+D/z4AAAR1ggSgxNJCE7g9gG3E2OPochVhMvjIj44niCjbFUhjl0e\nn591BVUUVNRySneVYjXllaWs31mF0aDRNS6EM/okMCI9uvGaf5zTi1qPjzqPnzqPjzqvj27xoYCq\n1Htil2jqvD7VFniNP1APsN7jZ9GGkmbtALeNzaBfx3CKq+q54b1lLcZ532mZXH5CKpuLqznlX4sA\nMBo0rCYV7N5/endO65XA+sIq7vpkZVOwbDZgNRm5bmQn0mNDmLOqgJs/WN7Yb6TDQlq0g+o6L4TB\npD6JnNQthuQIO0GmllXlWyz9EEKIY527Rq0r9Xkguos6t2wm7NoYSPMtVQFpdBeVygvwyiioyGne\nT8b4pmBW90NwHMR0VzOo9khIUA9EMZrgxt9UkBoU1iw7EVDbzoy+u/3erxCtJNWMW6OmBH58Xi1K\nN9uaNX2bvZMr3v6Vm8ek89eTuzQ1LHkOvvo/Brpf4ryRffnbKRmHd8ztZHP5ZmZvmc2crXPIq85r\nnH2dkDqBYYnDDuv2PfnV+SwvWk5+TT47qnY0pjHPHD8Tp9XJc8ue4+WVLwMQHhROnCOOWEcsT454\nEpvJxvrS9VR7qolzxBFjj8FskK2HxNHj562lfJu9k99zyli5o4J6r58Qq4kV956CwaCxYH0RNrOR\nrKSwdq+mq+s6bp8KaINMRtxeP1tLagKBro86rwqGu8aF0DHSQUl1PZ/+ntcYRDcEy5P6JtGvYzjr\nC6t46Iu1LdrvnpDJuB5xFFTUsmTTLtKiHaRFBRNml/8325yuq0qe9VUQ16PpnMxmC3H46bqaQa3I\nVVtEdg486P/2QTWhUrEDakvVuZhMuP5H9f3rp0De7yoIdQRmRxP7wUn3q/bV/1VfG2dPI9VrTPKA\nXxz9ZGue9uLzqP2pdvP8gk2M7R5L55iQppNbF8Hbp3GX/X6KY4fx2qUDOJ7ous6K4hXM3jKbedvm\nUVZfRqgllFNSTmFC6gT6xvY94lv1bK3YyuqS1eRX51PoKqSwppBdtbv4cOKHaJrG3T/czazNswDQ\n0IiyRdExtCNvjnsTgMV5i6n2VBNrjyXOEUe0LRqjQWZ8RNuq8/hYk1/RmC48/Zxe2CxGnvxyHa99\nv5XuiaH0SQ6nb0cnfTqEkxBmlfRZcXAKV8G2xZDzI+T+pNILU4bDZV+o9ucGqA+78b0hobda0xbV\nRe3ZKIQ4eJ46tTtGRS5UFao1pwCLnoLl76tgNbDEiqAwuCswm/r1fbBzDYQlqSMkXqUJN+yy4akF\nk1UeQonjkgSz7WHXZnh3Mkz4R9NTsz9orKhZWwZPpPB51FU8XnUqS+7aezrtsc7j97A0fymzt85m\nfs58ar21xDniODX1VCakTqBLeJej8sN3XnUe2yu2Nwa6hTWF+HQfjwx7BIAr513JT4U/Nb7eqBnp\nE9OnMdj9aP1HuH1u4h3xjbO+EdaIIx7Ei6OXruv4dZV6u2RzCU/MXcfagko8PvV7ODnCxpuXDaRz\nTDCVdR6CTIY9ptEKsV91lbDjF/Xv1qCr1bl3zoLN8yE0CToMVkfHoRDbXT2onfd3yF+ugl5vrbpm\n0HVw6uOqfeVHKsiNymhZrEWIPytdVym+FTkqKK3YAX0vBYsdlr6k9jutKWp+zV071BYyv7ymHjCF\nJam1ps5k9X1sDwlQxZ+eBLPtwVMLr4xUgep1P6r1BQFur5+/frScbnEh3DA6XZ18uiebLBmclHM5\ny+89Gafdsud+jyMuj4uFuQuZvXU2S/KW4NW9dHZ2ZnzqeManjScxOPFID7HVKt2VFFQXsNO1szHY\ntZvtXNnzSgAmfT6JjWUbm10zJH4Ir5zyCgDTf5mOyWAizhHXeCQEJxBqCT3s70UcGdX1XlbuKG+c\ndV2eW8bDZ/ZgXI94VuSW89jcbPp0CKdPspPeHZzEhFiP9JDFsSznJ1j1H8hZCkVr1Ho4gxnu2Ka2\nqSjKVh+gw5L23Y/fByUboGCFqjya1B8KV8NLah9eTFb1YTuht/rQLkVcxPHM64aqfBWklueqr30u\ngtB4Nav6xV+bHv40uP4niOkK6+bAhrkQ1qFpdtWZrP77j2tQhRDNSDDbXgpWwqujoctYOO/dZk/O\nbnjvd+atKeSLG4eTERcCH1yIK281mcWP8v5VgxnSKXIfHR9/yurK+GrbV8zZOoffi34HoHd0byak\nTWBsyljCreFHeISHRtd1yurLGgPdwppCImwRjEsZB8BZs85iW8U2vLq38ZrTO53OI8MeQdd1rv/2\neiKsEU3Brj2O9PB04hxxbT5Ov+7Hr/sxaAaMBiMev4cadw1e3Ytf9+Pz+/DpPiJtkdhMNqrcVeRX\n5+PTffh1P16/el2X8C4EW4LZWbOT9WXrm13r1/0MTRxKqCWUzeWbWVWyCqvRisVoIcgYRJAxiB5R\nPbCarFS6K3F5XI3ng4xBx3wKt9+vs6WkGk3T6BQdzI4yFyOeXNBYVCkt2kGf5HAuGNSBfh2P7Z99\ncYT5fbBztQpec5fCyQ+qD8k/v6qKFSb1h+TB0GEQJA1QAWxb3HPXZihYroLc/OVQuBLOfhPST4It\n38E396kU5fheKtCNyZS1eeLoputqn9SGGdWKHWotedZ5ai35hq/gvXOBP3xWvmQWpI2EHb+pdakN\nM6phSSpQtUfIzKoQh0iC2fa0+Fn4+h5VLa7vJY2nd1XXc8q/FpEYbuOT64Zi+mE6LHiE7nWv87eJ\n/Zg6LPUIDvrIyqvOY+7WuczeMptN5ZswaSaGJAxhQtoERiWPOm631vHrfnbV7lLBrquQaFs0vWN6\n4/K4uOrrqyisKaSktgR/YJ+2q3pexU19b6KivoJL515KiCUEn66CRZ/fx2U9LmNi2kS2VWzjum+u\na2xrCCpvH3g7E9MmsrJ4JZd/eXlje4PpJ05nbMpYluQt4Zpvrmkx3hdPepFhicP4evvXTFs4rUX7\nv0/9N31i+jBr0yzuXtyyiuHHp31MRkQG72W/x2M/P9aife6kuSSFJPHaqtd45vdnmrWZNBPzz51P\nuDWct1a/xX83/rdZIBxkDGLG6BmYjWZmb5nN7zt/x2K0YDWpgNlmtHFZj8sAWF60nCJXEUHGoMY+\n7GY7XSPUntAV9WprgYb2g00NX7i+iN9zylmWU8by3HKq6ryc3S+J6ef0Qtd1nl+wiR6JYfROdv4p\nMjNEOyvKhi/vUunD7mp1LiQBzn4DOg5R2UMG8+FLAfb7AV2tqd3yHXw/XQW6DVt3GMxw3RJVXbVk\no0p9ju0OZslAEIfA71NZB0az+hks26p+9r314K1Th7Oj2qfUXQMrPmjeVlOs9krtPAbyl6mMu90Z\nLXDWS9BjMpRtU7OvjTOqyWrN6h+KgQoh2l5rg1lZ9HIwhtwAm76GtZ9Dn4sbn75FBgfxwBndueG9\nZbz6/VauC6ReDXbks7Yg/UiO+IhLDE7kyp5XcmXPK9lQtqGxIvKd39+JzWRjVPIoJqRNYEjCkOOq\nqrBBMxBtjybaHk1Pejaet5vtzBw/E1BrjktcJRS6Com0qtl7t89Nx9CO1HhrMGkmjAYjBs1AsDkY\nAJvJRq+YXhg1ozoM6mtSsEofjLZFc1HmRRg1Y+NsrFEzku5UP4epYancOfDOZtcaNAOdnWoz86yo\nLP418l8YNAMmg0n1oRnp5OwEwPCk4cwcP7PZtSbNRGKISiM/o/MZDE8ajtvnpt5Xj9vnps5XR7Rd\nbR0zPHE44UHh1PvqGw+3z934UCPOEUe3iG7N2ivdlY2ztxvLNvJNzjeNbV6/F5upKZj9YP0HzN4y\nu9nfRYQ1gu/O+w6Aexbfw4LcBY1tFoOF1LBUPj79YwCeW/Ycle5KMiMzyYzMpENwCpuKalmWW069\nx8eVw9MAePCLtWwrqSEjLpTTeiXQJ9nJgJQIADRNa1pyIMSBqMhTM64NM699L4UBV6gZ1ppiVTym\nYeY1LLlpBuhwf8DePU0y7UR16LoKABpmcCMCD3F/eQ1+egkMJoju1jR72/+K4z/d0udVAX5tmTrq\nytXXbqepv7OClVC8DjSD+rvUjOr7jPHqwUTROlU4SNMCrzGo16QOV/2XblFrNndvN5ibthKsLFBB\n3e7tRotKkwVVPdfva2o3GFX/FnvT+BuvDfys7V75uqZEPVzx1DUFjBYHxAX+zVv7uXq/DW3eeojs\npIJFgNl/C7TXNwWlnUerPUx1HZ7OAo8rEJDWgt/btJbbWwcz+rb8Mx9+K4y5B9wumP2HB7O2CPVQ\nBSAiDU55pPmaVXtU089keAqMuuuQ/vqFEO1LZmYPVm0ZBIW2qPKo6zrXvvsby3PL+e6aDKwzevBv\n5/V8aBjP7JuGH6HBHp38up9lRcuYvWU2X23/ior6CpxBTsamjGVC2gR6R/c+KgtHiaOP1+9tFgwX\nu4opry9vFgwDDE0YCsCiHYvIqcxpFmiHWEIa10Pfvuh2FuYupDawDkr3m/FU9qK+4GzSY4J586pO\nxDni2FZSR3yYFUeQPBcUB8nvU8GEI1KtzXt+gAoGAcx2tc3GgCug+1lHdJiHrCIP8n5rnqZsNMPf\n1qn2r+9Ve2g2pCnH9VTrfI8Wuq4Cwt2D0fjeYA1VW6OsndW8rbYcpgRm9L7/J3z7QMs+b90EwdHw\n7UNqVvuP/q9QBbtz71APAnanGeG+wFYtn/0Flr/bvN0aBncGKuJ+dIka3+5Ck2DaGvX9u5Nh0zfN\n26My4Iaf1fevj1UPVdSNVVCb2A+u/FqdemEIFK1tfn3aSJWKC/B0T5W6u7tup8N576jvXxquglWT\ntelIPxmG3aLa/3ezer8mq5rVN1nV/TuPUTOzqz9W6ewma+CrLRCcJqr2mqKm80bL8f/wRIjjhKQZ\nHy7VRSq9KuucxlMl1fXoOkQHW+CpzqywD+acgotY8+BYzEb5JbonHp+HxfmLmbNlDgtyF1DnqyMx\nOLGxInLn8M5HeojiGFXn8VHmclNa46bc5aHM5eakbrFYzUa+zd7JFysLKHO5KXN5KHe5qan3svjO\n0ZgMcO/cBfy4YyVhYYV0jkjkpv5XkeAMYuj7Q/HrfrpGdG2cve0X24+kkP0U1hHCXaOCupyl6tjx\ni/pgfslnqn3e/6kP4smDVEB3GPfuPqx0XQV9dpXJwP9ugfVzoHpn4AWamrlsCHgKV6lZskNd/+vz\nqECzrhwc0WBzqkA7+3/NA9HaMjUjl9AHsr+A/1yqZgR3N/UrNTu+/H34/Eawhav+bOHqGP8UODuo\nVNbcn5vOW53qdRFp6oG4q1TdT/c3pdDqfrXm2GBQgWB1UdP5hiNlmBrHzjVQmd+8TTNChqrfwPYf\n1VrQxnafCvx6nq3a189VhY0a2nS/GmPfi1X7spktrw9NVA9ZQAXK7pqmgNEUpP5sGwqDlec0D0aN\nQVINWwixXxLMHi5z74CfX4HL5qg1S7vx+3VqXj8dX3URvXfey7xbRqjCUGKfajw1zM+Zz+yts1ma\nvxSf7qNLeBcmpE3g1JRTiQ+OP9JDFEeArutU1Xspr/EQFWLBbjGxtaSGBeuKKHe5Kd0tIH3kzJ6k\nRDl458dt3DNrTYu+Ftw6ktQoB28v2car328h3G7BaTcTbrcQbjdzw+h0okP2XLjG4/Pw5bYvWbtr\nLWt3rSW7NJtaby3X9rqWv/T+C9Xuap5d9izdI7uTGZlJalgqJoN8cPvTqixQs1YN27m9fTps/Q7Q\nIKabClrTTjz2Z17bSmWBmrktWK72vB14lZpde7yDSmWN7NQ0e9tplAr43TVqZrEhjbchGO0xSc0Q\nFmXDzHPUeXdV070mvQpZ58L2JfDmqepcUKgKNK1OGPc4pJyg0nxXftgUqFoDX+Oz1Ayo3x9Iw5VM\nIiGEaCsSzB4u9VXw0jD1tPLaH9Q/bAFPzVtH2OJHuNI0lwzX6zx5Xj/O6iMzNwdiV+0u5m2bx/+z\nd9/xbdX3/sdf52hLlmXJI3YcxyN7kISQMAJhhR1maGlLS0PLprT0tqW/UjpoSy+0pZReSlsoo4NQ\n9iZAIYQAZSVhZg+POIm3ZcnWHuf3x7GOJdsZQBI7yef5eOhxjs756ug4U299vuP5uuf5uO1jAA4b\ncRjza+ZzSuUpeGyeXVxBDGehWJI1TUH8vVVTPZDGOWd6OZNH5rOyoZMfPf4J/rB+Ptk7NfA/v3k4\nx44v5oVPmrhq0fsoChQ4LEYovXnBNCaUulm1LcCyDW14nVZ8LgsFTitep5WqIuceW781lU7REGzA\naXFS6iplbcdaFr640OiibDfZmeibyHcP+y6HjTiMRCphjGMWByB/A2xe0ld57WrQq1I/2qJ3m928\nVK/wjZqthyOxa6kk1C7VuyY3fahvg1vh2B/CiTfoVcnbJvW1N1n1sHniT/RJGoNNejff7CDqKNC/\nSPBW6t27Y936/99SMRRCiGFBwuy+1Pge3Hea3mVnwd3G4eZAlFv/cDO3cjvnJG/miDkn8OMzJu3k\nQmJnGrsbWVy7mOfrnqcuUIdZNXNM+THMr57PcRXH4TDL7IJDzR+KU9veQ1GejcpCF23dMW5evNbo\nxusPx/GH4vzo9ElceMRoVm0LcOYdb+Zcw2pSufWC6Zw9fSQbWrq57T8b8Lr0imkmrM4dV0ypx040\nkSIST5HvsGBSh09VJJVOUR+sN6q3azrW8MPDf8iUwim8WPciP3vrZ0zwTjC6KE8unEyNp0YC7o5k\nz5qbTkMs2NslM9n7SOljF+0efZKYjk36sXSyr523Gtwj9Il4Gt/re13mGhWH611Cg02w8aWs871t\nJp2pdwtt3wgfP6J3tcy+xtzvQ14JvH0nvPRjcJXoXVArjoTRR+rVRAlKn1k4EWa9fz2r2lexumM1\nq9s+5uZZ1zN19FxWtXzEqxseY3bp4cwoPxqHwytVUiGE2M9JmN3Xlt4My26BL/49p7vY4tfe4IzX\nzuQm87f4uOgsHr7iSJnU6HPSNI11netYXLeYxXWLaQ234jQ7mTd6HvNr5nNE2RHSrXMvygRIr8tK\nNJHihidXUdfeQ217iK5wAoBrThjLD06dQEdPjHPu/O+AbrxnHFLGETWFhGJJVjb4jfM+lxWn1XRA\n/x1Z3bGa5zY/l9NFGeCl819iZN5IljcvpynUxGSf3kV52AfcVEKvakHf+Me61/VlWOIhvVt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OeVMypv1GeedEwMX5nhJybVRDKdpD3Srn/pmooRT8WJp+KMzBtJqauUYDzI61tfN47HUjEKbAXM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\nqDmEWQAAAABAzSHMAgAAAABqzv8HgzUilCThg44AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VOKb9jMITUR6",
        "colab_type": "text"
      },
      "source": [
        "Let's try a much bigger lambda = 0.05"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hzkuiiC9TTNy",
        "colab_type": "code",
        "outputId": "70ea7497-fd24-4c64-cdca-7930c86d4c11",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "l2_model_3 = keras.models.Sequential([\n",
        "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.05),\n",
        "                       activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.05),\n",
        "                       activation=tf.nn.relu),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "l2_model_3.compile(optimizer='adam',\n",
        "                 loss='binary_crossentropy',\n",
        "                 metrics=['accuracy', 'binary_crossentropy'])\n",
        "\n",
        "l2_model_3_history = l2_model_3.fit(train_data, train_labels,\n",
        "                                epochs=20,\n",
        "                                batch_size=512,\n",
        "                                validation_data=(test_data, test_labels),\n",
        "                                verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 1.7982 - acc: 0.7037 - binary_crossentropy: 0.6177 - val_loss: 1.1604 - val_acc: 0.8317 - val_binary_crossentropy: 0.5145\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 1.0262 - acc: 0.8445 - binary_crossentropy: 0.4774 - val_loss: 0.9360 - val_acc: 0.8438 - val_binary_crossentropy: 0.4639\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.8694 - acc: 0.8499 - binary_crossentropy: 0.4502 - val_loss: 0.8149 - val_acc: 0.8446 - val_binary_crossentropy: 0.4473\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.7674 - acc: 0.8518 - binary_crossentropy: 0.4381 - val_loss: 0.7299 - val_acc: 0.8497 - val_binary_crossentropy: 0.4348\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.6970 - acc: 0.8479 - binary_crossentropy: 0.4285 - val_loss: 0.6694 - val_acc: 0.8496 - val_binary_crossentropy: 0.4273\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.6440 - acc: 0.8532 - binary_crossentropy: 0.4195 - val_loss: 0.6324 - val_acc: 0.8453 - val_binary_crossentropy: 0.4248\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.6071 - acc: 0.8540 - binary_crossentropy: 0.4114 - val_loss: 0.5961 - val_acc: 0.8514 - val_binary_crossentropy: 0.4130\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.5795 - acc: 0.8570 - binary_crossentropy: 0.4029 - val_loss: 0.5722 - val_acc: 0.8565 - val_binary_crossentropy: 0.4046\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.5595 - acc: 0.8582 - binary_crossentropy: 0.3957 - val_loss: 0.5614 - val_acc: 0.8495 - val_binary_crossentropy: 0.4037\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.5476 - acc: 0.8565 - binary_crossentropy: 0.3934 - val_loss: 0.5428 - val_acc: 0.8578 - val_binary_crossentropy: 0.3941\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.5330 - acc: 0.8587 - binary_crossentropy: 0.3858 - val_loss: 0.5332 - val_acc: 0.8582 - val_binary_crossentropy: 0.3890\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.5249 - acc: 0.8590 - binary_crossentropy: 0.3817 - val_loss: 0.5247 - val_acc: 0.8591 - val_binary_crossentropy: 0.3845\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.5169 - acc: 0.8596 - binary_crossentropy: 0.3780 - val_loss: 0.5179 - val_acc: 0.8572 - val_binary_crossentropy: 0.3797\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.5102 - acc: 0.8618 - binary_crossentropy: 0.3735 - val_loss: 0.5131 - val_acc: 0.8588 - val_binary_crossentropy: 0.3773\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.5041 - acc: 0.8615 - binary_crossentropy: 0.3696 - val_loss: 0.5054 - val_acc: 0.8618 - val_binary_crossentropy: 0.3726\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.4984 - acc: 0.8635 - binary_crossentropy: 0.3658 - val_loss: 0.4992 - val_acc: 0.8618 - val_binary_crossentropy: 0.3679\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.4940 - acc: 0.8643 - binary_crossentropy: 0.3622 - val_loss: 0.4960 - val_acc: 0.8619 - val_binary_crossentropy: 0.3660\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.4888 - acc: 0.8638 - binary_crossentropy: 0.3597 - val_loss: 0.4936 - val_acc: 0.8625 - val_binary_crossentropy: 0.3650\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.4850 - acc: 0.8660 - binary_crossentropy: 0.3563 - val_loss: 0.4879 - val_acc: 0.8632 - val_binary_crossentropy: 0.3607\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.4813 - acc: 0.8682 - binary_crossentropy: 0.3541 - val_loss: 0.4856 - val_acc: 0.8630 - val_binary_crossentropy: 0.3602\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AYRFXZW_Tthr",
        "colab_type": "code",
        "outputId": "8892a84a-1fb0-4a33-ef72-71d186298e68",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plot_history([('baseline', baseline_history),\n",
        "              ('l2.001', l2_model_history),\n",
        "              ('l2.01',l2_model_2_history),\n",
        "              ('l2.05',l2_model_3_history)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wjFXAFGDfRW3uB14zTfMMgGmap9o6ifXGGzG8vXFmZBBw/XUuCFtERERERLqVP0xs+izm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\nAAAAAKDkEGYBAAAAACXn/wPPBOkIEJ/JpgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "Kx1YHMsVxWjP"
      },
      "source": [
        "As you can see, the L2 regularized model has become much more resistant to overfitting than the baseline model, even though both models have the same number of parameters."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "HmnBNOOVxiG8"
      },
      "source": [
        "### Add dropout\n",
        "\n",
        "Dropout is one of the most effective and most commonly used regularization techniques for neural networks, developed by Hinton and his students at the University of Toronto. Dropout, applied to a layer, consists of randomly \"dropping out\" (i.e. set to zero) a number of output features of the layer during training. Let's say a given layer would normally have returned a vector [0.2, 0.5, 1.3, 0.8, 1.1] for a given input sample during training; after applying dropout, this vector will have a few zero entries distributed at random, e.g. [0, 0.5,\n",
        "1.3, 0, 1.1]. The \"dropout rate\" is the fraction of the features that are being zeroed-out; it is usually set between 0.2 and 0.5. At test time, no units are dropped out, and instead the layer's output values are scaled down by a factor equal to the dropout rate, so as to balance for the fact that more units are active than at training time.\n",
        "\n",
        "In tf.keras you can introduce dropout in a network via the Dropout layer, which gets applied to the output of layer right before.\n",
        "\n",
        "Let's add two Dropout layers in our IMDB network to see how well they do at reducing overfitting:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "OFEYvtrHxSWS",
        "outputId": "8fb17ec1-57d6-496d-bc34-382a718d1dcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "dpt_model = keras.models.Sequential([\n",
        "    keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dropout(0.1),\n",
        "    keras.layers.Dense(16, activation=tf.nn.relu),\n",
        "    keras.layers.Dropout(0.1),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "dpt_model.compile(optimizer='adam',\n",
        "                  loss='binary_crossentropy',\n",
        "                  metrics=['accuracy','binary_crossentropy'])\n",
        "\n",
        "dpt_model_history = dpt_model.fit(train_data, train_labels,\n",
        "                                  epochs=20,\n",
        "                                  batch_size=512,\n",
        "                                  validation_data=(test_data, test_labels),\n",
        "                                  verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.5571 - acc: 0.7270 - binary_crossentropy: 0.5571 - val_loss: 0.3997 - val_acc: 0.8456 - val_binary_crossentropy: 0.3997\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.3561 - acc: 0.8548 - binary_crossentropy: 0.3561 - val_loss: 0.3193 - val_acc: 0.8699 - val_binary_crossentropy: 0.3193\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.2974 - acc: 0.8805 - binary_crossentropy: 0.2974 - val_loss: 0.3033 - val_acc: 0.8738 - val_binary_crossentropy: 0.3033\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.2723 - acc: 0.8920 - binary_crossentropy: 0.2723 - val_loss: 0.2986 - val_acc: 0.8721 - val_binary_crossentropy: 0.2986\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.2531 - acc: 0.9013 - binary_crossentropy: 0.2531 - val_loss: 0.2989 - val_acc: 0.8730 - val_binary_crossentropy: 0.2989\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.2397 - acc: 0.9037 - binary_crossentropy: 0.2397 - val_loss: 0.3033 - val_acc: 0.8712 - val_binary_crossentropy: 0.3033\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.2282 - acc: 0.9097 - binary_crossentropy: 0.2282 - val_loss: 0.3084 - val_acc: 0.8704 - val_binary_crossentropy: 0.3084\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.2148 - acc: 0.9168 - binary_crossentropy: 0.2148 - val_loss: 0.3103 - val_acc: 0.8704 - val_binary_crossentropy: 0.3103\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.2027 - acc: 0.9238 - binary_crossentropy: 0.2027 - val_loss: 0.3193 - val_acc: 0.8698 - val_binary_crossentropy: 0.3193\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.1916 - acc: 0.9280 - binary_crossentropy: 0.1916 - val_loss: 0.3285 - val_acc: 0.8678 - val_binary_crossentropy: 0.3285\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.1811 - acc: 0.9323 - binary_crossentropy: 0.1811 - val_loss: 0.3310 - val_acc: 0.8669 - val_binary_crossentropy: 0.3310\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.1697 - acc: 0.9388 - binary_crossentropy: 0.1697 - val_loss: 0.3512 - val_acc: 0.8662 - val_binary_crossentropy: 0.3512\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.1575 - acc: 0.9437 - binary_crossentropy: 0.1575 - val_loss: 0.3557 - val_acc: 0.8640 - val_binary_crossentropy: 0.3557\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.1458 - acc: 0.9492 - binary_crossentropy: 0.1458 - val_loss: 0.3641 - val_acc: 0.8642 - val_binary_crossentropy: 0.3641\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.1356 - acc: 0.9541 - binary_crossentropy: 0.1356 - val_loss: 0.3785 - val_acc: 0.8624 - val_binary_crossentropy: 0.3785\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.1264 - acc: 0.9580 - binary_crossentropy: 0.1264 - val_loss: 0.3872 - val_acc: 0.8618 - val_binary_crossentropy: 0.3872\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.1153 - acc: 0.9628 - binary_crossentropy: 0.1153 - val_loss: 0.4053 - val_acc: 0.8602 - val_binary_crossentropy: 0.4053\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.1064 - acc: 0.9676 - binary_crossentropy: 0.1064 - val_loss: 0.4238 - val_acc: 0.8610 - val_binary_crossentropy: 0.4238\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.0986 - acc: 0.9690 - binary_crossentropy: 0.0986 - val_loss: 0.4490 - val_acc: 0.8613 - val_binary_crossentropy: 0.4490\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.0920 - acc: 0.9704 - binary_crossentropy: 0.0920 - val_loss: 0.4609 - val_acc: 0.8589 - val_binary_crossentropy: 0.4609\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab_type": "code",
        "id": "SPZqwVchx5xp",
        "outputId": "3579a036-3b8d-49f8-f74a-00a27f85d464",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plot_history([('baseline', baseline_history),\n",
        "              ('dropout=0.1', dpt_model_history)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LGDhwII2NjRw8eBCAwsJCiouL6d27N6+++ipFRUVs3ryZyspKMjMzmTBhAgAFBQVs3bqV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gQH/+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\n0K4dHeyD1dsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NJ7mt-XuUk0x",
        "colab_type": "text"
      },
      "source": [
        "Let's try a smaller drop out rate =0.25"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4bPlajh9Uov9",
        "colab_type": "code",
        "outputId": "43c100bc-9c5a-4576-8fec-afafcac29514",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "dpt_model_2 = keras.models.Sequential([\n",
        "    keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dropout(0.25),\n",
        "    keras.layers.Dense(16, activation=tf.nn.relu),\n",
        "    keras.layers.Dropout(0.25),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "dpt_model_2.compile(optimizer='adam',\n",
        "                  loss='binary_crossentropy',\n",
        "                  metrics=['accuracy','binary_crossentropy'])\n",
        "\n",
        "dpt_model_2_history = dpt_model_2.fit(train_data, train_labels,\n",
        "                                  epochs=20,\n",
        "                                  batch_size=512,\n",
        "                                  validation_data=(test_data, test_labels),\n",
        "                                  verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.6138 - acc: 0.6582 - binary_crossentropy: 0.6138 - val_loss: 0.4589 - val_acc: 0.8278 - val_binary_crossentropy: 0.4589\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.4148 - acc: 0.8276 - binary_crossentropy: 0.4148 - val_loss: 0.3326 - val_acc: 0.8662 - val_binary_crossentropy: 0.3326\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.3355 - acc: 0.8678 - binary_crossentropy: 0.3355 - val_loss: 0.3088 - val_acc: 0.8711 - val_binary_crossentropy: 0.3088\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.2925 - acc: 0.8866 - binary_crossentropy: 0.2925 - val_loss: 0.2982 - val_acc: 0.8766 - val_binary_crossentropy: 0.2982\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.2686 - acc: 0.8974 - binary_crossentropy: 0.2686 - val_loss: 0.2976 - val_acc: 0.8757 - val_binary_crossentropy: 0.2976\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.2515 - acc: 0.9061 - binary_crossentropy: 0.2515 - val_loss: 0.2991 - val_acc: 0.8736 - val_binary_crossentropy: 0.2991\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.2340 - acc: 0.9120 - binary_crossentropy: 0.2340 - val_loss: 0.3062 - val_acc: 0.8716 - val_binary_crossentropy: 0.3062\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.2222 - acc: 0.9191 - binary_crossentropy: 0.2222 - val_loss: 0.3137 - val_acc: 0.8704 - val_binary_crossentropy: 0.3137\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.2080 - acc: 0.9237 - binary_crossentropy: 0.2080 - val_loss: 0.3170 - val_acc: 0.8667 - val_binary_crossentropy: 0.3170\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.1936 - acc: 0.9310 - binary_crossentropy: 0.1936 - val_loss: 0.3287 - val_acc: 0.8692 - val_binary_crossentropy: 0.3287\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.1857 - acc: 0.9356 - binary_crossentropy: 0.1857 - val_loss: 0.3327 - val_acc: 0.8684 - val_binary_crossentropy: 0.3327\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.1727 - acc: 0.9424 - binary_crossentropy: 0.1727 - val_loss: 0.3446 - val_acc: 0.8668 - val_binary_crossentropy: 0.3446\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.1621 - acc: 0.9453 - binary_crossentropy: 0.1621 - val_loss: 0.3575 - val_acc: 0.8644 - val_binary_crossentropy: 0.3575\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.1502 - acc: 0.9517 - binary_crossentropy: 0.1502 - val_loss: 0.3681 - val_acc: 0.8641 - val_binary_crossentropy: 0.3681\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.1395 - acc: 0.9548 - binary_crossentropy: 0.1395 - val_loss: 0.3802 - val_acc: 0.8622 - val_binary_crossentropy: 0.3802\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.1327 - acc: 0.9572 - binary_crossentropy: 0.1327 - val_loss: 0.3836 - val_acc: 0.8610 - val_binary_crossentropy: 0.3836\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.1245 - acc: 0.9610 - binary_crossentropy: 0.1245 - val_loss: 0.3996 - val_acc: 0.8604 - val_binary_crossentropy: 0.3996\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.1113 - acc: 0.9638 - binary_crossentropy: 0.1113 - val_loss: 0.4347 - val_acc: 0.8613 - val_binary_crossentropy: 0.4347\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.1050 - acc: 0.9676 - binary_crossentropy: 0.1050 - val_loss: 0.4358 - val_acc: 0.8595 - val_binary_crossentropy: 0.4358\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.0974 - acc: 0.9687 - binary_crossentropy: 0.0974 - val_loss: 0.4514 - val_acc: 0.8585 - val_binary_crossentropy: 0.4514\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dzMQhkRbUxaK",
        "colab_type": "code",
        "outputId": "0000e1cd-a815-4938-bb1e-5e894b683481",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plot_history([('baseline', baseline_history),\n",
        "              ('dropout=0.1', dpt_model_history),\n",
        "              ('dropout=0.25',dpt_model_2_history)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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6O02PnZkFx3mzqpsVEREREZErueOOOwgNDQVg06ZN5OfnY7FYiIiIYNy4cRQX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\nkiRJktQ6/wOto4rY2WA1xgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EFCcX-JUWHBr",
        "colab_type": "text"
      },
      "source": [
        "Let's try 0.5 "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5yNtVvtnWEwP",
        "colab_type": "code",
        "outputId": "6641f5d1-28ea-45e4-d673-e1b0adf7c3a7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "dpt_model_3 = keras.models.Sequential([\n",
        "    keras.layers.Dense(16, activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dropout(0.5),\n",
        "    keras.layers.Dense(16, activation=tf.nn.relu),\n",
        "    keras.layers.Dropout(0.5),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "dpt_model_3.compile(optimizer='adam',\n",
        "                  loss='binary_crossentropy',\n",
        "                  metrics=['accuracy','binary_crossentropy'])\n",
        "\n",
        "dpt_model_3_history = dpt_model_3.fit(train_data, train_labels,\n",
        "                                  epochs=20,\n",
        "                                  batch_size=512,\n",
        "                                  validation_data=(test_data, test_labels),\n",
        "                                  verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.6695 - acc: 0.5783 - binary_crossentropy: 0.6695 - val_loss: 0.5749 - val_acc: 0.7897 - val_binary_crossentropy: 0.5749\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.5473 - acc: 0.7372 - binary_crossentropy: 0.5473 - val_loss: 0.4252 - val_acc: 0.8517 - val_binary_crossentropy: 0.4252\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.4503 - acc: 0.8113 - binary_crossentropy: 0.4503 - val_loss: 0.3450 - val_acc: 0.8660 - val_binary_crossentropy: 0.3450\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.3916 - acc: 0.8438 - binary_crossentropy: 0.3916 - val_loss: 0.3172 - val_acc: 0.8712 - val_binary_crossentropy: 0.3172\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.3520 - acc: 0.8636 - binary_crossentropy: 0.3520 - val_loss: 0.3053 - val_acc: 0.8736 - val_binary_crossentropy: 0.3053\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.3312 - acc: 0.8731 - binary_crossentropy: 0.3312 - val_loss: 0.3013 - val_acc: 0.8717 - val_binary_crossentropy: 0.3013\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.3156 - acc: 0.8780 - binary_crossentropy: 0.3156 - val_loss: 0.3012 - val_acc: 0.8742 - val_binary_crossentropy: 0.3012\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.2959 - acc: 0.8856 - binary_crossentropy: 0.2959 - val_loss: 0.3007 - val_acc: 0.8729 - val_binary_crossentropy: 0.3007\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.2864 - acc: 0.8951 - binary_crossentropy: 0.2864 - val_loss: 0.3041 - val_acc: 0.8727 - val_binary_crossentropy: 0.3041\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.2779 - acc: 0.8987 - binary_crossentropy: 0.2779 - val_loss: 0.3075 - val_acc: 0.8714 - val_binary_crossentropy: 0.3075\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.2711 - acc: 0.8978 - binary_crossentropy: 0.2711 - val_loss: 0.3070 - val_acc: 0.8710 - val_binary_crossentropy: 0.3070\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.2582 - acc: 0.9066 - binary_crossentropy: 0.2582 - val_loss: 0.3179 - val_acc: 0.8714 - val_binary_crossentropy: 0.3179\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.2498 - acc: 0.9099 - binary_crossentropy: 0.2498 - val_loss: 0.3183 - val_acc: 0.8706 - val_binary_crossentropy: 0.3183\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.2473 - acc: 0.9125 - binary_crossentropy: 0.2473 - val_loss: 0.3230 - val_acc: 0.8688 - val_binary_crossentropy: 0.3230\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.2363 - acc: 0.9160 - binary_crossentropy: 0.2363 - val_loss: 0.3354 - val_acc: 0.8700 - val_binary_crossentropy: 0.3354\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.2283 - acc: 0.9190 - binary_crossentropy: 0.2283 - val_loss: 0.3367 - val_acc: 0.8699 - val_binary_crossentropy: 0.3367\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.2228 - acc: 0.9218 - binary_crossentropy: 0.2228 - val_loss: 0.3400 - val_acc: 0.8693 - val_binary_crossentropy: 0.3400\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.2197 - acc: 0.9231 - binary_crossentropy: 0.2197 - val_loss: 0.3496 - val_acc: 0.8692 - val_binary_crossentropy: 0.3496\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.2127 - acc: 0.9248 - binary_crossentropy: 0.2127 - val_loss: 0.3446 - val_acc: 0.8677 - val_binary_crossentropy: 0.3446\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.2047 - acc: 0.9253 - binary_crossentropy: 0.2047 - val_loss: 0.3505 - val_acc: 0.8678 - val_binary_crossentropy: 0.3505\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cYXg7m11WNHQ",
        "colab_type": "code",
        "outputId": "06a83941-c63a-45fe-ec64-9647b2474023",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plot_history([('baseline', baseline_history),\n",
        "              ('dropout=0.1', dpt_model_history),\n",
        "              ('dropout=0.25',dpt_model_2_history),\n",
        "              ('dropout=0.5',dpt_model_3_history)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Jp36RkZGMHj36qnNlZmaSl5dn+7xp0yZSUlIYOHAgJSUlTJkyhcjISGJjY/nwww+vGZs9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lZnFx23RsMYMwsf7QbAkzPTySt1YTHriDTriTDpaBBlDhRW8vsFqqq0L72EuOrFLEBWvp0hk1cS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OqWSWzXwj6LpdMNAeagm49arucGcYn5Ydkn1CYVZERORVZJN29+SmIcpb8/ZBFySOUe7/kNsd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\n",
            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5AVExLFKa5XP",
        "colab_type": "code",
        "outputId": "1b65cceb-b8b5-4560-f2db-283d6b96c26f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 706
        }
      },
      "source": [
        "dpt_reg_model_1 = keras.models.Sequential([\n",
        "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.005),\n",
        "                       activation=tf.nn.relu, input_shape=(NUM_WORDS,)),\n",
        "    keras.layers.Dropout(0.2),\n",
        "    keras.layers.Dense(16, kernel_regularizer=keras.regularizers.l2(0.005),\n",
        "                       activation=tf.nn.relu),\n",
        "    keras.layers.Dropout(0.2),\n",
        "    keras.layers.Dense(1, activation=tf.nn.sigmoid)\n",
        "])\n",
        "\n",
        "dpt_reg_model_1.compile(optimizer='adam',\n",
        "                  loss='binary_crossentropy',\n",
        "                  metrics=['accuracy','binary_crossentropy'])\n",
        "\n",
        "dpt_reg_model_1_history = dpt_reg_model_1.fit(train_data, train_labels,\n",
        "                                  epochs=20,\n",
        "                                  batch_size=512,\n",
        "                                  validation_data=(test_data, test_labels),\n",
        "                                  verbose=2)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/20\n",
            "25000/25000 - 1s - loss: 0.7509 - acc: 0.7015 - binary_crossentropy: 0.5747 - val_loss: 0.5537 - val_acc: 0.8425 - val_binary_crossentropy: 0.4155\n",
            "Epoch 2/20\n",
            "25000/25000 - 1s - loss: 0.5183 - acc: 0.8400 - binary_crossentropy: 0.3904 - val_loss: 0.4560 - val_acc: 0.8658 - val_binary_crossentropy: 0.3369\n",
            "Epoch 3/20\n",
            "25000/25000 - 1s - loss: 0.4505 - acc: 0.8657 - binary_crossentropy: 0.3374 - val_loss: 0.4180 - val_acc: 0.8727 - val_binary_crossentropy: 0.3110\n",
            "Epoch 4/20\n",
            "25000/25000 - 1s - loss: 0.4188 - acc: 0.8750 - binary_crossentropy: 0.3159 - val_loss: 0.4070 - val_acc: 0.8721 - val_binary_crossentropy: 0.3084\n",
            "Epoch 5/20\n",
            "25000/25000 - 1s - loss: 0.3954 - acc: 0.8824 - binary_crossentropy: 0.3002 - val_loss: 0.3934 - val_acc: 0.8736 - val_binary_crossentropy: 0.3019\n",
            "Epoch 6/20\n",
            "25000/25000 - 1s - loss: 0.3854 - acc: 0.8844 - binary_crossentropy: 0.2962 - val_loss: 0.3922 - val_acc: 0.8734 - val_binary_crossentropy: 0.3053\n",
            "Epoch 7/20\n",
            "25000/25000 - 1s - loss: 0.3709 - acc: 0.8890 - binary_crossentropy: 0.2855 - val_loss: 0.3834 - val_acc: 0.8732 - val_binary_crossentropy: 0.3001\n",
            "Epoch 8/20\n",
            "25000/25000 - 1s - loss: 0.3625 - acc: 0.8903 - binary_crossentropy: 0.2808 - val_loss: 0.3788 - val_acc: 0.8744 - val_binary_crossentropy: 0.2989\n",
            "Epoch 9/20\n",
            "25000/25000 - 1s - loss: 0.3564 - acc: 0.8928 - binary_crossentropy: 0.2773 - val_loss: 0.3773 - val_acc: 0.8732 - val_binary_crossentropy: 0.2994\n",
            "Epoch 10/20\n",
            "25000/25000 - 1s - loss: 0.3487 - acc: 0.8951 - binary_crossentropy: 0.2713 - val_loss: 0.3806 - val_acc: 0.8703 - val_binary_crossentropy: 0.3043\n",
            "Epoch 11/20\n",
            "25000/25000 - 1s - loss: 0.3439 - acc: 0.8955 - binary_crossentropy: 0.2675 - val_loss: 0.3796 - val_acc: 0.8690 - val_binary_crossentropy: 0.3044\n",
            "Epoch 12/20\n",
            "25000/25000 - 1s - loss: 0.3387 - acc: 0.8965 - binary_crossentropy: 0.2639 - val_loss: 0.3729 - val_acc: 0.8730 - val_binary_crossentropy: 0.2988\n",
            "Epoch 13/20\n",
            "25000/25000 - 1s - loss: 0.3330 - acc: 0.8998 - binary_crossentropy: 0.2587 - val_loss: 0.3731 - val_acc: 0.8739 - val_binary_crossentropy: 0.2993\n",
            "Epoch 14/20\n",
            "25000/25000 - 1s - loss: 0.3293 - acc: 0.8997 - binary_crossentropy: 0.2557 - val_loss: 0.3720 - val_acc: 0.8725 - val_binary_crossentropy: 0.2988\n",
            "Epoch 15/20\n",
            "25000/25000 - 1s - loss: 0.3229 - acc: 0.9028 - binary_crossentropy: 0.2495 - val_loss: 0.3754 - val_acc: 0.8703 - val_binary_crossentropy: 0.3026\n",
            "Epoch 16/20\n",
            "25000/25000 - 1s - loss: 0.3200 - acc: 0.9040 - binary_crossentropy: 0.2470 - val_loss: 0.3744 - val_acc: 0.8717 - val_binary_crossentropy: 0.3017\n",
            "Epoch 17/20\n",
            "25000/25000 - 1s - loss: 0.3166 - acc: 0.9060 - binary_crossentropy: 0.2436 - val_loss: 0.3730 - val_acc: 0.8719 - val_binary_crossentropy: 0.3002\n",
            "Epoch 18/20\n",
            "25000/25000 - 1s - loss: 0.3155 - acc: 0.9059 - binary_crossentropy: 0.2423 - val_loss: 0.3759 - val_acc: 0.8730 - val_binary_crossentropy: 0.3030\n",
            "Epoch 19/20\n",
            "25000/25000 - 1s - loss: 0.3119 - acc: 0.9089 - binary_crossentropy: 0.2388 - val_loss: 0.3746 - val_acc: 0.8712 - val_binary_crossentropy: 0.3019\n",
            "Epoch 20/20\n",
            "25000/25000 - 1s - loss: 0.3086 - acc: 0.9094 - binary_crossentropy: 0.2353 - val_loss: 0.3829 - val_acc: 0.8648 - val_binary_crossentropy: 0.3095\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K8s9CGAwbnPN",
        "colab_type": "code",
        "outputId": "756c838d-f151-4cfa-ced9-818a1a01007c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 609
        }
      },
      "source": [
        "plot_history([('baseline', baseline_history),\n",
        "              ('combo1', dpt_reg_model_1_history),\n",
        "              ('dropout=0.5',dpt_model_3_history),\n",
        "              ('l2.01',l2_model_2_history)])"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jK15u586dREdHN7tv+fLlFBQUsG3bNsrLy4mNjWXMmDEAbNu2jcLCQvz8/AgKCmLevHls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\nM0f/OLOS2VRdT/Fm32fXOmJjid5/f1yzdyo0s0d7v7bD7CzAE/Oe4I8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7dmN9eS1LN1bS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            "text/plain": [
              "<Figure size 1152x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "colab_type": "text",
        "id": "gjfnkEeQyAFG"
      },
      "source": [
        "Adding dropout is a clear improvement over the baseline model.\n",
        "\n",
        "\n",
        "To recap: here the most common ways to prevent overfitting in neural networks:\n",
        "\n",
        "* Get more training data.\n",
        "* Reduce the capacity of the network.\n",
        "* Add weight regularization.\n",
        "* Add dropout.\n",
        "\n",
        "And two important approaches not covered in this guide are data-augmentation and batch normalization."
      ]
    }
  ]
}