multi_class_classification_of_handwritten_digits.ipynb

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      "name": "multi_class_classification_of_handwritten_digits.ipynb",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/douglaswchung/MNIST-classification/blob/master/multi_class_classification_of_handwritten_digits.ipynb)"
      ]
    },
    {
      "metadata": {
        "id": "JndnmDMp66FL",
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      "cell_type": "markdown",
      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
      "metadata": {
        "id": "hMqWDc_m6rUC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# 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": []
    },
    {
      "metadata": {
        "id": "mPa95uXvcpcn",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Classifying Handwritten Digits with Neural Networks"
      ]
    },
    {
      "metadata": {
        "id": "Fdpn8b90u8Tp",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST.png)"
      ]
    },
    {
      "metadata": {
        "id": "c7HLCm66Cs2p",
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      "cell_type": "markdown",
      "source": [
        "**Learning Objectives:**\n",
        "  * Train both a linear model and a neural network to classify handwritten digits from the classic [MNIST](http://yann.lecun.com/exdb/mnist/) data set\n",
        "  * Compare the performance of the linear and neural network classification models\n",
        "  * Visualize the weights of a neural-network hidden layer"
      ]
    },
    {
      "metadata": {
        "id": "HSEh-gNdu8T0",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Our goal is to map each input image to the correct numeric digit. We will create a NN with a few hidden layers and a Softmax layer at the top to select the winning class."
      ]
    },
    {
      "metadata": {
        "id": "2NMdE1b-7UIH",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Setup\n",
        "\n",
        "First, let's download the data set, import TensorFlow and other utilities, and load the data into a *pandas* `DataFrame`. Note that this data is a sample of the original MNIST training data; we've taken 20000 rows at random."
      ]
    },
    {
      "metadata": {
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        },
        "outputId": "08b7eb91-e836-43a1-dbb7-c574fce8a325"
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      "cell_type": "code",
      "source": [
        "!wget https://storage.googleapis.com/mledu-datasets/mnist_train_small.csv -O /tmp/mnist_train_small.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-04-05 15:41:28--  https://storage.googleapis.com/mledu-datasets/mnist_train_small.csv\r\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.141.128, 2607:f8b0:400c:c06::80\r\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.141.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 36523880 (35M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/mnist_train_small.csv’\n",
            "\n",
            "/tmp/mnist_train_sm 100%[===================>]  34.83M  77.2MB/s    in 0.5s    \n",
            "\n",
            "2018-04-05 15:41:29 (77.2 MB/s) - ‘/tmp/mnist_train_small.csv’ saved [36523880/36523880]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "4LJ4SD8BWHeh",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "import glob\n",
        "import io\n",
        "import math\n",
        "import os\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import scipy as sp\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn import metrics\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.data import Dataset\n",
        "\n",
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format\n",
        "\n",
        "mnist_dataframe = pd.read_csv(\n",
        "  io.open(\"/tmp/mnist_train_small.csv\", \"r\"),\n",
        "  sep=\",\",\n",
        "  header=None)\n",
        "\n",
        "# Use just the first 10,000 records for training/validation.\n",
        "mnist_dataframe = mnist_dataframe.head(10000)\n",
        "\n",
        "mnist_dataframe = mnist_dataframe.reindex(np.random.permutation(mnist_dataframe.index))\n",
        "mnist_dataframe.head()\n",
        "print(display.display(mnist_dataframe.describe()))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kg0-25p2mOi0",
        "colab_type": "text"
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      "cell_type": "markdown",
      "source": [
        "Each row represents one labeled example. Column 0 represents the label that a human rater has assigned for one handwritten digit. For example, if Column 0 contains '6', then a human rater interpreted the handwritten character as the digit '6'.  The ten digits 0-9 are each represented, with a unique class label for each possible digit. Thus, this is a multi-class classification problem with 10 classes."
      ]
    },
    {
      "metadata": {
        "id": "PQ7vuOwRCsZ1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png)"
      ]
    },
    {
      "metadata": {
        "id": "dghlqJPIu8UM",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Columns 1 through 784 contain the feature values, one per pixel for the 28×28=784 pixel values. The pixel values are on a gray scale in which 0 represents white, 255 represents black, and values between 0 and 255 represent shades of gray. Most of the pixel values are 0; you may want to take a minute to confirm that they aren't all 0.  For example, adjust the following text block to print out the values in column 72."
      ]
    },
    {
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        "id": "2ZkrL5MCqiJI",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "1ce03ab2-9e68-48bb-ea71-899e94ad2779"
      },
      "cell_type": "code",
      "source": [
        "print(display.display(mnist_dataframe.loc[:, range(69,784,100)].describe()))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
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              "mean      1.9     0.0   105.9     3.1    59.8    68.8     0.4     0.5\n",
              "std      18.7     0.0   109.7    24.3    97.4   102.3     8.3    10.2\n",
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      "source": [
        "Now, let's parse out the labels and features and look at a few examples. Note the use of `loc` which allows us to pull out columns based on original location, since we don't have a header row in this data set."
      ]
    },
    {
      "metadata": {
        "id": "JfFWWvMWDFrR",
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      "source": [
        "def parse_labels_and_features(dataset):\n",
        "  \"\"\"Extracts labels and features.\n",
        "  \n",
        "  This is a good place to scale or transform the features if needed.\n",
        "  \n",
        "  Args:\n",
        "    dataset: A Pandas `Dataframe`, containing the label on the first column and\n",
        "      monochrome pixel values on the remaining columns, in row major order.\n",
        "  Returns:\n",
        "    A `tuple` `(labels, features)`:\n",
        "      labels: A Pandas `Series`.\n",
        "      features: A Pandas `DataFrame`.\n",
        "  \"\"\"\n",
        "  labels = dataset[0]\n",
        "\n",
        "  # DataFrame.loc index ranges are inclusive at both ends.\n",
        "  features = dataset.loc[:,1:784]\n",
        "  # Scale the data to [0, 1] by dividing out the max value, 255.\n",
        "  features = features / 255\n",
        "\n",
        "  return labels, features"
      ],
      "execution_count": 0,
      "outputs": []
    },
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        "outputId": "0d0cf1d7-7922-4bf4-fced-21c23d8c10cf"
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      "cell_type": "code",
      "source": [
        "training_targets, training_examples = parse_labels_and_features(mnist_dataframe[:7500])\n",
        "training_examples.describe()"
      ],
      "execution_count": 0,
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          "output_type": "execute_result",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.8</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         1      2      3      4      5      6      7      8      9      10   \\\n",
              "count 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "\n",
              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
              "count  ...   7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0 7500.0   \n",
              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "max    ...      1.0    1.0    0.8    0.2    1.0    0.2    0.0    0.0    0.0   \n",
              "\n",
              "         784  \n",
              "count 7500.0  \n",
              "mean     0.0  \n",
              "std      0.0  \n",
              "min      0.0  \n",
              "25%      0.0  \n",
              "50%      0.0  \n",
              "75%      0.0  \n",
              "max      0.0  \n",
              "\n",
              "[8 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "metadata": {
        "id": "4-Vgg-1zu8Ud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "9f683d0f-9db5-4bad-ea8f-8c63f4a9edcb"
      },
      "cell_type": "code",
      "source": [
        "validation_targets, validation_examples = parse_labels_and_features(mnist_dataframe[7500:10000])\n",
        "validation_examples.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>784</th>\n",
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              "  <tbody>\n",
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              "    <tr>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>0.5</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "         1      2      3      4      5      6      7      8      9      10   \\\n",
              "count 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
              "mean     0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "std      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "min      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "25%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "50%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "75%      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "max      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "\n",
              "       ...      775    776    777    778    779    780    781    782    783  \\\n",
              "count  ...   2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0 2500.0   \n",
              "mean   ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "std    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "min    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "25%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "50%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "75%    ...      0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "max    ...      0.5    0.0    0.0    0.0    0.0    0.0    0.0    0.0    0.0   \n",
              "\n",
              "         784  \n",
              "count 2500.0  \n",
              "mean     0.0  \n",
              "std      0.0  \n",
              "min      0.0  \n",
              "25%      0.0  \n",
              "50%      0.0  \n",
              "75%      0.0  \n",
              "max      0.0  \n",
              "\n",
              "[8 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "metadata": {
        "id": "wrnAI1v6u8Uh",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Show a random example and its corresponding label."
      ]
    },
    {
      "metadata": {
        "id": "s-euVJVtu8Ui",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 360
        },
        "outputId": "80143972-54e6-46f4-cc48-147dcf5dac43"
      },
      "cell_type": "code",
      "source": [
        "rand_example = np.random.choice(training_examples.index)\n",
        "_, ax = plt.subplots()\n",
        "ax.matshow(training_examples.loc[rand_example].values.reshape(28, 28))\n",
        "ax.set_title(\"Label: %i\" % training_targets.loc[rand_example])\n",
        "ax.grid(False)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54cb942fd0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ScmYX7xdZMXE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Task 1: Build a Linear Model for MNIST\n",
        "\n",
        "First, let's create a baseline model to compare against. The `LinearClassifier` provides a set of *k* one-vs-all classifiers, one for each of the *k* classes.\n",
        "\n",
        "You'll notice that in addition to reporting accuracy, and plotting Log Loss over time, we also display a [**confusion matrix**](https://en.wikipedia.org/wiki/Confusion_matrix).  The confusion matrix shows which classes were misclassified as other classes. Which digits get confused for each other?\n",
        "\n",
        "Also note that we track the model's error using the `log_loss` function. This should not be confused with the loss function internal to `LinearClassifier` that is used for training."
      ]
    },
    {
      "metadata": {
        "id": "cpoVC4TSdw5Z",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def construct_feature_columns():\n",
        "  \"\"\"Construct the TensorFlow Feature Columns.\n",
        "\n",
        "  Returns:\n",
        "    A set of feature columns\n",
        "  \"\"\" \n",
        "  \n",
        "  # There are 784 pixels in each image.\n",
        "  return set([tf.feature_column.numeric_column('pixels', shape=784)])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kMmL89yGeTfz",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here, we'll make separate input functions for training and for prediction. We'll nest them in `create_training_input_fn()` and `create_predict_input_fn()`, respectively, so we can invoke these functions to return the corresponding `_input_fn`s to pass to our `.train()` and `.predict()` calls."
      ]
    },
    {
      "metadata": {
        "id": "OeS47Bmn5Ms2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_training_input_fn(features, labels, batch_size, num_epochs=None, shuffle=True):\n",
        "  \"\"\"A custom input_fn for sending MNIST data to the estimator for training.\n",
        "\n",
        "  Args:\n",
        "    features: The training features.\n",
        "    labels: The training labels.\n",
        "    batch_size: Batch size to use during training.\n",
        "\n",
        "  Returns:\n",
        "    A function that returns batches of training features and labels during\n",
        "    training.\n",
        "  \"\"\"\n",
        "  def _input_fn(num_epochs=None, shuffle=True):\n",
        "    # Input pipelines are reset with each call to .train(). To ensure model\n",
        "    # gets a good sampling of data, even when number of steps is small, we \n",
        "    # shuffle all the data before creating the Dataset object\n",
        "    idx = np.random.permutation(features.index)\n",
        "    raw_features = {\"pixels\":features.reindex(idx)}\n",
        "    raw_targets = np.array(labels[idx])\n",
        "   \n",
        "    ds = Dataset.from_tensor_slices((raw_features,raw_targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "    \n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(10000)\n",
        "    \n",
        "    # Return the next batch of data.\n",
        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
        "    return feature_batch, label_batch\n",
        "\n",
        "  return _input_fn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8zoGWAoohrwS",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def create_predict_input_fn(features, labels, batch_size):\n",
        "  \"\"\"A custom input_fn for sending mnist data to the estimator for predictions.\n",
        "\n",
        "  Args:\n",
        "    features: The features to base predictions on.\n",
        "    labels: The labels of the prediction examples.\n",
        "\n",
        "  Returns:\n",
        "    A function that returns features and labels for predictions.\n",
        "  \"\"\"\n",
        "  def _input_fn():\n",
        "    raw_features = {\"pixels\": features.values}\n",
        "    raw_targets = np.array(labels)\n",
        "    \n",
        "    ds = Dataset.from_tensor_slices((raw_features, raw_targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size)\n",
        "    \n",
        "        \n",
        "    # Return the next batch of data.\n",
        "    feature_batch, label_batch = ds.make_one_shot_iterator().get_next()\n",
        "    return feature_batch, label_batch\n",
        "\n",
        "  return _input_fn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G6DjSLZMu8Um",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_linear_classification_model(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets):\n",
        "  \"\"\"Trains a linear classification model for the MNIST digits dataset.\n",
        "  \n",
        "  In addition to training, this function also prints training progress information,\n",
        "  a plot of the training and validation loss over time, and a confusion\n",
        "  matrix.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: An `int`, the learning rate to use.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    training_examples: A `DataFrame` containing the training features.\n",
        "    training_targets: A `DataFrame` containing the training labels.\n",
        "    validation_examples: A `DataFrame` containing the validation features.\n",
        "    validation_targets: A `DataFrame` containing the validation labels.\n",
        "      \n",
        "  Returns:\n",
        "    The trained `LinearClassifier` object.\n",
        "  \"\"\"\n",
        "\n",
        "  periods = 10\n",
        "\n",
        "  steps_per_period = steps / periods  \n",
        "  # Create the input functions.\n",
        "  predict_training_input_fn = create_predict_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  predict_validation_input_fn = create_predict_input_fn(\n",
        "    validation_examples, validation_targets, batch_size)\n",
        "  training_input_fn = create_training_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  \n",
        "  # Create a LinearClassifier object.\n",
        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  classifier = tf.estimator.LinearClassifier(\n",
        "      feature_columns=construct_feature_columns(),\n",
        "      n_classes=10,\n",
        "      optimizer=my_optimizer,\n",
        "      config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n",
        "  )\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print \"Training model...\"\n",
        "  print \"LogLoss error (on validation data):\"\n",
        "  training_errors = []\n",
        "  validation_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    classifier.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "  \n",
        "    # Take a break and compute probabilities.\n",
        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
        "        \n",
        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
        "    \n",
        "    # Compute training and validation errors.\n",
        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
        "    # Occasionally print the current loss.\n",
        "    print \"  period %02d : %0.2f\" % (period, validation_log_loss)\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_errors.append(training_log_loss)\n",
        "    validation_errors.append(validation_log_loss)\n",
        "  print \"Model training finished.\"\n",
        "  # Remove event files to save disk space.\n",
        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
        "  \n",
        "  # Calculate final predictions (not probabilities, as above).\n",
        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
        "  final_probabilities = np.array([item['probabilities'] for item in final_predictions])\n",
        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
        "  \n",
        "  \n",
        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
        "  print \"Final accuracy (on validation data): %0.2f\" % accuracy  \n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"LogLoss\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"LogLoss vs. Periods\")\n",
        "  plt.plot(training_errors, label=\"training\")\n",
        "  plt.plot(validation_errors, label=\"validation\")\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  \n",
        "  # Output a plot of the confusion matrix.\n",
        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
        "  # in each class).\n",
        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
        "  ax.set_aspect(1)\n",
        "  plt.title(\"Confusion matrix\")\n",
        "  plt.ylabel(\"True label\")\n",
        "  plt.xlabel(\"Predicted label\")\n",
        "  plt.show()\n",
        "\n",
        "  return classifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ItHIUyv2u8Ur",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Spend 5 minutes seeing how well you can do on accuracy with a linear model of this form. For this exercise, limit yourself to experimenting with the hyperparameters for batch size, learning rate and steps.**\n",
        "\n",
        "Stop if you get anything above about 0.9 accuracy."
      ]
    },
    {
      "metadata": {
        "id": "yaiIhIQqu8Uv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "outputId": "b34e0bad-5b82-47f0-d3ef-01aeff3ebe3d"
      },
      "cell_type": "code",
      "source": [
        "classifier = train_linear_classification_model(\n",
        "             learning_rate=0.01,\n",
        "             steps=1000,\n",
        "             batch_size=100,\n",
        "             training_examples=training_examples,\n",
        "             training_targets=training_targets,\n",
        "             validation_examples=validation_examples,\n",
        "             validation_targets=validation_targets)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "LogLoss error (on validation data):\n",
            "  period 00 : 5.29\n",
            "  period 01 : 4.35\n",
            "  period 02 : 4.16\n",
            "  period 03 : 4.03\n",
            "  period 04 : 3.88\n",
            "  period 05 : 3.83\n",
            "  period 06 : 3.79\n",
            "  period 07 : 3.79\n",
            "  period 08 : 3.80\n",
            "  period 09 : 3.66\n",
            "Model training finished.\n",
            "Final accuracy (on validation data): 0.89\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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W3bcQQgjHJOFtBXHhviTHB3GqvJmjxQ1W3//00GmoVWqZ8y2EEAK4jvA+d84y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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54c46e31d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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PlyMkEREpRJahLVtbW4wdO7bRc97e3vD29pYjHBGR2dHS0BavIyEiUiUmEiIikoAVCRER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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54c3648ad0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "266KQvZoMxMv",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for one possible solution."
      ]
    },
    {
      "metadata": {
        "id": "lRWcn24DM3qa",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Here is a set of parameters that should attain roughly 0.9 accuracy."
      ]
    },
    {
      "metadata": {
        "id": "TGlBMrUoM1K_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_ = train_linear_classification_model(\n",
        "    learning_rate=0.03,\n",
        "    steps=1000,\n",
        "    batch_size=30,\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mk095OfpPdOx",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Task 2: Replace the Linear Classifier with a Neural Network\n",
        "\n",
        "**Replace the LinearClassifier above with a [`DNNClassifier`](https://www.tensorflow.org/api_docs/python/tf/estimator/DNNClassifier) and find a parameter combination that gives 0.95 or better accuracy.**\n",
        "\n",
        "You may wish to experiment with additional regularization methods, such as dropout. These additional regularization methods are documented in the comments for the `DNNClassifier` class."
      ]
    },
    {
      "metadata": {
        "id": "PDuLd2Hcu8VL",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_NN_classifier_model(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    hidden_units,\n",
        "    dropout,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets):\n",
        "  \"\"\"Trains a linear classification model for the MNIST digits dataset.\n",
        "  \n",
        "  In addition to training, this function also prints training progress information,\n",
        "  a plot of the training and validation loss over time, and a confusion\n",
        "  matrix.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: An `int`, the learning rate to use.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    training_examples: A `DataFrame` containing the training features.\n",
        "    training_targets: A `DataFrame` containing the training labels.\n",
        "    validation_examples: A `DataFrame` containing the validation features.\n",
        "    validation_targets: A `DataFrame` containing the validation labels.\n",
        "      \n",
        "  Returns:\n",
        "    The trained `LinearClassifier` object.\n",
        "  \"\"\"\n",
        "\n",
        "  periods = 10\n",
        "\n",
        "  steps_per_period = steps / periods  \n",
        "  # Create the input functions.\n",
        "  predict_training_input_fn = create_predict_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  predict_validation_input_fn = create_predict_input_fn(\n",
        "    validation_examples, validation_targets, batch_size)\n",
        "  training_input_fn = create_training_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  \n",
        "  # Create a LinearClassifier object.\n",
        "  my_optimizer = tf.train.AdadeltaOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  classifier = tf.estimator.DNNClassifier(\n",
        "      feature_columns=construct_feature_columns(),\n",
        "      hidden_units=hidden_units,\n",
        "      activation_fn=tf.nn.crelu,\n",
        "      dropout=dropout,\n",
        "      n_classes=10,\n",
        "      optimizer=my_optimizer,\n",
        "      config=tf.estimator.RunConfig(keep_checkpoint_max=1)\n",
        "  )\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print \"Training model...\"\n",
        "  print \"LogLoss error (on validation data):\"\n",
        "  training_errors = []\n",
        "  validation_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    classifier.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "  \n",
        "    # Take a break and compute probabilities.\n",
        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
        "        \n",
        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
        "    \n",
        "    # Compute training and validation errors.\n",
        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
        "    # Occasionally print the current loss.\n",
        "    print \"  period %02d : %0.2f\" % (period, validation_log_loss)\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_errors.append(training_log_loss)\n",
        "    validation_errors.append(validation_log_loss)\n",
        "  print \"Model training finished.\"\n",
        "  # Remove event files to save disk space.\n",
        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
        "  \n",
        "  # Calculate final predictions (not probabilities, as above).\n",
        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
        "  \n",
        "  \n",
        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
        "  print \"Final accuracy (on validation data): %0.2f\" % accuracy  \n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"LogLoss\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"LogLoss vs. Periods\")\n",
        "  plt.plot(training_errors, label=\"training\")\n",
        "  plt.plot(validation_errors, label=\"validation\")\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  \n",
        "  # Output a plot of the confusion matrix.\n",
        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
        "  # in each class).\n",
        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
        "  ax.set_aspect(1)\n",
        "  plt.title(\"Confusion matrix\")\n",
        "  plt.ylabel(\"True label\")\n",
        "  plt.xlabel(\"Predicted label\")\n",
        "  plt.show()\n",
        "\n",
        "  return classifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mblcHL0i83oy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "outputId": "ad815d5b-8cc4-440d-db9c-06e8925954ea"
      },
      "cell_type": "code",
      "source": [
        "classifier = train_NN_classifier_model(\n",
        "    learning_rate=1.25,\n",
        "    steps=1000,\n",
        "    batch_size=75,\n",
        "    hidden_units=[120,40,40],\n",
        "    dropout=0.02,\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "LogLoss error (on validation data):\n",
            "  period 00 : 4.79\n",
            "  period 01 : 3.72\n",
            "  period 02 : 3.43\n",
            "  period 03 : 2.90\n",
            "  period 04 : 2.62\n",
            "  period 05 : 2.42\n",
            "  period 06 : 2.29\n",
            "  period 07 : 2.38\n",
            "  period 08 : 2.24\n",
            "  period 09 : 2.03\n",
            "Model training finished.\n",
            "Final accuracy (on validation data): 0.94\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
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Hk/SVSlER4OTHxICxjPJJRAFy6/M5WZ3FjoK9lDaX42bngrudW/f3Jr/9Wob0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\n4UxERCQwDGciIiKB+X9L2fSs27EuQQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54b9d996d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EqsQgISIiCdiREBGRJIIVg4SIiCRgR0JERJIwSIiISNWSk5Nx7NgxCIKAmJgY+Pv7G5/7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\nBgkREUnCICEiIkkYJEREJMn/A1XQIaymrWxZAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54bf9b2f50>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "TOfmiSvqu8U9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Once you have a good model, double check that you didn't overfit the validation set by evaluating on the test data that we'll load below.\n"
      ]
    },
    {
      "metadata": {
        "id": "rZsMYFo7u8VB",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "4ba23990-d769-407f-fcc6-d26d419f0c73"
      },
      "cell_type": "code",
      "source": [
        "!wget https://storage.googleapis.com/mledu-datasets/mnist_test.csv -O /tmp/mnist_test.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-04-05 18:15:55--  https://storage.googleapis.com/mledu-datasets/mnist_test.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.141.128, 2607:f8b0:400c:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.141.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 18289443 (17M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/mnist_test.csv’\n",
            "\n",
            "/tmp/mnist_test.csv 100%[===================>]  17.44M   109MB/s    in 0.2s    \n",
            "\n",
            "2018-04-05 18:15:55 (109 MB/s) - ‘/tmp/mnist_test.csv’ saved [18289443/18289443]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "evlB5ubzu8VJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "dd7f3cd3-2daf-4267-ee5a-4033819e78ea"
      },
      "cell_type": "code",
      "source": [
        "mnist_test_dataframe = pd.read_csv(\n",
        "  io.open(\"/tmp/mnist_test.csv\", \"r\"),\n",
        "  sep=\",\",\n",
        "  header=None)\n",
        "\n",
        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
        "test_examples.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          1       2       3       4       5       6       7       8       9    \\\n",
              "count 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
              "mean      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
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              "max       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "          10    ...       775     776     777     778     779     780     781  \\\n",
              "count 10000.0   ...   10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
              "mean      0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "std       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "min       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "25%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "50%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "75%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "max       0.0   ...       1.0     1.0     0.6     0.0     0.0     0.0     0.0   \n",
              "\n",
              "          782     783     784  \n",
              "count 10000.0 10000.0 10000.0  \n",
              "mean      0.0     0.0     0.0  \n",
              "std       0.0     0.0     0.0  \n",
              "min       0.0     0.0     0.0  \n",
              "25%       0.0     0.0     0.0  \n",
              "50%       0.0     0.0     0.0  \n",
              "75%       0.0     0.0     0.0  \n",
              "max       0.0     0.0     0.0  \n",
              "\n",
              "[8 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 62
        }
      ]
    },
    {
      "metadata": {
        "id": "uDeqxO5tBg8V",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "22a0e340-e5aa-461f-c9bc-3486998fd9f8"
      },
      "cell_type": "code",
      "source": [
        "predict_test_input_fn = create_predict_input_fn(\n",
        "    test_examples, test_targets, batch_size=150)\n",
        "  \n",
        "# Calculate final predictions (not probabilities, as above).\n",
        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
        "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
        "  \n",
        "  \n",
        "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
        "print \"Final accuracy (on validation data): %0.2f\" % accuracy "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final accuracy (on validation data): 0.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "6sfw3LH0Oycm",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "### Solution\n",
        "\n",
        "Click below for a possible solution."
      ]
    },
    {
      "metadata": {
        "id": "XatDGFKEO374",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The code below is almost identical to the original `LinearClassifer` training code, with the exception of the NN-specific configuration, such as the hyperparameter for hidden units."
      ]
    },
    {
      "metadata": {
        "id": "kdNTx8jkPQUx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_nn_classification_model(\n",
        "    learning_rate,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    hidden_units,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets):\n",
        "  \"\"\"Trains a neural network classification model for the MNIST digits dataset.\n",
        "  \n",
        "  In addition to training, this function also prints training progress information,\n",
        "  a plot of the training and validation loss over time, as well as a confusion\n",
        "  matrix.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: An `int`, the learning rate to use.\n",
        "    steps: A non-zero `int`, the total number of training steps. A training step\n",
        "      consists of a forward and backward pass using a single batch.\n",
        "    batch_size: A non-zero `int`, the batch size.\n",
        "    hidden_units: A `list` of int values, specifying the number of neurons in each layer.\n",
        "    training_examples: A `DataFrame` containing the training features.\n",
        "    training_targets: A `DataFrame` containing the training labels.\n",
        "    validation_examples: A `DataFrame` containing the validation features.\n",
        "    validation_targets: A `DataFrame` containing the validation labels.\n",
        "      \n",
        "  Returns:\n",
        "    The trained `DNNClassifier` object.\n",
        "  \"\"\"\n",
        "\n",
        "  periods = 10\n",
        "  # Caution: input pipelines are reset with each call to train. \n",
        "  # If the number of steps is small, your model may never see most of the data.  \n",
        "  # So with multiple `.train` calls like this you may want to control the length \n",
        "  # of training with num_epochs passed to the input_fn. Or, you can do a really-big shuffle, \n",
        "  # or since it's in-memory data, shuffle all the data in the `input_fn`.\n",
        "  steps_per_period = steps / periods  \n",
        "  # Create the input functions.\n",
        "  predict_training_input_fn = create_predict_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  predict_validation_input_fn = create_predict_input_fn(\n",
        "    validation_examples, validation_targets, batch_size)\n",
        "  training_input_fn = create_training_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  \n",
        "  # Create the input functions.\n",
        "  predict_training_input_fn = create_predict_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  predict_validation_input_fn = create_predict_input_fn(\n",
        "    validation_examples, validation_targets, batch_size)\n",
        "  training_input_fn = create_training_input_fn(\n",
        "    training_examples, training_targets, batch_size)\n",
        "  \n",
        "  # Create feature columns.\n",
        "  feature_columns = [tf.feature_column.numeric_column('pixels', shape=784)]\n",
        "\n",
        "  # Create a DNNClassifier object.\n",
        "  my_optimizer = tf.train.AdagradOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  classifier = tf.estimator.DNNClassifier(\n",
        "      feature_columns=feature_columns,\n",
        "      n_classes=10,\n",
        "      hidden_units=hidden_units,\n",
        "      optimizer=my_optimizer,\n",
        "      config=tf.contrib.learn.RunConfig(keep_checkpoint_max=1)\n",
        "  )\n",
        "\n",
        "  # Train the model, but do so inside a loop so that we can periodically assess\n",
        "  # loss metrics.\n",
        "  print \"Training model...\"\n",
        "  print \"LogLoss error (on validation data):\"\n",
        "  training_errors = []\n",
        "  validation_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Train the model, starting from the prior state.\n",
        "    classifier.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "  \n",
        "    # Take a break and compute probabilities.\n",
        "    training_predictions = list(classifier.predict(input_fn=predict_training_input_fn))\n",
        "    training_probabilities = np.array([item['probabilities'] for item in training_predictions])\n",
        "    training_pred_class_id = np.array([item['class_ids'][0] for item in training_predictions])\n",
        "    training_pred_one_hot = tf.keras.utils.to_categorical(training_pred_class_id,10)\n",
        "        \n",
        "    validation_predictions = list(classifier.predict(input_fn=predict_validation_input_fn))\n",
        "    validation_probabilities = np.array([item['probabilities'] for item in validation_predictions])    \n",
        "    validation_pred_class_id = np.array([item['class_ids'][0] for item in validation_predictions])\n",
        "    validation_pred_one_hot = tf.keras.utils.to_categorical(validation_pred_class_id,10)    \n",
        "    \n",
        "    # Compute training and validation errors.\n",
        "    training_log_loss = metrics.log_loss(training_targets, training_pred_one_hot)\n",
        "    validation_log_loss = metrics.log_loss(validation_targets, validation_pred_one_hot)\n",
        "    # Occasionally print the current loss.\n",
        "    print \"  period %02d : %0.2f\" % (period, validation_log_loss)\n",
        "    # Add the loss metrics from this period to our list.\n",
        "    training_errors.append(training_log_loss)\n",
        "    validation_errors.append(validation_log_loss)\n",
        "  print \"Model training finished.\"\n",
        "  # Remove event files to save disk space.\n",
        "  _ = map(os.remove, glob.glob(os.path.join(classifier.model_dir, 'events.out.tfevents*')))\n",
        "  \n",
        "  # Calculate final predictions (not probabilities, as above).\n",
        "  final_predictions = classifier.predict(input_fn=predict_validation_input_fn)\n",
        "  final_predictions = np.array([item['class_ids'][0] for item in final_predictions])\n",
        "  \n",
        "  \n",
        "  accuracy = metrics.accuracy_score(validation_targets, final_predictions)\n",
        "  print \"Final accuracy (on validation data): %0.2f\" % accuracy  \n",
        "\n",
        "  # Output a graph of loss metrics over periods.\n",
        "  plt.ylabel(\"LogLoss\")\n",
        "  plt.xlabel(\"Periods\")\n",
        "  plt.title(\"LogLoss vs. Periods\")\n",
        "  plt.plot(training_errors, label=\"training\")\n",
        "  plt.plot(validation_errors, label=\"validation\")\n",
        "  plt.legend()\n",
        "  plt.show()\n",
        "  \n",
        "  # Output a plot of the confusion matrix.\n",
        "  cm = metrics.confusion_matrix(validation_targets, final_predictions)\n",
        "  # Normalize the confusion matrix by row (i.e by the number of samples\n",
        "  # in each class).\n",
        "  cm_normalized = cm.astype(\"float\") / cm.sum(axis=1)[:, np.newaxis]\n",
        "  ax = sns.heatmap(cm_normalized, cmap=\"bone_r\")\n",
        "  ax.set_aspect(1)\n",
        "  plt.title(\"Confusion matrix\")\n",
        "  plt.ylabel(\"True label\")\n",
        "  plt.xlabel(\"Predicted label\")\n",
        "  plt.show()\n",
        "\n",
        "  return classifier"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ZfzsTYGPPU8I",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 973
        },
        "outputId": "37651828-9a40-405d-916c-3c133a70ea74"
      },
      "cell_type": "code",
      "source": [
        "classifier = train_nn_classification_model(\n",
        "    learning_rate=0.05,\n",
        "    steps=1000,\n",
        "    batch_size=30,\n",
        "    hidden_units=[100, 100],\n",
        "    training_examples=training_examples,\n",
        "    training_targets=training_targets,\n",
        "    validation_examples=validation_examples,\n",
        "    validation_targets=validation_targets)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "LogLoss error (on validation data):\n",
            "  period 00 : 4.95\n",
            "  period 01 : 3.54\n",
            "  period 02 : 2.85\n",
            "  period 03 : 2.74\n",
            "  period 04 : 2.82\n",
            "  period 05 : 2.51\n",
            "  period 06 : 2.02\n",
            "  period 07 : 1.95\n",
            "  period 08 : 2.38\n",
            "  period 09 : 1.93\n",
            "Model training finished.\n",
            "Final accuracy (on validation data): 0.94\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tzXbbvhBCCOuScLayZZ3LSW620SM9e6JWqbl//F3MCJrC+bo8Xkp7lcb2JrvW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\nAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54c3b36550>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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EZHa01LXF80iIiFSJiYSIiCRgRUJERJIIVkwkREQkASsSIiKSREuJRDuLuRARkSqxIiEi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\nFexej6mzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54c1fea910>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "qXvrOgtUR-zD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next, we verify the accuracy on the test set."
      ]
    },
    {
      "metadata": {
        "id": "iDXpZ0jwSCaL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "e735d77f-9fd4-417d-9fe1-db8be63cfafb"
      },
      "cell_type": "code",
      "source": [
        "!wget https://storage.googleapis.com/mledu-datasets/mnist_test.csv -O /tmp/mnist_test.csv"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2018-04-05 19:29:30--  https://storage.googleapis.com/mledu-datasets/mnist_test.csv\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 74.125.141.128, 2607:f8b0:400c:c06::80\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|74.125.141.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 18289443 (17M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/mnist_test.csv’\n",
            "\n",
            "/tmp/mnist_test.csv 100%[===================>]  17.44M  60.5MB/s    in 0.3s    \n",
            "\n",
            "2018-04-05 19:29:31 (60.5 MB/s) - ‘/tmp/mnist_test.csv’ saved [18289443/18289443]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "scQNpDePSFjt",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 326
        },
        "outputId": "3e077941-f20d-402d-db49-3f94bfbc12a3"
      },
      "cell_type": "code",
      "source": [
        "mnist_test_dataframe = pd.read_csv(\n",
        "  io.open(\"/tmp/mnist_test.csv\", \"r\"),\n",
        "  sep=\",\",\n",
        "  header=None)\n",
        "\n",
        "test_targets, test_examples = parse_labels_and_features(mnist_test_dataframe)\n",
        "test_examples.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "      <th>783</th>\n",
              "      <th>784</th>\n",
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              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>10000.0</td>\n",
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              "      <td>10000.0</td>\n",
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              "      <td>10000.0</td>\n",
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              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
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              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
              "      <td>10000.0</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>50%</th>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
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              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.6</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>8 rows × 784 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          1       2       3       4       5       6       7       8       9    \\\n",
              "count 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
              "mean      0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "std       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "min       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "25%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "50%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "75%       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "max       0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "\n",
              "          10    ...       775     776     777     778     779     780     781  \\\n",
              "count 10000.0   ...   10000.0 10000.0 10000.0 10000.0 10000.0 10000.0 10000.0   \n",
              "mean      0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "std       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "min       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "25%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "50%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "75%       0.0   ...       0.0     0.0     0.0     0.0     0.0     0.0     0.0   \n",
              "max       0.0   ...       1.0     1.0     0.6     0.0     0.0     0.0     0.0   \n",
              "\n",
              "          782     783     784  \n",
              "count 10000.0 10000.0 10000.0  \n",
              "mean      0.0     0.0     0.0  \n",
              "std       0.0     0.0     0.0  \n",
              "min       0.0     0.0     0.0  \n",
              "25%       0.0     0.0     0.0  \n",
              "50%       0.0     0.0     0.0  \n",
              "75%       0.0     0.0     0.0  \n",
              "max       0.0     0.0     0.0  \n",
              "\n",
              "[8 rows x 784 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "metadata": {
        "id": "EVaWpWKvSHmu",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "a7d08538-df72-4a55-ea85-6fbfe551fa62"
      },
      "cell_type": "code",
      "source": [
        "predict_test_input_fn = create_predict_input_fn(\n",
        "    test_examples, test_targets, batch_size=150)\n",
        "\n",
        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
        "test_predictions = np.array([item['class_ids'][0] for item in test_predictions])\n",
        "  \n",
        "accuracy = metrics.accuracy_score(test_targets, test_predictions)\n",
        "print \"Accuracy on test data: %0.2f\" % accuracy "
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Accuracy on test data: 0.95\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "WX2mQBAEcisO",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Task 3: Visualize the weights of the first hidden layer.\n",
        "\n",
        "Let's take a few minutes to dig into our neural network and see what it has learned by accessing the `weights_` attribute of our model.\n",
        "\n",
        "The input layer of our model has `784` weights corresponding to the `28×28` pixel input images. The first hidden layer will have `784×N` weights where `N` is the number of nodes in that layer. We can turn those weights back into `28×28` images by *reshaping* each of the `N` `1×784` arrays of weights into `N` arrays of size `28×28`.\n",
        "\n",
        "Run the following cell to plot the weights. Note that this cell requires that a `DNNClassifier` called \"classifier\" has already been trained."
      ]
    },
    {
      "metadata": {
        "id": "eUC0Z8nbafgG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1390
        },
        "outputId": "b11e8cb9-52bb-4b16-a5e1-05f041b0b863"
      },
      "cell_type": "code",
      "source": [
        "print classifier.get_variable_names()\n",
        "\n",
        "weights0 = classifier.get_variable_value(\"dnn/hiddenlayer_0/kernel\")\n",
        "\n",
        "print \"weights0 shape:\", weights0.shape\n",
        "\n",
        "num_nodes = weights0.shape[1]\n",
        "num_rows = int(math.ceil(num_nodes / 10.0))\n",
        "fig, axes = plt.subplots(num_rows, 10, figsize=(20, 2 * num_rows))\n",
        "for coef, ax in zip(weights0.T, axes.ravel()):\n",
        "    # Weights in coef is reshaped from 1x784 to 28x28.\n",
        "    ax.matshow(coef.reshape(28, 28), cmap=plt.cm.pink)\n",
        "    ax.set_xticks(())\n",
        "    ax.set_yticks(())\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "['dnn/hiddenlayer_0/bias', 'dnn/hiddenlayer_0/bias/t_0/Adadelta', 'dnn/hiddenlayer_0/bias/t_0/Adadelta_1', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/Adadelta', 'dnn/hiddenlayer_0/kernel/t_0/Adadelta_1', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/Adadelta', 'dnn/hiddenlayer_1/bias/t_0/Adadelta_1', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/Adadelta', 'dnn/hiddenlayer_1/kernel/t_0/Adadelta_1', 'dnn/hiddenlayer_2/bias', 'dnn/hiddenlayer_2/bias/t_0/Adadelta', 'dnn/hiddenlayer_2/bias/t_0/Adadelta_1', 'dnn/hiddenlayer_2/kernel', 'dnn/hiddenlayer_2/kernel/t_0/Adadelta', 'dnn/hiddenlayer_2/kernel/t_0/Adadelta_1', 'dnn/logits/bias', 'dnn/logits/bias/t_0/Adadelta', 'dnn/logits/bias/t_0/Adadelta_1', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/Adadelta', 'dnn/logits/kernel/t_0/Adadelta_1', 'global_step']\n",
            "weights0 shape: (784, 120)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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rp/J0PHYxbBRzNkI2en0Vp7MHTkd+807FmBx752vWbtDLj+rYwgL54Mz3aEfH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\nverikqyMAbdYSBxCK5vZtVZC9934M2jMI+Jhz0c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9GLZXFragB1u6gC625YOtrE/aHbDh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osTnh7N+px475AWmxhh3WpobWXtQgN9tdhnJr5nzfUyLTa152tD1sF0LfaN88HfsTFj7WwDMUfM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fRfIYUIn4N7bOPKx1/BzjQdH7uI1P15j03XcXM5v/dZRzBmZ69dq7qbtOPf6bipmEuU6LpZ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Cf7jAlj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\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m6W3rZu1qzmCvN7LE8/i0F7CPXdu4jfUxdcS8MiXytpL9fG/2movzb287LSWBazeYP8T6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iAIgiD0IvLjjCAIgiAIgiAIgiAIQi8iP84IgiAIgiAIgiAIgiD0IvLjjCAIgiAIgiAIgiAIQi8i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jxZUp+Vp88bdLrI+vs7MWWwfZa/GDtgesnbmbNdr5od39325qcdSzA1mf+xuuanH/F19T+uCL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\n98Z61GmYMQvXxJJog5Xi9op73t+vxSPHwpr0+FFe9+Xuj+5R+mTxu6hnkPV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BnW7lMViB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g4WWlyTWoa/38DnmLkbzhl2YS5a3FxYz9qZkvdrK8W+2FXfrsVuSQGsT2c9zlHdrfiujoFD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\nXqOxDhyc8dw6O/nYN+QW4bt3YI5VN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T7skZTGR/dvpZRqKST74v82Nvj/BJ80UD5mjAr8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\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/Z/vgJ8aRT1AamvFsRY+MYccKjhEJyTzsMa1csKekkmSllKq+DVlE366QgpSd49L7vBsF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IJddBxqy+9j8qvfKWMSE4Trl3yN16Wj567y4xir8+5GfbOzZDwqpVRgbww2W3+M/fydvHZLPqml\nFhmP9bQ5HetV9s/8PJSajz2T1ueYscLgXhPb3w3Pk5pgX2EfqyXnNsP3S+qLcXnqIj9vjic1E3uY\n4fp4OWENpvU0lFLqxFuYCzPfn6KMQc1tXM+CC3nsNWp9607sqgOm4WzW2dHJ+tCaSNvW4Awy8q5h\nrF2oN67V5ndRh6+PP/7O/g0HWZ/GFpxVZq7EmeHWzwZjiaz5h9fu03HSKoy/prI61ofWiqN2u+4Z\nvK5aJbFUdo7AXLRyx9gZOIDXD6wgNeCMQVMpvouJF1+Xio5m6bjvcqw3Ob/hPOfu50K7KKcY2Ixb\n2GEuNpSVs3aNxFLdeyzOHGRKqJxDp2kXdp1obTi/SeGsXVsb1lMTM3wnj754bmus4s+ieX9hrXAf\ngPXJI4GvjamfY32NehD1ilorMbfDF45ifQ69iueMCD6Uu4z2eqxhbgm8Bk/G71hjTU1RA88jGOtK\n8fFs1sc9Hu/RWodzR8EZXmMofQ/ez5bUwKokdvXsXKCUOrb/Ij4DsWW3sOc1DpurcE1TD+GM5T0S\n4yV4Bq+tVXQN96fPfXgOqS/gZ76zP53R8RBSF4yOy4vv8TXEbwT+rvtE9bdI5owgCIIgCIIgCIIg\nCEI3Ij/OCIIgCIIgCIIgCIIgdCM9Omk+pCAIgiAIgiAIgiAIgvD/KZI5IwiCIAiCIAiCIAiC0I3I\njzOCIAiCIAiCIAiCIAjdiPw4IwiCIAiCIAiCIAiC0I3IjzOCIAiCIAiCIAiCIAjdiPw4IwiCIAiC\nIAiCIAiC0I3IjzOCIAiCIAiCIAiCIAjdiPw4IwiCIAiCIAiCIAiC0I3IjzOCIAiCIAiCIAiCIAjd\niPw4IwiCIAiCIAiCIAiC0I3IjzOCIAiCIAiCIAiCIAjdiPw4IwiCIAiCIAiCIAiC0I3IjzOCIAiC\nIAiCIAiCIAjdiPw4IwiCIAiCIAiCIAiC0I3IjzOCIAiCIAiCIAiCIAjdiPw4IwiCIAiCIAiCIAiC\n0I3IjzOCIAiCIAiCIAiCIAjdiPw4IwiCIAiCIAiCIAiC0I2Y/dOLKbs+13HmwXT2WuQdMTquvlGm\nY5e+3nhzK/72rXXNOvYIH6jjLx98jbW7Z8NLOr61czfez8Zcxy2VjaxP+fUSHRdXV+vYzd6etRu8\naomOGxoydbx15e86HjF/MOtj4WSlY+eQnvh3C1fW7tuH39JxfHwvHfdaMlXHF9f9wvq49PfScfT0\nB5UxeHvePB1PXpTEXvNKiNBx+fUsvNCjhw4dQ/n33PPKDh2Pe3GSjrO3pLB2flPw3iZm+B2wNrtK\nx+Z2FqxP8SHckxs3c3Q8/oWJrF1DUa2OC3fd0nFJFe591IwY1scxzE3HNbfLddxW18LaFR7Lxt9p\nwWvRS/rruC6zkvVprcHYjlv8hOpqzn31ro7bG1rZayHzMF5bGip0fGX9SR3nVVSwPgPGROvYc2iQ\njnP+SGXtLl/EvI/w8dGxiQnGh4WHDevjEInr3FrdpOOmknrWrqUcr5WVYUwUVSHuQcahUkqNuneE\njk3J+nJ442HWrlfvIB3bhTjruPoq1gnfqRG0i8rfflPHQ1auVsagtHSfjjs6+LjL3XFDx221eO2n\nnck6XjR7DOvjEof1dsNLP+j4xR9fYe0qszBHmsnambUnTceRC/uxPk0ldTq29XfUcfbPfJ53tnfo\nuNcjw3VcX4R9oexsPusTMnOojn9+8lMdd3R2snZz31mk41NvYy8IHIJ1+NDW06yPO1nzF6xfr7qa\nTQ88oOOQAG/2mrUf/raZvaWOS87k6ThwaiTrY+uHa1ubhXWlwuCaOZN73VSMe5N3LlfHdU1NrI+N\nJT5De3u7jqNmx7J2WTsw9r0G+eu48BTWYDovlVJq7AsTdNxAPk8jWZuVUsop0l3Hye8f1PGAWXE6\n3vVNMuvT0YEx9fSPPypjcHkLxoaFoyV77eB3x3Q887WZOqZzh84PpZSqulKs454LcH2r08tYO7p3\nlKYU6TjuqZH/9bMeW7tHx74hnjrOTitg7RysrXXsPShAxybm2H/tg5xZn6KD2HMD50TpOOuXq6yd\n97hQHbfVY30ysTTV8fb3drM+vX19dTx27VrV1ZSVJeu4+Hwae83MGufFEz+c0nFUvxAdewwJYH2K\nyPnDh3zf7J+vsXZO5NzmEOyi47amNh3TdVEppaquYe/Ju4a5bWZqytoNfGa0jitvYnzk7cZe3NLW\nxvrEPYl1N3cn5nJBCh8fg5+fpuOc3Rd0XH8LY9Il0Zf16WzF9+gz5X5lDNYtWKDjSXfxeeAUgfUj\ndxv2SHq+7GHCzwmt9Tgj/f7Gnzo2fB6Y9Np8Hf/w5Bc6tiD3ZOKT41mf8gu4pmEzx+n48vu/sXb+\ns7DO03EVvgj3qjSFP1fZkX22nYwlOpaVUsreBc8XZZkXdWzpiGeVDoPxd2kjzoNT3nlHdTWlpVjb\nW5v4HtDehPuR9SPmUkkl9pRWsj8ppdSQx5J0fHAdzk2NLfzcdOe7eG468vpPOg4ZFa5jw/Xd0hnr\nZFFylo47mvm8svF3UH+H70is761N/FmgMhXzvL0R71d9rZS1c4zGuPYdgrNX/jHcz5R911mf/gvi\ndRyWuEQZg2s7PtNxTSr/zNa+mD8m5pgjNn64Tnb+TqxP2Xmsda6xOMOYWvExffXD4zqOfQLn3LIr\nGTq+tesG65PwHOZmE3n2M7e3Yu2ay/Hsceoz/J1hT43ScQ9TvoZk/oz9r6MFc4nOS6WUcorx0LFv\nUh8dV97C2anN4JnNtQ+epTw8+LPtf5DMGUEQBEEQBEEQBEEQhG5EfpwRBEEQBEEQBEEQBEHoRv5R\n1nRrP9IjE5/j6fQ3NyKVPLMI6byjhwfpuNlAemTtYafjvLNHdXzfZ2+yds3NSKWy8rDVcWcHUt4z\n9/P0psp6pC01kbS38Ngg1q4kFamcruFIDZz6IqRHJadyWR8rd3yG1lak4dWXFbJ2SXck6tipl7v6\nO5z7ebL/zj8BCU309L/t8r/G2Q7X3cSCp9Bmbjmn49ocpIXZkO+874tDrE87kR78+gJSORMSolg7\nmvaWvwfpm4cOnNfxpIUjWJ+wJUN0bH8a8hj6Xkoptf8zfCYqPZq9ajrpw397vPYJ0jpdo3Af0s9k\nsHbhiUh7tslHemZdFu59zvEs1sc9xE0ZE/dBfjouOsz/9sV3d+nYf1yYjul9ih/PZQx1t5GKWemM\n+dtayWURcYMxR+qzMD7se+P7eg0LYn3q89HO1BJLTNXFYtbOhMiSes2EBC2kogGf7XwR61N2CvKQ\nkAVIBfVzcWHt6oprSTukgnolINW4rY3LErzGhShj06MHXXJ5em7wjAQdl92ADOkur8k6djRYV6hE\nYukKrGENVVwS8+XLkFM622JutxP5SOVn/Hr0v3OAjuvza3RM13ullGpohqTP/XKWjj9fh7Vh8aIJ\ntIu6vQXrP5WuOdtwidyu1ZCbRsZCykTHUk8PD9bHpzeXGnU1EVGBOj5+issd5t45S8cNhRiDnomQ\nCjkEc5motTXezyoKfY5tOs7aJfbB9zyz74qOk+4ehn//nku8hq2aq+P849j7HEL5emXvjvGSsh+p\n1FPWPoT+50+yPhc/OYH3s0GauHtSIGvXUIDv1Hci5JQtVVhr7v6ES0Ezth9RxubYtrM69nfj12Pk\nQuxDBfswF+kZxJfIXpRSqrEYZ5Dz7+Hzh0zkMjbPobg+vmPxHllbcU/9J3PJpROZs+ZEZj2YpP4r\npVT5BdxHem6x9UXaefVNLrOiqepZmzGez1/mMqEJw/C5d6yHzCAuJFjHE1eMZn1svOyUMSm7hr27\nwyDd3JrItyzNsO76jsceSaUeSinlHY21g55fDaU+DdnY4xpysDa2VmMtNEyTD5qLlHeHcKwBli58\nzbv+MeY9PcvGPZWk49RP+Oe+9hHmonNvrBMlROKvlFK3fsB7l+RC2u0bjTR7QwmNXQSXKRiDsbMg\nzTb8+/lEznXjWpaOW8pwf65kZdMuyp7I+6gcu7CSS1DaWnB9Zq3G2dGCyCJKTuewPp7kvFORA4mv\n9wR+frDxxHWj46KlCRLz2jQ+F2/8ASmFXyzOfFUGc/Z8BvbzifMhkzKPwr3vbOcSYc8wvk92NSVn\ncZ88B4az11o6cZ09x2Af7+mNtafoED+Hn/kEZ4TJr0P2duWDvaxdcyOew2KX4axHr3/pRX4PKy/h\nXBlMzjlNlVy6e/Mb7Jl9n4SE7fy6nTq2c7ZlfehzR3Ym/s6QR/izjo0rpJE5+87o2HdUb/Q/yq/J\niW8xz40la3IkaxMt6aGUUk5kDJmb48x9+DWMR3oeVEqpEU/jt4MDb+PehYbyNdWRrNf0nMxKRjw8\nhPVpIOUQjn+SrON+s+NYu9tEDjVgCUqqXPgE66Hh/KBlE7zHBenYyb8na9fejnVox4s/6zhmBJ6d\nnKO9WJ+mCnLW/i/TUjJnBEEQBEEQBEEQBEEQuhH5cUYQBEEQBEEQBEEQBKEbkR9nBEEQBEEQBEEQ\nBEEQupF/rDnj3xeax5ZaXj+mogY68gEzoe8qIzZzHgP9WJ+mctSSaC6DPvvCB1+xdjZBsJMLGAf7\n4sw/oafvM7sv6+PbD1q0/POwwXSM4DUazM2hQ7z+OWp1KKIlj35oNuvT0gJt7rE3/9Dx+DeeYe12\nvr1Kx4s+WqnjvS9t0DG1ulTq/7YKNgZUb21oAW3pCr2zswPs5qhetd3A3taO2LOOehhWZLS+iFL8\nu3U0wyavb1CQjqklrFJK/fzU1zru3w+6e0NLt3GPQMeYQXTyjt7Q/Z5ay23LHdyhb6U6xuHPcp18\nIanpQnWH1Mq9ycDO79p51CUYqLoea3foMa29uf7YrideKz6UpWO/eNS5sPLk2v8Kormltqhh9w5g\n7czNoT+l1s/ZO1E3qPpWOetjQ/5W1Q3cN0O7S5sAar+HOX9tB68nRYlZBl149n7o7j0N1prmUqw1\nZ96BzpXWBqLXTan/22LWGGx7AVaPpTU17LW7P75Hxx69UZ+gLRxr7am3d7A+VaTWVpAP6ig5hPAa\nPPe9BUvqm5ugo+45HbrYV57YyPr8fgr2sy88hv7xc/qzduWnseZ3tGCeP/EBvs/r93NL63xi7f75\nNth+l57m9b4asnGNHCJw70ozMAdO3ODj5ennpyhj0nMu9jsrT643r8uGBppaK/cww/8HaYnge6mZ\nGdZkc3OMQUNrUbomezlhHzO3w3o8/DFuQ5v+a7KOL55BDblRbrzOhW0w/m48sRduakINE7sAXnsi\ncDhqjTSR+ab4dqEUmfatNVhD9myDfn5CK/+uN0/f1nHcYmUUTMj+lPDsOPbare9Qj8aK1E2htb/y\n995ifYKmo66XLbEW7WFQ+4zaahYfzUIfcn1vb7rM+vR/BrWDqvJSdezs1Y+1MxmIv1V+CfOSrq/5\nRzJZn+iHUOuKWqJ7ZvLaUo2khhKtW0VrqxQf5O+dWYD3WLKRn6u6guoU7C+GtrfHPkrWMa1B0lKL\nvb+5lVucZp5H7ZK+pHZLXXoFa0frnXiNx5mjpQZ1lGhdGqWU+vY5rP0LVuNamFryenrZpfhOIQGo\nl9LWjLnjOzmM9WmuwOepIfVJ+pKacUrxOknOZN/uaMP8c+nlz/rc+oHUw3h8pjIG7vGYV3m7eK0j\nP1J/qecsnE82P7NJx9EB3BI94iGcwvJ24/1iBvPTWWUqxqetL+ZIbQ7WZFq7SSmlfn8ZzwBTH4eV\nr7mdBWtnbo79KvoRWJhbWOBMZWJ5W/036Hmz5+ze7DWnq3iP09uxn4/wQP0xlzBeG6M664IyJtRO\nOeUjXqfSzBbfxWcCxiA9e4bO5ntXzxkY07kHYC9tZVAH5a8123QcQGqHuYTj2c+1vw/rU5CMderK\nB/isYUv4c6W1I9aNX57+RsdmxGo9yoHvpeevY7x5OGJMGZ5t7EJwdjMl93rL81gnYqKCWZ+Bz4xV\nxqbsHPZ8Cyf+vJqzA3uPUxSuL11fg4fzWmwX1qOuy6CFg3TcTH4PUEqpqsuYi9VZqBHkkYC5XZvF\nn1+L9qImj6U5rqGtD98Lopdh3Sgnzz605qKLwRihduvH3scYcba9xNrFPj5Ux+NemoTPmo3Pmvod\nn3vltdhLF224Q/0dkjkjCIIgCIIgCIIgCILQjciPM4IgCIIgCIIgCIIgCN3IP8qa7EOp5SfPVa6o\nQ/pr+h6kldtZIeUs5cB11mfAQqQUeo9AqmLncC4ROf1Oso5tiAVkxiWknI6cGkO7qPS/IFFyJnZy\n+XvTWbu2WvytJpKC6j8RaaLXv97G+tC05KEvwK5230vvsnZz1yGNv7UVKU3+vZAuFTFvEutzeu33\nytgEexH7boOUc4cw3OOKS7CkoxICVzsuiWkiqcD525HC5xzH7cK2fAW7zaWv34n+P8Iu8M/tx1gf\nR2KlaxuENO8tm/azdvcRW8qIZZAZXHkPKaeDnjeUp5XgM5CUukYisVNKqWoioTIzxxT5dSPG2Mgo\nbhveb0m8MiadnbjmhlI9Ww9c97Y6pGy79MO462jtYH0yS3AtTC5ifJ/ZyVP2Bt+BOfvzRtgHThiK\nNMFDu8+yPqOnIU2eysKsA3iqYSOxKT/67kEdB4Xhczv35WOKWuz6joNdo6kpTz1urEBqdwuxBw+a\nCSvfgmQuSyg6kqVjf+4E2WW0tMHudcGrc9hrZZexvpWfguSDWnTaEkmhUkpF3oF1sOAvrHXtzdxW\ntuIkUmrLiJwqhFjU3z3awAaXpHNfP4X37jSQOfbqj8/XWIR94fQfkL49/eZdrM/YIct0nEnWg54L\nolm7ouYsHdfnQDIU/zRSoPcu5hKQmlzIOdyM4HCftxf7GpW5KKVUYwnWEtueWL/syFpm4cjTsq9v\nOKDjfk8s1LEnSYlWSqnTW8/peMIq7CN73sS6NHQZt5qsITKrGW9CkrD52c2sHbWb9RuN+0ltvnv0\n4Pbs5/+CXNCF7BEmqUWsnbMb5r1jNPbmkUOQQn7lcCrrM37NZGVsBo7E3GlvaWKvNREbZWqB3ET2\nirY6fm5pa8aeYk2kncc+TmbteobBQjR0IdbK42v/0rFPBF/3Ur7YrmMbP8hzbT15qjzdy0zI3K66\njvXe1oGnqt/+Bmt+cyO+k5U5tzS+sBPzzJncb99pOMsVGki9/Fy5bXxX40HsvYsOctvZyESc6dzi\ncc1LT+fp2D+K27nWZWG+dBCpXXsDX0/9ZsEevfIyxjvdkxoNUuun+eC+tVRjvFk4crng5Nfn6fji\nu3t0XH0T55LSI9wa2NILa3V1IeRUHq78Xptb4jOd+WW3jsP74Dq6xQSxPqELjSHU5qR9gb2i71Nc\nOpVzCPLlFiLfWvTRczq++dtO1idtIzmTENldbjGXwIbfDRvqwhOwxa4n4yBiMZeSzFiJa1p1Dfd+\nz5YTrN2it3Dmzd+HeeEcjfN42PTxrE9r1Z86diKyOtcALrepy8bZOnYQ5p+NJ8YY3VeUUsp7aKAy\nJjm/YQ0PWcY/bwH9/oGYIz898YmOxz7AJYZUnucQjo084ySf5wkzcBb1HQyZZ0UGzixNJbx8Qq/l\n6NPRhrOxm38Ca9c+EfM+Px/zz9cbn8d7Ipfx2IWQ+Uyksw05XOZYmgIZT8A4rFVh3t46rirm0veS\nM/juHkZSb5dcxnOgvQt/9vOfgXWvgchc65qwnpXs58/9/j4Yx1Re25DHr0fQndhnW4ncrTod46Da\noLyFhSvOUhOefkzHzc0FrF3WDkgzXQfgzGZHzmi7PuIW7dNW4gIHBGLOug3mss+M77F/WnpgHU45\nyeWZlJFPjfmvr/0HyZwRBEEQBEEQBEEQBEHoRuTHGUEQBEEQBEEQBEEQhG7kH2VNtFqxsxd36DA3\nRcocTdVPeAHSnsxDPE3IMwopZyff/EHHXnE8tTTugUQdH/sgWcfRk5Dy3tbCHS9aq5BWRR0zrL15\nWpbXFKROVedBRlCwCylwwQtjWZ+mcqQKW1hAUpJg4PKTtQtp5x4JSH2iMoBtz3PXkqT/F+lN/1uo\nU4hDJM/zz/gVTkeBM3hl//9gYuAo1Urut+80pCiWnuAp1iGeSAV7+b6PdfwgcdyxT+dpt1Puw/Vw\nCIbjzCLfWaxd6RmkJtekIO3NJhBpu00NPL2e3kcm7+rgMo2oRyENKDyCavpzh0zUsV0gT0Vua+Qp\nmV3NbZI655bIpRRV16/o2HsE0iPTv0QqX/jywazPpFcgz2uuQDp+1Vdc4rX9K8xz6jTmOQIpslMH\n8Crn5achf3DojfFGJYVKKdVQhb9b3YC4jTi61NzgzlSOfZAiSR0Vcv88ydrZBSB9spXID7K3IXXZ\nZ0wI62NlbwQNjAFTV+O63/7qInvt4DXMxfs+XKrjrJ8g+wmebyD7Sc7S8bkMpLy6FPF7cv4UUo7X\nfg1HtFXNkMFdycqiXdQzH6/QMZV2eo/mDgIv34u5PWMgUuBrSarr1o92sT4DByCtmKamdrZz+d3v\nv6FK/oIVkLokv473u/dRvjb88hZSw1f+skB1NQ7hkGpc//Ice42mAdeUI+23pxPSgTN/TWF9asjY\nv/wxnBrcnLisyc0F/118AnsX3X93buTyz5JqpA4ffxBuTaP78z0udBn2ZntHSDaTX/kUf8fA2WbM\ncyQln2wRNRnc2cbKDam+ZtbEOfA22lmY/eNRxCg0ZOLa5Bo4xKXkYi+reg9rYsKTSToOuqMP7aLK\nzmFPsiaSlsjBBhpJch44+TbkGHQNjE3kqdP0TONIUvwdHLi8uyAHa4XPMNzH27/C6TJ4Mb/3Jafw\nXRvPY+32ieZrSCT5uyc3Yb0tIXJQvykRtIvqNHTu6mLKz+Ka30jNZq9NWI31ojYTY43OX8dgLh8z\nN8eZozIT86WilKfg93SB/Pr4fuzNE4hMnEp6lVLKgrjMVKdAZma45rXW4twS/xxcunKPwj3vag6X\nNc26D1KgdiITbS7n5+Tdq3/Vcd8J2EtuHUIKfpgpP5PjBUvtAAAgAElEQVTd/CZZx+5PGue82vsR\nuAx98eA69tqY6ZCaWBKpbXMz5BdBU7nL5KbHP9fxvDcgH9780hbWrmcTrsHZv7AfxwzCnE1+9QfW\nx7snziAh8yFnH9PMHedcPHHmOnwJ63J/cga/8gmXl7oPgzMNcwo143tBQw7kLk1lWDdqbsOBK+we\n/syWvBbPY1G8ukKXQJ3PTC342G+twjmjvgpjd9pquFh1GMyDnlMwJjL/OqpjH3L9lVKqnZy9/w97\n7xVfVdV1Dy/Se++9J5CEAAmdhNB770VEBR97w4IidkVFhUcFOwKiIh0E6R0ivQRCEkjvvff6v/h+\n7x5znhd9L76cHzdzXE3Za53ss9dac619HGOO218jnwYS5yUDI85DcPLEGT91/w4trs3i5xT6DuLp\niXc/F3L+bS7jrkPUAbchH+PUXsf3Twtz5IPjmyDXH/EQvrdLb55Pk7+B/F/pSdbUTM4TPYiMSSml\nssnZxWcWHMTcAjEmljrvRq3EHa+OSJkc++g4aB3Bu5ZVENwjqRul+wh+ZqfvLnd+xTi26rxr9Hhs\nrBZXZCGvW/vi71AHLqWUStkEhyVjcm3/+qOsnZ8z5kVYFN55o0Zg/7UN5e8WOdvxHL1fv7+LoTBn\nBAKBQCAQCAQCgUAgEAgeIOTHGYFAIBAIBAKBQCAQCASCB4h/5RKXXgTdtfBYOrsWRSiRtIp/dgJk\nEGauXFLU0gKJQuxbT2pxZyevhF90B5TZoctRKb2cuAnRCs5KKZWdDDquRQau2Tlas3btjfhbTtGQ\nUzn2R2xpw6lThadAY0rcDBp7z4e5Qw+VVt38DhTU/q9CDuMziUuHarIIBZz/2S6DiRNoqmkHuCsG\npb27V/lpcSeR+jha82d4txDjsHc1oRESGZNS3C3BkNDCrINAFYzJ4F/aKcJfi5uq8Wws3bnzga4U\n6X9QeRMV0G3tOX07Y8tmLbYJA/34f83TWlDlbhyD1CTuqaFafGkdd5kKn8Lp5V2NW3cgWfEv45Xn\n3QeCClt2E2vWZxZodVSOp5RSTR1oR90rmnWkC+Pn4zsn7MXcp1TD5kpOnTayNdHiA1tOaXH/4GDW\nzptUqPe3wr0ammCu/PzONtancQ8okrOnwrEn/LkhrF32Poxbj2dBLz72LhxRMhM5Ndw3lOSDp2OV\nPnDvJ1AlAxbwOTPlOubk3rfgOtZIxmSoqwXrc+IMPi8mAHKjuvRK1u7MHVTQHzMUYzrxbcis/Ndf\nYH2KT2dqsdc40Ly/eOp71u6REcPxGQvxndyPY8/wHMelHVYrd2uxXQTyxuGPD7F2UX5+WmxsgznX\nczooy7+s2Uu7qCe/eFjpE4UH8b36LBvNrpVcgStF/iE4CxzbcFqLqXxFKe5+t2gOJFoGJpxma2ID\nGnTGJkgphszur8XddCSoNsRx8eRnoNZn5BaydnmrcK+hgzHuA5dDLtFQyfucXI19MTAEUsuwh7n0\n4cS7v2mxiz0oz64jkev79uPS5jIir3HTE32b0ugTzt1i18Y8jDVibI15l3cAlOi8VP48AmPh2pG0\nFRIJ3Zza9zHItt1i/bQ4aweoziVnuESnOAtnGo9SzJ/Odi4dp/daV4S90HsC6PF/vL6D9Zm8DOcT\nun+au/N9n1L0nW2wH6cnYy8pSC9mfcImEFfD+6um/3/BljhzDgrizlCZv0Pua2SFPYk6URoYmLA+\n7e3YW11DsW+UB3P3j9os5FcX4qrW1oCxpm4zSinVRmQNAbPhMOnszN2A2towvlnXIcMxd8E5ZeIr\n41gfel6rJnJKa2u+X/SbD9lpyh7skSbEmSth1R7Wx9aSf4Y+kH8c59J5H81i19qbcGa/sB7ylkbi\nFuM9kcsvhk3E96R7bp9gLsnd9Sb2oVmfwiWrLh9nT59JXL7YRqTAhoYYE+osq5RSB5Z/pMVDX0VO\nbCjCfXcz5P9/vJm4URlZYm6mJfMxiXgMz8jEBPO5ogLn0sPv7Gd9hr3IyzB0NcxNcb9ZO26za7fv\nZmmxn8LzrLgFCd+dY/zdxJbI6GPIfG+s5OvKzg2fd+UG3G/TN2OP9Nc5a1WWQ/LvNQzvCenbuTze\nawAkdc5DIG+1D/LT4qM6Z1Rff7gt2RF3Ql0ZT+4uSGmnvo1SD4dWQVo1JYo7bNXV8rODPtBzMd5r\nWxu4PKi9BdI9Q1P8fGBJyggkH+FuTdGLcT658jPOmO6ODqyd02CcIXLJ2TFwBvaQuz9fY30in8c5\n3cgC86+tnt937mnIemvTkLs9x2LPpk7TSinlPxkbVjVxO+zZ4cPa5ZZDSkj33+xjOAvahvH3L4e+\nfC7cD8KcEQgEAoFAIBAIBAKBQCB4gJAfZwQCgUAgEAgEAoFAIBAIHiDkxxmBQCAQCAQCgUAgEAgE\nggeIf605Q7W55s68LkdDIfTH4cR2rpsBNO92/lxHnvI99KLGtpe12CWW6+qy9kB7GDAbesKMk7C7\n1tVxD3g2TouvkHogZ65x7ePSx2itG3wGrUWTefgE62NAbGR7LoJl38UfzrN2fsHQkUU8At1e/ilo\nyWndBKWUOv17ghaHDNJPrYTCe9CB6+rqKuqgsb57EM+9vBa62N/OnGF9nhkPH74O4pV5LTOTtaO1\nFfpMg1Xrli9QI2LC8P6sT95xYgU6DDriqjSu7ze1gx7VhdRcobaUh1asZX3iV8JS8epq6HFd+nJr\n6uo7qL8wffXzuIcijGPYmB6sTyPREesDY19CbYuja7ldro81arfU56KGUH0m7FctZnD7wew90IWa\nOuBZDl0xhrVL/hoaUR8n5IMr32He+vbitq+0hkjLUayroMW9WDsrR+i/S5Mx7td+RW2btnZuT/nE\n+wu0uD4H3y91PdcKBz+OddpUiTmeUoDaAQNDeB0UMxf9a+tpjacwW74WE09j/cXNRl0KCw/Ufsjb\nk8r6zHoZNWNq06B9tY/kFrHPtKFdckqWFtN8baWT46mOuLEUz7C1jdcIM7JGvQJqJ2wVAJvCyju8\nFsWw+bCydOqOehjTP+WFKUxNod/OOIn6VtTK/umvH2V9Tqw6rMUL13Ob7a5A2FOob5S49ji7Fvk8\napUElEKj3vg3rGrHPcdrTBSdQN4sOIA9rqCS1w3yJBrtoCWoWdFAai+UX+G1MTz6wU41pAfypG4N\nIGo1SutplCZhvtXpWGTTfBC5BLm1sTGPtRv+9kItvklyfxN5Pq4DuY7b0MxY6Rvmnpjvk6aM5RfJ\nvpb461UtLif7ZWSvINblwgHo4QdNQf6xCeTaemonuv3jfbiHx1ATwtSJ56ILl5CvQydDg5+//676\nJ1jPwPqjNfronq+UUlW3sDZtQlG3Jed4GmvnHoN9MmIpanoc+OiAFscv5zWY8g/+8/11BXL+xPzU\ntUK1Dsb3t6KWuL1wVrz+319YH9/ZeLa0toVjDK8RQGsLBERg/7Pvjn228go/swQ/gho2tDbUnSM/\nsHa392MvpN/J2Q752G8er4OSvQvzw4/UctO1lKW2y1GPYgzpWjSy5Gsv+Y+bSt/obMO5jdaYUYrX\ntxtE8uubD6/R4qXEqlkppS4kop7Hn5dQX+SXgx+zdpmfY+63t+JZtTfjHuqLeB6uTsb5MP3vk1oc\ntYDbedO8R2sZlWShRlPAQl4X8e63eC+ie3NdBr8Hk1GYzzc3/6jFTn3xzjVn7busz50dv2uxD3do\n7hJ4z8De3aBzHo4mZx0LW9zj6cM4y4b08Wd9mkswV898yOvKUYx4B1+mkOyZfr44OzQU1LA+ZRdR\n08x3Os7y9MyilFKJa3Zqsdd0vI90dmKuhPbndTPdh+Fcu+9t5Pdpq+awdtSKOv8Q9n1q25zyLX/H\ndPDndbX0gRqyV7gO4u/m7qPx3WgNWPoO0e8JXv+xjdSt8QnCmNiG8zoslVdRfyhgOvJw5S3Uewl/\nZgDrk7kNdYVaylCvyUTnLG/ujr3eOgRrx9IV9xM9vQ/rY0b24GY3nMGdrPk7fGgv9Cs+h1pxHn2x\nLxjq1A+kz+ufIMwZgUAgEAgEAoFAIBAIBIIHCPlxRiAQCAQCgUAgEAgEAoHgAeJfZU0dzZAUXPsm\ngV0LnQTaUcVd0PwsPEH/oTImpTh12nsioaKd5nIYEyPcVnM5qG0j3n1ai8uyLrE+l4mUafjbD2mx\n3We7WLvkdae02CYctOzaFEgC8op1LBCJtOLyBlB4H5rN6emuxBZz7fM/afHyza9o8f43/2B9Fq9/\nX+kb6cWgbo6eyyln85aAitlK7Meoxd/5VC6lqCNWgpHxoDKGWXOpz5ltkJoYGIPWFR+OueM/J5L1\nSf8FNLWaLNz3vq8Ps3bTlk24732bE/pZWlER66Peh4Woqwvoix6xnON5cw1kbQ0NsLBO+hGUU9fe\nXLLnFsspmV2NJrIO+sRzSrNdKOiBdsSy7SCxUmyr4/TmsgLQP+0dYAFZcIVL2No7QDcOGo+xvrYD\nVP/Wak4pvv4Hrs19ebIWZ/2RxNpZ+EL+4DkSEgGfHni2QUO4dKCpDPRrCw/ct64MovgCbLJ3/gLq\n7NjBkHlY+tmxPh5DuR2nPkBtk/es2M2u0TxzcivorJNew1wPe2ow63Ptc8hqfAidXVeG2k4sXud/\n+bYWV1fC8vf8FT4+C+fN0+L1T23Av8/gee/wMeTiiTNgbVj2N8Y3cHFv1idnN2j4bQ2QgzjrSAyp\nBbw1kSaYO2Kdd3ZyGnxIlJ/SJ8zMMD9rG7mNfEUSvvPxg3gu4+ZBSnF0HZfN9umPeXebSM5mfvoY\na2dtjXWfdRVzxz0CVN+S09zWsywVYxo8H2Oz+7WN/B5GIA+7DAAdty4b0kHbHlwa6TMe9qRZp0Hv\n94nlc9TGBvfd73V8Rrdu2BNyrhxlfexCOOVZH/Acg/VSRawyleLy0MA45CCvcoz39QsprM+AsZBt\nOvaGDKYut4q127AWYxfbHTm1g9iUHljD9zsrU1CpqW3yntNczrmPSJBDfgUFfcXj87XYztKS9aF/\n12MUvmtTcT1rZ2CGc1lrTZMWT1oJyWQNkVYqpZT7CE7572pQGWHhuXvsWk0SzqXuw3Efh1b8V4tD\nR/KcT79X8lbIeWx0LKmZjWwQzhKPj31Ti0f34jLeg0sh1x0djWv+87nNr4MVcrc9KS3QTGj72dt4\nrj52Dfc6zQfyOCptVkqppkpC/SdSE3MXzInSi1yWGLEoWukbDbmQwZR28r9v7obn4RgFGcJjo2BP\n7T6KW2SPj0G7BathkX3+Uy5DHf8+pCZGRjhPlJVA9uIzlOczM4csLbYJwfiUJuSydge+gs29ux3O\nGlGzIINoruTWyA3kbN1zCGRn+YZXWLv2dvTzn4p2VRmQVZx9l8v6Le35uu9q2HlDKttQdJVds+2O\n51R+D+8TY9/Hu1rKBi7XtwrGfu8xAbnawZeff1N/h/X08JWw3C69iDOgsQ2XkFcWI78rIgn0msjl\nvmlHcK9eSLuqowN5wjGavwsc/wi5e/xruJ/CM1zimUr+m+bkXiPwfmTpw8+o1an83VQfcCLfh8ph\nlVKq6Ajeh7xnYu+y9cL7T+KaA6yPIXmXDHsa+frUB1yqNvYDlBzJOoEzUmkK3gODpg1jfTzH41xr\n5eCnxVV5fC/wCMGZtbkZe31NGUoJlJzJYX1qPSHj7vXI41qcvG8La2ftQsozmCB31d5Ff10b9dQ/\nErU4oM8CdT8Ic0YgEAgEAoFAIBAIBAKB4AFCfpwRCAQCgUAgEAgEAoFAIHiA6NbZSWwJdJDw+Yda\nnJiUzq71DAOVh9Ji/ReAonluzUnWJyjaT4vLkkEtciFURaWUMrIy0eKDv4GmO/O1SeqfcPqb0/g7\nHvi8ogpe5ZxWkrZwAh312ye/xd95kjs3/PkjqJCPfLlUi/e+8Ttr52KLavouPqiq3VoBClz3Zzgt\nq6UBtFMPn6lKH9jy1FNaXEzcYpRSatHaxVqcuRMSh4pM0JN9RnJpSVs9qGQnd8HNx8uBu1IEDsQc\nuXMG9MChL4OOamLF5RdNlbi/1I2QO5iacNmKqRtogD6TQa9LJdXuI18Yx/pk7AUF3JlQ9//69C/W\nLn4haKwGRqDeX90GamnPcVyO5UDmsIf3FNXV2PXii1ocMZPTpcsJDdl/Pqr/Hyaypt5jOXW6NrmM\n9ME13Sr7lEKfshNUvJvZoM9GB3BKsTeReJVcwL11Uxw2hLbqFAMq5aEvQAd2s+O0Tu/eGLfASaBv\nNzdzKjR1aKpMhLwt7TzymK5kysQe1NfuI5YofSDj2q9afPBLLuXo6e+nxWkFqITfbypo5dShSiml\nOpqQe6lTmTLgT9tvJqjAVSnIvQc3ntJiN5K/lFLKwAC/3feeCSp2xXUd5zRS1d49HmNvboWxuvMj\nX2ONhF5PXYkKKrgj0LMbPtHi5mbIFAwMkA/yEi6yPh4DsAbs7bkbXFegqgp5srYsg12rI05Hv68D\nvfeRd2drsYWrNevTUgsqu6MHXP5ufL2ZtYt5AbLexkZQcAuvgUJuaM6Vyq01+OyqG6AHe0/lcg5D\nIllpIxI4Mwfk5+w93PnQdaifFlcSx5+qRC4RchqAtU1lyoGTsX4rsu+wPjae6OPoGKv0gc1PgkY9\neAmX+6bvwHc1t4CkyDkWrlJU2qIUl4lU3MQaofulUkpdTcB37d0HNHr3UZDemNnzOXL6Q1D3qdTU\n0ID//zW6lsY8Eq/FN3dDLuwfzqWDbXW4v8JcrLELdzkNf+mbkIAc/g608yFjkRuo66VSSrkMwvPy\nCZuluhq39+PcVn6JO5W1EAmpMZHKF1Uhh9rrSLxciRyNuuWU3eASaVNLzIljV/Bs6V5I5cJKKXUl\nGVR7Kl3yd3Nl7Vzi4ZBi4Y55QB2i7qzjOe9aBvLQsDGQqv+waT9rN28I5nmPZ+O1uKUe566CY/ys\nn3gRZ7fHfuDOUl2F7c/DFdPTh8snqYOYa3/s2bW5JOcQh02llGomrlRtxMnJyI47rVCXqPCleL+o\nyoNksYO0UUoplyDISLe9BPcnXQdZ6ooWE4i1nVWKex35FH8fMHPEfDS3wTrd9ep3rN3kj7AWK1Jw\n9qm6iXlq7sXnH3XyG/bBB6qrkZmI9yEzBy4DTN8M2Z0pcdKx8MQ9Wurcb+qvWFcOAWQOkH1HKaXu\nbMC53M4D58XIJXhGbW38XEsltbe/36PF+bl8HlkQOalXP+Qy+0isWSdvfsYoSkWJDSNznFN0JUKV\nl7FHBC3FGa/gCPKEa5wf62Nuj7Xh5BSn9IGz70D2buLMx9FvJiRXzeQMV3wmS4vpO4NSSjlEI6fu\nXYt9bORU7rzkPQJ5q6EKz8aIlCyoTOaun2VnISWk79ZOTvGs3aXvPtViKmduLMEatXDn84+WUCi7\nRN6xdFydqNw8MwFnPlpKwjuOf9eDb6Lsybyvv1b3gzBnBAKBQCAQCAQCgUAgEAgeIOTHGYFAIBAI\nBAKBQCAQCASCBwj5cUYgEAgEAoFAIBAIBAKB4AHiX620gx6Ctsotz49do3UHDC1RI8bcFrU3pq5+\ni/U59MYqLR7xLvTeFfnXWbvMX29p8YKPoNXPJpZnujVw5q15QYvPvA+t/jRSN0cppYpyUM+i5Ap0\nuvNeRZ0Q3ZoAFbXQK66Y9Z4WPzab1zQxJprzaQtxPxtXrNBiU1OuL75I6p14fKqfmjMDHhmkxRZu\n/LtlbEddlxOnEX+7DZasOwI/ZX3SElFvZEA/2Ge7xPqydi2k3kEPYlNccRu62G46mnmvwbAFbG1H\n/ZjQhdzO8feVsMVeOA66fUtf6AYzdp9nfWy6w5616Djmz+DJMaxd6gHMMxsLaC5HvztTi8tuc/v3\n0svQJHp4qy5HPbFYNLXjtoAFOaQWx05YbFqZoR3VGyullOtQjFXWdtRX0K0ZQGtYWNLPI2uisYXb\ndK9dDe3xkhm8fhNF2EzM97TD0KL2HYZ6PpY+vA6KZx/Un2huRg4qPMPzAa25YuWP2lJ3d6BPTI9B\nrIuFM6+ZpA+UnEOtkMp6blV75Do01tXkWr9O5OHg2cNZn5pC5LCDnx3S4u5evK7Eja+wFmydkQNC\nPaAHLqupYX2CfNy0uKEA16oKed2qO5cx50ZbYy/oZoR13vf551iflhbUtMo6h9o7fhf4PM06h3xN\ny6MZmmBedjPi9XU+W/yRFn+4l9s1dgVaWvC97N147al7G7H3mJvgWZjammvxkXd5/Z1eY6BZvvsz\nagvEvDqHtTMyur8VqhVZI611fC16REKX7tkfY1iZw2u8tDVh3VfTenCDkCd6LJjB+lQWYd92j0NN\nhQsH+X4+PxaW7KVpuJbyC55D90UTWZ+7OzHujo/op+bMgIegAy86wvNHFVl/B65hX7S/jrpbLW08\nV85ZPEaLzVxRU8TQxJC1M70MDX1xFuoQ+FhhL035OoH1ofUsbmZlafGS13gdl9wfUB+v4jLWUmAU\n6iWU69Q+8IpDnajdx5EnHn6a106jdWZo3tixFf8+PILb3J47iZz2wi9dX3Pm1iHsXf7B3NI2YAJq\nT9G6BRFmqEvR2cnH0MgI49ZYg+dXk8Itwm26owZGQC7OdN598ZzNPXgNg2hSm/FqOvK2WxOvq2ZG\nanjV52PNGtug/oVuraHBMagF0VqNs8JDo+JZO/cxWKcZf1zS4px72BdjXx/F+nQ0tyt9o7YR9St8\npnVn1/a8ixw+OQjP3d4/VIuNyDuIUkrt/Qi1dvr2wdnTNtyZtaPPtKMDa8yE5Gs7R15joqkJ+X/o\nE7AGLjrBz4SfbsEZdc/fqHcYROpp1X3RxPos/hJ1xUpuY24Hu/O6nGW3cAan67xbN+yFnrF8b3If\nHKz0CfrelPHrTXatuAS15HqPxRxM+g37gZMXP3+FLuytxVZuqLXSWMHXIq2JSeudXf1koxZ3M+Tr\nJeI57DflRahBZWNuztrFvoV6nSm7duFvhiKX3dj2Fevj0h8vAA3FOCfbdee1lJqKscdYWOO8dubk\ndi2eQOa7UrwOlnJSeoE1yW2Jp5LZtYocjKNTIG6AfpeMfF6fq+AkauJNfwbvzEVHeb0+7xGo7dRK\n6vAlfY88ZWnFx6ff689qsaEh8mZOynbWztQB/WwckQ/cvEl9zMLDrI+9H+YVrSeXe5jX3nOLRd0a\nK2+cxSwccH6+u5XXl3Sy5u/h94MwZwQCgUAgEAgEAoFAIBAIHiDkxxmBQCAQCAQCgUAgEAgEggeI\nf5U1lV6HTdXVPZyqTCm9Qx8F7bi1FZSzc+/9yfoEjwGd6Monm7Q44nlO1e/xLD7P3By0as9xoB1a\nBdqzPjXFsH2c9NlnWtzQkMva2bmAdlt+DdTp2ru478+37GJ9sotA0/ptF6RZR9cfZ+1OEBriV8uW\nabGpDShReZe41GbIigVK32iugK1gVSKnnFn5gVI7yhQ2rhHeoOZVZnJ72wYisQmej7EqvMDpXjYh\noL1RWhi1U624xm15G6Mg+4h4DPdTcCSNtbMlcqM1T36vxYFuoJJ56lh7U9lZXhqeg1c3LotwcgA1\nLfzpCVrc0gIbN2ofr5RS5q7cEryrYWwIajyVICilVHA8ZF12RLpl2wPxud/+Zn2qD2IMwiZjTVwm\nduFKKdVrLKixDoQC3JPYQfZ+pB/rk5Kfr8WO/UDhzdrLKZKFd0Ddp3TNuruYb37juQVdaysoqLuX\nQ0LSM4ZTds1cIAHZuxZyH0pbPbqGUw3NjEF9X7h+pNIHjG3xDOe+PJldo3bGxaeytDhw+DQtLis4\nR7uw57bwy+VafHPdH6xd6DTkXip9sWtETh09bC7rc3MLbFO3/45cN31aPL/vAtCqqaW8jSPo6R0d\nXG6T/AdybNB0WCD6DOLWjasXva7F//likRZfXYc86h7mxvq8vPENpU/UZGOvuHeCrxcze8yvR9Yu\n1GJzC+xjAxcPZH2cQ6PIf2F8b63bw9pZB2PPCxwHuSC1dC7XsTmvTNytxdTWuPhMNmtn3xvPkI5h\nUykouybm3K6eWszeJdTjnpGBrJ25Of6uB6FM23hDDrfzFU4Nn/Sh/vfFKmL/7TTEh13r9jf+v9Uz\n8yF7rc9G/sm6mMX61BBKfUYRPrv/HJ4f/V1Ab88oRjsLK8wRu9783NJ+Fc86egZkFgd/PMHa9fLz\n02Jzb1Cn867i86g9rFJKndl+QYsfexXS3XO/8j1j8MheWtxUgv0jiOy5Z5N5jh/Ru6fSJ9qIrbi5\nB9+DM4l9r9dUSGASt2HfKa7mEs1hL8Le/fZPkFXT/Vcppfb/flqLpzyGvSLzGGxwez7Gx72yDtT/\nxV+9qMUdHY2snZERzmQl5yH9ayWW0FQGpJRSYROw/1Er24RNXB7n3Mil5/8DdyLpPfoel12629np\nNu9yUAnoznd43hsUhzlE97vKbJz5qWxUKaXGPYUxufYrxrEpJYe1m7MWZQrSz+Dv+g8Zr8UmJvwc\n2dCQpcVtZP98bf0G1u7777F3VVxBXj53HXkv0ptr4AsvoqSDlS+eu65Nt0sUznwNeZC+NRdhjuWd\nuMH6nNyHHP3CL1y61hU4/zHOCJY6OcYrGHtKC5HdDX4D+aZbN84VMDbGfldZiLVs4xrK2tm6kTlx\nDfud7zycXc0duYykvhLzoNcT2I/bGvhzzjwFub1TDM6yF7+GhbpuOYdb3yCfuvYmsvG/+f7pMQay\nmbTdyCdUMlqVVML6lCUgj3u9zmXGXQUTcoYZ/dZ4di1lPeZQyJzRWtzZiTwc1lrJ+lCpXelNnfID\nBH8s+1GLrck5PXI45L519/i7aM515CpTct+NxXWsXY+pD+Mz6pA3kg9u1GK/eP47RGUR1mLevhQt\ndtGxcje1wpnG0pLkYSLDd4zJYn3adebZ/SDMGYFAIBAIBAKBQCAQCASCBwj5cUYgEAgEAoFAIBAI\nBAKB4AHiX2VNx34FxdpZp7rwpE+XaPGGZ77U4iXrQa0PWtjA+jj7QqLQXAH6WcFZ7hxhGwo5xsWf\nIT1y9oNMJvzhaazP4TfXa3HFYFAIS3Vo3tGvTCcXLbkAACAASURBVNfigDGgsOZewHelsh2llPp9\nN6RMpedBKxu2iLtIBBxG1X5Kt22qQUX27CP3WB/qjmM7MUrpA1SC4xjDHQ2StsCJwos4DTi6gVJp\naG7M+hRVgdqd/ANo1VTCopRSNfdA/7cNxdiVX4HsxXN8COtjYw8Ka6sV6HE2IVWsXfYhyGqmxcN1\nx8QR0qW6LE5ZdhmA71d6DVIM6xBOW22twnhRSit1dmhv5LKAlIOYwyGDH1ZdDW8PrAkqY1CKjy+t\nSm9kBaqwbhV6O0vIfr77GM5cjjrr3JBUJh/4nyFa3L2HnxY3l3OKdUwgZA0m1qC3mplxqqsRkfF4\nxWHuU1lTTQF/zvU5mAejXwatsiq5lLUzJt99zgegf2ZugdtKUwNf51EvDlP6hs94UG3bmvlzu7L2\njBZT2npoY5YWO7r3Z31KMpC3Tr0LGZLfUC4tsfaClCLnLzyD0BlwLWhqymd9QmdC0jexAvdz/iSn\nS4cRGi6leBanQPJzeN0x1mfIZEgW6yqytLhdxxnkuR+e12ITE6yBvi9gnh/54CDr89sO/K01hw6p\nrkb5RdCTM7P5/sJcRxogJdv1zjot7j+Tu8MV3YCbgZkLpBl5CXzut9yENKybMSR5rcQVzzqI5zKn\nMFDAW5qwRmY/s5y1u1wMevD6JzCPwgntvvtoThW+uAfj6++Kve/ybS5taSzD/mligXXZ5wXkyRmr\nn2V9Nj+7Wouf2RSn9AEjC+xrlh487x1IBI258ya+z7jF8VocMo67ylTfAgW9IBl7V3MlX+fUeW/S\nh3D9OPkupN5UaqqUUi6DMA4Tx8PRZcyQIazdiNnYC639IAtob8K6ainn57Kxs5FHz393Vou7B3DJ\nBZXLNJRDPjF2JfJEUwX/7Pz9d5U+QeXNrbVcOulGXGGolJNKmcqI66BSSpVcwPnO3gNnoMvXUli7\nIWFY23VpGGv3CMg3rFz483MPw3quLYVTiZ0bH+vSdJzJnAbgM+yIs8iZD7axPlQy7BQFmVkHodYr\npVQnkSJSaXfcCuwDDte561BDLj9H6QMBYXCrGTM/nl3LO438eHcTyis4ku957zyXvUfNgfRvyDLI\nFdqbuSS8pgZ7mXsMziBpR+AQ5RXH8/WOV3/W4j79MQ/G9OGuTmXnsE+YeSKv21shdu7FXZjsI5BH\na9JxfrYP4S5ThX9DGkVz/rG/LmpxUAGX+w4azN2buhq9F0PGV65TrsDUEefP/GOQtlQT2U5BejHr\n05eUt7AjrohZJ3g5CZtgvFtY+MAhrakE+9WFb86yPkOJI1l9Pua37tn67A48T28n/B33KLzr2Hjx\ndR71AvaSuhzkhpBJ/J21PB+fnZOIvOPqhvG0j+JjmLL7ltI3qu/gnFB5nZfBCHwY0tY9r32jxR72\n2Gs8Yv1YH4+B6HNpO84MEQP5u1+3PKwXmrcce/M1QpGyA2dZ6vblPJjLlKkU0dgYZSvo+9K+1/7L\n+vj54u8WFWIt1u7g5yDXvngnsfBA7kzahtzSfRqX97rE3V9eSiHMGYFAIBAIBAKBQCAQCASCBwj5\ncUYgEAgEAoFAIBAIBAKB4AHiX2VNU1+fpMVVtzm9iVYipjKGvARU+PeL444n1RWgJN5LAA0x7o3R\nrF3RaVA+7R1AEbt9A32airewPkFxqJIcPhl06euV37B2JYmghWUfAuWW+vV89M5/WB+XUNAar25G\nterwR6ezdo1FoMiaEfeejlZQis9vv8T6pO8AlS9i4hNKHzCyBJW89AJ3gej/Kp59QwnkJAd3Qi6x\n+AvunGFgCueCohTMCycjTu9ziQnQ4rYW0J3p8/AKmcj63NwK56XmUlCnXXUqZE+Kh7yjKLdMi41K\ncG+Ri/uyPte+xHfyjcO92Xd3Ze2s7UBVra9P1eLWesidTu26yPrQCuv6gBOhtWdu5dRG95H4LnVZ\noFG2EHlW2PAw1seROLK8MB+UO1M7M9aupRqfkfQzKImxKyFrPP/BT6xP+OOgt576DBKTgAD+jGrJ\nvdp6oXK920jQtx+awp13/vvOM1pcfRv0y4BFvVi7zN9Q3d/KB/T0vGLMFeo4p5RSpuuIjPOdrncz\nUEqp2/9FVX4jY55+gyegKn1tOtZi6mbQeJ0H8zXmFTlGiz0+QmX95maerw0MIClrqwdNnFauz9rG\n55WBCdaSpS+ooHMWzGbtFJE7tLWCIlx+CTKpxhYuOchJAP3TzAnSBEtPG9bu4qeQ24Qvitbiipv4\nfmPemsD6hB/Sr5QiNQnyhEA/PqdbaiFZoc5Js794TYtzLnCHHZeeoGwXXICkoe8r41g7IyMqvcH/\nV+nsJLJGHee5S5/s0GIHQo1/a8kS1q6pAlTdwaGQQnkOR27JPMoluf2nYDxObse+H+XLKbvmDqC0\nNxBJzdVP4W5y4hZ3+nvym6VK36AyHV1ZpB2RywyYhX2kNhX0ZkNzvn69JiPHLpqOtZy2gTtdRr8I\nun5dAfIRpfFn6eR4m3CM3S8fvaXF815+k7WzMkP+HtkTed2AzIvWdi4d7HYc5y1/QuXuZsjnUjVx\nymgjn9FUgX265Cx3w7Hr6aL0ieoGzCeTVO5s0pgDF5uiSlDPByzA2eH4T6dZH9chmLuFJ/BcdJ0f\ng5Zi7ts5Qs6SexW5ur2dS6YCp8MVpiwZZ1k7N/6cnQPxeVmnTmpxZzskSTU6bk1eUZAF3TwJiTWV\nGyilVPkVyLmjl+B+Cs8jZ9alcUcUI2sTpW/Q892BNzeza/TZhz6K595Si7NJ40leGqH4CMauhuxd\nLaVcdmfhi/3GdzTKLjSRdr+9uJ71iZ2CfOBJnKQen/rPZQmsrJAbohbhb9bUJLJ22X9hb3YZiL2e\nulQppZRzBCSV1z+HK+7clZDONJOzm1JK3d4OmQX3EesatBB57f/KjbF4ZlXX8c5TTSRFw95+iPVp\nqEEu2f863PxMjPhnl+yBG1fPEJwdLx7FGXDMC/w8V5uNs+fx709pse45PoDIdbeeh0NkRAFkM+M9\nuSTWhLiaOgThvTTjxAH1T+j3DOSpObuJLFhHlhg+l59z9YG0u5AX9RrDJZeFRJIW+wTkxmVE6t1c\nwXNT/nnsf0OW4HsWn8hi7WZ+uliL723BWTyPSGNdh/nTLmrQMKy51J+Ry9N28P0zxxiyVP9Z4Vp8\n9wieddyyEaxPI5HFdZ7GONj15O+L+WdwlvWMx/35D8LZiTpDKqVUawXWZiBXTWoQ5oxAIBAIBAKB\nQCAQCAQCwQOE/DgjEAgEAoFAIBAIBAKBQPAAIT/OCAQCgUAgEAgEAoFAIBA8QPxrzZl9H+/X4iHj\notm1w2+h5kvsS7CqO/M5NLfX/uSWq6U10AAP6gfdl67VJNVXfrUb9xDuA53f2BXjWZ/is1n4O0W4\nB/te3IrMt/dULXbvDU1jUxNqsZRe5brp1B2oe0DrVGQe4/awTcXQXrsMxL3ue3efFns6OrI+XEmq\nH9A6IvnZ3JL6/CrY0FqaQis5hGha0zZcY30SkqDfm/QodHotOuOYtRtaQ9c4P7Qrpe34E4icvViL\n7/yJOdbW0MraUe2hUxO0uXd2QGea8gu/b79hqGtSmoDxduzNdaa/vfCpFge4QDPvTGzIdTXolfX1\nSp+48PvFf7zmUIE6AT7DIGBMXAONK7XAU4pbntr7+2mxgQG33C46C53twNfnanHS5l1a3PuleNan\nNhd1GXqOwjo3ttax0ia1kIquIlc4RGLN/vgzr6lA7W/L7qDGQGMx1/cb2+JvUfvM4StRR+vUB/tZ\nnx7P6qfODEXwI721OP/gvX9s5zECdbyq76EuRY1OPYAT2z/TYloHqKmJW1lSG/jGQmhpSy8i14Ut\nGsP6pO89pcXucVg7G5/fxNpNWoraYh3ECtuW1MmYPojXlrq7FevUkdiyp/50jrWzd4E+v7UOmnZq\nH//5El5XLJLUPIl5VHU5hr+CebLtrV3s2qNfv6TFDdV4tsnb9qBRRwftoixjoU127IVxq83nY1h2\nGTWfHPtgzSduQZ0CrwhP1ienDHOH6qFXb93K2kX0xvhmlqL+iuV1Sy22tbNifd5c+Z0Wv/XSIi32\nmzCQtaN5qNdLY7WY1q1zTQ1gfba98rsWP7NprNIHPEejHkDS1xfYNVrTw8wJz6A+E/unsS2vz3Xq\nC5w73GxR54I+T6WUMv6N2H/2x3jd3ITaCb0e7c/6bHob1sn0uc2bwOstGRqiThStMxM4B3WN9n72\nF+sz83H8rTs/4h58x3GrU5dI5PKawiwtPv0V6qLYWVoqihJSJzCCp4AuQb85qGVRcZ2vFweyRkxJ\nTaGjP57S4mnvc3vbc6sxhkH9MCeDSP5TSrFaEHV1qFtA602YmXE71+pS2B+bO+M51VZxm25rO9R8\novudtRfyaVgfvl6s/FBXbcQQ1BEsOZ/N2rkPQ7/U75BP3IYjN3iMDWZ9dGvc6QPFp7O02MqUnxMq\n67BfNZC6jnQ/cLTmdT/u5KIGxsAonOE6WnidOasAnONOvPubFje34rw5aExv1qe9CZ+RdxL7mGd8\nOGtXnY36Pjd2YI0Ez0OdmuZyfmZ2Jzk6ndSqqm/k9WPqc1CrpfcyLKxtL/+gxfPXvsT6XN96VekT\njYUYGwt3Ph7nPsR+M/iNmVpcnZelxbTGjFJKGZpi7jc0Y6zzK/gZaOHn87S47Bqe+fTHUEMo5Wt+\nrvCYhNxmQebb7Vxek3P4nEFaPKkJY+DqBVvtzV/sYX36khqsw99BzRbd9wwnl3gtLi8/o8Wp93AP\n+bl87+i9kNfR1Af6zcXf0K0BmHkWtZzM3HAe6GxHPjRz5XtAWz3WUhmpQ+jQlz+PqkyMv00I3pM7\n2nBeSv2V128b+AZquzn2R90td529ufgU6sLQ94Hiaqwj3VyZcAzvJD288Y7p1Iffd/px1MRpJ++p\nbsRSPHs3r4nlO5vnivtBmDMCgUAgEAgEAoFAIBAIBA8Q8uOMQCAQCAQCgUAgEAgEAsEDxL/KmryJ\nBMe5L6dLU5tCav2VUQKpwdixnJrrnQUKUWMJZCApmzjd7tAN0ImWjgKF3JFYWGXv5DQhpwGgNOXs\nwf3EPPkia3d7N2h/tXdBj6uvgczDK47bdVEq5KRVsNnOPnmetXMm1nc73tqtxWMeidfiusxK2kXl\n3spT+saVjZDE+IbycWzPBWWsF5Gnld0CxSznCJdfTPkPaLPUbtc6gEt9rGIhhUv+6ch97y3/3p/s\nv33CZmlx0BhQtgtvn2Xtmsowf67sgnwp/nl8h90f8c/+YhmxHBwCSze3u2WsXS6RAnT3xrwyJN9V\nV45G++gD/kReZRXCn7M5oRe2t4Mm20KoubpWjJTaR6VMtaUZrF0rsUfMPQvqP5Op1XF7yowdsMU1\nJraHnuM5XbqaUM3DZk/G37kISzz7cC5L7CQUx8hnIJ/QlTWZe4COWZ2Cv3PxhwQtjnmY56eiK8gp\njmOGKH0g/xBo/sb2XEJ24EdQ6scugE2hS188t+KL3Ca6nUhkaioIbd6aUy9Pvw9a8dCVkKflnUXu\nrcrj69xlMORB+9+EfGf6C1xS6hgCCWTyhsP47GzYB458Zx7rE/0y5nPBWTz30MeGsnZ1haApG1uB\nfmxghP+v8Nqk5axPSz2nPXc1/li5U4up5bJSStWVgz5L5bo3ErAnDXsinvW5ewSSlfps7JFW/nas\nnZkz/lbpBewbAbGgUR/dkcD61BJ5ztPPrNbidxYvZu1uXsPYxwwF5fbv44S2ryPlfHXBDC3+cw9o\n4+Z/XWLtfJ0hxxhsA+vLnETkY5tALvdtauUyVn2g8BRynXu8H7tWshfjUHAQa5bOu1tX+Hqhuc6d\nrJ0gH26x20wkpXVkvOPexPNM+fEk6zMwBDT8hNRULXYm8imllMosxprzJvIUS3cie5nJZWflNyEH\n6vUi1l/JZU7zrsqBjSq1su83GzT46iRuZ21dxOnlXY2ae5DQOhFau1JKVZF7cSBU9OGBmMfGZlx+\nYWoMKYUb2eOoXEIpvn/WEvlKRyvklh1+LaxP7j7Il1zjMD+oJF8ppUxd8HlG5rif0uto5xjN8zuV\n3pVeRm4w0dljdqxAHu8bDXvnpL2Q2rXryC493JyUvkHPkT0X8BIKisjzio8jv3ZfCrljbUo561JU\niXO2hRfWSNqZNNbu3Fl87xpiyz7zUZxxrf25HTmVVuUcxefpSu8rybkj9q3ntNjQEGvixMqPWR/X\n3jif387GGXzwFC5n2brhkBanrvlFi9f8ir3ws4dXsj5DwsKUPnHrFPa4Zp38HeSGc9yNLyAnt/LG\nOa0gtYj1iV0xXYvpfjDvw1msHbXFTvwL4xltBdl8h44ldVs91iad77pW2uMnPKHFvXuhNEAvf7wj\nxhAZk1JK+fTCe2BDbZYWm5jzPe7yf7/UYkOyzun9WOhI/Gh+0RfS9mMcQ6ZzK+3gsZhDBqbk5wPy\nfG2Deb6gkiereEjNTEycWbvk7du12H8S9qikdThTmpD8rJRSFz+G/NnQAHtzeS1/H6Cj7zcT32ny\n23jv6GjneW8okajahOBet7+6nbULJ5InAxM8kyKS143t+D6Y9BPkw96fzFT3gzBnBAKBQCAQCAQC\ngUAgEAgeIOTHGYFAIBAIBAKBQCAQCASCB4h/lTVFLe6nxXW51exaUw0qV0c8N1iL3UaA7mXhqkMZ\nNYMs6Y9l32rx7RxepZu6bThGgQ5HHTro31FKqZpUyEoiH52vxXf2/8TamTiA5hn0aB8triPUuHqd\n70ppkWVpkGzYhnD6VtFJUC5nrwJVKWMzZFrOhM6qlFL1V9KVvjFkGaQ+tPK1Uko1bkT165yD+G4H\n/wQ9/n/RXIkTVfUtUDev3+OSGHvi3NCXuCq01kIqY+HEn2FOKihjbY2gMtoF8ueW8DEcsKhTUtlV\nUILLiDuYUkqtnDNbi8urcS37MJeKULiOwjwrIw5PwSM5RdS7iMvFuhphz0BmU5TAqblWZH6WEneM\ngStgVZP43TbWx5RIJDx6glrq6MmlPp1DQAgsPIa52lwKCrC5J1/nUS9CkpP0JaR/NgGc1klp5y0t\nnJb8P6jL5e5iHuGx+LvmmIfdPLlsK+XUBi02NEeaG/xCvBYXHOHPUVf6pQ8UZ2K9VDdwOVgEoUeW\nkLnm0BM5sJsx/z39UhqRSa0HNXzgGw+xdpTymXXoby0OGI/cUJrK3fXsvTHHo8eCclydyiV8qTsx\ntwa8Bpqo2gh52u21B2kXlURcEQaOhRtGziHusJZ+GTnVzQH08uZm0JLNbTNZn4YqPFePVVNVV+OR\nr57RYiMjPvebm/GcOlqRN93tce8t1dx5o+oGpChV1XAmaSyqY+2MCU3beQjmfukZyE8ofVwppf4m\nEhhzQpH2784lIK0VuCenfrg20g/3ffl3LldqIrK15396Q4uTvjnA2kW/CClwacEJLbb1xXxP/uYE\n6/Pwf5cofYOOT/Yxngt8vHFWqanAOIQ/Aje8O59z2U/PYZD3NVdATmYTzPOeoRnyUWsVnnt9OeRF\n3Uz4Os8ijk/jRiJHU5cepZRqyMO+ln8E+dqajKPrQO48dP0LPHsq3U0/yWVbfV9AXs+5jjNbxzXs\nET3n9mF9XCw4Db2r4TcFkrGzH3EXKr8YPy1uLsUZwWMg5Ambn1/H+sR0J8+GyGmcovn+fu1LyPjy\nyrF39R+F+7n5Oae/O8dhzdYRtx0jHRfDK/uRh/uMBAX/4mFIDGPnDmB98vdinZdU4bPd3Pnc6+GF\ntU3XPB23fV8dZn2GrZys9A3bCMhcE3/lZQ4qiFvT7C/gQJT0M1xy6B6vlFJjlo/T4srbyMnU9Ucp\npdIKsebG98Ez2LEBMvwJ4wexPsZE7mBphfeJ5hLu2Emd8mw2QXrkNQFuXKHzdVw0iVx//KuQD9fl\n8HIIT3yK/d2QyEsufwX5Pz1PKKVUYjby1WjV9RjyBPKDqY6cTnUgRxiZ4+yZuPaUFo987ynWJWnj\nDi0e9xzKW9D3AqWUKj2Ps0TP8XDCaiHvqNk6jnkpm+EaNGJpPPks/i667atVWnzub7iWmRIJa/eJ\n3HnHLhRzuZO8OyWuOcTaOZNSHEEj4RrXoxVjfW31b6zPvT2QrofwadllCJqAfaz8Yj67Vl2I3BK+\nFL8PFB/Fu1/JZd4niLiTZV3F+0A3neO29zj83aKreBdtIxJSY1O+zt0GEOn9BuxjeTplJp5+E78J\nXPgMkuFhby/W4rMf/EK7qJ6P4fu1NeC8OebJEawddVyrI26q9N+dBvLzVo9HY9T/BWHOCAQCgUAg\nEAgEAoFAIBA8QMiPMwKBQCAQCAQCgUAgEAgEDxDy44xAIBAIBAKBQCAQCAQCwQPEv9acubkRGnP/\nQQHsmmMfdy0uINrks39BL2pjznWHkbHQlHUn2tfpH89m7b576kctbsiFhtrQFHroliqu2w+bAntY\nExNYJVrp2OCVXYIerpVo/7/9GhaDS5dwjW3ZdehSO4ll2OCVz7J2pnb4vkfeh/45fCBsMC2I7bFS\nSgUG6bdWiVJKbV+J7zZ0Irfk85oM/WsR0Q0OCIYN5+V0XhfnqzWoMfHotDFaPPKhWNbuKrG4do7E\n2JfchHVuXRG33nTyQ/2JW99Ds93al9tSnr2Dz1j65hwtNiIad6s/uX3ZkevQcvcLgrbcwpy3W/IR\n9Im3N17RYmrPFhPlyvrcuADNd8xjqstRk43nVE1qVCjFax85RUGDWXgLtUW8JoawPtnboF1N3grt\nttc43s7JB/UNrmfCStvCBPUvzFz5nK4rgGbW0gv1bBqKeQ2g1mrov6vzsrSY1kWi9slKKVWYDK1/\nZSKeg6kjzzVuAzGvc/7CuP/1I3SpIe7urE/083z+6gM+/TA+iSfvsGvhS7E293+Iuh2mv0Hr7D6W\n14sY3Rfrpc8Li7U44ySv8TLqg1e0OOcqtM/ZJ6FRN3XkttClKbCldB+C/O/gwO2uLc/CpruhAvr+\nskLMg4Bh3EZ9+EjUcso5iJpP1IJYKaV8gjFGhRlYA779/LTYpT/X1p/+7JjSJ7L+uqjFSQm8XtWQ\n/2AO1WWjXpL3ID8tvrqD11SgtYfi50FIfmU3r7/TVoSaa0ak/kzAQ6hz4XiP124KiIPNp9cu3Hdn\nK68jdvgKao/1KII+39cfz7/vvH6sT30Wxjf/b3wnh/58TytMxZpL/wNz2Y5YblZW8/o6O1/bosVP\nbBiu9IGKZMyn3HL+3CaROnpZ5J5ProXd/cDpfC/NP4PaR3fyYGfcT6cWhROpNRBAbKhr87F27qXm\nsj7D/xOvxebENnnt0z+wdn4uqHcw/QPUMbj5FWrIuffl6yV0Aepe0PoV/Xrx/FiTAT29lRn2zO4k\nb5XfKGR9HCL4PtnV6NYN9zvoVT5PGksxp2y9UO/l7m+ntHjG27wmVXUaahVcXntGi2ndE6WUCvbF\nHPeNRW48vh3PedryiaxP8TnU/DCyxPqtT+d11br38NPi3zeh/ktPUovRXGfPdRqCMQ0ORz2D1np+\nbjKzxXn45PukZss51Nro5edHu6iSazgXOo9ReoFtIHJBXkUFu+ZC7OKbm5GbTOwxB6+d4XvpseM4\ntyWRmpavLFvA2gX4wTp5zfa9WjwxGnbefx44z/q8vOkDLS4PQ27Y9cmfrB2tueg3PVKLq+4h77hF\nctvw5PPY99ubkO91a+pQWDtib416GO805Vd47Y/KBJ6HuhqH16JOz6in+Fq8SeoItbXjew18DmeJ\nzk5eS8ZrIs5wuXtT7vvvSinV68lFWnzvL4zhmT9hV+xP8qJSStk74VxK6/y4xvuxdhk7cU6m9uzL\nf0GNNUNDfvbMO4+5l30a707uUXxfpLWLbnyHGok+U/Gu1KljAV5cxXOFPtBIrOJ1n0egByyld7+O\nc9+MTxZrccH5W7SLsvfB9zEgNc0qk/i7X00Wcq+VN2qpmc9Grsveztf5oU2oa1hFao/OHRvP2tHz\nUsQ81JZK3oj1NnD5FNanvgx7WQ05V9HfJJRSyp78FmLfCzX/sg/hbFiiU8vIUWdvvR+EOSMQCAQC\ngUAgEAgEAoFA8AAhP84IBAKBQCAQCAQCgUAgEDxA/KusacS7j2vx7R92smt2hL5TcwdUwyhCvfSc\nwulnnywDBffzP2Gl/cdLn7N2o+JA9aN2f3Y9QQe28XdgfS599LUW93oZMpeau5yubO4O69NmQmd7\n8rkZWlybymmV1H5v5PsvaHFbG7fcLjqXpcV+zqB/tTeArletcz937xJLWaUfDIqDlZnnMG4B/ddK\njGvf6Xju1N51/MwhrM+erae02GcasV27ySnN0TPxeeWphOJ1MkuLAx6Ool3U7Q24H+sQ2EBSSZtS\nSr3889NavOUl2M1NWAKbM18yBkopZWyIz6A2h+ZEoqMUt2/26IM5F9UX8d2fueTAzY5bmnY1alKw\nxuz7erBrDYWg2dUS6nnmKdjDxq1cxPp0fxI04sw9oJx2M+S/12acgDyvvglrkdmrk3mvlFIRT8Pm\nk9pTF5/k7QIXgk7f2Y7PM3OAvKYmna+X0rOgB95Mg4zAT4e2em43JJmejphHk56GiWR1Crfba6nl\nNpv6wOFdoEjbEdqzUkpl7wBlc8zzsI6sSsbY12Xy3JSaifwRUgcKbn0Wp79+/eiLWvzYOuQwc3Pk\na2NjPocNDUG7Pfjaa1ocuzKQtTOxRbujq0FtDguBlIBSeJVSytIDeThwFuxijSz4Wqwl1oQubsj5\nVMp05vPjrE9JDaeddjUsiFQvZga3Di48DBqzcyy+/76vIU9ISElhfdb9+Y4W1xLL1HIdKYWvE9Zs\n94cmaHEJsUCvvM5zsCmRP/TuAUlcwuUk1m7pJ6D75+xM1uKUVEgxZujIfX//eY0WWxCb7nHvTWft\nCk5D8mlhh7V9MwF/Z+RL3Nz19tvchlgf8J2EvTDMmcsLOon1q8tQPy02IDbY5u5cWkIROwrzwj3e\nn13L3kHsUIcsJleOapG3I7dAziG0fisyw1vaOQAAIABJREFU/4Z0787apRcTqacFJEVDVsKaPPHb\nP1ifgDHINRdXbdJiI0O+52aQz57+KezRL30Cy9vIZ/gpJvkb5GHvD2aqrkZNXuE/XmssxvppqcT5\ng0pEShK4Hbptd5wZaH4+lcTXyzvff6/Fh0/8pMWj5uGsRCn8SilVS+2zybNNyefyE9dqyHhCPLDX\n91uMZ1umY1dr7ol8amsHWVNhIbeod3D302LvAJzhfaf1wP38cIV2UcVkzw3Xk6ypvQln5CA3N3Yt\n8j+QVrc2YV/zm4Dn4RbPyy7sexvyltmDIVGcPIuXItjy3tv4PHJepGf+iWP5nN7x8n+1eNgzw7Q4\nvaiItVu16wstPvb2z1rcfTyslzs6uJQnbAHss7NPQFbn0teHtftgAT67B7HMjh6EcTxz8jrrM+7h\neKVPDHsEVtpFxzPZNXdv7F2+M/D9myoh6S0ouMD6dLYjBwfOxxyoSOZr1soR5yN67nn8e7xXJm76\nmfXxGI29kJ556/P5O53ncMyrGe7Iu80NOJcmref33fe1WVrsG4dnknPuLGtnbI09M+rxxVpckoFx\nL6zkFuoD53BpsT5Ac+DZb86wa9GTIaOPjsM4Wlj4abHPUBvaRV36+HctdgjDZ+fezGPtYp5G7qTv\nYI2FkFk9+803rM+4/pgXZ0mOnv+fCaxdJpGnhT6Kvb7n0oVoc5aXArh7COeTSCKFopJUpZSy74F9\ntq0eeSPmFchaM/ZeZH0yfkPJALdlk9T9IMwZgUAgEAgEAoFAIBAIBIIHCPlxRiAQCAQCgUAgEAgE\nAoHgAeJfZU2H3gSFqPvoHuwalSuoboj/vAJK5H8mcocOb0LLzjwOuqW9Dr2/5xJQrFtaQB9L2wn6\nukdfTkP2mAQKa/Iv+7XY0teWtfv5a1SoX/b9E1rcRqraWwdwyZR9KeRPlSX4fh3t3PHCazgkOh3x\noOvlHQY9yjmaOyVM6h+u9A0rf8gVii9y56WwSFCui8+ALugxGtKF5D28+nZLW5sWtxHJVsIeToed\n+ekjWpx3AhTLgMWQs+Tu4xR/95H4u8aEPpa9i1fpTvoDVP6RM4jDyXbcA71PpZRKJg4ao6IwVoFz\nI1m70gRIRYyJZCOTUNE8RnIarbUvdwXrathHgep7azN3e/HsiSrwjXmgAA56HdXHr63exvo4EWmU\nxwg88yufn2btqENVSAjmbnEe1uW1TE5h9UzGs3HqDylYU3kDa2diAjpgWSrG99pv/zyGPiSHTH4T\ndMDt7+xm7UbOxJyYNA0SuNUtoDUX6FBGY8lP1b5cLdBlGD4ceev0KU47TkkDfbzbMdzM9Vtwwxu+\nkEsMI6ORY42NIYWoyeOyplkr4dzS2oprmfshswqfvZD1KcqBM5ZjAD77zq97WLuDR0DZtCTylntp\nWG9WQTyndhJHLgMiWfxtFf/soeHYd3znQv605XVIM5au51T1Dc+sU/qEd19Q2b99/E12bf6nkNRS\nueHUl0BXH1cez/oYGOGZNZc3arGpEd+eg+Iw1pnHsE7P7YMrxfiXuO4gi7iy1RFZ4oDefIKf/wqf\nF9of+aC7GWRvh1ZyZ6BQIrmg0oyUdedYO6c4UPLP7cfanvkJ6N/JX3NqeJin/l0Mm8uQj1J28T2u\niLhixM6HrMFzLMYgezuXutgTqfWFEze1eGqcH2tHc2d1Nf5uUyUo9X1emcP6UFe1nPPIt4YG/P+v\nDegFqdb+FZAojXgVsjFduU1ZBiS6xmTO2ffh8hIXS4zj7fVwuaDU+9rVXEYTvURfYu3/D42FOPdd\n2sP3xdBA7FcmzpDTuQzG96COakopZWqPdobkOek6GA18Fjmnmexr3YwwHnd+4uchlyg4dFj64kxW\ntZO76Lh4E0kbMfmouFagxQ69uduHYzBKCFSVQ0rmGNiTtUv8DhKD0MXxWnz4Lfx7j+E8N3S2tit9\no/oeJMY0TymlVCuRGzc14Vk59MLcurvlJOszbRXKFDQU4wyzJ3ota+c1EvtLWQXW34RPVmpx8t5f\nWR8q4awiJR36BnEnxcS1OJOMfBdn4aTv4OqUfXwr6+MzFGcn+h5SdoNLQKIDkaPNiCzfwhOSkgWf\nzWV9ti+H/LDH6KWqq3FvH/Jh+EIu921vwRxK/h77lT2R0LTpOIu5xlFHR+TT7jO5PDLvGt4LqQS1\nKB25KHwhz6fNzZBDXl0NCRx1nlNKqU1f4F1y/Ew4Md77AbnGVufsf/cPuEVGLnpIi611nIOr72LO\n59ZD9uwTM1aL+z7MZW/Ze/Au2X2E0gsqiDS676wYds3SA/OLOqSamWG/vrOdS5K9yPuFgTFyas2F\ne6xdM5G4jRkGy9svnn9ei3/68FXWh7pOTuqHseto4e8NoY9gPtJ51tSEc4trNHeqbSWO0FRu39HK\nZfMXVyP3RC3GPRg6QfZsF8HLLpTryFLvB2HOCAQCgUAgEAgEAoFAIBA8QMiPMwKBQCAQCAQCgUAg\nEAgEDxD/Kmsa/hZox9268ab3fgcNOmA2qDxLoyBVOPoNp7jOehrU7tp7oPYev8UpxS6fb9Bi61BQ\nPGlF9q+XrmJ9xk2CjKEkE1TDoQ9NZO2eJk4HRacytNiQSGjMHC1Yn6IToBH/vRUU/l5xXOrV2QFZ\nEKUOXzsDycapw5x66+UA6uLsLwcrfaDiEuiwQY9yuqGlN2Rfh9bCaSXAGbKfWh2a6dAe+N65u0Gz\nG/ZYHGtXmY7nVkqocpT669iX09cLj8Bh6K8zoD+O6snpubEr4AhCKbkUvWI5PTc2GJKQ798DnTRg\nKh/HgJmoiF54HmOXVViixdYl3E2j6iacLDyfV12OGz+DqtzSzmnG7sOwLqjrgYkJJEB24dy5ymMI\nnmfeSUjEej0xgLU7SZxw6om7mXcvUMadnLjLT2MBaMSV1+Bg4DuTS/hOvLtFi63MzLQ4+iE8/0sb\n/2Z9TOxAKU4mtPGtp7kcaxyhbO/fA5mLuRtcLQbYmtEu6vSXoCdGP6z0ArdhoOrOH8dplMdXgdpq\n5oIcNPZZuKm0N3Ka640rcCHJTgZVcvm337J2h6fDUaSjFbn3s3WQB406xyWGPXpiXjnGQMJSeIhL\nI3v6QCbg5ot5VpwD2u7Brbzqf/wQSBvv3gF3P9DVlbULWgoZ2OZlcGV7+AtIXzc+xyv4d/fyUvrE\nxVV4lv3CuHT3zCegNFOJ160cfMfJj3I+soEB9p7jf0BmFjeeU4odIvBsrF0wj1wH4vkbGHJXrN4v\ng96/61U8J28d94++cZAv1eeC3u9CaOdlBVwGSKU30UTabGDA7yHzOFyImolMsTYLTlypBQWsz+R3\npyh9I+M09prQSTw33f0e49hMZM3tjbh/cw8dtyYiMaKyrB9e+oU1e+zj+VpcUwwnqw4i9evoaGR9\nHHpCYkRlJj4juXtH8Q1IC0bNQ44vOoO9uKWMf3bKb8j/jv7Y16jsSyml8v/GuSrmZeQki8O3tTj1\nQhrroytV6GrQz+8/g68XEzvk95ZqnGFq0zHvjKy480bRaTyn4KX4POOdt1k7c+I2R92fKv5GDnYM\n4XuuQy/kUAMjrBfduZe4C+PhG4p5lJ+CM5RVAJdI5BzH+cAtFrnh3o7DrF0DkWCVpyKPd3bCGYdK\nTpVSqqOV/7c+UJWI8xN10lRKqcgKzNfLRLa+5RPIhgaG8L00dR3O6a4j8Tysg/i5zdAQ4+hki3eD\n9LO7tDh4Anepq76F/F+fhpzoZG3N2hVV4Fr1BzjreA/002L3sdz5kLrEGVkYa/HNfTdZu/5zse4b\niyDtqyNz+/CvfM+NjePOqF2N0BkoD2Boxt8XC/6ChMWNyAqp3Cls+gze5/YpLXYm8vja2kTWzorI\ninJI+YNgIgFvbuZOWrmn8R52PhU5OOltLh+b9ybeM3KJm6b7GIyblTcvnZH0DeZebS3ebamMSSml\n3AZCBpf2C9ZvcwXmtX0EPw/5TuFuu/oAXSNlF/jzaCFr0Ya46d7cDrdkKz/+PlBPpKPOxGVzzDv8\n3bwyGTng5xUr8HneWJd5Kdydz5e8h+TcwzV3D76ujC1xJmmpgUyyIh37la6zs6k99g9bTz8ttvHw\nZe3q0rHOM/7APuE9EXuTfRAvZ5JMynL8E4Q5IxAIBAKBQCAQCAQCgUDwACE/zggEAoFAIBAIBAKB\nQCAQPEDIjzMCgUAgEAgEAoFAIBAIBA8Q/1pzZv8K6CSpha1SSvV8cQxpB6u5pFzYEM+dN4r1MTDG\nb0H+01GnZnI61+KZOMCi69ge1JxYFL9Yi4PduZXg5VPQ9sXNhcVeYyO3+W0lejNqAZ51PoP8czfa\nRXlGQis8bwVsecsLeD0MaydoCP98HTUfpq6CnVpdIdfW01oi+oKRDfR2J94/xK41taKGRa9I3P/p\nL1EvaOAibofZWAyNq088apS0tPBxzDsCjWYH0TTXpkDbd/UQ149mFEN3OHkiNKO6VmSHVqL+xNCX\nR2ox1dYrnXGsvAHdaTCxgU3fzS1Ry2th69pK6rsE+qBP8plU1sfbjevLuxq+faDTNXPjtQ6MzWBF\nb2SKmgi/v/i5Fo98ejjrk74jQYvtIqFrbWvkFnTDluHZpm6ATtfcHffgFO3B+jSSGg1Um5+zk9uh\nZ5aghs+AiPtraXtP783+28wZ3zXtW9jLvjFrFmvXTLSx9UQTem4P6hhFBHDt6Ijlo5W+kbUVmtSb\naTw3XbiL+jEmxNLWKQXrKmwJr6sw9GGskaeWoA5XQs5O1q7kEvLyJx9t1uIIUi+m3/Ro1qf0HPp8\n8cZGLV4wcihrV1mP8T6+H3n48Weg1972cQLrM3J8fy3uMxFa+PO7LrN2TaTO0dL1L2hx1l9YoxMe\n5zVcbAK4bXdXw43UZ7Ek1qVKKRXqDP17VRo00OFWeLYpv3ILdTtSm2LJN+9p8Y0vN7N2zlNhjZr6\nF+wq2+qgbY6c+wjrQ60ix70FjXfufl5fqC4Ta6SJ1DBwjEKtkx7z+Fq8/S1sz41tscfo1hG7tB/f\nd97HWKfpm1FHYe4XvFBX+p/Yf9y5w3uXISAOuvTL27jtsbMNxrUhDzW0VDtsNDOy+F7edw7ONPX3\nUPshrgevaZa6EdbVoY9iXtA6DW1t3K6z+GyWFhfcwt+le7tSSnn2xz0U34SuPWA87N9Tyv9ifVx7\noiZH6RnURsosKGbtYmbiXo+/B/vZoN5+WtzeoVOvpE2/9Uqsg8ha7+TX6vNQO4nWCnIdhJzXplPD\nK+FX5JWiZHJemMzrwtA1m7MX+1r0q49qceIPv7E+hz7D2WvSO6hjcms3ryfSZwHGsDoVuX/IG6jZ\naGTE805jLXJN0VnsK3RvV0qpoBnIlZc/2abFUZORgyuv8voc/vMilb7hNwd/g1rNK6WUZwzqSdKa\ncf1KkKdObOT1VQZPIWsxF2up+/TZrF1hKurMWQSgVkZ9FupktMZwu3W73nim9uGIbVNKWbuSi6jX\nYUpqXlRex/N1jOLvMSc/R60re0ucdcZ/yK2vqXVx7o2DWmw5BHuT82BeV6z4JD9vdDWcwyK0OGM/\nH4/baajH2S0dOWbaKtTfKkjidQOvbsFZYOhyvEuamfGacvmJeIdqb8I6b6rB2qE2zUopFTgKaynu\nCvKp23B/1q42HXOxG3l/bSU1rPKT+bj3W459mlp2e8Xy81XWIbw/upJahJc24N9HDuLn4soknpP1\nAZtA1JJpImd5XXQjNdbo/h80eD5rl26I+p51uVhLxpW89plXDM6yBcfwPk7ra9nn8n2R1mUa9e5i\nLU7beZy1o7VzTBxQS6axADnE1JnXmjVzxTvO5U9x1ol5hdegonVxK0nupft5YyXPab2fHKT+Lwhz\nRiAQCAQCgUAgEAgEAoHgAUJ+nBEIBAKBQCAQCAQCgUAgeID4V1nTuPdgZ/n7y9yuuOJ90Oa794Ll\nau+xsG+kFoNKKWUXAor0vjdA+Qwnlq1KcfnTiEmgv1Nr4LHvc5pfYyNoc2ZmoArmJ3C6csAwULvr\ne8DejVK5rYM5LT5g5DgtTtqK5+A1ltuo1pbBlsuQUL7qikBts/PmfRxtkpW+0VxMZCYm3DpywOOw\n7zaywLWidaAEttY2sz6U8llbCvpZRxvnFVMJmbUzKGKUwudnxymzdi6g1zY2gv5YdjuDtTM0hFW5\npT2onI7RoPgnbea25S4hkEYFu2Eu6tq4DhgL+n7+FUg7HPtDvmPf4sb6tFRxil5Xw3M0rCL3rtjN\nrpntAk3e1Ra2ftFxoGK3VHE79OJ7kBRZ+oDOe/OPa6ydEZnHAYMhA7DvjmfZqmOX2s0QffIPY42F\nLuY27tOG+2lxJrFAzNqN2HUgt6ArInTHHv0hw6vQoRR3M4Ckza4XxsqtBPIN+958DAtP4rM99GSl\nHf405KCWB7mkcfZq2ENXp4P6nLAJkqCyNSdZH2o7PpjIJ4yMuL3jfz9brcV5xKr0mbmgaP69g0uK\nskvxTDOJ3PD0dW4ru/kgaNW/f/+BFn+5Bjbdz83n1sh/7YNl9FPfQa4UeiGftcvaDsmhxxjkk5pU\n0EQd+3BZnbkVp3N3NTz6YR5TO3illKJCSgOydvovi9fiAcunK4qSRMz3xlJI9XxmcSmFkRFyaOh4\n2I7mJ4HCa2zMx72s6JQWW9rSfZbLmpyiQZO/9T2sQINssRda2JuzPm0DsO5bibTq7jYuVZ1CqOut\nTZAIJaVjzy5552fWJ2go3yf1jdIaTpemtrh2PZHrOomt8LDZfHyufo05fZtYp1PpoFJKNbbgWeXu\nxThEPj5Ti+uquWw2cAqkhJ6jsF9d+Izng9sHsDbpXmBD7FGtdM435Zew5iz80CdQ5/x2aRvyA5U6\nn/n5nBYPmMKp+xd/xVwKGdz1STVjG76vxzAuSai8jHOXx0TMJ2pXXHGTS3iopbRrKCQr3tFcOllV\nhrOFDbGbp9a5frMjWJ+gBZCAV6RifoSN5NKF1J34jD7P4XxWchn7E5ULK8XtaqsTsbennrnL2oVP\nwDkl/Emcre/9iH3fby6/74MfQQa35IdpSh+oSsVek1XK9/I7myApaCDndP+5ODu62XH7Xt9hsVps\naAjJRVU533Nvb8H3nvzZZ1pcVLRfi01MuKSe2tpbOkBiU3SKy4ZciP0ztXx3HeKnxd8+t5H1WfQG\n8nrhAZydCq7y9xif/hh/AxOchdubIespIP2VUurqXVin80IFXYPMg9i73OP5O13YXcg86RrL3g/p\npZGlMevTczLeJduaID+8/s0O1s53Js49JkSWVESkoNQCWiml6nIgeey7fLEWX/54I2tX04h+1ubY\n/0oS8F4QtKgX69PSgrMJnTsXP97G2jkEICenbyNrfg6RuhpyqY2ujFofuPM1pJ3hzw5g17L34Kxi\n5gLZHX0nrKm5xfq4RuC8RGVeJZf5ekk7ANln9CuQH1blY4+kZyqllKrPwb5ddg9nRccYLq1uyEe7\n9BNYF3VkfK1K+fnmWgLezce+gnN7Rwd/H7bv4XrfmNrEm+nkJyMjnr/vB2HOCAQCgUAgEAgEAoFA\nIBA8QMiPMwKBQCAQCAQCgUAgEAgEDxD/KmsqvpilxQ9/yd0Uso6Aal+XBqmAgSk+0qSTOwl06wb6\nXVkt6M2GFpzOFjwDrik7Xv5Siy19QQ06+j2n87rbo6Jz3ApQ6O0jeLX6slxUwk75GdRUS0dQtFpr\nuUzj+NvrtHjIG6B1Zuzmbk3X/gb9at6al7V472v4DnHP8d/DAhZzSpw+4DgQ9Eofb057r80E3dDA\nCOMTMRvSnuZyXuncexJouLR/Sw2nexVngJ4aNhVU2WoiQQmcMJL1Kc0Edbr0AqiDqoNLpka/u0iL\ns4+eV/dDyHROz61KhDTjTDIoa7Hdu7N2d05jHD0cQAE/8Qvo25OWT2B9qu+2K32iPh+0QVsLTnWM\nexXPMP8waMzlyaA3G+u4evR5GnRpU2tSbfw6p3mXl4P+WXMb41ZLaKqeE0NYn58+hpPMpL5wFzIy\n5+s8bz/ulTr+UKeijFNprE/QCPytfZvg6NI/mMsgqKtTyRnIJ6g0y30glyXc/u9RpW8U3wAt1Hs0\nd3H5ayWkonVNkKFNfA1zrbGolvU58NURLV68ErKI/LOcWvrCq5CWlPyNdeUzDXM/4jHueNXUBPnE\n6Q92abERkRQqpdSfR5Af6XNf5gKXuuJT2azPjP+AJpp3EtTm4irujBE5FTLHH1eBFjxvAfrv+Xg/\n6zPzPeRoOzvuMNQV+OnpL7T4oTVcqlF8AdTxvPNZWlybgz3SNJQ7dCTtgQxo+NuwJrqwitOgrZZD\n4leehrWTtw8SmOo7a1kfU0fkCoPeGLek6+msHd2DvWNBDc85iLFpKubODW012CctA7E3Ryzpy9q1\nNoFSbGaJ7z58CaQ6zt15rr61Fm5AaqrSC5xisC+GnONjEv08ZBHZu0CXNvcCrZxKMZRSKnwu9vJu\nMKhQ94p4Tp38/FgtpnKH6hL8HbqvKqVUZyj2v3biMDR05VzWrq4UchnqZEHduQx18nB1KXKKA3He\nc4zm1HCTv+FyYUGcLCj1P+kId+SLjOd7a1cj9BFQ5jN/4XI6v/mQvVSRvbDiOqj17iMCWZ9Q4uxn\n7g7qefG9c6wdpclTWQl1f7r85VnWZ9ByjLtLOHG2+ZM72xgQl8kqclbyGgyJAZXQK6VURRnOaEFL\n8EysE3JYOypdLziGHMAkJZ38rDV4bn+lb5SS/SHci7vxUNdNek6wu43zXEBsEOuy/nHIax/9EvuQ\ngzN3SRm0HO8NtbU4E3a0QB6kKxWlz83YFpJA6pqnlFLO/ZCvqUsNlR718edSvIQNOMvWkzNA71tm\nrN3FExu0OOgh7JGpGyDTovJJpe7zXLsYfmNxpqTvekopZWCM+3Lsj7xSexcSoHYd57TAcXBoam/H\n/Pafxx1F6RrpbMU53Iq8L16/zMsihBMHoJYWzCPb7tx1NWoczq+5J+A6WHSFOHHZcffUtmbk05Ir\nWKfOEVxG7zYUY+9YCrleLZEI5VdxZ0dr4qSkL9iH4xmUXs5j15yIE2NHO/IEzaMJOucWj97o4z0K\n57GmEn6eqLoHub19JPYrRx/sq3bLQlmf/EtEpkjeEcsu8vt2I5JXL/I+S1Od7jtSDyId7SASpY4O\nvq6ayknZEBfMhbQtODvZ+NuzPgHT/u+cKswZgUAgEAgEAoFAIBAIBIIHCPlxRiAQCAQCgUAgEAgE\nAoHgAUJ+nBEIBAKBQCAQCAQCgUAgeID415ozhqR+jK41ayWp3+HUFzplIytoWhuLuKZs52uwoXaw\ngjaLanaVUqq1FbrfwfNh+tbMtGJcF3uW1BDx3AZtX5POPRgQm9/6ZtRIsTHD98u7wfVqo95fpsX3\nDsPG+JdtvEbFaz88qcVXPoHF6rTVr2ixrhYz6/xh3Lef0gtoHZGDv5xi1yYTa18LN2jsrn+DmkIe\nkdyqdu/7+7S4Z6CfFlv6c7swc2PomG/vvKnFnoGoA5S8+U/Wh9Y+CJ45XIvzE7jNb9K6A2i3FLrQ\nu9+jXV0Gr19h6gSrtOZW6Fur6vkciZoMDa8iusqhRCub8SvXtyfloo5H5OQnVVejiWjKBz4Vy66d\nXIU5VEt0yh0dsH11qud61xOrMXc9SV0dEwtutU4tZmltgXRSR2FWNP9sfxfYB166B82tax63V7Tt\n7oRr8X747B2wR7Ww5vZ2FcQeddggaFHNPXWsRfNw354TUI+m8jZqD+Sd5mPY2MxrJukDDbmo4dPi\nX8eu9Z0BC8Wt62FfunTG21rsZs+1q0tHQ5dtao/6IlS3rJRSjYXQQbuTZ51GLFQHruR2sRdXwd54\n4Ev/j733Cqyq2tq/Z3qvpPeEFBJKQgmQQCD0XkUQAUUQRey9HxTbOaKiAgpiwYICIkWQ3nsnlJCE\nEEhI770X/ldnPmPsD30v3uw338X4XQ3PmpPsvdZsa5/xjCeR/NvlrJ1LGKyCU1agFliXJ1FT5PLW\ny6zPsdVYrx//6lEdW3ty/bZLBNae3p2ha3aNxpjrdZOPq4YSMp+DVLsz9dXxOjYxMWHXvOKgia5K\ngYbajOxxR977lfU5lYaaMQnNGB+3i4pYu+hazDm/7qjL5hWJWhRXVm9gfTziA/EZzFG3IHFRImtX\ncR1/yz8Be27qz7BJ95vA9d75+1B7wTEccznzV261bmKBPdfcHjVJvIfhuWUfO8P6mNr849GkXaD1\nQXJKS9m1qBLMzaYSrHu+o7GWfPMKt1EPcodWP4isgSMfGMjaffnGjzqe2h/PLjUX9SsGzx7A+pRc\nwLXS86gFdVclsXYecahzUUH2fecY7LkmZvz/kzMj9qRpO1D3xjuM1+tz64+aFVkb0e7BpTN0XHye\nn52KTpP/nq7anTqyrnmN4vVjsn7HZ6R10ZL2Ynz6j+M21p2JLW45qVPT2sDrXPjGo65S1kHUHjS3\nwf7pG8XPTUVnUfeipQ5z/sxBvg8NW5Co46Zy7OfbX1+t4wGP8zPA9fOk1lUyxkffZwexdheJ3Ts9\n/w58foiOU7/lts10bkQOe1QZA8dumDtFl/LYtVOn8bzGLMCZcO93h3U89ukRtIsaFIN6cle/wFnW\ne2Aua1edTiyeW3Fe6v/8GzrOy9zK+rRUouaEjRf2qwYDu+a8fbDsLU7G+1J6Ps4wk/81kfVJXoPz\na+Rz+K6WjrzmTNk1zG1a6/HsTZy3uvn7sz4NzbymS3vT0oIzx7EP/2LXej7SV8eN5D5du0zq95jz\nNd8rG/Oi5Dyem7nB3mBuj1ohacfw/VvJ+TcgkK9lplak/mkq+jQU8DNZ2trDOq4j75/dn8QembLi\nJO2iQmbDArzsDD53m0HdzMKrGAcBg7EX+g1G/5yDfH23cuLjwBj4DsM+X5PHa59R6/mgAajj4hIS\npGO//oG0i3LriXXQwQHz0rUn3yuCJ2JNtbbG2K0qx9mxqbKB9bHzxXt72WXcz5Y6PtabSW23E/tx\nT/07oYZPZjGvIUfr2Pp6oF2nOF51UwTkAAAgAElEQVSLzSkMZ58D/0YNyL4zMeat3exYn9tbcd7p\nNJ+v5f9FMmcEQRAEQRAEQRAEQRA6EPlxRhAEQRAEQRAEQRAEoQP5x9xhmrKXe4HbAtYRmzbXaNhQ\nbngLdrBzv+TyjuYKpCT5jEAKaqNBqtKNH/G3LF2QxtVYjLSyAWN6sz42B2AdW3wb6eS5ZTwt67ej\nsC1c8Tnsrj1ikZof49SH9VkyHamcfUNh2WdoaZy1GSnbGYVIY+zVgtTbkpvc4tbcQEZiDFxj8Xy6\nlHMr3qIjsDA8n4I0zPvehh15bV4V61NA7G5LkvB9Zo2ewto5RiDdq7kKz5ja6lq58DQ9atNdVw2p\nkI0Pl62YOyCV0dYWYyn0EViepa3mUihrb4xnar3cexFPIS84jPTjrKtIvYt7Eam/tQaykc5NPG2y\nvbH1gYWrYYqrFZGPNbYg/Tp6EKyaDW0KJ3yIMd3WhjTT4x9uYe0SHiKW2y6QGA1owX3O3XaD9Rk4\nBel89XkYb4UHbrN2OYWYp2E9g3TsPxzPk0pDlFKqPBuSmrxcpD93s+ZWmskXkS7bywZpoh7xmOeZ\n6/hc9B/K0+KNgf8YWKgaSmK8+yCVc1h3jP1HP4IN9o2fuLWibTDSOusKca8PbD7F2o2YjudYRewr\nU4iUwuaHVawPtczO2gSJgGsfnq5vZ4e51ON5pII2E4nO4Nd42nnVYkgjGyuRSmxixu9J6VVYwSbd\nxvgJOojP0Hket8u2sOYppO0NnX8WFq7s2rWviNxyPixt21qQYt3nSb7e2H2LtezOLqTwDp03mLWj\nlo0XfsazsvHH2tBtwSTWJ/l73OfIubDybargKcXOUZDh5F+6oGMqM73y7VnWx5qsO6EzId0pv8Kt\nozv1IfabxJIyfR2+a2kNTycvJ//N71b7kfY9vmdnT75+5+/B+kHlZR7EQvn+uSNZn90bcG6hc/vk\n2jTWbuHL0Pc0E0mCJ/nOBQf5WtmpN/bw4BlYQ86u4Sn1F9cd0fHMD+/H9zmA79Pa0Mr6VBO5qn8X\nIlF34Nai9YX4fC598HlKiQzl5FYuiYkd2kMZE4dgzL/kr06za8FTkUJPJfG9J2K9MDHl603ObpyB\nTKkcL9Rwnu/QcXExl3n+l+xkLqHpPQA2zpWpGFNxo/n6RdcXxxCk0w8j5+zKdL4vdo2DbCv/Kp5H\nfTGfV67u2C+6JEJ+QG3XQ6ZzW3sbMheMhRuxbffoz+U4sQ6TdZxzHLKKKGIN7dqZy9Mq07DH9Zn2\npI6Tf/+Z/904/BuBPSExKi7erePG8jrWp6gA7xQeg3EPaw1k0UFEetupD/6OM5mLTPanlHJwv7d9\nb/IKPrbdYvBve/fBHLvv2bE6zv6Ln8uozMcYVGfjnSdsSDi7Zm6D8VWWBfmJmyP2rn4vJbI+v7yI\nMhj03maX8LH/4tuwSu/zOOZY8g9Yi0zMeR6COzlvXvj0gI4HvDmHtbv8xSYdO3XGGkDt1Ok6oZRS\nt36BHMvaA++IfuO5LLiN2H5b2GGtzT+N98g8A5loSRL2Vq8PJihj0FBG3hENpFghiThnZxzCWuk9\nEM+7uZzL+zz9sU9SG2pTc17iw94+UsdFWYfRh5SWMHwXdQzBM3Egz6c+m7/n3tyAs/79H03TcdIX\nx3VsbsqfY6+Z+B3AORTzd/+7m1m74Ytx5prw4QId3/oLvzVY2FmwPvad+X5yLyRzRhAEQRAEQRAE\nQRAEoQORH2cEQRAEQRAEQRAEQRA6kH+UNZlbIxXnrgNPiQschOrSRz9BWtiM9+7Tcd5JLhsII6nP\nZmZI37u+cwdrF/PEbB3/+eonOvZwQkqmSS5PW7KzRipoyAikWN1ed5y1Gx+LitB2JB38t5eQQpcw\niqcDdnLAZ+33YqKO6z9qYu3S0yBFuP+Tp3RsaYnU1HyDFFHnaOPKYZTiabJeUd7smrUHJACxRFKw\n4yOk5ze18jRomnI+fz5S69b9m0tiQkiqeJcYVPa+dgdShfOH+P3ofw3/Td0vfON5BXBbP4yF0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s44oxUHqe1y6yIeNyaHfUadx1EWcK/xA+91JTcI6INY6rvSo4iPXHktSmVEqpqEdQ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TzGYFyVHsJ6felSFms3aRmf9/9D0Q6sW85JfN2pScc9Tnp8meptTnywUosdEzzYa1bedlps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\nRwfuXvU5yCEOvXTuQP7Yp/WpGE/XMf5a3KV71yJrymsq7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DMGBniMu/Ep6R/E93ngjDCl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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f54c03fd3d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "kL8MEhNgrx9N",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "The first hidden layer of the neural network should be modeling some pretty low level features, so visualizing the weights will probably just show some fuzzy blobs or possibly a few parts of digits.  You may also see some neurons that are essentially noise -- these are either unconverged or they are being ignored by higher layers.\n",
        "\n",
        "It can be interesting to stop training at different numbers of iterations and see the effect.\n",
        "\n",
        "**Train the classifier for 10, 100 and respectively 1000 steps. Then run this visualization again.**\n",
        "\n",
        "What differences do you see visually for the different levels of convergence?"
      ]
    }
  ]
}