multi_class_classification_of_handwritten_digits.ipynb

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      "source": [
        "[View in Colaboratory](https://colab.research.google.com/github/DillipKS/MLCC_assignments/blob/master/multi_class_classification_of_handwritten_digits.ipynb)"
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      "source": [
        "#### Copyright 2017 Google LLC."
      ]
    },
    {
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        "colab": {}
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      "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",
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      "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)"
      ]
    },
    {
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        "id": "c7HLCm66Cs2p",
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      "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",
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      "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",
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      "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."
      ]
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      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import glob\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 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",
        "  \"https://dl.google.com/mlcc/mledu-datasets/mnist_train_small.csv\",\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()"
      ],
      "execution_count": 0,
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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",
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      "cell_type": "markdown",
      "source": [
        "![img](https://www.tensorflow.org/versions/r0.11/images/MNIST-Matrix.png)"
      ]
    },
    {
      "metadata": {
        "id": "dghlqJPIu8UM",
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      "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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        "outputId": "68825f6d-17cc-4205-dab5-0656939d59a6"
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      "source": [
        "mnist_dataframe.loc[:, 72:72]"
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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."
      ]
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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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    {
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        "outputId": "10f98855-0259-4ffa-80e7-0fb4a3527022"
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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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              "      <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>std</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>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",
              "      <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>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",
              "      <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>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",
              "      <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",
              "      <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": 4
        }
      ]
    },
    {
      "metadata": {
        "id": "4-Vgg-1zu8Ud",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "2a9a2102-9ceb-4e4d-8770-4db32d9a850a"
      },
      "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": {
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              "    <tr>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
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              "      <td>0.0</td>\n",
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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",
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              "      <td>1.0</td>\n",
              "      <td>0.6</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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              "    </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    ...      1.0    0.6    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": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "qHXzr2wVjr07",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 385
        },
        "outputId": "381b3319-f585-4400-9f73-534e89920540"
      },
      "cell_type": "code",
      "source": [
        "display.display(training_targets.hist())\n",
        "display.display(validation_targets.hist())"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2f578e7d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7fc2f578e7d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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MQJABADAAQQYAwAAEGQAAAxBkAAAMQJABADAAQQYAwAAEGQAAAxBkAAAMQJABADAAQQYA\nwAAEGQAAAxBkAAAMQJABADAAQQYAwAAEGQAAAxBkAAAM4Ez0hKGhIa1fv16fffaZPv/8cz311FOa\nM2eO1q1bp5GREfl8Pm3dulUul0vt7e1qbW1VRkaGampqVF1dnY5zAABg0ksY5Pfee0933323nnzy\nSZ0+fVpPPPGEiouLFQgEtHjxYm3btk3BYFBVVVVqaWlRMBhUVlaWli1bpsrKSuXl5aXjPAAAmNQS\nLlk/8sgjevLJJyVJZ8+e1bRp09Td3a2KigpJUnl5uUKhkHp6elRYWCi3263s7GwVFxcrHA6ndvQA\nANhEwhny/7dixQqdO3dOO3fu1I9+9CO5XC5JktfrlWVZikaj8ng8o8/3eDyyLCv5IwYAwIbGHeR9\n+/bp73//u37+858rHo+Pbr/6v6/2Vduvlp+fI6czc7xDAAAgrXw+d9qOlTDIx48fl9fr1a233qq7\n7rpLIyMjys3N1fDwsLKzsxWJROT3++X3+xWNRkdf19vbq3nz5o2571hs8MbPAACAFLGsC0nd31iB\nT3gP+ejRo9qzZ48kKRqNanBwUCUlJero6JAkdXZ2qqysTEVFRTp27Jj6+/s1MDCgcDis+fPnJ+kU\nAACwt4Qz5BUrVuiXv/ylAoGAhoeHtXHjRt19991qbGxUW1ubCgoKVFVVpaysLDU0NKiurk4Oh0P1\n9fVyu9M31QcAYDJzxMdzszdFkr0U8MTz7yZ1f6nwv753aKKHkNDQB/97oocAAEbYs35hUvd3Q0vW\nAAAg9QgyAAAGIMgAABhg3J9DBgAg2cx/X01y7yGPhRkyAAAGIMgAABiAIAMAYACCDACAAXhTFwAj\nmf5mH75AB8nGDBkAAAMQZAAADMCSNQBcB9OX1CWW1ScbZsgAABiAGTIA2NRkmMXjP2wVZP7nAwBM\nVrYKMm4Ok+EfXty7A/B1cQ8ZAAADMEMGUsD0WTwzeMA8zJABADAAQQYAwAAsWQM3IdOX1IGbETNk\nAAAMwAwZX8LsCQDSjxkyAAAGIMgAABiAIAMAYIBx3UPesmWL/vKXv+jy5cv6yU9+osLCQq1bt04j\nIyPy+XzaunWrXC6X2tvb1draqoyMDNXU1Ki6ujrV4wcAwBYSBvnIkSP6+OOP1dbWplgspiVLlmjB\nggUKBAJavHixtm3bpmAwqKqqKrW0tCgYDCorK0vLli1TZWWl8vLy0nEeAABMagmXrO+99169+OKL\nkqSpU6dqaGhI3d3dqqiokCSVl5crFAqpp6dHhYWFcrvdys7OVnFxscLhcGpHDwCATSScIWdmZion\nJ0eSFAwGdf/99+vPf/6zXC6XJMnr9cqyLEWjUXk8ntHXeTweWZY15r7z83PkdGbeyPgBAEgZn8+d\ntmON+3PI77zzjoLBoPbs2aOHHnpodHs8Hr/m879q+9ViscHxHh4AgLSzrAtJ3d9YgR/Xu6wPHz6s\nnTt36tVXX5Xb7VZOTo6Gh4clSZFIRH6/X36/X9FodPQ1vb298vv9Nzh0AABuDgmDfOHCBW3ZskWv\nvPLK6Bu0SkpK1NHRIUnq7OxUWVmZioqKdOzYMfX392tgYEDhcFjz589P7egBALCJhEvWBw8eVCwW\n0zPPPDO67fnnn9eGDRvU1tamgoICVVVVKSsrSw0NDaqrq5PD4VB9fb3c7vStvQMAMJk54uO52Zsi\nyV6br393XVL3BwC4ubUs3JLU/d3wPWQAAJBaBBkAAAMQZAAADECQAQAwAEEGAMAABBkAAAMQZAAA\nDECQAQAwAEEGAMAABBkAAAMQZAAADECQAQAwAEEGAMAABBkAAAMQZAAADECQAQAwAEEGAMAABBkA\nAAMQZAAADECQAQAwAEEGAMAABBkAAAMQZAAADECQAQAwAEEGAMAA4wryP//5Ty1atEi/+93vJEln\nz57VypUrFQgEtHbtWl26dEmS1N7erqVLl6q6ulr79+9P3agBALCZhEEeHBzUpk2btGDBgtFt27dv\nVyAQ0N69ezVz5kwFg0ENDg6qpaVFr732ml5//XW1traqr68vpYMHAMAuEgbZ5XLp1Vdfld/vH93W\n3d2tiooKSVJ5eblCoZB6enpUWFgot9ut7OxsFRcXKxwOp27kAADYiDPhE5xOOZ3//bShoSG5XC5J\nktfrlWVZikaj8ng8o8/xeDyyLCvJwwUAwJ4SBjmReDz+tbZfLT8/R05n5o0OAQCAlPD53Gk71nUF\nOScnR8PDw8rOzlYkEpHf75ff71c0Gh19Tm9vr+bNmzfmfmKxwes5PAAAaWFZF5K6v7ECf10feyop\nKVFHR4ckqbOzU2VlZSoqKtKxY8fU39+vgYEBhcNhzZ8///pGDADATSbhDPn48ePavHmzTp8+LafT\nqY6ODv32t7/V+vXr1dbWpoKCAlVVVSkrK0sNDQ2qq6uTw+FQfX293O70TfUBAJjMHPHx3OxNkWQv\nBdS/uy6p+wMA3NxaFm5J6v6SvmQNAACSiyADAGAAggwAgAEIMgAABiDIAAAYgCADAGAAggwAgAEI\nMgAABiDIAAAYgCADAGAAggwAgAEIMgAABiDIAAAYgCADAGAAggwAgAEIMgAABiDIAAAYgCADAGAA\nggwAgAEIMgAABiDIAAAYgCADAGAAggwAgAEIMgAABiDIAAAYwJnsHT733HPq6emRw+FQU1OT5s6d\nm+xDAABgO0kN8gcffKB//etfamtr08mTJ9XU1KS2trZkHgIAAFtK6pJ1KBTSokWLJEl33HGHzp8/\nr4sXLybzEAAA2FJSgxyNRpWfnz/6s8fjkWVZyTwEAAC2lPR7yFeLx+NjPu7zuZN6vD8sfzmp+wMA\nIF2SOkP2+/2KRqOjP/f29srn8yXzEAAA2FJSg/z9739fHR0dkqS//e1v8vv9mjJlSjIPAQCALSV1\nybq4uFjf/e53tWLFCjkcDv3qV79K5u4BALAtRzzRjV4AAJByfFMXAAAGIMgAABjANkF+7rnntHz5\ncq1YsUIffvjhRA/HtrZs2aLly5dr6dKl6uzsnOjh2Nrw8LAWLVqkN954Y6KHYmvt7e36wQ9+oMce\ne0xdXV0TPRxbGhgY0Jo1a7Ry5UqtWLFChw8fnughGSmln0NOF76yMz2OHDmijz/+WG1tbYrFYlqy\nZIkeeuihiR6Wbb388su65ZZbJnoYthaLxdTS0qIDBw5ocHBQO3bs0IMPPjjRw7KdN998U7fffrsa\nGhoUiUT0+OOP69ChQxM9LOPYIshf9ZWdfOQque69997RPxYydepUDQ0NaWRkRJmZmRM8Mvs5efKk\nTpw4QRxSLBQKacGCBZoyZYqmTJmiTZs2TfSQbCk/P1//+Mc/JEn9/f3/9Y2O+A9bLFnzlZ3pkZmZ\nqZycHElSMBjU/fffT4xTZPPmzVq/fv1ED8P2Pv30Uw0PD2v16tUKBAIKhUITPSRbevTRR3XmzBlV\nVlaqtrZWjY2NEz0kI9lihvw/8Umu1HrnnXcUDAa1Z8+eiR6KLb311luaN2+ebrvttokeyk2hr69P\nL730ks6cOaNVq1bpvffek8PhmOhh2crbb7+tgoIC7d69Wx999JGampp4b8Q12CLIfGVn+hw+fFg7\nd+7Url275HYn97vI8f90dXXp1KlT6urq0rlz5+RyufTNb35TJSUlEz002/F6vbrnnnvkdDo1Y8YM\n5ebm6t///re8Xu9ED81WwuGwSktLJUlz5sxRb28vt7uuwRZL1nxlZ3pcuHBBW7Zs0SuvvKK8vLyJ\nHo5tvfDCCzpw4ID+8Ic/qLq6Wk899RQxTpHS0lIdOXJEV65cUSwW0+DgIPc3U2DmzJnq6emRJJ0+\nfVq5ubnE+BpsMUPmKzvT4+DBg4rFYnrmmWdGt23evFkFBQUTOCrg+k2bNk0PP/ywampqJEkbNmxQ\nRoYt5ilGWb58uZqamlRbW6vLly+rubl5oodkJL46EwAAA/BPQQAADECQAQAwAEEGAMAABBkAAAMQ\nZAAADECQAQAwAEEGAMAABBkAAAP8Xw/0p9zViD5eAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc301267190>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "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": "bd610119-ae12-483c-91ef-136ac207dbca"
      },
      "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 0x7fc311e334d0>"
            ]
          },
          "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_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": 999
        },
        "outputId": "f6666861-8187-45f2-9029-a0954fe601be"
      },
      "cell_type": "code",
      "source": [
        "classifier = train_linear_classification_model(\n",
        "             learning_rate=0.02,\n",
        "             steps=800,\n",
        "             batch_size=20,\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 : 5.58\n",
            "  period 01 : 4.89\n",
            "  period 02 : 4.68\n",
            "  period 03 : 4.42\n",
            "  period 04 : 4.50\n",
            "  period 05 : 4.14\n",
            "  period 06 : 4.03\n",
            "  period 07 : 4.19\n",
            "  period 08 : 4.13\n",
            "  period 09 : 3.94\n",
            "Model training finished.\n",
            "Final accuracy (on validation data): 0.89\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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OHMK2bb9xxx0T2bJlMwMHDqFNm7YMHDiY3bsT+PLLz3n55deveN6aNato3boNjz32JOvX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\nem0Z0mfLkD5bhlJ97u3di+69urE7az/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50YFu3isYcT8d6Pc8zOPJnvR9hbfEndo6MdTO\niXGl7RwZinLoZAc1gSAHjofGLlMrK3Ky1O9j8Vwv2Vka6DZKv+dhlwpvnTYXEUkAs8nITWX53FSW\nT/9AhP3H2qgOBKk91cmJxi7+++3jXD/HzVK/j5vK8rFkaKCbXJrCW0QkwTKtZpZfX8jy6wvpPDvA\nh7UtVAeCfHwixMcnQtgsJhZe52GZv4C5JXma1lUuovAWEUmivGwra5aUsGZJCY1tPdQcbqEmEOT9\nT4f/ch0WvjzfxzJ/ATO9Dg10E0B93lO6Tpmc2jox1M6JkYh2jsZinGjoojoQZF9tK30DEQCK8u3D\nA93mF+B22uJaQ7Lp9zxMA9YuoB9G4qitE0PtnBiJbudzkSif1LVTczjIwRMhIkPD/2VfOzOXZX4f\ni+Z6sdsuf4a4VKHf8zANWBMRSUEZZiMLr/Ow8DoPveFz/N/RNqoPBTlaf4Zj9Wf4r7eOcUNpPsv8\nPm4ozSfDrIlgpgOFt4hIirDbMlh54wxW3jiD9q4wH9S2UH0oyP5jbew/1kaW1cyiuV6W+X18aWYu\nRvWPpy2Ft4hICnI7baxdOov/+HIJ9a091ARaqDkc5L2DTbx3sAlXjpWl8wtY5vdR5HEku1yZYgpv\nEZEUZjAYKPFlU+LL5oE7Sjn6r06qAy18dLSVN2pO80bNafKdNjy5mbidNtw5o39W3E4brhwbZpNO\ntacahbeISJowGg3Mu8bFvGtcrF99LR+fCFETaKGuqYva052TLmMAchwW8nOGg3xCwDuHQz4rDQfE\npTqFt4hIGrJkmFgyz8eSeT4ABs8N0d4dpqN7gPbuMO1d4QmPp4JnqWvqnnRdmVYT7snCfeS102FR\n/3qCKbxFRKYBS4aJQredQrd90s+j0RhdvYO0d4UJdfcPh/xowHeHCXWFaWjrnXRZk9FAXraV/JHT\n8O7xIe+04cq2arrXKabwFhERjCMBnJdtpQznRZ/HYjH6ByKEuiY5eh/5O/KvM5dcf05Wxlgfu3uS\ngLfbzJo97nNQeIuIyL9lMBjIsmVQYsugxDf5xCHnIlE6zobp6AoTGj1FPy7g61t7ONk8+cQr1gwT\nrpFBdO4cG9cU5ZKbaabIY8edY1OwX0DhLSIiUyLDbMSXl4UvL2vSz6OxGGd7BwmNHLVfGO7tXWGa\n2/sAePfjprHlbBYTRR47xR4HRfkjjx77tL6FqsJbREQSwmgw4HRYcTqslM64+NQ8QP9AhPbuMOGh\nGIfrQjS29dLQ1sPJprPUNU4cUOe0WyaGutfBDLcdqyX9+9cV3iIictXItJop9jjweLIpKzh/ev5c\nJEqwo4+Gtp6xQG9s6+XwqU4Onzp/GZwB8ORmUuSxU+RxUDzy6MvLTKvr2RXeIiJy1cswG5npdTDT\nO3G2uP6BCI2h82He2NZDQ1svB46HOHA8NPY9s8lAgcs+EuYjwZ5vx+1Mzf50hbeIiKSsTKuZsiIn\nZUXnT8PHYjG6ewdpCPVOOEpvDPXQ0NYzYXmbxURR/nCYj52C99jJucr70xXeIiKSVgzj+tb917jG\n3o/GYoS6wjS29owE+3CoTzZBTY7dMmFwXLHHwYz8LGyWqyM2r44qRERE4sxoMODNzcSbm8nN13rG\n3o8MRQm2j/Snjztarz3dedG0sp5cG0X5Doq99uFHjx2fKyvh/elxC+/+/n42bdpEe3s7AwMDbNy4\nka985Stjn+/du5df//rXmEwmVq5cyfe+9714lSIiInJJZpORYq+D4kn605tCvcN96q09Y33rH58I\n8fGJ8/3pJqOBQncW187MpeLOLyUkyOMW3rt27WLBggVs2LCBxsZGvv3tb08I7yeeeIJnn30Wn8/H\n+vXrWbNmDWVlZfEqR0RE5HPJtJopLXJSWjTxsrbu3sGJo95HjtbbuoL854o5ODJTOLzXrl079ry5\nuRmfzzf2ur6+HqfTSWFhIQC333471dXVCm8REbnq5dgtzLe7mH9Bf3o0GkvY6fO493lXVFQQDAZ5\n5plnxt5ra2vD5Tq/0S6Xi/r6+niXIiIiEhdGgwGjKXGXnMU9vHfs2EFtbS2PPvooO3fuvOLr6fLy\nsjCbp3bWHI9n8vl5ZeqprRND7ZwYaufEUDtfWtzC+9ChQ7jdbgoLC5k3bx5DQ0N0dHTgdrvxer2E\nQuc7+1taWvB6vZ+5vs7Ovimtz+PJpq1t8gnyZWqprRND7ZwYaufEUDsPu9QOTNxOzn/00Uc899xz\nAIRCIfr6+sjLywOguLiYnp4eGhoaiEQi7Nq1i+XLl8erFBERkbRiiMVisXisOBwO89hjj9Hc3Ew4\nHObhhx/mzJkzZGdns2rVKvbt28cvf/lLAFavXs13vvOdz1zfVO+Baa8ucdTWiaF2Tgy1c2KonYdd\n6sg7buE91RTeqUttnRhq58RQOyeG2nlYwk+bi4iISHwovEVERFKMwltERCTFKLxFRERSjMJbREQk\nxSi8RUREUkzKXComIiIiw3TkLSIikmIU3iIiIilG4S0iIpJiFN4iIiIpRuEtIiKSYhTeIiIiKWZa\nhvcvfvELysvLqaio4JNPPkl2OWnrySefpLy8nPvvv59//OMfyS4nrYXDYe666y5eeeWVZJeS1nbu\n3MnXvvY17rvvPnbv3p3sctJSb28vDz/8MJWVlVRUVLBnz55kl3RVMie7gET78MMPOX36NFVVVdTV\n1bF582aqqqqSXVbaqamp4fjx41RVVdHZ2cm9997L6tWrk11W2nr66adxOp3JLiOtdXZ2sm3bNl5+\n+WX6+vr43e9+xx133JHsstLOq6++yuzZs3nkkUdoaWnhW9/6Fm+++Wayy7rqTLvwrq6u5q677gKg\ntLSUrq4uenp6cDgcSa4svSxevJgbbrgBgJycHPr7+xkaGsJkMiW5svRTV1fHiRMnFCRxVl1dzbJl\ny3A4HDgcDn7+858nu6S0lJeXx9GjRwHo7u4mLy8vyRVdnabdafNQKDThx+ByuWhra0tiRenJZDKR\nlZUFwEsvvcTKlSsV3HGydetWNm3alOwy0l5DQwPhcJjvfve7rFu3jurq6mSXlJa++tWv0tTUxKpV\nq1i/fj0/+tGPkl3SVWnaHXlfSLPDxtfbb7/NSy+9xHPPPZfsUtLSX/7yF2666SZmzpyZ7FKmhTNn\nzvDUU0/R1NTEN7/5TXbt2oXBYEh2WWnltddeY8aMGTz77LMcOXKEzZs3ayzHJKZdeHu9XkKh0Njr\n1tZWPB5PEitKX3v27OGZZ57hj3/8I9nZ2ckuJy3t3r2b+vp6du/eTTAYxGKxUFBQwK233prs0tKO\n2+3m5ptvxmw2U1JSgt1up6OjA7fbnezS0sr+/fu57bbbAJg7dy6tra3qcpvEtDttvnz5cv7+978D\nEAgE8Hq96u+Og7Nnz/Lkk0/y+9//ntzc3GSXk7Z+85vf8PLLL/OnP/2JBx98kI0bNyq44+S2226j\npqaGaDRKZ2cnfX196o+Ng1mzZnHw4EEAGhsbsdvtCu5JTLsj71tuuQW/309FRQUGg4EtW7Yku6S0\n9MYbb9DZ2cn3v//9sfe2bt3KjBkzkliVyJXz+XysWbOGhx56CIAf//jHGI3T7vgn7srLy9m8eTPr\n168nEonw05/+NNklXZV0S1AREZEUo91GERGRFKPwFhERSTEKbxERkRSj8BYREUkxCm8REZEUo/AW\nSWMNDQ0sWLCAysrKsbs0PfLII3R3d1/2OiorKxkaGrrs73/961/ngw8+uJJyReQyKbxF0pzL5WL7\n9u1s376dHTt24PV6efrppy97+e3bt2uSDJGrzLSbpEVkulu8eDFVVVUcOXKErVu3EolEOHfuHD/5\nyU+YP38+lZWVzJ07l9raWp5//nnmz59PIBBgcHCQxx9/nGAwSCQS4Z577mHdunX09/fzgx/8gM7O\nTmbNmsXAwAAALS0t/PCHPwSG7zdeXl7OAw88kMxNF0kbCm+RaWRoaIi33nqLhQsX8uijj7Jt2zZK\nSkouugFEVlYWL7zwwoRlt2/fTk5ODr/61a8Ih8OsXbuWFStWsHfvXmw2G1VVVbS2tnLnnXcC8Le/\n/Y05c+bws5/9jIGBAf785z8nfHtF0pXCWyTNdXR0UFlZCUA0GmXRokXcf//9/Pa3v+Wxxx4b+15P\nTw/RaBQYnkb4QgcPHuS+++4DwGazsWDBAgKBAMeOHWPhwoXA8I1/5syZA8CKFSt48cUX2bRpE7ff\nfjvl5eVx3U6R6UThLZLmRvu8xzt79iwZGRkXvT8qIyPjovcuvPVlLBbDYDAQi8UmzPE9ugNQWlrK\nX//6V/bt28ebb77J888/z44dO77o5ogIGrAmMi1lZ2dTXFzMu+++C8DJkyd56qmnPnOZG2+8kT17\n9gDQ19dHIBDA7/dTWlrKgQMHAGhububkyZMAvP7663z66afceuutbNmyhebmZiKRSBy3SmT60JG3\nyDS1detWnnjiCf7whz8QiUTYtGnTZ36/srKSxx9/nG984xsMDg6yceNGiouLueeee3jnnXdYt24d\nxcXFXH/99QCUlZWxZcsWLBYLsViMDRs2YDbrvxyRqaC7iomIiKQYnTYXERFJMQpvERGRFKPwFhER\nSTEKbxERkRSj8BYREUkxCm8REZEUo/AWERFJMQpvERGRFPP/Rkv+YzMaywEAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc2efc844d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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HSSIiUogsQ1s2NjYYO3Zsk8e8vb3h7e0tRzkiIqOjpaEtXkdCRKRKDBIiIpKAPRIiIpJE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\n4H3Y4ogAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc2ec535450>"
            ]
          },
          "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": "rm8P_Ttwu8U4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# YOUR CODE HERE: Replace the linear classifier with a neural network.\n",
        "\n",
        "def train_DNN_model(\n",
        "    learning_rate,\n",
        "    regularization,\n",
        "    steps,\n",
        "    batch_size,\n",
        "    hidden_units,\n",
        "    training_examples,\n",
        "    training_targets,\n",
        "    validation_examples,\n",
        "    validation_targets):\n",
        "  \"\"\"Trains a DNN 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",
        "  steps_per_period = steps / periods  \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 a DNNClassifier object.\n",
        "  my_optimizer = tf.train.ProximalAdagradOptimizer(learning_rate=learning_rate,\n",
        "                                          l1_regularization_strength=regularization)\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",
        "      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",
        "  \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": "DMxA5Zn4slOD",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 999
        },
        "outputId": "dd643176-ea70-41c0-ae9e-ac3f76b13d4c"
      },
      "cell_type": "code",
      "source": [
        "classifier = train_DNN_model(\n",
        "             learning_rate=0.03,\n",
        "             regularization=0.001,\n",
        "             steps=500,\n",
        "             batch_size=50,\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)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training model...\n",
            "LogLoss error (on validation data):\n",
            "  period 00 : 6.31\n",
            "  period 01 : 4.78\n",
            "  period 02 : 4.05\n",
            "  period 03 : 3.29\n",
            "  period 04 : 3.23\n",
            "  period 05 : 3.04\n",
            "  period 06 : 2.65\n",
            "  period 07 : 2.80\n",
            "  period 08 : 3.07\n",
            "  period 09 : 2.83\n",
            "Model training finished.\n",
            "Final accuracy (on validation data): 0.92\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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3v3+00+0qFArOfRKFXq/rWN+lHkd5OWw2ct61axdjx47Fy8uLkJAQXnjhBVttqkvMV26b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\n7EvC2U7OPhBj/b4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cM/xWTCYjS3Lf50jNMUeXJIQQNiPhLAAYEOnL3b8YQpvWwKsr9lPX\n5Fy3WAEMCxrSEdBvS0ALIXoxCWfRYfSQUG68uj81De0sXpFLu9bg6JJ+ZljQEO5NvB0T8Hbu+xyu\nPurokoQQwuoknMV5rhnbj/HDw8gva+Tdbw5hdMKZ4BICr+A3w28zB/SBDzhU/YOjSxJCCKuScBbn\nUSgU3JY+mMExfmQdrWTFFue8fWlo4BXcN/x2FMA7Bz4kTwJaCNGLSDiLn1GrlDx443DCAz1Yu7eQ\nrTnOd4sVwJDAQfwm0RzQS3M/IK/6iKNLEkIIq5BwFhfk6aZh4a+S8HLX8PG6oxw85Xy3WAEMCRjE\nfYl3oFAoWJr7IQerDju6JCGE6DYJZ9GpED93Hjn3FqtK57vFCmBwwMAzAa3k3QP/4kDVIUeXJIQQ\n3SLhLC5qQJQvd10zhNZ2A699sZ96J7zFCswB/UDS2YD+SAJaCNGjSTiLS7pyaCg3XBVHdUM7i7/M\npV3nfLdYAQzyH8ADSXeiOhPQuZV5ji5JCCEui4SzsMgvxsUyflgYp043ssxJb7ECGOQf3xHQyw5+\nzH4JaCFEDyThLCyiUCi4bab5FqvMHyr5cqtz3mIFMNA/ngeS7kKlVLHs4EfkVB50dElCCNElEs7C\nYmqVkgduGE5ogAdr9hSyzUlvsQIY6N+fB5PuQq1U897Bj8mpOODokoQQwmISzqJLvNw1PParRLzc\nNXy07ig5RyscXVKnBvjF8WDSXWiUat7L+4RsCWghRA8h4Sy6LMTfg4duHI5SCX/+cB8nSuodXVKn\nzAF9Nxqlmn/mfUJWRa6jSxJCiEuScBaXZVC0H3f/YihtWgN//zyHQ/k1ji6pU/F+sTyUfDcuSg3v\n531KRnkORpPR0WUJIUSnFCaTc1x2W1nZaNX1BQd7W32d4ueOlzXxfx/tA+D+64YxYlCwgyvq3Kn6\nAt7IeY82QxsKFHio3fHUeOCh8TD/V+2Bl8YDD407nhpPPNXm/3Z8rXHHTeWGQqGwe+2yP9uP9No+\npM/mHnRGwll0S3CwN1v3FfD6l7no9SbuumYIY4eFObqsThU0FLGhYCsN2kaa9a0065pp0bViMFl2\n77ZSoTwT6uaw9tR44Kk+P8A9NZ4/+YwnLkpNt0Jd9mf7kV7bh/T54uGstmMdopdKiA3gt/NG8Oq/\n9/PuN4do1eqZkhLl6LIuqJ9PNHcPX3DeayaTiXZDO826Vpr15rBu1jWbv9a10KJvoVn34x/z181U\ntFRiwrLfbdUK1XmjdE+1x8W/PvOaRqWxRRuEEE5OwllYxYBIX34/fwSvLM/h4/VHaW3Xc83YWEeX\nZRGFQoGb2g03tRuB+Fu8nNFkpE3f/rPwbj7zdcsFvq5vb6CsucLiUNcoNXhqPPD38CHKI4pB/vEM\n9OuPt4vX5X67QogeQA5ri275aZ/Lalr4++fZ1DS0M3NMDHMmxjvkHK0zM5qMtOhbfwzvjhH5uSP2\n5vO+btA1ojPoOtYR4RnGQP/+DPKLZ4B/f7w0ng78jnoX+dlhH9JnOawt7CgswINFN6fy9+U5rNld\nSGu7gVtmDEIpAd1BqVDipfHsUqD6B3qQefIwR2tPcKz2BCfq8yltLmNb8fcARHqFM8gvnoH+8Qz0\ni8ND42Gr8oUQdiAjZ9EtnfW5vlnLK8tzKKpoYszQUO68Zghqldy5d7l+2me9UU9+QxHHak9wtPYE\nJxsK0Bv1AChQEOUVzkD/eAb5xzPALw53tbujSu9x5GeHfUif5WptYUMX63NLm45Xv8jleEk9yQOC\nuP/6BDRqlZ0r7B0utT/rDDryGwo5WnuCo3UnyK8vRH/mCnQFCqK9Ixl0JqzjfWNxU7vZq/QeR352\n2If0WcJZ2NCl+tyuNfDGV7nk5dcyOMaPh2cn4u4qZ1O6qqv7s9ag41R9AUfrzCPrgoaijtvFlAol\nMd7mi8sG+cXT3y8WV5WLrUrvceRnh31InyWchQ1Z0med3sg7q/LIOlpJXLgPj81NwstdbhHqiu7u\nz+0GLSfr8zvOWRc0FnfMkqZUKIn1ie44Z93fNxaXPnwLl/zssA/ps4SzsCFL+2wwGvlg9RF2Hiwj\nMtiTJ25Kxs/L1Q4V9g7W3p/b9G2cqC/oOGdd2FjccXuXWqGin09Mx2HwOJ+YPnW/tfzssA/ps4Sz\nsKGu9NloMvHZxmNsyiwm2M+N384bQbCfXKhkCVvvz636Vk7U5Xecsy5uLP0xrJVq4jrCegD9fKLR\nKHvvqQn52WEf0mcJZ2FDXe2zyWRi5Y5T/Pf7fPy9XXnipmQiguQe3Uux9/7comvheN2pjnPWJU2n\nO97TKDX09+3XMbKO8Y5C3YvCWn522If0WcJZ2NDl9nntnkL+veU4Xu4aHr8pidgwHxtU13s4en9u\n0jWbw/rMOevS5rKO91yUGuL94jrOWcd4R6JS9tyr8h3d697KYDRwpPYYmeX7ya3Kw9PFg4SAwSQG\nJTDQr3+P3mcul0PCubW1lSeffJLq6mra29t54IEHmDx5cqefl3DumbrT5205Jfxr7Q+4uapYOCeJ\nQdF+Vq6u93C2/blR28SxupMd56zLWio63nNVuRDvF8cV/gMYHZaCj0vnP4CckbP1uiczmowcrztJ\nRvl+cioO0KxvAcDP1RetUUuLrhUAd7UbQwOuIDE4gaEBV+Ch6RunuxwSzqtXr6akpIR77rmHkpIS\n7rzzTtatW9fp5yWce6bu9nnv4XLe/e8hlEoFD94wnMT4QCtW13s4+/5c397I8TOHwI/WnaCipQow\nj6qvjhrHtJiJPWY+cGfvtbMzmozkNxSSUb6f7IpcGrTmXvq4eJMSkkhqaDJxPjEEBHmy69h+cqsO\ncaDqEDVttYD57oFBfvEMDx5KYtBQAtwsn+++p3H4Ye2MjAwWL17Mv/71r04/I+HcM1mjz7knqnjz\nPwcxGk3c+8sERg0OsVJ1vUdP25/r2uvJqTzIhoKt1LXX46JyYVLUeKbGXO3084D3tF47A5PJRFFT\nCZnl+8ks309tex0AnhoPRgQPJzU0mQF+cSgVP84SeG6fTSYTJU2nya3K40DVIQobSzo+F+UVQWLQ\nUIYHDyXaK7JXzdXv0HCeN28eZWVlvP322wwePLjTz0k490zW6vMPhbW8tiKXdp2B29IHc3VShBWq\n6z166v6sM+jYWbqX9QWbqdc24qpyYXLUBKbEXI2nk87/3VN77Qinm8vJLM8hs3w/Fa3moyVuKjeS\nghNIDU1msP+ATs8lX6zPtW11HKg6TG5VHkdrT3RMoOPn6kti0FDzeWr//j3+QkSHj5wPHz7M73//\ne1atWtXpbz16vQG1TO3Ypx0rquWPS3fT2KLlrl8O4/qJ8Y4uSViJVq9lw4kdrDyynvq2Btw1blwz\naArXDJqKp4tzhrS4sLLGCr4vymRnYQZF9aWA+TqD1MhExkWnkhyeYNVJbFp0rewvO8S+klyySw/Q\nfM556uTwBEZFJpIcnoCXi3Mfkekqm4XzwYMHCQwMJDw8HIBZs2bx0UcfERh44XOKMnLumazd55Kq\nZl7+PJu6Ji2/HB/LdRPietVhrMvVW/ZnrUHL9pJdbCjYSpOuGXe1G1Oir2Jy9ASneThHb+m1NdW2\n1ZFZYT5kXdhYDJgnq0kIHExqaBLDgoZ2eQrYy+mzwWjgRP0pcisPkVuVR/U556kH+PU/M6oeSqB7\nQJfW6ygOGTl/8MEHlJSU8NRTT1FVVcWcOXPYvHkzSuWFn0wk4dwz2aLPlXWt/P3zbCrr2pg2Mop5\nUwf2+UdO9rb9ud2gZXvx92wo3EqzrgUPtTtTY65mUtR4hz+Uo7f1+nI1aBvJqsglq3w/J+rzAXMI\nDg4YSGpIEknBCd36haq7fTaZTJQ2l5Fbab6grKCxqOO9SK9w83nqoKHEeEc57S/4DgnntrY2nnrq\nKU6fPk1bWxsPPfQQU6ZM6fTzEs49k636XNvYzivLcyipamb88DBunzkYVSe/2PUFvXV/btO3sa34\nezYVbqdZ34KnxoNp0RO5OmocbmrHTO/aW3ttiWZdCzmVB8gs38/R2hOYMKFAwUC//qSGJpEcPNxq\nh4+t3ee69noOVB0it+oQR2uOdzyVzc/Vl2FBQ0gMSmCQf7xTzW7n8HPOlpBw7pls2eemVh2vLM8h\nv6yR1CuCuffaBDTqvhnQvX1/btW3sbVoJ5uKttOqb8VL48m0GHNI2/uJWb291z/Vqm8jtzKPrIr9\nHKo52vFAlDiffqSGJpESkoivq/UnCbJln9v0bRyuOUZuVR55VUc67q92Vbl03E+dEDjY4RclSjgL\nm7H5nM/tehavyOWHojoS4gJ46IbhuLr0vQsH+8r+3KpvZXPRd2wp2kGrvg1vjRfT+03iqsgxuNgp\npPtCr7UGLQerj5BZnsPB6iPojXoAor0jSQ1JIiUkiUB3295fbK8+G4wGTtbnk1t1iNzKPKraagDz\nIfp431gSgxNIDBpKkLv951iQcBY2Y48+a3UG3lp5kNwT1QyI8uXROYl4uPWdpyRB39ufW3QtbC7a\nwZai72gztOPj4s2MfpMZH3GlzR9n2Vt7rTPqOVJzlIzyHHKrDqE1aAEI8whhZGgyKaFJhHoE260e\nR/TZZDJxurncHNRVeRQ0/HieOsIzrON+6hjvqPPuybYVCWdhM/bqs95gZNk3h9h7uIKYEC8evykZ\nH0/7Hu50pL66PzfrWthcuJ0txd/RbtDi6+LNjNgpjA8fbbPHWPamXhuMBo7WniCzYj85lQdp1Ztv\nQwpyD2RkSBIpoUlEeIY55IIpZ+hzfXtDx3nqH2qPdxxB8HXxZviZC8qu8B9g032tMxLOolvs2Wej\n0cRH639gW04pYQEe/HZeMgE+jr2y1176+v7cpG1mU9F2thbvRGvQ4ufqS1q/KYyNGGX1C3x6eq+N\nJiMn6vLJrDBPn9mkawbMF0alhiSRGprkFFcwO1uf2/TtHKk5Sm7VIQ5WH6ZZZz5P7aJyYWjAIBKD\nEkgKTrDq3QQSzsJm7N1nk8nEF1tPsHZPIYE+rvx23ghCA3r/JBayP5s1apvYWLiNbcXfozPq8Hf1\nIz12CmPCR1pttqie2GuTyURBYxGZ5fvJqsilrr0eAG+NFymhiaSEJNHft59dDtVaypn7bD5PXXBm\nVJ1HZWs1AElBCdybeJvVtiPhLGzGUeeNvt1VwFfbT+Lj6cITNyUTHdIzHqpwuWR/Pl+DtpENBVvZ\nUbILnVFPoJs/6bFTuTIstduPHuwJvTaZTFS31VDYWEJ+fSE5lQepPnOhk4faneTg4aSGJjn1oxh7\nQp/B3OvylgoOVh8hxjuKQf7Wm7lQwlnYjCP7vCmzmE82HMXDVc1jc5OIj/R1SB32IPvzhdW3N5hD\nunQ3eqOeILcA0uOmMTp0xGWHkrP12mQyUdlaTVFjMUWNpRQ2FlPUWELLmfPHYL5FKDFoGCNDkxgc\nMLBHzDntbH12BAlnYTOO7vP3B0/zz2+PoFEreXj2cIbG9oxp+7rK0X12dnXt9awv2MLOkj3oTQaC\n3QOZGTuNUWEjunwo15G9NpqMVLVWU9hYYg7hhhKKmkpo1bed97kQ9yCivSOJ8YkixjuSWJ9+Nr+K\n3dpkn5ZwFjbkDH3OOlrJ218fBOD+64YxYpD9bgexF2foc09Q21bHuoItfF+6F4PJQKhHMDNjp5Ea\nmmRxSNur10aTkcqWqh+DuLGEosZS2gw/BrECBSEe5iCO9o4kxjuKaO8Ip5mHvDtkn5ZwFjbkLH3O\ny6/h9S9z0etN3HXNEMYOC3N0SVblLH3uKapba1lXsJldp/dhNBkJ8whhVtw0RoQkXjKkbdFro8lI\nRUslhY0lFJ0J4+LGUtoM7R2fMQdxMDHekcR4RxLtHUWUdwTuDp5r3FZkn5ZwFjbkTH0+XlLPq//e\nT0u7nltmDGJKSpSjS7IaZ+pzT1LVWsO6/E3sLsvEaDIS4RnGzLhpJAcP6zSku9tro8lIeUslhQ3F\nHUFc1FTaMekHmIM41DPkTBBHEe0dSZRXhMPmE3cE2aclnIUNOVufC8sbeWV5Dg0tOmZP7M81Y2Md\nXZJVOFufe5rKlmrW5m9iT1kmJkxEeoUzK246SUEJP7vftyu9NhgN5iBuLD4zKjaPiLVGXcdnFCgI\n9ww979B0lHeE3ecMdzayT0s4Cxtyxj6X1bTw98+zqWloZ+aYGOZMjHf4hAvd5Yx97okqWipZk7+J\nfWXZmDAR7RXBrLjpDA8a2rGPdNZrg9FAWUtFRwgXNpRQ3FSK7pwgViqUhHmEmEfDPmeC2CvcbvOC\n9ySyT0s4Cxty1j5X17fx9+U5lNe0MCk5gltmXIFS2XMD2ln73FOVN1ewOn8jmeX7MWEixjuSa+Jm\nkBA4mJAQH8rK6zjdXP5jEDeWUNJUiu7M9I5gDuJwz1BivKPOnCOOJNIrosddNe0osk9LOAsbcuY+\nNzRreWV5DoUVTYwZGsqd1wxBrXKeGZK6wpn73JOdbi5nzamNZFXkmkfS3pG4ajTk1xV3zLMM5iCO\n8AzruFArxieSSM9wm8253BfIPi3hLGzI2fvc0qbj1S9yOV5ST1J8IPdfPwwXjXPOmHQxzt7nnq60\nqYzVpzaQXXkAlVJ1fhB7RxLhGSZBbGWyT0s4CxvqCX1u1xp446tc8vJrGRzjx8OzE3F3df4ZlM7V\nE/rcGzRpm4kKC6Supu3SHxbdIvv0xcO5Zx7jE6ILXF1UPDInidRBwRwprONvn2VzvKQeJ/m9VDgR\nLxdPGSELpyDhLPoEjVrJfdcnMH54GPlljbz0USbPf5DBjtxStDqDo8sTQojz9Kxje0J0g0qp5M5Z\nQxiXEMamrBKyj1Xy/uoj/Hvzca5OimDyiEiC/Hr+tIhCiJ5Pwln0KQqFgiGxAQyJDaC6vo2tOSVs\nyyllzZ5C1u4pJGlAEFNToxgS64+yh98bLYTouSScRZ8V6OvG7Inx/HJ8LPuOVLAps4Sc41XkHK8i\nLMCDySmRjB8Wjoeb/DMRQtiX/NQRfZ5GrWLcsHDGDQvnZGkDm7OK2Xu4nM82HuOrbScZNyyMKSmR\nRAZ7ObpUIUQfIeEsxDn6R/jQP2Ioc6cMYMf+UrZkl3T8GRzjx9TUKJIHBqFSyrWUQgjbkXAW4gJ8\nPFy4Zmws6VfGsP94NZsyizlcUMuRwjr8vV2ZPCKSq5Mi8PGUOZOFENYn4SzERaiUSlIGBZMyKJiS\nqma2ZBWz82AZX20/yaqdpxg1OJSpqVH0j/BxdKlCiF5EwlkIC0UGeXLLjCuYPTGe7w+WsSmzmF15\nZezKKyM2zJupqVGMHhKCRt3zpgcVQjgXmb5TdEtf7rPJZOJQQS2bM4vJOV6FyQRe7hquTopg0ogI\ngnytd890X+6zvUmv7UP6fPHpO2XkLMRlUigUJMQGkBAbQFVdK1tyStix/zSrdxewZk8ByWfvme7n\n3+OfJy2EsC8JZyGsIAZf3fMAABPpSURBVMjPnV9NGsB14+PYe7iCTVnFZB+rIvtYFeGBHkxJiWLc\nsLAe98ANIYRjyE8KIazIRaNiQmI444eHcfJ0A5szi9l7uIJPNhzly20nGD8snCmpkYQHejq6VCGE\nE5NwFsIGFAoF8RG+xEf4MnfKQLbvL2VrdgmbsorZlFXM0Fh/pqZEkTQgCKVSDnkLIc4n4SyEjfl6\nunDtuFhmjYkh+2gVm7OKOZRfy6H8WgJ9XJmcEsVVieF4e8g900IIMwlnIexEpVQycnAIIweHUFzZ\nxOasEnYdLGPF1hOs3HGKK4eEMCU1irhwuWdaiL5OwlkIB4gK9uLWtCuYMzGenQdPsznTPLnJzoNl\n9I/wYWpKFCMHh6BRyzShQvRFEs5COJCHm5rpI6OZmhrFofwaNmeWsP94Fe+WHuLzzceYmBzBpOTI\ni94PKYTofSSchXACSoWCYXGBDIsLpLKulS3ZJezYX8o33xewelchI4eE4uOuxtVFhYtGhataiYuL\nClfNj39cNMpz/v7ja2qVjL6F6GkknIVwMsF+7sydPIDrJsSx91A5mzKL2Xuo7LLXp1IqzKHdabAr\nzwnz87++4Gdczn9NKROsCGF1Es5COClXjYqrkiKYkBiOUaWi5HQ97TqD+Y/WiFZv/rtWe+Y1nRHt\n2fd1BrQ645n//vhaa7ue+ibz3601ca9GrTwvrM+GuIermquSwkkeECQzpAnRRRLOQjg5hUJBWKAn\nKqPRaus0mUzoDUbadUbatYaOoG/Xnh/y2jOh/9OQP/sZ7Tlft2sNNLXqaG9oQ6sz15pzvIqEuADm\nTR1IZJBMvCKEpSScheiDFAoFGrUKjVqFl7vG6us3mkycrmpm+ebjHDxVwx/f28vklEiumxBnk+0J\n0dvIlSJCCKtTKhREBnvx2NwkFs5JJNjPjU2ZxSx6Zxebs4oxWPEogBC9kYychRA2o1AoSBoQREJc\nABszilm18xQfrz/KluwS5k8dyJDYAEeXKIRTkpGzEMLm1Col6VfG8OffjOWqxHBKK5v52+c5vPHV\nASrqWh1dnhBOR0bOQgi78fV04Y5ZQ5iSEsWnG4+SdbSS3BPVpI2O5pqx/XBzkR9JQoCMnIUQDtAv\nzJsnb07hvusS8PHU8O2uAhYt3c3OA6cxWuseLyF6MPk1VQjhEAqFgtFDQkkaEMTaPYWs2V3Ae98e\nZnNWCf/f3p0GR1mmaxz/dzr7vpAQQkhnAYwJKohxJLI5IpzSOTKAGoxEP5zyaHn8oKWWFC7MlNac\nClVT5VrqKJQWjpIxoOjooDIYiBIUAREjYQkJS0jI1tlIOkkv50NjDghBlnS/nc71+5JK99vpu++8\n1de7Pk/hLRPISokxukQZIg6nE2tHL01tPTS122hut3H1xESyRkfqHvhBmFwu39hMbWrqHNK/l5gY\nNeR/U86mPnvHSOhzS7uND8oO8t3eRgCm5SZzx+ws4qJCvFrHSOi1J5y09bvDt8126mcPzad+b+mw\n4XCeHTXpyVEsmp1F7gi9MPB8Y+YrnOWyqM/eMZL6vP9oG+9t3M+RE12EBJm5bZqFedePIyjQ7JX3\nH0m9vhh2h5OWDhvNp4Xv6WHc3Ws/5+tiIoJJjA0jMTaUxNgwRsWEERsZzPf7m9nyQx0AV1riWDQr\ni8yUkTVdqsJZPEZ99o6R1men08XXe+pZt7maju5+RsWEUvD78Vw7MdHjh0FHWq9/4XK56OrpP2PP\n9/QAbu20nXPI16DAAHf4xoSeCuGwgTAeFRNGSPC5N6oSE6P4fs9x1m6p5qdDrQBcOzGRBTMzR8xo\ncoaF84oVK9ixYwd2u50HHniAuXPnDrqswnl4Up+9Y6T2udtm559ba/ny+6M4nC6y02K5e85ExiVF\neuw9/bnX/XYnze09ZwVwc7v7d1uf45yvi4sKOUf4ugM4OiL4kjaYTu/zviNWSjdXU13XgckE+ZOS\nmT89g1ExYZf1eX2dIeG8bds2Vq5cyZtvvonVamXBggWUlZUNurzCeXhSn71jpPe5obWbkn8fYHd1\nCyYTzJ48lj/OyCAqPHjI32s499rlctFxss8dvu1nH3pu6+zlXF/4IUHmMw49n3kYOtQjpxR+3WeX\ny8UPB5tZt+UQdU0nCTSbuGlKKrflW4j2wP/ZFxgSzg6Hg97eXsLDw3E4HOTn57N161bM5nP/kxXO\nw5P67B3qs9ueQy2s+fcB6lu6CQ8JZP6MDG6aMnZI56weLr229dnZf7SNqsNtNLR2n7oSumdg0pHT\nmYD46BB32P4qfBNjw4gKC/L6VdOD9dnpdLHt5wY+Kq+hud1GSLCZeXnjmHd9GmEh/nWD0fnC2WOf\n1Gw2Ex4eDkBpaSkzZ84cNJhFRC7EVZkJXGmJ46uddXz0dQ3vbzxA2a467p4zgUkZCUaX51F2h5Oa\n+g721lr5ubaV6uMdZ1wBHRZiJjk+/KzDzomxYSREhw7pBownBQSYyJ80hrzs0WzZfZxPvqnh429q\n2bSzjj/kp3PTlBSvXRxoJI9fELZx40beeOMNVq1aRVTU4FsJdruDwBHQcBEZGu1dvfx9QxWfb6vF\n6YLrc5L5r9tzSUn03Plob3K5XBw50cnu/U38cKCJn6qb6el1nxM2mWB8aiyTJyZyzfhEMlNjiDRg\n79cbenrtfLylmnVlB+m22RkVG8Y9867gpqnjMA+TDY5L4dFwLi8v58UXX+Stt94iNjb2vMvqsPbw\npD57h/o8uCMnOnl/4wH2HW3DHGDilrxx/Gd++iUfAjWy160dNn6utfLz4Vb21lppP9k38Nzo+HBy\n0uPIscSTbYklInR4T715sX3u6unns4rDbNxxDLvDyZiEcBbOzPTKFfyeYsg5587OTgoLC3n77bdJ\nSPjtw00K5+FJffYO9fn8XC4XO/Y1UbLpIC0dNqLDg1g0K4sbrx5DwEV+cXuz1922fvYebmPv4VZ+\nrrXS0No98Fx0eBA56fFceSqQE2JCvVKTt1xqn1s7bHz8TS1f/+ge6jVjTDR3zMocljOcGRLOJSUl\nvPzyy2RkZAw8VlxcTEpKyjmXVzgPT+qzd6jPF6av38Hn24/yaUUtff1OLMlRFM6ZwITU8x+5O50n\ne91vd3CwroOfa91hXNvQMXDvcEiQmSvSYslJjycnPY6xoyKG7R7hhbjcPte3nOTD8hq+r3KPKJeT\n7h7IJGPM8BnIRIOQiMeoz96hPl8ca2cvpWUHqag8AcDvckZz5+ws4qN/e+9zKHvtdLk4eqLrVBi3\ncuBYO31299XU5gATmSnR7r1jSxyZKdHD5qKtoTBUfa5t6GDt5kNU1rgHMrnuCvdAJmMSfH8gE4Wz\neIz67B3q86U5WNfO+xv3U1PfSXBgALfeYGHe79IICRr84tPL7XVjW8/AnnHVYStdPf0Dz41NjCDH\n4t4znjgu1u9uDboYQ71O761tpXTzIWrq3QOZTL9qDPOnZ1zQBplRFM7iMeqzd6jPl87pclHxUwOl\nZdW0n+wjITqEO28aT1520jkPG19srzu6+6g6bB0I5OZ228Bz8dEhA2F8pSWOmEjvTuLhyzyxTrtc\nLnYdaGbt5mrqW7oJNAfw+2vHcts0i0cGrLlcCmfxGPXZO9Tny9fTa+fTisN8sf0IdoeLiakx3D1n\nIpbkM78gf6vXvX0O9h9rG7jf+Ehj18Bz4SGBXGmJc1/ElR7P6Lgwvz5vfDk8uU47nS62/tTA+q8P\n0dLRS2iwmf+4Po1b8sb51NEKhbN4jPrsHerz0Gm0dlOy6SC7DjRjAmZck8LCmZlER7j3rH7da4fT\nSW1958Ce8cG69oHBPwLNJiakxrpvcUqPxzI6ioAAhfGF8MY63W93UvZDHf/cWktndz9R4UH8IT+d\n2ZPHEhRo/Pl9hbN4jPrsHerz0KusbWXNxgPUNZ8kLMTM7TdmcPPUVJJHR/Nj1YmBMN531Pr/g38A\naclRA2E8YWwMwec5fy2D8+Y63dNr58vtR9nw3RFsfQ4SokP544wMpuUmG7oxpXAWj1GfvUN99gyH\n00nZruN8VH6IkzY7o2JCcbrc99L+Iik2bCCMsy1xRIYN78E/fIUR63Rndx+fVhxm08467A4nKaMi\nWDgzkykTRhly+kHhLB6jPnuH+uxZXT39rC+v4atddURFBHHFuFP3G1viGBXr39MWGsXIdbql3cb6\nb2r4Zk89LhdkpkRzx6wssi1xXq1D4Sweoz57h/rsHX39DsYkx9DS0vXbC8tl8YV1+njzST4sP8SO\nfU0A5GbEs2hWJunJ3hnIxJBZqUREhpvgILMu6BpBUkZF8D8LrqKmvoO1m6uprGmlsqaVvOwkFszM\nJDk+3LDaFM4iIjKiZYyJ5vHFU6isbWVtWTXbqxrZsa+J6VeP4fYb0w0ZyEThLCIiAuSmx5NzXxw7\n9zexbsshtuw+TkVlAzdPTeXWGyxevRhQ4SwiInKKyWRi6hVJTJ4wiq17Glj/TQ0bvj3C5h/qWDL3\nCqblJnulDuPvwhYREfEx5oAAZlyTwv/+9w0s/v14zAEB/HSoxWvvrz1nERGRQQQFmpl7fRpz8saB\nF+9tUjiLiIj8hgCTyT1EnLfez3tvJSIiIhdC4SwiIuJjFM4iIiI+RuEsIiLiYxTOIiIiPkbhLCIi\n4mMUziIiIj5G4SwiIuJjFM4iIiI+RuEsIiLiYxTOIiIiPsbkcrm8OJS3iIiI/BbtOYuIiPgYhbOI\niIiPUTiLiIj4GIWziIiIj1E4i4iI+BiFs4iIiI/xy3D+y1/+QkFBAYsXL+bHH380uhy/tWLFCgoK\nCli0aBFffPGF0eX4NZvNxpw5c1i3bp3Rpfitjz/+mNtvv52FCxdSVlZmdDl+6eTJkzz88MMUFRWx\nePFiysvLjS7JZwUaXcBQ++677zh8+DAlJSVUV1ezbNkySkpKjC7L72zbto0DBw5QUlKC1WplwYIF\nzJ071+iy/NZrr71GTEyM0WX4LavVyquvvsratWvp7u7m5ZdfZvbs2UaX5Xc+/PBDMjIyeOyxxzhx\n4gT33XcfGzZsMLosn+R34VxRUcGcOXMAyMrKor29na6uLiIjIw2uzL/k5eVx9dVXAxAdHU1PTw8O\nhwOz2WxwZf6nurqagwcPKiw8qKKigmnTphEZGUlkZCTPPfec0SX5pbi4OPbt2wdAR0cHcXFxBlfk\nu/zusHZzc/MZ//D4+HiampoMrMg/mc1mwsPDASgtLWXmzJkKZg8pLi5m6dKlRpfh144dO4bNZuPB\nBx+ksLCQiooKo0vyS7fddhvHjx/nlltuYcmSJTz55JNGl+Sz/G7P+dc0Oqlnbdy4kdLSUlatWmV0\nKX7po48+YvLkyYwbN87oUvxeW1sbr7zyCsePH+fee+/lq6++wmQyGV2WX1m/fj0pKSmsXLmSqqoq\nli1bpusoBuF34ZyUlERzc/PA742NjSQmJhpYkf8qLy/n9ddf56233iIqKsrocvxSWVkZR48epays\njIaGBoKDg0lOTiY/P9/o0vxKQkICU6ZMITAwkLS0NCIiImhtbSUhIcHo0vzKzp07mT59OgDZ2dk0\nNjbqdNgg/O6w9o033sjnn38OQGVlJUlJSTrf7AGdnZ2sWLGCN954g9jYWKPL8VsvvPACa9eu5R//\n+Ad33nknDz30kILZA6ZPn862bdtwOp1YrVa6u7t1PtQDLBYLu3fvBqCuro6IiAgF8yD8bs/52muv\nJTc3l8WLF2MymVi+fLnRJfmlzz77DKvVyiOPPDLwWHFxMSkpKQZWJXJpRo8ezbx587jrrrsAePrp\npwkI8Lt9F8MVFBSwbNkylixZgt1u509/+pPRJfksTRkpIiLiY7RpKCIi4mMUziIiIj5G4SwiIuJj\nFM4iIiI+RuEsIiLiYxTOIsPUsWPHmDRpEkVFRQOz/Dz22GN0dHRc8N8oKirC4XBc8PJ333033377\n7aWUKyIXQeEsMozFx8ezevVqVq9ezZo1a0hKSuK111674NevXr1ag0CI+CC/G4REZCTLy8ujpKSE\nqqoqiouLsdvt9Pf38+yzz5KTk0NRURHZ2dns3buXd955h5ycHCorK+nr6+OZZ56hoaEBu93O/Pnz\nKSwspKenh0cffRSr1YrFYqG3txeAEydO8PjjjwPuuaYLCgq44447jPzoIn5F4SziJxwOB19++SVT\np07liSee4NVXXyUtLe2sCQbCw8N59913z3jt6tWriY6O5q9//Ss2m41bb72VGTNmsHXrVkJDQykp\nKaGxsZGbb74ZgH/9619kZmby5z//md7eXj744AOvf14Rf6ZwFhnGWltbKSoqAsDpdHLdddexaNEi\nXnrpJZ566qmB5bq6unA6nYB7iNtf2717NwsXLgQgNDSUSZMmUVlZyf79+5k6dSrgnlQmMzMTgBkz\nZvDee++xdOlSZs2aRUFBgUc/p8hIo3AWGcZ+Oed8us7OToKCgs56/BdBQUFnPfbrqRFdLhcmkwmX\ny3XGGNO/BHxWVhaffvop27dvZ8OGDbzzzjusWbPmcj+OiJyiC8JE/ExUVBSpqals3rwZgJqaGl55\n5ZXzvuaaa66hvLwcgO7ubiorK8nNzSUrK4tdu3YBUF9fT01NDQCffPIJe/bsIT8/n+XLl1NfX4/d\nbvfgpxIZWbTnLOKHiouLef755/nb3/6G3W5n6dKl512+qKiIZ555hnvuuYe+vj4eeughUlNTmT9/\nPps2baKwsJDU1FSuuuoqAMaPH8/y5csJDg7G5XJx//33ExiorxORoaJZqURERHyMDmuLiIj4GIWz\niIiIj1E4i4iI+BiFs4iIiI9ROIuIiPgYhbOIiIiPUTiLiIj4GIWziIiIj/k/+vePfhfOY7UAAAAA\nSUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc2f63406d0>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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JOEdyF6IoauobQ0SkJC39vZTl8N8jR45g7NixmD59OrKysvD4449jxIgRGDNm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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc2f5b56c50>"
            ]
          },
          "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": "evlB5ubzu8VJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        },
        "outputId": "e9fb6687-e795-4358-a066-11116724e7a0"
      },
      "cell_type": "code",
      "source": [
        "mnist_test_dataframe = pd.read_csv(\n",
        "  \"https://dl.google.com/mlcc/mledu-datasets/mnist_test.csv\",\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": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "      <th>4</th>\n",
              "      <th>5</th>\n",
              "      <th>6</th>\n",
              "      <th>7</th>\n",
              "      <th>8</th>\n",
              "      <th>9</th>\n",
              "      <th>10</th>\n",
              "      <th>...</th>\n",
              "      <th>775</th>\n",
              "      <th>776</th>\n",
              "      <th>777</th>\n",
              "      <th>778</th>\n",
              "      <th>779</th>\n",
              "      <th>780</th>\n",
              "      <th>781</th>\n",
              "      <th>782</th>\n",
              "      <th>783</th>\n",
              "      <th>784</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\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",
              "      <td>10000.0</td>\n",
              "      <td>...</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",
              "      <td>10000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</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>std</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>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",
              "      <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>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",
              "      <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>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",
              "      <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",
              "      <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>0.6</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": 31
        }
      ]
    },
    {
      "metadata": {
        "id": "PDuLd2Hcu8VL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 55
        },
        "outputId": "5f8183a4-422a-41d1-cc53-f0bde9b3b938"
      },
      "cell_type": "code",
      "source": [
        "# YOUR CODE HERE: Calculate accuracy on the test set.\n",
        "\n",
        "predict_test_input_fn = create_predict_input_fn(\n",
        "  test_examples, test_targets, batch_size=20)\n",
        "\n",
        "test_predictions = classifier.predict(input_fn=predict_test_input_fn)\n",
        "test_pred_class_id = np.array([item['class_ids'][0] for item in test_predictions])\n",
        "test_pred_one_hot = tf.keras.utils.to_categorical(test_pred_class_id,10)    \n",
        "\n",
        "# Compute test errors.\n",
        "test_log_loss = metrics.log_loss(test_targets, test_pred_one_hot)\n",
        "accuracy = metrics.accuracy_score(test_targets, test_pred_class_id)\n",
        "print(\"Log Loss error (on test data): %0.2f\" % test_log_loss)\n",
        "print(\"Final accuracy (on test data): %0.2f\" % accuracy)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Log Loss error (on test data): 2.27\n",
            "Final accuracy (on test data): 0.93\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",
        "  \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": {}
      },
      "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": []
    },
    {
      "metadata": {
        "id": "qXvrOgtUR-zD",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Next, we verify the accuracy on the test set."
      ]
    },
    {
      "metadata": {
        "id": "scQNpDePSFjt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mnist_test_dataframe = pd.read_csv(\n",
        "  \"https://dl.google.com/mlcc/mledu-datasets/mnist_test.csv\",\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": []
    },
    {
      "metadata": {
        "id": "EVaWpWKvSHmu",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "predict_test_input_fn = create_predict_input_fn(\n",
        "    test_examples, test_targets, batch_size=100)\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": []
    },
    {
      "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": 1176
        },
        "outputId": "e31d7a9a-109c-4444-e5be-49e0904b3a05"
      },
      "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/ProximalAdagrad', 'dnn/hiddenlayer_0/kernel', 'dnn/hiddenlayer_0/kernel/t_0/ProximalAdagrad', 'dnn/hiddenlayer_1/bias', 'dnn/hiddenlayer_1/bias/t_0/ProximalAdagrad', 'dnn/hiddenlayer_1/kernel', 'dnn/hiddenlayer_1/kernel/t_0/ProximalAdagrad', 'dnn/logits/bias', 'dnn/logits/bias/t_0/ProximalAdagrad', 'dnn/logits/kernel', 'dnn/logits/kernel/t_0/ProximalAdagrad', 'global_step']\n",
            "weights0 shape: (784, 100)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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y1lmnvXe3tAINgtZ+3QOsT0RbbiJZt2F+gLXT0bCs7YNjCbW9+nqhvhAtGgvK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So2seNJRjm3i3CDDOnFY2Ao2QGQYhARBVL4fi+6JXZbIiLqusDHT/Twvc9YJKUF/fUyPHIiubqL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\ns354rXVnaVwUF/F9CrpdOmF/8zYpekf0uaiZM4qiKIqiKIqiKIqiKDOIf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A6/fmf1CtjMJyqsPx/f+luljr589nr1l9PdWsQHtYnNfGGFP9HNWzCIMtdn4Br02DNVxWLIe/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\nu4P5WXqu9ol6+YWcc+boofNOedNGuVfJ3cr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/+Sd/5pR/+q3/zm+ooYhytGS4vlCykhHe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sR/jc2fxr9GEZGdTv1vmXWL+mL9EaFRy2r1WFa1dGtv3/a068QvvcgUnabx74lQO2x7hc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\n/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ifXWRzfhe28TiSVS8h8lGMlpsajxATHCpF0WypuhvisstNrHmE56SjKGyplR5RqCVcK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irW0xH4G0O0VQgz9xjHJMFO3juSOYBrqaxmm4n/q9tIt22ujJV2qbizlmjDHGV0ka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\nHIRuQqKkXclY8hSmfnofr11W5hMtM2Gaf3jVaycpeW7+zjKvzbKmz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            "text/plain": [
              "<matplotlib.figure.Figure at 0x7fc2f5baf5d0>"
            ]
          },
          "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?"
      ]
    }
  ]
}