digit_recognition_project.ipynb

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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "## Setting up the data from Kaggle"
      ],
      "metadata": {
        "id": "6OlYYJA1cGu6"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4fMr70pravMT",
        "outputId": "288dcd2c-97e8-43b6-bbd1-5f8587f369ca"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/gdrive; to attempt to forcibly remount, call drive.mount(\"/content/gdrive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "os.environ[\"KAGGLE_CONFIG_DIR\"] = '/content/gdrive/MyDrive/Kaggle'"
      ],
      "metadata": {
        "id": "-g-w-TQJbUMF"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%cd gdrive/MyDrive/Kaggle"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3n5qU2iabhQo",
        "outputId": "7d586748-c651-4193-c174-e8812e901290"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/Kaggle\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle competitions download -c digit-recognizer"
      ],
      "metadata": {
        "id": "BWpSNjEtbkSl",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e0c5c7f9-80d9-49ab-bc07-6e89be910b4a"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading digit-recognizer.zip to /content/gdrive/MyDrive/Kaggle\n",
            "\r  0% 0.00/15.3M [00:00<?, ?B/s]\r 85% 13.0M/15.3M [00:00<00:00, 135MB/s]\n",
            "\r100% 15.3M/15.3M [00:00<00:00, 138MB/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip digit-recognizer.zip -d digit-recognizer"
      ],
      "metadata": {
        "id": "tLaEhpNLbpcX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dcc3bbbf-0b21-4c78-80f4-61fbf0ea4e6c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Archive:  digit-recognizer.zip\n",
            "  inflating: digit-recognizer/sample_submission.csv  \n",
            "  inflating: digit-recognizer/test.csv  \n",
            "  inflating: digit-recognizer/train.csv  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "%cd digit-recognizer"
      ],
      "metadata": {
        "id": "065jS4DBb0uG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c873cc7d-2858-4387-9e5e-9ca4c21959ff"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/content/gdrive/MyDrive/Kaggle/digit-recognizer\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing libraries"
      ],
      "metadata": {
        "id": "eNSFyShWdvKP"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "hcoXsM49dudu"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.__version__"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "bkZd0u9lcVPa",
        "outputId": "838bbd39-f405-45f2-f977-8e0ad40c3a32"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.11.0'"
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          "metadata": {},
          "execution_count": 8
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      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing the datasets"
      ],
      "metadata": {
        "id": "TuAugo2seFhU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train = pd.read_csv('train.csv')\n",
        "test = pd.read_csv('test.csv')"
      ],
      "metadata": {
        "id": "YuW4-mFWeEXJ"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(train.shape)\n",
        "print(test.shape)"
      ],
      "metadata": {
        "id": "Pf-6s8bMf2cO",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "36cccde3-128b-4c85-985a-e77caceed033"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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              "      <th>pixel782</th>\n",
              "      <th>pixel783</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>...</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 784 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-150d61a2-6293-4913-9012-befcc733bca7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-150d61a2-6293-4913-9012-befcc733bca7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-150d61a2-6293-4913-9012-befcc733bca7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Checking if the datasets are already clean"
      ],
      "metadata": {
        "id": "zamY9zSkemmT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "train.isnull().sum()"
      ],
      "metadata": {
        "id": "DYc0KIq_el64",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c6317ecc-cf38-4f0b-d297-c0d2da850044"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "label       0\n",
              "pixel0      0\n",
              "pixel1      0\n",
              "pixel2      0\n",
              "pixel3      0\n",
              "           ..\n",
              "pixel779    0\n",
              "pixel780    0\n",
              "pixel781    0\n",
              "pixel782    0\n",
              "pixel783    0\n",
              "Length: 785, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test.isnull().sum()"
      ],
      "metadata": {
        "id": "xHYG6oLSfYF2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "869930c8-4a20-4893-c97b-4a33d0e38a03"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "pixel0      0\n",
              "pixel1      0\n",
              "pixel2      0\n",
              "pixel3      0\n",
              "pixel4      0\n",
              "           ..\n",
              "pixel779    0\n",
              "pixel780    0\n",
              "pixel781    0\n",
              "pixel782    0\n",
              "pixel783    0\n",
              "Length: 784, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## The independent variables and the dependent variables"
      ],
      "metadata": {
        "id": "o1x_ikntg0O6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = train.iloc[:, 1:].values\n",
        "y = train.iloc[:, 0].values\n",
        "X_test = test.iloc[:, :].values"
      ],
      "metadata": {
        "id": "ehoAz36QgzR-"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X.shape)\n",
        "print(X_test.shape)\n",
        "print(y.shape)"
      ],
      "metadata": {
        "id": "RKK7CLd0fviV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eea1f31d-31ab-4b63-8239-257c0b3645b7"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(42000, 784)\n",
            "(28000, 784)\n",
            "(42000,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Spliting the training set into the training set and the cross validation set"
      ],
      "metadata": {
        "id": "x7hp8zRWh8aY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "X_train, X_cv, y_train, y_cv = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "metadata": {
        "id": "ZI5rjHWthmTL"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_cv.shape)\n",
        "print(y_train.shape)\n",
        "print(y_cv.shape)"
      ],
      "metadata": {
        "id": "WQqnfhk3i-OI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf9febf8-a57a-415a-e2f0-dd36409aa305"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(33600, 784)\n",
            "(8400, 784)\n",
            "(33600,)\n",
            "(8400,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Feature Scaling (without scaling the dependent variable)"
      ],
      "metadata": {
        "id": "orVpSAZFjK8m"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "sc_X = StandardScaler()\n",
        "X_train = sc_X.fit_transform(X_train)\n",
        "X_cv = sc_X.transform(X_cv)\n",
        "X_test = sc_X.transform(X_test)"
      ],
      "metadata": {
        "id": "VUZeiNW7jDI3"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_train.shape)\n",
        "print(X_cv.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_cv.shape)"
      ],
      "metadata": {
        "id": "D8nzCvuGmPkU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0278a838-f551-4c11-9d4b-853697dc34f0"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(33600, 784)\n",
            "(8400, 784)\n",
            "(28000, 784)\n",
            "(33600,)\n",
            "(8400,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#ANN"
      ],
      "metadata": {
        "id": "DxEEDdQusMJ3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Builiding and Training the ANN on the training set"
      ],
      "metadata": {
        "id": "8611PsaznSyX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "ann = tf.keras.models.Sequential()\n",
        "ann.add(tf.keras.layers.Dense(units=50, activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=50, activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=50, activation='relu'))\n",
        "ann.add(tf.keras.layers.Dense(units=10, activation='linear'))"
      ],
      "metadata": {
        "id": "n5MBGyFpm63n"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.losses import SparseCategoricalCrossentropy\n",
        "ann.compile(optimizer='adam', loss=SparseCategoricalCrossentropy(from_logits=True), metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "b_4bphDcn2tE"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#epochs=100, units=20,20\n",
        "#1050/1050 [==============================] - 3s 2ms/step - loss: 0.0077 - accuracy: 0.9978\n",
        "ann.fit(X_train, y_train, batch_size=32, epochs=20)"
      ],
      "metadata": {
        "id": "x7AkwC9WqRl5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2660fb97-1517-4db2-ef31-884efa58720b"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "1050/1050 [==============================] - 5s 3ms/step - loss: 0.3379 - accuracy: 0.8977\n",
            "Epoch 2/20\n",
            "1050/1050 [==============================] - 5s 5ms/step - loss: 0.1360 - accuracy: 0.9579\n",
            "Epoch 3/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0945 - accuracy: 0.9695\n",
            "Epoch 4/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0708 - accuracy: 0.9781\n",
            "Epoch 5/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0574 - accuracy: 0.9810\n",
            "Epoch 6/20\n",
            "1050/1050 [==============================] - 4s 4ms/step - loss: 0.0454 - accuracy: 0.9850\n",
            "Epoch 7/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0416 - accuracy: 0.9864\n",
            "Epoch 8/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0350 - accuracy: 0.9880\n",
            "Epoch 9/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0292 - accuracy: 0.9902\n",
            "Epoch 10/20\n",
            "1050/1050 [==============================] - 4s 4ms/step - loss: 0.0288 - accuracy: 0.9903\n",
            "Epoch 11/20\n",
            "1050/1050 [==============================] - 5s 4ms/step - loss: 0.0239 - accuracy: 0.9918\n",
            "Epoch 12/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0223 - accuracy: 0.9926\n",
            "Epoch 13/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0257 - accuracy: 0.9914\n",
            "Epoch 14/20\n",
            "1050/1050 [==============================] - 5s 4ms/step - loss: 0.0174 - accuracy: 0.9947\n",
            "Epoch 15/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0244 - accuracy: 0.9925\n",
            "Epoch 16/20\n",
            "1050/1050 [==============================] - 3s 2ms/step - loss: 0.0235 - accuracy: 0.9933\n",
            "Epoch 17/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0210 - accuracy: 0.9936\n",
            "Epoch 18/20\n",
            "1050/1050 [==============================] - 4s 3ms/step - loss: 0.0198 - accuracy: 0.9940\n",
            "Epoch 19/20\n",
            "1050/1050 [==============================] - 4s 4ms/step - loss: 0.0176 - accuracy: 0.9947\n",
            "Epoch 20/20\n",
            "1050/1050 [==============================] - 3s 3ms/step - loss: 0.0137 - accuracy: 0.9957\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f00305cddf0>"
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Testing the ANN on the cross validation set"
      ],
      "metadata": {
        "id": "MFaaT9J0skIU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "logits = ann(X_cv)\n",
        "y_pred = tf.nn.softmax(logits)\n",
        "y_pred = y_pred.numpy()"
      ],
      "metadata": {
        "id": "ibOaFB1ULVIK"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = np.argmax(y_pred, axis=1)"
      ],
      "metadata": {
        "id": "dff0dknXqZA6"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(y_pred.shape)\n",
        "print(y_cv.shape)"
      ],
      "metadata": {
        "id": "bYlI6TAozSmu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "beb64df9-eba9-4b5a-eb5a-9d6e2481e684"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8400,)\n",
            "(8400,)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import confusion_matrix, accuracy_score\n",
        "cm = confusion_matrix(y_cv, y_pred)\n",
        "print(cm)\n",
        "score = accuracy_score(y_cv, y_pred)\n",
        "print(score)"
      ],
      "metadata": {
        "id": "e3z4rHykywU2",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "228cd688-13c9-4234-dff6-67a2961de995"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[795   0   1   0   3   2   3   2   5   2]\n",
            " [  0 946   5   4   0   1   1   1   3   0]\n",
            " [  2   1 837   4   0   0   1  10   5   0]\n",
            " [  0   2   8 809   1  19   0   5  11   8]\n",
            " [  1   3   6   0 782   1   8   3   4  19]\n",
            " [  2   0   1   4   0 734   6   1   8   0]\n",
            " [  3   0   1   0   2   8 827   0   0   0]\n",
            " [  1   3   7   1   2   0   0 875   1   9]\n",
            " [  2   7   2   8   0  16   6   1 720   6]\n",
            " [  4   0   0   4   8   4   1  15   7 769]]\n",
            "0.9635714285714285\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(sum(y_pred - y_cv))"
      ],
      "metadata": {
        "id": "QqO-Bb5V0IYw",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3c2207a4-ec1b-4ad2-cd95-657d5a4d416a"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "106\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# This code evaluate the predictions automatically\n",
        "loss, accuracy = ann.evaluate(X_cv, y_cv)"
      ],
      "metadata": {
        "id": "eVcrppFV0KnV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f59c776b-9b06-4058-f4dd-68befd5b5834"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "263/263 [==============================] - 1s 2ms/step - loss: 0.2477 - accuracy: 0.9636\n"
          ]
        }
      ]
    }
  ]
}