Project_BHDR.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/YaminMahdi/Project_BHDR/blob/main/Project_BHDR.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Vo8V92aiWlKJ"
      },
      "source": [
        "###**Importing API & Libraries**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "UPsSL_i-Q_ic"
      },
      "outputs": [],
      "source": [
        "import gzip\n",
        "import pickle\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import sklearn\n",
        "from sklearn import metrics\n",
        "from google.colab import drive\n",
        "\n",
        "# Importing the required Keras modules containing model and layers\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Conv2D, Dropout, Flatten, MaxPool2D\n",
        "\n",
        "from google.colab import files\n",
        "from keras.preprocessing import image\n",
        "from PIL import Image, ImageDraw, ImageOps\n",
        "import cv2"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XK-NkjRZXxix"
      },
      "source": [
        "###**Loading Dataset From Drive**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "cIUbUM23Q_if",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08be667a-068e-44f8-d951-cbf1813f9ce6"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive/\n"
          ]
        }
      ],
      "source": [
        "drive.mount('/content/drive/')\n",
        "\n",
        "f = gzip.open('/content/drive/MyDrive/Project/Project_BHDR/bhand.pkl.gz','rb')\n",
        "trainSet,validSet,testSet = pickle.load(f,encoding=\"latin1\")\n",
        "f.close()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "\n",
        "\n",
        "```\n",
        "# This is formatted as code\n",
        "```\n",
        "\n",
        "###**Preprocessing Data**"
      ],
      "metadata": {
        "id": "DeYGwzudgN5v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "def make_square(im, min_size=30, fill_color=(0)):\n",
        "  x, y = im.size\n",
        "  size = max(min_size, x, y)\n",
        "  new_im = Image.new('L', (size, size), fill_color)\n",
        "  new_im.paste(im, (int((size - x) / 2), int((size - y) / 2)))\n",
        "  return new_im.resize((30,30))\n",
        "def normalize_data(im,x=32):\n",
        "  for i in range(x): # Outer loop  \n",
        "    for j in range(x): # inner loop  \n",
        "      if(im[i][j]<=0.5):\n",
        "        im[i][j]=0.0 \n",
        "def pre_prosess(path):\n",
        "  fig = plt.figure(figsize=(9, 12))\n",
        "  fig.subplots_adjust(left=0, right=2, bottom=0, top=2)\n",
        "\n",
        "  im= cv2.imread(path,0)\n",
        "\n",
        "  fig.add_subplot(1, 4, 1)\n",
        "  plt.imshow(im, 'gray')\n",
        "  plt.title(im.shape)\n",
        "  plt.xlabel(\"Selected Image\",fontsize=14)\n",
        "\n",
        "  img_reverted= cv2.bitwise_not(im)\n",
        "  im = img_reverted / 255\n",
        "  #print(type(im))\n",
        "\n",
        "  normalize_data(im,x=180) \n",
        "  #print(im)\n",
        "  #print(im.shape)\n",
        "\n",
        "  fig.add_subplot(1, 4, 2)\n",
        "  plt.imshow(im, cmap=plt.cm.binary)\n",
        "  plt.title(im.shape)\n",
        "  plt.xlabel(\"BG Noise Cleared\",fontsize=14)\n",
        "  plt.ylabel(\"↓ \",fontsize=25)\n",
        "\n",
        "  im= im.reshape(180,180,1)\n",
        "  #print(im)\n",
        "  im = image.array_to_img(im)\n",
        "  #print(image.img_to_array(im).shape)\n",
        "\n",
        "  imResize = im.crop(im.getbbox())\n",
        "\n",
        "  fig.add_subplot(1, 4, 3)\n",
        "  plt.imshow(imResize, cmap=plt.cm.binary)\n",
        "  plt.title(image.img_to_array(imResize).shape)\n",
        "  plt.xlabel(\"Croped Extra BG\",fontsize=14)\n",
        "  plt.ylabel(\"↓ \",fontsize=25)\n",
        "\n",
        "  #print(image.img_to_array(imResize).shape)\n",
        "  imResize = make_square(imResize)\n",
        "  #imResize.show()\n",
        "  # plt.imshow(imResize, cmap=plt.cm.binary)\n",
        "  # plt.show()\n",
        "  # print(image.img_to_array(imResize).shape)\n",
        "  imResize = ImageOps.expand(imResize, border=1, fill=(0))\n",
        "\n",
        "  fig.add_subplot(1, 4, 4)\n",
        "  plt.imshow(imResize, cmap=plt.cm.binary)\n",
        "  plt.title(image.img_to_array(imResize).shape)\n",
        "  plt.xlabel(\"Fitted To Square\",fontsize=14)\n",
        "  plt.ylabel(\"↓ \",fontsize=25)\n",
        "  plt.tight_layout()\n",
        "  plt.show()\n",
        "  #print(image.img_to_array(imResize).shape)\n",
        "  im=image.img_to_array(imResize)\n",
        "\n",
        "  return im\n",
        "\n",
        "pre_prosess('/content/drive/MyDrive/Project/Project_BHDR/test_image/d00142.png')\n",
        "print(\"\")"
      ],
      "metadata": {
        "id": "QGOx5IYrgNB1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        },
        "outputId": "88e2291c-9510-495a-eeee-f6fdfc915576"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uclV83PQYyEa"
      },
      "source": [
        "###**Reshaping Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "E67c7YldlmAu",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "4f9abeea-df1d-49b9-e132-074461ff5282"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "TrainSet type:  <class 'tuple'>\n",
            "Shape:  [50000, 50000]\n",
            "\n",
            "Spliting TrainSet Tuple into Arrays (X_train, y_train).\n",
            "\n",
            "Converting Arrays to DataFrame (X_train, y_train).\n",
            "\n",
            "X_train type:  <class 'pandas.core.frame.DataFrame'>\n",
            "Shape:  (50000, 1024)\n",
            "y_train type:  <class 'pandas.core.frame.DataFrame'>\n",
            "Shape:  (50000, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   0     1     2     3     4     5     6     7     8     9     ...  1014  \\\n",
              "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
              "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
              "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
              "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
              "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  ...   0.0   \n",
              "\n",
              "   1015  1016  1017  1018  1019  1020  1021  1022  1023  \n",
              "0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
              "1   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
              "2   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
              "3   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
              "4   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
              "\n",
              "[5 rows x 1024 columns]"
            ],
            "text/html": [
              "\n",
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              "    <div class=\"colab-df-container\">\n",
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              "      <th></th>\n",
              "      <th>0</th>\n",
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              "      <th>8</th>\n",
              "      <th>9</th>\n",
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              "    <tr>\n",
              "      <th>3</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>4</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",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 1024 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7be84354-644f-4fff-aa6f-f69ab43580d1')\"\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-7be84354-644f-4fff-aa6f-f69ab43580d1 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-7be84354-644f-4fff-aa6f-f69ab43580d1');\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": 6
        }
      ],
      "source": [
        "print(\"TrainSet type: \",type(trainSet))\n",
        "print(\"Shape: \", [len(a) for a in trainSet],end=\"\\n\\n\")\n",
        "\n",
        "print(\"Spliting TrainSet Tuple into Arrays (X_train, y_train).\",end=\"\\n\\n\")\n",
        "X_train, y_train = trainSet\n",
        "X_valid,y_valid = validSet\n",
        "X_test,y_test = testSet\n",
        "\n",
        "print(\"Converting Arrays to DataFrame (X_train, y_train).\",end=\"\\n\\n\")\n",
        "X_train = pd.DataFrame(X_train)\n",
        "X_test = pd.DataFrame(X_test)\n",
        "X_valid = pd.DataFrame(X_valid)\n",
        "\n",
        "\n",
        "y_train= pd.DataFrame(y_train)\n",
        "y_test= pd.DataFrame(y_test)\n",
        "y_valid = pd.DataFrame(y_valid)\n",
        "\n",
        "#marging trainSet and validSet\n",
        "X_test = pd.concat([X_test,X_valid])\n",
        "y_test = pd.concat([y_test,y_valid])\n",
        "\n",
        "print(\"X_train type: \",type(X_train))\n",
        "print(\"Shape: \", X_train.shape)\n",
        "print(\"y_train type: \",type(y_train))\n",
        "print(\"Shape: \", y_train.shape)\n",
        "\n",
        "X_train.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "XQ1BNOFAQ_ig",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "93ebfdbb-c52f-4e4b-e5eb-746989adb977"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Data Shape: (50000, 1024)\n",
            "\n",
            "Transforming a 50000x1024 dataframe to a 50000x32x32x1 4D tensor for Keras modelling.\n",
            "\n",
            "Reshaped Data Shape: (50000, 32, 32, 1)\n",
            "<class 'numpy.ndarray'>\n"
          ]
        }
      ],
      "source": [
        "print(\"Original Data Shape: \",end='')\n",
        "print(X_train.shape)\n",
        "\n",
        "print(\"\\nTransforming a 50000x1024 dataframe to a 50000x32x32x1 4D tensor for Keras modelling.\\n\")\n",
        "\n",
        "train_images = X_train.values.reshape(X_train.shape[0], 32, 32, 1)\n",
        "test_images = X_test.values.reshape(X_test.shape[0], 32, 32, 1)\n",
        "#valid_images = X_valid.values.reshape(X_valid.shape[0], 32, 32, 1)\n",
        "\n",
        "print(\"Reshaped Data Shape: \",end='')\n",
        "print(train_images.shape)\n",
        "print(type(train_images))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yviBkjyUvlgX"
      },
      "source": [
        "###**Visualizing Data**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "vjG4gQCDQ_ih",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 629
        },
        "outputId": "4ae3522e-3aa5-49b0-e61c-283400967788"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 64 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n"
          ]
        }
      ],
      "source": [
        "fig = plt.figure(figsize=(8, 8))\n",
        "fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.05, wspace=0.05)\n",
        "\n",
        "# plot the digits: each image is 32x32 pixels\n",
        "for i in range(64):\n",
        "    ax = fig.add_subplot(8, 8, i + 1, xticks=[], yticks=[])\n",
        "    ax.imshow(train_images[1000+i].reshape(32,32), cmap=plt.cm.binary, interpolation='nearest')\n",
        "    \n",
        "    # label of the image with the target value\n",
        "    ax.text(0, 7, str(y_train.values[1000+i]))\n",
        "plt.show()\n",
        "print(\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2Y_21LLgxt3Q"
      },
      "source": [
        "###**Creating a model**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "FozJQWuwQ_ii",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "233ce80f-9929-493f-b3d3-c1184c066c24"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 30, 30, 64)        640       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 15, 15, 64)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 14400)             0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               1843328   \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 10)                1290      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,845,258\n",
            "Trainable params: 1,845,258\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "# Creating a Sequential Model and adding the layers\n",
        "model = Sequential([\n",
        "                      Conv2D(64,(3,3),input_shape=(32,32,1)),\n",
        "                      MaxPool2D(2,2),\n",
        "                      Flatten(),\n",
        "                      Dense(128,activation='relu'),\n",
        "                      Dropout(0.2),\n",
        "                      Dense(10, input_shape=(train_images.shape[1],),activation='softmax')\n",
        "])\n",
        "# model.add(Conv2D(28, kernel_size=(3,3), input_shape=input_shape))\n",
        "model.summary()\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "ug5aWR4eU3vf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "44c3d78c-b14c-4bfb-ca95-c46dee655e1f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "========================== Compiling  model ==========================\n",
            "\n",
            "\n",
            "============================= Model- Fit =============================\n",
            "Epoch 1/100\n",
            "1250/1250 [==============================] - 56s 44ms/step - loss: 0.3061 - acc: 0.9064 - val_loss: 0.1124 - val_acc: 0.9660\n",
            "Epoch 2/100\n",
            "1250/1250 [==============================] - 52s 41ms/step - loss: 0.1020 - acc: 0.9685 - val_loss: 0.0973 - val_acc: 0.9719\n",
            "Epoch 3/100\n",
            "1250/1250 [==============================] - 53s 42ms/step - loss: 0.0646 - acc: 0.9803 - val_loss: 0.0872 - val_acc: 0.9733\n",
            "Epoch 4/100\n",
            "1250/1250 [==============================] - 51s 41ms/step - loss: 0.0446 - acc: 0.9858 - val_loss: 0.0935 - val_acc: 0.9741\n",
            "Epoch 5/100\n",
            "1250/1250 [==============================] - 53s 42ms/step - loss: 0.0359 - acc: 0.9883 - val_loss: 0.0920 - val_acc: 0.9754\n",
            "Epoch 6/100\n",
            "1250/1250 [==============================] - ETA: 0s - loss: 0.0300 - acc: 0.9902\n",
            "\n",
            "Model has reached >99% accuracy :)\n",
            "\n",
            "1250/1250 [==============================] - 52s 42ms/step - loss: 0.0300 - acc: 0.9902 - val_loss: 0.0878 - val_acc: 0.9783\n",
            "\n"
          ]
        }
      ],
      "source": [
        "print(\"\")\n",
        "\n",
        "# Model Compile\n",
        "print(\"========================== Compiling  model ==========================\")\n",
        "model.compile(optimizer='adam',metrics=['acc'],loss='sparse_categorical_crossentropy')\n",
        "print(\"\")\n",
        "\n",
        "# Accuracy Callback\n",
        "class handwriting_acc_callback(keras.callbacks.Callback):\n",
        "  def on_epoch_end(self,epoch,logs={}):\n",
        "    if(logs.get('acc')>=0.99):\n",
        "       print(\"\\n\\nModel has reached >99% accuracy :)\\n\")\n",
        "       self.model.stop_training = True\n",
        "\n",
        "handwriting_acc_callback = handwriting_acc_callback()\n",
        "print(\"\")\n",
        "# Model Fitting\n",
        "print(\"============================= Model- Fit =============================\")\n",
        "model.fit(train_images,y_train,epochs=100,validation_split=.2,callbacks=[handwriting_acc_callback])\n",
        "print(\"\")"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "###**Result**"
      ],
      "metadata": {
        "id": "5Hqrc773ifu2"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "OiHhnHfkB3N1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 674
        },
        "outputId": "b5a986da-3378-4cd1-d260-45f1d3933f6a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "========================== Model Evaluation ==========================\n",
            "625/625 [==============================] - 7s 11ms/step - loss: 0.1078 - acc: 0.9736\n",
            "\n",
            "==================== Tensorflow Confusion Matrix =====================\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x576 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "print(\"========================== Model Evaluation ==========================\")\n",
        "model.evaluate(test_images,y_test)\n",
        "\n",
        "print(\"\\n==================== Tensorflow Confusion Matrix =====================\")\n",
        "import seaborn as sns\n",
        "classes=[0,1,2,3,4,5,6,7,8,9]\n",
        "y_pred=model.predict(test_images)\n",
        "y_pred=np.argmax(y_pred,axis=1)\n",
        "con_mat = tf.math.confusion_matrix(labels=y_test, predictions=y_pred).numpy()\n",
        "con_mat_norm = np.around(con_mat.astype('float') / con_mat.sum(axis=1)[:, np.newaxis], decimals=2)\n",
        "\n",
        "con_mat_df = pd.DataFrame(con_mat_norm, index = classes, columns = classes)\n",
        "figure = plt.figure(figsize=(8, 8))\n",
        "sns.heatmap(con_mat_df, annot=True,cmap=plt.cm.Blues)\n",
        "plt.tight_layout()\n",
        "plt.ylabel('True label')\n",
        "plt.xlabel('Predicted label')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ot20uicfFteY"
      },
      "source": [
        "###**Model Prediction Example**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "VgXXCVoQmdWA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1d38a405-8cdb-4ac9-d250-4197d374353f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True Label     : [7. 0. 7. 1. 7. 4. 5. 7. 2. 1. 6. 5. 5. 2. 3. 2. 1. 3. 7. 3. 4. 4. 7. 8.]\n",
            "Pridcted Label : [7, 0, 7, 1, 7, 4, 5, 7, 2, 1, 6, 5, 5, 2, 3, 2, 1, 3, 7, 3, 4, 4, 7, 8]\n"
          ]
        }
      ],
      "source": [
        "pred=[]\n",
        "label=np.array([])\n",
        "for i in range(24):\n",
        "    pred.append(model.predict(X_valid.values[i].reshape(1,32,32)).argmax())\n",
        "    label=np.concatenate((label, y_valid.values[i]))\n",
        "\n",
        "print(\"True Label     :\",label)\n",
        "print(\"Pridcted Label :\",pred)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "jQSIm4DRn_tY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 204
        },
        "outputId": "9583cab0-f437-4a1b-fdb9-d70f2f80a003"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 648x864 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Predicted Value is: 8\n"
          ]
        }
      ],
      "source": [
        "im=pre_prosess('/content/drive/MyDrive/Project/Project_BHDR/test_image/d00110.png')\n",
        "class_label=model.predict(im.reshape(1,32,32)).argmax()\n",
        "print(\"Predicted Value is:\",class_label)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "Project_BHDR.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "interpreter": {
      "hash": "e7370f93d1d0cde622a1f8e1c04877d8463912d04d973331ad4851f04de6915a"
    },
    "kernelspec": {
      "display_name": "Python 3.9.7 64-bit",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}