DeMe_Net.ipynb

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  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "DeMe Net.ipynb",
      "provenance": [],
      "collapsed_sections": [
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      "mount_file_id": "1qo1vMszfnreywj7KOco5xX0qTmq-nf4H",
      "authorship_tag": "ABX9TyMo0lJpYg5JUrZUSKhClbdb",
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Rajsoni03/DeMe-Net/blob/main/DeMe_Net.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6FbrUtcmgFYr"
      },
      "source": [
        "# Importing Libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uuo_ebeAfyTp"
      },
      "source": [
        "#import the important libraries\n",
        "\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "from PIL import Image\n",
        "import pandas as pd\n",
        "import datetime\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import shutil\n",
        "\n",
        "from tensorflow.keras.utils import plot_model\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from keras.preprocessing.image import array_to_img, img_to_array\n",
        "from tensorflow.keras.models import Sequential, Model, load_model\n",
        "from tensorflow.keras.layers import Conv2D, Flatten, BatchNormalization, Dropout, Dense, MaxPool2D, Input, Concatenate, MaxPooling2D\n",
        "from keras.callbacks import ReduceLROnPlateau\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix, classification_report"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3yTDVojcgt0u"
      },
      "source": [
        "# Clone Data from Kaggle"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jmzyqjt1gsxs"
      },
      "source": [
        "# os.environ['KAGGLE_CONFIG_DIR'] = '/content/drive/MyDrive/AI Colab/Kaggle'\n",
        "\n",
        "# !kaggle competitions download --force -c siim-isic-melanoma-classification -f train.csv\n",
        "# !kaggle datasets download -d sarques/siic-melanoma-384x384-test\n",
        "\n",
        "# !kaggle datasets download -d kmader/skin-cancer-mnist-ham10000 -f hmnist_28_28_RGB.csv.csv"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fqjqmmW04I7o"
      },
      "source": [
        "# !unzip /content/siic-melanoma-384x384-test.zip && rm /content/siic-melanoma-384x384-test.zip\n",
        "\n",
        "# !unzip /content/skin-cancer-mnist-ham10000.zip && rm /content/skin-cancer-mnist-ham10000.zip"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yrAXozF2c7mG",
        "outputId": "15970e76-354c-41c8-bb76-4b4ccbeeebfa"
      },
      "source": [
        "shutil.copy('/content/drive/MyDrive/Colab Notebooks/Skin Disease Classification/data/hmnist_28_28_RGB.csv.zip', '/content')\n",
        "!unzip hmnist_28_28_RGB.csv.zip"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Archive:  hmnist_28_28_RGB.csv.zip\n",
            "  inflating: hmnist_28_28_RGB.csv    \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "k8IfYedh4Uva"
      },
      "source": [
        "# Prepair Data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NCpMMJycW--",
        "outputId": "2cc01284-c932-42b2-fe27-f07eb4fde768"
      },
      "source": [
        "# Load image data\n",
        "\n",
        "df = pd.read_csv('/content/hmnist_28_28_RGB.csv')\n",
        "df.shape"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10015, 2353)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "dneUoLbVeR80",
        "outputId": "25700e38-ec08-4896-fb3e-296fe859e23f"
      },
      "source": [
        "# Randomly suffle images\n",
        "\n",
        "df = df.sample(frac = 1)\n",
        "df.head(5)"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
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              "      <td>196</td>\n",
              "      <td>106</td>\n",
              "      <td>112</td>\n",
              "      <td>193</td>\n",
              "      <td>99</td>\n",
              "      <td>103</td>\n",
              "      <td>190</td>\n",
              "      <td>96</td>\n",
              "      <td>99</td>\n",
              "      <td>188</td>\n",
              "      <td>100</td>\n",
              "      <td>107</td>\n",
              "      <td>184</td>\n",
              "      <td>101</td>\n",
              "      <td>111</td>\n",
              "      <td>180</td>\n",
              "      <td>97</td>\n",
              "      <td>103</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8893</th>\n",
              "      <td>147</td>\n",
              "      <td>113</td>\n",
              "      <td>117</td>\n",
              "      <td>158</td>\n",
              "      <td>128</td>\n",
              "      <td>130</td>\n",
              "      <td>161</td>\n",
              "      <td>131</td>\n",
              "      <td>132</td>\n",
              "      <td>172</td>\n",
              "      <td>144</td>\n",
              "      <td>147</td>\n",
              "      <td>178</td>\n",
              "      <td>149</td>\n",
              "      <td>152</td>\n",
              "      <td>182</td>\n",
              "      <td>154</td>\n",
              "      <td>159</td>\n",
              "      <td>186</td>\n",
              "      <td>160</td>\n",
              "      <td>165</td>\n",
              "      <td>187</td>\n",
              "      <td>160</td>\n",
              "      <td>165</td>\n",
              "      <td>189</td>\n",
              "      <td>162</td>\n",
              "      <td>167</td>\n",
              "      <td>192</td>\n",
              "      <td>166</td>\n",
              "      <td>171</td>\n",
              "      <td>193</td>\n",
              "      <td>166</td>\n",
              "      <td>171</td>\n",
              "      <td>191</td>\n",
              "      <td>166</td>\n",
              "      <td>171</td>\n",
              "      <td>191</td>\n",
              "      <td>167</td>\n",
              "      <td>171</td>\n",
              "      <td>190</td>\n",
              "      <td>...</td>\n",
              "      <td>188</td>\n",
              "      <td>161</td>\n",
              "      <td>163</td>\n",
              "      <td>185</td>\n",
              "      <td>155</td>\n",
              "      <td>155</td>\n",
              "      <td>184</td>\n",
              "      <td>154</td>\n",
              "      <td>155</td>\n",
              "      <td>181</td>\n",
              "      <td>150</td>\n",
              "      <td>152</td>\n",
              "      <td>181</td>\n",
              "      <td>150</td>\n",
              "      <td>153</td>\n",
              "      <td>181</td>\n",
              "      <td>151</td>\n",
              "      <td>154</td>\n",
              "      <td>178</td>\n",
              "      <td>150</td>\n",
              "      <td>153</td>\n",
              "      <td>174</td>\n",
              "      <td>143</td>\n",
              "      <td>145</td>\n",
              "      <td>167</td>\n",
              "      <td>135</td>\n",
              "      <td>134</td>\n",
              "      <td>163</td>\n",
              "      <td>131</td>\n",
              "      <td>131</td>\n",
              "      <td>161</td>\n",
              "      <td>129</td>\n",
              "      <td>131</td>\n",
              "      <td>155</td>\n",
              "      <td>124</td>\n",
              "      <td>124</td>\n",
              "      <td>144</td>\n",
              "      <td>111</td>\n",
              "      <td>110</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2353 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      pixel0000  pixel0001  pixel0002  ...  pixel2350  pixel2351  label\n",
              "2500        195        172        183  ...        178        188      1\n",
              "7145        216        206        212  ...        201        210      4\n",
              "9422        194        171        164  ...        184        169      4\n",
              "3739        217        144        134  ...         97        103      4\n",
              "8893        147        113        117  ...        111        110      4\n",
              "\n",
              "[5 rows x 2353 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "91T6jYw6cW8T",
        "outputId": "8c08eccb-4d80-489b-c92d-9fa538c693ee"
      },
      "source": [
        "# crop image pixles from DataFrame\n",
        "\n",
        "data = df.iloc[:,:-1]\n",
        "data.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10015, 2352)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DTFNu5gkcW5D",
        "outputId": "e5f39328-344e-4cc5-f0e1-cf48a9df882e"
      },
      "source": [
        "# crop labels from DataFrame\n",
        "\n",
        "labels = df.iloc[:,-1:]\n",
        "labels.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10015, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "tIqMCFzLcW3E",
        "outputId": "00fda639-66dc-455f-cc0f-b8fe2da81b28"
      },
      "source": [
        "# Plot Bar Chart for Image Type Count\n",
        "type_of_cancer = {0 : 'akiec',\n",
        "                  1 : 'bcc',\n",
        "                  2 : 'bkl',\n",
        "                  3 : 'df',\n",
        "                  4 : 'nv',\n",
        "                  5 : 'vasc',\n",
        "                  6 : 'mel'}\n",
        "\n",
        "labels_count = dict()\n",
        "for i in type_of_cancer.keys():\n",
        "  labels_count[type_of_cancer[i]] = len(df[df['label'] == i])\n",
        "  print(type_of_cancer[i], ':', len(df[df['label'] == i]))\n",
        "\n",
        "plt.figure(figsize = (8,6))\n",
        "plt.bar(labels_count.keys(), labels_count.values())\n",
        "plt.ylabel('No. of images')\n",
        "plt.xlabel('Classes')\n",
        "plt.show()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "akiec : 327\n",
            "bcc : 514\n",
            "bkl : 1099\n",
            "df : 115\n",
            "nv : 6705\n",
            "vasc : 142\n",
            "mel : 1113\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZTG99EnHm4bT"
      },
      "source": [
        "# Handle Class Unbalance Problem"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "65TMpUSqcWmP",
        "outputId": "08877df4-d80c-4920-fbd6-22136c87ccdd"
      },
      "source": [
        "# Duplicate data for balance the classes\n",
        "\n",
        "df = df.sort_values('label')\n",
        "df = df.reset_index()\n",
        "\n",
        "\n",
        "index0 = df[df['label'] == 0].index.values\n",
        "index1 = df[df['label'] == 1].index.values\n",
        "index2 = df[df['label'] == 2].index.values\n",
        "index3 = df[df['label'] == 3].index.values\n",
        "index5 = df[df['label'] == 5].index.values\n",
        "index6 = df[df['label'] == 6].index.values\n",
        "\n",
        "\n",
        "df0 = df.iloc[int(min(index0)):int(max(index0)+1)]\n",
        "df1 = df.iloc[int(min(index1)):int(max(index1)+1)]\n",
        "df2 = df.iloc[int(min(index2)):int(max(index2)+1)]\n",
        "df3 = df.iloc[int(min(index3)):int(max(index3)+1)]\n",
        "df5 = df.iloc[int(min(index5)):int(max(index5)+1)]\n",
        "df6 = df.iloc[int(min(index6)):int(max(index6)+1)]\n",
        "\n",
        "\n",
        "df_index0 = df0.append([df0]*19, ignore_index = True)\n",
        "df_index1 = df1.append([df1]*11, ignore_index = True)\n",
        "df_index2 = df2.append([df2]*4, ignore_index = True)\n",
        "df_index3 = df3.append([df3]*57, ignore_index = True)\n",
        "df_index5 = df5.append([df5]*45, ignore_index = True)\n",
        "df_index6 = df6.append([df6]*4, ignore_index = True)\n",
        "\n",
        "frames = [df, df_index0, df_index1, df_index2, df_index3, df_index5, df_index6]\n",
        "\n",
        "final_df = pd.concat(frames)\n",
        "final_df.drop('index', inplace = True, axis = 1)\n",
        "final_df = final_df.sample(frac = 1)\n",
        "data = final_df.iloc[:,:-1]\n",
        "labels = final_df.iloc[:,-1:]\n",
        "print(final_df.shape)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(46985, 2353)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 0
        },
        "id": "n9tJtNBAvJv1",
        "outputId": "c6fddb77-5d7f-4343-8dac-0e30d9cbfff3"
      },
      "source": [
        "# Plot Bar Chart for Image Type Count\n",
        "\n",
        "type_of_cancer = {0 : 'akiec',\n",
        "                  1 : 'bcc',\n",
        "                  2 : 'bkl',\n",
        "                  3 : 'df',\n",
        "                  4 : 'nv',\n",
        "                  5 : 'vasc',\n",
        "                  6 : 'mel'}\n",
        "\n",
        "labels_count = dict()\n",
        "for i in type_of_cancer.keys():\n",
        "  labels_count[type_of_cancer[i]] = len(final_df[final_df['label'] == i])\n",
        "  print(type_of_cancer[i], ':', len(final_df[final_df['label'] == i]))\n",
        "\n",
        "plt.figure(figsize = (8,6))\n",
        "plt.bar(labels_count.keys(), labels_count.values())\n",
        "plt.ylabel('No. of images')\n",
        "plt.xlabel('Classes')\n",
        "plt.show()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "akiec : 6867\n",
            "bcc : 6682\n",
            "bkl : 6594\n",
            "df : 6785\n",
            "nv : 6705\n",
            "vasc : 6674\n",
            "mel : 6678\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WtyEmZhLhPBD"
      },
      "source": [
        "# Data Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K5YxmpkP0mly"
      },
      "source": [
        "# Convart DataFrame to numpy array\n",
        "\n",
        "X = np.array(data)\n",
        "Y = np.array(labels)"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mYTbvg-Y0mad",
        "outputId": "bbdd79ae-598d-4178-9dd5-7736cbadd932"
      },
      "source": [
        "# convart pixles to standard (28 x 28 X 3) image\n",
        "\n",
        "X = X.reshape(-1,28,28,3)\n",
        "\n",
        "print( X.shape)\n",
        "print( Y.shape)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(46985, 28, 28, 3)\n",
            "(46985, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pENecXCE1NME"
      },
      "source": [
        "# Normalize Data using \n",
        "\n",
        "X = (X-np.mean(X))/np.std(X)"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r5HqX63p1NCg",
        "outputId": "671677ec-2ea2-4a96-d922-a2077cee1a2d"
      },
      "source": [
        "# split data into Train, val, test set in ratio of 8:2\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=1)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(Y_train.shape)\n",
        "print(Y_test.shape)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(37588, 28, 28, 3)\n",
            "(9397, 28, 28, 3)\n",
            "(37588, 1)\n",
            "(9397, 1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fIibZo68hE_A"
      },
      "source": [
        "# Image Data Augmentation \n",
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   brightness_range = [-0.2, 0.2],\n",
        "                                  rotation_range = 10,\n",
        "                                  width_shift_range = 0.2,\n",
        "                                  height_shift_range = 0.2,\n",
        "                                  shear_range = 0.2,\n",
        "                                  horizontal_flip = True,\n",
        "                                  vertical_flip = True,\n",
        "                                  fill_mode = 'nearest')\n",
        "train_datagen.fit(X_train)\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "test_datagen.fit(X_test)\n",
        "\n",
        "train_data = train_datagen.flow(X_train, Y_train, batch_size = 64)\n",
        "test_data = test_datagen.flow(X_test, Y_test, batch_size = 64)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5CUXyfqbz_kT"
      },
      "source": [
        "# Creating Model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Uos6sMgEz8RE",
        "outputId": "0cdc48cc-8e1e-41ca-a3d4-cdf1a844ba71"
      },
      "source": [
        "Input_X = Input(shape=(28, 28, 3))\n",
        "\n",
        "# 3x3 conv\n",
        "X1 = Conv2D(32, (3,3), padding='same', activation='relu')(Input_X)\n",
        "X1 = Conv2D(32, (3,3), padding='same', activation='relu')(X1)\n",
        "X1 = BatchNormalization()(X1)\n",
        "X1 = MaxPooling2D((2,2), padding='same')(X1)\n",
        "\n",
        "# 5x5 conv\n",
        "X2 = Conv2D(24, (5,5), padding='same', activation='relu')(Input_X)\n",
        "X2 = Conv2D(24, (5,5), padding='same', activation='relu')(X2)\n",
        "X2 = BatchNormalization()(X2)\n",
        "X2 = MaxPooling2D((2,2), padding='same')(X2)\n",
        "\n",
        "# 7x7 conv\n",
        "X3 = Conv2D(16, (7,7), padding='same', activation='relu')(Input_X)\n",
        "X3 = Conv2D(16, (7,7), padding='same', activation='relu')(X3)\n",
        "X3 = BatchNormalization()(X3)\n",
        "X3 = MaxPooling2D((2,2), padding='same')(X3)\n",
        "\n",
        "# 11x11 conv\n",
        "X4 = Conv2D(8, (3,3), padding='same', activation='relu')(Input_X)\n",
        "X4 = Conv2D(8, (3,3), padding='same', activation='relu')(X4)\n",
        "X4 = BatchNormalization()(X4)\n",
        "X4 = MaxPooling2D((2,2), padding='same')(X4)\n",
        "\n",
        "X = Concatenate(axis=-1)([X1, X2, X3, X4])\n",
        "\n",
        "# 3x3 Conv \n",
        "X1 = Conv2D(64, (3,3), padding='same', activation='relu')(X)\n",
        "X1 = Conv2D(64, (3,3), padding='same', activation='relu')(X1)\n",
        "X1 = Conv2D(64, (3,3), padding='same', activation='relu')(X1)\n",
        "X1 = BatchNormalization()(X1)\n",
        "X1 = MaxPooling2D((2,2), padding='same')(X1)\n",
        "\n",
        "# 5x5 Conv \n",
        "X2 = Conv2D(32, (5,5), padding='same', activation='relu')(X)\n",
        "X2 = Conv2D(32, (5,5), padding='same', activation='relu')(X2)\n",
        "X2 = Conv2D(32, (5,5), padding='same', activation='relu')(X2)\n",
        "X2 = BatchNormalization()(X2)\n",
        "X2 = MaxPooling2D((2,2), padding='same')(X2)\n",
        "\n",
        "X = Concatenate(axis=-1)([X1, X2])\n",
        "\n",
        "X = Conv2D(96, (3,3), padding='same', activation='relu')(X)\n",
        "X = BatchNormalization()(X)\n",
        "X = MaxPooling2D((2,2), padding='same')(X)\n",
        "\n",
        "X = Flatten()(X)\n",
        "X = Dropout(0.9)(X)\n",
        "X = Dense(256, activation='relu')(X)\n",
        "X = Dense(128, activation='relu')(X)\n",
        "X = Dense(7, activation='softmax')(X)\n",
        "\n",
        "model = Model(inputs = Input_X, outputs = X)\n",
        "print(model.summary())\n",
        "plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"model\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 28, 28, 3)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d (Conv2D)                 (None, 28, 28, 32)   896         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_2 (Conv2D)               (None, 28, 28, 24)   1824        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_4 (Conv2D)               (None, 28, 28, 16)   2368        input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_6 (Conv2D)               (None, 28, 28, 8)    224         input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_1 (Conv2D)               (None, 28, 28, 32)   9248        conv2d[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_3 (Conv2D)               (None, 28, 28, 24)   14424       conv2d_2[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_5 (Conv2D)               (None, 28, 28, 16)   12560       conv2d_4[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_7 (Conv2D)               (None, 28, 28, 8)    584         conv2d_6[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization (BatchNorma (None, 28, 28, 32)   128         conv2d_1[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_1 (BatchNor (None, 28, 28, 24)   96          conv2d_3[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_2 (BatchNor (None, 28, 28, 16)   64          conv2d_5[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_3 (BatchNor (None, 28, 28, 8)    32          conv2d_7[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D)    (None, 14, 14, 32)   0           batch_normalization[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2D)  (None, 14, 14, 24)   0           batch_normalization_1[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2D)  (None, 14, 14, 16)   0           batch_normalization_2[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_3 (MaxPooling2D)  (None, 14, 14, 8)    0           batch_normalization_3[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate (Concatenate)       (None, 14, 14, 80)   0           max_pooling2d[0][0]              \n",
            "                                                                 max_pooling2d_1[0][0]            \n",
            "                                                                 max_pooling2d_2[0][0]            \n",
            "                                                                 max_pooling2d_3[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_8 (Conv2D)               (None, 14, 14, 64)   46144       concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_11 (Conv2D)              (None, 14, 14, 32)   64032       concatenate[0][0]                \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_9 (Conv2D)               (None, 14, 14, 64)   36928       conv2d_8[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_12 (Conv2D)              (None, 14, 14, 32)   25632       conv2d_11[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_10 (Conv2D)              (None, 14, 14, 64)   36928       conv2d_9[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_13 (Conv2D)              (None, 14, 14, 32)   25632       conv2d_12[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_4 (BatchNor (None, 14, 14, 64)   256         conv2d_10[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_5 (BatchNor (None, 14, 14, 32)   128         conv2d_13[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_4 (MaxPooling2D)  (None, 7, 7, 64)     0           batch_normalization_4[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_5 (MaxPooling2D)  (None, 7, 7, 32)     0           batch_normalization_5[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "concatenate_1 (Concatenate)     (None, 7, 7, 96)     0           max_pooling2d_4[0][0]            \n",
            "                                                                 max_pooling2d_5[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "conv2d_14 (Conv2D)              (None, 7, 7, 96)     83040       concatenate_1[0][0]              \n",
            "__________________________________________________________________________________________________\n",
            "batch_normalization_6 (BatchNor (None, 7, 7, 96)     384         conv2d_14[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "max_pooling2d_6 (MaxPooling2D)  (None, 4, 4, 96)     0           batch_normalization_6[0][0]      \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 1536)         0           max_pooling2d_6[0][0]            \n",
            "__________________________________________________________________________________________________\n",
            "dropout (Dropout)               (None, 1536)         0           flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense (Dense)                   (None, 256)          393472      dropout[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense_1 (Dense)                 (None, 128)          32896       dense[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "dense_2 (Dense)                 (None, 7)            903         dense_1[0][0]                    \n",
            "==================================================================================================\n",
            "Total params: 788,823\n",
            "Trainable params: 788,279\n",
            "Non-trainable params: 544\n",
            "__________________________________________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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iaWnp6uoaEBBw/vz5M2fOSFvXrFnTrVu31atXm5mZDR8+PCgo6Isvvrhy5Yq0ddGiRYMGDZo4caL6xj3QcVB1WwOqLgAAAJoD7U8AAACgY9mxY0d+fn6LHe7atWshISGrV6+Wy+Wa425ubv7+/rm5uZ988kmLJVOvQYMGJScnz5gxw9jY+FlzysrKlixZEhMT00oi5+Tk2NraymQy6aO9vb0Q4vr160KIqqqqQ4cOubu7q7dOmDBBpVIdPHhQvXtoaOj58+ef96AAtEfVrQNVFwAAAM2B9icAAACgeydPnuzRo4dMJtu0aZMQIi4uzszMzNTU9ODBgxMmTFAoFEqlcs+ePdLk2NhYuVzetWvXjz76yNbWVi6Xu7m5qded+Pn5GRkZdevWTfq4YMECMzMzmUxWUFAghPD39w8MDMzMzJTJZE5OTkKII0eOKBSKiIiIZjq12NhYlUr15ptvPrkpPDy8d+/e27dvP378+FP3ValUUVFRffv2NTY2tra2njJlinoBTd2XSAhRXV396aef9ujRw8TEZODAgQkJCU11RitWrFiwYIGNjU1TBWxkZEdHR83OivQKOkdHRyFEVlZWcXFxjx491Ft79eolhLh48aJ6xNra2t3dPSYmpn0/CBSohar71H2putqg6gIAALR+tD8BAAAA3Rs5cuQPP/yg/jh//vzFixeXlZVZWFgkJCRkZmY6OjrOnTu3srJSCOHn5+fj41NaWrpo0aLs7OyzZ89WVVX96U9/ysnJEULExsZ6enqqQ23evHn16tXqjzExMZMnT+7Vq5dKpbp27ZoQorq6WghRU1PTTKd26NChPn36mJqaPrnJxMTkiy++0NPTmzt3bklJyZMTQkNDly1btmLFivz8/NTU1JycnFGjRt25c0fUd4mEEMHBwevXr4+Ojs7Ly5s8efL06dN//vnnxp/O999/n5mZOX369MaHaqrIy5cvv3379saNG4uKitLT02NiYsaNGzds2DDx+015zadfyuVyExMT6Rqqvfzyy7m5uRcuXGj0SQBtBlWXqkvVBQAAaMdofwIAAACtl5ubm0KhsLGx8fb2LikpuXHjhnqTgYGBtECnX79+cXFxRUVFO3fubMAhJk2aVFhYGBIS0nRZ/09JSclvv/0mrX15quHDhy9evDg7Ozs4OLjWprKysqioqHfeeWfmzJmWlpYDBgzYunVrQUHBtm3bNKc99RKVl5fHxcW9/fbbU6dOtbKyWrlypaGhYcOuT62U/P394+LiGhmnaSO7u7sHBQX5+fkpFAoXF5eioqLt27dLmx4/fiyE0NfX15xvaGhYVlamOfLSSy8JIdLS0hqYPdCOUHWpuvWi6gIAALR+tD8BAACANsDIyEgIoV5kU8uQIUNMTU3VjyhsPfLz81Uq1VMXIamFh4f36dNn8+bNJ0+e1BxPT08vLi4eMmSIemTo0KFGRkbqB07WonmJrl69Wlpa6uLiIm0yMTHp1q1b46/P8uXLP/zwQzs7u0bGadrIK1as2LZt2zfffFNcXJyVleXm5jZ8+HBpUZr05r+qqirN+RUVFSYmJpoj0hdUa3ES0MFRdQVV9xmougAAAK0f7U8AAACgPTA2Nr57966us6itvLxcCGFsbFzHHLlcvnPnTplMNmvWLM31MQ8fPhRCmJuba062srIqKiqq97jSQx1Xrlwp+93169dLS0sbdhaSkydPpqWlzZkzpzFBmjxyXl7eunXrPvzwwz/84Q9mZmY9e/aMj4+/devWhg0bhBDSywgLCwvV80tLS8vLy21tbTWDSPflpS8LgJaoupqoulRdAACAVoX2JwAAANDmVVZWPnz4UKlU6jqR2qQ7vNKL7uowfPjwgICAjIyMNWvWqAetrKyEELVuu2t5mjY2NkKI6OholYZTp0414BTUduzY8c033+jp6Ul39qVDREREyGSyRr7frjGRMzIyqquru3fvrh5RKBSdOnVKT08XQvTs2dPCwuL69evqrdKrBwcOHKgZpKKiQvz+ZQHQBlW3FqouVRcAAKBVof0JAAAAtHkpKSkqlWrYsGHSRwMDg2c9sLGFde3aVSaTPXr0qN6Za9ascXZ2PnfunHrExcXF3Nxc8070mTNnKioqXnnllXqj2dvby+Xy8+fPNyztp9q5c6fmbX1p1deKFStUKpXmsyJbOLLUlsjLy1OPFBUV3b9/397eXghhYGAwceLE1NTUmpoaaevhw4dlMtmbb76pGUT6gl544YXGnAXQoVB1a6HqUnUBAABaFdqfAAAAQJtUU1Pz4MGDqqqqixcv+vv79+jRw8fHR9rk5OR0//79AwcOVFZW3r17V3MZihCiU6dOt27dys7OLioqqqysPHz4sEKhiIiIaI4kTU1NHR0db968We9M6WGM+vr6miOBgYH79+/ftWtXYWFhWlravHnzbG1tfX19tYn2wQcf7NmzJy4urrCwsLq6+ubNm9Ldam9v7xdeeOHs2bONOa+n0knknj17jhkzJj4+PjU1taysLCcnR7o+s2fPliaEhITcuXNn1apVJSUlp06d2rBhg4+PT58+fTSDSF/QgAEDmjxzoD2h6tYdjaorTaDqAgAAtAa0PwEAAADd27Rp09ChQ4UQQUFBb731VlxcXHR0tBBi4MCBWVlZ8fHxgYGBQojx48dnZGRIu5SXlw8YMMDExGTUqFG9e/f+9ttv1S97mz9//pgxY959990+ffqsWbNGerze8OHDc3JyhBDz5s3r2rVrv379Jk6ceP/+/eY+tUmTJqWnp6tfL/fPf/7TyckpMzNz6NChH3/8sebMYcOGBQQEaI6sWrUqMjIyLCysS5cu7u7uDg4OKSkpZmZmQoh6L1FMTMzixYvXrVvXuXNnW1tbf3//Bw8eCCEqKiry8/MPHjz41GxPnz49cuTI7t27nzlz5sKFC7a2tiNGjEhNTdXmTHUSWSaTJSUleXt7z54929raul+/fjdu3EhOTh41apQ0oX///kePHj127Fjnzp2nTp06a9asLVu21Ary008/2dnZ1Xo2I9C+UXUFVbdBkam6AAAAbYJMpVLpOgcAAIA2LDEx0cvLi7+poEkmkyUkJHh6ejbfIT766KOkpKR79+413yHq5eHhIYRISkqqe9q1a9f69u27c+fOmTNntkhe9aipqRk9erSPj8+sWbOILIS4d++eUqkMDw+XOhl1a4HftkTLXxcgoepqouq25siitVZdAACAdobVnwAAAECbVF1dresUtOLk5BQWFhYWFlZcXKzrXER1dfWBAweKioq8vb2JLAkNDXV1dfXz82uO4EB7QtVtgLZYG6m6AAAA7QDtTwAAAADNa9myZR4eHt7e3o8ePdJtJikpKcnJyYcPHzY1NSWyECIqKur8+fNff/21oaFhkwcHoCtU3dYZWVB1AQAAWgrtTwAAgPYpLCysX79+CoXC2NjYyclp6dKlz1oFMmfOHAsLC5lMdv78eS2Df/nll0OHDrWwsHjxxRc/+OCD27dvP1duV69e/fjjj/v3729hYWFgYGBpadm7d+9JkyadOnXqueI0QB2XJTk52dHRUabByMioa9euo0eP3rBhg/QCs1Zi+fLlO3fufPToUc+ePfft26frdLQSERHh5+f32Wef6TaNN954Y/fu3d26dSOyEOLgwYOPHz9OSUmxtrZu8uDtVbOWViFETU1NdHS0m5vbc22qF1W3kai6DdYWayNVFwAAoB2g/QkAANA+nThxYuHChdnZ2QUFBZGRkTExMdI7w560ffv2+Ph47SMnJCTMmDHDw8Pj5s2bBw8eTE1NnTBhQlVVlZa779ixY8CAARcvXoyKisrJySkpKTl37tyaNWsePnyYlpamfRoNU8dlmTp1alZWVq9evSwtLVUqVU1NTX5+fmJiYs+ePYOCgvr37//zzz83d3paioyMfPz4sUql+u2336ZNm6brdLQ1duzYtWvX6joL/M9bb721bNkyfX19XSfSljRfaRVCZGRkvP766wEBAaWlpdpvqhdVt/GoumgSVF0AAIAWY6DrBAAAANAszM3NfX19pVtsnp6eycnJiYmJOTk59vb2jYz8l7/8pXv37kuWLJHJZK6urgEBAQsXLjxz5syIESPq3ff06dO+vr7u7u5Hjx41MPh/f4s6Ojo6OjpaWVllZGQ0Mrd6aX9ZZDKZlZXV6NGjR48ePWnSJC8vr0mTJv3666+WlpbNnSSAVqv5SuuFCxfCwsLmzZtXUlKiUqm03FQvqi4AAACADojVnwAAAO3TV199pbm8oEuXLkKIZy0bkslk2kfOycmxtbVV7yLdwr5+/bo2+4aHh1dXV3/22Wfqu/Bq48aNW7hwofZpNMxzXRa1adOm+fj45Ofnb926tXnzA9C6NV9pHTRoUHJy8owZM4yNjbXfVC+qLgAAAIAOiPYnAABAC/nHP/4xZMgQuVxuZmbm4OCwZs0aIYRKpYqKiurbt6+xsbG1tfWUKVOuXLkizY+LizMzMzM1NT148OCECRMUCoVSqdyzZ4+0tW/fvjKZTE9P75VXXpHuIy9dutTS0lIul3/xxRdPHj03N9fExKRnz57SR5VKtWHDhj59+hgbG1taWi5ZskT7E3F0dMzPz1d/lF786ejoKH08cuSIQqGIiIh4cseKiopvvvmmc+fOr776at2H0NVlqYOPj48Q4vDhw/XOBNCS2k1pbTCqLgAAAADUQvsTAACgJcTExLz//vvTpk27devWzZs3ly9ffvXqVSFEaGjosmXLVqxYkZ+fn5qampOTM2rUqDt37ggh5s+fv3jx4rKyMgsLi4SEhMzMTEdHx7lz51ZWVgohLl265ODgYG9v/+OPP5qamgoh1q9fP3v27LVr10q3jDWVlpaeOHFi7ty5RkZG0khISEhQUJCvr++dO3du374dHBys/bksX7789u3bGzduLCoqSk9Pj4mJGTdu3LBhw6St1dXVQoiampond7x+/Xp5eflLL71U7yF0dVnq4OrqKoTIysqqdyaAFtOeSmuDUXUBAAAAoBbanwAAAM2usrJy9erVY8aMCQ4O7tSpk7W19ezZs4cOHVpWVhYVFfXOO+/MnDnT0tJywIABW7duLSgo2LZtm+bubm5uCoXCxsbG29u7pKTkxo0bQgh9ff1FixbduHFj//790rTS0tLk5ORZs2Y9mUBkZKStrW14eLj0saysLDo6+o9//GNAQICVlZWJiUmnTp20Px13d/egoCA/Pz+FQuHi4lJUVLR9+3b11kmTJhUWFoaEhDy5Y2FhoRDC3Ny87vi6uix1s7CwkMlkRUVF2kwG0ALaWWltMKouAAAAANRC+xMAAKDZXbx48eHDh+PGjVOPSPeL09PTi4uLhwwZoh4fOnSokZHRmTNnnhpHWi4jLbgRQsyZM8fS0jImJkb6uGvXrilTpigUilp77d+/PzEx8ejRoxYWFtLItWvXSktL33jjjYadzooVK7Zt2/bNN98UFxdnZWW5ubkNHz48Jyen3h2lW/D1vvJNV5elbiUlJSqV6sk4z+Ll5SVr7/bt27dv3z5dZ4EWpeXvv2W0s9LaHKi67QxVtwPS8vcPAACAWgx0nQAAAED7J62/sbKyqjX+8OFD8cS6HCsrKy0Xu5ibm3/44YcbNmz48ccfX3311S1btuzbt6/WnL1790ZFRaWkpHTv3l09ePPmTSGEjY3N85+KyMvLW7du3bJly/7whz8IIXr27BkfH29tbb1hw4bY2Ni693VwcJDL5b/++mvd03R1Weompe3s7KzlfH9//+HDh2s5uY2Kjo4WQixevFjXiaDleHl56TqF/2lPpbWZUHXbGapuB9Sqqi4AAEAbQvsTAACg2Ul3ewsKCmqNS3fta91ffvjwoVKp1DKyn59fTExMdHT0vHnz7O3te/Xqpbl148aNR48ePXHiRK2b2nK5XAjx+PHj5zwPIYTIyMiorq7WvH+tUCg6deqUnp5e777Gxsbjxo07ePDg999/P2LEiFpb79+/v3Tp0u3bt+vqstTtyJEjQogJEyZoOX/48OGenp7ax2+LkpKShBDt/jShqVXdiG9PpbWZUHXbGapuB9Sqqi4AAEAbwsNvAQAAmp2Dg0OnTp2OHTtWa9zFxcXc3Pznn39Wj5w5c6aiouKVV17RMrJSqfT09Ny3b19ISIi/v796XKVSBQUFpaWlHThw4MnbzS4uLnp6ev/9738bcC7S3fC8vDz1SFFR0f379+3t7bXZPTQ01NjYOCAgoKysrNamS5cuGRgYCN1dljrcvn07OjpaqVQ+9W12AHSiPZXW5kPVBQAAANAB0f4EAABodsbGxsuXL09NTfXz88vNza2pqSkqKrp8+bJcLg+j5cSeAAAgAElEQVQMDNy/f/+uXbsKCwvT0tLmzZtna2vr6+urffDAwMCqqqoHDx5IT6OVXL58ef369fHx8YaGhppvkPr888+FEDY2NlOnTt23b9+OHTsKCwsvXry4bds2LQ/Xs2fPMWPGxMfHp6amlpWV5eTkSNnOnj1bmnD48GGFQhEREfHU3V1dXXfv3n3p0qVRo0Z9/fXXjx49qqys/O233+Lj42fPnm1oaCiE0NVlUVOpVMXFxTU1NSqV6u7duwkJCSNGjNDX1z9w4ID2b6ED0NzaU2ltDKouAAAAANRC+xMAAKAlBAYGbtq0KSUlxcnJyczMzN3dPSUlRQixatWqyMjIsLCwLl26uLu7Ozg4pKSkmJmZCSHi4uKkt3wNHDgwKysrPj4+MDBQCDF+/PiMjAx15JdffnnMmDGLFi3SPJxKpao7n7/+9a8ffPBBUFCQnZ3dggULRo0aJYSYPHnyxYsX695RJpMlJSV5e3vPnj3b2tq6X79+N27cSE5OliJoY+rUqVeuXBkzZkxwcLBSqTQxMRk8ePCWLVvc3d3fffddaY5OLsu///3vQYMG5eXllZeXW1pa6uvr6+vr9+7dOyoqysfHJz09XfuFUABaRrsprUKI06dPjxw5snv37mfOnLlw4YKtre2IESNSU1Pr3qQNqi4AAACAjkZW7/+/AQAAoA6JiYleXl78TQVNMpksISGh3b+ezcPDQ/z+Ljp0EC322+bXhedC1UV71UF+2wAAAE2O1Z8AAAAAAAAAAAAA2gnanwAAAPifK1euyJ7N29tb1wmiDTt+/PiyZcuSk5MdHR2lX9R7772nOWHs2LEWFhb6+vr9+/c/e/asrvIUQtTU1ERHR7u5udUxp7y83NnZeeXKla0hcmVlZWRkpJOTk5GRkZWVlYuLS3Z2dr2R//Wvf61bt666uvp5zgANQWmFTlB1mynyunXrnJ2dTUxMzMzMnJ2dQ0JCCgsL1VvDwsL69eunUCiMjY2dnJyWLl1aXFwsbaLqAgAAtBjanwAAAPgfZ2dn1bPt3btX1wmirVq1alVsbOzy5cunTp2alZXVq1evzp0779q169ChQ+o5x44dS0pKmjx5cnp6+uDBg3WVakZGxuuvvx4QEFBaWlrHtBUrVly9erWVRPby8vr73/++e/fu0tLSX375pVevXuq77XVEfvPNN+Vy+RtvvPHw4cPnOhyeF6UVLY+q23yRv/vuu7lz5964cePOnTtr1qxZt27dtGnT1FtPnDixcOHC7OzsgoKCyMjImJgY6anFgqoLAADQgmh/AgAAAG1MWVlZ3QtZdBKqDmvXrt27d29iYqKFhYV6MDY2Vk9Pz9fX99GjR82dgPYuXLgQHBw8b948V1fXOqb98MMPly5daiWR9+7de+DAgaSkpNdee83AwMDW1vbgwYMuLi7aRF60aNGgQYMmTpxYVVX1XAcFOhSqbvNpi1XXyMhowYIFNjY25ubmHh4eU6ZM+c9//pOXlydtNTc39/X17dSpk4WFhaen59tvv33kyJGcnBxpK1UXAACgZdD+BAAAANqYHTt25Ofnt7ZQz3Lt2rWQkJDVq1fL5XLNcTc3N39//9zc3E8++aRZE3gugwYNSk5OnjFjhrGx8bPmlJWVLVmyJCYmppVE3rJly+DBgwcMGFDHnDoih4aGnj9//nkPCnQoVN3m0xar7v79+zWvrZ2dnRBCveb+q6++0tfXV2/t0qWLEEJz+SlVFwAAoAXQ/gQAAAB0QKVSRUVF9e3b19jY2NraesqUKVeuXJE2+fn5GRkZdevWTfq4YMECMzMzmUxWUFAghPD39w8MDMzMzJTJZE5OTrGxsXK5vGvXrh999JGtra1cLndzcztz5kwDQgkhjhw5olAoIiIimvBMY2NjVSrVm2+++eSm8PDw3r17b9++/fjx4897leLi4szMzExNTQ8ePDhhwgSFQqFUKvfs2aPet7q6+tNPP+3Ro4eJicnAgQMTEhKa6oxWrFghrftpqoCNiVxRUXH69Om61zbVHdna2trd3T0mJkalUj1fukCbQtUVVN3miZyRkWFlZfXiiy8+dWtubq6JiUnPnj3VI1RdAACAFkD7EwAAANCB0NDQZcuWrVixIj8/PzU1NScnZ9SoUXfu3BFCxMbGenp6qmdu3rx59erV6o8xMTGTJ0/u1auXSqW6du2an5+fj49PaWnpokWLsrOzz549W1VV9ac//Ul6zt5zhRJCVFdXCyFqamqa8EwPHTrUp08fU1PTJzeZmJh88cUXenp6c+fOLSkpeXJCHVdp/vz5ixcvLisrs7CwSEhIyMzMdHR0nDt3bmVlpbRvcHDw+vXro6Oj8/LyJk+ePH369J9//rnxp/P9999nZmZOnz698aGaJPKtW7cqKir+7//+b8yYMVInpm/fvps3b9a8q15v5Jdffjk3N/fChQsNzx5o9ai6gqrbpJErKytzc3M3bdp0/PjxjRs3GhkZPTmntLT0xIkTc+fOrbWVqgsAANDcaH8CAAAALa2srCwqKuqdd96ZOXOmpaXlgAEDtm7dWlBQsG3btoYFNDAwkBbr9OvXLy4urqioaOfOnQ2IM2nSpMLCwpCQkIal8aSSkpLffvutV69ez5owfPjwxYsXZ2dnBwcH19qk5VVyc3NTKBQ2Njbe3t4lJSU3btwQQpSXl8fFxb399ttTp061srJauXKloaFhw65JrZT8/f3j4uIaGacJI0uPW7SxsYmIiEhPT79z586UKVMWLlz45Zdfah/5pZdeEkKkpaU1KHegDaDqqlF1myqyvb29UqkMDQ1dv369l5fXU+dERkba2tqGh4fXGqfqAgAANDfanwAAAEBLS09PLy4uHjJkiHpk6NChRkZG6scnNsaQIUNMTU3VjyvUrfz8fJVK9dRFSGrh4eF9+vTZvHnzyZMnNcef9ypJa2ukdUhXr14tLS11cXGRNpmYmHTr1q3x12T58uUffvih9Jq3ptXgyNI77fr37+/m5tapUydLS8vVq1dbWlqq2xXaRJa+IGmBF9AuUXU1UXWbJHJOTk5+fv6XX375t7/97eWXX37yla779+9PTEw8evSohYVFrU1UXQAAgOZG+xMAAABoaQ8fPhRCmJubaw5aWVkVFRU1SXxjY+O7d+82SahGKi8vF7+36J5FLpfv3LlTJpPNmjWrrKxMPd6YqyQ91HHlypWy312/fr20tLRhZyE5efJkWlranDlzGhOkySPb2toKIaTXCkqMjIxefPHFzMxM7SObmJiI378soF2i6mqi6jZJZENDQxsbm7Fjx+7duzc9PT0yMlJz6969e9euXZuSkuLg4PDkvlRdAACA5kb7EwAAAGhpVlZWQohaN5QfPnyoVCobH7yysrKpQjWedIdXerldHYYPHx4QEJCRkbFmzRr1YGOuko2NjRAiOjpapeHUqVMNOAW1HTt2fPPNN3p6etKdfekQERERMpmske+3a0xkc3Pzl1566fLly5qDVVVVlpaW2keuqKgQv39ZQLtE1a2FqtuEkZ2cnPT19dPT09UjGzdu3LVr14kTJ7p37/7UXai6AAAAzY32JwAAANDSXFxczM3NNe+xnjlzpqKi4pVXXpE+GhgYSI8TbICUlBSVSjVs2LDGh2q8rl27ymSyR48e1TtzzZo1zs7O586dU4/Ue5XqYG9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AAElFTkSuQmCC\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v29y05n_ht84"
      },
      "source": [
        "# Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A5_IEPyLz0Ee"
      },
      "source": [
        "learning_rate_reduction = ReduceLROnPlateau(monitor='val_accuracy', \n",
        "                                            patience=2, \n",
        "                                            verbose=1, \n",
        "                                            factor=0.5, \n",
        "                                            min_lr=0.00001)\n",
        "\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate = 0.00075,\n",
        "                                     beta_1 = 0.9,\n",
        "                                     beta_2 = 0.999,\n",
        "                                     epsilon = 1e-8)\n",
        "\n",
        "EPOCHS = 30\n",
        "\n",
        "model.compile(loss='sparse_categorical_crossentropy', optimizer=optimizer, metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RhlAlP6nz7TN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b8e9f3a1-a662-4a12-974f-816b7296c4a8"
      },
      "source": [
        "history = model.fit(X_train, Y_train,\n",
        "                    validation_split=0.2,\n",
        "                    batch_size = 64,\n",
        "                    epochs = EPOCHS,\n",
        "                    callbacks=[learning_rate_reduction]) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "470/470 [==============================] - 41s 20ms/step - loss: 1.6938 - accuracy: 0.3824 - val_loss: 1.2511 - val_accuracy: 0.5129\n",
            "Epoch 2/30\n",
            "470/470 [==============================] - 8s 17ms/step - loss: 1.0958 - accuracy: 0.5766 - val_loss: 1.1736 - val_accuracy: 0.5829\n",
            "Epoch 3/30\n",
            "470/470 [==============================] - 8s 17ms/step - loss: 0.8344 - accuracy: 0.6806 - val_loss: 0.7729 - val_accuracy: 0.7018\n",
            "Epoch 4/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.6858 - accuracy: 0.7385 - val_loss: 0.5857 - val_accuracy: 0.7795\n",
            "Epoch 5/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.5730 - accuracy: 0.7817 - val_loss: 0.4847 - val_accuracy: 0.8093\n",
            "Epoch 6/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.4785 - accuracy: 0.8157 - val_loss: 0.4514 - val_accuracy: 0.8268\n",
            "Epoch 7/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.4178 - accuracy: 0.8408 - val_loss: 0.3629 - val_accuracy: 0.8536\n",
            "Epoch 8/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.3554 - accuracy: 0.8632 - val_loss: 0.3714 - val_accuracy: 0.8581\n",
            "Epoch 9/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.3205 - accuracy: 0.8803 - val_loss: 0.3194 - val_accuracy: 0.8818\n",
            "Epoch 10/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.2759 - accuracy: 0.8960 - val_loss: 0.3344 - val_accuracy: 0.8802\n",
            "Epoch 11/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.2473 - accuracy: 0.9077 - val_loss: 0.2571 - val_accuracy: 0.9020\n",
            "Epoch 12/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.2208 - accuracy: 0.9197 - val_loss: 0.2282 - val_accuracy: 0.9203\n",
            "Epoch 13/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.1950 - accuracy: 0.9275 - val_loss: 0.2199 - val_accuracy: 0.9166\n",
            "Epoch 14/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.1755 - accuracy: 0.9355 - val_loss: 0.1778 - val_accuracy: 0.9342\n",
            "Epoch 15/30\n",
            "470/470 [==============================] - 9s 19ms/step - loss: 0.1635 - accuracy: 0.9403 - val_loss: 0.2192 - val_accuracy: 0.9229\n",
            "Epoch 16/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.1563 - accuracy: 0.9435 - val_loss: 0.1472 - val_accuracy: 0.9516\n",
            "Epoch 17/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.1301 - accuracy: 0.9533 - val_loss: 0.2168 - val_accuracy: 0.9259\n",
            "Epoch 18/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.1280 - accuracy: 0.9532 - val_loss: 0.3780 - val_accuracy: 0.8964\n",
            "\n",
            "Epoch 00018: ReduceLROnPlateau reducing learning rate to 0.000375000003259629.\n",
            "Epoch 19/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0682 - accuracy: 0.9766 - val_loss: 0.1016 - val_accuracy: 0.9713\n",
            "Epoch 20/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0469 - accuracy: 0.9837 - val_loss: 0.1106 - val_accuracy: 0.9671\n",
            "Epoch 21/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0436 - accuracy: 0.9846 - val_loss: 0.1147 - val_accuracy: 0.9667\n",
            "\n",
            "Epoch 00021: ReduceLROnPlateau reducing learning rate to 0.0001875000016298145.\n",
            "Epoch 22/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0260 - accuracy: 0.9916 - val_loss: 0.0944 - val_accuracy: 0.9755\n",
            "Epoch 23/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0160 - accuracy: 0.9947 - val_loss: 0.1109 - val_accuracy: 0.9743\n",
            "Epoch 24/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0181 - accuracy: 0.9939 - val_loss: 0.0792 - val_accuracy: 0.9808\n",
            "Epoch 25/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0189 - accuracy: 0.9937 - val_loss: 0.1094 - val_accuracy: 0.9763\n",
            "Epoch 26/30\n",
            "470/470 [==============================] - 9s 18ms/step - loss: 0.0164 - accuracy: 0.9947 - val_loss: 0.1042 - val_accuracy: 0.9771\n",
            "\n",
            "Epoch 00026: ReduceLROnPlateau reducing learning rate to 9.375000081490725e-05.\n",
            "Epoch 27/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0085 - accuracy: 0.9974 - val_loss: 0.0893 - val_accuracy: 0.9816\n",
            "Epoch 28/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0065 - accuracy: 0.9981 - val_loss: 0.1019 - val_accuracy: 0.9806\n",
            "Epoch 29/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0050 - accuracy: 0.9985 - val_loss: 0.1014 - val_accuracy: 0.9815\n",
            "\n",
            "Epoch 00029: ReduceLROnPlateau reducing learning rate to 4.6875000407453626e-05.\n",
            "Epoch 30/30\n",
            "470/470 [==============================] - 8s 18ms/step - loss: 0.0040 - accuracy: 0.9985 - val_loss: 0.1011 - val_accuracy: 0.9806\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hTabEXbjy2dg"
      },
      "source": [
        "# Evaluate the model on testing data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NifO46NNPDK5"
      },
      "source": [
        "# load Model if already trained \n",
        "# model = tf.keras.models.load_model('/content/drive/MyDrive/Colab Notebooks/Skin Disease Classification/models/best_model.h5')"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8NocZvlIVU5y",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbf913b7-c514-4bf9-8b41-96b7110d8933"
      },
      "source": [
        "model_acc_test = model.evaluate(X_test, Y_test, verbose=0)[1]\n",
        "print(\"Model accuracy on test data =\", model_acc_test * 100)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model accuracy on test data = 98.3398973941803\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 422
        },
        "id": "4nxg4yOLMRaK",
        "outputId": "f740af44-46fc-4df6-ff91-c19f811ece49"
      },
      "source": [
        "ACC = history.history['accuracy']\n",
        "VAL_ACC = history.history['val_accuracy']\n",
        "LOSS = history.history['loss']\n",
        "VAL_LOSS = history.history['val_loss']\n",
        "\n",
        "plt.figure(figsize=(8,6))\n",
        "plt.title(\"the accuracy / loss of the training and validation phase of the model\")\n",
        "plt.ylabel('Accuracy / Loss')\n",
        "plt.xlabel('Epoche')\n",
        "plt.plot(ACC, label = 'train_acc')\n",
        "plt.plot(VAL_ACC, label = 'val_acc')\n",
        "plt.plot(LOSS, label = 'train_loss')\n",
        "plt.plot(VAL_LOSS, label = 'val_loss')\n",
        "plt.legend()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f5647e28550>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "iTTqQmgHMYTF",
        "outputId": "79107731-337b-4bce-b666-5838db6285ca"
      },
      "source": [
        "Y_true = np.array([Y_test])\n",
        "\n",
        "Y_pred = model.predict(X_test)\n",
        "Y_pred = np.array(list(map(lambda x: np.argmax(x), Y_pred)))\n",
        "\n",
        "cm1 = confusion_matrix(Y_true, Y_pred)\n",
        "cm1 = pd.DataFrame(cm1, index=['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc','mel'], columns =['akiec', 'bcc', 'bkl', 'df', 'nv', 'vasc','mel'])\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "# plt.title('the confusion matrix of the model in the train')\n",
        "plt.figure(figsize = (10,7))\n",
        "plt.xlabel('Actual Value')\n",
        "plt.ylabel('Predicted Value')\n",
        "sns.heatmap(cm1, annot = True, fmt = 'g' ,vmin = 0, cmap = 'binary', cbar=False)\n",
        "plt.plot()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f56304aa750>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5JbvLi9UMs8n",
        "outputId": "cfd743c7-7ea2-41f6-9e65-d3a86581e6f5"
      },
      "source": [
        "classification_report_model = classification_report(Y_true, Y_pred, target_names=type_of_cancer.values())\n",
        "print(classification_report_model)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       akiec       1.00      1.00      1.00      1382\n",
            "         bcc       0.99      1.00      1.00      1340\n",
            "         bkl       0.96      1.00      0.98      1265\n",
            "          df       1.00      1.00      1.00      1382\n",
            "          nv       1.00      0.88      0.94      1285\n",
            "        vasc       1.00      1.00      1.00      1361\n",
            "         mel       0.94      1.00      0.97      1382\n",
            "\n",
            "    accuracy                           0.98      9397\n",
            "   macro avg       0.98      0.98      0.98      9397\n",
            "weighted avg       0.98      0.98      0.98      9397\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kwnmnXpzM9F_"
      },
      "source": [
        "model.save('/content/drive/MyDrive/Colab Notebooks/Skin Disease Classification/models/best_model.h5')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PGAiJRmVRUbd"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}