Skin_Cancer_Detection.ipynb

{
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
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      "source": [
        "<a href=\"https://colab.research.google.com/github/charanhu/Skin_Cancer_Detection_MNIST/blob/main/Skin_Cancer_Detection.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "PdUxjmfownUy"
      },
      "source": [
        "# Skin_Cancer_Detection using CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_WEqtd_FwnU5"
      },
      "source": [
        "## The dataset consists of 10015 dermatoscopic images which can serve as a training set for academic machine learning purposes. The objective to build deep learning model to classify given query image into one of the 7 different classes of skin cancer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "78ZwJ3EGwnU7"
      },
      "source": [
        "#### Sourses:\n",
        "Data: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WZnWmP-JwnU8"
      },
      "source": [
        "This the HAM10000 (\"Human Against Machine with 10000 training images\") dataset.It consists of 10015 dermatoscopicimages which are released as a training set for academic machine learning purposes and are publiclyavailable through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts.\n",
        "\n",
        "It has 7 different classes of skin cancer which are listed below :\n",
        "1. Melanocytic nevi\n",
        "2. Melanoma\n",
        "3. Benign keratosis-like lesions\n",
        "4. Basal cell carcinoma\n",
        "5. Actinic keratoses\n",
        "6. Vascular lesions\n",
        "7. Dermatofibroma\n",
        "\n",
        "Here I will try to detect 7 different classes of skin cancer using Convolution Neural Network with keras tensorflow in backend and then analyse the result to see how the model can be useful in practical scenario.\n",
        "We will move step by step process to classify 7 classes of cancer."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AmuzHRbYwnU9"
      },
      "source": [
        "![](https://github.com/charanhu/Skin_Cancer_Detection_MNIST/blob/main/model_architecture.png?raw=true)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g87hZJFUwVcc"
      },
      "source": [
        "In this kernel I have followed following 14 steps for model building and evaluation which are as follows :\n",
        "\n",
        "Step 1: Importing Essential Libraries\n",
        "\n",
        "Step 2: Loading data and Making labels\n",
        "\n",
        "Step 3: Train Test Split\n",
        "\n",
        "Step 4: Exploratory data analysis (EDA)\n",
        "\n",
        "Step 5: Model Building (CNN)\n",
        "\n",
        "Step 6: Setting Optimizer & Annealing\n",
        "\n",
        "Step 7: Fitting the model\n",
        "\n",
        "Step 8: Model Evaluation"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CITglgXP2wiK"
      },
      "source": [
        "# Step 1: Importing Essential Libraries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "kES_rD67rrG6"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import warnings \n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g9adCBqmu3BD",
        "outputId": "371ce213-3ea2-46e2-b0a8-80cf63ace603"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2022-01-05 05:43:53--  https://storage.googleapis.com/kaggle-data-sets/54339/104884/compressed/hmnist_28_28_RGB.csv.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20220105%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20220105T054322Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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\n",
            "Resolving storage.googleapis.com (storage.googleapis.com)... 142.250.157.128, 142.251.8.128, 74.125.203.128, ...\n",
            "Connecting to storage.googleapis.com (storage.googleapis.com)|142.250.157.128|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 27520440 (26M) [application/zip]\n",
            "Saving to: ‘hmnist_28_28_RGB.csv.zip’\n",
            "\n",
            "hmnist_28_28_RGB.cs 100%[===================>]  26.25M  16.5MB/s    in 1.6s    \n",
            "\n",
            "2022-01-05 05:43:57 (16.5 MB/s) - ‘hmnist_28_28_RGB.csv.zip’ saved [27520440/27520440]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget --header=\"Host: storage.googleapis.com\" --header=\"User-Agent: Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/96.0.4664.110 Safari/537.36\" --header=\"Accept: text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8,application/signed-exchange;v=b3;q=0.9\" --header=\"Accept-Language: en-US,en;q=0.9\" --header=\"Referer: https://www.kaggle.com/\" \"https://storage.googleapis.com/kaggle-data-sets/54339/104884/compressed/hmnist_28_28_RGB.csv.zip?X-Goog-Algorithm=GOOG4-RSA-SHA256&X-Goog-Credential=gcp-kaggle-com%40kaggle-161607.iam.gserviceaccount.com%2F20220105%2Fauto%2Fstorage%2Fgoog4_request&X-Goog-Date=20220105T054322Z&X-Goog-Expires=259199&X-Goog-SignedHeaders=host&X-Goog-Signature=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\" -c -O 'hmnist_28_28_RGB.csv.zip'"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xp6IRzqhu1Q9"
      },
      "source": [
        "# Step 2: Loading data and Making labels"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vwGBAfcav3Bz",
        "outputId": "deb956c8-d407-48da-b6b3-0b702961483c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Archive:  hmnist_28_28_RGB.csv.zip\n",
            "  inflating: hmnist_28_28_RGB.csv    \n"
          ]
        }
      ],
      "source": [
        "!unzip hmnist_28_28_RGB.csv.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g7tDvsg5sNZK"
      },
      "outputs": [],
      "source": [
        "#Dataset used: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000\n",
        "\n",
        "path='hmnist_28_28_RGB.csv'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "TSlPUvq-sfo4"
      },
      "outputs": [],
      "source": [
        "df=pd.read_csv(path)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        },
        "id": "-XZB67tXslAf",
        "outputId": "bf1e7171-0aea-487a-8835-d052283ea720"
      },
      "outputs": [
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              "      <td>210</td>\n",
              "      <td>242</td>\n",
              "      <td>216</td>\n",
              "      <td>215</td>\n",
              "      <td>239</td>\n",
              "      <td>214</td>\n",
              "      <td>213</td>\n",
              "      <td>234</td>\n",
              "      <td>208</td>\n",
              "      <td>208</td>\n",
              "      <td>224</td>\n",
              "      <td>198</td>\n",
              "      <td>196</td>\n",
              "      <td>206</td>\n",
              "      <td>180</td>\n",
              "      <td>177</td>\n",
              "      <td>226</td>\n",
              "      <td>202</td>\n",
              "      <td>201</td>\n",
              "      <td>214</td>\n",
              "      <td>192</td>\n",
              "      <td>190</td>\n",
              "      <td>204</td>\n",
              "      <td>181</td>\n",
              "      <td>178</td>\n",
              "      <td>181</td>\n",
              "      <td>159</td>\n",
              "      <td>153</td>\n",
              "      <td>172</td>\n",
              "      <td>151</td>\n",
              "      <td>145</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10013</th>\n",
              "      <td>160</td>\n",
              "      <td>124</td>\n",
              "      <td>146</td>\n",
              "      <td>164</td>\n",
              "      <td>131</td>\n",
              "      <td>152</td>\n",
              "      <td>167</td>\n",
              "      <td>127</td>\n",
              "      <td>146</td>\n",
              "      <td>169</td>\n",
              "      <td>124</td>\n",
              "      <td>142</td>\n",
              "      <td>173</td>\n",
              "      <td>134</td>\n",
              "      <td>149</td>\n",
              "      <td>177</td>\n",
              "      <td>139</td>\n",
              "      <td>156</td>\n",
              "      <td>181</td>\n",
              "      <td>141</td>\n",
              "      <td>159</td>\n",
              "      <td>183</td>\n",
              "      <td>137</td>\n",
              "      <td>154</td>\n",
              "      <td>185</td>\n",
              "      <td>139</td>\n",
              "      <td>160</td>\n",
              "      <td>185</td>\n",
              "      <td>145</td>\n",
              "      <td>165</td>\n",
              "      <td>186</td>\n",
              "      <td>148</td>\n",
              "      <td>162</td>\n",
              "      <td>189</td>\n",
              "      <td>157</td>\n",
              "      <td>170</td>\n",
              "      <td>190</td>\n",
              "      <td>155</td>\n",
              "      <td>169</td>\n",
              "      <td>191</td>\n",
              "      <td>...</td>\n",
              "      <td>189</td>\n",
              "      <td>141</td>\n",
              "      <td>159</td>\n",
              "      <td>192</td>\n",
              "      <td>167</td>\n",
              "      <td>179</td>\n",
              "      <td>193</td>\n",
              "      <td>173</td>\n",
              "      <td>183</td>\n",
              "      <td>192</td>\n",
              "      <td>171</td>\n",
              "      <td>181</td>\n",
              "      <td>190</td>\n",
              "      <td>164</td>\n",
              "      <td>171</td>\n",
              "      <td>190</td>\n",
              "      <td>162</td>\n",
              "      <td>171</td>\n",
              "      <td>190</td>\n",
              "      <td>168</td>\n",
              "      <td>179</td>\n",
              "      <td>194</td>\n",
              "      <td>177</td>\n",
              "      <td>187</td>\n",
              "      <td>192</td>\n",
              "      <td>170</td>\n",
              "      <td>180</td>\n",
              "      <td>186</td>\n",
              "      <td>163</td>\n",
              "      <td>165</td>\n",
              "      <td>185</td>\n",
              "      <td>162</td>\n",
              "      <td>167</td>\n",
              "      <td>184</td>\n",
              "      <td>157</td>\n",
              "      <td>166</td>\n",
              "      <td>185</td>\n",
              "      <td>162</td>\n",
              "      <td>172</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10014</th>\n",
              "      <td>175</td>\n",
              "      <td>142</td>\n",
              "      <td>121</td>\n",
              "      <td>181</td>\n",
              "      <td>150</td>\n",
              "      <td>134</td>\n",
              "      <td>181</td>\n",
              "      <td>150</td>\n",
              "      <td>133</td>\n",
              "      <td>178</td>\n",
              "      <td>145</td>\n",
              "      <td>127</td>\n",
              "      <td>177</td>\n",
              "      <td>144</td>\n",
              "      <td>122</td>\n",
              "      <td>177</td>\n",
              "      <td>146</td>\n",
              "      <td>123</td>\n",
              "      <td>179</td>\n",
              "      <td>148</td>\n",
              "      <td>126</td>\n",
              "      <td>185</td>\n",
              "      <td>157</td>\n",
              "      <td>140</td>\n",
              "      <td>177</td>\n",
              "      <td>142</td>\n",
              "      <td>113</td>\n",
              "      <td>174</td>\n",
              "      <td>138</td>\n",
              "      <td>112</td>\n",
              "      <td>177</td>\n",
              "      <td>141</td>\n",
              "      <td>117</td>\n",
              "      <td>175</td>\n",
              "      <td>139</td>\n",
              "      <td>108</td>\n",
              "      <td>178</td>\n",
              "      <td>145</td>\n",
              "      <td>117</td>\n",
              "      <td>181</td>\n",
              "      <td>...</td>\n",
              "      <td>183</td>\n",
              "      <td>150</td>\n",
              "      <td>136</td>\n",
              "      <td>178</td>\n",
              "      <td>141</td>\n",
              "      <td>116</td>\n",
              "      <td>184</td>\n",
              "      <td>151</td>\n",
              "      <td>135</td>\n",
              "      <td>188</td>\n",
              "      <td>160</td>\n",
              "      <td>147</td>\n",
              "      <td>183</td>\n",
              "      <td>153</td>\n",
              "      <td>131</td>\n",
              "      <td>180</td>\n",
              "      <td>146</td>\n",
              "      <td>131</td>\n",
              "      <td>173</td>\n",
              "      <td>137</td>\n",
              "      <td>115</td>\n",
              "      <td>157</td>\n",
              "      <td>88</td>\n",
              "      <td>68</td>\n",
              "      <td>146</td>\n",
              "      <td>32</td>\n",
              "      <td>47</td>\n",
              "      <td>145</td>\n",
              "      <td>24</td>\n",
              "      <td>48</td>\n",
              "      <td>159</td>\n",
              "      <td>79</td>\n",
              "      <td>82</td>\n",
              "      <td>174</td>\n",
              "      <td>137</td>\n",
              "      <td>125</td>\n",
              "      <td>175</td>\n",
              "      <td>139</td>\n",
              "      <td>126</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>5 rows × 2353 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-216ee3aa-a4ce-4c7b-a34d-0110fbfd3a32')\"\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-216ee3aa-a4ce-4c7b-a34d-0110fbfd3a32 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-216ee3aa-a4ce-4c7b-a34d-0110fbfd3a32');\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",
              "  "
            ],
            "text/plain": [
              "       pixel0000  pixel0001  pixel0002  ...  pixel2350  pixel2351  label\n",
              "10010        183        165        181  ...        187        189      0\n",
              "10011          2          3          1  ...          4          1      0\n",
              "10012        132        118        118  ...        151        145      0\n",
              "10013        160        124        146  ...        162        172      0\n",
              "10014        175        142        121  ...        139        126      6\n",
              "\n",
              "[5 rows x 2353 columns]"
            ]
          },
          "execution_count": 126,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.tail()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y4Cw9UFa1rsr"
      },
      "source": [
        "# Step 3: Train Test Split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iILXCW2hsoDl"
      },
      "outputs": [],
      "source": [
        "fractions=np.array([0.8,0.2])\n",
        "\n",
        "df=df.sample(frac=1)\n",
        "\n",
        "train_set, test_set = np.array_split(df, (fractions[:-1].cumsum() * len(df)).astype(int))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "As38rXlQs1PY",
        "outputId": "2fea275b-f97f-413f-90ca-fbe82daa4972"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "8012\n"
          ]
        }
      ],
      "source": [
        "print(len(train_set))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VY83gf_ss-x1",
        "outputId": "95ac1324-07f0-466f-d01d-e11e688a675b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "2003\n"
          ]
        }
      ],
      "source": [
        "print(len(test_set))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3w2H4w0_ubRT",
        "outputId": "66c0a7ae-3ab5-4512-8991-1641377bbcc6"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "array([4, 1, 6, 5, 2, 0, 3])"
            ]
          },
          "execution_count": 130,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.label.unique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lHk4p-sdugSa"
      },
      "outputs": [],
      "source": [
        "# reference: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000/discussion/183083\n",
        "classes={\n",
        "    0:('akiec', 'actinic keratoses and intraepithelial carcinomae'),\n",
        "         \n",
        "    1:('bcc' , 'basal cell carcinoma'),\n",
        "         \n",
        "    2:('bkl', 'benign keratosis-like lesions'),\n",
        "         \n",
        "    3:('df', 'dermatofibroma'),\n",
        "         \n",
        "    4:('nv', ' melanocytic nevi'),\n",
        "         \n",
        "    5:('vasc', ' pyogenic granulomas and hemorrhage'),\n",
        "         \n",
        "    6:('mel', 'melanoma'),\n",
        "}"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2W5sBRkZtBr9"
      },
      "outputs": [],
      "source": [
        "y_train=train_set['label']\n",
        "\n",
        "x_train=train_set.drop(columns=['label'])\n",
        "\n",
        "y_test=test_set['label']\n",
        "\n",
        "x_test=test_set.drop(columns=['label'])\n",
        "\n",
        "columns=list(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JzQneFTdtWO1",
        "outputId": "b8016af4-5370-474f-8991-208944e47404"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "cuda:0\n"
          ]
        }
      ],
      "source": [
        "import torch\n",
        "\n",
        "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n",
        "\n",
        "print(device)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5i_16okiu1Rf"
      },
      "source": [
        "# Step 4: Exploratory Data Analysis and Preprocessing"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "Tv4196QOhtmI",
        "outputId": "17c53836-3a24-4662-b652-48f82890a0f2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f69d35edcd0>"
            ]
          },
          "execution_count": 134,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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amW/Dk+YGd1VtBbZO4rOS7Kqq2Ul81jhY/3RZ//Qs59phuvUvl26o/cApQ+vrW5skaQKWS1jcAWxMclqSpwAXAzumXJMkrRjLohuqqg4neT1wM7AK2FZVd0+xpIl0d42R9U+X9U/Pcq4dplj/srjBLUmaruXSDSVJmiLDQpLUZVgs0nKediTJtiQHknxu2rUsVpJTktyW5J4kdyd5w7RrWowkJyT5ZJLPtPp/f9o1HY0kq5J8OsmHpl3LYiX5QpK7ktyZZNe061msJCcmuSHJ55PsSfJjE/1871mMrk078n+Bnwb2MRil9ZqqumeqhY0oyUuBR4FrqupHpl3PYiQ5GTi5qj6V5JnAbuDCZfTfPsDTq+rRJMcD/wC8oapun3Jpi5LkvwGzwLOq6pXTrmcxknwBmK2qZfmjvCTbgf9dVe9po0KfVlVfmdTne2WxOMt62pGq+hhwaNp1HI2qerCqPtWWvwbsAdZNt6rR1cCjbfX49lpWf6klWQ/8DPCeadey0iR5NvBS4GqAqvrWJIMCDIvFWgc8MLS+j2X0D9aTRZINwIuAT0y3ksVpXTh3AgeAnVW1rOoH/gT4beA70y7kKBXwd0l2t+mBlpPTgIPAe1s34HuSPH2SBRgWWlaSPAP4IPDrVfXVadezGFX17ap6IYMZCM5Msmy6ApO8EjhQVbunXcu/w09U1RkMZq++vHXLLhergTOAd1fVi4B/ASZ6z9SwWBynHZmi1tf/QeDaqvrraddztFr3wW3ApmnXsghnAz/X+v2vA85J8oHplrQ4VbW/vR8AbmTQrbxc7AP2DV2N3sAgPCbGsFgcpx2ZknaD+GpgT1X98bTrWawkM0lObMvfw2CQxOenW9XoqupNVbW+qjYw+P/+1qp67ZTLGlmSp7eBEbTum/OAZTMqsKoeAh5I8rzWdC4w0cEdy2K6j2PFMTjtyKIk+Qvgp4CTkuwDrqiqq6db1cjOBl4H3NX6/QF+p6o+PMWaFuNkYHsbUXcccH1VLbvhp8vYWuDGwd8crAb+vKo+Mt2SFu1XgWvbH6r3A5dO8sMdOitJ6rIbSpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFtASSPNrZvmGxs/0meV+Si/59lUlLw7CQJHUZFtISSvKMJLck+VR7dsLwrMSrk1zbnkVwQ5KntWNenOSjbYK7m9t07NIxxbCQltY3gJ9vE9a9DHhrm6oE4HnAu6rqh4GvAv+1zXf1DuCiqnoxsA24agp1S0fkdB/S0grwh21G0+8wmMJ+bdv2QFV9vC1/APg14CPAjwA7W6asAh6caMXSCAwLaWn9EjADvLiq/rXN0npC2/b4uXWKQbjcXVUTfUSmtFh2Q0lL69kMnvvwr0leBnz/0LZTh56b/IsMHq16LzAz157k+CTPn2jF0ggMC2lpXQvMJrkLuITHTkN+L4OH7uwB1jB4kM23gIuAtyT5DHAn8OMTrlnqctZZSVKXVxaSpC7DQpLUZVhIkroMC0lSl2EhSeoyLCRJXYaFJKnr/wMX5g0MsNri8AAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "\n",
        "sns.countplot(train_set['label'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "CKIlAJY9pwh1"
      },
      "outputs": [],
      "source": [
        "from imblearn.over_sampling import RandomOverSampler \n",
        "\n",
        "oversample = RandomOverSampler()\n",
        "\n",
        "x_train,y_train  = oversample.fit_resample(x_train,y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        },
        "id": "tM_GikKFiJuK",
        "outputId": "15f6e0ce-f063-4d88-e257-d4de54f9e276"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f695c669050>"
            ]
          },
          "execution_count": 136,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.countplot(y_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 809
        },
        "id": "84ADzEUx5R9I",
        "outputId": "93d80c0d-cad7-4f5d-fecf-a8ea8706c8ee"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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FC62V2xu3Si5DtOyy1f7Irt1m019tecq078JO4LNkyJ4DUNcYLg+sY3aY6MF+24ffVLJTiY0PR+Jzh8Ihl431kdDgSPrvY5EsZ82F8OG9KHKeOzhi29uHR0z70H47fLd8brjz3Z12+u5zzlobtN216UdBm5/ZHScRXOyOkwgudsdJBBe74ySCi91xEsHF7jiJ4GJ3nESoqp+9UFegoz2cWnjtGXYZXKvcs0ZrzdgLaKQ0lUyE48KfefgBs+22o3bZ5K41drnpwQG7bmajke25obXVbPvsfjvN9eDTu0z7ygV2QPu4UQp7mZGfAKAYSdc8EvnS1ZgbcTgyf0AO29/ZkjpbOr1b7TLcp178+0FbS7Ndq/q01eF49yYjJbqf2R0nEVzsjpMILnbHSQQXu+MkgovdcRLBxe44ieBid5xEqKqfvb6ujh6jtPKKpeEytgAFw7ep9ZFdiZRVttYNML53b9D2+CNbzLbldjv/OeNDplmNHAAAda3hePa6SD795ctXmvbxATsn/ljRjmfff3Q4aDt9+WqzbZOd8p5ia3jOBkDvnh1B22jfQbNt33h4fgBAS6TEd13JniNQVwjPESjU2+teelK4tHmDUR8hemYXkS+KyAER2VrxWY+I3CUiT+X/7QLijuPUnKlcxt8MXHHCZx8G7lbVNcDd+XvHceYxUbGr6iag74SPrwJuyV/fAlw9y/1yHGeWme49+xJV3Ze/7gWWhBYUkRuAGwCaI/ePjuPMHTN+Gq+qihFloqobVXWDqm5oaLAn+DuOM3dMV+z7RWQZQP7ffmTrOE7Nma7Y7wCuz19fD3x7drrjOM5cEb1nF5GvAZcBi0RkD/Ax4JPAbSLyHmAXcO2UNlZfz6KenqB9YXfEg2flfo/UV9dIXniJ+OkfvOObQdtjT2w32zYtsOuIj5ftuOxI6DUjR058fvob2lrsePOmFnu/Fy0L1wIHGDlo50fv6Ap/311ddu13BgdN89hE2IcPMDEezpm/sCvsqwaYONBr2jua7UkAi049zbQ3tIf3vThu++gXGG0LhkaiYlfV6wKm18XaOo4zf/Dpso6TCC52x0kEF7vjJIKL3XESwcXuOIlQ/RDXznC4Z3ubHbIIhvusaIckSr3tmou1VyNtcVHttrE01kPHjpn2Y2O2m6ehLjwu4+P2ugcHj5r21m477LhtcTitMUCL4f5qbrTdoY3L7PDb/kORvreEj6fDfXZ67iWRctIn9YTDigFOWneRaacQnk1aEDtsuLnJahseUz+zO04iuNgdJxFc7I6TCC52x0kEF7vjJIKL3XESwcXuOIlQ3ZLNUqDVSE0V8YTb6aAN/yKAREJgSxF/867HHw/ajhrlnAFa2uxQzs7GBaZ9995nTfvoaNjPPz5mx8e2RLIHLai32w+MGfWigZGRkaCtc8QOYRW1z0XFSMrlfmP+Qn3JnvvQsyDynS2y54Q0dtrzE7BKhEfKjzcZqcXF0Iif2R0nEVzsjpMILnbHSQQXu+MkgovdcRLBxe44ieBid5xEqKqfXQQajZKytqc8tvLI71ad7U8ePXzYtPfufi5sbLJ9roVyOKYbQCdsx2p390K7fTns5+9aaPt768fCaagBjh61y0kPl+xDyPpaBiNx/E0jdqrokQn7Ox0YDrcfHbB9/G0TdjrnzkZ7bsUlkfTgVlp0qbOP5Tpjzog13cTP7I6TCC52x0kEF7vjJIKL3XESwcXuOIngYnecRHCxO04iVNXPDnaorlglmQGssstG7nQALdm53Rs77XLRJ689K2i7/75HzLbFI7ZPtqfLLqvc1WjbWzpPChuHbT/62FC/aR/Hjlff84wda9/eFm5f6LZjxpede45pP7R1m2kfHRoI2yL5DfZE8gCcWbDzxje22TkKKMdqDYTRWMB7gOiZXUS+KCIHRGRrxWc3icheEXko/7tyWlt3HKdqTOUy/mbgikk+/ztVXZ//fX92u+U4zmwTFbuqbgLsa0HHceY9M3lAd6OIPJJf5gdveEXkBhHZLCKbh418ZI7jzC3TFfvngNOB9cA+4NOhBVV1o6puUNUNrS32wx7HceaOaYldVferaklVy8DngUjJSsdxas20xC4iyyreXgNsDS3rOM78IOpnF5GvAZcBi0RkD/Ax4DIRWU/mNt8J/NlUNlZWZWwsHCdcsl2bYNrt3y2N+DUbuhaZ9pVnrgvamh/YYrYttto+14U9tn0skpe+NBau512YsGPpS2ofAi3tti/85BXLTfvmJ54M2lo7Ws227W2dpr2h3Y7z72nZH7QNFSO+6liOgQURP3uLvW/ZRXHQaLadMI4HNdpGxa6q103y8Rdi7RzHmV/4dFnHSQQXu+MkgovdcRLBxe44ieBid5xEqGqIa7lUZmAonJrYcstlKzDcFRHXmhQiuzpipzXevjUcxnps1HZvtTTabhpt7rHtZTs0YXQonBa5pHZZ44GiHRrcZnv9eGr/QdM+YnynI312muqhYft46Ii4BZcbKbi7Sva6u1vt4+XKa99m2ht6bFduuRjefqxU9agxpmXD9eZndsdJBBe74ySCi91xEsHF7jiJ4GJ3nERwsTtOIrjYHScRqupnL5aK9PUfCdqHI75u1emn342Vgy4avmqAvoO9QVt9JAX2xHg4BBVgbNi2D4/Y9qP94XLTbR1dZltps8NEH9xmpyo4uOsZ037WwnA56xWL7fkFsTLcPQsipbKXLA3aTpYxs+2/e9efmvYl520w7Sp2vLYYtZXLJXtyw6BxrJaNlOl+ZnecRHCxO04iuNgdJxFc7I6TCC52x0kEF7vjJIKL3XESocp+9hKHjoRLBPf1275u1PCWF20fvA4Nm/a6Rjv17ysv+b2g7Znbf2C2HY5UwjnQa5c97t0fTokMUCDsh+/sWmy2JZJqeqDP3vb5a8+w1z8c/k5PX7fGbDp07KhpL1jHA9DdHs4jsLTTHpeeNeebdhrtks92cXKQctg+MWHPqzh4ODyvYsLQgZ/ZHScRXOyOkwgudsdJBBe74ySCi91xEsHF7jiJ4GJ3nESYSsnmVcCtwBIy5+FGVf2siPQA/wKcQla2+VpVDQerA8VikUNHwj7CvQcOmH0pGTWdCxORnPP1Daa50BYpwbsgXFa5cWzEbDs0aPuLxyI57xd02zHp/ca47d7xtNl28erTTPvLzzjVtK9absek17e/LGjTOjsefShSR6A4aOesXzASzrd/2qv/wGzbECnJrNgx5xJJvTAxGp730Xdon9l217O7grbx8XCc/lTO7EXgA6q6Dngl8D4RWQd8GLhbVdcAd+fvHceZp0TFrqr7VPWB/PUgsA1YAVwF3JIvdgtw9Vx10nGcmfOi7tlF5BTgAuBXwBJVPX690Ut2me84zjxlymIXkXbgm8D7VXWg0qaqSmAysIjcICKbRWRz0ciP5TjO3DIlsYtIA5nQv6Kq38o/3i8iy3L7MmDSp0SqulFVN6jqhvq6WPCA4zhzRVTskqXB/AKwTVU/U2G6A7g+f3098O3Z757jOLPFVEJcXw28A9giIg/ln30E+CRwm4i8B9gFXBtbUalc4ujgQND+5E47LfHIWNit0F5vh5FqXSSZdCRN9cp15wZt5yy1XUi7R+3SxP2RUM2+SAnfY+OGG6hstx0+sMe0N7c0mfaBvnDIMsCiurDL80i/HVZcjpTCHthnhwYv7Aqv/4wNF5lto8oo2iGsseTlE8YxcTjynTxhuFNHDddbVOyqei/hnr8u1t5xnPmBz6BznERwsTtOIrjYHScRXOyOkwgudsdJBBe74yRCVVNJa1nNELwtjz9mtj9klHtub7f97BJzi5btErvdp64N2q543wfNto/cfadpP/ScPb+g95g9B6CzLexPru+2SzLvGLLDb/uHw/MiADoa7FmR5WLY39zQZIcGFyJzH0rH7PkLr31rOIy1zSjnDKCRsskxtGSng65rCB+vR0btY3HrU08EbSPG3AQ/sztOIrjYHScRXOyOkwgudsdJBBe74ySCi91xEsHF7jiJUF0/O1DWsA/xiae3m+23bQ/bly9eZLZtMErkAmC7NqEuPFQLN1xsNv29lXY65vLoMdPef+iQaX/u0S1B21NPhn2yWWM7xXZ/pPRw+Wg4NThA8WjYF360bO/32Ljtq37Dy9eb9vWvuSJoU4nkN4jsN+N2mutSJA322ETYj7/tGTtOf8fucCrpMaNffmZ3nERwsTtOIrjYHScRXOyOkwgudsdJBBe74ySCi91xEqGqfnZQSqWw/7L3oF2y+Z777gvaLlgXjjcHOKnLLntciPhdC/XNQVt5wo7Lbl6y2LRTsH9zW5etMO1L15wTtDV85ytm28cfftC0tzWFS1UDtJXtuO/urnDp48F6O298S4ttv+Kqa0x7Y2e470qkFFlk3oVGymxTsI+ng/3hPAI/v9/+To4cDbctGSXW/MzuOIngYnecRHCxO04iuNgdJxFc7I6TCC52x0kEF7vjJELUzy4iq4BbgSVkQb4bVfWzInIT8F7gYL7oR1T1+9a6ymW1420jvsl77vtV0Hb5Ja8y23att2OfmxsbTTsT4Xz3BSPWHUAjfnQiPn6tt3OzF9rC21917nlm29deutO0jzfYtefrIwn5V58XroNe32nnIGhrt+dGdC1fYtq1ZMSUS+TQV3u/NFJnYCQSi79566NB230PPmC2LRlzG9SIw5/KpJoi8AFVfUBEOoD7ReSu3PZ3qvqpKazDcZwaExW7qu4D9uWvB0VkG2BP6XIcZ97xou7ZReQU4ALg+PX0jSLyiIh8UUS6A21uEJHNIrLZusRwHGdumbLYRaQd+CbwflUdAD4HnA6sJzvzf3qydqq6UVU3qOoGIZb3y3GcuWJKYheRBjKhf0VVvwWgqvtVtaSqZeDzQPhJjOM4NScqdhER4AvANlX9TMXnyyoWuwbYOvvdcxxntpjK0/hXA+8AtojIQ/lnHwGuE5H1ZO64ncCfxVakqowbLonGBjut8fadO4K27/34x2bbU1fYzxRXrVhp2usk7P6KuWEkYqfe/hokMi5qhDX2nHm+2fbS1XZo8NiEnRIZY1wAtDD9KOpY2LHU2+cqy64R11qMktp927HbTgf9ze99L2jrPdhrti0WjfBaY7em8jT+Xpj0Ztv0qTuOM7/wGXSOkwgudsdJBBe74ySCi91xEsHF7jiJ4GJ3nESocippKBs+YSv8FewQ2Dvvuddse8G6cLplgMtb7bTFPUYq6kJ9JByyLuIPjmQljmH68etsPzgt4RTZAI0NkUMk4gsvF42+RVzdhYgfvRDpmxrlwWPbjrnhD/b1m/bv/2STab/3178M2kqRNNVjRjnosnEs+JndcRLBxe44ieBid5xEcLE7TiK42B0nEVzsjpMILnbHSQSZaVzvi9qYyEFgV8VHi4BDVevAi2O+9m2+9gu8b9NlNvu2WlVPmsxQVbH/1sZFNqvqhpp1wGC+9m2+9gu8b9OlWn3zy3jHSQQXu+MkQq3FvrHG27eYr32br/0C79t0qUrfanrP7jhO9aj1md1xnCrhYnecRKiJ2EXkChF5QkS2i8iHa9GHECKyU0S2iMhDIrK5xn35oogcEJGtFZ/1iMhdIvJU/n/SGns16ttNIrI3H7uHROTKGvVtlYj8REQeE5FHReQ/55/XdOyMflVl3Kp+zy4idcCTwBuAPcCvgetU9bGqdiSAiOwENqhqzSdgiMilwBBwq6qem3/2P4A+Vf1k/kPZrar/dZ707SZgqNZlvPNqRcsqy4wDVwPvooZjZ/TrWqowbrU4s18EbFfVHao6DnwduKoG/Zj3qOomoO+Ej68Cbslf30J2sFSdQN/mBaq6T1UfyF8PAsfLjNd07Ix+VYVaiH0FUFkbZw/zq967AneKyP0ickOtOzMJS1R1X/66F1hSy85MQrSMdzU5ocz4vBm76ZQ/nyn+gO63uVhVXw68CXhffrk6L9HsHmw++U6nVMa7WkxSZvx5ajl20y1/PlNqIfa9wKqK9yvzz+YFqro3/38AuJ35V4p6//EKuvn/AzXuz/PMpzLek5UZZx6MXS3Ln9dC7L8G1ojIqSLSCPwhcEcN+vFbiEhb/uAEEWkDLmf+laK+A7g+f3098O0a9uUFzJcy3qEy49R47IIc/xIAAACCSURBVGpe/lxVq/4HXEn2RP5p4KO16EOgX6cBD+d/j9a6b8DXyC7rJsiebbwHWAjcDTwF/AjomUd9+xKwBXiETFjLatS3i8ku0R8BHsr/rqz12Bn9qsq4+XRZx0kEf0DnOIngYnecRHCxO04iuNgdJxFc7I6TCC52x0kEF7vjJML/B4/R1wcDzEW6AAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import random\n",
        "\n",
        "num=random.randint(0,8000)\n",
        "x_train=np.array(x_train, dtype=np.uint8).reshape(-1,28,28,3)\n",
        "\n",
        "plt.imshow(x_train[num].reshape(28,28,3))\n",
        "plt.title(\"Random image from training data\")\n",
        "plt.show()\n",
        "num=random.randint(0,8000)\n",
        "plt.imshow(x_train[num].reshape(28,28,3))\n",
        "plt.title(\"Random image from training data\")\n",
        "plt.show()\n",
        "\n",
        "num=random.randint(0,8000)\n",
        "plt.imshow(x_train[num].reshape(28,28,3))\n",
        "plt.title(\"Random image from training data\")\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MQlw_baKxPQw"
      },
      "source": [
        "# Step 5: Model Building (CNN)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "obmDZia7Sxnn"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, Flatten, Dense, MaxPool2D\n",
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vsll2hd2wzuS",
        "outputId": "add27fd4-5383-4f03-8dd4-f1f7d50478cc"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
            "Wall time: 6.44 µs\n",
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_10 (Conv2D)          (None, 28, 28, 16)        448       \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPooling  (None, 14, 14, 16)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_12 (Bat  (None, 14, 14, 16)       64        \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_11 (Conv2D)          (None, 12, 12, 32)        4640      \n",
            "                                                                 \n",
            " conv2d_12 (Conv2D)          (None, 10, 10, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPooling  (None, 5, 5, 64)         0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " batch_normalization_13 (Bat  (None, 5, 5, 64)         256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " conv2d_13 (Conv2D)          (None, 3, 3, 128)         73856     \n",
            "                                                                 \n",
            " conv2d_14 (Conv2D)          (None, 1, 1, 256)         295168    \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 256)               0         \n",
            "                                                                 \n",
            " dropout_6 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 256)               65792     \n",
            "                                                                 \n",
            " batch_normalization_14 (Bat  (None, 256)              1024      \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dropout_7 (Dropout)         (None, 256)               0         \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 128)               32896     \n",
            "                                                                 \n",
            " batch_normalization_15 (Bat  (None, 128)              512       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_12 (Dense)            (None, 64)                8256      \n",
            "                                                                 \n",
            " batch_normalization_16 (Bat  (None, 64)               256       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dropout_8 (Dropout)         (None, 64)                0         \n",
            "                                                                 \n",
            " dense_13 (Dense)            (None, 32)                2080      \n",
            "                                                                 \n",
            " batch_normalization_17 (Bat  (None, 32)               128       \n",
            " chNormalization)                                                \n",
            "                                                                 \n",
            " dense_14 (Dense)            (None, 7)                 231       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 504,103\n",
            "Trainable params: 502,983\n",
            "Non-trainable params: 1,120\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "#https://keras.io/api/models/sequential/\n",
        "#https://keras.io/api/layers/core_layers/dense/\n",
        "#https://keras.io/api/layers/merging_layers/add/\n",
        "#https://keras.io/api/layers/convolution_layers/convolution2d\n",
        "#https://keras.io/api/layers/convolution_layers/convolution2d\n",
        "#https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization\n",
        "\n",
        "\n",
        "%time \n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(16, \n",
        "                 kernel_size = (3,3), \n",
        "                 input_shape = (28, 28, 3), \n",
        "                 activation = 'relu', \n",
        "                 padding = 'same'))\n",
        "\n",
        "model.add(MaxPool2D(pool_size = (2,2)))\n",
        "model.add(tf.keras.layers.BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(32, \n",
        "                 kernel_size = (3,3), \n",
        "                 activation = 'relu'))\n",
        "\n",
        "model.add(Conv2D(64, \n",
        "                 kernel_size = (3,3), \n",
        "                 activation = 'relu'))\n",
        "\n",
        "model.add(MaxPool2D(pool_size = (2,2)))\n",
        "\n",
        "model.add(tf.keras.layers.BatchNormalization())\n",
        "\n",
        "model.add(Conv2D(128, \n",
        "                 kernel_size = (3,3), \n",
        "                 activation = 'relu'))\n",
        "\n",
        "model.add(Conv2D(256, \n",
        "                 kernel_size = (3,3), \n",
        "                 activation = 'relu'))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(Dense(256,activation='relu'))\n",
        "\n",
        "model.add(tf.keras.layers.BatchNormalization())\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(Dense(128,activation='relu'))\n",
        "\n",
        "model.add(tf.keras.layers.BatchNormalization())\n",
        "model.add(Dense(64,activation='relu'))\n",
        "\n",
        "model.add(tf.keras.layers.BatchNormalization())\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(Dense(32,activation='relu'))\n",
        "\n",
        "model.add(tf.keras.layers.BatchNormalization())\n",
        "model.add(Dense(7,activation='softmax'))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KEKYqILDx4He"
      },
      "source": [
        "# Step 6: Setting Optimizer & Annealing\n",
        "\n",
        "---\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g4B4-f7ehUIW"
      },
      "outputs": [],
      "source": [
        "#reference: https://www.kaggle.com/dhruv1234/ham10000-skin-disease-classification\n",
        "\n",
        "callback = tf.keras.callbacks.ModelCheckpoint(filepath='best_model.h5',\n",
        "                                              monitor='val_acc', \n",
        "                                              mode='max',\n",
        "                                              verbose=1,\n",
        "                                              save_best_only=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wPmBHtmLxxR4",
        "outputId": "d01e165f-d76c-4e40-c963-359c09e5fb9d"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "CPU times: user 3 µs, sys: 0 ns, total: 3 µs\n",
            "Wall time: 6.44 µs\n"
          ]
        }
      ],
      "source": [
        "%time \n",
        "\n",
        "optimizer=tf.keras.optimizers.Adam(lr=0.001)\n",
        "\n",
        "model.compile(loss = 'sparse_categorical_crossentropy',\n",
        "              optimizer =optimizer,\n",
        "              metrics = ['accuracy'])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XXveDlVpu1Rv"
      },
      "source": [
        "# Step 7: Fitting the model"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1XgBEIh5hY63",
        "outputId": "d24b72b5-b707-4363-ccc7-bcc4cb3a83fd"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 1.1990 - accuracy: 0.5744WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 8s 24ms/step - loss: 1.1990 - accuracy: 0.5744 - val_loss: 2.6021 - val_accuracy: 0.0402\n",
            "Epoch 2/50\n",
            "236/238 [============================>.] - ETA: 0s - loss: 0.4169 - accuracy: 0.8618WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.4168 - accuracy: 0.8618 - val_loss: 1.7786 - val_accuracy: 0.3873\n",
            "Epoch 3/50\n",
            "236/238 [============================>.] - ETA: 0s - loss: 0.2618 - accuracy: 0.9108WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 4s 19ms/step - loss: 0.2621 - accuracy: 0.9108 - val_loss: 0.7704 - val_accuracy: 0.6166\n",
            "Epoch 4/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.2004 - accuracy: 0.9324WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 4s 19ms/step - loss: 0.2003 - accuracy: 0.9324 - val_loss: 0.7994 - val_accuracy: 0.6335\n",
            "Epoch 5/50\n",
            "236/238 [============================>.] - ETA: 0s - loss: 0.1560 - accuracy: 0.9472WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.1559 - accuracy: 0.9473 - val_loss: 0.8395 - val_accuracy: 0.5850\n",
            "Epoch 6/50\n",
            "236/238 [============================>.] - ETA: 0s - loss: 0.1329 - accuracy: 0.9538WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 4s 19ms/step - loss: 0.1331 - accuracy: 0.9538 - val_loss: 1.2162 - val_accuracy: 0.5410\n",
            "Epoch 7/50\n",
            "236/238 [============================>.] - ETA: 0s - loss: 0.1176 - accuracy: 0.9601WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 4s 19ms/step - loss: 0.1175 - accuracy: 0.9601 - val_loss: 0.5929 - val_accuracy: 0.7492\n",
            "Epoch 8/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0961 - accuracy: 0.9653WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0963 - accuracy: 0.9653 - val_loss: 0.8171 - val_accuracy: 0.6011\n",
            "Epoch 9/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0932 - accuracy: 0.9683WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 19ms/step - loss: 0.0932 - accuracy: 0.9683 - val_loss: 0.6400 - val_accuracy: 0.7062\n",
            "Epoch 10/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0779 - accuracy: 0.9724WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 19ms/step - loss: 0.0779 - accuracy: 0.9724 - val_loss: 0.6522 - val_accuracy: 0.7326\n",
            "Epoch 11/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0780 - accuracy: 0.9737WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 19ms/step - loss: 0.0780 - accuracy: 0.9737 - val_loss: 0.4177 - val_accuracy: 0.8322\n",
            "Epoch 12/50\n",
            "236/238 [============================>.] - ETA: 0s - loss: 0.0549 - accuracy: 0.9815WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 19ms/step - loss: 0.0548 - accuracy: 0.9815 - val_loss: 0.2289 - val_accuracy: 0.9010\n",
            "Epoch 13/50\n",
            "236/238 [============================>.] - ETA: 0s - loss: 0.0599 - accuracy: 0.9796WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 19ms/step - loss: 0.0599 - accuracy: 0.9796 - val_loss: 0.2284 - val_accuracy: 0.8958\n",
            "Epoch 14/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0608 - accuracy: 0.9805WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0608 - accuracy: 0.9805 - val_loss: 0.8634 - val_accuracy: 0.7003\n",
            "Epoch 15/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0593 - accuracy: 0.9799WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0593 - accuracy: 0.9799 - val_loss: 0.1789 - val_accuracy: 0.9300\n",
            "Epoch 16/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0403 - accuracy: 0.9869WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0404 - accuracy: 0.9869 - val_loss: 0.4147 - val_accuracy: 0.8716\n",
            "Epoch 17/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0363 - accuracy: 0.9877WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0363 - accuracy: 0.9878 - val_loss: 0.0858 - val_accuracy: 0.9623\n",
            "Epoch 18/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0432 - accuracy: 0.9861WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0432 - accuracy: 0.9861 - val_loss: 0.1961 - val_accuracy: 0.9199\n",
            "Epoch 19/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0399 - accuracy: 0.9867WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0402 - accuracy: 0.9867 - val_loss: 0.1060 - val_accuracy: 0.9625\n",
            "Epoch 20/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0345 - accuracy: 0.9884WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0345 - accuracy: 0.9884 - val_loss: 0.3731 - val_accuracy: 0.8930\n",
            "Epoch 21/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0312 - accuracy: 0.9895WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 19ms/step - loss: 0.0312 - accuracy: 0.9895 - val_loss: 0.1376 - val_accuracy: 0.9490\n",
            "Epoch 22/50\n",
            "236/238 [============================>.] - ETA: 0s - loss: 0.0315 - accuracy: 0.9893WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 19ms/step - loss: 0.0316 - accuracy: 0.9893 - val_loss: 0.1788 - val_accuracy: 0.9294\n",
            "Epoch 23/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0283 - accuracy: 0.9909WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0283 - accuracy: 0.9909 - val_loss: 0.3028 - val_accuracy: 0.8953\n",
            "Epoch 24/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0245 - accuracy: 0.9922WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0246 - accuracy: 0.9921 - val_loss: 0.5647 - val_accuracy: 0.8206\n",
            "Epoch 25/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0343 - accuracy: 0.9893WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0343 - accuracy: 0.9893 - val_loss: 0.9184 - val_accuracy: 0.7457\n",
            "Epoch 26/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0175 - accuracy: 0.9942WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0175 - accuracy: 0.9942 - val_loss: 0.3065 - val_accuracy: 0.9003\n",
            "Epoch 27/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0298 - accuracy: 0.9903WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0298 - accuracy: 0.9903 - val_loss: 0.5699 - val_accuracy: 0.8152\n",
            "Epoch 28/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0212 - accuracy: 0.9934WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0212 - accuracy: 0.9934 - val_loss: 0.9193 - val_accuracy: 0.7299\n",
            "Epoch 29/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0169 - accuracy: 0.9942WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0169 - accuracy: 0.9942 - val_loss: 0.1894 - val_accuracy: 0.9381\n",
            "Epoch 30/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0292 - accuracy: 0.9910WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0292 - accuracy: 0.9910 - val_loss: 0.1340 - val_accuracy: 0.9508\n",
            "Epoch 31/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0228 - accuracy: 0.9926WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0228 - accuracy: 0.9926 - val_loss: 0.1757 - val_accuracy: 0.9392\n",
            "Epoch 32/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0145 - accuracy: 0.9949WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0145 - accuracy: 0.9949 - val_loss: 0.1225 - val_accuracy: 0.9549\n",
            "Epoch 33/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0142 - accuracy: 0.9955WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0142 - accuracy: 0.9955 - val_loss: 0.3244 - val_accuracy: 0.8890\n",
            "Epoch 34/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0319 - accuracy: 0.9903WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0319 - accuracy: 0.9903 - val_loss: 0.8834 - val_accuracy: 0.7906\n",
            "Epoch 35/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0170 - accuracy: 0.9952WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0170 - accuracy: 0.9952 - val_loss: 0.1086 - val_accuracy: 0.9612\n",
            "Epoch 36/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0124 - accuracy: 0.9959WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0124 - accuracy: 0.9959 - val_loss: 1.6297 - val_accuracy: 0.6708\n",
            "Epoch 37/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0142 - accuracy: 0.9956WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0142 - accuracy: 0.9956 - val_loss: 0.1980 - val_accuracy: 0.9438\n",
            "Epoch 38/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0174 - accuracy: 0.9945WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0174 - accuracy: 0.9945 - val_loss: 0.5500 - val_accuracy: 0.8686\n",
            "Epoch 39/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0145 - accuracy: 0.9958WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0145 - accuracy: 0.9958 - val_loss: 0.7592 - val_accuracy: 0.7896\n",
            "Epoch 40/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0116 - accuracy: 0.9973WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0116 - accuracy: 0.9973 - val_loss: 0.2226 - val_accuracy: 0.9189\n",
            "Epoch 41/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0219 - accuracy: 0.9935WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0219 - accuracy: 0.9935 - val_loss: 0.1346 - val_accuracy: 0.9625\n",
            "Epoch 42/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0172 - accuracy: 0.9951WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0172 - accuracy: 0.9951 - val_loss: 0.1400 - val_accuracy: 0.9521\n",
            "Epoch 43/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0145 - accuracy: 0.9957WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0144 - accuracy: 0.9957 - val_loss: 0.0915 - val_accuracy: 0.9698\n",
            "Epoch 44/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0132 - accuracy: 0.9960WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0132 - accuracy: 0.9960 - val_loss: 0.0998 - val_accuracy: 0.9661\n",
            "Epoch 45/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0127 - accuracy: 0.9960WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0127 - accuracy: 0.9960 - val_loss: 0.6396 - val_accuracy: 0.8156\n",
            "Epoch 46/50\n",
            "237/238 [============================>.] - ETA: 0s - loss: 0.0111 - accuracy: 0.9968WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0111 - accuracy: 0.9967 - val_loss: 0.2005 - val_accuracy: 0.9190\n",
            "Epoch 47/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0173 - accuracy: 0.9946WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 20ms/step - loss: 0.0173 - accuracy: 0.9946 - val_loss: 0.7843 - val_accuracy: 0.8056\n",
            "Epoch 48/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0136 - accuracy: 0.9961WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0136 - accuracy: 0.9961 - val_loss: 0.1037 - val_accuracy: 0.9623\n",
            "Epoch 49/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0090 - accuracy: 0.9972WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0090 - accuracy: 0.9972 - val_loss: 0.2112 - val_accuracy: 0.9287\n",
            "Epoch 50/50\n",
            "238/238 [==============================] - ETA: 0s - loss: 0.0066 - accuracy: 0.9981WARNING:tensorflow:Can save best model only with val_acc available, skipping.\n",
            "238/238 [==============================] - 5s 21ms/step - loss: 0.0066 - accuracy: 0.9981 - val_loss: 0.0176 - val_accuracy: 0.9961\n",
            "Duration: 0:04:24.568061\n"
          ]
        }
      ],
      "source": [
        "from datetime import datetime\n",
        "\n",
        "start_time = datetime.now()\n",
        "\n",
        "history = model.fit(x_train,\n",
        "                    y_train,\n",
        "                    validation_split=0.2,\n",
        "                    batch_size = 128,\n",
        "                    epochs = 50,\n",
        "                    shuffle=True,\n",
        "                    callbacks=[callback])\n",
        "\n",
        "end_time = datetime.now()\n",
        "\n",
        "print('Duration: {}'.format(end_time - start_time))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nfIapcND3kGk"
      },
      "source": [
        "# Step 8: Model Evaluation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "qieCeDDGnEh3",
        "outputId": "52fdd55f-ea6b-41f6-bad6-874ca1236128"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plot of accuracy vs epoch\n",
        "\n",
        "plt.plot(history.history['accuracy'])\n",
        "\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "\n",
        "plt.title('model accuracy')\n",
        "\n",
        "plt.ylabel('accuracy')\n",
        "\n",
        "plt.xlabel('epoch')\n",
        "\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "8gK_9yQotuky",
        "outputId": "28e090aa-f2da-439f-b06a-daf0aaefaa15"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "#plot of loss vs epoch\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "\n",
        "plt.plot(history.history['val_loss'])\n",
        "\n",
        "plt.title('model loss')\n",
        "\n",
        "plt.ylabel('loss')\n",
        "\n",
        "plt.xlabel('epoch')\n",
        "\n",
        "plt.legend(['train', 'val'], loc='upper left')\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "w9uqTVqftz1w"
      },
      "outputs": [],
      "source": [
        "model.load_weights('best_model.h5')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lzqOpvxMt2Xb",
        "outputId": "5fdc5e94-cac2-41ba-d569-3a230c4eea63"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "63/63 - 1s - loss: 0.6087 - accuracy: 0.8842 - 650ms/epoch - 10ms/step\n"
          ]
        }
      ],
      "source": [
        "x_test=np.array(x_test).reshape(-1,28,28,3)\n",
        "\n",
        "loss, acc = model.evaluate(x_test, y_test, verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KHh_xm9e0kgT"
      },
      "outputs": [],
      "source": [
        "from sklearn.metrics import confusion_matrix\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "y_pred = np.argmax(y_pred, axis=1)\n",
        "\n",
        "conf_mat = confusion_matrix(y_test, y_pred)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4ahpCr92gEL",
        "outputId": "a640666c-359e-4c98-9c7d-17be2c7b5f8a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[[  56    5    4    0    4    0    1]\n",
            " [   2   86    7    1    5    0    2]\n",
            " [   6    3  211    1   32    0    4]\n",
            " [   0    0    2   20    0    0    0]\n",
            " [   4    9   31    2 1198    0   30]\n",
            " [   0    1    0    0    2   24    1]\n",
            " [   4    1   21    0   47    0  176]]\n"
          ]
        }
      ],
      "source": [
        "print(conf_mat)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "vYbAXbHR-JZC",
        "outputId": "5f74222e-1ee9-4796-e147-318824c57961"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots(figsize=(10,10)) \n",
        "\n",
        "\n",
        "ax = sns.heatmap(conf_mat, annot=True, cmap='Blues', linewidths=.9, ax=ax)\n",
        "\n",
        "ax.set_title('Confusion Matrix with labels\\n\\n');\n",
        "\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['0','1','2','3','4','5','6'])\n",
        "\n",
        "ax.yaxis.set_ticklabels(['0','1','2','3','4','5','6'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "LSI3BUvg2o4R",
        "outputId": "e5b749b9-f01f-49ab-ef93-f754e48bce07"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 720x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "fig, ax = plt.subplots(figsize=(10,10)) \n",
        "\n",
        "\n",
        "ax = sns.heatmap(conf_mat/np.sum(conf_mat), annot=True, fmt='.2%', cmap='Blues', linewidths=.9, ax=ax)\n",
        "\n",
        "ax.set_title('Confusion Matrix with labels\\n\\n');\n",
        "\n",
        "ax.set_xlabel('\\nPredicted Values')\n",
        "\n",
        "ax.set_ylabel('Actual Values ');\n",
        "\n",
        "ax.xaxis.set_ticklabels(['0','1','2','3','4','5','6'])\n",
        "\n",
        "ax.yaxis.set_ticklabels(['0','1','2','3','4','5','6'])\n",
        "\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tBWa4PFe8qCQ",
        "outputId": "bbda8134-01c6-41a7-f042-ea382666cce9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[9.4849083e-06 2.5722728e-05 4.0849736e-05 5.5863543e-06 9.9334717e-01\n",
            " 1.0107847e-05 6.5611666e-03]\n",
            "('nv', ' melanocytic nevi')\n"
          ]
        }
      ],
      "source": [
        "#https://pillow.readthedocs.io/en/stable/\n",
        "\n",
        "import PIL\n",
        "\n",
        "image=PIL.Image.open('test.jpg')\n",
        "\n",
        "image=image.resize((28,28))\n",
        "\n",
        "img=x_test[1]\n",
        "\n",
        "img=np.array(image).reshape(-1,28,28,3)\n",
        "\n",
        "result=model.predict(img)\n",
        "\n",
        "print(result[0])\n",
        "\n",
        "result=result.tolist()\n",
        "\n",
        "max_prob=max(result[0])\n",
        "\n",
        "class_ind=result[0].index(max_prob)\n",
        "\n",
        "print(classes[class_ind])"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "collapsed_sections": [],
      "name": "Skin_Cancer_Detection.ipynb",
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.8.8"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}