DeepLearning.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "PhTWvEZZ_c3U"
      },
      "outputs": [],
      "source": [
        "#imports\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mimg\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "import cv2\n",
        "import PIL\n",
        "from PIL import Image\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "OrM7z2CCaabX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "9084183e-fa83-417d-cad0-d31853ccc4aa"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0026fc9e-f447-4e57-943f-1b27edcba208\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-0026fc9e-f447-4e57-943f-1b27edcba208\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script>// Copyright 2017 Google LLC\n",
              "//\n",
              "// Licensed under the Apache License, Version 2.0 (the \"License\");\n",
              "// you may not use this file except in compliance with the License.\n",
              "// You may obtain a copy of the License at\n",
              "//\n",
              "//      http://www.apache.org/licenses/LICENSE-2.0\n",
              "//\n",
              "// Unless required by applicable law or agreed to in writing, software\n",
              "// distributed under the License is distributed on an \"AS IS\" BASIS,\n",
              "// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
              "// See the License for the specific language governing permissions and\n",
              "// limitations under the License.\n",
              "\n",
              "/**\n",
              " * @fileoverview Helpers for google.colab Python module.\n",
              " */\n",
              "(function(scope) {\n",
              "function span(text, styleAttributes = {}) {\n",
              "  const element = document.createElement('span');\n",
              "  element.textContent = text;\n",
              "  for (const key of Object.keys(styleAttributes)) {\n",
              "    element.style[key] = styleAttributes[key];\n",
              "  }\n",
              "  return element;\n",
              "}\n",
              "\n",
              "// Max number of bytes which will be uploaded at a time.\n",
              "const MAX_PAYLOAD_SIZE = 100 * 1024;\n",
              "\n",
              "function _uploadFiles(inputId, outputId) {\n",
              "  const steps = uploadFilesStep(inputId, outputId);\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  // Cache steps on the outputElement to make it available for the next call\n",
              "  // to uploadFilesContinue from Python.\n",
              "  outputElement.steps = steps;\n",
              "\n",
              "  return _uploadFilesContinue(outputId);\n",
              "}\n",
              "\n",
              "// This is roughly an async generator (not supported in the browser yet),\n",
              "// where there are multiple asynchronous steps and the Python side is going\n",
              "// to poll for completion of each step.\n",
              "// This uses a Promise to block the python side on completion of each step,\n",
              "// then passes the result of the previous step as the input to the next step.\n",
              "function _uploadFilesContinue(outputId) {\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  const steps = outputElement.steps;\n",
              "\n",
              "  const next = steps.next(outputElement.lastPromiseValue);\n",
              "  return Promise.resolve(next.value.promise).then((value) => {\n",
              "    // Cache the last promise value to make it available to the next\n",
              "    // step of the generator.\n",
              "    outputElement.lastPromiseValue = value;\n",
              "    return next.value.response;\n",
              "  });\n",
              "}\n",
              "\n",
              "/**\n",
              " * Generator function which is called between each async step of the upload\n",
              " * process.\n",
              " * @param {string} inputId Element ID of the input file picker element.\n",
              " * @param {string} outputId Element ID of the output display.\n",
              " * @return {!Iterable<!Object>} Iterable of next steps.\n",
              " */\n",
              "function* uploadFilesStep(inputId, outputId) {\n",
              "  const inputElement = document.getElementById(inputId);\n",
              "  inputElement.disabled = false;\n",
              "\n",
              "  const outputElement = document.getElementById(outputId);\n",
              "  outputElement.innerHTML = '';\n",
              "\n",
              "  const pickedPromise = new Promise((resolve) => {\n",
              "    inputElement.addEventListener('change', (e) => {\n",
              "      resolve(e.target.files);\n",
              "    });\n",
              "  });\n",
              "\n",
              "  const cancel = document.createElement('button');\n",
              "  inputElement.parentElement.appendChild(cancel);\n",
              "  cancel.textContent = 'Cancel upload';\n",
              "  const cancelPromise = new Promise((resolve) => {\n",
              "    cancel.onclick = () => {\n",
              "      resolve(null);\n",
              "    };\n",
              "  });\n",
              "\n",
              "  // Wait for the user to pick the files.\n",
              "  const files = yield {\n",
              "    promise: Promise.race([pickedPromise, cancelPromise]),\n",
              "    response: {\n",
              "      action: 'starting',\n",
              "    }\n",
              "  };\n",
              "\n",
              "  cancel.remove();\n",
              "\n",
              "  // Disable the input element since further picks are not allowed.\n",
              "  inputElement.disabled = true;\n",
              "\n",
              "  if (!files) {\n",
              "    return {\n",
              "      response: {\n",
              "        action: 'complete',\n",
              "      }\n",
              "    };\n",
              "  }\n",
              "\n",
              "  for (const file of files) {\n",
              "    const li = document.createElement('li');\n",
              "    li.append(span(file.name, {fontWeight: 'bold'}));\n",
              "    li.append(span(\n",
              "        `(${file.type || 'n/a'}) - ${file.size} bytes, ` +\n",
              "        `last modified: ${\n",
              "            file.lastModifiedDate ? file.lastModifiedDate.toLocaleDateString() :\n",
              "                                    'n/a'} - `));\n",
              "    const percent = span('0% done');\n",
              "    li.appendChild(percent);\n",
              "\n",
              "    outputElement.appendChild(li);\n",
              "\n",
              "    const fileDataPromise = new Promise((resolve) => {\n",
              "      const reader = new FileReader();\n",
              "      reader.onload = (e) => {\n",
              "        resolve(e.target.result);\n",
              "      };\n",
              "      reader.readAsArrayBuffer(file);\n",
              "    });\n",
              "    // Wait for the data to be ready.\n",
              "    let fileData = yield {\n",
              "      promise: fileDataPromise,\n",
              "      response: {\n",
              "        action: 'continue',\n",
              "      }\n",
              "    };\n",
              "\n",
              "    // Use a chunked sending to avoid message size limits. See b/62115660.\n",
              "    let position = 0;\n",
              "    do {\n",
              "      const length = Math.min(fileData.byteLength - position, MAX_PAYLOAD_SIZE);\n",
              "      const chunk = new Uint8Array(fileData, position, length);\n",
              "      position += length;\n",
              "\n",
              "      const base64 = btoa(String.fromCharCode.apply(null, chunk));\n",
              "      yield {\n",
              "        response: {\n",
              "          action: 'append',\n",
              "          file: file.name,\n",
              "          data: base64,\n",
              "        },\n",
              "      };\n",
              "\n",
              "      let percentDone = fileData.byteLength === 0 ?\n",
              "          100 :\n",
              "          Math.round((position / fileData.byteLength) * 100);\n",
              "      percent.textContent = `${percentDone}% done`;\n",
              "\n",
              "    } while (position < fileData.byteLength);\n",
              "  }\n",
              "\n",
              "  // All done.\n",
              "  yield {\n",
              "    response: {\n",
              "      action: 'complete',\n",
              "    }\n",
              "  };\n",
              "}\n",
              "\n",
              "scope.google = scope.google || {};\n",
              "scope.google.colab = scope.google.colab || {};\n",
              "scope.google.colab._files = {\n",
              "  _uploadFiles,\n",
              "  _uploadFilesContinue,\n",
              "};\n",
              "})(self);\n",
              "</script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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          ]
        }
      ],
      "source": [
        "!pip install -q kaggle\n",
        "from google.colab import files\n",
        "files.upload()\n",
        "!mkdir ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/\n",
        "!chmod 600 ~/.kaggle/kaggle.json\n",
        "!kaggle datasets download -d omkargurav/face-mask-dataset\n",
        "!unzip *.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "Fe0yr0nAaCeK"
      },
      "outputs": [],
      "source": [
        "#Sets up data set training\n",
        "with_mask_file=os.listdir('./data/with_mask')\n",
        "without_mask_file=os.listdir('./data/without_mask')\n",
        "with_mask_label=[1]*len(with_mask_file)\n",
        "without_mask_label=[0]*len(without_mask_file)\n",
        "labels=with_mask_label+without_mask_label"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "tYxNYACGbfEm",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "eaa709e1-0281-45eb-ea6d-ddc08e59f8ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/PIL/Image.py:1054: UserWarning: Palette images with Transparency expressed in bytes should be converted to RGBA images\n",
            "  warnings.warn(\n"
          ]
        }
      ],
      "source": [
        "#Set up training split\n",
        "with_mask_path = './data/with_mask/'\n",
        "without_mask_path = './data/without_mask/'\n",
        "data = []\n",
        "\n",
        "for img_file in with_mask_file:\n",
        "    image = Image.open(with_mask_path + img_file)\n",
        "    image = image.resize((128,128))\n",
        "    image = image.convert('RGB')\n",
        "    image = np.array(image)\n",
        "    data.append(image)\n",
        "\n",
        "for img_file in without_mask_file:\n",
        "    image = Image.open(without_mask_path + img_file)\n",
        "    image = image.resize((128,128))\n",
        "    image = image.convert('RGB')\n",
        "    image = np.array(image)\n",
        "    data.append(image)\n",
        "\n",
        "X=np.array(data)\n",
        "Y=np.array(labels)\n",
        "x_train,x_test,y_train,y_test=train_test_split(X,Y,test_size=0.1,random_state=2)\n",
        "x_train_scl=x_train/255\n",
        "x_test_scl=x_test/255"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "sIOQEj41dGyD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4a0d454f-bd61-4e59-8c63-1065bdef06c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/layers/convolutional/base_conv.py:107: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
            "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
          ]
        }
      ],
      "source": [
        "model1=keras.Sequential()\n",
        "model1.add(keras.layers.Conv2D(32,kernel_size=(3,3),activation='relu',input_shape=(128,128,3)))\n",
        "model1.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model1.add(keras.layers.Conv2D(64,kernel_size=(3,3),activation='relu'))\n",
        "model1.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model1.add(keras.layers.Flatten())\n",
        "\n",
        "model1.add(keras.layers.Dense(128,activation='relu'))\n",
        "model1.add(keras.layers.Dropout(0.50))\n",
        "\n",
        "model1.add(keras.layers.Dense(64,activation='relu'))\n",
        "model1.add(keras.layers.Dropout(0.50))\n",
        "\n",
        "model1.add(keras.layers.Dense(2,activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "Yp0eV2h_dMzm"
      },
      "outputs": [],
      "source": [
        "model2=keras.Sequential()\n",
        "model2.add(keras.layers.Conv2D(32,kernel_size=(3,3),activation='elu',input_shape=(128,128,3)))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model2.add(keras.layers.Conv2D(64,kernel_size=(3,3),activation='elu'))\n",
        "model2.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model2.add(keras.layers.Flatten())\n",
        "\n",
        "model2.add(keras.layers.Dense(128,activation='elu'))\n",
        "model2.add(keras.layers.Dropout(0.50))\n",
        "\n",
        "model2.add(keras.layers.Dense(64,activation='elu'))\n",
        "model2.add(keras.layers.Dropout(0.50))\n",
        "\n",
        "model2.add(keras.layers.Dense(2,activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "U2VXVaYGdcUF"
      },
      "outputs": [],
      "source": [
        "model3=keras.Sequential()\n",
        "model3.add(keras.layers.Conv2D(32,kernel_size=(3,3),activation='leaky_relu',input_shape=(128,128,3)))\n",
        "model3.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model3.add(keras.layers.Conv2D(64,kernel_size=(3,3),activation='leaky_relu'))\n",
        "model3.add(keras.layers.MaxPooling2D(pool_size=(2,2)))\n",
        "\n",
        "model3.add(keras.layers.Flatten())\n",
        "\n",
        "model3.add(keras.layers.Dense(128,activation='leaky_relu'))\n",
        "model3.add(keras.layers.Dropout(0.50))\n",
        "\n",
        "model3.add(keras.layers.Dense(64,activation='leaky_relu'))\n",
        "model3.add(keras.layers.Dropout(0.50))\n",
        "\n",
        "model3.add(keras.layers.Dense(2,activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VCW7JqMKeOxL",
        "outputId": "3d7cd8d7-d471-4279-b510-5c466b582cd5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 805ms/step - accuracy: 0.6673 - loss: 0.7397 - val_accuracy: 0.8632 - val_loss: 0.3400\n",
            "Epoch 2/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m193s\u001b[0m 761ms/step - accuracy: 0.8775 - loss: 0.3194 - val_accuracy: 0.8485 - val_loss: 0.3468\n",
            "Epoch 3/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 755ms/step - accuracy: 0.8884 - loss: 0.2983 - val_accuracy: 0.8824 - val_loss: 0.2639\n",
            "Epoch 4/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 761ms/step - accuracy: 0.9063 - loss: 0.2331 - val_accuracy: 0.8897 - val_loss: 0.2486\n",
            "Epoch 5/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 760ms/step - accuracy: 0.9246 - loss: 0.1959 - val_accuracy: 0.8853 - val_loss: 0.2778\n",
            "Epoch 6/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 742ms/step - accuracy: 0.9066 - loss: 0.2411 - val_accuracy: 0.9074 - val_loss: 0.2456\n",
            "Epoch 7/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m144s\u001b[0m 748ms/step - accuracy: 0.9301 - loss: 0.1822 - val_accuracy: 0.8985 - val_loss: 0.2947\n",
            "Epoch 8/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m146s\u001b[0m 761ms/step - accuracy: 0.9397 - loss: 0.1474 - val_accuracy: 0.9074 - val_loss: 0.2589\n",
            "Epoch 9/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m207s\u001b[0m 787ms/step - accuracy: 0.9547 - loss: 0.1144 - val_accuracy: 0.9132 - val_loss: 0.2497\n",
            "Epoch 10/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m198s\u001b[0m 766ms/step - accuracy: 0.9647 - loss: 0.0975 - val_accuracy: 0.9162 - val_loss: 0.2817\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 173ms/step - accuracy: 0.9102 - loss: 0.2601\n",
            "The Accuracy =  91 %\n"
          ]
        }
      ],
      "source": [
        "model1.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "Model1= model1.fit(x_train_scl,y_train,validation_split=0.1,epochs=10)\n",
        "\n",
        "\n",
        "\n",
        "loss,acc=model1.evaluate(x_test_scl,y_test)\n",
        "print('The Accuracy = ',int(acc*100),'%')\n",
        "\n",
        "h1 = Model1"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ToRXlVSneUB8",
        "outputId": "31d498c3-5aba-410b-9cc5-3fe36ccace8d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 815ms/step - accuracy: 0.7337 - loss: 1.1903 - val_accuracy: 0.8779 - val_loss: 0.2867\n",
            "Epoch 2/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 821ms/step - accuracy: 0.8540 - loss: 0.3909 - val_accuracy: 0.8912 - val_loss: 0.2693\n",
            "Epoch 3/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 820ms/step - accuracy: 0.8858 - loss: 0.3043 - val_accuracy: 0.9074 - val_loss: 0.2546\n",
            "Epoch 4/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m159s\u001b[0m 828ms/step - accuracy: 0.8932 - loss: 0.2661 - val_accuracy: 0.9015 - val_loss: 0.2823\n",
            "Epoch 5/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 827ms/step - accuracy: 0.9135 - loss: 0.2302 - val_accuracy: 0.9044 - val_loss: 0.2588\n",
            "Epoch 6/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 830ms/step - accuracy: 0.9274 - loss: 0.2003 - val_accuracy: 0.9059 - val_loss: 0.2767\n",
            "Epoch 7/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 827ms/step - accuracy: 0.9374 - loss: 0.1601 - val_accuracy: 0.9088 - val_loss: 0.2350\n",
            "Epoch 8/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 829ms/step - accuracy: 0.9441 - loss: 0.1474 - val_accuracy: 0.9147 - val_loss: 0.2730\n",
            "Epoch 9/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 830ms/step - accuracy: 0.9547 - loss: 0.1148 - val_accuracy: 0.8941 - val_loss: 0.3547\n",
            "Epoch 10/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 820ms/step - accuracy: 0.9636 - loss: 0.1036 - val_accuracy: 0.9147 - val_loss: 0.3107\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m5s\u001b[0m 227ms/step - accuracy: 0.9175 - loss: 0.3270\n",
            "The Accuracy =  92 %\n"
          ]
        }
      ],
      "source": [
        "model2.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "Model2= model2.fit(x_train_scl,y_train,validation_split=0.1,epochs=10)\n",
        "\n",
        "\n",
        "\n",
        "loss,acc=model2.evaluate(x_test_scl,y_test)\n",
        "print('The Accuracy = ',int(acc*100),'%')\n",
        "\n",
        "h2 = Model2"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "S9-oNhplfIiZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "47bf59f8-4e68-4026-f28f-2eaf3023f9ce"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m156s\u001b[0m 799ms/step - accuracy: 0.9732 - loss: 0.0682 - val_accuracy: 0.9074 - val_loss: 0.4024\n",
            "Epoch 2/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 795ms/step - accuracy: 0.9796 - loss: 0.0716 - val_accuracy: 0.9132 - val_loss: 0.4128\n",
            "Epoch 3/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 787ms/step - accuracy: 0.9869 - loss: 0.0459 - val_accuracy: 0.9074 - val_loss: 0.2843\n",
            "Epoch 4/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 774ms/step - accuracy: 0.9802 - loss: 0.0686 - val_accuracy: 0.9250 - val_loss: 0.3521\n",
            "Epoch 5/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m202s\u001b[0m 773ms/step - accuracy: 0.9871 - loss: 0.0398 - val_accuracy: 0.9044 - val_loss: 0.4117\n",
            "Epoch 6/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 770ms/step - accuracy: 0.9898 - loss: 0.0414 - val_accuracy: 0.9176 - val_loss: 0.3622\n",
            "Epoch 7/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m203s\u001b[0m 773ms/step - accuracy: 0.9867 - loss: 0.0462 - val_accuracy: 0.9265 - val_loss: 0.3944\n",
            "Epoch 8/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m200s\u001b[0m 768ms/step - accuracy: 0.9882 - loss: 0.0321 - val_accuracy: 0.9176 - val_loss: 0.4020\n",
            "Epoch 9/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m149s\u001b[0m 777ms/step - accuracy: 0.9900 - loss: 0.0361 - val_accuracy: 0.9132 - val_loss: 0.4546\n",
            "Epoch 10/10\n",
            "\u001b[1m192/192\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m201s\u001b[0m 771ms/step - accuracy: 0.9930 - loss: 0.0222 - val_accuracy: 0.9191 - val_loss: 0.4745\n",
            "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 179ms/step - accuracy: 0.9334 - loss: 0.4517\n",
            "The Accuracy =  94 %\n"
          ]
        }
      ],
      "source": [
        "model3.compile(\n",
        "    optimizer='adam',\n",
        "    loss='sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")\n",
        "\n",
        "Model3= model3.fit(x_train_scl,y_train,validation_split=0.1,epochs=10)\n",
        "\n",
        "\n",
        "\n",
        "loss,acc=model3.evaluate(x_test_scl,y_test)\n",
        "print('The Accuracy = ',int(acc*100),'%')\n",
        "\n",
        "h3 = Model3"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(h1.history['loss'], label='train loss relu')\n",
        "plt.plot(h1.history['val_loss'], label='validation loss relu')\n",
        "plt.plot(h2.history['loss'], label='train loss elu')\n",
        "plt.plot(h2.history['val_loss'], label='validation loss elu')\n",
        "plt.plot(h3.history['loss'], label='train loss leaky relu')\n",
        "plt.plot(h3.history['val_loss'], label='validation loss leaky relu')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "96dRp6-xxSfx",
        "outputId": "0a6da3bc-e1e8-4170-bc05-08553bd94f84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(h1.history['accuracy'], label='train accuracy relu')\n",
        "plt.plot(h1.history['val_accuracy'], label='validation accuracy relu')\n",
        "plt.plot(h2.history['accuracy'], label='train accuracy elu')\n",
        "plt.plot(h2.history['val_accuracy'], label='validation accuracy elu')\n",
        "plt.plot(h3.history['accuracy'], label='train accuracy leaky relu')\n",
        "plt.plot(h3.history['val_accuracy'], label='validation accuracy leaky relu')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "RCUKHkihxyGt",
        "outputId": "4942cb40-debe-48d5-d23b-d88b21135306",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "YHjutMczxVT5"
      }
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}