train.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "main_Attempt2.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "94c2d3e8201dfce7460585f5b03ed3e477bbc91e7814b2e8adc32983670ef3a6"
    },
    "kernelspec": {
      "display_name": "Python 3.8.5 64-bit ('MiniProjectSem3': conda)",
      "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": "3.8.12"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7T1HwcJyENJC"
      },
      "source": [
        "## Import packages"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5j-TJN0FKyZ",
        "outputId": "9e3303df-9743-4eb8-ad8e-8085f3e44896"
      },
      "source": [
        "!pip install mediapipe"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: mediapipe in /usr/local/lib/python3.7/dist-packages (0.8.8)\n",
            "Requirement already satisfied: opencv-contrib-python in /usr/local/lib/python3.7/dist-packages (from mediapipe) (4.1.2.30)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from mediapipe) (1.19.5)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from mediapipe) (1.15.0)\n",
            "Requirement already satisfied: attrs>=19.1.0 in /usr/local/lib/python3.7/dist-packages (from mediapipe) (21.2.0)\n",
            "Requirement already satisfied: protobuf>=3.11.4 in /usr/local/lib/python3.7/dist-packages (from mediapipe) (3.17.3)\n",
            "Requirement already satisfied: wheel in /usr/local/lib/python3.7/dist-packages (from mediapipe) (0.37.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.7/dist-packages (from mediapipe) (0.12.0)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from mediapipe) (3.2.2)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mediapipe) (0.10.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mediapipe) (2.4.7)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mediapipe) (1.3.2)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->mediapipe) (2.8.2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZgPDPwmiDi3F"
      },
      "source": [
        "import cv2\n",
        "import time\n",
        "import numpy as np\n",
        "import mediapipe as mp\n",
        "import os\n",
        "import tensorflow as tf\n",
        "from tqdm import tqdm\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, Flatten, Activation, Dropout"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DeaTW288Fhpk"
      },
      "source": [
        "## Get Dataset"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfVErpkZFmJ0",
        "outputId": "182029d3-a76f-4d63-e88e-6e4a0a84406d"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uCRh-E1FFtju",
        "outputId": "a0c30913-d05f-42cc-c38d-0f7f3298539b"
      },
      "source": [
        "!unzip \"/content/drive/MyDrive/Datasets/Dataset.zip\""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
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            "  inflating: data/without_mask/without_mask_999.jpg  \n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z7X3oemEFJIA"
      },
      "source": [
        "## Model Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Jkc8HwKLRO2N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "48b7d44f-b133-4cd8-fe0f-4aa03937da16"
      },
      "source": [
        "img_size = 100\n",
        "datadir = r'data'  # root data directory\n",
        "CATEGORIES = os.listdir(datadir)\n",
        "print(CATEGORIES)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['without_mask', 'with_mask']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K2jkYCcPFJIB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1b3c7fe-99db-416b-bee4-fe142b8dbda3"
      },
      "source": [
        "# Define two empty list to contain image data\n",
        "x, y = [], []\n",
        "\n",
        "# Modifies all the images to one size for better results\n",
        "def PreProcess():\n",
        "    for category in CATEGORIES:\n",
        "        path = os.path.join(datadir, category)\n",
        "        classIndex = CATEGORIES.index(category)\n",
        "        print(path)\n",
        "        for imgs in tqdm(os.listdir(path)):\n",
        "            img_arr = cv2.imread(os.path.join(path, imgs))\n",
        "            \n",
        "            # resize the image\n",
        "            resized_array = cv2.resize(img_arr, (img_size, img_size))\n",
        "            #cv2.imshow(\"images\", resized_array)\n",
        "            cv2.waitKey(1)\n",
        "            resized_array = resized_array/255.0\n",
        "            x.append(resized_array)\n",
        "            y.append(classIndex)\n",
        "          \n",
        "PreProcess()\n",
        "cv2.destroyAllWindows()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data/without_mask\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3828/3828 [00:04<00:00, 783.89it/s]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "data/with_mask\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 3725/3725 [00:13<00:00, 266.72it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tu4eQRUeFJIB"
      },
      "source": [
        "# Split data for training and testing\n",
        "X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.20, random_state=42)\n",
        "\n",
        "# Convert and resize the data to a numpy array\n",
        "X_train = np.array(X_train).reshape(-1, img_size, img_size, 3)\n",
        "y_train = np.array(y_train)\n",
        "X_test = np.array(X_test).reshape(-1, img_size, img_size, 3)\n",
        "y_test = np.array(y_test)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uVvMl3haFJIC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a94b296-d840-47f6-e20e-e1aeb48708a7"
      },
      "source": [
        "# Create the model architecture \n",
        "\n",
        "model = Sequential()\n",
        "\n",
        "model.add(Conv2D(64,(3, 3), input_shape=(img_size, img_size, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "model.add(Conv2D(256, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "model.add(Conv2D(128, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(Dropout(0.25))\n",
        "\n",
        "model.add(Conv2D(32, (3, 3)))\n",
        "model.add(Activation('relu'))\n",
        "model.add(MaxPooling2D(pool_size = (2, 2)))\n",
        "\n",
        "model.add(Dropout(0.25))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(100, activation='relu'))\n",
        "model.add(Dense(16, activation='relu'))\n",
        "\n",
        "model.add(Dense(len(CATEGORIES)))\n",
        "model.add(Activation('softmax'))\n",
        "\n",
        "# compile the model\n",
        "\n",
        "model.compile(optimizer='adam', loss = 'sparse_categorical_crossentropy', \n",
        "metrics=['accuracy'])\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "conv2d (Conv2D)              (None, 98, 98, 64)        1792      \n",
            "_________________________________________________________________\n",
            "activation (Activation)      (None, 98, 98, 64)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d (MaxPooling2D) (None, 49, 49, 64)        0         \n",
            "_________________________________________________________________\n",
            "conv2d_1 (Conv2D)            (None, 47, 47, 256)       147712    \n",
            "_________________________________________________________________\n",
            "activation_1 (Activation)    (None, 47, 47, 256)       0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_1 (MaxPooling2 (None, 23, 23, 256)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_2 (Conv2D)            (None, 21, 21, 128)       295040    \n",
            "_________________________________________________________________\n",
            "activation_2 (Activation)    (None, 21, 21, 128)       0         \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 21, 21, 128)       0         \n",
            "_________________________________________________________________\n",
            "conv2d_3 (Conv2D)            (None, 19, 19, 32)        36896     \n",
            "_________________________________________________________________\n",
            "activation_3 (Activation)    (None, 19, 19, 32)        0         \n",
            "_________________________________________________________________\n",
            "max_pooling2d_2 (MaxPooling2 (None, 9, 9, 32)          0         \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 9, 9, 32)          0         \n",
            "_________________________________________________________________\n",
            "flatten (Flatten)            (None, 2592)              0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 100)               259300    \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 16)                1616      \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 2)                 34        \n",
            "_________________________________________________________________\n",
            "activation_4 (Activation)    (None, 2)                 0         \n",
            "=================================================================\n",
            "Total params: 742,390\n",
            "Trainable params: 742,390\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qmks7DdjFJIC"
      },
      "source": [
        "## Train model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ZWl31nwFJIC",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "da84d057-c3c0-4305-b9ab-fd7bffa36d7b"
      },
      "source": [
        "batch_size = 32\n",
        "epochs = 10\n",
        "t1 = time.time()\n",
        "\n",
        "cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path, save_weights_only=True, verbose=1)\n",
        "\n",
        "# fit the model\n",
        "history = model.fit(X_train, y_train, batch_size = batch_size, epochs=epochs, validation_split=0.3, verbose = 1, callbacks=[cp_callback])\n",
        "model.save('{}.h5'.format(\"model\"))\n",
        "t2 = time.time()\n",
        "print('Time taken: ',t2-t1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "133/133 [==============================] - 270s 2s/step - loss: 0.0512 - accuracy: 0.9820 - val_loss: 0.1793 - val_accuracy: 0.9564\n",
            "Epoch 2/10\n",
            "133/133 [==============================] - 270s 2s/step - loss: 0.0279 - accuracy: 0.9898 - val_loss: 0.1345 - val_accuracy: 0.9630\n",
            "Epoch 3/10\n",
            "133/133 [==============================] - 271s 2s/step - loss: 0.0251 - accuracy: 0.9913 - val_loss: 0.1684 - val_accuracy: 0.9581\n",
            "Epoch 4/10\n",
            "133/133 [==============================] - 270s 2s/step - loss: 0.0257 - accuracy: 0.9917 - val_loss: 0.1641 - val_accuracy: 0.9520\n",
            "Epoch 5/10\n",
            "133/133 [==============================] - 270s 2s/step - loss: 0.0336 - accuracy: 0.9879 - val_loss: 0.1545 - val_accuracy: 0.9570\n",
            "Epoch 6/10\n",
            "133/133 [==============================] - 270s 2s/step - loss: 0.0160 - accuracy: 0.9943 - val_loss: 0.1708 - val_accuracy: 0.9559\n",
            "Epoch 7/10\n",
            "133/133 [==============================] - 270s 2s/step - loss: 0.0142 - accuracy: 0.9941 - val_loss: 0.1998 - val_accuracy: 0.9647\n",
            "Epoch 8/10\n",
            "133/133 [==============================] - 270s 2s/step - loss: 0.0523 - accuracy: 0.9827 - val_loss: 0.1666 - val_accuracy: 0.9614\n",
            "Epoch 9/10\n",
            "133/133 [==============================] - 269s 2s/step - loss: 0.0309 - accuracy: 0.9920 - val_loss: 0.2354 - val_accuracy: 0.9553\n",
            "Epoch 10/10\n",
            "133/133 [==============================] - 270s 2s/step - loss: 0.0243 - accuracy: 0.9924 - val_loss: 0.1846 - val_accuracy: 0.9625\n",
            "Time taken:  2725.3561301231384\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COAYJYc7FJID",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f6058a8a-da98-45a7-8601-e2191cc5e5bd"
      },
      "source": [
        "validation_loss, validation_accuracy = model.evaluate(X_test, y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "48/48 [==============================] - 23s 489ms/step - loss: 0.1613 - accuracy: 0.9596\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 301
        },
        "id": "lmytFX-SPfKo",
        "outputId": "eb5e4021-1896-4247-c3f1-83ccb91d0152"
      },
      "source": [
        "plt.plot(history.history['accuracy'], label = 'Accuracy', linestyle = '--' , color='crimson')\n",
        "plt.plot(history.history['val_accuracy'], label = 'Validation Accuracy', linestyle = 'dashdot', color='springgreen')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.ylim(0, 1)\n",
        "plt.xlim(0, 3)\n",
        "plt.legend(loc = \"lower right\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7f282f5a4c10>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}