Assignment01_Q2.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "authorship_tag": "ABX9TyMyxst8eSrInoYiwvGbtpY8",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ksathperera/MLOM/blob/main/Assignment01_Q2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "G0gohLcV-xYQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4345e197-1fdb-4766-95a1-cc13429131c3"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_path='/content/drive/MyDrive/Colab Notebooks/MLOM/Assignments/Assignment_01'"
      ],
      "metadata": {
        "id": "rSqGeYLKAxN_"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "Xa2eR0MHBDZH"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "datagen = ImageDataGenerator(rescale = 1./255, validation_split = 0.2)\n",
        "\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    dataset_path,\n",
        "    target_size = (124,124),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'categorical',\n",
        "    subset = 'training',\n",
        "    shuffle = 'True')\n",
        "\n",
        "validation_generator = datagen.flow_from_directory(\n",
        "    dataset_path,\n",
        "    target_size = (124, 124),\n",
        "    batch_size = 32,\n",
        "    class_mode = 'categorical',\n",
        "    subset = 'validation',\n",
        "    shuffle = 'True')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5gJ4zaxHBFzY",
        "outputId": "96f73819-5a11-45ed-c54b-5db431f9c6ff"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 523 images belonging to 5 classes.\n",
            "Found 128 images belonging to 5 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "\n",
        "    tf.keras.layers.Conv2D(filters=16, kernel_size=3, activation='relu', strides=1, input_shape=(124,124,3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "\n",
        "\n",
        "    tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=2),\n",
        "\n",
        "    #newly added the third convolution\n",
        "    tf.keras.layers.Conv2D(filters=32, kernel_size=3, strides=1, activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=2),\n",
        "\n",
        "    #newly added the fourth convolution\n",
        "    tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=1, activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=2),\n",
        "\n",
        "    #newly added the fifth convolution\n",
        "    tf.keras.layers.Conv2D(filters=64, kernel_size=3, strides=1, activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=2),\n",
        "\n",
        "    tf.keras.layers.Flatten(),\n",
        "\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "\n",
        "    #newly added 512 neuron hidden layer\n",
        "    tf.keras.layers.Dense(512, activation=\"relu\"),\n",
        "\n",
        "    tf.keras.layers.Dropout(0.5),\n",
        "\n",
        "    tf.keras.layers.Dense(5, activation='softmax'),\n",
        "\n",
        "])"
      ],
      "metadata": {
        "id": "boNiIxIOFRdq"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001)\n",
        "model.compile(optimizer=optimizer,\n",
        "loss='categorical_crossentropy',\n",
        "metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "EQZEyuS1GsV_"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_generator,\n",
        "    validation_data=validation_generator,\n",
        "    #enhance the no of epochs\n",
        "    epochs=50\n",
        ")"
      ],
      "metadata": {
        "id": "zS6-ZcF9IaRI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "501e4721-33f4-4314-df29-ed7c3451890a"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "17/17 [==============================] - 10s 451ms/step - loss: 1.5989 - accuracy: 0.2639 - val_loss: 1.5850 - val_accuracy: 0.2656\n",
            "Epoch 2/50\n",
            "17/17 [==============================] - 8s 430ms/step - loss: 1.5836 - accuracy: 0.2677 - val_loss: 1.5775 - val_accuracy: 0.2656\n",
            "Epoch 3/50\n",
            "17/17 [==============================] - 9s 516ms/step - loss: 1.5787 - accuracy: 0.2677 - val_loss: 1.5776 - val_accuracy: 0.2656\n",
            "Epoch 4/50\n",
            "17/17 [==============================] - 6s 372ms/step - loss: 1.5750 - accuracy: 0.2696 - val_loss: 1.5710 - val_accuracy: 0.2656\n",
            "Epoch 5/50\n",
            "17/17 [==============================] - 6s 370ms/step - loss: 1.5686 - accuracy: 0.2658 - val_loss: 1.5642 - val_accuracy: 0.2656\n",
            "Epoch 6/50\n",
            "17/17 [==============================] - 9s 524ms/step - loss: 1.5441 - accuracy: 0.2639 - val_loss: 1.5538 - val_accuracy: 0.2734\n",
            "Epoch 7/50\n",
            "17/17 [==============================] - 6s 362ms/step - loss: 1.5342 - accuracy: 0.2639 - val_loss: 1.5325 - val_accuracy: 0.3125\n",
            "Epoch 8/50\n",
            "17/17 [==============================] - 9s 525ms/step - loss: 1.5121 - accuracy: 0.3193 - val_loss: 1.5056 - val_accuracy: 0.3828\n",
            "Epoch 9/50\n",
            "17/17 [==============================] - 9s 510ms/step - loss: 1.5170 - accuracy: 0.3117 - val_loss: 1.5449 - val_accuracy: 0.2656\n",
            "Epoch 10/50\n",
            "17/17 [==============================] - 8s 419ms/step - loss: 1.5257 - accuracy: 0.3059 - val_loss: 1.4859 - val_accuracy: 0.3672\n",
            "Epoch 11/50\n",
            "17/17 [==============================] - 9s 530ms/step - loss: 1.4770 - accuracy: 0.3499 - val_loss: 1.4200 - val_accuracy: 0.3594\n",
            "Epoch 12/50\n",
            "17/17 [==============================] - 7s 420ms/step - loss: 1.4228 - accuracy: 0.3728 - val_loss: 1.3995 - val_accuracy: 0.4062\n",
            "Epoch 13/50\n",
            "17/17 [==============================] - 8s 478ms/step - loss: 1.3901 - accuracy: 0.3977 - val_loss: 1.3617 - val_accuracy: 0.4375\n",
            "Epoch 14/50\n",
            "17/17 [==============================] - 9s 544ms/step - loss: 1.3702 - accuracy: 0.4187 - val_loss: 1.3329 - val_accuracy: 0.4531\n",
            "Epoch 15/50\n",
            "17/17 [==============================] - 6s 369ms/step - loss: 1.3103 - accuracy: 0.4493 - val_loss: 1.2825 - val_accuracy: 0.4688\n",
            "Epoch 16/50\n",
            "17/17 [==============================] - 8s 442ms/step - loss: 1.2432 - accuracy: 0.4895 - val_loss: 1.2086 - val_accuracy: 0.4609\n",
            "Epoch 17/50\n",
            "17/17 [==============================] - 9s 509ms/step - loss: 1.1513 - accuracy: 0.5163 - val_loss: 1.2535 - val_accuracy: 0.4609\n",
            "Epoch 18/50\n",
            "17/17 [==============================] - 7s 415ms/step - loss: 1.1973 - accuracy: 0.5010 - val_loss: 1.1168 - val_accuracy: 0.6016\n",
            "Epoch 19/50\n",
            "17/17 [==============================] - 9s 554ms/step - loss: 1.1001 - accuracy: 0.5698 - val_loss: 1.1037 - val_accuracy: 0.5781\n",
            "Epoch 20/50\n",
            "17/17 [==============================] - 7s 410ms/step - loss: 1.0135 - accuracy: 0.5966 - val_loss: 1.0609 - val_accuracy: 0.5469\n",
            "Epoch 21/50\n",
            "17/17 [==============================] - 9s 492ms/step - loss: 0.9378 - accuracy: 0.6386 - val_loss: 1.0387 - val_accuracy: 0.6250\n",
            "Epoch 22/50\n",
            "17/17 [==============================] - 8s 462ms/step - loss: 0.8837 - accuracy: 0.6444 - val_loss: 0.9995 - val_accuracy: 0.6172\n",
            "Epoch 23/50\n",
            "17/17 [==============================] - 7s 401ms/step - loss: 0.8740 - accuracy: 0.6520 - val_loss: 0.9746 - val_accuracy: 0.6328\n",
            "Epoch 24/50\n",
            "17/17 [==============================] - 9s 517ms/step - loss: 0.8996 - accuracy: 0.6444 - val_loss: 1.1164 - val_accuracy: 0.5781\n",
            "Epoch 25/50\n",
            "17/17 [==============================] - 6s 376ms/step - loss: 0.7707 - accuracy: 0.6998 - val_loss: 1.0791 - val_accuracy: 0.6016\n",
            "Epoch 26/50\n",
            "17/17 [==============================] - 9s 509ms/step - loss: 0.6949 - accuracy: 0.7208 - val_loss: 0.9350 - val_accuracy: 0.6406\n",
            "Epoch 27/50\n",
            "17/17 [==============================] - 7s 375ms/step - loss: 0.6819 - accuracy: 0.7361 - val_loss: 1.0437 - val_accuracy: 0.5859\n",
            "Epoch 28/50\n",
            "17/17 [==============================] - 6s 367ms/step - loss: 0.6538 - accuracy: 0.7361 - val_loss: 0.9582 - val_accuracy: 0.6328\n",
            "Epoch 29/50\n",
            "17/17 [==============================] - 9s 489ms/step - loss: 0.6262 - accuracy: 0.7572 - val_loss: 0.9044 - val_accuracy: 0.6953\n",
            "Epoch 30/50\n",
            "17/17 [==============================] - 8s 449ms/step - loss: 0.5935 - accuracy: 0.7744 - val_loss: 0.9538 - val_accuracy: 0.6875\n",
            "Epoch 31/50\n",
            "17/17 [==============================] - 7s 414ms/step - loss: 0.5329 - accuracy: 0.7916 - val_loss: 0.9392 - val_accuracy: 0.7109\n",
            "Epoch 32/50\n",
            "17/17 [==============================] - 7s 402ms/step - loss: 0.4650 - accuracy: 0.8031 - val_loss: 0.8412 - val_accuracy: 0.7188\n",
            "Epoch 33/50\n",
            "17/17 [==============================] - 10s 563ms/step - loss: 0.4966 - accuracy: 0.8317 - val_loss: 0.9928 - val_accuracy: 0.6797\n",
            "Epoch 34/50\n",
            "17/17 [==============================] - 7s 377ms/step - loss: 0.4699 - accuracy: 0.8184 - val_loss: 0.8633 - val_accuracy: 0.7188\n",
            "Epoch 35/50\n",
            "17/17 [==============================] - 9s 575ms/step - loss: 0.4203 - accuracy: 0.8585 - val_loss: 1.0826 - val_accuracy: 0.6953\n",
            "Epoch 36/50\n",
            "17/17 [==============================] - 9s 504ms/step - loss: 0.5257 - accuracy: 0.8050 - val_loss: 1.4145 - val_accuracy: 0.6328\n",
            "Epoch 37/50\n",
            "17/17 [==============================] - 7s 374ms/step - loss: 0.4954 - accuracy: 0.8107 - val_loss: 1.2267 - val_accuracy: 0.6406\n",
            "Epoch 38/50\n",
            "17/17 [==============================] - 10s 562ms/step - loss: 0.3623 - accuracy: 0.8853 - val_loss: 1.1269 - val_accuracy: 0.6641\n",
            "Epoch 39/50\n",
            "17/17 [==============================] - 8s 450ms/step - loss: 0.3595 - accuracy: 0.8681 - val_loss: 1.0454 - val_accuracy: 0.6953\n",
            "Epoch 40/50\n",
            "17/17 [==============================] - 7s 424ms/step - loss: 0.3537 - accuracy: 0.8662 - val_loss: 1.0271 - val_accuracy: 0.6875\n",
            "Epoch 41/50\n",
            "17/17 [==============================] - 9s 533ms/step - loss: 0.3105 - accuracy: 0.8910 - val_loss: 1.0390 - val_accuracy: 0.7344\n",
            "Epoch 42/50\n",
            "17/17 [==============================] - 7s 390ms/step - loss: 0.3116 - accuracy: 0.8815 - val_loss: 1.0661 - val_accuracy: 0.7188\n",
            "Epoch 43/50\n",
            "17/17 [==============================] - 8s 434ms/step - loss: 0.3121 - accuracy: 0.8872 - val_loss: 1.1806 - val_accuracy: 0.7109\n",
            "Epoch 44/50\n",
            "17/17 [==============================] - 10s 562ms/step - loss: 0.3113 - accuracy: 0.9006 - val_loss: 1.1823 - val_accuracy: 0.6875\n",
            "Epoch 45/50\n",
            "17/17 [==============================] - 8s 453ms/step - loss: 0.2574 - accuracy: 0.9235 - val_loss: 1.1049 - val_accuracy: 0.7266\n",
            "Epoch 46/50\n",
            "17/17 [==============================] - 7s 382ms/step - loss: 0.2934 - accuracy: 0.8853 - val_loss: 1.2631 - val_accuracy: 0.6875\n",
            "Epoch 47/50\n",
            "17/17 [==============================] - 8s 420ms/step - loss: 0.2936 - accuracy: 0.8815 - val_loss: 1.2389 - val_accuracy: 0.7188\n",
            "Epoch 48/50\n",
            "17/17 [==============================] - 9s 518ms/step - loss: 0.2766 - accuracy: 0.9006 - val_loss: 1.0654 - val_accuracy: 0.7266\n",
            "Epoch 49/50\n",
            "17/17 [==============================] - 7s 377ms/step - loss: 0.2674 - accuracy: 0.9006 - val_loss: 1.1812 - val_accuracy: 0.7109\n",
            "Epoch 50/50\n",
            "17/17 [==============================] - 6s 375ms/step - loss: 0.1924 - accuracy: 0.9254 - val_loss: 1.1751 - val_accuracy: 0.7422\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "#plot the loss\n",
        "plt.plot(history.history['loss'], label='train loss')\n",
        "plt.plot(history.history['val_loss'], label='validation loss')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('LossVal_loss')"
      ],
      "metadata": {
        "id": "LVMpAqCCIt2C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "outputId": "b4e9294d-06e9-46a7-cc79-80e9cfe04bb6"
      },
      "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": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plot the accuracy\n",
        "plt.plot(history.history['accuracy'], label='train acc')\n",
        "plt.plot(history.history['val_accuracy'], label='validation acc')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "RiGRiYy_N-0M",
        "outputId": "04852cf4-a342-49f7-bbae-c3f5ce378ea2"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "TvocyZMWOEDT"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}