Assignment01_Q1.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "mount_file_id": "1NVXRZ06Wd7OioUaONQhsELugTPh4qnLl",
      "authorship_tag": "ABX9TyMHO7GpdWJZ0O/UsPcDp0J5",
      "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_Q1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "I7XOGBfaUkYS",
        "outputId": "370f4c14-69b9-49b5-ea46-82ad18eecd11"
      },
      "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": "JetE1wYoYIes"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow import keras"
      ],
      "metadata": {
        "id": "Zngq9z7jdCtZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#image preprocessing\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "#all train images will be rescaled by 1./255\n",
        "datagen = ImageDataGenerator(rescale = 1./255, validation_split = 0.2)\n",
        "\n",
        "#flow tarining images in batches of 128 using train_datagen generator\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",
        "\n",
        "#apply predefined specification to test dataset\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": "e5X-5GKel4ja",
        "outputId": "41140b78-409c-4499-f695-758e6ceb96d2"
      },
      "execution_count": null,
      "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": [
        "#define the model\n",
        "model = tf.keras.models.Sequential([\n",
        "    #Note the input shape is the desired size of the image 300*300 with 3 bytes color\n",
        "    #This is the first convolution\n",
        "    tf.keras.layers.Conv2D(filters=16, kernel_size=3, activation='sigmoid', strides=1, input_shape=(124,124,3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "\n",
        "    #The second convolution\n",
        "    tf.keras.layers.Conv2D(filters=16, kernel_size=3, strides=1, activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(pool_size=2),\n",
        "\n",
        "    #flatten the results to feed into a DNN\n",
        "    tf.keras.layers.Flatten(),\n",
        "\n",
        "    #50 neuron hidden layer\n",
        "    tf.keras.layers.Dense(50, activation='relu'),\n",
        "\n",
        "    #5 output neuron. It will contain a value from\n",
        "    tf.keras.layers.Dense(5, activation='softmax'),\n",
        "\n",
        "])"
      ],
      "metadata": {
        "id": "JGN_3wmAdNXr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kKPUYzEQBuIr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#complie the model\n",
        "optimizer = tf.keras.optimizers.Adam(learning_rate = 0.001)\n",
        "model.compile(optimizer=optimizer,\n",
        "loss='categorical_crossentropy',\n",
        "metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "89wyLWVC39cV"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#to get sumamry of the model\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jlPo4MV_Byy1",
        "outputId": "e854e9af-6dfe-4b4f-b018-f376c429989b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d_4 (Conv2D)           (None, 122, 122, 16)      448       \n",
            "                                                                 \n",
            " max_pooling2d_4 (MaxPoolin  (None, 61, 61, 16)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 59, 59, 16)        2320      \n",
            "                                                                 \n",
            " max_pooling2d_5 (MaxPoolin  (None, 29, 29, 16)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " flatten_2 (Flatten)         (None, 13456)             0         \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 50)                672850    \n",
            "                                                                 \n",
            " dense_5 (Dense)             (None, 5)                 255       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 675873 (2.58 MB)\n",
            "Trainable params: 675873 (2.58 MB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#fit the model to train the model\n",
        "history = model.fit(\n",
        "    train_generator,\n",
        "    validation_data=validation_generator,\n",
        "    epochs=10\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hQfUfJ4j4u9Y",
        "outputId": "f07b7197-bbcf-49c7-d997-45d7a5fa38c5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "17/17 [==============================] - 5s 253ms/step - loss: 1.8776 - accuracy: 0.2447 - val_loss: 1.5934 - val_accuracy: 0.2656\n",
            "Epoch 2/10\n",
            "17/17 [==============================] - 3s 197ms/step - loss: 1.5892 - accuracy: 0.2677 - val_loss: 1.5946 - val_accuracy: 0.2656\n",
            "Epoch 3/10\n",
            "17/17 [==============================] - 4s 216ms/step - loss: 1.5929 - accuracy: 0.2677 - val_loss: 1.5841 - val_accuracy: 0.2656\n",
            "Epoch 4/10\n",
            "17/17 [==============================] - 3s 195ms/step - loss: 1.5913 - accuracy: 0.2486 - val_loss: 1.5894 - val_accuracy: 0.2656\n",
            "Epoch 5/10\n",
            "17/17 [==============================] - 3s 196ms/step - loss: 1.5862 - accuracy: 0.2677 - val_loss: 1.5906 - val_accuracy: 0.2656\n",
            "Epoch 6/10\n",
            "17/17 [==============================] - 3s 196ms/step - loss: 1.5866 - accuracy: 0.2677 - val_loss: 1.5827 - val_accuracy: 0.2656\n",
            "Epoch 7/10\n",
            "17/17 [==============================] - 4s 202ms/step - loss: 1.5840 - accuracy: 0.2677 - val_loss: 1.5823 - val_accuracy: 0.2656\n",
            "Epoch 8/10\n",
            "17/17 [==============================] - 5s 291ms/step - loss: 1.5904 - accuracy: 0.2677 - val_loss: 1.6005 - val_accuracy: 0.2734\n",
            "Epoch 9/10\n",
            "17/17 [==============================] - 10s 586ms/step - loss: 1.5884 - accuracy: 0.2677 - val_loss: 1.5813 - val_accuracy: 0.2656\n",
            "Epoch 10/10\n",
            "17/17 [==============================] - 8s 475ms/step - loss: 1.5837 - accuracy: 0.2677 - val_loss: 1.5797 - val_accuracy: 0.2656\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\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": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "l1n7Hoeh52V3",
        "outputId": "a9ea001f-09a3-447c-b3bc-e6451b65810e"
      },
      "execution_count": null,
      "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 accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='validation accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "plt.savefig('AccVal_acc')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "B25tulk38a2j",
        "outputId": "7437fde8-de5c-4623-a4d6-e8f759c5e1c9"
      },
      "execution_count": null,
      "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": "Gu4HPQyd9KwQ"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}