ipl.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vofv7t4teVg5"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D , MaxPool2D , Flatten , Dropout \n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "\n",
        "from sklearn.metrics import classification_report, confusion_matrix\n",
        "\n",
        "import tensorflow as tf\n",
        "\n",
        "import cv2\n",
        "import os\n",
        "\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "oVGH098je8mx"
      },
      "outputs": [],
      "source": [
        "labels = ['logo', 'ads']\n",
        "img_size = 90\n",
        "def get_data(data_dir):\n",
        "    data = [] \n",
        "    for label in labels: \n",
        "        path = os.path.join(data_dir, label)\n",
        "        class_num = labels.index(label)\n",
        "        for img in os.listdir(path):\n",
        "            if img.endswith('png'):\n",
        "              img_arr = cv2.imread(os.path.join(path, img))[...,::-1] #convert BGR to RGB format\n",
        "              resized_arr = cv2.resize(img_arr, (img_size, img_size)) # Reshaping images to preferred size\n",
        "              data.append([resized_arr, class_num])\n",
        "    return np.array(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {},
      "outputs": [],
      "source": [
        "# auth for google drive, needed to run only once\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TcnFZDQfcP8T",
        "outputId": "addb5b29-69e0-4937-9b97-93c6c35f6ba4"
      },
      "outputs": [],
      "source": [
        "!unzip ./drive/MyDrive/ipl-assets/upload.zip -d ./drive/MyDrive/ipl-assets/"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8sE62D8egNhe",
        "outputId": "59df7bd9-e519-4fed-9796-0fc94f8705b9"
      },
      "outputs": [],
      "source": [
        "train = get_data('./drive/MyDrive/ipl-assets/upload/train')\n",
        "val = get_data('./drive/MyDrive/ipl-assets/upload/test')\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "MgpWDGrLj64W"
      },
      "outputs": [],
      "source": [
        "x_train = []\n",
        "y_train = []\n",
        "x_val = []\n",
        "y_val = []\n",
        "\n",
        "for feature, label in train:\n",
        "  x_train.append(feature)\n",
        "  y_train.append(label)\n",
        "\n",
        "for feature, label in val:\n",
        "  x_val.append(feature)\n",
        "  y_val.append(label)\n",
        "\n",
        "# Normalize the data\n",
        "x_train = np.array(x_train) / 255\n",
        "x_val = np.array(x_val) / 255\n",
        "\n",
        "x_train.reshape(-1, img_size, img_size, 1)\n",
        "y_train = np.array(y_train)\n",
        "\n",
        "x_val.reshape(-1, img_size, img_size, 1)\n",
        "y_val = np.array(y_val)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "s0iM_Dhzkoyk"
      },
      "outputs": [],
      "source": [
        "datagen = ImageDataGenerator(\n",
        "        featurewise_center=False,  # set input mean to 0 over the dataset\n",
        "        samplewise_center=False,  # set each sample mean to 0\n",
        "        featurewise_std_normalization=False,  # divide inputs by std of the dataset\n",
        "        samplewise_std_normalization=False,  # divide each input by its std\n",
        "        zca_whitening=False,  # apply ZCA whitening\n",
        "        rotation_range = 30,  # randomly rotate images in the range (degrees, 0 to 180)\n",
        "        zoom_range = 0.2, # Randomly zoom image \n",
        "        width_shift_range=0.1,  # randomly shift images horizontally (fraction of total width)\n",
        "        height_shift_range=0.1,  # randomly shift images vertically (fraction of total height)\n",
        "        horizontal_flip = True,  # randomly flip images\n",
        "        vertical_flip=False)  # randomly flip images\n",
        "\n",
        "\n",
        "datagen.fit(x_train)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R6op5Pluk7E8",
        "outputId": "d544bd27-0190-41ae-d3bf-d5714cb11e86"
      },
      "outputs": [],
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(32,3,padding=\"same\", activation=\"relu\", input_shape=(img_size,img_size,3)))\n",
        "model.add(MaxPool2D())\n",
        "\n",
        "model.add(Conv2D(32, 3, padding=\"same\", activation=\"relu\"))\n",
        "model.add(MaxPool2D())\n",
        "\n",
        "model.add(Conv2D(64, 3, padding=\"same\", activation=\"relu\"))\n",
        "model.add(MaxPool2D())\n",
        "model.add(Dropout(0.4))\n",
        "\n",
        "model.add(Flatten())\n",
        "model.add(Dense(128,activation=\"relu\"))\n",
        "model.add(Dense(len(labels), activation=\"softmax\"))\n",
        "\n",
        "model.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MCs1GInYlESq",
        "outputId": "bb9db18a-5921-4d2c-b577-d9c2c0ce3bf3"
      },
      "outputs": [],
      "source": [
        "opt = Adam(lr=0.000001)\n",
        "model.compile(optimizer = opt , loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True) , metrics = ['accuracy'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fwDMSPBilI3y",
        "outputId": "9e4c0544-a5f7-4d1a-9d77-ed76d7cbc419"
      },
      "outputs": [],
      "source": [
        "history = model.fit(x_train,y_train,epochs = 500 , validation_data = (x_val, y_val))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 435
        },
        "id": "uYZhbm3hpY5i",
        "outputId": "cedefdb9-83cf-412d-a48a-304afaa3ed4e"
      },
      "outputs": [],
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "\n",
        "epochs_range = range(500)\n",
        "\n",
        "plt.figure(figsize=(15, 15))\n",
        "plt.subplot(2, 2, 1)\n",
        "plt.plot(epochs_range, acc, label='Training Accuracy')\n",
        "plt.plot(epochs_range, val_acc, label='Validation Accuracy')\n",
        "plt.legend(loc='lower right')\n",
        "plt.title('Training and Validation Accuracy')\n",
        "\n",
        "plt.subplot(2, 2, 2)\n",
        "plt.plot(epochs_range, loss, label='Training Loss')\n",
        "plt.plot(epochs_range, val_loss, label='Validation Loss')\n",
        "plt.legend(loc='upper right')\n",
        "plt.title('Training and Validation Loss')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "96D_sGRTEY6U",
        "outputId": "11938784-a9fa-4d5a-ddf7-c78f097118f5"
      },
      "outputs": [],
      "source": [
        "model.save('./drive/MyDrive/ipl_model')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MUg_NUvIkHbJ",
        "outputId": "54d43903-7584-4f8a-9e1d-5ff02b24123c"
      },
      "outputs": [],
      "source": [
        "predictions = model.predict(x_val)\n",
        "predictions"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "ipl.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}