{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "TPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ChetanKnowIt/PML_Notes/blob/main/SportsCelebrity.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": {
        "id": "8osKUPz5POr7"
      },
      "outputs": [],
      "source": [
        "!pip install -q kaggle"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!mkdir -p ~/.kaggle\n",
        "!cp kaggle.json ~/.kaggle/"
      ],
      "metadata": {
        "id": "gzNTlex5PtwE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!chmod 600 /root/.kaggle/kaggle.json"
      ],
      "metadata": {
        "id": "tWpY9RG6P5mJ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets list "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "laaZhq71QlAS",
        "outputId": "35185a1e-ce6d-4668-a9ad-2f4c7b014f42"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ref                                                           title                                               size  lastUpdated          downloadCount  voteCount  usabilityRating  \n",
            "------------------------------------------------------------  -------------------------------------------------  -----  -------------------  -------------  ---------  ---------------  \n",
            "meirnizri/covid19-dataset                                     COVID-19 Dataset                                     5MB  2022-11-13 15:47:17          15711        443  1.0              \n",
            "devrimtuner/list-of-moststreamed-songs-on-spotify             Top 100 Spotify Songs👑🎤🎧🎼                            3KB  2022-12-30 05:42:54            608         39  1.0              \n",
            "thedevastator/analyzing-credit-card-spending-habits-in-india  Credit Card Spending Habits in India               319KB  2022-12-14 07:30:37           1878         63  1.0              \n",
            "die9origephit/fifa-world-cup-2022-complete-dataset            Fifa World Cup 2022: Complete Dataset                7KB  2022-12-18 22:51:11           3647        127  1.0              \n",
            "michals22/coffee-dataset                                      Coffee dataset                                      24KB  2022-12-15 20:02:12           3991         90  1.0              \n",
            "heemalichaudhari/netflix-movies-and-series                    Netflix Movies and Series                            2MB  2022-12-22 13:34:22           1226         32  0.8235294        \n",
            "sejungjenn/spotify-best-songs-of-2022                         Spotify: Winner Tracks Audio Features🎹              38KB  2022-12-28 08:06:49            315         22  1.0              \n",
            "thedevastator/unlock-profits-with-e-commerce-sales-data       E-Commerce Sales Dataset                             6MB  2022-12-03 09:27:17           3269         79  1.0              \n",
            "aklimarimi/qs-world-ranked-universities-20182022              QS World ranked Universities (2018-2022)            51KB  2022-12-28 03:53:39            650         32  1.0              \n",
            "rajeshrampure/black-friday-sale                               Black Friday Sale                                    5MB  2022-12-24 09:37:49           1029         32  1.0              \n",
            "devrimtuner/highestpaid-athletes                              HIGHEST-PAID ATHLETES⚽️🏀🏈⚾️🥎🎾                        1KB  2022-12-29 01:29:51            312         27  1.0              \n",
            "heemalichaudhari/shopping                                     Shopping                                            12KB  2022-12-26 14:25:07            515         28  0.9411765        \n",
            "milanvaddoriya/old-car-price-prediction                       Old car price prediction                           105KB  2022-12-24 15:38:56            489         30  1.0              \n",
            "thedevastator/how-does-daily-yoga-impact-screen-time-habits   How Does Daily Yoga Impact Screen Time Habits       742B  2022-12-14 04:10:56            738         24  1.0              \n",
            "thedevastator/uncovering-factors-that-affect-used-car-prices  Used Cars                                           18MB  2022-12-06 13:36:08           1196         36  1.0              \n",
            "devrimtuner/list-of-mostfollowed-instagram-accounts           (TOP 50)List of most-followed Instagram accounts👑    2KB  2022-12-30 07:52:00            366         27  1.0              \n",
            "thedevastator/jobs-dataset-from-glassdoor                     Salary Prediction                                    3MB  2022-11-16 13:52:31           8568        182  1.0              \n",
            "dansbecker/melbourne-housing-snapshot                         Melbourne Housing Snapshot                         451KB  2018-06-05 12:52:24         103465       1191  0.7058824        \n",
            "mattop/best-selling-game-boy-video-games                      Best Selling Game Boy Video Games                    2KB  2022-12-17 18:41:38            395         27  0.9705882        \n",
            "rajeshrampure/zomato-dataset                                  Zomato Dataset                                      89MB  2022-12-23 07:38:07            628         28  1.0              \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!kaggle datasets download -d yaswanthgali/sport-celebrity-image-classification"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CVfzYYGYRGux",
        "outputId": "7b1ec38c-14e5-471c-f5db-a2fb61e4c2b1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading sport-celebrity-image-classification.zip to /content\n",
            "\r  0% 0.00/14.8M [00:00<?, ?B/s]\r 61% 9.00M/14.8M [00:00<00:00, 93.8MB/s]\n",
            "\r100% 14.8M/14.8M [00:00<00:00, 128MB/s] \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!unzip \"/content/sport-celebrity-image-classification.zip\" -d \"/content/drive/MyDrive/Kaggle Datasets\""
      ],
      "metadata": {
        "id": "ZuC5PzG_R56a"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "dir = \"/content/drive/MyDrive/Kaggle Datasets/Sports-celebrity images\""
      ],
      "metadata": {
        "id": "T794fWOuSw_C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "# All images will be rescaled by 1./255.\n",
        "datagen = ImageDataGenerator( rescale = 1.0/255. , validation_split=0.1, \n",
        "                             rotation_range=20,\n",
        "                             width_shift_range=0.2,\n",
        "                             height_shift_range=0.2,\n",
        "                             shear_range=60,\n",
        "                             horizontal_flip=True)\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    directory=dir, target_size=(100, 100), color_mode='rgb', class_mode='categorical', batch_size=32, \n",
        "    shuffle=True, seed=2022, subset='training')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ApqVOga8S8S5",
        "outputId": "3910659e-a2dd-4417-b3b7-f3cb75595c19"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 399 images belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "validation_generator = datagen.flow_from_directory(\n",
        "    directory=dir, target_size=(100, 100), color_mode='rgb', class_mode='categorical', \n",
        "    batch_size=32, shuffle=True, seed=2022, subset='validation')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OtRfJvrKTEfp",
        "outputId": "ae2d49a8-4f6e-4057-aa0d-33d710df89b6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 42 images belonging to 4 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf"
      ],
      "metadata": {
        "id": "foN7Dk5ZVxXB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.random.set_seed(2022)\n",
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(16, (3,3), activation='relu', input_shape=(100, 100, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2,1),\n",
        "    tf.keras.layers.Flatten(), \n",
        "    tf.keras.layers.Dense(1024, activation='relu'), \n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Dense(512, activation='relu'), \n",
        "    tf.keras.layers.BatchNormalization(),\n",
        "    tf.keras.layers.Dense(4, activation='softmax')  \n",
        "])\n",
        "from tensorflow.keras.callbacks import EarlyStopping\n",
        "monitor = EarlyStopping(monitor='val_loss', min_delta=1e-3, patience=5,verbose=3,restore_best_weights=True)\n",
        "model.compile(optimizer='adam',loss='categorical_crossentropy', metrics = ['accuracy'])"
      ],
      "metadata": {
        "id": "VOWJOy4hV0MB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(train_generator, validation_data=validation_generator, epochs=50,\n",
        "                    verbose=2, callbacks=[monitor])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9dpFB_8tWXej",
        "outputId": "14c6b4cb-0856-4723-d1b9-c9880f7db52c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.8/dist-packages/keras/preprocessing/image.py:1663: UserWarning: This ImageDataGenerator specifies `featurewise_center`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n",
            "/usr/local/lib/python3.8/dist-packages/keras/preprocessing/image.py:1671: UserWarning: This ImageDataGenerator specifies `featurewise_std_normalization`, but it hasn't been fit on any training data. Fit it first by calling `.fit(numpy_data)`.\n",
            "  warnings.warn('This ImageDataGenerator specifies '\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "13/13 - 28s - loss: 1.7844 - accuracy: 0.3860 - val_loss: 6.7128 - val_accuracy: 0.3810 - 28s/epoch - 2s/step\n",
            "Epoch 2/50\n",
            "13/13 - 26s - loss: 1.1777 - accuracy: 0.5388 - val_loss: 3.2453 - val_accuracy: 0.3333 - 26s/epoch - 2s/step\n",
            "Epoch 3/50\n",
            "13/13 - 26s - loss: 1.0512 - accuracy: 0.5514 - val_loss: 3.0801 - val_accuracy: 0.4524 - 26s/epoch - 2s/step\n",
            "Epoch 4/50\n",
            "13/13 - 26s - loss: 1.0572 - accuracy: 0.5664 - val_loss: 2.9439 - val_accuracy: 0.4286 - 26s/epoch - 2s/step\n",
            "Epoch 5/50\n",
            "13/13 - 26s - loss: 0.9758 - accuracy: 0.5940 - val_loss: 1.2449 - val_accuracy: 0.5952 - 26s/epoch - 2s/step\n",
            "Epoch 6/50\n",
            "13/13 - 26s - loss: 0.8903 - accuracy: 0.6491 - val_loss: 1.0821 - val_accuracy: 0.5714 - 26s/epoch - 2s/step\n",
            "Epoch 7/50\n",
            "13/13 - 25s - loss: 0.9012 - accuracy: 0.6366 - val_loss: 1.7198 - val_accuracy: 0.5238 - 25s/epoch - 2s/step\n",
            "Epoch 8/50\n",
            "13/13 - 25s - loss: 0.8918 - accuracy: 0.6366 - val_loss: 1.7779 - val_accuracy: 0.4048 - 25s/epoch - 2s/step\n",
            "Epoch 9/50\n",
            "13/13 - 33s - loss: 0.9311 - accuracy: 0.6541 - val_loss: 1.2837 - val_accuracy: 0.5476 - 33s/epoch - 3s/step\n",
            "Epoch 10/50\n",
            "13/13 - 29s - loss: 0.8496 - accuracy: 0.6617 - val_loss: 1.2064 - val_accuracy: 0.5714 - 29s/epoch - 2s/step\n",
            "Epoch 11/50\n",
            "13/13 - 27s - loss: 0.8027 - accuracy: 0.6767 - val_loss: 0.9762 - val_accuracy: 0.6429 - 27s/epoch - 2s/step\n",
            "Epoch 12/50\n",
            "13/13 - 25s - loss: 0.8506 - accuracy: 0.6591 - val_loss: 1.1437 - val_accuracy: 0.4762 - 25s/epoch - 2s/step\n",
            "Epoch 13/50\n",
            "13/13 - 26s - loss: 0.7915 - accuracy: 0.6892 - val_loss: 1.1869 - val_accuracy: 0.5476 - 26s/epoch - 2s/step\n",
            "Epoch 14/50\n",
            "13/13 - 26s - loss: 0.8202 - accuracy: 0.6867 - val_loss: 1.6896 - val_accuracy: 0.4524 - 26s/epoch - 2s/step\n",
            "Epoch 15/50\n",
            "13/13 - 25s - loss: 0.8408 - accuracy: 0.6617 - val_loss: 0.9959 - val_accuracy: 0.6190 - 25s/epoch - 2s/step\n",
            "Epoch 16/50\n",
            "Restoring model weights from the end of the best epoch: 11.\n",
            "13/13 - 26s - loss: 0.7455 - accuracy: 0.6842 - val_loss: 1.0675 - val_accuracy: 0.5476 - 26s/epoch - 2s/step\n",
            "Epoch 16: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "Bc1d-hg0YOjK"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}