Custom_Image_Classification/Custom_image_clasification.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "final hackerEarth gala classification.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "lqv7memQqco7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SfVqsrCAq033",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "from shutil import copy2\n",
        "import csv"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RzzvPCdWq5XZ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from __future__ import absolute_import, division, print_function, unicode_literals\n",
        "\n",
        "try:\n",
        "  # The %tensorflow_version magic only works in colab.\n",
        "  %tensorflow_version 2.x\n",
        "except Exception:\n",
        "  pass\n",
        "import tensorflow as tf\n",
        "\n",
        "import os\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pGB8rH8trecP",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!pip install tensorflow_hub"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FXt9rLUuFI5B",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import tensorflow_hub as hub"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-y5ROM_6FLHl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "35_iMApcFRMg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import pandas as pd\n",
        "\n",
        "# Increase precision of presented data for better side-by-side comparison\n",
        "pd.set_option(\"display.precision\", 8)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2bdsezDTFUXQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "data_root = (\"/content/drive/My Drive/Dataset/hackerrank deep learning project/tfDataset\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5rNgw-IyFzcY",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IMAGE_SHAPE = (224, 224)\n",
        "TRAINING_DATA_DIR = str(data_root)\n",
        "\n",
        "datagen_kwargs = dict(rescale=1./255, validation_split=.20)\n",
        "valid_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**datagen_kwargs)\n",
        "valid_generator = valid_datagen.flow_from_directory(\n",
        "    TRAINING_DATA_DIR, \n",
        "    subset=\"validation\", \n",
        "    shuffle=True,\n",
        "    target_size=IMAGE_SHAPE\n",
        ")\n",
        "\n",
        "train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(**datagen_kwargs)\n",
        "train_generator = train_datagen.flow_from_directory(\n",
        "    TRAINING_DATA_DIR, \n",
        "    subset=\"training\", \n",
        "    shuffle=True,\n",
        "    target_size=IMAGE_SHAPE)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8Bg0xKAnGEWV",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for image_batch, label_batch in train_generator:\n",
        "  break\n",
        "image_batch.shape, label_batch.shape"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P645UYzUGLEF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "print (train_generator.class_indices)\n",
        "\n",
        "labels = '\\n'.join(sorted(train_generator.class_indices.keys()))\n",
        "\n",
        "with open('labels.txt', 'w') as f:\n",
        "  f.write(labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0F75KwUvGRMv",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "!cat labels.txt"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MFNnREvqGbG4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "IMAGE_SIZE = 224"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XZqfkQllGe0L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model = tf.keras.Sequential([\n",
        "  hub.KerasLayer(\"https://tfhub.dev/google/tf2-preview/mobilenet_v2/feature_vector/4\", \n",
        "                 output_shape=[1280],\n",
        "                 trainable=False),\n",
        "  tf.keras.layers.Dropout(0.4),\n",
        "  tf.keras.layers.Dense(train_generator.num_classes, activation='softmax')\n",
        "])\n",
        "model.build([None, 224, 224, 3])\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7za6jFDG10A",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "optimizer = tf.keras.optimizers.Adam(lr=1e-3)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QPiP6C70GhSf",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(\n",
        "  optimizer=optimizer,\n",
        "  loss='categorical_crossentropy',\n",
        "  metrics=['acc'])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfti_jBLGn_E",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "steps_per_epoch = np.ceil(train_generator.samples/train_generator.batch_size)\n",
        "val_steps_per_epoch = np.ceil(valid_generator.samples/valid_generator.batch_size)\n",
        "\n",
        "hist = model.fit(\n",
        "    train_generator, \n",
        "    epochs=100,\n",
        "    verbose=1,\n",
        "    steps_per_epoch=steps_per_epoch,\n",
        "    validation_data=valid_generator,\n",
        "    validation_steps=val_steps_per_epoch).history"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-iNI4dNpGvg7",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "final_loss, final_accuracy = model.evaluate(valid_generator, steps = val_steps_per_epoch)\n",
        "print(\"Final loss: {:.2f}\".format(final_loss))\n",
        "print(\"Final accuracy: {:.2f}%\".format(final_accuracy * 100))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hiJWQnyDHTTE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "plt.figure()\n",
        "plt.ylabel(\"Loss (training and validation)\")\n",
        "plt.xlabel(\"Training Steps\")\n",
        "plt.ylim([0,50])\n",
        "plt.plot(hist[\"loss\"])\n",
        "plt.plot(hist[\"val_loss\"])\n",
        "\n",
        "plt.figure()\n",
        "plt.ylabel(\"Accuracy (training and validation)\")\n",
        "plt.xlabel(\"Training Steps\")\n",
        "plt.ylim([0,1])\n",
        "plt.plot(hist[\"acc\"])\n",
        "plt.plot(hist[\"val_acc\"])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OWbM1Jm4F1qA",
        "colab_type": "text"
      },
      "source": [
        "save trained model\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oURjCe69CfD8",
        "colab_type": "text"
      },
      "source": [
        "checking the performance of the model\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hJB0_RgOAwk9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "val_image_batch, val_label_batch = next(iter(valid_generator))\n",
        "true_label_ids = np.argmax(val_label_batch, axis=-1)\n",
        "print(\"Validation batch shape:\", val_image_batch.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hRp2KjvjHd_k",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "dataset_labels = sorted(train_generator.class_indices.items(), key=lambda pair:pair[1])\n",
        "dataset_labels = np.array([key.title() for key, value in dataset_labels])\n",
        "print(dataset_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJwXwdjYHj2i",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "tf_model_predictions = model.predict(val_image_batch)\n",
        "print(\"Prediction results shape:\", tf_model_predictions.shape)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mwPc50YCHkMQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "predicted_ids = np.argmax(tf_model_predictions, axis=-1)\n",
        "predicted_labels = dataset_labels[predicted_ids]\n",
        "print(predicted_labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KOdzDhFGHkw6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "plt.figure(figsize=(10,9))\n",
        "plt.subplots_adjust(hspace=0.5)\n",
        "for n in range((len(predicted_labels)-2)):\n",
        "  plt.subplot(6,5,n+1)\n",
        "  plt.imshow(val_image_batch[n])\n",
        "  color = \"green\" if predicted_ids[n] == true_label_ids[n] else \"red\"\n",
        "  plt.title(predicted_labels[n].title(), color=color)\n",
        "  plt.axis('off')\n",
        "_ = plt.suptitle(\"Model predictions (green: correct, red: incorrect)\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2DJAgRTLGLja",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ln1E1AmcGhDZ",
        "colab_type": "text"
      },
      "source": [
        ""
      ]
    }
  ]
}