Transfer_Learning_Inception_V3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "-8_MSfEg-ENl"
   },
   "source": [
    "## Transfer Learning Inception V3 using Keras"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "YtCp6yxu-ENp"
   },
   "source": [
    "Please download the dataset from the below url"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "HEbP6wdl-ENp"
   },
   "outputs": [],
   "source": [
    "from tensorflow.compat.v1 import ConfigProto\n",
    "from tensorflow.compat.v1 import InteractiveSession\n",
    "\n",
    "config = ConfigProto()\n",
    "config.gpu_options.per_process_gpu_memory_fraction = 0.5\n",
    "config.gpu_options.allow_growth = True\n",
    "session = InteractiveSession(config=config)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "TzE9Iec1-ENq"
   },
   "outputs": [],
   "source": [
    "# import the libraries as shown below\n",
    "\n",
    "from tensorflow.keras.layers import Input, Lambda, Dense, Flatten\n",
    "from tensorflow.keras.models import Model\n",
    "from tensorflow.keras.applications.inception_v3 import InceptionV3\n",
    "from tensorflow.keras.applications.inception_v3 import preprocess_input\n",
    "from tensorflow.keras.preprocessing import image\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator,load_img\n",
    "from tensorflow.keras.models import Sequential\n",
    "import numpy as np\n",
    "from glob import glob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "fr1p4gYP-ENr"
   },
   "outputs": [],
   "source": [
    "# re-size all the images to this\n",
    "IMAGE_SIZE = [224, 224]\n",
    "\n",
    "train_path = 'Datasets/train'\n",
    "valid_path = 'Datasets/test'\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "ROgevyTP-ENr",
    "outputId": "188ab255-0b10-4a91-8cbf-a7544442f34b"
   },
   "outputs": [],
   "source": [
    "# Import the Vgg 16 library as shown below and add preprocessing layer to the front of VGG\n",
    "# Here we will be using imagenet weights\n",
    "\n",
    "inception = InceptionV3(input_shape=IMAGE_SIZE + [3], weights='imagenet', include_top=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "XEy3Si5G-ENr"
   },
   "outputs": [],
   "source": [
    "# don't train existing weights\n",
    "for layer in inception.layers:\n",
    "    layer.trainable = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "7V3NrpdA-ENs"
   },
   "outputs": [],
   "source": [
    "  # useful for getting number of output classes\n",
    "folders = glob('Datasets/train/*')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "B-xXTtxy-ENs"
   },
   "outputs": [],
   "source": [
    "# our layers - you can add more if you want\n",
    "x = Flatten()(inception.output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "58iKXxu4-ENt"
   },
   "outputs": [],
   "source": [
    "prediction = Dense(len(folders), activation='softmax')(x)\n",
    "\n",
    "# create a model object\n",
    "model = Model(inputs=inception.input, outputs=prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "CLdqlJia-ENt",
    "outputId": "99fbbe0a-402f-41f1-e887-89e41580fac2"
   },
   "outputs": [],
   "source": [
    "\n",
    "# view the structure of the model\n",
    "model.summary()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "JY1R_XNz-ENu"
   },
   "outputs": [],
   "source": [
    "# tell the model what cost and optimization method to use\n",
    "model.compile(\n",
    "  loss='categorical_crossentropy',\n",
    "  optimizer='adam',\n",
    "  metrics=['accuracy']\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "52M9wsUn-ENu"
   },
   "outputs": [],
   "source": [
    "# Use the Image Data Generator to import the images from the dataset\n",
    "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
    "\n",
    "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
    "                                   shear_range = 0.2,\n",
    "                                   zoom_range = 0.2,\n",
    "                                   horizontal_flip = True)\n",
    "\n",
    "test_datagen = ImageDataGenerator(rescale = 1./255)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "DMqkF1nD-ENv",
    "outputId": "a9a524c6-6d71-4b9d-c495-44719fde9540"
   },
   "outputs": [],
   "source": [
    "# Make sure you provide the same target size as initialied for the image size\n",
    "training_set = train_datagen.flow_from_directory('Datasets/train',\n",
    "                                                 target_size = (224, 224),\n",
    "                                                 batch_size = 32,\n",
    "                                                 class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "yZV6MwPu-ENv",
    "outputId": "95bdc0e0-ec35-4251-cfa9-fd4419f35cdf"
   },
   "outputs": [],
   "source": [
    "test_set = test_datagen.flow_from_directory('Datasets/test',\n",
    "                                            target_size = (224, 224),\n",
    "                                            batch_size = 32,\n",
    "                                            class_mode = 'categorical')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "h4tyZlRB-ENv",
    "outputId": "6705fb5d-848e-4e03-e4ce-2dbb20077db8",
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# fit the model\n",
    "# Run the cell. It will take some time to execute\n",
    "r = model.fit_generator(\n",
    "  training_set,\n",
    "  validation_data=test_set,\n",
    "  epochs=50,\n",
    "  steps_per_epoch=len(training_set),\n",
    "  validation_steps=len(test_set)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ACu9NLIL-ENw"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 530
    },
    "id": "FVTZkb3Y-ENw",
    "outputId": "f8a4b9e8-0ef9-475b-f2b2-9d22f51b73c3"
   },
   "outputs": [],
   "source": [
    "# plot the loss\n",
    "plt.plot(r.history['loss'], label='train loss')\n",
    "plt.plot(r.history['val_loss'], label='val loss')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('LossVal_loss')\n",
    "\n",
    "# plot the accuracy\n",
    "plt.plot(r.history['accuracy'], label='train acc')\n",
    "plt.plot(r.history['val_accuracy'], label='val acc')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "plt.savefig('AccVal_acc')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "yNas9f2y-ENw"
   },
   "outputs": [],
   "source": [
    "# save it as a h5 file\n",
    "\n",
    "\n",
    "from tensorflow.keras.models import load_model\n",
    "\n",
    "model.save('model_inception_aug.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "PCK43IFM-ENw"
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "8PGSwplw-ENw"
   },
   "outputs": [],
   "source": [
    "\n",
    "y_pred = model.predict(test_set)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "igZCsz1D-ENx",
    "outputId": "050a39d9-5d12-4f27-8aaa-d20bd1eed192"
   },
   "outputs": [],
   "source": [
    "y_pred"
   ]
  }
 ],
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