VGG16_implementation_Practice.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "VGG16_implementation_on_Dogs&Cats.ipynb",
      "private_outputs": true,
      "provenance": [],
      "authorship_tag": "ABX9TyMZpMckDvdVoXlvBhFWkix4",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/SahilSunda/ML_Project/blob/master/VGG16_implementation_on_Dogs%26Cats.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R42ayJqf9Qtr"
      },
      "source": [
        "import keras,os\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense, Conv2D, MaxPool2D, Flatten\n",
        "from keras.preprocessing.image import ImageDataGenerator\n",
        "import numpy as np"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gc9mPU4L-LSB"
      },
      "source": [
        "trdata = ImageDataGenerator()\n",
        "traindata = trdata.flow_from_directory(directory=\"/Users/sahil/My Mac/Documents/Coding/ML/dogs-vs-cats\",target_size=(224,224))\n",
        "tsdata = ImageDataGenerator()\n",
        "testdata = tsdata.flow_from_directory(directory=\"/Users/sahil/My Mac/Documents/Coding/ML/dogs-vs-cats\",target_size=(224,224))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ikIU87_s-R71"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Conv2D(input_shape=(224,224,3),filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
        "model.add(Conv2D(filters=64,kernel_size=(3,3),padding=\"same\", activation=\"relu\"))\n",
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(Conv2D(filters=128, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(Conv2D(filters=256, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(Conv2D(filters=512, kernel_size=(3,3), padding=\"same\", activation=\"relu\"))\n",
        "model.add(MaxPool2D(pool_size=(2,2),strides=(2,2)))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SV5JKWTa-S7P"
      },
      "source": [
        "model.add(Flatten())\n",
        "model.add(Dense(units=4096,activation=\"relu\"))\n",
        "model.add(Dense(units=4096,activation=\"relu\"))\n",
        "model.add(Dense(units=2, activation=\"softmax\"))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_EoLFAQ4-Xvu"
      },
      "source": [
        "from keras.optimizers import Adam\n",
        "opt = Adam(lr=0.001)\n",
        "model.compile(optimizer=opt, loss=keras.losses.categorical_crossentropy, metrics=['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K7hLjFxd-bUS"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}