Image_classification.ipynb

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  "metadata": {
    "colab": {
      "name": "Image_classification.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tanishka423/Deep_Learning/blob/main/Image_classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3DR-eO17geWu"
      },
      "source": [
        "# Convolutional Neural Network"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EMefrVPCg-60"
      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3EqORuDH3XZj"
      },
      "source": [
        "import tensorflow as tf\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oxQxCBWyoGPE"
      },
      "source": [
        "## Part 1 - Data Preprocessing"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MvE-heJNo3GG"
      },
      "source": [
        "### Preprocessing the Training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHj5Y9kh3sfN"
      },
      "source": [
        "training_data = ImageDataGenerator( rescale= 1./255 , shear_range=0.2 , zoom_range=0,horizontal_flip=False )\n",
        "training_set = training_data.flow_from_directory('dataset/training_set', target_size =(64,64), batch_size= 32 , class_mode = 'binary')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrCMmGw9pHys"
      },
      "source": [
        "### Preprocessing the Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ONkz-7Bd6nAU"
      },
      "source": [
        "test_data = ImageDataGenerator( rescale= 1./255 )\n",
        "test_set = test_data.flow_from_directory('dataset/test_set', target_size =(64,64), batch_size= 32 , class_mode = 'binary')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "af8O4l90gk7B"
      },
      "source": [
        "## Part 2 - Building the CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ces1gXY2lmoX"
      },
      "source": [
        "### Initialising the CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AtlaBDrT60mo"
      },
      "source": [
        "cnn = tf.keras.models.Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5YJj_XMl5LF"
      },
      "source": [
        "### Step 1 - Convolution"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_UO6HGKO7o1Z"
      },
      "source": [
        "cnn.add(tf.keras.layers.Conv2D(filters=32 , kernel_size= 3 , activation='relu', input_shape=[64,64,3]))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tf87FpvxmNOJ"
      },
      "source": [
        "### Step 2 - Pooling"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iiODwRR286_u"
      },
      "source": [
        "cnn.add(tf.keras.layers.MaxPool2D(strides=2 , pool_size= 2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaTOgD8rm4mU"
      },
      "source": [
        "### Adding a second convolutional layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_n5V_uih9IvG"
      },
      "source": [
        "cnn.add(tf.keras.layers.Conv2D(filters=32 , kernel_size= 3 , activation='relu'))\n",
        "cnn.add(tf.keras.layers.MaxPool2D(strides=2 , pool_size= 2))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmiEuvTunKfk"
      },
      "source": [
        "### Step 3 - Flattening"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gLwmSlWN9WgQ"
      },
      "source": [
        "cnn.add(tf.keras.layers.Flatten())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAoSECOm203v"
      },
      "source": [
        "### Step 4 - Full Connection"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cTIYwuRl9_W8"
      },
      "source": [
        "cnn.add(tf.keras.layers.Dense(units = 128 , activation='relu'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTldFvbX28Na"
      },
      "source": [
        "### Step 5 - Output Layer"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iKZwqsJp-Jt2"
      },
      "source": [
        "cnn.add(tf.keras.layers.Dense(units =1 , activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "D6XkI90snSDl"
      },
      "source": [
        "## Part 3 - Training the CNN"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vfrFQACEnc6i"
      },
      "source": [
        "### Compiling the CNN"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "22pn2Ksw-XWE"
      },
      "source": [
        "cnn.compile(optimizer= 'adam' , loss='binary_crossentropy' , metrics = ['accuracy'])"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ehS-v3MIpX2h"
      },
      "source": [
        "### Training the CNN on the Training set and evaluating it on the Test set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CYVnDVNSB_Cc"
      },
      "source": [
        "cnn.fit( x= tarining_set , validation_data = test_set ,epochs =25 )"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3PZasO0006Z"
      },
      "source": [
        "## Part 4 - Making a single prediction"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QEQ-ZiyxDFkq"
      },
      "source": [
        "import numpy as np\n",
        "from keras.preprocessing import image\n",
        "test_image = image.load_img('dataset/single_prediction/cat_or_dog_1.jpg', target_size=(64,64))\n",
        "test_image = image.img_to_array(test_image)\n",
        "test_image = np.expand_dims(test_image, axis = 0)\n",
        "result = cnn.predict(test_image)\n",
        "training_set.class_indices\n",
        "if result[0][0] == 1:\n",
        "  prediction = 'dog'\n",
        "else:\n",
        "  prediction = 'cat'"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ED9KB3I54c1i"
      },
      "source": [
        "print(prediction)"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}