convolutional_neural_network.ipynb

{
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  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArmaanMistry/ml-beginner-projects/blob/main/convolutional_neural_network.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": "code",
      "source": [
        "print('trying to add version 2 of this ipynb file')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0LC6Q7Fk5cVk",
        "outputId": "4992d141-b5c8-4e7a-9b2d-04fc0ef078d4"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "trying to add version 2 of this ipynb file\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "EMefrVPCg-60"
      },
      "source": [
        "### Importing the libraries"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow as tf\n",
        "from keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "T2DxFAfV4Tsx"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "tf.__version__"
      ],
      "metadata": {
        "id": "fKRHElcA4rS2"
      },
      "execution_count": null,
      "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",
      "source": [
        "train_datagen = ImageDataGenerator(rescale = 1./255,\n",
        "                                   shear_range = 0.2,\n",
        "                                   zoom_range = 0.2,\n",
        "                                   horizontal_flip = True)\n",
        "training_set = train_datagen.flow_from_directory('dataset/training_set',\n",
        "                                                 target_size = (64, 64),\n",
        "                                                 batch_size = 32,\n",
        "                                                 class_mode = 'binary')"
      ],
      "metadata": {
        "id": "pZZPTOOx4uvS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mrCMmGw9pHys"
      },
      "source": [
        "### Preprocessing the Test set"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_datagen = ImageDataGenerator(rescale = 1./255)\n",
        "test_set = test_datagen.flow_from_directory('dataset/test_set',\n",
        "                                            target_size = (64, 64)\n",
        "                                            batch_size = 32,\n",
        "                                            class_mode = 'binary')"
      ],
      "metadata": {
        "id": "E2od-VXA6aev"
      },
      "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",
      "source": [
        "cnn = tf.keras.models.Sequential()"
      ],
      "metadata": {
        "id": "4MwWi73O66cQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "u5YJj_XMl5LF"
      },
      "source": [
        "### Step 1 - Convolution"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = 3, activation  ='relu', input_shape = [64, 64, 3]))"
      ],
      "metadata": {
        "id": "0q9x63Xp7BYz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tf87FpvxmNOJ"
      },
      "source": [
        "### Step 2 - Pooling"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(tf.keras.layers.MaxPool2D(pool_size = 2, strides = 2))"
      ],
      "metadata": {
        "id": "xHJCKgEQ7_ye"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xaTOgD8rm4mU"
      },
      "source": [
        "### Adding a second convolutional layer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(tf.keras.layers.Conv2D(filters = 32, kernel_size = 3, activation = 'relu'))\n",
        "cnn.add(tf.keras.layers.MaxPool2D(pool_size = 2, strides = 2))"
      ],
      "metadata": {
        "id": "wL9TG_2P8h8D"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tmiEuvTunKfk"
      },
      "source": [
        "### Step 3 - Flattening"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(tf.keras.layers.Flatten())"
      ],
      "metadata": {
        "id": "rJiEcO4i9AGl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dAoSECOm203v"
      },
      "source": [
        "### Step 4 - Full Connection"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(tf.keras.layers.Dense(units=128, activation='relu'))"
      ],
      "metadata": {
        "id": "XGvyES_-9EtW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yTldFvbX28Na"
      },
      "source": [
        "### Step 5 - Output Layer"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "cnn.add(tf.keras.layers.Dense(units=1, activation='sigmoid'))"
      ],
      "metadata": {
        "id": "QNF_yFRp9tVW"
      },
      "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",
      "source": [
        "cnn.compile(optimizer='adam', loss='binary-crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "VVsY2nu190pD"
      },
      "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",
      "source": [
        "cnn.fit(x = training_set, validation_data = test_set, epochs = 25)"
      ],
      "metadata": {
        "id": "fYNVM8hP-CT0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "U3PZasO0006Z"
      },
      "source": [
        "## Part 4 - Making a single prediction"
      ]
    },
    {
      "cell_type": "code",
      "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'"
      ],
      "metadata": {
        "id": "VxtIwZ55-Tsh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(prediction)"
      ],
      "metadata": {
        "id": "AlbjTrKIAI1y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}