Flask Image Classification and Drawing App

Overview

This project is a Flask web application that enables users to upload images for classification of the MNIST Fashion dataset using a custom neural network. Additionally, users can draw numbers which are processed and displayed. The neural network is built from scratch without using high-level libraries like Keras, relying instead on custom implementations of neural network layers.

Features

• Image Classification: Upload an image to classify it using a custom-built neural network.
• Number Drawing: Draw a number and view the generated image.
• Custom Neural Network: Implemented using custom classes for convolutional layers, dense layers, activation functions, and more.

Requirements

• Python 3.x
• Flask
• Flask-WTF
• Pandas
• NumPy
• Pillow
• Matplotlib

Installation

1. Clone the repository:

   git clone https://github.com/koushik2k3/Neural-Networks.git
   cd Neural-Networks

2. Install the required packages:

   It is recommended to use a virtual environment. Install the dependencies using pip:

   pip install -r requirements.txt

   Create a requirements.txt file with the following content if it does not already exist:

   Flask
   Flask-WTF
   pandas
   numpy
   pillow
   matplotlib

3. Prepare the dataset and weights:

   Ensure you have the following files in the project directory:

   • weights1.xlsx
   • weights2.xlsx
   • bias1.xlsx
   • bias2.xlsx
   • weights.xlsx

   These files are used to initialize the neural network's weights and biases.

4. Run the application:

   Start the Flask development server:

   python app.py

   By default, the server will run on http://127.0.0.1:4000/.

Drawing Feature

The drawing feature allows users to create images of numbers and view them. Here’s how it works:

1. Number Drawing:

   • When a number is entered on the /draw route, the application processes it to generate an image.
   • The draw function uses a custom neural network to produce an image representing the given number. The network performs a forward pass with the number encoded as a one-hot vector.
   • The resulting output is a 28x28 image, which is saved as a PNG file. This file is stored in the static/drawings directory.

2. Multiple Digits:

   • For numbers with multiple digits, the drawmorethan1digit function processes each digit separately and concatenates the images to form a composite image.
   • Each digit is converted into a 28x28 image, which is then placed side by side to create a single image representing the entire number.

3. Displaying the Image:

   • The generated image is saved and can be accessed through the /static/drawings/<filename> route.
   • The filename is generated dynamically based on the number drawn.

Custom Convolutional Neural Network (CNN)

This project demonstrates how to build a Convolutional Neural Network (CNN) from scratch. Here's a breakdown of how the custom CNN works:

1. Network Architecture:

   The custom CNN is composed of the following layers:

   • Convolutional Layer: Applies convolution operations to extract features from input images.
   • Activation Layer: Applies activation functions (e.g., ReLU, sigmoid) to introduce non-linearity.
   • Reshape Layer: Reshapes the output of the convolutional layer to fit the input requirements of fully connected layers.
   • Dense Layer: Implements fully connected layers to process the features extracted by the convolutional layers.
   • Softmax Layer: Provides a probability distribution over classes for classification tasks.

2. Layer Details:

   • Convolutional Layer:

     • Custom Convolutional class performs 2D convolution operations using kernels and biases.
     • Implemented with methods for forward and backward propagation, weight updating, and saving/loading weights.

   • Dense Layer:

     • Custom Dense class implements fully connected layers with weight and bias parameters.
     • Includes methods for forward and backward propagation and weight updates.

   • Activation Functions:

     • Implementations for various activation functions such as ReLU and sigmoid are included.
     • Custom classes for activation functions handle both forward and backward propagation.

   • Softmax Layer:

     • Custom Softmax class applies the softmax function to produce class probabilities from the network output.

3. Training and Prediction:

   • Training:

     • The train function performs training using backpropagation and gradient descent.
     • Loss functions such as mean squared error and binary cross-entropy are used for calculating the error and gradients.

   • Prediction:

     • The predict function applies the trained network to input data and provides output predictions.

Usage

1. Upload Image for Classification:

   Visit the root URL to access the image upload form. Upload an image from the fashion_mnist folder, and it will be classified using the custom neural network. The classification result will be displayed.

2. Draw a Number:

   Navigate to the /draw route. Enter a number to draw and submit the form. The generated image of the number will be displayed.

Directory Structure

• app.py - The main Flask application file.
• libv2.py - Contains custom neural network implementations, including layers and activation functions.
• libv3.py - Additional custom functions or utilities (if applicable).
• static/ - Directory for static files, including uploaded images and generated drawings.
• templates/ - Directory for HTML templates.
• weights1.xlsx, weights2.xlsx, bias1.xlsx, bias2.xlsx, weights.xlsx - Files with pre-trained model weights and biases.

Contributing

Contributions are welcome! Feel free to open issues or submit pull requests for improvements or bug fixes.

Acknowledgments

• This project utilizes custom implementations of neural network components.
• Inspired by foundational concepts in neural networks and machine learning.