🧠 ThreeLayerConvNet

A from-scratch implementation of a three-layer convolutional neural network (CNN) in NumPy with custom forward and backward passes for convolution, pooling, activation, affine layers, and softmax loss.

🔧 Architecture

Input → Conv → ReLU → MaxPool(2x2) → Affine → ReLU → Affine → Softmax

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This minimal CNN supports:

• Custom weight initialization

• Naive forward/backward implementations (no deep learning libraries used)

• Layer composability through modular design

• Debugging-friendly shape printing for each major block

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📁 Files Overview

File	Description
CNN.py	Defines the ThreeLayerConvNet class with full forward/backward logic
Conv2d.py	Naive 2D convolution forward and backward pass
ReLU.py	ReLU activation forward and backward pass
Maxpool.py	Naive 2x2 max pooling forward and backward
Affine_layer.py	Fully-connected (affine) layer logic
Softmax_loss.py	Softmax loss and gradient implementation
Sandwich_layers.py	Helper functions for chaining layers: conv-relu-pool, affine-relu
Implementation.ipynb	Notebook demonstrating usage and testing of the network
Images	Test Images for out Convolution Layers

The model can classify images of shape (3, 32, 32) into 10 classes. You can also plug it into datasets like CIFAR-10 (after appropriate preprocessing).

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📌 Features

NumPy-only: No TensorFlow/PyTorch/Keras used

Backpropagation from scratch: Every layer implements its own gradient logic

Debug output: Key print statements show internal shape flows

Extensible design: Each component is modular and reusable

This repository is designed not just to use a CNN — but to help you learn how it actually works under the hood. Every major layer of a convolutional neural network has been manually implemented from scratch using just NumPy, with readable and modular Python code.

Here’s how you can use this repo to gain a deep understanding of CNN fundamentals:

Study Each Layer in Isolation

Each layer (e.g., convolution, ReLU, max-pooling, affine, softmax) is implemented in its own file:

• Conv2d.py: Learn how convolutions apply filters using nested loops and stride/padding logic.

• ReLU.py: Understand how non-linear activations shape model capacity.

• Maxpool.py: Observe how spatial downsampling is performed via max-pooling.

• Affine_layer.py: Explore fully-connected transformations using matrix reshaping and dot products.

• Softmax_loss.py: Learn how classification loss and gradients are computed for training.

Clone the Repo

git clone https://github.com/vansh-py04/Convolution-Net-from-Scratch.git
cd ThreeLayerConvNet

🙋‍♂️ Credits

Built by Yuganter Pratap — inspired by CS231n and DIY deep learning educational projects.