Simple Neural Network Implementation

This repository contains a basic implementation of a neural network, including classes for individual neurons, layers, and a multilayer perceptron (MLP).

Components

Neuron: A single neuron with n inputs. It computes the weighted sum of inputs and applies a ReLU activation function.
Layer: A layer of neurons. Each neuron in the layer expects inputs from a single, separate input vector.
MLP: A multilayer perceptron consisting of multiple layers of neurons, where the output of one layer is the input to the next.

Usage

To use this neural network, create an instance of the MLP class with the desired input dimension and a list indicating the number of neurons per layer. Then, pass an input through the network by calling the instance with an input vector.

from your_module import MLP

# Define the MLP structure
input_dim = 5
neurons_per_layer = [10, 10, 5]  # Example: 3 layers with 10, 10, and 5 neurons
mlp = MLP(input_dim, neurons_per_layer)

# Pass an input vector
input_vector = [Value(0.5), Value(0.1), Value(-0.2), Value(0.4), Value(0.9)]
output = mlp(input_vector)

ShujaKhalid/micrograd

Simple Neural Network Implementation

Components

Usage