Simple Neural Network (simplenn)

A simple neural network implementation using Python.

Overview

This repository contains a straightforward neural network (NN) implemented in Python. The neural network is designed for simplicity and serves for understanding the basics of neural networks, including layers, activations, loss functions, and optimizers.

Features

• Customizable neural network architecture
• Various activation functions and loss functions
• Different optimizers (e.g., SGD, Adam, AdaGrad)
• Easy-to-use interface for training, evaluating, and predicting

Prerequisites

• Python 3.x

Installation

1. Clone the repository:

   https://github.com/berkayguzel06/Simple_nn.git
   cd simplenn
   

Usage

1. Import the Model class and other necessary components from the simplenn package:

   from simplenn.model import Model
   from simplenn.dense import Layer_Dense
   from simplenn.engine import ReLU, Softmax, Accuracy_Categorical, Categorical_Cross_Entropy, Adam

2. Create an instance of the Model class and define your neural network architecture:

   model = Model()
   model.add(Layer_Dense(input_size, 16))
   model.add(ReLU())
   model.add(Layer_Dense(16, 3))
   model.add(Softmax())

3. Set the loss function, optimizer, and accuracy metric:

   model.set(
      loss=Categorical_Cross_Entropy(),
      optimizer=Adam(lr=0.005, decay=1e-7),
      accuracy=Accuracy_Categorical()
   )

4. Finalize the model and train it on your data:

   model.finalize()
   model.train(data, target, epochs=1000, print_every=100, validation=(validation_data, validation_target))

Examples

Check the nnDemo.py script for an example of using the simplenn library on classification problems.