Neural Network

This is a neural network library in Python which can be used to feedforward arrays of inputs, generate outputs, and be trained with expected outputs.

Install this package using pip:

pip install -e git+https://github.com/javidahmed64592/neural-network#egg=neural_network

To update the package:

pip update -e git+https://github.com/javidahmed64592/neural-network#egg=neural_network

Note: It is recommended to install this into a virtual environment.

Table of Contents

• Installing Dependencies
• Using the Neural Network
  • Neuroevolution
• Testing
• Linting and Formatting

Installing Dependencies

Install the required dependencies using pip:

pip install -e .

To install with dev and test dependencies:

pip install -e .[dev,test]

Using the Neural Network

For a complete example of how to create and train the neural network, see example_training.ipynb where it is trained on binary lists.

The neural network can be created in the following way:

from neural_network.math.activation_functions import LinearActivation, SigmoidActivation
from neural_network.neural_network import NeuralNetwork
from neural_network.layer import HiddenLayer, InputLayer, OutputLayer


input_layer = InputLayer(size=num_inputs, activation=LinearActivation)
hidden_layers = [
    HiddenLayer(size=size, activation=SigmoidActivation, weights_range=[-1, 1], bias_range=[-1, 1])
    for size in hidden_layer_sizes
]
output_layer = OutputLayer(size=num_outputs, activation=SigmoidActivation, weights_range=[-1, 1], bias_range=[-1, 1])

nn = NeuralNetwork(layers=[input_layer, *hidden_layers, output_layer])

where

• num_inputs: Number of inputs to pass through neural network
• num_outputs: Number of outputs to be generated
• hidden_layer_sizes: List of number of nodes in each hidden layer

To feedforward an array of inputs:

outputs = nn.feedforward([x_i, ..., x_n]) # n: Number of inputs

The neural network can also be trained by providing an array of inputs and expected outputs, and backpropagating the error using gradient descent.

inputs = [x_i, ..., x_n] # n: Number of inputs
expected_outputs = [y_i, ..., y_m] # m: Number of outputs

errors = nn.train(inputs, expected_outputs)

The neural network weights and biases can be saved to a json file:

nn.save("/path/to/nn_model.json")

To load a neural network from a file:

nn = NeuralNetwork.from_file("/path/to/nn_model.json")

Neuroevolution

New weights and biases can also be calculated via crossover:

nn_1 = NeuralNetwork(num_inputs, num_outputs, [i])
nn_2 = NeuralNetwork(num_inputs, num_outputs, [j])
nn_3 = NeuralNetwork(num_inputs, num_outputs, [k])

nn_1.weights, nn_1.bias = nn_1.crossover(nn_2, nn_3, mutation_rate)

where

• mutation_rate: Percentage of weights and biases to be randomised

Testing

This library uses Pytest for the unit tests. These tests are located in the tests directory. To run the tests:

python -m pytest tests

Linting and Formatting

This library uses ruff for linting and formatting. This is configured in pyproject.toml.

To check the code for linting errors:

python -m ruff check .

To format the code:

python -m ruff format .