ff-neural-network

A Python module for the generation and training of an entry-level feedforward neural network.

This repository serves as a repurposing of a 2019 project I did as an initiation into machine learning.

Usage

Creating a network:

network = Network(layer_sizes, bias_value)

• layer_sizes: Number of neurons in each layer. Ex: [2, 5, 1] will generate a network that can be visualized as such:

• bias_value: Value of the bias nodes (standardized at 1):

Bias nodes are added to a feed-forward neural network to help facilitate learning patterns. They function like an input node that always produces a value of 1.0 or other constant.

network.randomize()

• Initializes the weights between all neurons with a random value.

network.train(input_data, target_data, learning_rate)

• input_data : The input data, a good approach is to have it normalized into a proper range.

• target_data : The data that the model learns from.

• learning_rate : Controls how quickly or slowly the network model learns the problem.

Example

For an (output = X) pattern learning data:

Which should lead to:

from network import Network
from data_set import DataSet

# Initializing a network with a 2-2-1 structure
network = Network([2, 2, 1], 1.0)

# Randomizing initial weights between all neurons
network.randomize()

# Initializing data_set with input and output training data
inputs = [[0, 1], [1, 0], [1, 1]]
outputs = [[0], [1], [1]]
data_set = DataSet(inputs, outputs)

# Training the network for 10000 intervals
for _ in  range(10000):
	for index in  range(0, data_set.get_size()):
		network.train(data_set.get_input(index),data_set.get_target(index), 1.0)

# Printing output prediction for input = [0, 0]
print(network.calculate_outputs([0, 0]))

We get :

output : [0.0023672395614975253]