feedforwardnet-shine7 0.0.4

Installation

$ [sudo] pip install feedforwardnet-shine7

Usage

from Neural_Network import NeuralNetwork

# Create a Neural Network
inputs = 2
output_neurons = 1
hidden_layers = 2
each_hidden_nodes = [2, 3]
network = NeuralNetwork(inputs, hidden_layers, output_neurons, each_hidden_nodes)

Building a dataset

Dataset must be python list of data_samples, where each data_sample is a list of input and target.
For Eg: Input: [1, 1], Target: [1] => [[1, 1], [1]] is a data sample.

A typical XOR function's dataset looks something like :

>>> XOR_data = 
[
	[			### ####
		[0, 0], # Input   Data
		[0] # Output	 Sample
	],			### ####
	[
		[0, 1],
		[1]
	],
	[
		[1, 0],
		[1]
	],
	[
		[1, 1],
		[0]
	]
]

>>> size = 4 # Length of the data

Training The network

The library provides a Train function which accepts the dataset, dataset size, and three optional parameters MAX_EPOCHS, graph, and log_outputs.

def Train(dataset, size, MAX_EPOCHS=10000, graph=False, log_outputs=True) :
	....
	....

For Eg: If you want to train your network for 5000 epochs and display epoch vs error graph after training.

>>> network.Train(XOR_data, size, MAX_EPOCHS=5000, graph=True)

Notice that I didn't change the value of log_outputs as I want the output to printed for each epoch.

Debugging

If you want to look at the network's weights at any point of time, the library provides a print_weights function.

>>> network.print_weights()