NeuralNetwork/python/lab/generator.py

# Generate neural network

from lab import neural
import numpy as np

# Random generators
def uniform(layer):
	"""
	To generate network weights
	with a uniform distribution between -1.0 and 1.0
	"""
	return np.random.uniform(low = -1.0, high = 1.0, size = layer.shape)

def gaussUnitDev(layer):
	"""
	To generate network weights
	with a gaussian distribution.
	"""
	return np.random.normal(size = layer.shape)

def gaussAdaptedDev(layer):
	"""
	To generate network weights
	with a gaussian distribution
	where standard deviation is adpted 1 / sqrt(nl - 1)
	"""
	nl, _ = layer.shape
	stdDev = 1 / np.sqrt(nl - 1)
	return np.random.normal(scale = stdDev, size = layer.shape)

# Network weight initialization
def generate(activation, derivative, hiddenLength = 30, weightGenerator = None):
	"""
	activation : function used on network outputs
	derivative : the derivative of the activation function
	hiddenLength : number of neurons in the hidden layer
	weightGenerator : is one of
	None
	generator.uniform
	generator.gaussUnitDev
	generator.gaussAdaptedDev
	"""
	net = neural.Network(activation, derivative, hiddenLength)

	if (weightGenerator is not None):
		net.layer1 = weightGenerator(net.layer1)
		net.layer2 = weightGenerator(net.layer2)

	return net