NeuralNetwork/python/lab/generator.py

# Generate neural network

from lab import neural
import numpy as np

# Random generators
def uniform(layer):
	return np.random.uniform(low = -1.0, high = 1.0, size = layer.shape)

def gaussUnitDev(layer):
	return np.random.normal(size = layer.shape)

def gaussAdaptedDev(layer):
	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, weightGenerator = None):
	"""
	Weight generator can be
	None
	generator.uniform
	generator.gaussUnitDev
	generator.gaussAdaptedDev
	"""
	net = neural.Network(activation, derivative)

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

	return net