def __init__(self, dim_list, eta = 0.1):
"""
Constructor for network.
Params:
dim_list: a list of the number of dimension for each layer.
eta: learning rate for each gradient descent step
"""
depth = len(dim_list)
self.depth = depth
self.dim_list = dim_list
self.eta = eta
# 1. Initiate each layer: output, partial_output and weight,
# although partial_output is useless for the input layer, similarly
# weight and bias are useless for the output layer.
#
# 2. Partial_weight is an internal variable and will not be stored in
# a layer.
#
self.layers = [ {'output':Vector.fromIterable(0 for i in xrange(dim_list[l])),
'partial_output':Vector.fromIterable(0 for i in xrange(dim_list[l])),
'weight':Matrix.fromRandom(dim_list[l + 1], dim_list[l]),
'bias':Vector.fromRandom(dim_list[l + 1])}
for l in xrange(depth - 1) ]
# output layer
self.layers.append({'output':Vector.fromList([0] * dim_list[depth - 1]),
'partial_output':Vector.fromList([0] * dim_list[depth - 1]),
'weight': None, 'bias': None})
