def __init__(self, dim1, dim2):
"""Constructor
@param dim1 -- number of neurons in each row layer.
@param dim2 -- number of neurons in each column of the layer.
"""
self.activations = cp.get_filled_matrix(dim1, dim2, 0.0)
self.deltas = cp.get_filled_matrix(dim1, dim2, 0.0)
def __init__(self, dim1, dim2):
"""Constructor
@param dim1 -- number of neurons in each row layer.
@param dim2 -- number of neurons in each column of the layer.
"""
self.activations = cp.get_filled_matrix(dim1, dim2, 0.0)
self.deltas = cp.get_filled_matrix(dim1, dim2, 0.0)
def __init__(self, source_layer, target_layer):
"""Constructor
@param source_layer pointer to the previous neuron layer.
@param target_layer pointer to the next neuron layer.
"""
self.source=source_layer
self.target=target_layer
dim1 = self.target.activations.h
dim2 = self.source.activations.h
self.weight = cp.get_filled_matrix(dim1, dim2, 0.0)
cp.fill_rnd_uniform(self.weight)
cp.apply_scalar_functor(self.weight, cp.scalar_functor.SUBTRACT, 0.5)
cp.apply_scalar_functor(self.weight, cp.scalar_functor.DIV, 10)
self.bias = cp.get_filled_matrix(dim1, 1, 0)
def weight_update(self, learnrate=0.01, decay=0.0):
"""Updates the weights and the bias
using source activations and target deltas.
@param learnrate how strongly the gradient influences the weights
@param decay large values result in a regularization with
to the squared weight value"""
batch_size=self.source.activations.w
h = cp.dev_matrix_cmf(self.weight.h, self.weight.w)
cp.prod(h, self.target.deltas, self.source.activations, 'n', 't')
cp.learn_step_weight_decay(self.weight, h, learnrate/batch_size, decay)
h.dealloc()
h = cp.get_filled_matrix(self.target.activations.h, 1, 0)
cp.reduce_to_col(h.vec, self.target.deltas)
cp.learn_step_weight_decay(self.bias, h, learnrate/batch_size, decay)
h.dealloc()
def __init__(self, source_layer, target_layer):
"""Constructor
@param source_layer reference to previous neuron layer.
@param target_layer reference to next neuron layer.
"""
self.source = source_layer
self.target = target_layer
dim1 = self.target.activations.shape[0]
dim2 = self.source.activations.shape[0]
self.weight = cp.get_filled_matrix(dim1, dim2, 0.0)
cp.fill_rnd_uniform(self.weight)
self.weight -= 0.5
self.weight /= 10.0
self.bias = cp.dev_tensor_float(dim1)
cp.fill(self.bias, 0)