def _init_weights(self, weight_shape, bias_shape):
if self.initB is None:
self.initB = 0.0
if self.initW is None:
self.initW = 1.0 / np.sqrt(np.prod(weight_shape))
self.bias.shape = bias_shape
self.weight.shape = weight_shape
if self.weight.wt is None:
self.weight.set_weight(to_gpu(col_randn(weight_shape, np.float32) * self.initW))
if self.bias.wt is None:
self.bias.set_weight(to_gpu((np.ones(bias_shape, dtype=np.float32) * self.initB)))
def _init_weights(self, weight_shape, bias_shape):
if self.initB is None:
self.initB = 0.0
if self.initW is None:
self.initW = 1.0 / np.sqrt(np.prod(weight_shape))
self.bias.shape = bias_shape
self.weight.shape = weight_shape
if self.weight.wt is None:
self.weight.set_weight(to_gpu(col_randn(weight_shape, np.float32) * self.initW))
if self.bias.wt is None:
self.bias.set_weight(to_gpu((np.ones(bias_shape, dtype=np.float32) * self.initB)))
def fprop(self, input, output, train=TRAIN):
gpu_copy_to(dot(self.weight.wt, input), output)
add_vec_to_rows(output, self.bias.wt)
if train == TEST:
if self.dropRate > 0.0:
output *= (1.0 - self.dropRate)
else:
if self.dropRate > 0.0:
self.dropMask = to_gpu(np.random.uniform(0, 1, output.size).astype(np.float32).reshape(output.shape))
bigger_than_scaler(self.dropMask, self.dropRate)
gpu_copy_to(output * self.dropMask, output)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
gpu_copy_to(dot(self.weight.wt, input), output)
add_vec_to_rows(output, self.bias.wt)
if train == TEST:
if self.dropRate > 0.0:
output *= (1.0 - self.dropRate)
else:
if self.dropRate > 0.0:
self.dropMask = to_gpu(np.random.uniform(0, 1, output.size).astype(np.float32).reshape(output.shape))
bigger_than_scaler(self.dropMask, self.dropRate)
gpu_copy_to(output * self.dropMask, output)
if PFout:
print_matrix(output, self.name)
def __init__(self, name, type, epsW, epsB, initW, initB, momW, momB, wc, weight, bias,
weightIncr , biasIncr, disable_bprop=False):
Layer.__init__(self, name, type, disable_bprop)
self.initW = initW
self.initB = initB
self.weight = WEIGHTS.empty('weight.' + self.name, epsW, momW, wc)
self.bias = WEIGHTS.empty('bias.' + self.name, epsB, momB, 0.0)
if weight is not None:
self.weight.set_weight(weight)
if weightIncr is not None:
self.weight.set_incr(weightIncr)
if bias is not None:
self.bias.set_weight(bias)
if biasIncr is not None:
self.bias.set_incr(to_gpu(biasIncr))
def __init__(self, name, type, epsW, epsB, initW, initB, momW, momB, wc, weight, bias,
weightIncr , biasIncr, disable_bprop=False):
Layer.__init__(self, name, type, disable_bprop)
self.initW = initW
self.initB = initB
self.weight = WEIGHTS.empty('weight.' + self.name, epsW, momW, wc)
self.bias = WEIGHTS.empty('bias.' + self.name, epsB, momB, 0.0)
if weight is not None:
self.weight.set_weight(weight)
if weightIncr is not None:
self.weight.set_incr(weightIncr)
if bias is not None:
self.bias.set_weight(bias)
if biasIncr is not None:
self.bias.set_incr(to_gpu(biasIncr))
