def fprop(self, input, output, train=TRAIN):
cudaconv2.localFilterActs(input, self.weight.wt, output, self.img_size, self.outputSize,
self.outputSize, -self.padding, self.stride, self.numColor, 1)
#util.log_info('%s', output.get().mean())
self.tmp = gpuarray.empty((self.numFilter,
self.get_single_img_size() * self.batch_size / self.numFilter),
dtype=np.float32)
add_vec_to_rows(output, self.bias.wt)
if PFout:
print_matrix(output, self.name)
def fprop(self, input, output, train=TRAIN):
cudaconv2.localFilterActs(input, self.weight.wt, output, self.img_size, self.outputSize,
self.outputSize, -self.padding, self.stride, self.numColor, 1)
#util.log_info('%s', output.get().mean())
self.tmp = gpuarray.empty((self.numFilter,
self.get_single_img_size() * self.batch_size / self.numFilter),
dtype=np.float32)
add_vec_to_rows(output, self.bias.wt)
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 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)
