def __init__(self, layer1, layer2, cfg, layernum):
self.mat = cp.dev_tensor_float_cm([layer1.size, layer2.size])
cp.fill(self.mat, 0)
cp.add_rnd_normal(self.mat)
fact = 1.0
if layer2.unit_type == UnitType.binary or layer1.unit_type == UnitType.binary:
# the 0.5 stems from the fact that our upper layer has activation 0.5 on average, not 0, if we use binary hidden units.
fact = 0.5
self.mat *= fact / math.sqrt(max(layer1.size, layer2.size))
self.allocBias(layer1, layer2)
def __init__(self, layer1, layer2, cfg, layernum):
self.mat = cp.dev_tensor_float_cm([layer1.size, layer2.size])
cp.fill(self.mat, 0)
cp.add_rnd_normal(self.mat)
fact = 1.0
if layer2.unit_type == UnitType.binary or layer1.unit_type == UnitType.binary:
# the 0.5 stems from the fact that our upper layer has activation 0.5 on average, not 0, if we use binary hidden units.
fact = 0.5
self.mat *= fact / math.sqrt(max(layer1.size, layer2.size))
self.allocBias(layer1, layer2)
def setMiniBatch(self, mb, dst_layer):
self.sampleset_ = mb
self.sampleset = cp.dev_tensor_uc_cm(self.sampleset_.copy('F'))
shift = np.random.randint(-self.maxmov,self.maxmov+1, 2).astype('int32')
cp.image_move(dst_layer,self.sampleset,self.src_size, self.dst_size, self.src_num_maps, shift[0],shift[1])
if self.noise_std != 0:
cp.add_rnd_normal(dst_layer,self.noise_std)
if self.norm:
self.norm(dst_layer)
self.sampleset.dealloc()
self.sampleset = dst_layer.copy()
def setMiniBatch(self, mb, dst_layer):
self.sampleset_ = mb
self.sampleset = cp.dev_tensor_uc_cm(self.sampleset_.copy('F'))
shift = np.random.randint(-self.maxmov, self.maxmov + 1,
2).astype('int32')
cp.image_move(dst_layer, self.sampleset, self.src_size, self.dst_size,
self.src_num_maps, shift[0], shift[1])
if self.noise_std != 0:
cp.add_rnd_normal(dst_layer, self.noise_std)
if self.norm:
self.norm(dst_layer)
self.sampleset.dealloc()
self.sampleset = dst_layer.copy()
def sample(self):
if self.unit_type == UnitType.gaussian or self.unit_type == UnitType.cont:
cp.add_rnd_normal(self.act)
elif self.unit_type == UnitType.binary:
cp.rnd_binarize(self.act)
def sample(self):
if self.unit_type == UnitType.gaussian or self.unit_type == UnitType.cont:
cp.add_rnd_normal(self.act)
elif self.unit_type == UnitType.binary:
cp.rnd_binarize(self.act)
