def setup_from_shape(self, in_shape):
batch_size = in_shape[0]
self.shape = (batch_size,)
self.array = ca.zeros(self.shape)
self.grad_array = ca.ones(self.shape)
self.axis = tuple(range(1, len(in_shape)))
self.bcast_shape = tuple([batch_size, ] + [1, ]*len(self.axis))
def fun_grad(x):
ca.random.seed(seed)
src.array = ca.array(x)
graph.fprop()
sink.grad_array = ca.ones(sink.shape, dtype=ca.float_)
graph.bprop()
x_grad = np.array(src.grad_array)
return x_grad
def fun_grad(x):
ca.random.seed(seed)
src.array = ca.array(x)
graph.fprop()
sink.grad_array = ca.ones(sink.shape, dtype=ca.float_)
graph.bprop()
x_grad = np.array(src.grad_array)
return x_grad
def setup_from_shape(self, in_shape):
batch_size = in_shape[0]
self.shape = (batch_size, )
self.array = ca.zeros(self.shape)
self.grad_array = ca.ones(self.shape)
self.axis = tuple(range(1, len(in_shape)))
self.bcast_shape = tuple([
batch_size,
] + [
1,
] * len(self.axis))
def setup(self):
super(BatchNormalization, self).setup()
if len(self.out_shape) != 2:
raise ValueError('Only 1D data supported')
reduced_shape = 1, self.out_shape[1]
self.running_mean = ca.zeros(reduced_shape)
self.running_std = ca.ones(reduced_shape)
self._tmp_batch_centered = ca.zeros(self.out_shape)
self._tmp_batch_inv_std = ca.zeros(reduced_shape)
if self.affine:
self.gamma.setup(reduced_shape)
self.beta.setup(reduced_shape)
def setup(self):
super(BatchNormalization, self).setup()
reduced_shape = (1,) + self.out_shape[1:]
if self.running_mean is None:
self.running_mean = ca.zeros(reduced_shape)
self.running_std = ca.ones(reduced_shape)
if self.affine:
self.gamma.setup(reduced_shape)
self.beta.setup(reduced_shape)
else:
if self.running_mean.shape != reduced_shape:
raise ValueError('New input shape is not compatible')
self._tmp_batch_inv_std = ca.zeros(reduced_shape)
self._tmp_batch_centered = ca.zeros(self.out_shape)
def setup(self):
super(BatchNormalization, self).setup()
reduced_shape = (1, ) + self.out_shape[1:]
if self.running_mean is None:
self.running_mean = ca.zeros(reduced_shape)
self.running_std = ca.ones(reduced_shape)
if self.affine:
self.gamma.setup(reduced_shape)
self.beta.setup(reduced_shape)
else:
if self.running_mean.shape != reduced_shape:
raise ValueError('New input shape is not compatible')
self._tmp_batch_inv_std = ca.zeros(reduced_shape)
self._tmp_batch_centered = ca.zeros(self.out_shape)
def setup(self):
super(SpatialBatchNormalization, self).setup()
if len(self.out_shape) != 4:
raise ValueError('Only 4D data supported')
reduced_shape = 1, self.out_shape[1], 1, 1
if self.running_mean is None:
self.running_mean = ca.zeros(reduced_shape)
self.running_std = ca.ones(reduced_shape)
if self.affine:
self.gamma.setup(reduced_shape)
self.beta.setup(reduced_shape)
else:
if self.running_mean.shape != reduced_shape:
raise ValueError('New input shape is not compatible')
self._tmp_batch_inv_std = ca.zeros(reduced_shape)
self._tmp_batch_centered = ca.zeros(self.out_shape)
def setup(self):
super(SpatialBatchNormalization, self).setup()
if len(self.out_shape) != 4:
raise ValueError('Only 4D data supported')
reduced_shape = 1, self.out_shape[1], 1, 1
if self.running_mean is None:
self.running_mean = ca.zeros(reduced_shape)
self.running_std = ca.ones(reduced_shape)
if self.affine:
self.gamma.setup(reduced_shape)
self.beta.setup(reduced_shape)
else:
if self.running_mean.shape != reduced_shape:
raise ValueError('New input shape is not compatible')
self._tmp_batch_inv_std = ca.zeros(reduced_shape)
self._tmp_batch_centered = ca.zeros(self.out_shape)
def array(self, shape):
return ca.ones(shape)*self.c
def array(self, shape):
return ca.ones(shape) * self.value
def setup(self):
self.out_shape = (self.pred.out_shape[0], 1)
self.out = ca.empty(self.out_shape)
self.out_grad = ca.ones(self.out_shape)
def setup(self):
self.out_shape = (len(self.mu.out), 1)
self.out = ca.empty(self.out_shape)
self.out_grad = ca.ones(self.out_shape)
