def backward_impl(self, inputs, outputs, prop_down, accum):
# inputs: [inputs_fwd_graph] + [inputs_bwd_graph] or
# [inputs_fwd_graph] + [outputs_fwd_graph] + [inputs_bwd_graph]
# Args
axes = self.forward_func.info.args["axes"]
# Inputs
x0 = inputs[0].data
dy = inputs[1].data
# Outputs
dx0 = outputs[0].data
# Grads of inputs
g_x0 = inputs[0].grad
g_dy = inputs[1].grad
# Grads of outputs
g_dx0 = outputs[0].grad
# Compute
# TODO: Optimize by creating max_pooling with indeces
if prop_down[1]:
# dx0 is not accumulated on the backward graph
mask = F.not_equal_scalar(dx0, 0.0)
if accum[1]:
g_dy_ = F.sum(g_dx0 * mask, axes)
g_dy += g_dy_
else:
g_dy_ = F.sum(g_dx0 * mask, axes)
g_dy.copy_from(g_dy_)
def backward_impl(self, inputs, outputs, prop_down, accum):
# inputs: [inputs_fwd_graph] + [inputs_bwd_graph] or
# [inputs_fwd_graph] + [outputs_fwd_graph] + [inputs_bwd_graph]
# Args
p = self.forward_func.info.args["p"]
# Inputs
x0 = inputs[0].data
dy = inputs[1].data
# Outputs
dx0 = outputs[0].data
# Grads of inputs
g_x0 = inputs[0].grad
g_dy = inputs[1].grad
# Grads of outputs
g_dx0 = outputs[0].grad
# Computation
if prop_down[1]:
# TODO: Optimize by creating dropout with mask
# dx0 is not accumulated in the backward graph
mask = F.not_equal_scalar(dx0, 0.0)
if accum[1]:
g_dy += g_dx0 * mask / (1.0 - p)
else:
g_dy.copy_from(g_dx0 * mask / (1.0 - p))
def dropout_backward(inputs, p=0.5, seed=-1):
"""
Args:
inputs (list of nn.Variable): Incomming grads/inputs to/of the forward function.
kwargs (dict of arguments): Dictionary of the corresponding function arguments.
Return:
list of Variable: Return the gradients wrt inputs of the corresponding function.
"""
dy = inputs[0]
x0 = inputs[1]
y0 = get_output(x0, "Dropout")
m0 = F.not_equal_scalar(y0, 0)
m0 = no_grad(m0)
dx0 = dy * m0 / (1 - p)
return dx0
def sinc_backward(inputs):
"""
Args:
inputs (list of nn.Variable): Incomming grads/inputs to/of the forward function.
kwargs (dict of arguments): Dictionary of the corresponding function arguments.
Return:
list of Variable: Return the gradients wrt inputs of the corresponding function.
"""
dy = inputs[0]
x0 = inputs[1]
m0 = F.not_equal_scalar(x0, 0)
m0 = no_grad(m0)
y0 = get_output(x0, "Sinc")
dx0 = dy * (F.cos(x0) - y0) / x0
c0 = F.constant(0, x0.shape)
dx0 = F.where(m0, dx0, c0)
return dx0
