def do_check():
# Generate an initialized temporary link that is already forward
# propagated. This link is only used to generate necessary data,
# i.e. inputs, outputs and parameters for the later gradient check
# and the link itself will be discarded.
inits = self._generate_params()
link, inputs, outputs = self._create_initialized_link(
inits, backend_config)
# Extract the parameter ndarrays from the initialized link.
params = _get_link_params(link, self.param_names)
params = [p.array for p in params]
# Prepare inputs, outputs and upstream gradients for the gradient
# check.
cpu_device = backend.CpuDevice()
outputs = [cpu_device.send(output) for output in outputs]
grad_outputs = self._generate_grad_outputs(outputs)
grad_outputs = backend_config.get_array(grad_outputs)
inputs = self._to_noncontiguous_as_needed(inputs)
params = self._to_noncontiguous_as_needed(params)
grad_outputs = self._to_noncontiguous_as_needed(grad_outputs)
# Create the link used for the actual forward propagation in the
# gradient check.
forward_link, _, _ = self._create_initialized_link(
inits, backend_config)
def forward(inputs, ps):
# Use generated parameters.
with forward_link.init_scope():
for param_name, p in zip(self.param_names, ps):
setattr(forward_link, param_name, p)
return self._forward(forward_link, inputs, backend_config)
with LinkTestError.raise_if_fail(
'backward is not implemented correctly'):
gradient_check._check_backward_with_params(
forward,
inputs,
grad_outputs,
params=params,
dtype=numpy.float64,
detect_nondifferentiable=self.dodge_nondifferentiable,
**self.check_backward_options)
def do_check():
# Generate an initialized temporary link that is already forward
# propagated. This link is only used to generate necessary data,
# i.e. inputs, outputs and parameters for the later gradient check
# and the link itself will be discarded.
inits = self._generate_params()
link, inputs, outputs = self._create_initialized_link(
inits, backend_config)
# Extract the parameter ndarrays from the initialized link.
params = _get_link_params(link, self.param_names)
params = [p.array for p in params]
# Prepare inputs, outputs and upstream gradients for the gradient
# check.
cpu_device = backend.CpuDevice()
outputs = [cpu_device.send(output) for output in outputs]
grad_outputs = self._generate_grad_outputs(outputs)
grad_outputs = backend_config.get_array(grad_outputs)
inputs = self._to_noncontiguous_as_needed(inputs)
params = self._to_noncontiguous_as_needed(params)
grad_outputs = self._to_noncontiguous_as_needed(grad_outputs)
# Create the link used for the actual forward propagation in the
# gradient check.
forward_link, _, _ = self._create_initialized_link(
inits, backend_config)
def forward(inputs, ps):
# Use generated parameters.
with forward_link.init_scope():
for param_name, p in zip(self.param_names, ps):
setattr(forward_link, param_name, p)
return self._forward(forward_link, inputs, backend_config)
with LinkTestError.raise_if_fail(
'backward is not implemented correctly'):
gradient_check._check_backward_with_params(
forward, inputs, grad_outputs, params=params,
dtype=numpy.float64,
detect_nondifferentiable=self.dodge_nondifferentiable,
**self.check_backward_options)