def grad_fn(*args):
"""Computes the gradient of the wrapped function."""
tape.push_new_tape()
end_node = f(*args)
variables = tape.top_tape_watched_variables()
sources = [x.handle for x in variables]
grad = imperative_grad(end_node, sources)
return end_node, list(zip(grad, variables))
def grad_fn(*args): """Computes the gradient of the wrapped function.""" tape.push_new_tape() end_node = f(*args) variables = tape.top_tape_watched_variables() sources = [x.handle for x in variables] grad = imperative_grad(end_node, sources) return end_node, list(zip(grad, variables))
def grad_fn(*args):
"""Computes the gradient of the wrapped function."""
tape.push_new_tape()
end_node = f(*args)
variables = tape.top_tape_watched_variables()
sources = [x.handle for x in variables]
if not sources:
raise ValueError("no trainable variables were accessed while the "
"function was being computed.")
grad = imperative_grad.imperative_grad(_default_vspace,
tape.pop_tape(),
nest.flatten(end_node), sources)
return end_node, list(zip(grad, variables))
def grad_fn(*args):
"""Computes the gradient of the wrapped function."""
tape.push_new_tape()
end_node = f(*args)
variables = tape.top_tape_watched_variables()
sources = [x.handle for x in variables]
if not sources:
raise ValueError("no trainable variables were accessed while the "
"function was being computed.")
grad = imperative_grad.imperative_grad(_default_vspace,
tape.pop_tape(),
nest.flatten(end_node),
sources)
return end_node, list(zip(grad, variables))
def grad_fn(*args):
"""Computes the gradient of the wrapped function."""
tape.push_new_tape()
try:
end_node = f(*args)
if end_node is None:
raise ValueError(
"Cannot differentiate a function that returns None; "
"did you forget to return a value from {}?".format(
f.__name__))
variables = tape.top_tape_watched_variables()
finally:
popped_tape = tape.pop_tape()
sources = [x.handle for x in variables]
if not sources:
raise ValueError("No trainable variables were accessed while the "
"function was being computed.")
grad = imperative_grad.imperative_grad(_default_vspace, popped_tape,
nest.flatten(end_node), sources)
return end_node, list(zip(grad, variables))
