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TensorFlow 1 version
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View source on GitHub
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A decorator for registering the gradient function for an op type.
tf.RegisterGradient(
op_type
)
This decorator is only used when defining a new op type. For an op
with m inputs and n outputs, the gradient function is a function
that takes the original Operation and n Tensor objects
(representing the gradients with respect to each output of the op),
and returns m Tensor objects (representing the partial gradients
with respect to each input of the op).
For example, assuming that operations of type "Sub" take two
inputs x and y, and return a single output x - y, the
following gradient function would be registered:
@tf.RegisterGradient("Sub")
def _sub_grad(unused_op, grad):
return grad, tf.negative(grad)
The decorator argument op_type is the string type of an
operation. This corresponds to the OpDef.name field for the proto
that defines the operation.
Args | |
|---|---|
op_type
|
The string type of an operation. This corresponds to the
OpDef.name field for the proto that defines the operation.
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Raises | |
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TypeError
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If op_type is not string.
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Methods
__call__
__call__(
f
)
Registers the function f as gradient function for op_type.