tf.RegisterGradient
| View source on GitHub |
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. |
| Raises | |
|---|---|
TypeError | If op_type is not string. |
Methods
__call__
__call__(
f
)
Registers the function f as gradient function for op_type.
© 2020 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/versions/r2.3/api_docs/python/tf/RegisterGradient