IdentityN
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Returns a list of tensors with the same shapes and contents as the input
tensors.
This op can be used to override the gradient for complicated functions. For
example, suppose y = f(x) and we wish to apply a custom function g for backprop
such that dx = g(dy). In Python,
with tf.get_default_graph().gradient_override_map(
{'IdentityN': 'OverrideGradientWithG'
):
y, _ = identity_n([f(x), x])
Inherited Methods
From class
java.lang.Object
boolean
|
equals
(Object arg0)
|
final
Class<?>
|
getClass
()
|
int
|
hashCode
()
|
final
void
|
notify
()
|
final
void
|
notifyAll
()
|
String
|
toString
()
|
final
void
|
wait
(long arg0, int arg1)
|
final
void
|
wait
(long arg0)
|
final
void
|
wait
()
|
From interface
java.lang.Iterable
abstract
void
|
forEach
(Consumer<? super T> arg0)
|
abstract
Iterator<
Operand
<Object>>
|
iterator
()
|
abstract
Spliterator<
Operand
<Object>>
|
spliterator
()
|
Public Methods
Factory method to create a class wrapping a new IdentityN operation.
Returns
-
a new instance of IdentityN
public
Iterator<
Operand
<Object>>
iterator
()
public
List<
Output
<?>>
output
()
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates. Some content is licensed under the numpy license.
Last updated 2021-05-14 UTC.
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