tf.identity_n
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Returns a list of tensors with the same shapes and contents as the input
tf.identity_n( input, name=None )
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]) @tf.RegisterGradient('OverrideGradientWithG') def ApplyG(op, dy, _): return [None, g(dy)] # Do not backprop to f(x).
Args |
|---|
input | A list of Tensor objects. |
name | A name for the operation (optional). |
Returns |
|---|
A list of Tensor objects. Has the same type as input. |
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Last updated 2024-04-26 UTC.
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