#include <array_ops.h>
Compute gradients for a FakeQuantWithMinMaxVars operation.
Arguments:
Optional attributes (see Attrs
):
Returns:
Output
backprops_wrt_input: Backpropagated gradients w.r.t. inputs: gradients * (inputs >= min && inputs <= max)
.Output
backprop_wrt_min: Backpropagated gradients w.r.t. min parameter: sum(gradients * (inputs < min))
.Output
backprop_wrt_max: Backpropagated gradients w.r.t. max parameter: sum(gradients * (inputs > max))
. Constructors and Destructors | |
---|---|
FakeQuantWithMinMaxVarsGradient(const ::tensorflow::Scope & scope, ::tensorflow::Input gradients, ::tensorflow::Input inputs, ::tensorflow::Input min, ::tensorflow::Input max) | |
FakeQuantWithMinMaxVarsGradient(const ::tensorflow::Scope & scope, ::tensorflow::Input gradients, ::tensorflow::Input inputs, ::tensorflow::Input min, ::tensorflow::Input max, const FakeQuantWithMinMaxVarsGradient::Attrs & attrs) |
Public attributes | |
---|---|
backprop_wrt_max | |
backprop_wrt_min | |
backprops_wrt_input |
Public static functions | |
---|---|
NarrowRange(bool x) | |
NumBits(int64 x) |
Structs | |
---|---|
tensorflow::ops::FakeQuantWithMinMaxVarsGradient::Attrs | Optional attribute setters for FakeQuantWithMinMaxVarsGradient. |
::tensorflow::Output backprop_wrt_max
::tensorflow::Output backprop_wrt_min
::tensorflow::Output backprops_wrt_input
FakeQuantWithMinMaxVarsGradient( const ::tensorflow::Scope & scope, ::tensorflow::Input gradients, ::tensorflow::Input inputs, ::tensorflow::Input min, ::tensorflow::Input max )
FakeQuantWithMinMaxVarsGradient( const ::tensorflow::Scope & scope, ::tensorflow::Input gradients, ::tensorflow::Input inputs, ::tensorflow::Input min, ::tensorflow::Input max, const FakeQuantWithMinMaxVarsGradient::Attrs & attrs )
Attrs NarrowRange( bool x )
Attrs NumBits( int64 x )
© 2017 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/api_docs/cc/class/tensorflow/ops/fake-quant-with-min-max-vars-gradient.html