org.tensorflow.op.core

Class BatchNormWithGlobalNormalizationGrad<T>

java.lang.Object

org.tensorflow.op.PrimitiveOp

org.tensorflow.op.core.BatchNormWithGlobalNormalizationGrad<T>

Type Parameters:

T - data type for dx() output

All Implemented Interfaces:

Op

@Operator
public final class BatchNormWithGlobalNormalizationGrad<T>
extends PrimitiveOp

Gradients for batch normalization.

This op is deprecated. See `tf.nn.batch_normalization`.

Field Summary

Fields inherited from class org.tensorflow.op.PrimitiveOp

operation

Method Summary

Modifier and Type	Method and Description
static <T> BatchNormWithGlobalNormalizationGrad<T>	create(Scope scope, Operand<T> t, Operand<T> m, Operand<T> v, Operand<T> gamma, Operand<T> backprop, Float varianceEpsilon, Boolean scaleAfterNormalization) Factory method to create a class to wrap a new BatchNormWithGlobalNormalizationGrad operation to the graph.
Output<T>	db() 1D backprop tensor for beta.
Output<T>	dg() 1D backprop tensor for gamma.
Output<T>	dm() 1D backprop tensor for mean.
Output<T>	dv() 1D backprop tensor for variance.
Output<T>	dx() 4D backprop tensor for input.

Methods inherited from class org.tensorflow.op.PrimitiveOp

equals, hashCode, toString

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Method Detail

create

public static <T> BatchNormWithGlobalNormalizationGrad<T> create(Scope scope,
                                                                 Operand<T> t,
                                                                 Operand<T> m,
                                                                 Operand<T> v,
                                                                 Operand<T> gamma,
                                                                 Operand<T> backprop,
                                                                 Float varianceEpsilon,
                                                                 Boolean scaleAfterNormalization)

Factory method to create a class to wrap a new BatchNormWithGlobalNormalizationGrad operation to the graph.

Parameters:

scope - current graph scope

t - A 4D input Tensor.

m - A 1D mean Tensor with size matching the last dimension of t. This is the first output from tf.nn.moments, or a saved moving average thereof.

v - A 1D variance Tensor with size matching the last dimension of t. This is the second output from tf.nn.moments, or a saved moving average thereof.

gamma - A 1D gamma Tensor with size matching the last dimension of t. If "scale_after_normalization" is true, this Tensor will be multiplied with the normalized Tensor.

backprop - 4D backprop Tensor.

varianceEpsilon - A small float number to avoid dividing by 0.

scaleAfterNormalization - A bool indicating whether the resulted tensor needs to be multiplied with gamma.

Returns:

a new instance of BatchNormWithGlobalNormalizationGrad

dx

public Output<T> dx()

4D backprop tensor for input.

dm

public Output<T> dm()

1D backprop tensor for mean.

dv

public Output<T> dv()

1D backprop tensor for variance.

db

public Output<T> db()

1D backprop tensor for beta.

dg

public Output<T> dg()

1D backprop tensor for gamma.