U
- data type for result()
output@Operator(group="nn") public final class QuantizedBatchNormWithGlobalNormalization<U> extends PrimitiveOp
This op is deprecated and will be removed in the future. Prefer `tf.nn.batch_normalization`.
operation
Modifier and Type | Method and Description |
---|---|
static <U,T> QuantizedBatchNormWithGlobalNormalization<U> |
create(Scope scope,
Operand<T> t,
Operand<Float> tMin,
Operand<Float> tMax,
Operand<T> m,
Operand<Float> mMin,
Operand<Float> mMax,
Operand<T> v,
Operand<Float> vMin,
Operand<Float> vMax,
Operand<T> beta,
Operand<Float> betaMin,
Operand<Float> betaMax,
Operand<T> gamma,
Operand<Float> gammaMin,
Operand<Float> gammaMax,
Class<U> outType,
Float varianceEpsilon,
Boolean scaleAfterNormalization)
Factory method to create a class wrapping a new QuantizedBatchNormWithGlobalNormalization operation.
|
Output<U> |
result() |
Output<Float> |
resultMax() |
Output<Float> |
resultMin() |
equals, hashCode, op, toString
public static <U,T> QuantizedBatchNormWithGlobalNormalization<U> create(Scope scope, Operand<T> t, Operand<Float> tMin, Operand<Float> tMax, Operand<T> m, Operand<Float> mMin, Operand<Float> mMax, Operand<T> v, Operand<Float> vMin, Operand<Float> vMax, Operand<T> beta, Operand<Float> betaMin, Operand<Float> betaMax, Operand<T> gamma, Operand<Float> gammaMin, Operand<Float> gammaMax, Class<U> outType, Float varianceEpsilon, Boolean scaleAfterNormalization)
scope
- current scopet
- A 4D input Tensor.tMin
- The value represented by the lowest quantized input.tMax
- The value represented by the highest quantized input.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.mMin
- The value represented by the lowest quantized mean.mMax
- The value represented by the highest quantized mean.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.vMin
- The value represented by the lowest quantized variance.vMax
- The value represented by the highest quantized variance.beta
- A 1D beta Tensor with size matching the last dimension of t.
An offset to be added to the normalized tensor.betaMin
- The value represented by the lowest quantized offset.betaMax
- The value represented by the highest quantized offset.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.gammaMin
- The value represented by the lowest quantized gamma.gammaMax
- The value represented by the highest quantized gamma.outType
- varianceEpsilon
- A small float number to avoid dividing by 0.scaleAfterNormalization
- A bool indicating whether the resulted tensor
needs to be multiplied with gamma.Copyright © 2022. All rights reserved.