QuantizedBatchNormWithGlobalNormalization

public final class QuantizedBatchNormWithGlobalNormalization

Quantized Batch normalization.

This op is deprecated and will be removed in the future. Prefer `tf.nn.batch_normalization`.

Constants

String OP_NAME The name of this op, as known by TensorFlow core engine

Public Methods

static <U extends TType , T extends TType > QuantizedBatchNormWithGlobalNormalization <U>
create ( Scope scope, Operand <T> t, Operand < TFloat32 > tMin, Operand < TFloat32 > tMax, Operand <T> m, Operand < TFloat32 > mMin, Operand < TFloat32 > mMax, Operand <T> v, Operand < TFloat32 > vMin, Operand < TFloat32 > vMax, Operand <T> beta, Operand < TFloat32 > betaMin, Operand < TFloat32 > betaMax, Operand <T> gamma, Operand < TFloat32 > gammaMin, Operand < TFloat32 > gammaMax, Class<U> outType, Float varianceEpsilon, Boolean scaleAfterNormalization)
Factory method to create a class wrapping a new QuantizedBatchNormWithGlobalNormalization operation.
Output <U>
Output < TFloat32 >
Output < TFloat32 >

Inherited Methods

Constants

public static final String OP_NAME

The name of this op, as known by TensorFlow core engine

Constant Value: "QuantizedBatchNormWithGlobalNormalization"

Public Methods

public static QuantizedBatchNormWithGlobalNormalization <U> create ( Scope scope, Operand <T> t, Operand < TFloat32 > tMin, Operand < TFloat32 > tMax, Operand <T> m, Operand < TFloat32 > mMin, Operand < TFloat32 > mMax, Operand <T> v, Operand < TFloat32 > vMin, Operand < TFloat32 > vMax, Operand <T> beta, Operand < TFloat32 > betaMin, Operand < TFloat32 > betaMax, Operand <T> gamma, Operand < TFloat32 > gammaMin, Operand < TFloat32 > gammaMax, Class<U> outType, Float varianceEpsilon, Boolean scaleAfterNormalization)

Factory method to create a class wrapping a new QuantizedBatchNormWithGlobalNormalization operation.

Parameters
scope current scope
t 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.
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 QuantizedBatchNormWithGlobalNormalization

public Output <U> result ()

public Output < TFloat32 > resultMax ()

public Output < TFloat32 > resultMin ()