FractionalAvgPoolGrad

public final class FractionalAvgPoolGrad

Computes gradient of the FractionalAvgPool function.

Unlike FractionalMaxPoolGrad, we don't need to find arg_max for FractionalAvgPoolGrad, we just need to evenly back-propagate each element of out_backprop to those indices that form the same pooling cell. Therefore, we just need to know the shape of original input tensor, instead of the whole tensor.

Nested Classes

class FractionalAvgPoolGrad.Options Optional attributes for FractionalAvgPoolGrad

Constants

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

Public Methods

Output<T>	asOutput() Returns the symbolic handle of the tensor.
static <T extends TNumber> FractionalAvgPoolGrad<T>	create(Scope scope, Operand<TInt64> origInputTensorShape, Operand<T> outBackprop, Operand<TInt64> rowPoolingSequence, Operand<TInt64> colPoolingSequence, Options... options) Factory method to create a class wrapping a new FractionalAvgPoolGrad operation.
Output<T>	output() 4-D.
static FractionalAvgPoolGrad.Options	overlapping(Boolean overlapping)

Inherited Methods

From class org.tensorflow.op.RawOp

final boolean	equals(Object obj)
final int	hashCode()
Operation	op() Return this unit of computation as a single `Operation`.
final String	toString()

From class java.lang.Object

boolean	equals(Object arg0)
final Class<?>	getClass()
int	hashCode()
final void	notify()
final void	notifyAll()
String	toString()
final void	wait(long arg0, int arg1)
final void	wait(long arg0)
final void	wait()

From interface org.tensorflow.op.Op

abstract ExecutionEnvironment	env() Return the execution environment this op was created in.
abstract Operation	op() Return this unit of computation as a single `Operation`.

From interface org.tensorflow.Operand

abstract Output<T>	asOutput() Returns the symbolic handle of the tensor.
abstract T	asTensor() Returns the tensor at this operand.
abstract Shape	shape() Returns the (possibly partially known) shape of the tensor referred to by the `Output` of this operand.
abstract Class<T>	type() Returns the tensor type of this operand

From interface org.tensorflow.ndarray.Shaped

abstract int	rank()
abstract Shape	shape()
abstract long	size() Computes and returns the total size of this container, in number of values.

Constants

public static final String OP_NAME

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

Constant Value: "FractionalAvgPoolGrad"

Public Methods

public Output<T> asOutput ()

Returns the symbolic handle of the tensor.

Inputs to TensorFlow operations are outputs of another TensorFlow operation. This method is used to obtain a symbolic handle that represents the computation of the input.

public static FractionalAvgPoolGrad<T> create (Scope scope, Operand<TInt64> origInputTensorShape, Operand<T> outBackprop, Operand<TInt64> rowPoolingSequence, Operand<TInt64> colPoolingSequence, Options... options)

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

Parameters

scope	current scope
origInputTensorShape	Original input tensor shape for `fractional_avg_pool`
outBackprop	4-D with shape `[batch, height, width, channels]`. Gradients w.r.t. the output of `fractional_avg_pool`.
rowPoolingSequence	row pooling sequence, form pooling region with col_pooling_sequence.
colPoolingSequence	column pooling sequence, form pooling region with row_pooling sequence.
options	carries optional attributes values

Returns

a new instance of FractionalAvgPoolGrad

public Output<T> output ()

4-D. Gradients w.r.t. the input of `fractional_avg_pool`.

public static FractionalAvgPoolGrad.Options overlapping (Boolean overlapping)

Parameters

overlapping	When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example: `index 0 1 2 3 4` `value 20 5 16 3 7` If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.

overlapping

When set to True, it means when pooling, the values at the boundary of adjacent pooling cells are used by both cells. For example:

`index 0 1 2 3 4`

`value 20 5 16 3 7`

If the pooling sequence is [0, 2, 4], then 16, at index 2 will be used twice. The result would be [41/3, 26/3] for fractional avg pooling.