org.tensorflow.op.core

Class BatchToSpace<T>

java.lang.Object

org.tensorflow.op.PrimitiveOp

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

Type Parameters:

T - data type for output() output

All Implemented Interfaces:

Op, Operand<T>

@Operator
public final class BatchToSpace<T>
extends PrimitiveOp
implements Operand<T>

BatchToSpace for 4-D tensors of type T.

This is a legacy version of the more general BatchToSpaceND.

Rearranges (permutes) data from batch into blocks of spatial data, followed by cropping. This is the reverse transformation of SpaceToBatch. More specifically, this op outputs a copy of the input tensor where values from the `batch` dimension are moved in spatial blocks to the `height` and `width` dimensions, followed by cropping along the `height` and `width` dimensions.

Field Summary

Fields inherited from class org.tensorflow.op.PrimitiveOp

operation

Method Summary

Modifier and Type	Method and Description
Output<T>	asOutput() Returns the symbolic handle of a tensor.
static <T,U extends Number> BatchToSpace<T>	create(Scope scope, Operand<T> input, Operand<U> crops, Long blockSize) Factory method to create a class wrapping a new BatchToSpace operation.
Output<T>	output() 4-D with shape `[batch, height, width, depth]`, where:

Methods inherited from class org.tensorflow.op.PrimitiveOp

equals, hashCode, op, toString

Methods inherited from class java.lang.Object

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

Method Detail

create

public static <T,U extends Number> BatchToSpace<T> create(Scope scope,
                                                          Operand<T> input,
                                                          Operand<U> crops,
                                                          Long blockSize)

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

Parameters:

scope - current scope

input - 4-D tensor with shape `[batchblock_sizeblock_size, height_pad/block_size, width_pad/block_size, depth]`. Note that the batch size of the input tensor must be divisible by `block_size * block_size`.

crops - 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies how many elements to crop from the intermediate result across the spatial dimensions as follows:

crops = [[crop_top, crop_bottom], [crop_left, crop_right]]

blockSize -

Returns:

a new instance of BatchToSpace

output

public Output<T> output()

4-D with shape `[batch, height, width, depth]`, where:

height = height_pad - crop_top - crop_bottom width = width_pad - crop_left - crop_right

The attr `block_size` must be greater than one. It indicates the block size.

Some examples:

(1) For the following input of shape `[4, 1, 1, 1]` and block_size of 2:

 [[[[1]]], [[[2]]], [[[3]]], [[[4]]]]
 

The output tensor has shape `[1, 2, 2, 1]` and value:

 x = [[[[1], [2]], [[3], [4]]]]
 

(2) For the following input of shape `[4, 1, 1, 3]` and block_size of 2:

 [[[[1, 2, 3]]], [[[4, 5, 6]]], [[[7, 8, 9]]], [[[10, 11, 12]]]]
 

The output tensor has shape `[1, 2, 2, 3]` and value:

 x = [[[[1, 2, 3], [4, 5, 6]],
       [[7, 8, 9], [10, 11, 12]]]]
 

(3) For the following input of shape `[4, 2, 2, 1]` and block_size of 2:

 x = [[[[1], [3]], [[9], [11]]],
      [[[2], [4]], [[10], [12]]],
      [[[5], [7]], [[13], [15]]],
      [[[6], [8]], [[14], [16]]]]
 

The output tensor has shape `[1, 4, 4, 1]` and value:

 x = [[[[1],   [2],  [3],  [4]],
      [[5],   [6],  [7],  [8]],
      [[9],  [10], [11],  [12]],
      [[13], [14], [15],  [16]]]]
 

(4) For the following input of shape `[8, 1, 2, 1]` and block_size of 2:

 x = [[[[1], [3]]], [[[9], [11]]], [[[2], [4]]], [[[10], [12]]],
      [[[5], [7]]], [[[13], [15]]], [[[6], [8]]], [[[14], [16]]]]
 

The output tensor has shape `[2, 2, 4, 1]` and value:

 x = [[[[1], [3]], [[5], [7]]],
      [[[2], [4]], [[10], [12]]],
      [[[5], [7]], [[13], [15]]],
      [[[6], [8]], [[14], [16]]]]
 

asOutput

public Output<T> asOutput()

Description copied from interface: Operand

Returns the symbolic handle of a 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.

Specified by:

asOutput in interface Operand<T>

See Also:

OperationBuilder.addInput(Output)