BatchToSpace for N-D tensors of type T.
This operation reshapes the "batch" dimension 0 into `M + 1` dimensions of shape `block_shape + [batch]`, interleaves these blocks back into the grid defined by the spatial dimensions `[1, ..., M]`, to obtain a result with the same rank as the input. The spatial dimensions of this intermediate result are then optionally cropped according to `crops` to produce the output. This is the reverse of SpaceToBatch. See below for a precise description.
Public Methods
Output<T> |
asOutput()
Returns the symbolic handle of a tensor.
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static <T, U extends Number, V extends Number> BatchToSpaceNd<T> | |
Output<T> |
output()
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Inherited Methods
Public Methods
public Output<T> asOutput ()
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.
public static BatchToSpaceNd<T> create (Scope scope, Operand<T> input, Operand<U> blockShape, Operand<V> crops)
Factory method to create a class wrapping a new BatchToSpaceNd operation.
Parameters
scope | current scope |
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input | N-D with shape `input_shape = [batch] + spatial_shape + remaining_shape`, where spatial_shape has M dimensions. |
blockShape | 1-D with shape `[M]`, all values must be >= 1. |
crops | 2-D with shape `[M, 2]`, all values must be >= 0.
`crops[i] = [crop_start, crop_end]` specifies the amount to crop from input
dimension `i + 1`, which corresponds to spatial dimension `i`. It is
required that
`crop_start[i] + crop_end[i] <= block_shape[i] * input_shape[i + 1]`.
This operation is equivalent to the following steps: 1. Reshape `input` to `reshaped` of shape: [block_shape[0], ..., block_shape[M-1], batch / prod(block_shape), input_shape[1], ..., input_shape[N-1]] 2. Permute dimensions of `reshaped` to produce `permuted` of shape [batch / prod(block_shape), input_shape[1], block_shape[0], ..., input_shape[M], block_shape[M-1], input_shape[M+1], ..., input_shape[N-1]] 3. Reshape `permuted` to produce `reshaped_permuted` of shape [batch / prod(block_shape), input_shape[1] * block_shape[0], ..., input_shape[M] * block_shape[M-1], input_shape[M+1], ..., input_shape[N-1]] 4. Crop the start and end of dimensions `[1, ..., M]` of `reshaped_permuted` according to `crops` to produce the output of shape: [batch / prod(block_shape), input_shape[1] * block_shape[0] - crops[0,0] - crops[0,1], ..., input_shape[M] * block_shape[M-1] - crops[M-1,0] - crops[M-1,1], input_shape[M+1], ..., input_shape[N-1]] Some examples: (1) For the following input of shape `[4, 1, 1, 1]`, `block_shape = [2, 2]`, and `crops = [[0, 0], [0, 0]]`:
The output tensor has shape `[1, 2, 2, 1]` and value:
(2) For the following input of shape `[4, 1, 1, 3]`, `block_shape = [2, 2]`, and
`crops = [[0, 0], [0, 0]]`:
The output tensor has shape `[1, 2, 2, 3]` and value:
(3) For the following input of shape `[4, 2, 2, 1]`, `block_shape = [2, 2]`, and
`crops = [[0, 0], [0, 0]]`:
The output tensor has shape `[1, 4, 4, 1]` and value:
(4) For the following input of shape `[8, 1, 3, 1]`, `block_shape = [2, 2]`, and
`crops = [[0, 0], [2, 0]]`:
The output tensor has shape `[2, 2, 4, 1]` and value:
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Returns
- a new instance of BatchToSpaceNd