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# tf.nn.space_to_batch

SpaceToBatch for 4-D tensors of type T.

This is a legacy version of the more general SpaceToBatchND.

Zero-pads and then rearranges (permutes) blocks of spatial data into batch. More specifically, this op outputs a copy of the input tensor where values from the `height` and `width` dimensions are moved to the `batch` dimension. After the zero-padding, both `height` and `width` of the input must be divisible by the block size.

`input` A `Tensor`. 4-D with shape `[batch, height, width, depth]`.
`paddings` A `Tensor`. Must be one of the following types: `int32`, `int64`. 2-D tensor of non-negative integers with shape `[2, 2]`. It specifies the padding of the input with zeros across the spatial dimensions as follows:

The effective spatial dimensions of the zero-padded input tensor will be:

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

• Non-overlapping blocks of size `block_size x block size` in the height and width dimensions are rearranged into the batch dimension at each location.
• The batch of the output tensor is `batch * block_size * block_size`.

The shape of the output will be:

Some examples:

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

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

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

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

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

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

The output tensor has shape `[4, 1, 1, 3]` and value:

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

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

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

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

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

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

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

The output tensor has shape `[8, 1, 2, 1]` and value:

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

Among others, this operation is useful for reducing atrous convolution into regular convolution.

`block_size` An `int` that is `>= 2`.
`name` A name for the operation (optional).

A `Tensor`. Has the same type as `input`.

[{ "type": "thumb-down", "id": "missingTheInformationINeed", "label":"Missing the information I need" },{ "type": "thumb-down", "id": "tooComplicatedTooManySteps", "label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]
[{ "type": "thumb-up", "id": "easyToUnderstand", "label":"Easy to understand" },{ "type": "thumb-up", "id": "solvedMyProblem", "label":"Solved my problem" },{ "type": "thumb-up", "id": "otherUp", "label":"Other" }]