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# tf.unstack

Unpacks the given dimension of a rank-`R` tensor into rank-`(R-1)` tensors.

Unpacks `num` tensors from `value` by chipping it along the `axis` dimension. If `num` is not specified (the default), it is inferred from `value`'s shape. If `value.shape[axis]` is not known, `ValueError` is raised.

For example, given a tensor of shape `(A, B, C, D)`;

If `axis == 0` then the i'th tensor in `output` is the slice `value[i, :, :, :]` and each tensor in `output` will have shape `(B, C, D)`. (Note that the dimension unpacked along is gone, unlike `split`).

If `axis == 1` then the i'th tensor in `output` is the slice `value[:, i, :, :]` and each tensor in `output` will have shape `(A, C, D)`. Etc.

This is the opposite of stack.

`value` A rank `R > 0` `Tensor` to be unstacked.
`num` An `int`. The length of the dimension `axis`. Automatically inferred if `None` (the default).
`axis` An `int`. The axis to unstack along. Defaults to the first dimension. Negative values wrap around, so the valid range is `[-R, R)`.
`name` A name for the operation (optional).

The list of `Tensor` objects unstacked from `value`.

`ValueError` If `num` is unspecified and cannot be inferred.
`ValueError` If `axis` is out of the range [-R, R).

[{ "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"label":"Too complicated / too many steps" },{ "type": "thumb-down", "id": "outOfDate", "label":"Out of date" },{ "type": "thumb-down", "id": "samplesCodeIssue", "label":"Samples / code issue" },{ "type": "thumb-down", "id": "otherDown", "label":"Other" }]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" }]