tf.unstack

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

tf.unstack(
    value, num=None, axis=0, name='unstack'
)

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.

Args:

• 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).

Returns:

The list of Tensor objects unstacked from value.

Raises:

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