def unpack(value, num=None, name="unpack"):
"""Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
Unpacks `num` tensors from `value` along the first dimension.
If `num` is not specified (the default), it is inferred from `value`'s shape.
If `value.shape[0]` is not known, `ValueError` is raised.
The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
`output` has shape `value.shape[1:]`.
This is the opposite of pack. The numpy equivalent is
tf.unpack(x, n) = list(x)
Args:
value: A rank `R > 0` `Tensor` to be unpacked.
num: An `int`. The first dimension of value. Automatically inferred if
`None` (the default).
name: A name for the operation (optional).
Returns:
The list of `Tensor` objects unpacked from `value`.
Raises:
ValueError: If `num` is unspecified and cannot be inferred.
"""
if num is None:
value = ops.convert_to_tensor(value)
shape = value.get_shape()
num = shape[0].value
if num is None:
raise ValueError("Cannot infer num from shape %s" % shape)
return gen_array_ops._unpack(value, num=num, name=name)
def unpack(value, num=None, name="unpack"):
"""Unpacks the outer dimension of a rank-`R` tensor into rank-`(R-1)` tensors.
Unpacks `num` tensors from `value` along the first dimension.
If `num` is not specified (the default), it is inferred from `value`'s shape.
If `value.shape[0]` is not known, `ValueError` is raised.
The ith tensor in `output` is the slice `value[i, ...]`. Each tensor in
`output` has shape `value.shape[1:]`.
This is the opposite of pack. The numpy equivalent is
tf.unpack(x, n) = list(x)
Args:
value: A rank `R > 0` `Tensor` to be unpacked.
num: An `int`. The first dimension of value. Automatically inferred if
`None` (the default).
name: A name for the operation (optional).
Returns:
The list of `Tensor` objects unpacked from `value`.
Raises:
ValueError: If `num` is unspecified and cannot be inferred.
"""
if num is None:
value = ops.convert_to_tensor(value)
shape = value.get_shape()
num = shape[0].value
if num is None:
raise ValueError("Cannot infer num from shape %s" % shape)
return gen_array_ops._unpack(value, num=num, name=name)