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Stacks a list of rank-R tensors into one rank-(R+1) tensor in parallel.
View aliases
Compat aliases for migration
See Migration guide for more details.
tf.parallel_stack(
values, name='parallel_stack'
)
Requires that the shape of inputs be known at graph construction time.
Packs the list of tensors in values into a tensor with rank one higher than
each tensor in values, by packing them along the first dimension.
Given a list of length N of tensors of shape (A, B, C); the output
tensor will have the shape (N, A, B, C).
For example:
x = tf.constant([1, 4])
y = tf.constant([2, 5])
z = tf.constant([3, 6])
tf.parallel_stack([x, y, z]) # [[1, 4], [2, 5], [3, 6]]
The difference between stack and parallel_stack is that stack requires
all the inputs be computed before the operation will begin but doesn't require
that the input shapes be known during graph construction.
parallel_stack will copy pieces of the input into the output as they become
available, in some situations this can provide a performance benefit.
Unlike stack, parallel_stack does NOT support backpropagation.
This is the opposite of unstack. The numpy equivalent is
tf.parallel_stack([x, y, z]) = np.asarray([x, y, z])
Args | |
|---|---|
values
|
A list of Tensor objects with the same shape and type.
|
name
|
A name for this operation (optional). |
Returns | |
|---|---|
output
|
A stacked Tensor with the same type as values.
|
Raises | |
|---|---|
RuntimeError
|
if executed in eager mode. |
eager compatibility
parallel_stack is not compatible with eager execution.