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Stacks a list of rankR
tensors into one rank(R+1)
tensor in parallel.
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.