tf.parallel_stack( values, name='parallel_stack' )
Defined in tensorflow/python/ops/array_ops.py
.
See the guide: Tensor Transformations > Slicing and Joining
Stacks a list of rank-R
tensors into one rank-(R+1)
tensor in parallel.
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])
values
: A list of Tensor
objects with the same shape and type.name
: A name for this operation (optional).output
: A stacked Tensor
with the same type as values
.
© 2018 The TensorFlow Authors. All rights reserved.
Licensed under the Creative Commons Attribution License 3.0.
Code samples licensed under the Apache 2.0 License.
https://www.tensorflow.org/api_docs/python/tf/parallel_stack