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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.

Compat aliases