tf.stack(values, axis=0, name='stack')

tf.stack(values, axis=0, name='stack')

See the guides: Layers (contrib) > Higher level ops for building neural network layers, Tensor Transformations > Slicing and Joining

Stacks a list of rank-R tensors into one rank-(R+1) tensor.

Packs the list of tensors in values into a tensor with rank one higher than each tensor in values, by packing them along the axis dimension. Given a list of length N of tensors of shape (A, B, C);

if axis == 0 then the output tensor will have the shape (N, A, B, C). if axis == 1 then the output tensor will have the shape (A, N, B, C). Etc.

For example:

# 'x' is [1, 4]
# 'y' is [2, 5]
# 'z' is [3, 6]
stack([x, y, z]) => [[1, 4], [2, 5], [3, 6]]  # Pack along first dim.
stack([x, y, z], axis=1) => [[1, 2, 3], [4, 5, 6]]

This is the opposite of unstack. The numpy equivalent is

tf.stack([x, y, z]) = np.asarray([x, y, z])

Args:

• values: A list of Tensor objects with the same shape and type.
• axis: An int. The axis to stack along. Defaults to the first dimension. Supports negative indexes.
• name: A name for this operation (optional).

Returns:

• output: A stacked Tensor with the same type as values.

Raises:

• ValueError: If axis is out of the range [-(R+1), R+1).

Defined in tensorflow/python/ops/array_ops.py.