tf.stack

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

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.stack

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

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 = tf.constant([1, 4])
y = tf.constant([2, 5])
z = tf.constant([3, 6])
tf.stack([x, y, z])
<tf.Tensor: shape=(3, 2), dtype=int32, numpy=
array([[1, 4],
       [2, 5],
       [3, 6]], dtype=int32)>

tf.stack([x, y, z], axis=1)

This is the opposite of unstack. The numpy equivalent is np.stack

np.array_equal(np.stack([x, y, z]), tf.stack([x, y, z]))
True

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. Negative values wrap around, so the valid range is [-(R+1), R+1).
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).