tf.stack

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

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).