jax.numpy.vstack — JAX documentation (original) (raw)

jax.numpy.vstack#

jax.numpy.vstack(tup, dtype=None)[source]#

Vertically stack arrays.

JAX implementation of numpy.vstack().

For arrays of two or more dimensions, this is equivalent tojax.numpy.concatenate() with axis=0.

Parameters:

• tup (np.ndarray | Array | Sequence [ ArrayLike ]) – a sequence of arrays to stack; each must have the same shape along all but the first axis. If a single array is given it will be treated equivalently to tup = unstack(tup), but the implementation will avoid explicit unstacking.
• dtype (DTypeLike | None | None) – optional dtype of the resulting array. If not specified, the dtype will be determined via type promotion rules described in Type promotion semantics.

Returns:

the stacked result.

Return type:

Array

See also

• jax.numpy.stack(): stack along arbitrary axes
• jax.numpy.concatenate(): concatenation along existing axes.
• jax.numpy.hstack(): stack horizontally, i.e. along axis 1.
• jax.numpy.dstack(): stack depth-wise, i.e. along axis 2.

Examples

Scalar values:

jnp.vstack([1, 2, 3]) Array([[1], [2], [3]], dtype=int32, weak_type=True)

1D arrays:

x = jnp.arange(4) y = jnp.ones(4) jnp.vstack([x, y]) Array([[0., 1., 2., 3.], [1., 1., 1., 1.]], dtype=float32)

2D arrays:

x = x.reshape(1, 4) y = y.reshape(1, 4) jnp.vstack([x, y]) Array([[0., 1., 2., 3.], [1., 1., 1., 1.]], dtype=float32)