jax.numpy.vstack#

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

Vertically stack arrays.

JAX implementation ofnumpy.vstack().

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

Parameters:

• tup (np.ndarray |Array |Sequence[ArrayLike]) – a sequence of arrays to stack; each must have the same shape along allbut the first axis. If a single array is given it will be treatedequivalently totup = unstack(tup), but the implementation will avoidexplicit unstacking.

• dtype (DTypeLike |None) – optional dtype of the resulting array. If not specified, the dtypewill be determined via type promotion rules described inType 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)