jax.nn.one_hot#

jax.nn.one_hot(x, num_classes, *, dtype=<class 'jax.numpy.float64'>, axis=-1)[source]#

One-hot encodes the given indicies.

Each index in the input x is encoded as a vector of zeros of length num_classes with the element at index set to one:

>>> jax.nn.one_hot(jnp.array([0, 1, 2]), 3)
Array([[1., 0., 0.],
       [0., 1., 0.],
       [0., 0., 1.]], dtype=float32)

Indicies outside the range [0, num_classes) will be encoded as zeros:

>>> jax.nn.one_hot(jnp.array([-1, 3]), 3)
Array([[0., 0., 0.],
       [0., 0., 0.]], dtype=float32)
Parameters:

  • x (Any) – A tensor of indices.

  • num_classes (int) – Number of classes in the one-hot dimension.

  • dtype (Any) – optional, a float dtype for the returned values (default jnp.float_).

  • axis (Union[int, Hashable]) – the axis or axes along which the function should be computed.

Return type:

Any