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one_hot

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one_hot#

class brainpy.math.one_hot(x, num_classes, *, dtype=None, 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:

>>> import jax.numpy as jnp
>>> 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:

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

  • x – A tensor of indices.

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

  • dtype – optional, a float dtype for the returned values (default float64 if jax_enable_x64 is true, otherwise float32).

  • axis – the axis or axes along which the function should be computed.

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