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tfa.losses.sparsemax_loss

Sparsemax loss function 1.

tfa.losses.sparsemax_loss(
    logits: tfa.types.TensorLike,
    sparsemax: tfa.types.TensorLike,
    labels: tfa.types.TensorLike,
    name: Optional[str] = None
) -> tf.Tensor

Computes the generalized multi-label classification loss for the sparsemax function. The implementation is a reformulation of the original loss function such that it uses the sparsemax properbility output instead of the internal $ au $ variable. However, the output is identical to the original loss function.

Args
`logits`	A `Tensor`. Must be one of the following types: `float32`, `float64`.
`sparsemax`	A `Tensor`. Must have the same type as `logits`.
`labels`	A `Tensor`. Must have the same type as `logits`.
`name`	A name for the operation (optional).

Returns
A `Tensor`. Has the same type as `logits`.

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Last updated 2021-05-17 UTC.

tfa.losses.sparsemax_loss

Args

Returns