tfa.activations.sparsemax
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Sparsemax activation function.
tfa.activations.sparsemax(
logits: tfa.types.TensorLike ,
axis: int = -1
) -> tf.Tensor
For each batch \(i\), and class \(j\), compute sparsemax activation function:
\[ \mathrm{sparsemax}(x)[i, j] = \max(\mathrm{logits}[i, j] - \tau(\mathrm{logits}[i, :]), 0). \]
See From Softmax to Sparsemax: A Sparse Model of Attention and Multi-Label Classification.
Usage:
x = tf.constant([[-1.0, 0.0, 1.0], [-5.0, 1.0, 2.0]])tfa.activations.sparsemax(x)<tf.Tensor: shape=(2, 3), dtype=float32, numpy=array([[0., 0., 1.],[0., 0., 1.]], dtype=float32)>
Args | |
|---|---|
logits
|
A Tensor.
|
axis
|
int, axis along which the sparsemax operation is applied.
|
Returns | |
|---|---|
A Tensor, output of sparsemax transformation. Has the same type and
shape as logits.
|
Raises | |
|---|---|
ValueError
|
In case dim(logits) == 1.
|