Upstream multiclass_perceptron_loss jaxopt loss to optax.

PiperOrigin-RevId: 619473957

optax/losses/_classification.py

@@ -78,7 +78,7 @@ def hinge_loss(
     targets: Target values. Target values should be strictly in the set {-1, 1}.
 
   Returns:
-    Binary Hinge Loss.
+    loss value.
   """
   return jnp.maximum(0, 1 - predictor_outputs * targets)
 
@@ -99,6 +99,7 @@ def perceptron_loss(
   Returns:
     loss value.
   """
+  chex.assert_equal_shape([predictor_outputs, targets])
   return jnp.maximum(0, - predictor_outputs * targets)
 
 
@@ -172,6 +173,30 @@ def multiclass_logistic_loss(logits, labels):
   return softmax_cross_entropy_with_integer_labels(logits, labels)
 
 
+def multiclass_perceptron_loss(
+    scores: chex.Array,
+    label: chex.Array,
+) -> chex.Array:
+  """Binary perceptron loss.
+
+  References:
+    Michael Collins. Discriminative training methods for Hidden Markov Models:
+    Theory and experiments with perceptron algorithms. EMNLP 2002
+
+  Args:
+    scores: score produced by the model.
+    label: ground-truth integer label.
+
+  Returns:
+    loss value.
+
+  .. versionadded:: 0.2.2
+  """
+  one_hot_label = jax.nn.one_hot(label, scores.shape[-1])
+  dot_last_dim = jnp.vectorize(jnp.dot, signature='(n),(n)->()')
+  return jnp.max(scores, axis=-1) - dot_last_dim(scores, one_hot_label)
+
+
 @functools.partial(chex.warn_only_n_pos_args_in_future, n=2)
 def poly_loss_cross_entropy(
     logits: chex.Array,

optax/losses/_classification_test.py

@@ -259,6 +259,47 @@ def test_batched(self):
     )
 
 
+class PerceptronTest(parameterized.TestCase):
+
+  def test_binary(self):
+    label = jnp.array(1)
+    signed_label = jnp.array(2.0 * label - 1.0)
+    score = jnp.array(10.)
+    def reference_impl(label, logit) -> float:
+      return jax.nn.relu(- logit * (2.0 * label - 1.0))
+    expected = reference_impl(label, score)
+    result = _classification.perceptron_loss(score, signed_label)
+    np.testing.assert_allclose(result, expected, atol=1e-4)
+
+  def test_batched_binary(self):
+    labels = jnp.array([1, 0])
+    signed_labels = jnp.array(2.0 * labels - 1.0)
+    scores = jnp.array([10., 20.])
+    def reference_impl(label, logit) -> float:
+      return jax.nn.relu(- logit * (2.0 * label - 1.0))
+    expected = jax.vmap(reference_impl)(labels, scores)
+    result = _classification.perceptron_loss(scores, signed_labels)
+    np.testing.assert_allclose(result, expected, atol=1e-4)
+
+  def test_multi_class(self):
+    label = jnp.array(1)
+    scores = jnp.array([10., 3.])
+    def reference_impl(label, scores):
+      return jnp.max(scores) - scores[label]
+    expected = reference_impl(label, scores)
+    result = _classification.multiclass_perceptron_loss(scores, label)
+    np.testing.assert_allclose(result, expected, atol=1e-4)
+
+  def test_batched_multi_class(self):
+    label = jnp.array([1, 0])
+    scores = jnp.array([[10., 3.], [11., -2.]])
+    def reference_impl(label, scores):
+      return jnp.max(scores) - scores[label]
+    expected = jax.vmap(reference_impl)(label, scores)
+    result = _classification.multiclass_perceptron_loss(scores, label)
+    np.testing.assert_allclose(result, expected, atol=1e-4)
+
+
 class KLDivergenceTest(parameterized.TestCase):
 
   def setUp(self):