Add PReLU Activation

docs/flax.linen.rst

@@ -114,6 +114,7 @@ Activation functions
     softmax
     softplus
     swish
+    PReLU
 
 
 Attention primitives

flax/linen/__init__.py

@@ -19,7 +19,7 @@
 # re-export commonly used modules and functions
 from .activation import (celu, elu, gelu, glu, leaky_relu, log_sigmoid,
                          log_softmax, relu, sigmoid, soft_sign, softmax,
-                         softplus, swish, silu, tanh)
+                         softplus, swish, silu, tanh, PReLU)
 from .attention import (MultiHeadDotProductAttention, SelfAttention,
                         dot_product_attention, make_attention_mask,
                         make_causal_mask, combine_masks)

flax/linen/activation.py

@@ -40,3 +40,35 @@
 
 from jax.numpy import tanh
 # pylint: enable=unused-import
+
+from typing import Any
+
+from flax.linen.module import Module, compact
+import jax.numpy as jnp
+
+
+Array = Any
+
+
+class PReLU(Module):
+  """Parametric Rectified Linear Unit (PReLU) activation function.
+
+  Attributes:
+    negative_slope_init: the value to initialize the negative slope.
+  """
+  negative_slope_init: float = 0.01
+  @compact
+  def __call__(self, inputs: Array) -> Array:
+    """Applies an activation to the inputs.
+
+    Args:
+      inputs: the nd-array to apply the activation function to.
+
+    Returns:
+      The transformed input.
+    """
+    negative_slope = self.param(
+      'negative_slope',
+      lambda k: jnp.asarray(self.negative_slope_init, jnp.float32)
+    )
+    return jnp.where(inputs >= 0, inputs, jnp.asarray(negative_slope, inputs.dtype) * inputs)

tests/linen/linen_activation_test.py

@@ -0,0 +1,42 @@
+# Copyright 2021 The Flax Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Tests for flax.nn.activation."""
+
+from absl.testing import absltest
+from absl.testing import parameterized
+
+from flax import linen as nn
+
+import jax
+from jax import random
+import jax.numpy as jnp
+
+
+# Parse absl flags test_srcdir and test_tmpdir.
+jax.config.parse_flags_with_absl()
+
+
+class ActivationTest(parameterized.TestCase):
+
+  def test_prelu(self):
+    rng = random.PRNGKey(0)
+    x = jnp.ones((4, 6, 5))
+    act = nn.PReLU()
+    y, _ = act.init_with_output(rng, x)
+    self.assertEqual(y.shape, x.shape)
+
+
+if __name__ == '__main__':
+  absltest.main()