Snake layer and activation to learn periodic functions

.github/CODEOWNERS

@@ -39,6 +39,8 @@
 /tensorflow_addons/activations/tests/sparsemax_test.py @andreasmadsen
 /tensorflow_addons/activations/tanhshrink.py @fsx950223
 /tensorflow_addons/activations/tests/tanhshrink_test.py @fsx950223
+/tensorflow_addons/activations/snake.py @failure-to-thrive
+/tensorflow_addons/activations/tests/snake_test.py @failure-to-thrive
 
 /tensorflow_addons/callbacks/average_model_checkpoint.py @squadrick
 /tensorflow_addons/callbacks/time_stopping.py @shun-lin
@@ -99,6 +101,8 @@
 /tensorflow_addons/layers/tests/wrappers_test.py @seanpmorgan
 /tensorflow_addons/layers/esn.py @pedrolarben
 /tensorflow_addons/layers/tests/esn_test.py @pedrolarben
+/tensorflow_addons/layers/snake.py @failure-to-thrive
+/tensorflow_addons/layers/tests/snake_test.py @failure-to-thrive
 
 /tensorflow_addons/losses/contrastive.py @windqaq
 /tensorflow_addons/losses/tests/contrastive_test.py @windqaq

tensorflow_addons/activations/__init__.py

@@ -20,5 +20,6 @@
 from tensorflow_addons.activations.mish import mish
 from tensorflow_addons.activations.softshrink import softshrink
 from tensorflow_addons.activations.rrelu import rrelu
+from tensorflow_addons.activations.snake import snake
 from tensorflow_addons.activations.sparsemax import sparsemax
 from tensorflow_addons.activations.tanhshrink import tanhshrink

tensorflow_addons/activations/snake.py

@@ -0,0 +1,37 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+
+import tensorflow as tf
+
+from tensorflow_addons.utils import types
+
+
+@tf.keras.utils.register_keras_serializable(package="Addons")
+def snake(logits: types.TensorLike, frequency: types.Number = 1) -> tf.Tensor:
+    """Snake activation to learn periodic functions.
+
+    https://arxiv.org/abs/2006.08195
+
+    Args:
+        logits: Input tensor.
+        frequency: A scalar, frequency of the periodic part.
+    Returns:
+        Tensor of the same type and shape as `logits`.
+    """
+
+    logits = tf.convert_to_tensor(logits)
+    frequency = tf.cast(frequency, logits.dtype)
+
+    return logits + (1 - tf.cos(2 * frequency * logits)) / (2 * frequency)

tensorflow_addons/activations/tests/activations_test.py

@@ -27,6 +27,7 @@
     "softshrink",
     "sparsemax",
     "tanhshrink",
+    "snake",
 ]
 
 

tensorflow_addons/activations/tests/snake_test.py

@@ -0,0 +1,29 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+
+import pytest
+
+import numpy as np
+from tensorflow_addons.activations import snake
+from tensorflow_addons.utils import test_utils
+
+
+@pytest.mark.usefixtures("maybe_run_functions_eagerly")
+@pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64])
+def test_activation(dtype):
+    x = dtype(np.random.rand(2, 5))
+    a = dtype(np.random.randn())
+    expected_result = x + np.power(np.sin(a * x), 2) / a
+    test_utils.assert_allclose_according_to_type(snake(x, a), expected_result)

tensorflow_addons/layers/__init__.py

@@ -31,6 +31,7 @@
 from tensorflow_addons.layers.optical_flow import CorrelationCost
 from tensorflow_addons.layers.poincare import PoincareNormalize
 from tensorflow_addons.layers.polynomial import PolynomialCrossing
+from tensorflow_addons.layers.snake import Snake
 from tensorflow_addons.layers.sparsemax import Sparsemax
 from tensorflow_addons.layers.spatial_pyramid_pooling import SpatialPyramidPooling2D
 from tensorflow_addons.layers.tlu import TLU

tensorflow_addons/layers/snake.py

@@ -0,0 +1,53 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# 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.
+# ==============================================================================
+"""Implements Snake layer."""
+
+import tensorflow as tf
+from typeguard import typechecked
+
+from tensorflow_addons.activations.snake import snake
+
+from tensorflow_addons.utils import types
+
+
+@tf.keras.utils.register_keras_serializable(package="Addons")
+class Snake(tf.keras.layers.Layer):
+    """Snake layer to learn periodic functions with the trainable `frequency` scalar.
+
+    https://arxiv.org/abs/2006.08195
+
+    Arguments:
+        frequency_initializer: Initializer for the `frequency` scalar.
+    """
+
+    @typechecked
+    def __init__(self, frequency_initializer: types.Initializer = "ones", **kwargs):
+        super().__init__(**kwargs)
+        self.frequency_initializer = tf.keras.initializers.get(frequency_initializer)
+        self.frequency = self.add_weight(
+            initializer=frequency_initializer, trainable=True
+        )
+
+    def call(self, inputs):
+        return snake(inputs, self.frequency)
+
+    def get_config(self):
+        config = {
+            "frequency_initializer": tf.keras.initializers.serialize(
+                self.frequency_initializer
+            ),
+        }
+        base_config = super().get_config()
+        return {**base_config, **config}

tensorflow_addons/layers/tests/snake_test.py

@@ -0,0 +1,39 @@
+# Copyright 2020 The TensorFlow Authors. All Rights Reserved.
+#
+# 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 Snake layer."""
+
+import pytest
+
+import numpy as np
+import tensorflow as tf
+
+from tensorflow_addons.layers.snake import Snake
+from tensorflow_addons.activations.snake import snake
+
+from tensorflow_addons.utils import test_utils
+
+
+@pytest.mark.usefixtures("maybe_run_functions_eagerly")
+@pytest.mark.parametrize("dtype", [np.float16, np.float32, np.float64])
+def test_layer(dtype):
+    x = np.random.rand(2, 5).astype(dtype)
+    a = np.random.randn()
+    val = snake(x, a)
+    test_utils.layer_test(
+        Snake,
+        kwargs={"frequency_initializer": tf.constant_initializer(a), "dtype": dtype},
+        input_data=x,
+        expected_output=val,
+    )