Add Parametric Soft Exponential Unit (PSEU) activation layer

CODEOWNERS

@@ -26,6 +26,7 @@ keras_contrib/callbacks/snapshot.py @titu1994
 
 
 # layers
+keras_contrib/layers/advanced_activations/pseu.py @SriRangaTarun
 keras_contrib/layers/advanced_activations/sinerelu.py @wilderrodrigues
 keras_contrib/layers/advanced_activations/swish.py @gabrieldemarmiesse
 keras_contrib/layers/convolutional/subpixelupscaling.py @titu1994

keras_contrib/layers/__init__.py

@@ -1,6 +1,7 @@
 from __future__ import absolute_import
 
 from .advanced_activations.pelu import PELU
+from .advanced_activations.pseu import PSEU
 from .advanced_activations.srelu import SReLU
 from .advanced_activations.swish import Swish
 from .advanced_activations.sinerelu import SineReLU

keras_contrib/layers/advanced_activations/pseu.py

@@ -0,0 +1,75 @@
+# -*- coding: utf-8 -*-
+from keras import backend as K
+from keras.layers import Layer
+
+
+class PSEU(Layer):
+    """Parametric Soft Exponential Unit
+    See: https://arxiv.org/pdf/1602.01321.pdf by Godfrey and Gashler
+    Reference: https://github.com/keras-team/keras/issues/3842 (@hobson)
+
+    Soft Exponential f(α, x):
+        α == 0:  x
+        α  > 0:  (exp(αx)-1) / α + α
+        α  < 0:  -ln(1-α(x + α)) / α
+
+    # Input shape
+        Arbitrary. Use the keyword argument `input_shape`
+        (tuple of integers, does not include the samples axis)
+        when using this layer as the first layer in a model.
+
+    # Output shape
+        Same shape as the input.
+
+    # Arguments
+        alpha: Value of the alpha weights (float)
+
+        NOTE : This function can become unstable for
+               negative values of α. In particular, the
+               function returns NaNs when α < 0 and x <= 1/α
+               (where x is the input).
+               If the function starts returning NaNs for α < 0,
+               try decreasing the magnitude of α.
+               Alternatively, you can normalize the data into fixed
+               ranges before passing it to PSEU.
+               Adjust α based on your specific dataset
+               and use-case.
+
+    # Example
+        model = Sequential()
+        model.add(Dense(10, input_shape=(5,))
+        model.add(PSEU(alpha=0.2))
+    """
+    def __init__(self,
+                 alpha=0.1,
+                 **kwargs):
+
+        super(PSEU, self).__init__(**kwargs)
+        self.alpha = alpha
+
+    def alpha_initializer(self, input_shape):
+        return self.alpha * K.ones(input_shape)
+
+    def build(self, input_shape):
+        new_input_shape = input_shape[1:]
+        self.alphas = self.add_weight(shape=new_input_shape,
+                                      name='{}_alphas'.format(self.name),
+                                      initializer=self.alpha_initializer,
+                                      trainable=False)
+        self.build = True
+
+    def call(self, x):
+        if self.alpha < 0:
+            return - K.log(1 - self.alphas * (x + self.alphas)) / self.alphas
+        elif self.alpha > 0:
+            return self.alphas + (K.exp(self.alphas * x) - 1.) / self.alphas
+        else:
+            return x
+
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
+    def get_config(self):
+        config = {'alpha': self.alpha}
+        base_config = super(PSEU, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))

tests/keras_contrib/layers/advanced_activations/test_pseu.py

@@ -0,0 +1,15 @@
+# -*- coding: utf-8 -*-
+import pytest
+from keras_contrib.utils.test_utils import layer_test
+from keras_contrib.layers import PSEU
+
+
+@pytest.mark.parametrize('alpha', [-0.1, 0., 0.1])
+def test_pseu(alpha):
+    layer_test(PSEU,
+               kwargs={'alpha': alpha},
+               input_shape=(2, 3, 4))
+
+
+if __name__ == '__main__':
+    pytest.main([__file__])