diff --git a/keras_contrib/layers/advanced_activations.py b/keras_contrib/layers/advanced_activations.py
index 179856c..7bf349e 100644
--- a/keras_contrib/layers/advanced_activations.py
+++ b/keras_contrib/layers/advanced_activations.py
@@ -236,3 +236,50 @@ class SReLU(Layer):
         return dict(list(base_config.items()) + list(config.items()))
 
 get_custom_objects().update({'SReLU': SReLU})
+
+
+class Swish(Layer):
+    """ Swish (Ramachandranet al., 2017)
+
+    # 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
+        beta: float >= 0. Scaling factor
+            if set to 1 and trainable set to False (default), Swish equals the SiLU activation (Elfwing et al., 2017)
+        trainable: whether to learn the scaling factor during training or not
+
+    # References
+        - [Searching for Activation Functions](https://arxiv.org/abs/1710.05941)
+        - [Sigmoid-weighted linear units for neural network function approximation in reinforcement learning](https://arxiv.org/abs/1702.03118)
+    """
+
+    def __init__(self, beta=1.0, trainable=False, **kwargs):
+        super(Swish, self).__init__(**kwargs)
+        self.supports_masking = True
+        self.beta = beta
+        self.trainable = trainable
+
+    def build(self, input_shape):
+        self.scaling_factor = K.variable(self.beta,
+                                         dtype=K.floatx(),
+                                         name='scaling_factor')
+        if self.trainable:
+            self._trainable_weights.append(self.scaling_factor)
+        super(Swish, self).build(input_shape)
+
+    def call(self, inputs, mask=None):
+        return inputs * K.sigmoid(self.scaling_factor * inputs)
+
+    def get_config(self):
+        config = {'beta': self.get_weights()[0] if self.trainable else self.beta,
+                  'trainable': self.trainable}
+        base_config = super(Swish, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+get_custom_objects().update({'Swish': Swish})
diff --git a/tests/keras_contrib/layers/test_advanced_activations.py b/tests/keras_contrib/layers/test_advanced_activations.py
index f07a690..8c71426 100644
--- a/tests/keras_contrib/layers/test_advanced_activations.py
+++ b/tests/keras_contrib/layers/test_advanced_activations.py
@@ -26,5 +26,18 @@ def test_srelu_share():
     layer_test(advanced_activations.SReLU, kwargs={'shared_axes': 1},
                input_shape=(2, 3, 4))
 
+
+@keras_test
+def test_swish_constant():
+    layer_test(advanced_activations.Swish, kwargs={'beta': 1.0, 'trainable': False},
+               input_shape=(2, 3, 4))
+
+
+@keras_test
+def test_swish_trainable():
+    layer_test(advanced_activations.Swish, kwargs={'beta': 1.0, 'trainable': True},
+               input_shape=(2, 3, 4))
+
+
 if __name__ == '__main__':
     pytest.main([__file__])