Add compute_output_shape methods explicitly when shape unchanged.

keras/layers/advanced_activations.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-"""Advanced activation layers.
+"""Layers that act as activation functions.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -50,6 +50,9 @@ def get_config(self):
         base_config = super(LeakyReLU, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class PReLU(Layer):
     """Parametric Rectified Linear Unit.
@@ -142,6 +145,9 @@ def get_config(self):
         base_config = super(PReLU, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class ELU(Layer):
     """Exponential Linear Unit.
@@ -178,6 +184,9 @@ def get_config(self):
         base_config = super(ELU, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class ThresholdedReLU(Layer):
     """Thresholded Rectified Linear Unit.
@@ -214,6 +223,9 @@ def get_config(self):
         base_config = super(ThresholdedReLU, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class Softmax(Layer):
     """Softmax activation function.
@@ -242,3 +254,6 @@ def get_config(self):
         config = {'axis': self.axis}
         base_config = super(Softmax, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
+
+    def compute_output_shape(self, input_shape):
+        return input_shape

keras/layers/core.py

@@ -71,6 +71,9 @@ def get_config(self):
         base_config = super(Masking, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class Dropout(Layer):
     """Applies Dropout to the input.
@@ -127,6 +130,9 @@ def get_config(self):
         base_config = super(Dropout, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class SpatialDropout1D(Dropout):
     """Spatial 1D version of Dropout.
@@ -301,6 +307,9 @@ def get_config(self):
         base_config = super(Activation, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class Reshape(Layer):
     """Reshapes an output to a certain shape.
@@ -902,3 +911,6 @@ def get_config(self):
                   'l2': self.l2}
         base_config = super(ActivityRegularization, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
+
+    def compute_output_shape(self, input_shape):
+        return input_shape

keras/layers/noise.py

@@ -1,5 +1,5 @@
 # -*- coding: utf-8 -*-
-"""Noise regularization layers.
+"""Layers that operate regularization via the addition of noise.
 """
 from __future__ import absolute_import
 from __future__ import division
@@ -9,7 +9,6 @@
 from .. import backend as K
 import numpy as np
 from ..legacy import interfaces
-from ..engine import InputSpec
 
 
 class GaussianNoise(Layer):
@@ -52,6 +51,9 @@ def get_config(self):
         base_config = super(GaussianNoise, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class GaussianDropout(Layer):
     """Apply multiplicative 1-centered Gaussian noise.
@@ -96,6 +98,9 @@ def get_config(self):
         base_config = super(GaussianDropout, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
+    def compute_output_shape(self, input_shape):
+        return input_shape
+
 
 class AlphaDropout(Layer):
     """Applies Alpha Dropout to the input.
@@ -142,7 +147,8 @@ def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed):
                 scale = 1.0507009873554804934193349852946
                 alpha_p = -alpha * scale
 
-                kept_idx = K.greater_equal(K.random_uniform(noise_shape, seed=seed), rate)
+                kept_idx = K.greater_equal(K.random_uniform(noise_shape,
+                                                            seed=seed), rate)
                 kept_idx = K.cast(kept_idx, K.floatx())
 
                 # Get affine transformation params
@@ -162,3 +168,6 @@ def get_config(self):
         config = {'rate': self.rate}
         base_config = super(AlphaDropout, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
+
+    def compute_output_shape(self, input_shape):
+        return input_shape

keras/layers/normalization.py

@@ -211,3 +211,6 @@ def get_config(self):
         }
         base_config = super(BatchNormalization, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
+
+    def compute_output_shape(self, input_shape):
+        return input_shape