Merge pull request #40 from philipperemy/issue_39

dilation name to make it unique

tcn/tcn.py

@@ -44,14 +44,15 @@ def wave_net_activation(x):
     return keras.layers.multiply([tanh_out, sigm_out])
 
 
-def residual_block(x, s, i, activation, nb_filters, kernel_size, padding, dropout_rate=0, name=''):
-    # type: (Layer, int, int, str, int, int, str, float, str) -> Tuple[Layer, Layer]
+def residual_block(x, s, i, c, activation, nb_filters, kernel_size, padding, dropout_rate=0, name=''):
+    # type: (Layer, int, int, int, str, int, int, str, float, str) -> Tuple[Layer, Layer]
     """Defines the residual block for the WaveNet TCN
 
     Args:
         x: The previous layer in the model
         s: The stack index i.e. which stack in the overall TCN
         i: The dilation power of 2 we are using for this residual block
+        c: The dilation name to make it unique. In case we have same dilation twice: [1, 1, 2, 4].
         activation: The name of the type of activation to use
         nb_filters: The number of convolutional filters to use in this block
         kernel_size: The size of the convolutional kernel
@@ -67,7 +68,7 @@ def residual_block(x, s, i, activation, nb_filters, kernel_size, padding, dropou
     original_x = x
     conv = Conv1D(filters=nb_filters, kernel_size=kernel_size,
                   dilation_rate=i, padding=padding,
-                  name=name + '_d_%s_conv_%d_tanh_s%d' % (padding, i, s))(x)
+                  name=name + '_d_%s_conv_%d-%d_tanh_s%d' % (padding, i, c, s))(x)
     if activation == 'norm_relu':
         x = Activation('relu')(conv)
         x = Lambda(channel_normalization)(x)
@@ -76,7 +77,7 @@ def residual_block(x, s, i, activation, nb_filters, kernel_size, padding, dropou
     else:
         x = Activation(activation)(conv)
 
-    x = SpatialDropout1D(dropout_rate, name=name + '_spatial_dropout1d_%d_s%d_%f' % (i, s, dropout_rate))(x)
+    x = SpatialDropout1D(dropout_rate, name=name + '_spatial_dropout1d_%d-%d_s%d_%f' % (i, c, s, dropout_rate))(x)
 
     # 1x1 conv.
     x = Convolution1D(nb_filters, 1, padding='same')(x)
@@ -156,8 +157,8 @@ def __call__(self, inputs):
         x = Convolution1D(self.nb_filters, 1, padding=self.padding, name=self.name + '_initial_conv')(x)
         skip_connections = []
         for s in range(self.nb_stacks):
-            for i in self.dilations:
-                x, skip_out = residual_block(x, s, i, self.activation, self.nb_filters,
+            for i, d in enumerate(self.dilations):
+                x, skip_out = residual_block(x, s, d, i, self.activation, self.nb_filters,
                                              self.kernel_size, self.padding, self.dropout_rate, name=self.name)
                 skip_connections.append(skip_out)
         if self.use_skip_connections: