Added A activation

prednet.py

@@ -33,6 +33,7 @@ class PredNet(Recurrent):
         pixel_max: the maximum pixel value.
             Used to clip the pixel-layer prediction.
         error_activation: activation function for the error (E) units.
+        A_activation: activation function for the target (A) and prediction (A_hat) units.
         LSTM_activation: activation function for the cell and hidden states of the LSTM.
         LSTM_inner_activation: activation function for the gates in the LSTM.
         output_mode: either 'error', 'prediction', or 'all'.
@@ -58,7 +59,7 @@ class PredNet(Recurrent):
     '''
     def __init__(self, stack_sizes, R_stack_sizes,
                  A_filt_sizes, Ahat_filt_sizes, R_filt_sizes,
-                 pixel_max=1., error_activation='relu',
+                 pixel_max=1., error_activation='relu', A_activation='relu',
                  LSTM_activation='tanh', LSTM_inner_activation='hard_sigmoid',
                  output_mode='error', extrap_start_time = None,
                  dim_ordering=K.image_dim_ordering(), **kwargs):
@@ -75,6 +76,7 @@ def __init__(self, stack_sizes, R_stack_sizes,
 
         self.pixel_max = pixel_max
         self.error_activation = activations.get(error_activation)
+        self.A_activation = activations.get(A_activation)
         self.LSTM_activation = activations.get(LSTM_activation)
         self.LSTM_inner_activation = activations.get(LSTM_inner_activation)
 
@@ -163,13 +165,14 @@ def build(self, input_shape):
 
         for l in range(self.nb_layers):
             for c in ['i', 'f', 'c', 'o']:
-                act = self.LSTM_activation.__name__ if c == 'c' else self.LSTM_inner_activation.__name__
+                act = self.LSTM_activation if c == 'c' else self.LSTM_inner_activation
                 self.conv_layers[c].append(Convolution2D(self.R_stack_sizes[l], self.R_filt_sizes[l], self.R_filt_sizes[l], border_mode='same', activation=act, dim_ordering=self.dim_ordering))
 
-            self.conv_layers['ahat'].append(Convolution2D(self.stack_sizes[l], self.Ahat_filt_sizes[l], self.Ahat_filt_sizes[l], border_mode='same', activation='relu', dim_ordering=self.dim_ordering))
+            act = 'relu' if l == 0 else self.A_activation
+            self.conv_layers['ahat'].append(Convolution2D(self.stack_sizes[l], self.Ahat_filt_sizes[l], self.Ahat_filt_sizes[l], border_mode='same', activation=act, dim_ordering=self.dim_ordering))
 
             if l < self.nb_layers - 1:
-                self.conv_layers['a'].append(Convolution2D(self.stack_sizes[l+1], self.A_filt_sizes[l], self.A_filt_sizes[l], border_mode='same', activation='relu', dim_ordering=self.dim_ordering))
+                self.conv_layers['a'].append(Convolution2D(self.stack_sizes[l+1], self.A_filt_sizes[l], self.A_filt_sizes[l], border_mode='same', activation=self.A_activation, dim_ordering=self.dim_ordering))
 
         self.upsample = UpSampling2D()
         self.pool = MaxPooling2D()
@@ -269,6 +272,7 @@ def get_config(self):
                   'R_filt_sizes': self.R_filt_sizes,
                   'pixel_max': self.pixel_max,
                   'error_activation': self.error_activation.__name__,
+                  'A_activation': self.A_activation.__name__,
                   'LSTM_activation': self.LSTM_activation.__name__,
                   'LSTM_inner_activation': self.LSTM_inner_activation.__name__,
                   'dim_ordering': self.dim_ordering,