Parameter checking for case where with_bn==True and is_training is not passed in.
@@ -158,6 +158,8 @@ class ConvolutionalLayer(TrainableLayer):
output_tensor = conv_layer(input_tensor)
if self.with_bn:
+ if is_training is None:
+ raise ValueError('is_training argument should be True or False unless with_bn is False')
bn_layer = BNLayer(
regularizer=self.regularizers['w'],
moving_decay=self.moving_decay,
