regularization and constraints for Convolution1D and Convolution2D
diff --git a/keras/layers/convolutional.py b/keras/layers/convolutional.py
index a250ed461b1..6d4db99ec74 100644
--- a/keras/layers/convolutional.py
+++ b/keras/layers/convolutional.py
@@ -13,7 +13,8 @@
 class Convolution1D(Layer):
     def __init__(self, nb_filter, stack_size, filter_length,
         init='uniform', activation='linear', weights=None,
-        image_shape=None, border_mode='valid', subsample_length=1):
+        image_shape=None, border_mode='valid', subsample_length=1,
+        W_regularizer=None, b_regularizer=None, W_constraint=None, b_constraint=None):
 
         nb_row = 1
         nb_col = filter_length
@@ -35,6 +36,9 @@ def __init__(self, nb_filter, stack_size, filter_length,
 
         self.params = [self.W, self.b]
 
+        self.regularizers = [W_regularizer, b_regularizer]
+        self.constraints = [W_constraint, b_constraint]
+
         if weights is not None:
             self.set_weights(weights)
 
@@ -82,7 +86,8 @@ def get_config(self):
 class Convolution2D(Layer):
     def __init__(self, nb_filter, stack_size, nb_row, nb_col, 
         init='glorot_uniform', activation='linear', weights=None, 
-        image_shape=None, border_mode='valid', subsample=(1,1)):
+        image_shape=None, border_mode='valid', subsample=(1,1),
+        W_regularizer=None, b_regularizer=None, W_constraint=None, b_constraint=None):
         super(Convolution2D,self).__init__()
 
         self.init = initializations.get(init)
@@ -102,6 +107,9 @@ def __init__(self, nb_filter, stack_size, nb_row, nb_col,
 
         self.params = [self.W, self.b]
 
+        self.regularizers = [W_regularizer, b_regularizer]
+        self.constraints = [W_constraint, b_constraint]
+
         if weights is not None:
             self.set_weights(weights)
 

fix for #170, including constraints. Nothing fancy, just copied the code from the dense layer, but I suppose it should work the same.
