Merge branch 'master' of https://github.com/fchollet/keras

docs/sources/regularizers.md

@@ -15,3 +15,4 @@ model.add(Dense(64, 64, W_regularizer = l2(.01)))
 
 - __l1__(l=0.01): L1 regularization penalty, also known as LASSO
 - __l2__(l=0.01): L2 regularization penalty, also known as weight decay, or Ridge
+- __l1l2__(l1=0.01, l2=0.01): L1-L2 regularization penalty, also known as ElasticNet

examples/kaggle_otto_nn.py

@@ -53,7 +53,7 @@ def preprocess_data(X, scaler=None):
     X = scaler.transform(X)
     return X, scaler
 
-def preprocess_labels(y, encoder=None, categorical=True):
+def preprocess_labels(labels, encoder=None, categorical=True):
     if not encoder:
         encoder = LabelEncoder()
         encoder.fit(labels)

keras/regularizers.py

@@ -15,5 +15,12 @@ def l2(l=.01):
         return g
     return l2wrap
 
+def l1l2(l1=.01, l2=.01):
+    def l1l2wrap(g, p):
+        g += T.sgn(p) * l1
+        g += p * l2
+        return g
+    return l1l2wrap
+
 def identity(g, p):
     return g