update theano TUT

Author: Mofan Zhou

theanoTUT/theano12_regularization/full_code.py

@@ -50,8 +50,8 @@ def minmax_normalization(data):
 
 # the way to compute cost
 cost = T.mean(T.square(l2.outputs - y))      # without regularization
-# cost = T.mean(T.square(l2.outputs - y)) + 0.1 * (l1.W ** 2).sum() + (l2.W ** 2).sum()  # with l2 regularization
-# cost = T.mean(T.square(l2.outputs - y)) + 0.1 * abs(l1.W).sum() + abs(l2.W).sum()  # with l1 regularization
+# cost = T.mean(T.square(l2.outputs - y)) + 0.1 * ((l1.W ** 2).sum() + (l2.W ** 2).sum())  # with l2 regularization
+# cost = T.mean(T.square(l2.outputs - y)) + 0.1 * (abs(l1.W).sum() + abs(l2.W).sum())  # with l1 regularization
 gW1, gb1, gW2, gb2 = T.grad(cost, [l1.W, l1.b, l2.W, l2.b])
 
 learning_rate = 0.01