update theano TUT

Mofan Zhou · Mofan Zhou · commit 65d0d7ba451e · 2016-07-29T12:43:52.000+10:00
diff --git a/theanoTUT/theano5_function.py b/theanoTUT/theano5_function.py
@@ -13,12 +13,12 @@
 
 # activation function example
 x = T.dmatrix('x')
-s = 1 / (1 + T.exp(-x))
+s = 1 / (1 + T.exp(-x))    # logistic or soft step
 logistic = theano.function([x], s)
 print(logistic([[0, 1],[-1, -2]]))
 
 # multiply outputs for a function
-a , b = T.dmatrices('a', 'b')
+a, b = T.dmatrices('a', 'b')
 diff = a - b
 abs_diff = abs(diff)
 diff_squared = diff ** 2
diff --git a/theanoTUT/theano7_activation_function.py b/theanoTUT/theano7_activation_function.py
@@ -7,9 +7,9 @@
 
 """
 The available activation functions in theano can be found in this link:
-https://www.google.com/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwjjhYfkvJPOAhVDto8KHVciCqUQFgghMAA&url=http%3A%2F%2Fdeeplearning.net%2Fsoftware%2Ftheano%2Flibrary%2Ftensor%2Fnnet%2Fnnet.html&usg=AFQjCNE_BGK4eZpKd0vBdsn6mSjtAhBhsA&sig2=F57jVSqVcHq-M505idTbaw
+http://deeplearning.net/software/theano/library/tensor/nnet/nnet.html
 
-It includes but not limited to softplus, sigmoid, relu, softmax, elu, tanh...
+The activation functions include but not limited to softplus, sigmoid, relu, softmax, elu, tanh...
 
 For the hidden layer, we could use relu, tanh...
 For classification problems, we could use softplus or softmax for the output layer.
