added comment.

Author: nouiz

code/mlp.py

@@ -1,4 +1,4 @@
-"""
+7"""
 This tutorial introduces the multilayer perceptron using Theano.  
 
  A multilayer perceptron is a logistic regressor where
@@ -69,6 +69,7 @@ def __init__(self, rng, input, n_in, n_out, activation = T.tanh):
         #        For example, results presented in [Xavier10] suggest that you 
         #        should use 4 times larger initial weights for sigmoid 
         #        compared to tanh
+        #        We have no info for other function, so we use the same as tanh.
         W_values = numpy.asarray( rng.uniform(
                 low  = - numpy.sqrt(6./(n_in+n_out)),
                 high = numpy.sqrt(6./(n_in+n_out)),

doc/mlp.txt

@@ -161,6 +161,7 @@ both upward (activations flowing from inputs to outputs) and backward
         #        For example, results presented in [Xavier10]_ suggest that you 
         #        should use 4 times larger initial weights for sigmoid 
         #        compared to tanh
+        #        We have no info for other function, so we use the same as tanh.
         W_values = numpy.asarray( rng.uniform(
                 low  = - numpy.sqrt(6./(n_in+n_out)),
                 high =   numpy.sqrt(6./(n_in+n_out)),