doc update

doc/modules/neural_networks_supervised.rst

@@ -81,7 +81,7 @@ training samples::
     >>> y = [0, 1]
     >>> clf = MultilayerPerceptronClassifier(n_hidden=[5, 2], random_state=1)
     >>> clf.fit(X, y)
-    MultilayerPerceptronClassifier(activation='tanh', algorithm='l-bfgs',
+    MultilayerPerceptronClassifier(activation='relu', algorithm='l-bfgs',
                     alpha=1e-05, batch_size=200, learning_rate='constant',
                     learning_rate_init=0.5, max_iter=200, n_hidden=[5, 2],
                     power_t=0.5, random_state=1, shuffle=False, tol=1e-05,
@@ -104,7 +104,7 @@ following command,
 use :meth:`MultilayerPerceptronClassifier.decision_function`::
 
     >>> clf.decision_function([[2., 2.], [1., 2.]])
-    array([ 10.63831359,  10.62624867])
+    array([ 11.55408143,  11.55408143])
 
 Currently, :class:`MultilayerPerceptronClassifier` supports only the 
 Cross-Entropy loss function, which allows probability estimates by running the 
@@ -115,8 +115,8 @@ Cross-Entropy loss function, giving a vector of probability estimates
 :math:`P(y|x)` per sample :math:`x`:: 
 
     >>> clf.predict_proba([[2., 2.], [1., 2.]])
-    array([[  2.39788691e-05,   9.99976021e-01],
-           [  2.42699174e-05,   9.99975730e-01]])
+    array([[  9.59670230e-06,   9.99990403e-01],
+           [  9.59670230e-06,   9.99990403e-01]])
 
 :class:`MultilayerPerceptronClassifier` supports multi-class classification by 
 applying `Softmax <http://en.wikipedia.org/wiki/Softmax_activation_function>`_
@@ -131,11 +131,11 @@ value is `1` represents the assigned classes of that samples::
 
     >>> X = [[0., 0.], [1., 1.]]
     >>> y = [[0, 1], [1]]
-    >>> clf = MultilayerPerceptronClassifier(n_hidden=[2], random_state=1)
+    >>> clf = MultilayerPerceptronClassifier(n_hidden=[15], random_state=1)
     >>> clf.fit(X, y)
-    MultilayerPerceptronClassifier(activation='tanh', algorithm='l-bfgs',
+    MultilayerPerceptronClassifier(activation='relu', algorithm='l-bfgs',
                     alpha=1e-05, batch_size=200, learning_rate='constant',
-                    learning_rate_init=0.5, max_iter=200, n_hidden=[2],
+                    learning_rate_init=0.5, max_iter=200, n_hidden=[15],
                     power_t=0.5, random_state=1, shuffle=False, tol=1e-05,
                     verbose=False, warm_start=False)
     >>> clf.predict([1., 2.])