Fix for the documentation error and support for Python3 in the cifar1…

…0 example.

docs/guide.rst

@@ -60,15 +60,17 @@ Verbose Mode
 
 To see the output of the neural network's training, you need to configure two things: first setting up the Python logger (mandatory), and secondly to specify a verbose mode if you want more information during training (optional).
 
+The first step is to configure either the ``sknn`` logger specifically, or do so globally (easier) as follows:
+
 .. code:: python
 
+    import sys
     import logging
 
-    log = logging.getLogger('sknn')
-    log.basicConfig(
-            format="%(message)s",
-            level=logging.DEBUG,
-            stream=sys.stdout)
+    logging.basicConfig(
+                format="%(message)s",
+                level=logging.DEBUG,
+                stream=sys.stdout)
 
 Then you can optionally create your neural networks using an additional ``verbose`` parameter to show the output during training:
 

examples/bench_cifar10.py

@@ -1,18 +1,24 @@
-import cPickle
+# -*- coding: utf-8 -*-
+from __future__ import (absolute_import, unicode_literals, print_function)
+
+import pickle
 import numpy as np
 
 def load(name):
+    print("\t"+name)
     try:
         with open(name, 'rb') as f:
-            return cPickle.load(f)
+            return pickle.load(f, encoding='latin1')
     except IOError:
         import gzip
         with gzip.open(name+'.gz', 'rb') as f:
-            return cPickle.load(f)
+            return pickle.load(f, encoding='latin1')
 
+print("Loading...")
 dataset1 = load('data_batch_1')
 dataset2 = load('data_batch_2')
 dataset3 = load('data_batch_3')
+print("")
 
 data_train = np.vstack([dataset1['data'], dataset2['data']])
 labels_train = np.hstack([dataset1['labels'], dataset2['labels']])
@@ -48,6 +54,6 @@ def load(name):
 expected = labels_test
 predicted = net.predict(data_test)
 
-print "Classification report for classifier %s:\n%s\n" % (
-    net, classification_report(expected, predicted))
-print "Confusion matrix:\n%s" % confusion_matrix(expected, predicted)
+print("Classification report for classifier %s:\n%s\n" % (
+    net, classification_report(expected, predicted)))
+print("Confusion matrix:\n%s" % confusion_matrix(expected, predicted))