Merge branch 'dev' of http://github.com/ragavvenkatesan/yann into dev

ragavvenkatesan · Mar 11, 2017 · 82abc8e · 82abc8e
2 parents 96aa3f5 + 151fa79
commit 82abc8e
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Showing 3 changed files with 12 additions and 10 deletions.
diff --git a/yann/modules/resultor.py b/yann/modules/resultor.py
@@ -191,13 +191,13 @@ def _store_confusion_img (self, confusion, filename, verbose = 2):
         """
         if verbose >= 3:
             print ("... Saving the file down")
-        confusion = confusion / confusion.max() 
+        confusion = confusion / confusion.sum(axis = 0)
         fig = plt.figure()
         plt.matshow(confusion)
         plt.title('Confusion matrix')
-        plt.set_cmap('Greens')
+        plt.set_cmap('GnBu')
         plt.colorbar()
         plt.ylabel('True labels')
-        plt.xlabel('Predicated labels')
+        plt.xlabel('Predicted labels')
         plt.savefig(filename)
         plt.close('all')
diff --git a/yann/network.py b/yann/network.py
@@ -1935,13 +1935,15 @@ def _initialize_confusion (self, classifier = None, verbose = 2):
             print("... initializing confusion matrix function")   
 
         _classes = T.scalar('num_classes')
-        _predictions = self.inference_layers[classifier].predictions
-        _labels = self.y 
+        _predictions = self.inference_layers[classifier].predictions.dimshuffle(0, 'x')
+        _labels = self.y.dimshuffle(0, 'x')
+        _order = T.arange(self.num_classes_to_classify)
+
+        oneHot_labels = T.eq(_labels, _order).astype('int32')
+        oneHot_predictions = T.eq(_predictions, _order).astype('int32')
 
-        confusion = T.dot(  T.eq( _predictions.dimshuffle(0, 'x'), \
-                            T.arange(self.num_classes_to_classify).astype('int32')).T, \
-                            T.eq( _labels.dimshuffle(0,'x'), \
-                            T.arange(self.num_classes_to_classify).astype('int32')))
+        confusion = T.dot(oneHot_labels.T, oneHot_predictions)
+        # confusion_normalized = confusion / confusion.sum(axis = 0)     
 
         index = T.lscalar('index')
         self.mini_batch_confusion = theano.function(

diff --git a/yann/special/gan.py b/yann/special/gan.py
@@ -810,7 +810,7 @@ def train ( self, verbose, **kwargs):
                     if self.softmax_head is True:
                         softmax_cost = self.mini_batch_train_softmax (minibatch, epoch_counter)
                     disc_cost = self.mini_batch_train_discriminator (minibatch, epoch_counter)
-                    if minibatch % k == 0:
+                    if (minibatch + 1) * (batch + 1 ) * (epoch_counter + 1) % k == 0:
                         gen_cost = self.mini_batch_train_generator (minibatch, epoch_counter)
 
                     if numpy.isnan(gen_cost) or \