diff --git a/keras/models.py b/keras/models.py
index e89ef8eb0..ae9302bd1 100644
--- a/keras/models.py
+++ b/keras/models.py
@@ -159,6 +159,14 @@ class Sequential(object):
                 if verbose:
                     print("Train on %d samples, validate on %d samples" % (len(y), len(y_val)))
         
+        history = {'epoch':[], 'loss':[]}
+        if show_accuracy:
+            history['acc'] = []
+        if do_validation:
+            history['val_loss'] = []
+            if show_accuracy:
+                history['val_acc'] = []
+
         index_array = np.arange(len(X))
         for epoch in range(nb_epoch):
             if verbose:
@@ -167,21 +175,29 @@ class Sequential(object):
             if shuffle:
                 np.random.shuffle(index_array)
 
+            av_loss = 0.
+            av_acc = 0.
+            seen = 0
+
             batches = make_batches(len(X), batch_size)
             for batch_index, (batch_start, batch_end) in enumerate(batches):
                 if shuffle:
                     batch_ids = index_array[batch_start:batch_end]
                 else:
                     batch_ids = slice(batch_start, batch_end)
+                seen += len(batch_ids)
                 X_batch = X[batch_ids]
                 y_batch = y[batch_ids]
 
                 if show_accuracy:
                     loss, acc = self._train_with_acc(X_batch, y_batch)
                     log_values = [('loss', loss), ('acc.', acc)]
+                    av_loss += loss * len(batch_ids)
+                    av_acc += acc * len(batch_ids)
                 else:
                     loss = self._train(X_batch, y_batch)
                     log_values = [('loss', loss)]
+                    av_loss += loss * len(batch_ids)
 
                 # validation
                 if do_validation and (batch_index == len(batches) - 1):
@@ -196,6 +212,16 @@ class Sequential(object):
                 if verbose:
                     progbar.update(batch_end, log_values)
 
+            history['epoch'].append(epoch)
+            history['loss'].append(av_loss/seen)
+            if do_validation:
+                history['val_loss'].append(float(val_loss))
+            if show_accuracy:
+                history['acc'].append(av_acc/seen)
+                if do_validation:
+                    history['val_acc'].append(float(val_acc))
+        return history
+
             
     def predict_proba(self, X, batch_size=128, verbose=1):
         batches = make_batches(len(X), batch_size)
diff --git a/keras/utils/generic_utils.py b/keras/utils/generic_utils.py
index ad39d041e..4865c876a 100644
--- a/keras/utils/generic_utils.py
+++ b/keras/utils/generic_utils.py
@@ -39,11 +39,12 @@ class Progbar(object):
         '''
         for k, v in values:
             if k not in self.sum_values:
-                self.sum_values[k] = [v, 1]
+                self.sum_values[k] = [v * (current-self.seen_so_far), current-self.seen_so_far]
                 self.unique_values.append(k)
             else:
                 self.sum_values[k][0] += v * (current-self.seen_so_far)
                 self.sum_values[k][1] += (current-self.seen_so_far)
+        self.seen_so_far = current
 
         now = time.time()
         if self.verbose == 1:
@@ -84,7 +85,6 @@ class Progbar(object):
 
             sys.stdout.write(info)
             sys.stdout.flush()
-            self.seen_so_far = current
 
             if current >= self.target:
                 sys.stdout.write("\n")