diff --git a/gconv_experiments/MNIST_ROT/train.py b/gconv_experiments/MNIST_ROT/train.py
index 3220ac0..72b84ff 100644
--- a/gconv_experiments/MNIST_ROT/train.py
+++ b/gconv_experiments/MNIST_ROT/train.py
@@ -80,7 +80,7 @@ def preprocess_mnist_data(train_data, test_data, train_labels, test_labels):
 def train_epoch(train_data, train_labels, model, optimizer, batchsize, transformations, silent, gpu=0, finetune=False):
 
     N = train_data.shape[0]
-    pbar = ProgressBar(0, N)
+    pbar = ProgressBar(maxval=N).start()
     perm = np.random.permutation(N)
     sum_accuracy = 0
     sum_loss = 0
@@ -100,7 +100,7 @@ def train_epoch(train_data, train_labels, model, optimizer, batchsize, transform
             x_batch = cuda.to_gpu(x_batch.astype(np.float32))
             y_batch = cuda.to_gpu(y_batch.astype(np.int32))
 
-        optimizer.zero_grads()
+        model.cleargrads()
         x = Variable(x_batch)
         t = Variable(y_batch)
 
@@ -114,12 +114,13 @@ def train_epoch(train_data, train_labels, model, optimizer, batchsize, transform
         if not silent:
             pbar.update(i + y_batch.size)
 
+    pbar.finish()
     return sum_loss, sum_accuracy
 
 
 def validate(test_data, test_labels, model, batchsize, silent, gpu):
     N_test = test_data.shape[0]
-    pbar = ProgressBar(0, N_test)
+    pbar = ProgressBar(maxval=N_test).start()
     sum_accuracy = 0
     sum_loss = 0
 
diff --git a/gconv_experiments/conv_bn_act.py b/gconv_experiments/conv_bn_act.py
index e72a708..e430a4e 100644
--- a/gconv_experiments/conv_bn_act.py
+++ b/gconv_experiments/conv_bn_act.py
@@ -1,6 +1,7 @@
 
 from chainer import Chain
 import chainer.functions as F
+import chainer.links as L
 
 
 class ConvBNAct(Chain):
@@ -13,7 +14,7 @@ def __init__(self,
 
         if bn:
             out_channels = self.conv.W.data.shape[0]
-            self.add_link('bn', F.BatchNormalization(out_channels))
+            self.add_link('bn', L.BatchNormalization(out_channels))
         else:
             self.bn = None
 
@@ -24,7 +25,7 @@ def __call__(self, x, train, finetune):
         y = self.conv(x)
 
         if self.bn:
-            y = self.bn(y, test=not train, finetune=finetune)
+            y = self.bn(y, finetune=finetune)
         if self.act:
             y = self.act(y)