diff --git a/layers.py b/layers.py
index f1531f7..4a5296e 100644
--- a/layers.py
+++ b/layers.py
@@ -19,7 +19,7 @@ class Layer(nn.Module):
     ACTIVATION = F.leaky_relu
 
     BATCH_NORM = True
-    BATCH_NORM_TRAINING = False
+    BATCH_NORM_TRAINING = True
     BATCH_NORM_MOMENTUM = 0.01
 
     IS_TRAINING = False
@@ -28,7 +28,7 @@ class Layer(nn.Module):
     LOGGER = DummyLogger()
 
     def __init__(self, activation, batch_norm):
-        super(Layer, self).__init__()
+        super().__init__()
         self.name = 'Layer'
         self.info = LayerInfo()
 
@@ -55,7 +55,7 @@ class Conv1d(Layer):
         self.batch_norm = nn.BatchNorm1d(
             out_channels,
             momentum=Layer.BATCH_NORM_MOMENTUM,
-            track_running_stats=not Layer.BATCH_NORM_TRAINING) if self.batch_norm else None
+            track_running_stats=Layer.BATCH_NORM_TRAINING) if self.batch_norm else None
 
     def forward(self, input_data: torch.Tensor) -> torch.Tensor:
         return super().forward(self.conv(input_data))
@@ -87,7 +87,7 @@ class Conv3d(Layer):
         self.batch_norm = nn.BatchNorm3d(
             out_channels,
             momentum=Layer.BATCH_NORM_MOMENTUM,
-            track_running_stats=not Layer.BATCH_NORM_TRAINING) if self.batch_norm else None
+            track_running_stats=Layer.BATCH_NORM_TRAINING) if self.batch_norm else None
 
     def forward(self, input_data: torch.Tensor) -> torch.Tensor:
         return super().forward(self.conv(input_data))