diff --git a/configs/model/transformer_dbn_classifier.yaml b/configs/model/transformer_dbn_classifier.yaml
index 23f770e..f47e9ee 100644
--- a/configs/model/transformer_dbn_classifier.yaml
+++ b/configs/model/transformer_dbn_classifier.yaml
@@ -27,9 +27,9 @@ nn:
   _target_: src.models.components.transformer.TransformerDBN
   embedding_dim: 256
   output_dim: ${model.nn.num_embedding}
-  dbn_after_each_layer: True
+  dbn_after_each_layer: False
   dbn_last_layer: True
-  shared_embedding_dbn: True
+  shared_embedding_dbn: False
   num_embedding: 10
   seq_len: 11 # TODO: set this automatically based on the data config file or take it form some higher level folder. 
   emb_dropout: 0.1  
diff --git a/src/models/components/discrete_layers/vqvae.py b/src/models/components/discrete_layers/vqvae.py
index 61db136..cb0dc22 100644
--- a/src/models/components/discrete_layers/vqvae.py
+++ b/src/models/components/discrete_layers/vqvae.py
@@ -30,8 +30,8 @@ def project_matrix(self,x):
         return x
     
     def discretize(self, x, **kwargs) -> dict: 
-        probs = self.kernel( - self.codebook_distances(x) / self.temperature)
         x = self.project_matrix(x)
+        probs = self.kernel( - self.codebook_distances(x) / self.temperature)
         indices = torch.argmax(probs, dim=-1)
 
         if self.hard:
diff --git a/src/models/transformer_dbn_classifier.py b/src/models/transformer_dbn_classifier.py
index 6130c5b..07a698d 100644
--- a/src/models/transformer_dbn_classifier.py
+++ b/src/models/transformer_dbn_classifier.py
@@ -97,7 +97,7 @@ def training_step(
         self.train_loss(loss)
         self.train_acc(preds, targets)
         self.log("train/loss", self.train_loss, on_step=False, on_epoch=True, prog_bar=True)
-        self.log("tain/disc_loss", disc_loss, on_step=False, on_epoch=True, prog_bar=True)
+        self.log("train/disc_loss", disc_loss, on_step=False, on_epoch=True, prog_bar=True)
         self.log("train/acc", self.train_acc, on_step=False, on_epoch=True, prog_bar=True)
 
         # return loss or backpropagation will fail