diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
index fa9d47c35..1870818eb 100644
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/scaling.py
@@ -502,7 +502,6 @@ class ActivationBalancer(torch.nn.Module):
             min_prob: float = 0.1,
     ):
         super(ActivationBalancer, self).__init__()
-
         # CAUTION: this code expects self.batch_count to be overwritten in the main training
         # loop.
         self.batch_count = 0
@@ -998,8 +997,9 @@ class ScheduledFloat(torch.nn.Module):
     def __init__(self,
                  *args):
         super().__init__()
-        # self.batch_count will be written to in the training loop.
+        # self.batch_count and self.name will be written to in the training loop.
         self.batch_count = 0
+        self.name = ''
         assert len(args) >= 1
         for (x,y) in args:
             assert x >= 0
@@ -1012,17 +1012,27 @@ class ScheduledFloat(torch.nn.Module):
                                                     self.schedule)
 
     def __float__(self):
+        print_prob = 0.0001
+        def maybe_print(ans):
+            if random.random() < print_prob:
+                logging.info(f"ScheduledFloat: name={self.name}, batch_count={self.batch_count}, ans={ans}")
         batch_count = self.batch_count
         if batch_count <= self.schedule[0][0]:
-            return self.schedule[0][1]
+            ans = self.schedule[0][1]
+            maybe_print(ans)
+            return ans
         elif batch_count >= self.schedule[-1][0]:
-            return self.schedule[-1][1]
+            ans = self.schedule[-1][1]
+            maybe_print(ans)
+            return ans
         else:
             cur_x, cur_y = self.schedule[0]
             for i in range(1, len(self.schedule)):
                 next_x, next_y = self.schedule[i]
                 if batch_count >= cur_x and batch_count <= next_x:
-                    return cur_y + (next_y - cur_y) * (batch_count - cur_x) / (next_x - cur_x)
+                    ans = cur_y + (next_y - cur_y) * (batch_count - cur_x) / (next_x - cur_x)
+                    maybe_print(ans)
+                    return ans
                 cur_x, cur_y = next_x, next_y
             assert False
 
diff --git a/egs/librispeech/ASR/pruned_transducer_stateless7/train.py b/egs/librispeech/ASR/pruned_transducer_stateless7/train.py
index 47be7f4f3..138cba3db 100755
--- a/egs/librispeech/ASR/pruned_transducer_stateless7/train.py
+++ b/egs/librispeech/ASR/pruned_transducer_stateless7/train.py
@@ -95,9 +95,11 @@ def set_batch_count(
     if isinstance(model, DDP):
         # get underlying nn.Module
         model = model.module
-    for module in model.modules():
+    for name, module in model.named_modules():
         if hasattr(module, 'batch_count'):
             module.batch_count = batch_count
+            if hasattr(module, 'name'):
+                module.name = name
 
 
 def add_model_arguments(parser: argparse.ArgumentParser):