fix twice gradient cut

model_training_nnn_tpu/trainer_tf.py

@@ -88,9 +88,7 @@ class BrainToTextDecoderTrainerTF:
 
         # Build model within strategy scope
         with self.strategy.scope():
-            print("🔨 Building model within TPU strategy scope...")
             self.model = self._build_model()
-            print("⚙️ Creating optimizer...")
             self.optimizer = self._create_optimizer()
             print("🔧 Pre-building optimizer state for TPU...")
             # For TPU, we must ensure optimizer is completely ready before training
@@ -125,13 +123,8 @@ class BrainToTextDecoderTrainerTF:
                 print(f"Full traceback: {traceback.format_exc()}")
                 raise RuntimeError(f"Optimizer pre-build failed: {e}") from e
 
-            print("📅 Setting up learning rate scheduler...")
             self.lr_scheduler = self._create_lr_scheduler()
-            print("✅ LR scheduler ready")
-
-            print("🎯 Initializing CTC loss...")
             self.ctc_loss = CTCLoss(blank_index=0, reduction='none')
-            print("✅ CTC loss initialized")
 
         # Log model information
         self._log_model_info()
@@ -452,12 +445,9 @@ class BrainToTextDecoderTrainerTF:
                 beta_1=self.args['beta0'],
                 beta_2=self.args['beta1'],
                 epsilon=self.args['epsilon'],
-                weight_decay=0.0,  # Disabled for TPU compatibility
-                # TPU-specific settings
-                global_clipnorm=self.args.get('grad_norm_clip_value', 0.0) if self.args.get('grad_norm_clip_value', 0.0) > 0 else None
+                weight_decay=0.0  # Disabled for TPU compatibility
+                # REMOVE global_clipnorm to avoid double clipping with manual tf.clip_by_global_norm
             )
-            print(f"⚠️  Weight decay disabled for TPU compatibility (was {self.args['weight_decay']})")
-            print("💡 Consider implementing manual L2 regularization if needed")
         else:
             print("Using standard optimizer configuration")
             optimizer = tf.keras.optimizers.AdamW(