f

model_training_nnn_tpu/trainer_tf.py

@@ -107,17 +107,12 @@ class BrainToTextDecoderTrainerTF:
                 self.optimizer.build(self.model.trainable_variables)
                 print("✅ Optimizer built with model variables")
 
-                # Verify optimizer is properly built
+                # Verify optimizer is properly built - just check iterations
                 print(f"Optimizer iterations: {self.optimizer.iterations}")
-                print(f"Optimizer built: {self.optimizer.built}")
 
-                # Print optimizer variable names for debugging
-                if hasattr(self.optimizer, 'variables') and self.optimizer.variables:
-                    print(f"Optimizer has {len(self.optimizer.variables)} internal variables")
-                else:
-                    print("⚠️  Optimizer has no internal variables - this might cause issues")
+                # Simple check - if we have iterations, optimizer is ready
+                print("✅ Optimizer ready for training")
 
-                print("✅ Optimizer pre-build completed successfully")
                 print("📝 Note: Optimizer state will be fully initialized on first training step")
 
             except Exception as e:
@@ -481,7 +476,10 @@ class BrainToTextDecoderTrainerTF:
 
     def _log_model_info(self):
         """Log model architecture and parameter information"""
-        self.logger.info("Initialized TripleGRUDecoder model")
+        if self.logger:
+            self.logger.info("Initialized TripleGRUDecoder model")
+        else:
+            print("Initialized TripleGRUDecoder model")
 
         # Build the model by calling it once with dummy data
         dummy_batch_size = 2
@@ -494,7 +492,11 @@ class BrainToTextDecoderTrainerTF:
 
         # Count parameters
         total_params = sum([tf.size(w).numpy() for w in self.model.trainable_weights])
-        self.logger.info(f"Model has {total_params:,} trainable parameters")
+
+        if self.logger:
+            self.logger.info(f"Model has {total_params:,} trainable parameters")
+        else:
+            print(f"Model has {total_params:,} trainable parameters")
 
     def _train_step(self, batch, step):
         """Single training step with gradient tape"""