pus

diagnose_model.py

@@ -0,0 +1,58 @@
+# Save as: diagnose_model.py
+import h5py
+import tensorflow as tf
+import json
+
+print("🔍 Diagnosing hybrid_model.keras...")
+
+# 1. Check file structure
+print("\n1. HDF5 File Structure:")
+with h5py.File('hybrid_model.keras', 'r') as f:
+    print("Top-level keys:", list(f.keys()))
+    
+    # Check for model config
+    if 'model_config' in f:
+        config = f['model_config'][()]
+        if isinstance(config, bytes):
+            config = config.decode('utf-8')
+        
+        # Try to parse JSON
+        try:
+            model_config = json.loads(config)
+            print("\n2. Model Configuration:")
+            print(f"   Model class: {model_config.get('class_name', 'Unknown')}")
+            print(f"   Config keys: {list(model_config.get('config', {}).keys())}")
+            
+            # Print layers
+            if 'layers' in model_config.get('config', {}):
+                layers = model_config['config']['layers']
+                print(f"\n3. Model Layers ({len(layers)} total):")
+                for i, layer in enumerate(layers):
+                    class_name = layer.get('class_name', 'Unknown')
+                    name = layer.get('config', {}).get('name', 'No name')
+                    print(f"   {i:3d}: {class_name:30s} - {name}")
+        except:
+            print("   Could not parse model config")
+    
+    # Check weights
+    if 'model_weights' in f:
+        print("\n4. Model Weights:")
+        weight_layers = list(f['model_weights'].keys())
+        print(f"   Weight groups: {weight_layers[:10]}")  # First 10
+    
+    # Try to load with Keras
+    print("\n5. Attempting Keras load...")
+    try:
+        model = tf.keras.models.load_model('hybrid_model.keras', compile=False)
+        print(f"   ✅ Success! Model loaded")
+        print(f"   Input shape: {model.input_shape}")
+        print(f"   Output shape: {model.output_shape}")
+        print(f"   Layers: {len(model.layers)}")
+        
+        # Show layer names
+        print("\n   Layer Names:")
+        for i, layer in enumerate(model.layers[:10]):  # First 10
+            print(f"      {i}: {layer.name} ({layer.__class__.__name__})")
+        
+    except Exception as e:
+        print(f"   ❌ Failed: {e}")