File size: 2,197 Bytes
810396d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 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}") |