| name | keras |
| description | Keras model loading and structure-inspection workflow for `.keras`, SavedModel, and HDF5 artifacts. Use when you need to inspect layers, summaries, configs, weights, or quick inference behavior from TensorFlow/Keras model files. |
| compatibility | Linux, Windows, macOS; Python 3; TensorFlow/Keras installed |
| metadata | {"author":"AeonDave","version":"1.0"} |
Keras
High-level model inspection when the artifact speaks layers, configs, and summaries.
When to use Keras
Use Keras when you need to:
- load a saved Keras or TensorFlow model artifact
- print a structural summary and inspect layers quickly
- review configuration, weights, or input/output expectations
- run a small inference sanity check without rebuilding the whole training stack
Quick Start
from keras.saving import load_model
model = load_model("model.keras", compile=False, safe_mode=True)
model.summary()
High-Value Workflows
Layer and config inspection
for layer in model.layers:
print(layer.name, layer.__class__.__name__)
config = model.get_config()
print(config.keys())
Weight inspection
for layer in model.layers:
weights = layer.get_weights()
if weights:
print(layer.name, [w.shape for w in weights])
Practical Notes
- Use
compile=False when you only need inspection; it avoids unnecessary optimizer/loss restoration.
- Keep
safe_mode=True unless you are intentionally loading trusted custom objects that require otherwise.
model.summary() is the quickest overview; get_config() is better for structured downstream tooling.
Caveats
- Custom layers, losses, or metrics may require explicit
custom_objects support.
- SavedModel,
.keras, and old .h5 artifacts do not behave identically across all environments.
- Deserialization of untrusted custom objects is a real risk; stay conservative.
Resources
No bundled scripts/, references/, or assets/.
Use the official Keras saving and serialization documentation for format differences and safe_mode behavior.
