Keras

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.