Save and load models

Save the model instance after training, and keep its architecture and preprocessing configuration alongside it. A parameter JSON file describes construction choices; it does not contain trained weights.

Save a Keras model

model.save("model.keras")

Use the EDGE loader in a fresh process after selecting the appropriate backend:

from helia_edge.models import load_model
model = load_model("model.keras")

This loader registers supported EDGE custom objects. A bare import helia_edge uses lazy imports and does not register every Keras class as a side effect.

Use the Keras loader directly

import helia_edge
helia_edge.register_keras_serializables()

import keras
model = keras.saving.load_model("model.keras")

Models whose layer normalization runs over spatial axes (TCN, UNet and UNext with norm="layer") save helia_edge>LayerNormalization, so plain keras.saving.load_model needs this registration first; helia_edge.models.load_model registers automatically. On the Torch backend, helia_edge.models.load_model also adapts .keras files saved by earlier helia-edge versions: it renames names containing . (layers, the model, and compile keys), which Torch rejects, and loads Keras LayerNormalization over spatial axes as the helia-edge class. Other formats, such as .h5, load unchanged.

Registration initializes the selected backend. It does not turn a TensorFlow-only component into a portable component. See the backend guide for the tested round-trip boundaries.

Reconstruct from configuration and weights

Construct the same architecture and input signature, then call model.load_weights(checkpoint_path). Check that graph connections, shapes and layer configuration agree with the checkpoint. Do not use skipped mismatches as a substitute for compatibility.

The model architectures guide explains reading older TCN configurations, and specs and trained weights the handling of historical trained artifacts. Cross-backend loading depends on the model’s custom components and naming conventions; preserve the original backend and dependency versions with the checkpoint.

Include the experiment context

Record the model configuration, preprocessing policy, class or target definitions, package versions and evaluation results. Record seed policy too, but do not assume a seed guarantees identical weights or random sequences across backends.