def load_model(model_name: str) -> Model:
""" Loads and returns a model. Attaches the model name and that model's
history. """
model_path = path_from_model_name(model_name)
model_dir = os.path.dirname(model_path)
model = kload_model(model_path)
history = load_history_from_path(model_dir)
# Attach our extra data to the model
model.__asf_model_name = model_name
model.__asf_model_history = history
return model
def load_model(saved_model_dir, model_name, custom_objects={}):
"""
Loads a model from a .h5 file. Also returns the metadata required to use this model.
The .h5 can either consist of weights + architecture + optimizer state, or
just the weights. If the latter, an architection json file of the same
file basename is expected.
Parameters
----------
saved_model_dir: String.
Path to a directory where the model is saved.
model_name: String.
Model name, which is the file basename (without the extension).
Returns
-------
Two objects:
- A keras model, not yet compiled.
- Metadata required to run this model.
"""
file = os.path.join(saved_model_dir, model_name)
metadata = get_metadata(saved_model_dir, model_name)
if metadata[
"save_weights_only"]: # The .h5 file contains only the weights.
# load architecture
with open(file + ".json", "r") as f2:
model = model_from_json(f2.read(), custom_objects=custom_objects)
# load weights
model.load_weights(file + ".h5")
else: # The .h5 file contains weights + architecture + optimizer state.
model = kload_model(file + ".h5", custom_objects=custom_objects)
return model, metadata