Note: it is not recommended to use pickle or cPickle to save a Keras model.
1) Whole-model saving (configuration + weights)
Whole-model saving means creating a file that will contain:
- the architecture of the model, allowing you to re-create the model
- the weights of the model
- the training configuration (loss, optimizer)
- the state of the optimizer, allowing you to resume training exactly where you left off.
The default and recommended way to save a whole model is to just do: model.save(your_file_path.keras).
After saving a model in either format, you can reinstantiate it via model = keras.models.load_model(your_file_path.keras).
Example:
from keras.saving import load_model
model.save('my_model.keras')
del model # deletes the existing model
# returns a compiled model
# identical to the previous one
model = load_model('my_model.keras')
2) Weights-only saving
If you need to save the weights of a model, you can do so in HDF5 with the code below, using the file extension .weights.h5:
model.save_weights('my_model.weights.h5')
Assuming you have code for instantiating your model, you can then load the weights you saved into a model with the same architecture:
model.load_weights('my_model.weights.h5')
If you need to load the weights into a different architecture (with some layers in common), for instance for fine-tuning or transfer-learning, you can load them by layer name:
model.load_weights('my_model.weights.h5', by_name=True)
Example:
"""
Assuming the original model looks like this:
model = Sequential()
model.add(Dense(2, input_dim=3, name='dense_1'))
model.add(Dense(3, name='dense_2'))
...
model.save_weights(fname)
"""
# new model
model = Sequential()
model.add(Dense(2, input_dim=3, name='dense_1')) # will be loaded
model.add(Dense(10, name='new_dense')) # will not be loaded
# load weights from the first model; will only affect the first layer, dense_1.
model.load_weights(fname, by_name=True)
See also How can I install HDF5 or h5py to save my models? for instructions on how to install h5py.
3) Configuration-only saving (serialization)
If you only need to save the architecture of a model, and not its weights or its training configuration, you can do:
# save as JSON
json_string = model.to_json()
The generated JSON file is human-readable and can be manually edited if needed.
You can then build a fresh model from this data:
# model reconstruction from JSON:
from keras.models import model_from_json
model = model_from_json(json_string)
4) Handling custom layers (or other custom objects) in saved models
If the model you want to load includes custom layers or other custom classes or functions, you can pass them to the loading mechanism via the custom_objects argument:
from keras.models import load_model
# Assuming your model includes instance of an "AttentionLayer" class
model = load_model('my_model.h5', custom_objects={'AttentionLayer': AttentionLayer})
Alternatively, you can use a custom object scope:
from keras.utils import CustomObjectScope
with CustomObjectScope({'AttentionLayer': AttentionLayer}):
model = load_model('my_model.h5')
Custom objects handling works the same way for load_model & model_from_json:
from keras.models import model_from_json
model = model_from_json(json_string, custom_objects={'AttentionLayer': AttentionLayer})
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