Save tf.keras

Saving a model whole is super easy.

model.save('my_model')

This will create a directory called my_model with assets, saved_model.pb, and variables as contents. This only works if using tf.keras instead of native keras. See below if using native keras.

To load it again:

model = tf.keras.models.load_model('my_model')

Save keras

Saving the model as a single HDF5 is an option, however some items are not saved, such as custom layers and external losses and metrics. These can be quite annoying to add back later if you get a model from someone else.

H5:

model.save('model.h5')
model = tf.keras.models.load_model('model.h5')

Other Save/Load Functions

• model.get_weights()
• model.set_weights(weights)
• model.save_weights('file_path.h5')
• model.load_weights('file_path.h5')
• model.to_json()
• model = tf.keras.models.model_from_json(config)
• new_model = tf.keras.models.clone_model(model)