About Keras models

There are two types of models available in Keras: the Sequential model and the Model class used with functional API.

These models have a number of methods in common:

  • model.summary(): prints a summary representation of your model. Shortcut for utils.print_summary
  • model.get_config(): returns a dictionary containing the configuration of the model. The model can be reinstantiated from its config via:
config = model.get_config()
model = Model.from_config(config)
# or, for Sequential:
model = Sequential.from_config(config)
  • model.get_weights(): returns a list of all weight tensors in the model, as Numpy arrays.
  • model.set_weights(weights): sets the values of the weights of the model, from a list of Numpy arrays. The arrays in the list should have the same shape as those returned by get_weights().
  • model.to_json(): returns a representation of the model as a JSON string. Note that the representation does not include the weights, only the architecture. You can reinstantiate the same model (with reinitialized weights) from the JSON string via:
from keras.models import model_from_json

json_string = model.to_json()
model = model_from_json(json_string)
  • model.to_yaml(): returns a representation of the model as a YAML string. Note that the representation does not include the weights, only the architecture. You can reinstantiate the same model (with reinitialized weights) from the YAML string via:
from keras.models import model_from_yaml

yaml_string = model.to_yaml()
model = model_from_yaml(yaml_string)
  • model.save_weights(filepath): saves the weights of the model as a HDF5 file.
  • model.load_weights(filepath, by_name=False): loads the weights of the model from a HDF5 file (created by save_weights). By default, the architecture is expected to be unchanged. To load weights into a different architecture (with some layers in common), use by_name=True to load only those layers with the same name.