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.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 byget_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 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 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): loads the weights of the model from a HDF5 file (created bysave_weights).