Is it possible to continue training a Keras estimator with all the hyperparameters (including decreasing learning rate) and weights saved from previous epochs, as one does in scikit-learn with the warm_start parameter? Something like this:
estimator = KerasRegressor(build_fn=create_model, epochs=20, batch_size=40, warm_start=True)
Specifically, warm start should do this:
warm_start : bool, optional, default False When set to True, reuse the solution of the previous call to fit as initialization, otherwise, just erase the previous solution.
Is there anything like that in Keras?