title: Keras Training warm_start
body: <p>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 <code>warm_start</code> parameter? Something like this:</p>
  
  <pre><code>estimator = KerasRegressor(build_fn=create_model, epochs=20, batch_size=40, warm_start=True)
  </code></pre>
  
  <p>Specifically, warm start should do this:</p>
  
  <blockquote>
    <p>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.</p>
  </blockquote>
  
  <p>Is there anything like that in Keras?</p>