thread_example.py

from concurrent.futures import ThreadPoolExecutor
import keras


def f():
    # NOTE: removing this line causes the `get_weights` call later to fail
    # The initialization performed in `get_session` is important somehow?
    print('Info:', keras.backend.get_session(), keras.backend.get_session().graph)

    model = keras.models.Sequential()
    model.add(keras.layers.Dense(4, input_shape=(4,)))
    model.compile(loss='mse', optimizer='sgd')
    print('Before:', model.predict(np.ones((1, 4))))
    return model


def recreate(model):
    w = model.get_weights()  # can't access after recreating the session
    keras.backend.clear_session()
    model = keras.models.model_from_config(model._updated_config())
    model.set_weights(w)
    return model


if __name__ == '__main__':
    e = ThreadPoolExecutor(max_workers=1)
    m = e.submit(f).result()
    m = recreate(m)
    print('After:', m.predict(np.ones((1, 4))))