experiment.py 文件源码

def create_optimiser(optimiser):
    """
    Creates a function that returns an optimiser and (optional) a learn
    rate schedule
    """

    if optimiser['schedule'] is not None:
        # if we have a learn rate schedule, create a theano shared
        # variable and a corresponding update
        lr = theano.shared(np.float32(optimiser['params']['learning_rate']))

        # create a copy of the optimiser config dict so we do not change
        # it
        from copy import deepcopy
        optimiser = deepcopy(optimiser)
        optimiser['params']['learning_rate'] = lr
        lrs = nn.LearnRateSchedule(learning_rate=lr, **optimiser['schedule'])
    else:
        lrs = None

    return partial(getattr(lnn.updates, optimiser['name']),
                   **optimiser['params']), lrs