parsing.py 文件源码

def create_updates(loss, network, opt, learning_rate, momentum, beta1, beta2):
    params = lasagne.layers.get_all_params(network, trainable=True)
    grads = theano.grad(loss, params)
    # if max_norm:
    #     names = ['crf.U', 'crf.W_h', 'crf.W_c', 'crf.b']
    #     constraints = [grad for param, grad in zip(params, grads) if param.name in names]
    #     assert len(constraints) == 4
    #     scaled_grads = total_norm_constraint(constraints, max_norm=max_norm)
    #     counter = 0
    #     for i in xrange(len(params)):
    #         param = params[i]
    #         if param.name in names:
    #             grads[i] = scaled_grads[counter]
    #             counter += 1
    #     assert counter == 4
    if opt == 'adam':
        updates = adam(grads, params=params, learning_rate=learning_rate, beta1=beta1, beta2=beta2)
    elif opt == 'momentum':
        updates = nesterov_momentum(grads, params=params, learning_rate=learning_rate, momentum=momentum)
    else:
        raise ValueError('unkown optimization algorithm: %s' % opt)

    return updates