Created by: FrankRouter

日志

Tue Sep  5 15:16:57 2017[1,37]<stdout>:Test with Pass 2, Cost 4.149080, {}
Tue Sep  5 15:17:00 2017[1,34]<stdout>:Test with Pass 2, Cost 4.421460, {}
Tue Sep  5 15:17:02 2017[1,46]<stdout>:Test with Pass 2, Cost 4.200185, {}
Tue Sep  5 15:17:08 2017[1,39]<stdout>:Test with Pass 2, Cost 4.398165, {}
Tue Sep  5 15:17:09 2017[1,17]<stdout>:Test with Pass 2, Cost 4.678599, {}

网络配置

def fc_net(dict_dim, class_dim=2):
    """
    dnn network definition

    :param dict_dim: size of word dictionary
    :type input_dim: int
    :params class_dim: number of instance class
    :type class_dim: int
    """

    # input layers
    data = paddle.layer.data("word", paddle.data_type.sparse_binary_vector(dict_dim))
    label = paddle.layer.data("label", paddle.data_type.dense_vector(1))

    # hidden
    h_size = 128
    h = paddle.layer.fc(
        input=data,
        size=h_size,
        act=paddle.activation.Tanh())

    # output layer
    output = paddle.layer.fc(
        input=h,
        size=1,
        act=paddle.activation.Linear())

    cost = paddle.layer.smooth_l1_cost(input=output, label=label)

    return cost, output, label

训练参数

    parameters = paddle.parameters.create(cost)
    # create optimizer
    adagrad_optimizer = paddle.optimizer.DecayedAdaGrad(
        learning_rate=0.01,
        regularization=paddle.optimizer.L2Regularization(rate=0.01),
        rho=0.95,
        epsilon=1e-6,
     )

    # create trainer
    trainer = paddle.trainer.SGD(
        cost=cost,
        parameters=parameters,
        update_equation=adagrad_optimizer
    )