Created by: ARDUJS

环境

• paddle = 1.7.2
• python = 3.7.5

问题

    unpad_feature, seq_len_used = embedding_layer()

    res = []
    ## 标签1的发射状态特征权重
    emission_1 = fluid.layers.fc(size=class_num,input=unpad_feature,param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Uniform(low=-0.1, high=0.1),
                regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=1e-4)))
    size = emission_1.shape[1]
    ## 标签1的转移特征权重
    w1 = fluid.layers.create_parameter(
            shape=[size + 2, size], dtype=emission_1.dtype, name='crfw_1')

    
    ## 预测标签1
    ret_infers_1 = fluid.layers.crf_decoding(
            input=emission_1, param_attr=fluid.ParamAttr(name='crfw_1'))
    infers_1 = fluid.layers.assign(ret_infers_1)
    res.append(infers_1)

    ## 标签2的发射状态特征权重
    emission_2 = fluid.layers.fc(size=class_num,input=emission_1,param_attr=fluid.ParamAttr(
                initializer=fluid.initializer.Uniform(low=-0.1, high=0.1),
                regularizer=fluid.regularizer.L2DecayRegularizer(regularization_coeff=1e-4)))
    ## 标签2的转移特征权重
    w2 = layers.fc(size=size, input=w1, name="crfw_2")
    # w2 = fluid.layers.create_parameter(
    #         shape=[size + 2, size], dtype=emission_1.dtype, name='crfw_2')
    ## 预测标签2
    ret_infers_2 = fluid.layers.crf_decoding(
            input=emission_2, param_attr=fluid.ParamAttr(name='crfw_2'))
    infers_2 = fluid.layers.assign(ret_infers_2)
    res.append(infers_2)

传入 fluid.layers.crf_decoding的param_attr参数，我想是w1通过fc所获得的变量传入，我在实现上出了问题，求大佬帮助