Created by: qingqing01

可以采用如下方式：

w_param = fluid.layers.create_parameter(shape=[nid_dict_size, 31], dtype='float32', name='nce_w')
b_param = fluid.layers.create_parameter(shape=[nid_dict_size, 31], dtype='float32', name='nce_b')

if is_train:
    nid_freq_arr = np.array(nid_freq).astype('float32')
     cost = fluid.layers.nce(intput=fc, label=next_id, num_total_classes=nid_dict_size,
                                          sample_weight=weight,
                                           param_attr=fluid.ParamAttr(name='nce_w'),
                                           param_attr=fluid.ParamAttr(name='nce_b'),
                                           num_neg_samples=5)
else:
    fc = fluid.layers.matmul(x=fc, y=w_param, tranpose_y=True)
    fc_trans = fluid.layers.tranpose(fc, perm=[1,0]) + b_param
    fc_trans = fluid.layers.tranpose(fc, perm=[1,0])
    prediction = fluid.layers.softmax(fc_trans)
    pred_topvalue, pred_topindices = fluid.layers.topk(prediction, k=100)