代码

class LRLayer(nn.Layer):
    def __init__(self, params):
    ¦   super(LRLayer, self).__init__()
    ¦   self.init_value_ = 0.0
    ¦   self.reg = 0.001

    def forward(self, inputs, is_infer=False):
    ¦   input_idxs, input_weights = inputs

    ¦   embs = []
    ¦   for idx in input_idxs:
    ¦   ¦   emb = fluid.layers.embedding(idx,
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   [10240000, 1],
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   is_sparse=True,
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   is_distributed=True,
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   param_attr=fluid.ParamAttr(
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   name="emb",
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   # initializer=paddle.nn.initializer.Constant(value=0)
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   # initializer=paddle.nn.initializer.TruncatedNormal(mean=0.0, std=self.init_value_),
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   # regularizer=paddle.regularizer.L1Decay(self.reg)
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ))
    ¦   ¦   #paddle.static.Print(idx)
    ¦   ¦   embs.append(emb)

    ¦   #paddle.static.Print(input_weights)

    ¦   embs = paddle.concat(x=embs, axis=1)

    ¦   self.b_linear = paddle.create_parameter(shape=[1],
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   dtype='float32',
    ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   ¦   default_initializer=paddle.nn.initializer.Constant(value=0))
    ¦   y_first_order = paddle.sum(embs * input_weights, 1, keepdim=True)

    ¦   predict = paddle.nn.functional.sigmoid(y_first_order + self.b_linear)
    ¦   return predict, y_first_order, embs

• 训练信息

   1）CPU 多机

idx 生成方式

1. OK

feasign = sign_dict.get(txt, 0)

1. 不 ok

feasign = sign.sign(txt)

运行方式

fleetrun --worker_num=5 --server_num=1 trainer.py ps -c wd_config.yaml