Created by: yuanxiangxie

核心训练代码如下

    def train_model(self):
        """
            训练模型
        """
        train_data_reader = paddle.batch(paddle.reader.shuffle(self.data_reader(train_data_dir), buf_size = train_buf_size), batch_size = train_batch_size)
        test_data_reader = paddle.batch(self.data_reader(test_data_dir), batch_size = test_batch_size)
        (content_feature, word_feature) = self.build_model_feature()
        label = paddle.layer.data(name = "label", type = paddle.data_type.dense_vector(1))
        inference = paddle.layer.cos_sim(a = content_feature, b = word_feature, size = 1)
        cost = paddle.layer.square_error_cost(input = (inference + 1.0) * 0.5, label = label)
        parameters = paddle.parameters.create(cost)
        adam_optimizer = paddle.optimizer.Adam(
                learning_rate=1e-3,
                regularization=paddle.optimizer.L2Regularization(rate=1e-3),
                model_average=paddle.optimizer.ModelAverage(average_window=0.5, max_average_window = 10000))
        trainer = paddle.trainer.SGD(
                cost = cost,
                extra_layers=paddle.evaluator.classification_error(input= (inference + 1.0) * 0.5, label=label),
                parameters=parameters,
                update_equation=adam_optimizer)
        feeding = {
                "content": 0,
                "word": 1,
                "word_len": 2,
                "label": 3
                }
        def event_handler(event):
            """
                事件监听
            """
            if isinstance(event, paddle.event.EndIteration):
                if event.batch_id % 10 == 0:
                    print >> sys.stderr, "[NOTICE] Pass:{} Batch:{} Cost:{:.2f} {}".format(event.pass_id, event.batch_id, event.cost, event.metrics)
            if isinstance(event, paddle.event.EndPass):
                if test_data_reader is not None:
                    result = trainer.test(reader = test_data_reader, feeding = feeding)
                    print >> sys.stderr, "[NOTICE] Test at Pass:{} {}".format(event.pass_id, result.metrics)
                if not os.path.exists(model_save_dir):
                    os.mkdir(model_save_dir)
                with gzip.open(os.path.join(model_save_dir, "ctr_model_{}.tar.gz".format(event.pass_id)), "w") as out_file:
                    trainer.save_parameter_to_tar(out_file)
                print >> sys.stderr, "[NOTICE] move model to output dir ..."
                shutil.move(model_save_dir, "output/model_params")
        print >> sys.stderr, "[NOTICE] embedding feature building finished ..."
        print >> sys.stderr, "[NOTICE] train ctr model start ..."
        trainer.train(
                reader = train_data_reader,
                event_handler = event_handler,
                feeding = feeding,
                num_passes = num_passes)

模型输出信息如下