官网文档手册：https://www.paddlepaddle.org.cn/documentation/docs/zh/tutorial/quick_start/linear_regression/linear_regression.html

里面的最后部分，高层API来做线性回归训练，发现官网案例效果极差，loss几乎没有收敛。

训练loss没有收敛，eval loss在每次减少2个点的收敛..... 至少这不是一个合格的例子。 我试了下，提高lr可以改善一点，另外就是用SGD效果要好很多。

The loss value printed in the log is the current step, and the metric is the average value of previous steps.
Epoch 1/5
step 51/51 [==============================] - loss: 624.0728 - 2ms/step
Eval begin...
step 13/13 [==============================] - loss: 397.2567 - 878us/step
Eval samples: 102
Epoch 2/5
step 51/51 [==============================] - loss: 422.2296 - 1ms/step
Eval begin...
step 13/13 [==============================] - loss: 394.6901 - 750us/step
Eval samples: 102
Epoch 3/5
step 51/51 [==============================] - loss: 417.4614 - 1ms/step
Eval begin...
step 13/13 [==============================] - loss: 392.1667 - 810us/step
Eval samples: 102
Epoch 4/5
step 51/51 [==============================] - loss: 423.6764 - 1ms/step
Eval begin...
step 13/13 [==============================] - loss: 389.6587 - 772us/step
Eval samples: 102
Epoch 5/5
step 51/51 [==============================] - loss: 461.0751 - 1ms/step
Eval begin...
step 13/13 [==============================] - loss: 387.1344 - 828us/step

官网代码：

import paddle
paddle.set_default_dtype("float64")

# step1:用高层API定义数据集，无需进行数据处理等，高层API为你一条龙搞定
train_dataset = paddle.text.datasets.UCIHousing(mode='train')
eval_dataset = paddle.text.datasets.UCIHousing(mode='test')

# step2:定义模型
class UCIHousing(paddle.nn.Layer):
    def __init__(self):
        super(UCIHousing, self).__init__()
        self.fc = paddle.nn.Linear(13, 1, None)

    def forward(self, input):
        pred = self.fc(input)
        return pred

# step3:训练模型
model = paddle.Model(UCIHousing())
model.prepare(paddle.optimizer.Adam(parameters=model.parameters()),
              paddle.nn.MSELoss())
model.fit(train_dataset, eval_dataset, epochs=5, batch_size=8, verbose=1)