在做一个项目学习paddle,大概是用天气预报数据，预测发电机功率，功率是有负值的。

paddle版本是2.3

意思是label必须为正值？那么model.fit怎么做线性回归？可是我看到官方案例就有波士顿房价预测的例子，为什么label有负数就报错了

报错信息为

D:\study_software\annoconda\envs\fd\lib\site-packages\paddle\nn\functional\loss.py in cross_entropy(input, label, weight, ignore_index, reduction, soft_label, axis, use_softmax, name)
   1718             if label_min < 0:
   1719                 raise ValueError("Target {} is out of lower bound.".format(
-> 1720                     label_min.item()))
   1721             if label_max >= input.shape[axis]:
   1722                 raise ValueError("Target {} is out of upper bound.".format(

ValueError: Target -0.9184724688529968 is out of lower bound.

代码：

class Regressor(paddle.nn.Layer):
    def __init__(self):
        super(Regressor, self).__init__()
        self.fc = paddle.nn.Linear(27, 1,)

    def forward(self, inputs):
        pred = self.fc(inputs)
        return pred
model = Regressor()

import paddle
import paddle.nn as nn
import paddle.vision.transforms as T
from paddle.static import InputSpec
device = paddle.set_device('cpu') # or 'gpu'
net = nn.Sequential(
    
    nn.Linear(27, 1))
# inputs and labels are not required for dynamic graph.
input = InputSpec([None, 27], 'float32', 'x')
label = InputSpec([None, 1], 'float32', 'label')
model = paddle.Model(net, input, label)
optim = paddle.optimizer.SGD(learning_rate=1e-3,
    parameters=model.parameters())
model.prepare(optim,
            paddle.nn.CrossEntropyLoss(),
            paddle.metric.Accuracy())


model.fit(dataset, epochs=2, batch_size=32, verbose=1)