咸鱼求大神带

import torch
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
import matplotlib.pyplot as plt
import numpy as np
import torch.nn.functional as F
x_train =np.array([[3.3],[4.4],[5.5],[6.71],[6.93],[6.93],[4.168],[9.779],[6.182],[7.59],[2.167],[7.042],[10.791],[5.313],[7.997],[3.1]],dtype=np.float32)
y_train =np.array([[1.7],[2.76],[2.09],[3.19],[1.69],[1.694],[1.573],[3.366],[2.596],[2.53],[1.221],[2.827],[3.465],[1.65],[2.904],[1.3]],dtype=np.float32)
x_train=torch.from_numpy(x_train)
y_train=torch.from_numpy(y_train)
class LinearRegression(nn.Module):
def init(self):
super(LinearRegression,self).init()
self.hidden=nn.Linear(1,2)
self.fc=nn.Linear(2,1)
def forward(self,x):
out=F.relu(self.hidden(x))
out=F.relu(self.fc(out))

    return out

if torch.cuda.is_available():
model = LinearRegression().cuda()
else:
model = LinearRegression()

criterion = nn.MSELoss() #create the object for loss function
optimizer = optim.SGD(model.parameters(),lr=1e-3)
num_epochs =1000
for epoch in range(num_epochs):
if torch.cuda.is_available():
inputs = Variable(x_train).cuda()
target = Variable(y_train).cuda()
else:
inputs = Variable(x_train)
target = Variable(y_train)
#forward
out = model(inputs)
loss = criterion(out, target)
#backward
optimizer.zero.grad() #initial
loss.backgrad()
optimizer.step()

if (epoch+1) % 20 == 0:
    print('Epoch[{}/{}],loss:{:.6f}'.format(epoch+1, num_epochs, loss.data[0]))

model.eval()
model.cpu()
predict = model(Variable(x_train))
predict = predict.data.numpy()
plt.plot(x_train.numpy(), y_train.numpy,‘ro’,label=‘Original data’)
plt.plot(x_train.numpy(), predict, label=‘Fitting Line’)
plt.show()