用pytorch进行多变量线性回归

我正在使用Pytorch进行linear_regression .
我用一个变量成功了 . 但是使用pytorch的multi_variable linear_regression .
得到了一些错误 . 我应该如何使用多变量进行线性回归？

TypeError Traceback（最近一次调用last）in（）9 optimizer.zero_grad（）#gradient 10 outputs = model（inputs）#output ---> 11 loss = criterion（outputs，targets）#loss function 12 loss.backward（ ）#backward propogation 13 optimizer.step（）＃1步优化（gradeint descent）/anaconda/envs/tensorflow/lib/python3.6/site-packages/torch/nn/modules/module.py in call（self， 输入，* kwargs）204 205 def调用（self，* input，** kwargs）： - > 206 result = self.forward（* input，** kwargs）207 for hook in self._forward_hooks.values（） ：208 hook_result = hook（self，input，result）/anaconda/envs/tensorflow/lib/python3.6/site-packages/torch/nn/modules/loss.py in forward（self，input，target）22 _assert_no_grad（ target）23 backend_fn = getattr（self._backend，type（self）.name）---> 24 return backend_fn（self.size_average）（输入，目标）25 26 /anaconda/envs/tensorflow/lib/python3.6/ site-packages / torch / nn / _functions / thnn / auto.py in forward（self，input，target）39 output = input.new（1）40 getattr（self._backend， update_output.name）（self._backend.library_state，input，target，---> 41 output，* self.additional_args）42返回输出43 TypeError：FloatMSECriterion_updateOutput接收到无效的参数组合 - got（int，torch.FloatTensor，torch .DoubleTensor，torch.FloatTensor，bool），但是预期（int state，torch.FloatTensor输入，torch.FloatTensor目标，torch.FloatTensor输出，bool sizeAverage）

这是代码

#import
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
from torch.autograd import Variable

#input_size = 1
input_size = 3
output_size = 1
num_epochs = 300
learning_rate = 0.002

#Data set
#x_train = np.array([[1.564],[2.11],[3.3],[5.4]], dtype=np.float32)
x_train = np.array([[73.,80.,75.],[93.,88.,93.],[89.,91.,90.],[96.,98.,100.],[73.,63.,70.]],dtype=np.float32)
#y_train = np.array([[8.0],[19.0],[25.0],[34.45]], dtype= np.float32)
y_train = np.array([[152.],[185.],[180.],[196.],[142.]])
print('x_train:\n',x_train)
print('y_train:\n',y_train)

class LinearRegression(nn.Module):
    def __init__(self,input_size,output_size):
        super(LinearRegression,self).__init__()
        self.linear = nn.Linear(input_size,output_size)

    def forward(self,x):
        out = self.linear(x) #Forward propogation 
        return out

model = LinearRegression(input_size,output_size)

#Lost and Optimizer
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(),lr=learning_rate)

#train the Model
for epoch in range(num_epochs):
    #convert numpy array to torch Variable
    inputs = Variable(torch.from_numpy(x_train)) #convert numpy array to torch tensor
    #inputs = Variable(torch.Tensor(x_train))    
    targets = Variable(torch.from_numpy(y_train)) #convert numpy array to torch tensor

    #forward+ backward + optimize
    optimizer.zero_grad() #gradient
    outputs = model(inputs) #output
    loss = criterion(outputs,targets) #loss function
    loss.backward() #backward propogation
    optimizer.step() #1-step optimization(gradeint descent)

    if(epoch+1) %5 ==0:
        print('epoch [%d/%d], Loss: %.4f' % (epoch +1, num_epochs, loss.data[0]))
        predicted = model(Variable(torch.from_numpy(x_train))).data.numpy()
        plt.plot(x_train,y_train,'ro',label='Original Data')
        plt.plot(x_train,predicted,label='Fitted Line')
        plt.legend()
        plt.show()