基于pytorch的多变量线性回归

Question

我正在研究Pytorch的线性回归问题
我在单变量情况下取得了成功，但是当我执行多变量线性回归时，我得到了以下错误。我应该如何执行多变量线性回归？

TypeError Traceback（最近调用最后一次）in（）9optimizer.zero_grad（）#渐变10输出=模型（输入）#输出---

/anaconda/envs/tensorflow/lib/python3.6/site-packages/torch/nn/modules/module.py在调用（自我，*输入，**kwargs）204 205 def调用（自我，*输入，**kwargs）：-

/巨蟒/envs/tensorflow/lib/python3。6/现场包/火炬/nn/模块/损耗。前向py（自我，输入，目标）22 _断言_否_梯度（目标）23后端_fn=getattr（自我._后端，键入（自我）.name）---

/anaconda/envs/tensorflow/lib/python3.6/site-packages/torch/nn/_functions/thnn/auto.py正向（自我，输入，目标）39输出=input.new（1）40 getattr（自我。_backend，update_output.name）（自我。_<--plhd_state输入目标

TypeError:FloatMseCriteria_updateOutput接收到无效的参数组合-Get（int，torch.FloatTensor，torch.DoubleTensor，torch.FloatTensor，bool），但应为（int状态，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()

Answer 1

您需要确保数据具有相同的类型。在这种情况下x_train是32位浮点数，而y_train是双。你必须使用：

y_train = np.array([[152.],[185.],[180.],[196.],[142.]],dtype=np.float32)