멀티 변수 선형 회귀와 피팅

나는 Pytorch와 linear_regression에서 일하고 있습니다.
하나의 변수로 성공했습니다. 하지만 pytorch와 함께 multi_variable linear_regression 있습니다.
약간의 오류가 발생했습니다. 어떻게 다중 변수로 선형 회귀 연산을해야합니까? 여기멀티 변수 선형 회귀와 피팅

TypeError Traceback (most recent call 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-step optimization(gradeint descent)

/anaconda/envs/tensorflow/lib/python3.6/site-packages/torch/nn/modules/module.py in call(self, *input, **kwargs) 204 205 def call(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)(input, target) 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 return output 43

TypeError: FloatMSECriterion_updateOutput received an invalid combination of arguments - got (int, torch.FloatTensor, torch.DoubleTensor, torch.FloatTensor, bool), but expected (int state, torch.FloatTensor input, torch.FloatTensor target, torch.FloatTensor output, bool sizeAverage)

코드

당신은 데이터가 동일한 유형이 있는지 확인해야합니다

#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()

출처

2017-04-22 jojo kim

입니다. 이 경우 x_train은 32 비트 float이고 y_train은 Double입니다. 사용해야하는 항목 :

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

출처

2017-05-05 18:59:42

멀티 변수 선형 회귀와 피팅

답변

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