一、用PyTorch做一個簡單的神經網絡迴歸模型
import torch
from torch.autograd import Variable
import torch.nn.functional as F
import matplotlib.pyplot as plt
#實現簡單迴歸模型
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)
y = x.pow(2) + 0.2*torch.rand(x.size())
x, y = Variable(x), Variable(y)
class Net(torch.nn.Module):
def __init__(self, n_feature, n_hidden, n_output):
super(Net, self).__init__()
self.hidden = torch.nn.Linear(n_feature, n_hidden)
self.predict = torch.nn.Linear(n_hidden, n_output)
def forward(self, x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
net = Net(1, 10, 1)
optimizer = torch.optim.SGD(net.parameters(), lr=0.5)
for i in range(3000):
#這裏分別是清空上一步的更新參數值、進行誤差的反向傳播、計算新的更新參數值、將計算得到的更新值賦給net.parameters()
prediction = net(x)
loss = torch.nn.MSELoss()(prediction, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i+1)%100 == 0:
print(loss.data)