Selected program examples: python+torch linear regression model machine learning. If you need to install the operating environment or remote debugging, please see your personal QQ business card at the bottom of the article for remote assistance from professional technicians!
Preface
This blog writes code for "Python+Torch Linear Regression Model Machine Learning". The code is clean, regular and easy to read. Recommended for learning and application.
Article directory
1. Required tools and software
2. Usage steps
1. Main code
2. Operation results
3. Online assistance
1. Required tools and software
1. Python
2. Pycharm
2. Usage steps
The code is as follows (example):
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)
#plt.scatter(x.data.numpy(), y.data.numpy())
#plt.show()
#定义层
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)
#搭建层
def forward(self, x):
x = F.relu(self.hidden(x))
x = self.predict(x)
return x
net = Net(1, 10, 1)
print(net)
plt #something about plotting
plt.show()
for t in range(100):
prediction = net(x)
loss = loss_func(prediction, y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
operation result
3. Online assistance:
If you need to install the operating environment or remote debugging, please see your personal QQ business card at the bottom of the article for remote assistance from professional technicians!
1) Remote installation and running environment, code debugging
2) Visual Studio, Qt, C++, Python programming language introductory guide
3) Interface beautification
4) Software production 5
) Cloud server application
6) Website production
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