python中的Pytorch建模流程汇总

以下是Python中的PyTorch建模流程汇总的完整攻略，包括两个示例说明。

1. 建立简单的神经网络模型

以下是建立简单的神经网络模型的步骤：

1. 导入必要的库

python
import torch
import torch.nn as nn
import torch.optim as optim

1. 定义神经网络模型

```python
class Net(nn.Module):
def init(self):
super(Net, self).init()
self.fc1 = nn.Linear(784, 128)
self.fc2 = nn.Linear(128, 10)

   def forward(self, x):
       x = x.view(-1, 784)
       x = torch.relu(self.fc1(x))
       x = self.fc2(x)
       return x

net = Net()
```

1. 定义损失函数和优化器

python
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

1. 训练模型

```python
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0

print('Finished Training')
```

1. 测试模型

```python
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```

运行上述代码，即可建立简单的神经网络模型并进行训练和测试。

2. 建立卷积神经网络模型

以下是建立卷积神经网络模型的步骤：

1. 导入必要的库

python
import torch
import torch.nn as nn
import torch.optim as optim

1. 定义卷积神经网络模型

```python
class Net(nn.Module):
def init(self):
super(Net, self).init()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 4 * 4, 120)
self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10)

   def forward(self, x):
       x = self.pool(torch.relu(self.conv1(x)))
       x = self.pool(torch.relu(self.conv2(x)))
       x = x.view(-1, 16 * 4 * 4)
       x = torch.relu(self.fc1(x))
       x = torch.relu(self.fc2(x))
       x = self.fc3(x)
       return x

net = Net()
```

1. 定义损失函数和优化器

python
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)

1. 训练模型

```python
for epoch in range(10):
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 2000 == 1999:
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 2000))
running_loss = 0.0

print('Finished Training')
```

1. 测试模型

```python
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
outputs = net(images)
_, predicted = torch.max(outputs.data, 1)
total += labels.size(0)
correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (
100 * correct / total))
```

运行上述代码，即可建立卷积神经网络模型并进行训练和测试。

以上就是Python中的PyTorch建模流程汇总的完整攻略，包括两个示例说明。