How to build a neural network model in PyTorch?

Building a neural network model in PyTorch typically involves the following steps:

  1. Import the necessary libraries.
import torch
import torch.nn as nn
  1. A neural network module
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.fc1 = nn.Linear(in_features, out_features)
        self.fc2 = nn.Linear(out_features, out_features)
        # 添加其他层
  1. send onward
def forward(self, x):
    x = self.fc1(x)
    x = self.fc2(x)
    # 添加其他层和激活函数
    return x
  1. Instantiate the model and set the optimizer and loss function.
model = MyModel()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()
  1. Training the model.
for epoch in range(num_epochs):
    optimizer.zero_grad()
    outputs = model(inputs)
    loss = criterion(outputs, labels)
    loss.backward()
    optimizer.step()

The above are the basic steps to build a neural network model, you can add more layers, optimizers, and loss functions based on your specific task and requirement. PyTorch offers a wealth of APIs and tools to help you easily construct and train neural network models.

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