PyTorch Neural Network Tutorial: Build a Model

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.