How to create a network model using PyTorch?

2 years ago
Olivia Parker
2 minutes

Building a neural network model with PyTorch involves the following steps:

  1. Import the necessary libraries: Firstly, you need to import the PyTorch library.
import torch
import torch.nn as nn
import torch.optim as optim
  1. Module of type nn
  2. initialize
class MyModel(nn.Module):
    def __init__(self):
        super(MyModel, self).__init__()
        self.layer1 = nn.Linear(input_size, hidden_size)
        self.layer2 = nn.Linear(hidden_size, output_size)

    def forward(self, x):
        x = self.layer1(x)
        x = torch.relu(x)
        x = self.layer2(x)
        return x
  1. Instantiate a network model: create an instance of a network model.
model = MyModel()
  1. Select the appropriate loss function and optimizer to train the network model.
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=learning_rate)
  1. Train the network model: Use the defined loss function and optimizer on the training data to train the network model.
for epoch in range(num_epochs):
    # 前向传播
    outputs = model(inputs)
    loss = criterion(outputs, labels)

    # 反向传播和优化
    optimizer.zero_grad()
    loss.backward()
    optimizer.step()
  1. Predict using a network model: Use the trained network model to make predictions on test data.
with torch.no_grad():
    outputs = model(test_inputs)
    _, predicted = torch.max(outputs.data, 1)

The above are simple steps for building a network model using PyTorch. Depending on the specific problem, more network structure definitions and training operations may be needed.

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