在Pytorch中搭建神经网络通常需要遵循以下步骤:
torch.nn.Module
的类来定义神经网络的结构,其中包含网络的层和操作。import torch
import torch.nn as nn
class MyNetwork(nn.Module):
def __init__(self):
super(MyNetwork, self).__init__()
self.fc1 = nn.Linear(784, 128)
self.relu = nn.ReLU()
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = self.fc1(x)
x = self.relu(x)
x = self.fc2(x)
return x
model = MyNetwork()
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=0.01)
for epoch in range(num_epochs):
for i, (inputs, labels) in enumerate(train_loader):
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
outputs = model(inputs)
predictions = torch.argmax(outputs, dim=1)
这就是在Pytorch中搭建自己的神经网络的基本步骤。您可以根据自己的需求和数据集来调整网络结构、损失函数和优化器等参数以获得更好的性能。