pytorch nn.Linear源代码解读

从这些引用内容中可以看出，nn.Linear是PyTorch中的一个模块，用于定义线性变换。它的主要功能是将输入数据进行线性变换，并返回输出。具体来说，nn.Linear的forward函数接受输入数据并将其与权重矩阵相乘，然后加上偏置项，最后输出结果。

在PyTorch的源代码中，nn.Linear是继承自nn.Module的一个类。它有两个主要的成员变量：weight和bias，分别代表权重和偏置项。在forward函数中，输入数据通过torch.matmul函数与权重矩阵相乘，并加上偏置项。最后，输出结果的大小由权重矩阵的形状决定。

通过调用nn.Linear类的实例，可以创建一个线性变换的模型。在给定输入数据后，通过调用该模型的forward函数，可以得到输出结果。

相关问题

pytorch对fashion mnist 分类 源代码

以下是使用PyTorch对Fashion MNIST数据集进行分类的源代码示例:

import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision import datasets, transforms

# Define transform to normalize data
transform = transforms.Compose([
                  transforms.ToTensor(),
                  transforms.Normalize([0.5], [0.5])])

# Download and load the training and testing datasets
trainset = datasets.FashionMNIST('~/.pytorch/FashionMNIST_data/', download=True, train=True, transform=transform)
trainloader = DataLoader(trainset, batch_size=64, shuffle=True)
testset = datasets.FashionMNIST('~/.pytorch/FashionMNIST_data/', download=True, train=False, transform=transform)
testloader = DataLoader(testset, batch_size=64, shuffle=True)

# Define the neural network architecture
model = nn.Sequential(nn.Linear(784, 256),
                      nn.ReLU(),
                      nn.Linear(256, 128),
                      nn.ReLU(),
                      nn.Linear(128, 64),
                      nn.ReLU(),
                      nn.Linear(64, 10))

# Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01)

# Train the model
epochs = 10
for e in range(epochs):
    running_loss = 0
    for images, labels in trainloader:
        # Flatten images into a 784 long vector
        images = images.view(images.shape[0], -1)
        
        # Forward pass, backward pass and optimize
        optimizer.zero_grad()
        output = model.forward(images)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()
        
        running_loss += loss.item()
    else:
        print(f"Training loss: {running_loss/len(trainloader)}")

# Test the trained network on the test set
accuracy = 0
with torch.no_grad():
    model.eval()
    for images, labels in testloader:
        # Flatten images into a 784 long vector
        images = images.view(images.shape[0], -1)
        
        # Forward pass and calculate accuracy
        logps = model(images)
        ps = torch.exp(logps)
        top_p, top_class = ps.topk(1, dim=1)
        equals = top_class == labels.view(*top_class.shape)
        accuracy += torch.mean(equals.type(torch.FloatTensor))
    else:
        print(f"Accuracy: {accuracy/len(testloader)}")

这段代码使用了一个简单的前馈神经网络，包含四个全连接层和ReLU激活函数。训练使用了随机梯度下降优化器和交叉熵损失函数，测试使用了测试集计算模型的准确率。 Fashion MNIST是一个流行的图像分类数据集，由Zalando的研究团队收集。它包含62,000个训练图像和10,000个测试图像，每个图像的大小为28x28像素。

基于pytorch的vgg16的源代码

以下是基于PyTorch的VGG16的源代码：

import torch
import torch.nn as nn

class VGG16(nn.Module):
    def __init__(self, num_classes=1000):
        super(VGG16, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, padding=1)
        self.pool1 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.conv4 = nn.Conv2d(128, 128, kernel_size=3, padding=1)
        self.pool2 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv5 = nn.Conv2d(128, 256, kernel_size=3, padding=1)
        self.conv6 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.conv7 = nn.Conv2d(256, 256, kernel_size=3, padding=1)
        self.pool3 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv8 = nn.Conv2d(256, 512, kernel_size=3, padding=1)
        self.conv9 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv10 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool4 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv11 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv12 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.conv13 = nn.Conv2d(512, 512, kernel_size=3, padding=1)
        self.pool5 = nn.MaxPool2d(kernel_size=2, stride=2)
        self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
        self.fc1 = nn.Linear(512 * 7 * 7, 4096)
        self.fc2 = nn.Linear(4096, 4096)
        self.fc3 = nn.Linear(4096, num_classes)
        self.relu = nn.ReLU(inplace=True)
    
    def forward(self, x):
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.pool1(x)
        x = self.relu(self.conv3(x))
        x = self.relu(self.conv4(x))
        x = self.pool2(x)
        x = self.relu(self.conv5(x))
        x = self.relu(self.conv6(x))
        x = self.relu(self.conv7(x))
        x = self.pool3(x)
        x = self.relu(self.conv8(x))
        x = self.relu(self.conv9(x))
        x = self.relu(self.conv10(x))
        x = self.pool4(x)
        x = self.relu(self.conv11(x))
        x = self.relu(self.conv12(x))
        x = self.relu(self.conv13(x))
        x = self.pool5(x)
        x = self.avgpool(x)
        x = torch.flatten(x, 1)
        x = self.relu(self.fc1(x))
        x = self.relu(self.fc2(x))
        x = self.fc3(x)
        return x

该模型包括VGG16的所有层（共13层卷积层和3个全连接层），并且使用ReLU激活函数。在前向传播时，输入数据首先通过卷积层和池化层进行特征提取，然后通过全连接层进行分类。最后的输出是一个含有num_classes个元素的向量，表示每个类别的概率分数。