Learning to Quantify的pytorch代码

以下是使用PyTorch实现Learning to Quantify的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim

class Quantifier(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(Quantifier, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_dim, output_dim)

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        out = torch.sigmoid(out)
        return out

def train(model, train_loader, num_epochs, learning_rate):
    criterion = nn.MSELoss()
    optimizer = optim.Adam(model.parameters(), lr=learning_rate)

    for epoch in range(num_epochs):
        for i, (data, target) in enumerate(train_loader):
            optimizer.zero_grad()
            output = model(data)
            loss = criterion(output, target)
            loss.backward()
            optimizer.step()

            if (i+1) % 10 == 0:
                print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

def test(model, test_loader):
    correct = 0
    total = 0
    with torch.no_grad():
        for data, target in test_loader:
            output = model(data)
            predicted = torch.round(output)
            total += target.size(0)
            correct += (predicted == target).sum().item()

    print('Accuracy of the model on the test set: {:.2f}%'.format(100 * correct / total))

if __name__ == '__main__':
    input_dim = 784
    hidden_dim = 100
    output_dim = 1
    num_epochs = 10
    learning_rate = 0.001

    train_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
    train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=100, shuffle=True)

    test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
    test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=100, shuffle=False)

    model = Quantifier(input_dim, hidden_dim, output_dim)
    train(model, train_loader, num_epochs, learning_rate)
    test(model, test_loader)

在这个例子中，我们使用了MNIST数据集来训练一个“量化器”模型。该模型使用一个简单的两层神经网络，其中包含一个隐藏层和一个输出层（使用Sigmoid作为激活函数），用于将输入图像映射到一个0到1之间的输出值。我们使用均方误差作为损失函数，并使用Adam优化器进行反向传播和权重更新。在训练过程中，我们输出每个时期的损失，并在测试集上评估模型的准确性。