宝石数据集相对于MNIST数据集的网络模型复杂度

相对于MNIST数据集，宝石数据集的网络模型复杂度可能会更高。这是因为宝石数据集中的图像通常具有更高的分辨率和更多的细节，因此需要更深更复杂的卷积神经网络（CNN）来提取其特征。此外，宝石数据集中可能会出现一些形状和颜色变化，需要更加复杂的网络结构来处理这些变化。因此，相对于MNIST数据集，宝石数据集可能需要更多的卷积层、池化层和全连接层等网络层次。

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pytorch训练mnist数据集使用网络模型的代码

下面是使用PyTorch训练MNIST数据集的示例代码：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 定义网络模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = nn.functional.relu(nn.functional.max_pool2d(self.conv1(x), 2))
        x = nn.functional.relu(nn.functional.max_pool2d(self.drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = nn.functional.relu(self.fc1(x))
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

# 加载数据集
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())

# 定义训练参数
batch_size = 64
learning_rate = 0.01
epochs = 10

# 定义优化器和损失函数
net = Net()
optimizer = optim.SGD(net.parameters(), lr=learning_rate, momentum=0.5)
criterion = nn.CrossEntropyLoss()

# 开始训练
for epoch in range(epochs):
    for i, (images, labels) in enumerate(torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)):
        optimizer.zero_grad()
        outputs = net(images)
        loss = criterion(outputs, labels)
        loss.backward()
        optimizer.step()
        
        if (i+1) % 100 == 0:
            print('Epoch [%d/%d], Iter [%d/%d] Loss: %.4f' % (epoch+1, epochs, i+1, len(train_dataset)//batch_size, loss.item()))

# 测试模型
correct = 0
total = 0
with torch.no_grad():
    for images, labels in torch.utils.data.DataLoader(test_dataset, batch_size=batch_size):
        outputs = net(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

print('Accuracy of the network on the 10000 test images: %d %%' % (100 * correct / total))

这段代码定义了一个包含两个卷积层和两个全连接层的神经网络模型，并使用SGD作为优化器和交叉熵损失函数进行训练。在循环中，我们使用DataLoader来加载数据集批次，并对每个批次进行前向传播、反向传播和参数更新。最后，我们使用测试集来评估模型的性能。

pytorch训练mnist数据集使用LeNet网络模型的代码

以下是使用PyTorch训练MNIST数据集的LeNet网络模型的代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.datasets as datasets
import torchvision.transforms as transforms

# 定义LeNet网络模型
class LeNet(nn.Module):
    def __init__(self):
        super(LeNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 6, kernel_size=5)
        self.pool1 = nn.MaxPool2d(kernel_size=2)
        self.conv2 = nn.Conv2d(6, 16, kernel_size=5)
        self.pool2 = nn.MaxPool2d(kernel_size=2)
        self.fc1 = nn.Linear(16*4*4, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        x = self.pool1(torch.relu(self.conv1(x)))
        x = self.pool2(torch.relu(self.conv2(x)))
        x = x.view(-1, 16*4*4)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# 加载MNIST数据集
train_dataset = datasets.MNIST(root='data/', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='data/', train=False, transform=transforms.ToTensor(), download=True)

# 定义训练参数
batch_size = 64
learning_rate = 0.001
num_epochs = 10

# 创建数据加载器
train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=False)

# 创建LeNet网络模型和优化器
model = LeNet()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练模型
for epoch in range(num_epochs):
    for i, (images, labels) in enumerate(train_loader):
        # 前向传播
        outputs = model(images)
        loss = nn.CrossEntropyLoss()(outputs, labels)

        # 反向传播和优化
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

        if (i+1) % 100 == 0:
            print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, len(train_loader), loss.item()))

# 测试模型
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Accuracy of the network on the 10000 test images: {} %'.format(100 * correct / total))