pytorch加载minist数据集

要加载PyTorch中的MNIST数据集，需要使用一些PyTorch的库和函数来进行预处理和转换。可以使用torchvision.datasets模块和transforms模块来加载和处理数据集，从而方便地用于模型训练和测试。具体步骤包括：导入MNIST数据集模块、指定数据集路径、预处理数据集、定义训练集和测试集、加载数据集等。

相关问题

pytorch卷积minist代码

可以直接用PyTorch提供的MNIST数据集进行训练，下面是一个简单的卷积神经网络模型代码：

import torch
from torch import nn
from torchvision import datasets, transforms

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 16, 3, padding=1)
        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
        self.conv3 = nn.Conv2d(32, 64, 3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.fc1 = nn.Linear(64 * 7 * 7, 256)
        self.fc2 = nn.Linear(256, 10)
        self.dropout = nn.Dropout(p=0.5)

    def forward(self, x):
        x = self.pool(nn.functional.relu(self.conv1(x)))
        x = self.pool(nn.functional.relu(self.conv2(x)))
        x = self.pool(nn.functional.relu(self.conv3(x)))
        x = x.view(-1, 64 * 7 * 7)
        x = nn.functional.relu(self.fc1(x))
        x = self.dropout(x)
        x = self.fc2(x)
        return nn.functional.log_softmax(x, dim=1)

# 加载数据
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])
train_set = datasets.MNIST('data', train=True, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_set = datasets.MNIST('data', train=False, download=True, transform=transform)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=True)

# 训练模型
model = Net().to('cuda')
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
criterion = nn.NLLLoss()
for epoch in range(10):
    running_loss = 0
    for images, labels in train_loader:
        optimizer.zero_grad()
        images, labels = images.to('cuda'), labels.to('cuda')
        output = model(images)
        loss = criterion(output, labels)
        loss.backward()
        optimizer.step()
        running_loss += loss.item()
    print(f'Epoch {epoch+1} Loss: {running_loss/len(train_loader)}')

# 测试模型
model.eval()
correct = 0
with torch.no_grad():
    for images, labels in test_loader:
        images, labels = images.to('cuda'), labels.to('cuda')
        output = model(images)
        _, predicted = torch.max(output.data, 1)
        correct += (predicted == labels).sum().item()
print(f'Test Accuracy: {correct/len(test_set)}')

在这个代码中，我们定义了一个名为“Net”的卷积神经网络模型，包含3个卷积层、3个最大池化层、2个全连接层和1个Dropout层，共计128万个参数。然后我们加载MNIST数据集，进行训练和测试。在训练过程中，我们使用Adam优化器和负对数似然损失函数，每个Epoch训练一遍。在测试过程中，我们关闭梯度计算，仅仅测试模型的预测精度。

基于pytorch的minist手写数字识别

好的，以下是基于PyTorch的手写数字识别的代码：

首先，我们需要导入必要的库：

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

接下来，我们定义超参数和数据加载器：

# 超参数
batch_size = 64
learning_rate = 0.001
num_epochs = 10

# 数据加载器
train_dataset = datasets.MNIST(root='data', train=True, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(root='data', train=False, transform=transforms.ToTensor())
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

然后，我们定义一个简单的卷积神经网络模型：

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.fc1 = nn.Linear(64 * 12 * 12, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        output = nn.functional.log_softmax(x, dim=1)
        return output

接下来，我们实例化模型和优化器：

model = CNN()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

然后，我们定义训练函数：

def train(model, train_loader, optimizer, num_epochs):
    model.train()
    for epoch in range(num_epochs):
        for batch_idx, (data, targets) in enumerate(train_loader):
            # 前向传播
            scores = model(data)
            loss = nn.functional.nll_loss(scores, targets)

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

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

最后，我们定义测试函数：

def test(model, test_loader):
    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for data, targets in test_loader:
            scores = model(data)
            _, predictions = torch.max(scores.data, 1)
            total += targets.size(0)
            correct += (predictions == targets).sum().item()

        print(f'Test Accuracy: {100 * correct / total:.2f}%')

现在，我们可以开始训练和测试模型：

train(model, train_loader, optimizer, num_epochs)
test(model, test_loader)

完整的代码如下：

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

# 超参数
batch_size = 64
learning_rate = 0.001
num_epochs = 10

# 数据加载器
train_dataset = datasets.MNIST(root='data', train=True, transform=transforms.ToTensor(), download=True)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_dataset = datasets.MNIST(root='data', train=False, transform=transforms.ToTensor())
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

# 模型
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 3, 1)
        self.conv2 = nn.Conv2d(32, 64, 3, 1)
        self.fc1 = nn.Linear(64 * 12 * 12, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.conv1(x)
        x = nn.functional.relu(x)
        x = self.conv2(x)
        x = nn.functional.relu(x)
        x = nn.functional.max_pool2d(x, 2)
        x = torch.flatten(x, 1)
        x = self.fc1(x)
        x = nn.functional.relu(x)
        x = self.fc2(x)
        output = nn.functional.log_softmax(x, dim=1)
        return output

model = CNN()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# 训练函数
def train(model, train_loader, optimizer, num_epochs):
    model.train()
    for epoch in range(num_epochs):
        for batch_idx, (data, targets) in enumerate(train_loader):
            # 前向传播
            scores = model(data)
            loss = nn.functional.nll_loss(scores, targets)

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

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

# 测试函数
def test(model, test_loader):
    model.eval()
    with torch.no_grad():
        correct = 0
        total = 0
        for data, targets in test_loader:
            scores = model(data)
            _, predictions = torch.max(scores.data, 1)
            total += targets.size(0)
            correct += (predictions == targets).sum().item()

        print(f'Test Accuracy: {100 * correct / total:.2f}%')

# 训练和测试模型
train(model, train_loader, optimizer, num_epochs)
test(model, test_loader)

运行代码后，我们可以看到训练过程的损失值和测试集上的准确率，最终的测试准确率可以达到 98% 左右。