pytorch实现cnn手写数字识别
时间: 2023-06-05 14:47:08 浏览: 127
PyTorch可以用来实现CNN手写数字识别。具体步骤包括:
1. 准备数据集:可以使用MNIST数据集,其中包含了大量手写数字的图片和对应的标签。
2. 定义模型:可以使用PyTorch提供的nn模块来定义卷积神经网络模型,包括卷积层、池化层、全连接层等。
3. 训练模型:使用PyTorch提供的优化器和损失函数来训练模型,可以使用GPU来加速训练过程。
4. 测试模型:使用测试集来评估模型的性能,可以计算准确率、精确率、召回率等指标。
5. 部署模型:将训练好的模型部署到生产环境中,可以使用PyTorch提供的ONNX格式来导出模型,或者使用PyTorch提供的C++接口来进行部署。
总之,PyTorch是一个非常强大的深度学习框架,可以用来实现各种各样的神经网络模型,包括CNN手写数字识别。
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pytorch实现cnn手写数字识别并计算查准率,查全率以及ALU的代码
以下是使用PyTorch实现CNN手写数字识别并计算查准率、查全率和ALU的示例代码:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
import torchvision.datasets as datasets
# 设置超参数
num_epochs = 10
num_classes = 10
batch_size = 100
learning_rate = 0.001
# 加载数据集并进行预处理
train_dataset = datasets.MNIST(root='./data', train=True, transform=transforms.ToTensor(), download=True)
test_dataset = datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor())
# 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)
# 定义CNN模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.layer1 = nn.Sequential(
nn.Conv2d(1, 16, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(16),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.layer2 = nn.Sequential(
nn.Conv2d(16, 32, kernel_size=5, stride=1, padding=2),
nn.BatchNorm2d(32),
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2))
self.fc = nn.Linear(7*7*32, num_classes)
def forward(self, x):
out = self.layer1(x)
out = self.layer2(out)
out = out.reshape(out.size(0), -1)
out = self.fc(out)
return out
# 实例化CNN模型
model = CNN()
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)
# 训练模型
total_step = len(train_loader)
for epoch in range(num_epochs):
for i, (images, labels) in enumerate(train_loader):
# 前向传播
outputs = model(images)
loss = criterion(outputs, labels)
# 反向传播并优化
optimizer.zero_grad()
loss.backward()
optimizer.step()
# 每训练100个batch就输出一次信息
if (i+1) % 100 == 0:
print('Epoch [{}/{}], Step [{}/{}], Loss: {:.4f}'.format(epoch+1, num_epochs, i+1, total_step, loss.item()))
# 测试模型
model.eval()
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('准确率: {} %'.format(100 * correct / total))
# 计算查准率、查全率和ALU
with torch.no_grad():
TP, TN, FP, FN = 0, 0, 0, 0
for images, labels in test_loader:
outputs = model(images)
_, predicted = torch.max(outputs.data, 1)
for i in range(len(labels)):
if labels[i] == predicted[i]:
if labels[i] == 1:
TP += 1
else:
TN += 1
else:
if labels[i] == 1:
FN += 1
else:
FP += 1
precision = TP / (TP + FP)
recall = TP / (TP + FN)
ALU = (TN + TP) / (TN + TP + FP + FN)
print('查准率: {:.4f}, 查全率: {:.4f}, ALU: {:.4f}'.format(precision, recall, ALU))
```
在以上代码中,我们首先加载MNIST数据集并进行预处理。然后,我们定义了一个CNN模型,并且实例化了该模型。接着,我们定义了损失函数和优化器,并在训练过程中使用它们。最后,我们测试了模型的准确率,并计算了查准率、查全率和ALU。
请注意,查准率(precision)是指正确预测为正类的样本数(True Positive)占所有预测为正类的样本数(True Positive + False Positive)的比例;查全率(recall)是指正确预测为正类的样本数(True Positive)占所有真实为正类的样本数(True Positive + False Negative)的比例;ALU(Accuracy of Labeling Unlabeled data)是指正确预测为负类的样本数(True Negative)和正确预测为正类的样本数(True Positive)占所有样本数(True Negative + True Positive + False Negative + False Positive)的比例。
pytorch+cnn手写数字识别
基于PyTorch搭建CNN实现手写数字识别的步骤如下:
1. 导入所需的库和模块:
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
```
2. 准备数据集:
```python
transform = transforms.Compose(
[transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))])
trainset = torchvision.datasets.MNIST(root='./data', train=True,
download=True, transform=transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,
shuffle=True, num_workers=2)
testset = torchvision.datasets.MNIST(root='./data', train=False,
download=True, transform=transform)
testloader = torch.utils.data.DataLoader(testset, batch_size=64,
shuffle=False, num_workers=2)
```
3. 定义CNN模型:
```python
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.conv1 = nn.Conv2d(1, 6, 5)
self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5)
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.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 4 * 4)
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
x = self.fc3(x)
return x
net = Net()
```
4. 定义损失函数和优化器:
```python
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
```
5. 训练模型:
```python
for epoch in range(10): # 进行10个epoch的训练
running_loss = 0.0
for i, data in enumerate(trainloader, 0):
inputs, labels = data
optimizer.zero_grad()
outputs = net(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
if i % 200 == 199: # 每200个batch打印一次损失值
print('[%d, %5d] loss: %.3f' %
(epoch + 1, i + 1, running_loss / 200))
running_loss = 0.0
```
6. 测试模型:
```python
correct = 0
total = 0
with torch.no_grad():
for data in testloader:
images, labels = data
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: %.2f %%' % (
100 * correct / total))
```