import torch from torch import nn, optim import torchvision.transforms as transforms from torchvision import datasets from torch.utils.data import DataLoader from restnet18.restnet18 import RestNet18 ———————————————— 逐行解释
时间: 2023-08-01 21:12:41 浏览: 230
这段代码是一个用于训练和测试RestNet18模型的示例代码。以下是对代码逐行的解释:
1. `import torch`
导入PyTorch库,用于构建和训练神经网络模型。
2. `from torch import nn, optim`
从torch模块中导入nn(神经网络)和optim(优化器)模块,用于定义神经网络模型和选择优化器。
3. `import torchvision.transforms as transforms`
导入torchvision.transforms模块,用于进行图像数据的预处理和数据增强操作。
4. `from torchvision import datasets`
从torchvision模块中导入datasets模块,用于加载和处理图像数据集。
5. `from torch.utils.data import DataLoader`
从torch.utils.data模块中导入DataLoader类,用于构建数据加载器,方便批量加载和处理数据。
6. `from restnet18.restnet18 import RestNet18`
从restnet18.restnet18模块中导入RestNet18类,该类是我们之前定义的RestNet18模型。
8. 这一行是空行,没有实际的代码。
9. 这一行是空行,没有实际的代码。
这段代码的目的是导入所需的库和模块,为后续的训练和测试过程做准备。
相关问题
import torch import torch.nn.functional as F import torch.optim as optim from torchvision import transforms from torchvision import datasets from torch.utils. data import DataI oader 补全代码
下面是一个基本的 PyTorch MNIST 数据集训练代码示例,包括数据加载、模型定义、优化器和损失函数的定义以及训练过程。
```
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
# 定义数据预处理
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
])
# 加载数据集
train_dataset = datasets.MNIST('data', train=True, download=True, transform=transform)
test_dataset = datasets.MNIST('data', train=False, download=True, transform=transform)
train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=64, shuffle=False)
# 定义模型
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.fc1 = nn.Linear(320, 50)
self.fc2 = nn.Linear(50, 10)
def forward(self, x):
x = F.relu(F.max_pool2d(self.conv1(x), 2))
x = F.relu(F.max_pool2d(self.conv2(x), 2))
x = x.view(-1, 320)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return F.log_softmax(x, dim=1)
net = Net()
# 定义优化器和损失函数
optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.5)
criterion = nn.CrossEntropyLoss()
# 训练模型
def train(epoch):
net.train()
for batch_idx, (data, target) in enumerate(train_loader):
optimizer.zero_grad()
output = net(data)
loss = criterion(output, target)
loss.backward()
optimizer.step()
if batch_idx % 10 == 0:
print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
epoch, batch_idx * len(data), len(train_loader.dataset),
100. * batch_idx / len(train_loader), loss.item()))
def test():
net.eval()
test_loss = 0
correct = 0
with torch.no_grad():
for data, target in test_loader:
output = net(data)
test_loss += criterion(output, target)
pred = output.argmax(dim=1, keepdim=True)
correct += pred.eq(target.view_as(pred)).sum().item()
test_loss /= len(test_loader.dataset)
print('\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format(
test_loss, correct, len(test_loader.dataset),
100. * correct / len(test_loader.dataset)))
for epoch in range(1, 5):
train(epoch)
test()
```
这是一个简单的卷积神经网络模型,用于对 MNIST 手写数字进行分类。训练过程中使用了交叉熵损失函数和随机梯度下降优化器。
import torch import torch.nn as nn import torch.optim as optim import torchvision # datasets and pretrained neural nets import torch.utils.data import torch.nn.functional as F import torchvision.transforms as transforms transform = transforms.Compose([transforms.ToTensor(), ]) trainset = torchvision.datasets.MNIST(root='.\mydata', train=True, download=True, transform=transform) #文件夹地址可自定义 testset = torchvision.datasets.MNIST(root='.\mydata', train=False, download=True, transform=transform) #文件夹地址可自定义 trainloader = torch.utils.data.DataLoader(trainset, batch_size =32, shuffle=True) testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)写一个构建网络定义损失函数,优化器训练 迭代次数为10次 模型评测尝试将数据集更改为FashionMNIST或者CIFIR10再训练模型,或对比卷积神经网络和全连接神经网络模型的预测准确度,或尝试数据增强方法的代码
好的,这里是一个简单的代码示例,包括构建网络、定义损失函数、优化器、训练和评估模型的步骤。同时,也包括了针对FashionMNIST数据集和CIFAR10数据集的代码,以及卷积神经网络和全连接神经网络模型的比较,以及数据增强方法的代码。
```python
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torch.utils.data
import torch.nn.functional as F
import torchvision.transforms as transforms
# 数据集选项
dataset = 'MNIST' # 可以选择'MNIST', 'FashionMNIST'或者'CIFAR10'
data_root = './data' # 数据集存储的文件夹地址
# 构建网络
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义损失函数和优化器
net = Net()
criterion = nn.CrossEntropyLoss()
optimizer = optim.SGD(net.parameters(), lr=0.001, momentum=0.9)
# 加载数据集
if dataset == 'MNIST':
trainset = torchvision.datasets.MNIST(root=data_root, train=True, download=True, transform=transforms.ToTensor())
testset = torchvision.datasets.MNIST(root=data_root, train=False, download=True, transform=transforms.ToTensor())
elif dataset == 'FashionMNIST':
trainset = torchvision.datasets.FashionMNIST(root=data_root, train=True, download=True, transform=transforms.ToTensor())
testset = torchvision.datasets.FashionMNIST(root=data_root, train=False, download=True, transform=transforms.ToTensor())
elif dataset == 'CIFAR10':
trainset = torchvision.datasets.CIFAR10(root=data_root, train=True, download=True, transform=transforms.ToTensor())
testset = torchvision.datasets.CIFAR10(root=data_root, train=False, download=True, transform=transforms.ToTensor())
else:
raise ValueError('Invalid dataset name')
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
testloader = torch.utils.data.DataLoader(testset, batch_size=32, shuffle=False)
# 训练模型
for epoch in range(10):
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 % 100 == 99:
print('[%d, %5d] loss: %.3f' % (epoch + 1, i + 1, running_loss / 100))
running_loss = 0.0
print('Finished Training')
# 评估模型
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: %d %%' % (100 * correct / total))
```
针对FashionMNIST数据集和CIFAR10数据集的代码仅需要改变数据集的名称和地址即可。卷积神经网络和全连接神经网络模型的比较可以使用以下代码:
```python
# 定义卷积神经网络
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=5, padding=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=5, padding=2)
self.fc1 = nn.Linear(32 * 7 * 7, 256)
self.fc2 = nn.Linear(256, 10)
def forward(self, x):
x = F.relu(self.conv1(x))
x = F.max_pool2d(x, 2)
x = F.relu(self.conv2(x))
x = F.max_pool2d(x, 2)
x = x.view(-1, 32 * 7 * 7)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义全连接神经网络
class FCNet(nn.Module):
def __init__(self):
super(FCNet, self).__init__()
self.fc1 = nn.Linear(28 * 28, 128)
self.fc2 = nn.Linear(128, 10)
def forward(self, x):
x = x.view(-1, 28 * 28)
x = F.relu(self.fc1(x))
x = self.fc2(x)
return x
if dataset == 'MNIST' or dataset == 'FashionMNIST':
net = FCNet()
elif dataset == 'CIFAR10':
net = ConvNet()
else:
raise ValueError('Invalid dataset name')
# 训练和评估模型的代码和之前相同
```
最后,以下是数据增强的代码示例,可以在训练数据集上应用随机的图像变换,从而增加数据集的多样性和泛化性能。
```python
# 数据增强
train_transform = transforms.Compose([
transforms.RandomCrop(28, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
])
trainset = torchvision.datasets.MNIST(root=data_root, train=True, download=True, transform=train_transform)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=32, shuffle=True)
# 训练模型的代码和之前相同
```
需要注意的是,在评估模型时,不应该使用数据增强,因为这会导致测试结果不准确。
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