train_set = torchvision.datasets.CIFAR10(root="./dataset_test",train=True,transform=dataset_transfrom,download=True)
时间: 2024-05-31 08:07:23 浏览: 13
This code creates a dataset object called `train_set` using the CIFAR10 dataset from the `torchvision.datasets` module. The dataset is stored in the "./dataset_test" directory and is meant for training (train=True). The data is transformed using the `dataset_transfrom` function, which applies various image transformations such as cropping, flipping, and normalization. If the dataset is not already downloaded, the `download=True` argument will download it from the internet.
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这段python代码什么意思:import torch import torchvision import torchvision.transforms as transforms import PIL train_transform = transforms.Compose( [transforms.RandomHorizontalFlip(p=0.5), transforms.RandomAffine(degrees=(-5, 5), translate=(0.1, 0.1), scale=(0.9, 1.1), interpolation=PIL.Image.BILINEAR), transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) test_transform = transforms.Compose( [transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=train_transform) train_set, val_set = torch.utils.data.random_split(dataset, [40000, 10000]) train_loader = torch.utils.data.DataLoader(train_set, batch_size=128, shuffle=True, num_workers=8) val_loader = torch.utils.data.DataLoader(val_set, batch_size=128, shuffle=False, num_workers=8) test_set = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform) test_loader = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False, num_workers=8) classes = ['plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck']
这段 Python 代码是用 PyTorch 和 torchvision 库来处理 CIFAR-10 数据集的。具体来说,它的作用是:
1. 导入所需的库和模块。这里包括了 PyTorch 和 torchvision 库,以及数据预处理模块 transforms 和 PIL(Python Imaging Library)库。
2. 定义了两个数据集的预处理方式。其中,train_transform 用于训练集数据的预处理,包括随机水平翻转、随机仿射变换、转化为张量并进行归一化;test_transform 用于测试集数据的预处理,只包括转化为张量并进行归一化。
3. 加载 CIFAR-10 数据集,并进行数据集划分。这里使用了 torchvision.datasets.CIFAR10() 函数来加载 CIFAR-10 数据集,root 参数指定数据集存放的根目录,train 参数指定是否加载训练集数据,download 参数指定是否下载数据集,transform 参数指定对数据进行的转换。接着使用 torch.utils.data.random_split() 函数将数据集划分为训练集和验证集,划分的比例为 4:1。
4. 定义了数据加载器。这里使用了 torch.utils.data.DataLoader() 函数来定义数据加载器,分别对训练集、验证集和测试集进行定义。其中,batch_size 参数指定批量大小,shuffle 参数指定是否打乱数据集顺序,num_workers 参数指定加载数据的线程数。
5. 定义了 CIFAR-10 数据集中的类别。这里定义了 10 个类别,分别是飞机、汽车、鸟、猫、鹿、狗、青蛙、马、船和卡车。
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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