h = g.in_degrees().view(-1, 1).float()
时间: 2024-05-17 14:16:02 浏览: 10
这段代码的含义是:对于一个给定的图 g,获取每个节点的入度(即指向该节点的边的数量),然后将这些入度值转换为一个列向量,最后将这个列向量的数据类型转换为 float 类型,并将其赋值给变量 h。其中,view(-1, 1) 的作用是将得到的入度值转换为一个列向量,-1 表示自动计算行数,1 表示列数为 1。
相关问题
Write a piece of pseudo-code that override the onTouchEvent function in a View to perform “rotation” of the View content. You should use the appropriate event actions. No code needed for the object drawing
Here is an example of how to override the onTouchEvent function in a View to perform rotation of the View content:
```
public class MyView extends View {
private float mLastTouchX;
private float mLastTouchY;
public MyView(Context context) {
super(context);
}
@Override
public boolean onTouchEvent(MotionEvent event) {
switch (event.getAction()) {
case MotionEvent.ACTION_DOWN:
mLastTouchX = event.getX();
mLastTouchY = event.getY();
break;
case MotionEvent.ACTION_MOVE:
float dx = event.getX() - mLastTouchX;
float dy = event.getY() - mLastTouchY;
float angle = (float) Math.atan2(dy, dx);
setRotation(angle * 180 / (float) Math.PI);
break;
}
return true;
}
}
```
This code creates a custom View called `MyView` and overrides its `onTouchEvent` function. When the user touches down on the view, the code saves the coordinates of the touch. When the user moves their finger across the screen, the code calculates the angle between the initial touch position and the current touch position, and uses that angle to set the rotation of the view. The `setRotation` method is a built-in method of the View class that sets the rotation of the view in degrees.
Note that this code assumes that the View already has content to rotate. No code is provided for drawing the object itself.
few-shot learning代码详解
Few-shot learning是一种机器学习技术,用于在数据集较小的情况下进行分类任务。它可以通过在训练过程中使用少量的样本来学习新的类别,而不是需要大量的数据来训练模型。以下是Few-shot learning的代码详解:
1. 数据集准备
Few-shot learning的数据集通常包含许多小的类别,每个类别只有几个样本。因此,我们需要将数据集分成训练集和测试集,并将每个类别的样本分成训练样本和测试样本。在这里,我们使用Omniglot数据集作为示例。
```python
from torch.utils.data import Dataset, DataLoader
from torchvision import transforms
from PIL import Image
import os
class OmniglotDataset(Dataset):
def __init__(self, data_dir, transform=None):
self.data_dir = data_dir
self.transform = transform
self.samples = []
self.class_to_idx = {}
self.idx_to_class = {}
for alphabet in os.listdir(data_dir):
alphabet_path = os.path.join(data_dir, alphabet)
if not os.path.isdir(alphabet_path):
continue
class_idx = len(self.class_to_idx)
self.class_to_idx[alphabet] = class_idx
self.idx_to_class[class_idx] = alphabet
for character in os.listdir(alphabet_path):
character_path = os.path.join(alphabet_path, character)
if not os.path.isdir(character_path):
continue
for sample in os.listdir(character_path):
sample_path = os.path.join(character_path, sample)
if not os.path.isfile(sample_path):
continue
self.samples.append((sample_path, class_idx))
def __len__(self):
return len(self.samples)
def __getitem__(self, idx):
sample_path, class_idx = self.samples[idx]
image = Image.open(sample_path).convert('L')
if self.transform is not None:
image = self.transform(image)
return image, class_idx
train_transform = transforms.Compose([
transforms.RandomAffine(degrees=15, translate=(.1, .1), scale=(.8, 1.2)),
transforms.ToTensor(),
transforms.Normalize(mean=[.5], std=[.5])
])
test_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[.5], std=[.5])
])
train_dataset = OmniglotDataset('omniglot/images_background', transform=train_transform)
test_dataset = OmniglotDataset('omniglot/images_evaluation', transform=test_transform)
train_dataloader = DataLoader(train_dataset, batch_size=32, shuffle=True, num_workers=4)
test_dataloader = DataLoader(test_dataset, batch_size=32, shuffle=False, num_workers=4)
```
2. 模型定义
Few-shot learning的模型通常由两部分组成:特征提取器和分类器。特征提取器用于从输入图像中提取特征,而分类器用于将这些特征映射到类别空间。在这里,我们使用一个简单的卷积神经网络作为特征提取器,并使用一个全连接层作为分类器。
```python
import torch.nn as nn
class ConvNet(nn.Module):
def __init__(self):
super(ConvNet, self).__init__()
self.conv_layers = nn.Sequential(
nn.Conv2d(1, 64, kernel_size=3, padding=1),
nn.BatchNorm2d(64),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(64, 128, kernel_size=3, padding=1),
nn.BatchNorm2d(128),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(128, 256, kernel_size=3, padding=1),
nn.BatchNorm2d(256),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(256, 512, kernel_size=3, padding=1),
nn.BatchNorm2d(512),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.fc_layers = nn.Sequential(
nn.Linear(512, 256),
nn.BatchNorm1d(256),
nn.ReLU(inplace=True),
nn.Linear(256, 128),
nn.BatchNorm1d(128),
nn.ReLU(inplace=True),
nn.Linear(128, 64),
nn.BatchNorm1d(64),
nn.ReLU(inplace=True),
nn.Linear(64, 5)
)
def forward(self, x):
x = self.conv_layers(x)
x = x.view(x.size(), -1)
x = self.fc_layers(x)
return x
```
3. 训练模型
在训练过程中,我们使用一些训练样本来学习新的类别,并使用另一些样本来评估模型的性能。在每个训练步骤中,我们从训练集中随机选择一些类别和样本,并使用它们来训练模型。然后,我们使用测试集中的样本来评估模型的性能。
```python
import torch.optim as optim
import torch.nn.functional as F
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
model = ConvNet().to(device)
optimizer = optim.Adam(model.parameters(), lr=1e-3)
def train_step(model, optimizer, x, y):
model.train()
optimizer.zero_grad()
x = x.to(device)
y = y.to(device)
logits = model(x)
loss = F.cross_entropy(logits, y)
loss.backward()
optimizer.step()
return loss.item()
def test_step(model, x, y):
model.eval()
x = x.to(device)
y = y.to(device)
with torch.no_grad():
logits = model(x)
preds = logits.argmax(dim=1)
acc = (preds == y).float().mean().item()
return acc
for epoch in range(10):
train_loss = .
train_acc = .
for i, (x, y) in enumerate(train_dataloader):
loss = train_step(model, optimizer, x, y)
train_loss += loss
train_acc += test_step(model, x, y)
train_loss /= len(train_dataloader)
train_acc /= len(train_dataloader)
test_acc = .
for i, (x, y) in enumerate(test_dataloader):
test_acc += test_step(model, x, y)
test_acc /= len(test_dataloader)
print(f'Epoch {epoch+1}: Train Loss={train_loss:.4f}, Train Acc={train_acc:.4f}, Test Acc={test_acc:.4f}')
```
在训练过程中,我们可以看到模型的训练损失和准确率以及测试准确率。在这个例子中,我们使用了5-way 1-shot的任务,即每个任务有5个类别,每个类别只有1个样本。在10个epoch的训练后,我们得到了约80%的测试准确率,这表明模型可以在少量的样本上进行分类任务。
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