帮我用pytorch写一段根据原有数据自动生成较少类别样本的代码
时间: 2024-05-16 22:12:08 浏览: 18
好的,我可以帮您写一段根据原有数据自动生成较少类别样本的代码,以下是示例代码:
```Python
import torch
from torch import nn, optim
from torch.utils.data import DataLoader, Dataset
import numpy as np
# 定义原有数据
data = np.array([[1, 5, 9], [2, 6, 10], [3, 7, 11], [4, 8, 12]])
# 定义标签
labels = np.array([0, 1, 0, 1])
# 定义数据集类
class MyDataset(Dataset):
def __init__(self, data, labels):
self.data = torch.tensor(data, dtype=torch.float32)
self.labels = torch.tensor(labels)
def __getitem__(self, idx):
return self.data[idx], self.labels[idx]
def __len__(self):
return len(self.labels)
# 定义生成器
def generator(z_dim=3, hidden_dim=10):
return nn.Sequential(
nn.Linear(z_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, hidden_dim),
nn.ReLU(),
nn.Linear(hidden_dim, 3)
)
# 定义训练函数
def train(generator, dataset, batch_size=2, epochs=100):
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
generator.to(device)
criterion = nn.BCEWithLogitsLoss()
optimizer = optim.Adam(generator.parameters(), lr=0.001)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
for epoch in range(epochs):
for data, _ in dataloader:
z = torch.randn(batch_size, 3).to(device)
generated_data = generator(z)
optimizer.zero_grad()
loss = criterion(generated_data, data)
loss.backward()
optimizer.step()
# 生成新样本
def generate_samples(generator, num_samples=2):
with torch.no_grad():
z = torch.randn(num_samples, 3)
generated_data = generator(z).numpy()
return generated_data
# 训练生成器
dataset = MyDataset(data, labels)
generator = generator()
train(generator, dataset)
# 生成新样本
new_data = generate_samples(generator)
print("生成的新样本:")
print(new_data)
```
运行这段代码可以生成新的数据样本。
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