基于pytorch读取csv文件(每行121列)一维数据(未归一化)用dcgan模型生成类似数据的代码(未归一化)并保存模型,用保存的模型生成数据并保存到csv文件
时间: 2024-03-07 10:47:47 浏览: 105
好的,可以按照以下步骤进行操作:
1. 导入必要的库
```python
import torch
import torch.nn as nn
import torch.optim as optim
import pandas as pd
```
2. 定义生成器和判别器
```python
# 定义生成器
class Generator(nn.Module):
def __init__(self, nz=100, ngf=64, nc=121):
super(Generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose1d(nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm1d(ngf * 8),
nn.ReLU(True),
nn.ConvTranspose1d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm1d(ngf * 4),
nn.ReLU(True),
nn.ConvTranspose1d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm1d(ngf * 2),
nn.ReLU(True),
nn.ConvTranspose1d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm1d(ngf),
nn.ReLU(True),
nn.ConvTranspose1d(ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
return self.main(input)
# 定义判别器
class Discriminator(nn.Module):
def __init__(self, ndf=64, nc=121):
super(Discriminator, self).__init__()
self.main = nn.Sequential(
nn.Conv1d(nc, ndf, 4, 2, 1, bias=False),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv1d(ndf, ndf * 2, 4, 2, 1, bias=False),
nn.BatchNorm1d(ndf * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv1d(ndf * 2, ndf * 4, 4, 2, 1, bias=False),
nn.BatchNorm1d(ndf * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv1d(ndf * 4, ndf * 8, 4, 2, 1, bias=False),
nn.BatchNorm1d(ndf * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Conv1d(ndf * 8, 1, 4, 1, 0, bias=False),
nn.Sigmoid()
)
def forward(self, input):
return self.main(input)
```
3. 定义训练函数
```python
def train(generator, discriminator, dataloader, num_epochs=50, lr=0.0002, beta1=0.5, ngpu=1, nz=100, device=torch.device("cuda:0" if (torch.cuda.is_available() and ngpu > 0) else "cpu")):
# 初始化生成器和判别器的参数
generator.apply(weights_init)
discriminator.apply(weights_init)
# 定义优化器
optimizer_g = optim.Adam(generator.parameters(), lr=lr, betas=(beta1, 0.999))
optimizer_d = optim.Adam(discriminator.parameters(), lr=lr, betas=(beta1, 0.999))
# 定义损失函数
criterion = nn.BCELoss()
# 定义真和假的标签
real_label = 1
fake_label = 0
# 开始训练
for epoch in range(num_epochs):
for i, data in enumerate(dataloader, 0):
# 训练判别器
discriminator.zero_grad()
real_data = data[0].to(device)
b_size = real_data.size(0)
label = torch.full((b_size,), real_label, device=device)
output = discriminator(real_data)
err_d_real = criterion(output.view(-1), label)
err_d_real.backward()
D_x = output.mean().item()
noise = torch.randn(b_size, nz, 1, device=device)
fake_data = generator(noise)
label.fill_(fake_label)
output = discriminator(fake_data.detach())
err_d_fake = criterion(output.view(-1), label)
err_d_fake.backward()
D_G_z1 = output.mean().item()
err_d = err_d_real + err_d_fake
optimizer_d.step()
# 训练生成器
generator.zero_grad()
label.fill_(real_label)
output = discriminator(fake_data)
err_g = criterion(output.view(-1), label)
err_g.backward()
D_G_z2 = output.mean().item()
optimizer_g.step()
# 打印训练信息
if i % 50 == 0:
print('[%d/%d][%d/%d]\tLoss_D: %.4f\tLoss_G: %.4f\tD(x): %.4f\tD(G(z)): %.4f / %.4f'
% (epoch, num_epochs, i, len(dataloader),
err_d.item(), err_g.item(), D_x, D_G_z1, D_G_z2))
# 保存模型
torch.save(generator.state_dict(), "generator.pth")
```
4. 定义生成函数
```python
def generate(generator, num_samples=100, nz=100, device=torch.device("cuda:0" if torch.cuda.is_available() else "cpu")):
# 生成噪声
noise = torch.randn(num_samples, nz, 1, device=device)
# 生成数据
fake_data = generator(noise)
# 将生成的数据转换为numpy格式
fake_data = fake_data.cpu().detach().numpy()
# 返回生成的数据
return fake_data
```
5. 加载数据集
```python
# 读取csv文件
data = pd.read_csv("data.csv")
# 转换为tensor格式
data = torch.tensor(data.values, dtype=torch.float32)
# 构建数据集
dataset = torch.utils.data.TensorDataset(data)
# 构建数据加载器
dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)
```
6. 训练模型并保存
```python
# 初始化生成器和判别器
generator = Generator().to(device)
discriminator = Discriminator().to(device)
# 训练模型
train(generator, discriminator, dataloader, num_epochs=50, lr=0.0002, beta1=0.5, ngpu=1, nz=100, device=device)
# 生成数据并保存到csv文件
fake_data = generate(generator, num_samples=100, nz=100, device=device)
fake_data = pd.DataFrame(fake_data)
fake_data.to_csv("fake_data.csv", index=False)
```
这样就完成了基于pytorch读取csv文件(每行121列)一维数据(未归一化)用dcgan模型生成类似数据的代码并保存模型,用保存的模型生成数据并保存到csv文件的任务。
阅读全文