基于pytorch读取多行121列csv文件用dcgan模型生成一维数据代码并将他们保存到csv文件
时间: 2023-10-05 15:09:09 浏览: 58
好的,下面是基于PyTorch读取多行121列CSV文件用DCGAN模型生成一维数据的代码,并将其保存到CSV文件中。
首先,需要安装必要的库,包括torch、numpy、pandas和csv:
```python
import torch
import numpy as np
import pandas as pd
import csv
```
接下来,读取CSV文件并将其转换为numpy数组:
```python
data = pd.read_csv('data.csv', header=None).values
```
然后,定义数据集和数据加载器:
```python
class Dataset(torch.utils.data.Dataset):
def __init__(self, data):
self.data = data
def __getitem__(self, index):
return torch.tensor(self.data[index], dtype=torch.float)
def __len__(self):
return len(self.data)
dataset = Dataset(data)
dataloader = torch.utils.data.DataLoader(dataset, batch_size=64, shuffle=True)
```
定义生成器和判别器:
```python
class Generator(torch.nn.Module):
def __init__(self, input_size, output_size):
super(Generator, self).__init__()
self.fc1 = torch.nn.Linear(input_size, 256)
self.fc2 = torch.nn.Linear(256, 512)
self.fc3 = torch.nn.Linear(512, 1024)
self.fc4 = torch.nn.Linear(1024, output_size)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = torch.relu(self.fc3(x))
x = torch.tanh(self.fc4(x))
return x
class Discriminator(torch.nn.Module):
def __init__(self, input_size):
super(Discriminator, self).__init__()
self.fc1 = torch.nn.Linear(input_size, 1024)
self.fc2 = torch.nn.Linear(1024, 512)
self.fc3 = torch.nn.Linear(512, 256)
self.fc4 = torch.nn.Linear(256, 1)
def forward(self, x):
x = torch.relu(self.fc1(x))
x = torch.relu(self.fc2(x))
x = torch.relu(self.fc3(x))
x = torch.sigmoid(self.fc4(x))
return x
```
定义训练过程:
```python
def train(generator, discriminator, dataloader, epochs, lr):
criterion = torch.nn.BCELoss()
gen_optimizer = torch.optim.Adam(generator.parameters(), lr=lr)
dis_optimizer = torch.optim.Adam(discriminator.parameters(), lr=lr)
for epoch in range(epochs):
for i, real_data in enumerate(dataloader):
# Train discriminator
dis_optimizer.zero_grad()
real_labels = torch.ones(real_data.shape[0], 1)
fake_labels = torch.zeros(real_data.shape[0], 1)
real_outputs = discriminator(real_data)
real_loss = criterion(real_outputs, real_labels)
noise = torch.randn(real_data.shape[0], 100)
fake_data = generator(noise)
fake_outputs = discriminator(fake_data.detach())
fake_loss = criterion(fake_outputs, fake_labels)
dis_loss = real_loss + fake_loss
dis_loss.backward()
dis_optimizer.step()
# Train generator
gen_optimizer.zero_grad()
fake_outputs = discriminator(fake_data)
gen_loss = criterion(fake_outputs, real_labels)
gen_loss.backward()
gen_optimizer.step()
if i % 10 == 0:
print('Epoch [{}/{}], Step [{}/{}], Discriminator Loss: {:.4f}, Generator Loss: {:.4f}'
.format(epoch, epochs, i, len(dataloader), dis_loss.item(), gen_loss.item()))
return generator
```
最后,训练模型并生成数据,将其保存到CSV文件中:
```python
generator = Generator(100, 121)
discriminator = Discriminator(121)
generator = train(generator, discriminator, dataloader, 100, 0.0002)
generated_data = generator(torch.randn(1000, 100)).detach().numpy()
np.savetxt('generated_data.csv', generated_data, delimiter=',')
```
以上就是基于PyTorch读取多行121列CSV文件用DCGAN模型生成一维数据的代码,并将其保存到CSV文件中。