pytorch如何将csv数据输入cnn模型中,数据需要归一化
时间: 2024-05-06 11:19:57 浏览: 16
首先,需要使用pandas库读取csv数据,并将其转换为numpy数组。然后,可以使用sklearn库中的MinMaxScaler函数将数据进行归一化处理。最后,将归一化后的数据作为输入传递给CNN模型即可。
以下是一个示例代码:
```python
import pandas as pd
import numpy as np
from sklearn.preprocessing import MinMaxScaler
import torch
import torch.nn as nn
# 读取csv数据,假设有两列x和y
data = pd.read_csv('data.csv')
x = data['x'].values
y = data['y'].values
# 将数据转换为numpy数组
x = np.array(x).reshape(-1, 1)
y = np.array(y).reshape(-1, 1)
# 归一化数据
scaler = MinMaxScaler()
x = scaler.fit_transform(x)
y = scaler.fit_transform(y)
# 将数据转换为tensor格式
x = torch.from_numpy(x).type(torch.FloatTensor)
y = torch.from_numpy(y).type(torch.FloatTensor)
# 定义CNN模型
class CNN(nn.Module):
def __init__(self):
super(CNN, self).__init__()
self.conv1 = nn.Conv2d(1, 16, kernel_size=3, padding=1)
self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
self.conv2 = nn.Conv2d(16, 32, kernel_size=3, padding=1)
self.fc1 = nn.Linear(32 * 7 * 7, 10)
self.fc2 = nn.Linear(10, 1)
def forward(self, x):
x = x.view(-1, 1, 28, 28)
x = self.pool(nn.functional.relu(self.conv1(x)))
x = self.pool(nn.functional.relu(self.conv2(x)))
x = x.view(-1, 32 * 7 * 7)
x = nn.functional.relu(self.fc1(x))
x = self.fc2(x)
return x
# 定义损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
# 将数据输入CNN模型中进行训练
for epoch in range(10):
optimizer.zero_grad()
outputs = model(x)
loss = criterion(outputs, y)
loss.backward()
optimizer.step()
print('Epoch [{}/{}], Loss: {:.4f}'.format(epoch+1, 10, loss.item()))
```