帮我用pytorch写一段CNN和GRU结合的网络模型代码,输入时.csv文件
时间: 2024-04-08 08:35:35 浏览: 80
当您提到输入是.csv文件时,我假设您的.csv文件包含文本数据。在这种情况下,您需要将文本数据加载到PyTorch模型中。以下是一个示例代码,用于将.csv文件中的文本数据加载到CNN和GRU结合的网络模型中:
```python
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from torch.utils.data import Dataset, DataLoader
from sklearn.feature_extraction.text import CountVectorizer
# 自定义数据集类
class CustomDataset(Dataset):
def __init__(self, csv_file, vectorizer):
self.data = pd.read_csv(csv_file)
self.vectorizer = vectorizer
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
text = self.data.iloc[idx]['text']
label = self.data.iloc[idx]['label']
vectorized_text = self.vectorizer.transform([text]).toarray().squeeze()
return vectorized_text, label
# 自定义CNN-GRU模型类
class CNN_GRU_Model(nn.Module):
def __init__(self, input_dim, hidden_dim, output_dim, num_filters, filter_sizes, dropout):
super(CNN_GRU_Model, self).__init__()
# Convolutional layers
self.convs = nn.ModuleList([
nn.Conv2d(in_channels=1,
out_channels=num_filters,
kernel_size=(fs, input_dim))
for fs in filter_sizes
])
# GRU layer
self.gru = nn.GRU(input_dim*num_filters, hidden_dim)
# Dropout layer
self.dropout = nn.Dropout(dropout)
# Fully-connected layer
self.fc = nn.Linear(hidden_dim, output_dim)
def forward(self, text):
# text shape: (batch_size, input_dim)
text = text.unsqueeze(1)
# text shape: (batch_size, 1, input_dim)
conved = [F.relu(conv(text)).squeeze(3) for conv in self.convs]
# conved[i] shape: (batch_size, num_filters, input_dim - filter_sizes[i] + 1)
pooled = [F.max_pool1d(conv, conv.shape[2]).squeeze(2) for conv in conved]
# pooled[i] shape: (batch_size, num_filters)
cat = self.dropout(torch.cat(pooled, dim=1))
# cat shape: (batch_size, num_filters * len(filter_sizes))
_, hidden = self.gru(cat.unsqueeze(0))
# hidden shape: (1, batch_size, hidden_dim)
hidden = self.dropout(hidden.squeeze(0))
# hidden shape: (batch_size, hidden_dim)
output = self.fc(hidden)
# output shape: (batch_size, output_dim)
return output
# 加载数据
csv_file = 'your_data.csv'
data = pd.read_csv(csv_file)
# 创建文本向量化器
vectorizer = CountVectorizer()
vectorizer.fit(data['text'])
# 创建自定义数据集
dataset = CustomDataset(csv_file, vectorizer)
# 设置超参数
input_dim = len(vectorizer.get_feature_names())
hidden_dim = 100
output_dim = 2
num_filters = 100
filter_sizes = [3, 4, 5]
dropout = 0.5
# 创建CNN-GRU模型实例
model = CNN_GRU_Model(input_dim, hidden_dim, output_dim, num_filters, filter_sizes, dropout)
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# 创建数据加载器
dataloader = DataLoader(dataset, batch_size=32, shuffle=True)
# 训练模型
for epoch in range(10):
running_loss = 0.0
for inputs, labels in dataloader:
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f"Epoch {epoch+1} Loss: {running_loss/len(dataloader)}")
```
请注意,上述代码仅提供了一个基本的框架,您可能需要根据您的具体数据和任务进行适当的修改。在这个示例中,我们使用了一个简单的文本向量化器(CountVectorizer)将文本转换为向量表示,然后使用自定义数据集类将数据加载到模型中。您可以调整超参数、网络结构和训练过程来满足您的需求。
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