此实验报告,构建深度学习模型代码
时间: 2023-11-09 11:12:34 浏览: 78
深度学习实验报告+代码
本实验报告使用Python语言编写深度学习模型,主要使用了PyTorch框架。以下是代码实现的基本过程:
1. 数据预处理:读入数据集,对数据进行预处理,如分词、去除停用词、编码等。
```python
import pandas as pd
import jieba
import torch
from torch.utils.data import Dataset, DataLoader
# 读入数据集
data = pd.read_csv('data.csv')
# 分词
data['text'] = data['text'].apply(lambda x: list(jieba.cut(x)))
# 去除停用词
stop_words = ['的', '了', '在', '是', '我', '有', '和', '就', '不', '人', '都', '一', '一个', '上', '也', '很', '到', '说', '要', '去', '你', '会', '着', '没有', '看', '好', '自己', '这']
data['text'] = data['text'].apply(lambda x: [word for word in x if word not in stop_words])
# 编码
word2idx = {'PAD': 0, 'UNK': 1}
idx2word = {0: 'PAD', 1: 'UNK'}
for text in data['text']:
for word in text:
if word not in word2idx:
idx = len(word2idx)
word2idx[word] = idx
idx2word[idx] = word
data['text'] = data['text'].apply(lambda x: [word2idx[word] if word in word2idx else word2idx['UNK'] for word in x])
```
2. 定义模型:使用PyTorch框架定义深度学习模型,包括神经网络结构、损失函数、优化器等。
```python
import torch.nn as nn
# 定义模型
class MyModel(nn.Module):
def __init__(self, vocab_size, embedding_dim, hidden_dim):
super(MyModel, self).__init__()
self.embedding = nn.Embedding(vocab_size, embedding_dim)
self.gru = nn.GRU(embedding_dim, hidden_dim, batch_first=True)
self.fc = nn.Linear(hidden_dim, 2)
def forward(self, x):
x = self.embedding(x)
_, h_n = self.gru(x)
h_n = h_n[-1]
x = self.fc(h_n)
return x
# 定义损失函数和优化器
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)
```
3. 训练模型:使用训练数据集对模型进行训练,并在验证集上进行验证,调整模型参数。
```python
# 定义数据集和数据加载器
class MyDataset(Dataset):
def __init__(self, data):
self.data = data
def __getitem__(self, index):
text = self.data.iloc[index]['text']
label = self.data.iloc[index]['label']
return torch.tensor(text), torch.tensor(label)
def __len__(self):
return len(self.data)
train_data = MyDataset(data[:800])
val_data = MyDataset(data[800:])
train_loader = DataLoader(train_data, batch_size=32, shuffle=True)
val_loader = DataLoader(val_data, batch_size=32)
# 训练模型
model.train()
for epoch in range(10):
for batch_idx, (text, label) in enumerate(train_loader):
optimizer.zero_grad()
output = model(text)
loss = criterion(output, label)
loss.backward()
optimizer.step()
# 在验证集上进行验证
model.eval()
correct = 0
total = 0
with torch.no_grad():
for text, label in val_loader:
output = model(text)
_, predicted = torch.max(output.data, 1)
total += label.size(0)
correct += (predicted == label).sum().item()
accuracy = 100 * correct / total
print('Epoch: {}, Validation Accuracy: {:.2f}%'.format(epoch+1, accuracy))
model.train()
```
4. 测试模型:使用测试数据集对训练好的模型进行测试,并计算模型的准确率。
```python
# 定义测试数据集和数据加载器
test_data = MyDataset(data[800:])
test_loader = DataLoader(test_data, batch_size=32)
# 测试模型
model.eval()
correct = 0
total = 0
with torch.no_grad():
for text, label in test_loader:
output = model(text)
_, predicted = torch.max(output.data, 1)
total += label.size(0)
correct += (predicted == label).sum().item()
accuracy = 100 * correct / total
print('Test Accuracy: {:.2f}%'.format(accuracy))
```
以上就是本实验报告深度学习模型代码的基本实现过程,具体实现细节可能会有所不同,但整体流程基本相同。
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