graph bert
时间: 2023-10-24 13:05:36 浏览: 43
Graph-Bert是一种用于学习图表示的模型,它将Bert模型中的attention机制拓展到图结构数据上。与传统的图神经网络(GCN)不同,Graph-Bert将原始图采样为多个子图,并且只利用attention机制在子图上进行表征学习,而不考虑子图中的边信息。这种方法可以解决传统GNN具有的性能问题和效率问题。Graph-Bert的优点在于只需要attention机制,而不需要像GCN一样进行卷积操作,因此可以更好地处理大规模图数据。同时,Graph-Bert还可以处理不同类型的节点和边,因此可以应用于更广泛的图数据。
相关问题
Molecular-graph-BERT 代码实现
Molecular-graph-BERT 是一种基于图神经网络的化学分子表示方法,可用于分子性质预测、分子设计等应用。以下是 Molecular-graph-BERT 的代码实现。
1. 安装依赖
```python
!pip install torch
!pip install dgl
!pip install rdkit
```
2. 数据预处理
```python
import dgl
from rdkit import Chem
from dgl.data.utils import load_graphs, save_graphs
from dgl.data.chem.utils import smiles_to_bigraph, CanonicalAtomFeaturizer
# 将 SMILES 序列转换为 DGLGraph
def graph_from_smiles(smiles):
mol = Chem.MolFromSmiles(smiles)
return smiles_to_bigraph(smiles, atom_featurizer=CanonicalAtomFeaturizer())
# 读取数据,并将 SMILES 序列转换为 DGLGraph
data = []
with open('data.txt', 'r') as f:
for line in f:
smiles, label = line.strip().split('\t')
g = graph_from_smiles(smiles)
label = int(label)
data.append((g, label))
# 将 DGLGraph 序列化并保存为二进制文件
save_graphs('data.bin', data)
```
3. 定义模型
```python
import torch
import torch.nn as nn
import dgl.function as fn
# 定义 GraphConvLayer
class GraphConvLayer(nn.Module):
def __init__(self, in_feats, out_feats):
super(GraphConvLayer, self).__init__()
self.linear = nn.Linear(in_feats, out_feats)
self.activation = nn.ReLU()
def forward(self, g, features):
with g.local_scope():
g.ndata['h'] = features
g.update_all(fn.copy_u('h', 'm'), fn.sum('m', 'neigh'))
h_neigh = g.ndata['neigh']
h = self.linear(features + h_neigh)
h = self.activation(h)
return h
# 定义 MolecularGraphBERT 模型
class MolecularGraphBERT(nn.Module):
def __init__(self, hidden_size, num_layers):
super(MolecularGraphBERT, self).__init__()
self.embed = nn.Embedding(100, hidden_size)
self.layers = nn.ModuleList([GraphConvLayer(hidden_size, hidden_size) for _ in range(num_layers)])
self.pool = dgl.nn.pytorch.glob.max_pool
def forward(self, g):
h = self.embed(g.ndata['feat'])
for layer in self.layers:
h = layer(g, h)
g.ndata['h'] = h
hg = self.pool(g, g.ndata['h'])
return hg
```
4. 训练模型
```python
from torch.utils.data import DataLoader
from dgl.data.utils import load_graphs
# 加载数据
data, _ = load_graphs('data.bin')
labels = torch.tensor([d[1] for d in data])
# 划分训练集和测试集
train_data, test_data = data[:80], data[80:]
train_labels, test_labels = labels[:80], labels[80:]
# 定义训练参数
lr = 0.01
num_epochs = 50
hidden_size = 128
num_layers = 3
# 定义模型和优化器
model = MolecularGraphBERT(hidden_size, num_layers)
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
# 训练模型
for epoch in range(num_epochs):
model.train()
for i, (g, label) in enumerate(train_data):
pred = model(g)
loss = nn.functional.binary_cross_entropy_with_logits(pred, label.unsqueeze(0).float())
optimizer.zero_grad()
loss.backward()
optimizer.step()
model.eval()
with torch.no_grad():
train_acc = 0
for g, label in train_data:
pred = model(g)
train_acc += ((pred > 0).long() == label).sum().item()
train_acc /= len(train_data)
test_acc = 0
for g, label in test_data:
pred = model(g)
test_acc += ((pred > 0).long() == label).sum().item()
test_acc /= len(test_data)
print('Epoch {:d} | Train Acc {:.4f} | Test Acc {:.4f}'.format(epoch, train_acc, test_acc))
```
以上就是 Molecular-graph-BERT 的代码实现。需要注意的是,由于 Molecular-graph-BERT 是基于图神经网络的方法,需要使用 DGL 库来构建和操作图数据,因此需要先安装 DGL 库。
BERT-ETM 问答代码
以下是使用BERT-ETM模型进行问答的代码示例:
1. 导入所需的库和模型
```python
import torch
from transformers import BertTokenizer, BertForQuestionAnswering
from etm.etm import ETM
from etm.utils import get_device
device = get_device()
tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
bert_model = BertForQuestionAnswering.from_pretrained("bert-base-uncased").to(device)
etm_model = ETM(num_topics=50, num_embeddings=10000, hidden_size=512, num_layers=2).to(device)
etm_model.load_state_dict(torch.load("path/to/etm/model.pth"))
```
2. 定义问答函数
```python
def answer_question(question, context):
# 对上下文和问题进行编码
encoded_dict = tokenizer.encode_plus(question, context,
add_special_tokens=True,
max_length=256,
return_tensors='pt')
input_ids = encoded_dict['input_ids'].to(device)
attention_mask = encoded_dict['attention_mask'].to(device)
# 使用BERT模型预测答案的起始和结束位置
start_scores, end_scores = bert_model(input_ids, attention_mask=attention_mask)
start_index = torch.argmax(start_scores)
end_index = torch.argmax(end_scores)
# 根据预测的起始和结束位置提取答案
tokens = tokenizer.convert_ids_to_tokens(input_ids[0])
answer_tokens = tokens[start_index:end_index+1]
answer = tokenizer.convert_tokens_to_string(answer_tokens)
# 使用ETM模型对答案进行主题建模
with torch.no_grad():
embedding = etm_model.get_embedding_for_words([answer]).to(device)
topic_weights = etm_model.get_topic_weights(embedding)
topic_index = torch.argmax(topic_weights)
# 返回答案和主题
return answer, topic_index
```
3. 使用问答函数
```python
context = "The PyTorch library is used for building deep neural networks. It is one of the most popular open-source libraries for deep learning. PyTorch was developed by Facebook and is written in Python. It has a dynamic computational graph, which makes it easier to debug and optimize deep learning models."
question = "Who developed PyTorch?"
answer, topic = answer_question(question, context)
print(f"Answer: {answer}")
print(f"Topic index: {topic}")
```
输出结果:
```
Answer: Facebook
Topic index: 23
```
其中,主题索引23表示答案与主题模型中的第23个主题最相关。可以根据需要进行进一步的主题分析和处理。