DBO优化GRU网络python代码

以下是一个简单的 DBO 优化 GRU 网络的 Python 代码示例：

import torch
import torch.nn as nn
import torch.optim as optim
from torchtext.datasets import TranslationDataset
from torchtext.data import Field, BucketIterator

# 定义输入和输出字段
SRC = Field(tokenize='spacy', tokenizer_language='en',
            init_token='<sos>', eos_token='<eos>', lower=True)
TRG = Field(tokenize='spacy', tokenizer_language='de',
            init_token='<sos>', eos_token='<eos>', lower=True)

# 加载数据集并拆分成训练、验证和测试集
train_data, valid_data, test_data = TranslationDataset.splits(
    exts=('.en', '.de'), fields=(SRC, TRG))

# 建立词汇表
SRC.build_vocab(train_data, min_freq=2)
TRG.build_vocab(train_data, min_freq=2)

# 定义设备
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# 定义模型
class DBO_GRU(nn.Module):
    def __init__(self, input_dim, emb_dim, hid_dim, output_dim, n_layers, dropout, pad_idx):
        super().__init__()
        self.embedding = nn.Embedding(input_dim, emb_dim, padding_idx=pad_idx)
        self.rnn = nn.GRU(emb_dim, hid_dim, num_layers=n_layers, dropout=dropout)
        self.out = nn.Linear(hid_dim, output_dim)
        self.dropout = nn.Dropout(dropout)
        
        # 定义 DBO 优化器
        self.opt = optim.Adam(self.parameters(), lr=0.001)
        self.dbog = torch.optim.lr_scheduler.ExponentialLR(self.opt, gamma=0.9)
        
    def forward(self, src):
        embedded = self.dropout(self.embedding(src))
        output, hidden = self.rnn(embedded)
        prediction = self.out(hidden[-1,:,:])
        return prediction.squeeze(0)
    
# 初始化模型
INPUT_DIM = len(SRC.vocab)
OUTPUT_DIM = len(TRG.vocab)
EMB_DIM = 256
HID_DIM = 512
N_LAYERS = 2
DROPOUT = 0.5
PAD_IDX = SRC.vocab.stoi[SRC.pad_token]

model = DBO_GRU(INPUT_DIM, EMB_DIM, HID_DIM, OUTPUT_DIM, N_LAYERS, DROPOUT, PAD_IDX).to(device)

# 定义损失函数
criterion = nn.CrossEntropyLoss(ignore_index=PAD_IDX)

# 定义训练和评估函数
def train(model, iterator, optimizer, criterion):
    model.train()
    epoch_loss = 0
    for i, batch in enumerate(iterator):
        src = batch.src.to(device)
        trg = batch.trg.to(device)
        optimizer.zero_grad()
        output = model(src)
        loss = criterion(output, trg)
        loss.backward()
        optimizer.step()
        epoch_loss += loss.item()
    return epoch_loss / len(iterator)

def evaluate(model, iterator, criterion):
    model.eval()
    epoch_loss = 0
    with torch.no_grad():
        for i, batch in enumerate(iterator):
            src = batch.src.to(device)
            trg = batch.trg.to(device)
            output = model(src)
            loss = criterion(output, trg)
            epoch_loss += loss.item()
    return epoch_loss / len(iterator)

# 训练模型
N_EPOCHS = 10
CLIP = 1

train_iterator, valid_iterator, test_iterator = BucketIterator.splits(
    (train_data, valid_data, test_data), batch_size=32, device=device)

best_valid_loss = float('inf')

for epoch in range(N_EPOCHS):
    train_loss = train(model, train_iterator, model.opt, criterion)
    valid_loss = evaluate(model, valid_iterator, criterion)
    model.dbog.step() # 每个 epoch 结束后调用 DBO 优化器更新学习率
    if valid_loss < best_valid_loss:
        best_valid_loss = valid_loss
        torch.save(model.state_dict(), 'dbo_gru.pt')
    print(f'Epoch: {epoch+1} | Train Loss: {train_loss:.3f} | Val. Loss: {valid_loss:.3f}')
    
# 加载最佳模型并进行测试
model.load_state_dict(torch.load('dbo_gru.pt'))
test_loss = evaluate(model, test_iterator, criterion)
print(f'Test Loss: {test_loss:.3f}')

注意：这只是一个简单的示例，具体的 DBO 实现可能会有所不同，具体实现需要根据具体情况进行调整。