我正在尝试使用 pytorch 构建一个连体神经网络,在其中我提供 BERT 词嵌入并试图找出两个句子是否相似(想象重复的帖子匹配、产品匹配等)。这是模型:
class SiameseNetwork(torch.nn.Module):
def __init__(self):
super(SiameseNetwork, self).__init__()
self.brothers = torch.nn.Sequential(
torch.nn.Linear(512 * 768, 512),
torch.nn.BatchNorm1d(512),
torch.nn.ReLU(inplace=True),
torch.nn.Linear(512, 256),
torch.nn.BatchNorm1d(256),
torch.nn.ReLU(inplace=True),
torch.nn.Linear(256, 32),
)
self.final = torch.nn.Sequential(
torch.nn.Linear(32, 16),
torch.nn.ReLU(inplace=True),
torch.nn.Linear(16, 2),
)
def forward(self, left, right):
outputLeft = self.brothers(left)
outputRight = self.brothers(right)
output = self.final((outputLeft - outputRight) ** 2)
return output
bros = SiameseNetwork()
bros = bros.to(device)
标准和优化器:
criterion = torch.nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(params=bros.parameters(), lr=0.001)
训练循环:
for batch in tqdm(tLoader, desc=f"Train epoch: {epoch+1}"):
a = batch[0].to(device)
b = batch[1].to(device)
y = torch.unsqueeze(batch[2].type(torch.FloatTensor), 1).to(device)
optimizer.zero_grad()
output = bros(a,b)
loss = criterion(output, y)
loss.backward()
trainingLoss += loss.item()
optimizer.step()
现在,这似乎奏效了,因为它产生了合理的结果,但验证错误在仅仅一个时期后就停止下降到 0.13。使用 Pytorch 在这种 NN 上找不到很多东西。有没有办法优化它?难道我做错了什么?