Python 当你的模型不能过拟合一小批数据时，这意味着什么

I am trying to train RNN model to classify sentences into 4 classes, but it doesn’t seem to work. I tried to overfit 4 examples (blue line) which worked, but even as little as 8 examples (red line) is not working, let alone the whole dataset.

我try 了不同的学习速度和大小的hidden_size和embedding_size，但似乎没有帮助，我错过了什么？我知道，如果模型不能过度适应小批量，这意味着应该增加产能，但在这种情况下，增加产能没有效果.

class RNN(nn.Module):
    def __init__(self, embedding_size=256, hidden_size=128, num_classes=4):
        super().__init__()
        self.embedding = nn.Embedding(len(vocab), embedding_size, 0)
        self.rnn = nn.RNN(embedding_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, num_classes)

    def forward(self, x):
        #x=[batch_size, sequence_length]
        x = self.embedding(x) #x=[batch_size, sequence_length, embedding_size]
        _, h_n = self.rnn(x)  #h_n=[1, batch_size, hidden_size]
        h_n = h_n.squeeze(0)
        out = self.fc(h_n)  #out=[batch_size, num_classes]
        return out

输入数据是标记化的句子，用0填充到该批中最长的句子，因此作为一个示例，一个样本是:[278495441321120,0,0].数据来自torchtext数据集的AG_新闻数据集.

培训代码:

model = RNN().to(device)
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=LR)
model.train()

for epoch in range(NUM_EPOCHS):
    epoch_losses = []
    correct_predictions = []
    for batch_idx, (labels, texts) in enumerate(train_loader):
        scores = model(texts)
        loss = criterion(scores, labels)
        
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()
    
        epoch_losses.append(loss.item())
        correct = (scores.max(1).indices==labels).sum()
        correct_predictions.append(correct)
        
    epoch_avg_loss = sum(epoch_losses)/len(epoch_losses)
    epoch_avg_accuracy = float(sum(correct_predictions))/float(len(labels))