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machine-learning - Band RNN 的计算精度

问题描述

所以我想弄清楚如何计算 BandRNN 的准确性。

BandRnn 是一个对角RNN 模型，每个神经元的连接数不同。例如： 在此处输入图像描述 这里 C 是每个神经元的连接数。

我目前的模型训练如下：

model = ModelLSTM(m, k).to(device)

model.train()

opt = torch.optim.Adam(model.parameters(), lr=args.lr)

best_test = 1e7
best_validation = 1e7

for ep in range(1, args.epochs + 1):

init_time = datetime.now()
processed = 0
step = 1

for batch_idx, (batch_x, batch_y, len_batch) in enumerate(train_loader):
    batch_x, batch_y, len_batch = batch_x.to(device), batch_y.to(device), len_batch.to(device)

    opt.zero_grad()

    logits = model(batch_x)
   
    loss = model.loss(logits, batch_y, len_batch)

    acc = sum(logits == batch_y) * 1.0 / len(logits)
    print(acc)

    loss.backward()

    if args.clip > 0:
        nn.utils.clip_grad_norm_(model.parameters(), args.clip)

    opt.step()

    processed += len(batch_x)
    step += 1
    print("   batch_idx {}\tLoss: {:.2f} ".format(batch_idx, loss))

print("Epoch {}, LR {:.5f} \tLoss: {:.2f} ".format(ep, opt.param_groups[0]['lr'], loss))

我的模型测试如下：

model.eval()
with torch.no_grad():

for batch_x, batch_y, len_batch in test_loader:
    batch_x, batch_y, len_batch = batch_x.to(device), batch_y.to(device), len_batch.to(device)
    logits = model(batch_x)
    loss_test = model.loss(logits, batch_y, len_batch)
    
    acc = sum(logits == batch_y) * 1.0 / len(logits)
    

for batch_x, batch_y, len_batch in val_loader:
    batch_x, batch_y, len_batch = batch_x.to(device), batch_y.to(device), len_batch.to(device)
    logits = model(batch_x)
    loss_val = model.loss(logits, batch_y, len_batch)

if loss_val < best_validation:
    best_validation = loss_val.item()
    best_test = loss_test.item()

print()
print("Val:  Loss: {:.2f}\tBest: {:.2f}".format(loss_val, best_validation))
print("Test: Loss: {:.2f}\tBest: {:.2f}".format(loss_test, best_test))
print()

model.train()

我正在努力思考一种计算该模型准确性的方法，我想收到一些关于这样做的建议。谢谢你。

标签： machine-learningpytorchrecurrent-neural-network

解决方案

我相信您代码中的这一行已经在尝试计算准确性：

acc = sum(logits == batch_y) * 1.0 / len(logits)

尽管您可能希望在与标签进行比较之前对 logits 进行 argmax：

preds = logits.argmax(dim=-1)
acc = sum(preds == batch_y) * 1.0 / len(logits)

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