test_loss /= num_batches correct /= size
时间: 2023-06-16 15:04:06 浏览: 65
这是一个在训练神经网络时常用的计算方法。其中,test_loss 代表测试集上的损失值,num_batches 代表测试集中的批次数,correct 代表模型在测试集上的正确预测数量,size 代表测试集的大小。
test_loss /= num_batches 的作用是,将测试集的总损失值除以批次数,得到每个批次的平均损失值。这可以帮助我们更好地理解模型在测试集上的表现。
correct /= size 的作用是,将模型在测试集上的正确预测数量除以测试集的大小,得到模型在测试集上的准确率。这可以帮助我们评估模型的性能,并进行比较。
相关问题
为以下代码添加注释def test(dataloader, model, loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() test_loss, correct = 0, 0 with torch.no_grad(): for X, y in dataloader: X, y = X.to(device), y.to(device) pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= num_batches correct /= size print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
# 定义一个测试函数,用于测试模型
def test(dataloader, model, loss_fn):
# 获取数据集大小和批次数
size = len(dataloader.dataset)
num_batches = len(dataloader)
# 将模型设为评估模式
model.eval()
# 初始化测试损失和正确率
test_loss, correct = 0, 0
# 关闭梯度计算
with torch.no_grad():
# 遍历数据集
for X, y in dataloader:
# 将数据和标签移动到设备上
X, y = X.to(device), y.to(device)
# 前向传播计算预测值
pred = model(X)
# 计算损失
test_loss += loss_fn(pred, y).item()
# 统计正确率
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
# 计算平均测试损失和正确率
test_loss /= num_batches
correct /= size
# 输出测试结果
print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) model.train() LossList = [] for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device) y=y.long() # Compute prediction error pred = model(X) loss = loss_fn(pred, y) # Backpropagation optimizer.zero_grad() loss.backward() optimizer.step() if batch % 50 == 0: loss, current = loss.item(), (batch + 1) * len(X) print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]") LossList.append(loss) return Losslist def test(dataloader, model, loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() LossList = [] AccList = [] test_loss, correct = 0, 0 with torch.no_grad(): for X, y in dataloader: X, y = X.to(device), y.to(device) y=y.long() pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1) == y).type(torch.float).sum().item() test_loss /= num_batches correct /= size LossList.append(test_loss) AccList.append(correct) print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n") return LossList,acclist报错显示lostlist未定义
在这段代码中,`losslist` 和 `acclist` 都没有被定义。你需要在函数内部定义这两个变量,或者在函数调用前将它们定义并且传入函数。例如,你可以在函数内部这样定义它们:
```
def train(dataloader, model, loss_fn, optimizer):
size = len(dataloader.dataset)
model.train()
losslist = [] # 在这里定义 losslist
for batch, (X, y) in enumerate(dataloader):
X, y = X.to(device), y.to(device)
y = y.long()
# Compute prediction error
pred = model(X)
loss = loss_fn(pred, y)
# Backpropagation
optimizer.zero_grad()
loss.backward()
optimizer.step()
if batch % 50 == 0:
loss, current = loss.item(), (batch + 1) * len(X)
print(f"loss: {loss:>7f} [{current:>5d}/{size:>5d}]")
losslist.append(loss) # 将 loss 添加到 losslist 中
return losslist
def test(dataloader, model, loss_fn):
size = len(dataloader.dataset)
num_batches = len(dataloader)
model.eval()
losslist = [] # 在这里定义 losslist
acclist = [] # 在这里定义 acclist
test_loss, correct = 0, 0
with torch.no_grad():
for X, y in dataloader:
X, y = X.to(device), y.to(device)
y = y.long()
pred = model(X)
test_loss += loss_fn(pred, y).item()
correct += (pred.argmax(1) == y).type(torch.float).sum().item()
test_loss /= num_batches
correct /= size
losslist.append(test_loss) # 将 test_loss 添加到 losslist 中
acclist.append(correct) # 将 correct 添加到 acclist 中
print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f} \n")
return losslist, acclist
```
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