with torch.no_grad():

This is a context manager in PyTorch that disables gradient computation. When a tensor is created under this context, it will have its requires_grad attribute set to False. This is useful when you want to evaluate a model without updating its parameters. By default, all tensors in a model have requires_grad set to True, which means that gradients will be computed and accumulated during backpropagation. However, when you only want to make predictions, you don't need to compute gradients, so using torch.no_grad() can save memory and speed up computations.

相关问题

def test(): correct = 0 total = 0 with torch.no_grad: for data in test_loader: x, y =data y_pred = model(x) _, predicted = torch.max(y_pred, dim=1) total += y.size(0) correct += (predicted == y).sum().item() print('accuracy on test_data:%d %%' % (100 *correct/total))

这段代码存在一个语法错误，在第 4 行应该调用 `torch.no_grad()` 方法而不是 `torch.no_grad` 属性。`torch.no_grad()` 方法是一个上下文管理器，它使得在该上下文中计算的所有张量都不会被跟踪梯度，从而提高了计算效率。

正确的代码应该是：

def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            x, y = data
            y_pred = model(x)
            _, predicted = torch.max(y_pred, dim=1)
            total += y.size(0)
            correct += (predicted == y).sum().item()
    print('accuracy on test_data:%d %%' % (100 * correct/total))

注意，在 `with` 语句中调用 `torch.no_grad()` 方法之后，所有在该上下文中计算的张量都不会被跟踪梯度，这有助于提高计算效率。然而，如果需要计算梯度，则需要退出该上下文并调用 `backward()` 方法。

with torch.no_grad

with torch.no_grad是PyTorch中的一个上下文管理器，用于在评估模型时禁用梯度计算，以减少内存消耗和加速计算。在这个上下文中，所有的张量操作都不会被记录在计算图中，也不会计算梯度。这个上下文管理器通常用于测试和验证模型，以避免不必要的计算和内存消耗。