torch.no_grad

torch.no_grad是一个上下文管理器，用于在PyTorch中禁止梯度计算，并在计算完成后自动启用梯度计算。在该上下文管理器内部，PyTorch将不会记录计算图中的操作，从而减少内存的使用并加快代码的执行速度。通常，当我们进行推断或评估时，我们不需要计算梯度，因此使用torch.no_grad可以提高代码的效率。例如，以下代码将使用torch.no_grad来计算神经网络在测试集上的准确率：

with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

test_accuracy = 100 * correct / total
print('Test Accuracy: {:.2f}%'.format(test_accuracy))

相关问题

with torch.no_grad

`torch.no_grad()`是一个上下文管理器，用于在进行模型推理时禁用梯度计算。在这个上下文中，不会记录梯度信息，从而减少了内存消耗并提高了推理速度。这通常用于测试集上的模型推理，因为在测试集上不需要更新模型的参数。例如：

with torch.no_grad():
    output = model(input)

在这段代码中，`input`是输入数据，`model`是神经网络模型，`output`是模型的输出结果。在`with torch.no_grad()`的上下文中，模型的参数不会被更新，并且梯度计算不会被记录，从而加速了模型的推理过程。

torch.no_grad()

torch.no_grad() is a context manager provided by PyTorch that disables gradient calculation. When we are working with deep learning models, we are often interested in computing gradients for the parameters of the model so that we can update them using an optimization algorithm. However, sometimes we may want to perform some operations on the model's output without computing gradients, such as when we are using the model for inference or evaluation. In such cases, we can use the torch.no_grad() context manager to disable gradient calculation, which can significantly reduce memory usage and speed up computation.

Here is an example of how to use torch.no_grad():

import torch

# create a tensor with requires_grad=True
x = torch.tensor([3., 4.], requires_grad=True)

# compute the gradient of a function
y = x.sum()
y.backward()

# update the parameter using an optimizer
optimizer = torch.optim.SGD([x], lr=0.1)
optimizer.step()

# use the tensor without gradient calculation
with torch.no_grad():
    z = x * 2
    print(z)

In this example, we create a tensor `x` with `requires_grad=True`, which means that gradients will be computed for this tensor. We then compute the gradient of a function `y` with respect to `x` using `y.backward()`. We also create an optimizer and update the parameter `x` using `optimizer.step()`. Finally, we use the `x` tensor without gradient calculation by wrapping the code inside the `with torch.no_grad():` context manager. In this block, we compute `z = x * 2`, but no gradients are computed for `z`.