with torch.no_grad():
时间: 2024-05-16 17:15:05 浏览: 17
"with torch.no_grad()" is a context manager in PyTorch that temporarily disables gradient computation. This can be useful when you want to evaluate a model and don't need to update its parameters. By using "with torch.no_grad()", you can reduce memory usage and speed up computation. Here's an example:
```
with torch.no_grad():
# Forward pass through model
outputs = model(inputs)
# Compute loss
loss = loss_fn(outputs, targets)
```
In this example, "model" is a PyTorch model, "inputs" are input data, "outputs" are the model's predictions, "targets" are the ground-truth labels, and "loss_fn" is a loss function. By using "with torch.no_grad()", the gradients of the model's parameters won't be computed during the forward pass, which can save memory and speed up computation.
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