.backward(retain_graph=true)
时间: 2023-04-24 10:05:56 浏览: 83
.backward(retain_graph=true)是PyTorch中的一个方法,用于计算梯度并反向传播。其中,retain_graph=true表示在计算梯度后保留计算图,以便进行多次反向传播。
相关问题
loss.backward(retain_graph=False)
This function call computes the gradients of the loss with respect to all the trainable parameters in the model, and propagates them backwards through the computation graph, accumulating the gradients in the leaves of the graph.
The `retain_graph` argument determines whether or not to keep the computation graph after the backward pass has completed. If `retain_graph=True`, the graph is retained and can be used for multiple backward passes. If `retain_graph=False`, the graph is released after the backward pass, and cannot be used for further computations.
In general, `retain_graph=True` is only necessary if you need to perform multiple backward passes through the same graph (e.g. for computing second-order gradients). Otherwise, it is usually more efficient to set `retain_graph=False` to save memory.
retain_graph=True。如何设置
如果您想在反向传播(backpropagation)中计算多个梯度(gradients),则应将retain_graph参数设置为True。您可以通过以下代码将其设置为True:
```python
loss.backward(retain_graph=True)
```
需要注意的是,retain_graph=True可以在内存消耗方面造成严重负担,因此应当避免滥用。在计算完所有需要的梯度后,应将其设置为False以释放内存。