retain_graph (bool, optional) – If False, the graph used to compute the grad will be freed. Note that in nearly all cases setting this option to True is not needed and often can be worked around in a much more efficient way. Defaults to the value of create_graph. 大意是如果设置为Fa...
1 # 假如你需要执行两次backward,先执行第一个的backward,再执行第二个backward 2 loss1.backward(retain_graph=True)# 这里参数表明保留backward后的中间参数。 3 loss2.backward() # 执行完这个后,所有中间变量都会被释放,以便下一次的循环 4 #如果是在训练网络optimizer.step() # 更新参数 create_graph参数比...
retain_graph:保存计算图;由于pytorch采用动态图机制,在每一次反向传播结束之后,计算图都会释放掉。如果想继续使用计算图,就需要设置参数retain_graph为True create_graph:创建导数计算图,用于高阶求导,例如二阶导数、三阶导数等等 torch.autograd.backward(tensors, grad_tensors=None,\ retain_graph=None, create_gra...
def backward(self, gradient=None, retain_graph=None, create_graph=False): torch.autograd.backward(self, gradient, retain_graph, create_graph) 这样就清楚了,这个backward函数就是在调用这个自动求导的函数。 backward()里面有个参数叫做retain_graph, 这个是控制是否需要保留计算图的,默认是不保留的,即一次反...
retain_graph (bool, optional) – If False, the graph used to compute the grad will be freed. Note that in nearly all cases setting this option to True is not needed and often can be worked around in a much more efficient way. Defaults to the value of create_graph. ...
其实retain_graph这个参数在平常中我们是用不到的,但是在特殊的情况下我们会用到它: 假设一个我们有一个输入x,y = x **2, z = y*4,然后我们有两个输出,一个output_1 = z.mean(),另一个output_2 = z.sum()。然后我们对两个output执行backward。
pytorch retain_graph==True的作用说明 总的来说进行一次backward之后,各个节点的值会清除,这样进行第二次backward会报错,如果加上retain_graph==True后,可以再来一次backward。 retain_graph参数的作用 官方定义: retain_graph (bool, optional) – If False, the graph used to compute the grad will be freed....
retain_graph:保存计算图 create_graph:创建导数计算图,用于高阶求导 grad_tensors:多梯度权重(当有多个loss需要计算梯度时,需要设置各个loss之间的权重比例) w =torch.tensor([1.],requires_grad=True)x =torch.tensor([2.],requires_grad=True)a=torch.add(w,x)b=torch.add(w,1)y0=torch.mul(a,b)y1...
def backward(self, gradient=None, retain_graph=None, create_graph=False) | V #torch.autograd.backward(self, gradient, retain_graph, create_graph) #torch/autograd/__init__.py def backward(tensors, grad_tensors=None, retain_graph=None, create_graph=False,...
retain_graph: If set to False, the computation graph will be freed. Default value depends on thecreate_graphparameter. create_graph: If set to True, the graph of the derivative will be constructed, allowing higher-order derivative products. Default is False. ...