a = torch.randn(1, requires_grad=True, dtype=torch.float, device=device) b = torch.randn(1, requires_grad=True, dtype=torch.float, device=device) for epoch in range(n_epochs): yhat = a + b * x_train_tensor error
https://www.cnblogs.com/marsggbo/p/11549631.html 标签:python,pytorch 思念殇千寻 粉丝-20关注 -11 +加关注 posted @2019-10-28 17:01思念殇千寻阅读(611) 评论(0)收藏举报
from torch import nn from torch.autograd import Function from torch.optim import Adam class GradDecay(Function): alpha = 0 @staticmethod def forward(ctx, *args, **kwargs): return args[0].view_as(args[0]) @staticmethod def backward(ctx, *grad_outputs): return GradDecay.alpha*grad_outputs...
backward.py importtorchfromtorch.autogradimportVariable x= Variable(torch.ones(2,2),requires_grad=True) y= x + 2print("x =", x)print("y =", y) z= y * y * 3print("z =", z)#o = z.sum()o =z.mean()print("o =", o)print("o.grad_fn =", o.grad_fn) o.backward()p...
from torch.autograd import Variable print("start training") losses=[] losses_end_of_epoch=[] number_of_steps_each_epoch=[] loss_function = nn.MSELoss(reduction='mean') #each epoch for epoch in range(2): count=0 target_reached=False ...
x = torch.ones(2, 2, requires_grad=True) print(x) #对tensor进行操作 y = x + 2 print(y) # y是操作的结果,所以它有grad_fn属性 print(y.grad_fn) #对y进行更多操作 z = y * y * 3 out = z.mean() print(z, out) 上述操作的结果如下: ...
// PyG的C++扩展就选择的是直接继承PyTorch的C++端的torch::autograd类进行扩展// 下面是PyG的一个ScatterSum算子的扩展示例// 不用纠结这个算子的具体内容,对扩展的算子的结构有一个大致了解即可classScatterSum:publictorch::autograd::Function<ScatterSum>{public:// AutogradContext *ctx指针可以操作staticvariable...
inputs.requires_grad = True x = model(inputs) # instead of using loss.backward(), use torch.autograd.grad() to compute gradients # https://pytorch.org/docs/stable/autograd.html#torch.autograd.grad loss_grads = grad(x, inputs, create_graph=True) drv = grad(loss_grads[0][0][2], ...
复制 cuda = torch.device('cuda') 这会将我们的默认 CUDA 设备的名称设置为'cuda'。 然后,我们可以通过在任何张量操作中手动指定设备参数来执行对这个设备的操作。 代码语言:javascript 代码运行次数:0 运行 复制 x = torch.tensor([5., 3.], device=cuda) 另外,我们可以通过调用cuda方法来做到这一点: 代码...
autograd import gradcheck # Check gradients computed via small finite differences against analytical gradients # 检查的是 inputs 中 requires_grad=True 的梯度, # 一定要记得 double() 一下!!! input = (Variable(torch.randn(20, 20).double(), requires_grad=True), Variable(torch.randn(30, 20)....