梯度控制 在进行反向传播之前,必须要用 zero_grad() 清空梯度。具体的方法是遍历 self.param_groups 中全部参数,根据 grad 属性做清除。 例如: for input, target in dataset: def closure(): optimizer.zero_grad() output = model(input) loss = loss_fn(output, target) loss.backward() return loss opt...
2 importtorch.utils.dataasData 3 importtorch.nn.functionalasF 4 importmatplotlib.pyplotasplt 5 6 torch.manual_seed(1)# reproducible 7 8 LR=0.01 9 BATCH_SIZE=32 10 EPOCH=12 11 12 # fake dataset 13 x=torch.unsqueeze(torch.linspace(-1,1,1000),dim=1) ...
shape[0])) xs[0, :] = x v = 0 for i in range(max_iter): v = v + grad(x)**2 x = x - alpha * grad(x) / (eps + np.sqrt(v)) xs[i+1, :] = x return xs ###L=x^2+100y^2_Ir0.01 with Adadelta このコードはうまく収束していますが、今一つ理論が不明です。
lossshould take no arguments. Minimization (and gradient computation) is done with respect to the elements ofvar_listif not None, else with respect to any trainable variables created during the execution of thelossfunction.gate_gradients,aggregation_method,colocate_gradients_with_opsandgrad_lossare ...
def drumhead_height(n, k, distance, angle, t):kth_zero = special.jn_zeros(n, k)[-1]return np.cos(t) * np.cos(n*angle) * special.jn(n, distance*kth_zero) theta = np.r_[0:2*np.pi:50j]radius = np.r_[0:1:50j]x = np.array([r * np.cos(theta) for r in radius])...
平均梯度 随机梯度 在my_FAD.py里增加一个Optim类,如下 my_FAD.py importmathimportrandomclassFAD:def__init__(self,x,name=None,dx=None):self.x=xifname!=None:self.dx=dict()self.dx[name]=1.0else:self.dx=dx# print(self.x,self.dx)def__str__(self):info=''for(key,grad)inself.dx.ite...
[Zero-Dim] fix Tensor.numpy, cntrol whether to hack process to 1D (#51757) zhwesky2010authoredMar 20, 2023 Verified d703545 Commits on Mar 15, 2023 refine amp scaler (#51340) wanghuancoderauthoredMar 15, 2023 Verified 1e232e2 Commits on Mar 10, 2023 Delete duplicate code in op...