b_q = torch.nn.Parameter(torch.zeros(num_outputs, device=device, dtype=torch.float32), requires_grad=True)returnnn.ParameterList([W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q]) 開發者ID:wdxtub,項目名稱:deep-learning-note,代碼...
torch.nn.Parameter(torch.zeros(num_hiddens, device=device, dtype=torch.float32), requires_grad=True)) W_xz, W_hz, b_z = _three()# 更新门参数W_xr, W_hr, b_r = _three()# 重置门参数W_xh, W_hh, b_h = _three()# 候选隐藏层参数# 输出层参数W_hq = _one((num_hiddens, num...
BatchNorm1d(2) print(f"r: {norm.weight}") print(f"b: {norm.bias}") # r: Parameter containing: # tensor([1., 1.], requires_grad=True) # b: Parameter containing: # tensor([0., 0.], requires_grad=True) 这样描述还是过于抽象,我们可以做一些实验: case1. 输入 (N,C) ,即 N ...
torch.autograd.grad(outputs, inputs, grad_ outputs=None, retain graph= None,create_ graph=False, only_ inputs =True, allow_ unused= False) 计算和返回output关 于inputs的梯度的和。 outputs :函数的因变量,即需要求导的那个函数 inputs :函数的自变量, grad_ _outputs :同backward only_ inputs:...
x.requires_grad_(True) # 等价于 `x = torch.arange(4.0, requires_grad=True)` x.requires_grad # 默认值是False # 计算y # x是一个长度为4的向量,计算x和x的内积,得到了我们赋值给y的标量输出。 y = 2 * torch.dot(x, x) y tensor(28., grad_fn=<MulBackward0>) ...
6.torch.zeros(size,dtype=None,device=None, requires_grad=False)--生成一个指定形状为size的全0张量 torch.zeros(2) tensor([0., 0.]) torch.zeros(2,3) tensor([[0., 0., 0.], [0., 0., 0.]]) 7.torch.ones(size,dtype=None,device=None, requires_grad=False)--生成一个指定形状为si...
model = [torch.nn.Parameter(torch.randn(2, 2, requires_grad=True))] optimizer = SGD(model, 0.1) scheduler1 = ExponentialLR(optimizer, gamma=0.9) scheduler2 = StepLR(optimizer, step_size=3, gamma=0.1) forepochinrange(4): print(epoch, scheduler2.get_last_lr[0]) ...
deffn(x):withtorch.enable_grad():out=x+1returnoutopt_fn=torch.compile(fn,backend="inductor")# also, aot_eager, aot_eager_decomp_partition, but not eagerwithtorch.no_grad():res=fn(torch.zeros(10,requires_grad=True))opt_res=opt_fn(torch.zeros(10,requires_grad=True))assertres.requires...
torch.zeros(*size, out=None, dtype=None, layout=torch.strided, device=None, requires_grad=False) → TensorReturns...Default: False.Example:>>> torch.zeros(2, 3)tensor([[ 0., 0., 0.], [ 0., 0., 0.]])>>> torch.zeros ...
nn.Parameter(torch.zeros(num_outputs, device=device, dtype=torch.float32), requires_grad=True) return nn.ParameterList([W_xi, W_hi, b_i, W_xf, W_hf, b_f, W_xo, W_ho, b_o, W_xc, W_hc, b_c, W_hq, b_q]) Example #3...