torch.ones_like(input) 等同于 torch.ones(input.size(), dtype=input.dtype, layout=input.layout, device=input.device)。警告 从0.4 开始,此函数不支持 out 关键字。作为替代方案,旧的 torch.ones_like(input, out=output) 等效于 torch.ones(input.size(), out=output)。例子:...
tensor([0.]), torch.tensor([1.]))).sample_(zerod.shape) oned = Parameter(MultivariateNormal(torch.zeros(2), torch.eye(2))).sample_(zerod.shape) mu = torch.zeros((3, 3)) norm = Independent(Normal(mu, torch.ones_like(mu)), 2) twod = Parameter(norm).sample_(zerod.shape) # ...
loss_real=adversarial_loss(discriminator_output_real,torch.ones_like(discriminator_output_real))# 总的对抗损失total_adversarial_loss=loss_fake+loss_real# 输出损失值print("总的对抗损失:",total_adversarial_loss.item()) 代码语言:bash AI代码解释 总的对抗损失:1.528 点关注,防走丢,如有纰漏之处,请留...
10b = torch.ones_like(x) # 创建与给定张量形状相同且元素为1的张量 11c = torch.randn(5, 6) # 创建服从正态分布的随机张量 12 13# 指定设备(CPU 或 GPU) 14if torch.cuda.is_available(): 15 device = torch.device('cuda') 16 d = torch.tensor([1, 2, 3], device=device) # 创建在 G...
x = x.new_ones(5, 3, dtype=torch.double) # new_* methods take in sizes print(x) x = torch.randn_like(x, dtype=torch.float) # 覆盖类型! print(x) # result 的size相同 1. 2. 3. 4. 5. tensor([[1., 1., 1.], [1., 1., 1.], ...
>>>input=torch.empty(2,3)>>>torch.ones_like(input)tensor([[1.,1.,1.],[1.,1.,1.]]) 说一下zeros与zeroslike和类似函数的区别。zeros是指定输出张量的形状size,然后返回张量,zeroslike则是根据一个张量,返回这个张量形状的张量。ones和ones_like类似 ...
ones_like(theta) pi = torch.randn_like(theta) x = torch.randint_like(mu, high=20) dist1 = ZeroInflatedNegativeBinomial(mu=mu, theta=theta, zi_logits=pi) dist2 = NegativeBinomial(mu=mu, theta=theta) assert dist1.log_prob(x).shape == size assert dist2.log_prob(x).shape == size...
torch.ones_like(input)返回跟input的tensor一个size的全一tensor torch.arange(start=0, end, step=1)返回一个从start到end的序列,可以只输入一个end参数,就跟python的range() 一样了。实际上PyTorch也有range(),但是这个要被废掉了,替换成arange了
from math import pi import matplotlib.pyplot as plt import numpy as np import torch import torchaudio 部分代码如下: sr = 1e4 t = torch.arange(0, 2.5, 1/sr) f = torch.sin(2*pi*t) * 1e2 + 1e2 * torch.ones_like(t) + 5e1 * t x = (torch.sin(torch.cumsum(f, dim=0) /...
torch.full和torch.full_like可以看作是torch.ones和torch.ones_like衍生出来的函数。 arange, range, linspace, logspace:arange、range和linspace用来构造公差数列。logspace用来构造指数的幂为等差数列的数组,他们的用法如下: torch.arange(start=0, end, step=1, *, out=None, dtype=None, layout=torch.stride...