功能:依据input 形状创建全 0 张量 intput : 创建与 input 同形状的全 0 张量 dtype : 数据类型 layout 内存中布局形式 2.3 torch. ones() 2.4 torch. ones_like() 功能:input 形状创建全 1 张量 其他参数一样同上。 2.5 torch. full() 2.6 torch.full_like() 功能:依据input 形状创建指定数据的张量 ...
torch.full()和torch.full_like() 作用:torch.full((3,4), 5),给定一个值fill_value和一个size,创建一个矩阵元素全为fill_value的大小为size的tensor。 reference:https://blog.csdn.net/Fluid_ray/article/details/109855155
「通过 torch.full_like(input, fill_value) 函数来创建全为自定义数值 fill_value 的张量,形状由参数 input 的形状指定,input 必须是张量。」 本文参与
mul(tensor) z3 = torch.rand_like(tensor) torch.mul(tensor, tensor, out=z3) 7.3 in-place操作 in-place版本的操作,也称为原位操作,特点时:操作的结果不是创建一个新的变量,而是原位赋值给原来的变量。通常都以下划线结尾。 x = torch.randn(3, 4) y = torch.randn(3, 4) x + y # add_ 没...
torch.full([2, 2], 4) 生成给定维度,全部数据相等的数据 不常用 torch.arange(start=0, end, step=1, *, out=None) 生成指定间隔的数据 易用常用 torch.ones(*size, *, out=None) 生成给定 size 且值全为1 的矩阵数据 简单 zeros()/zeros_like()/eye() 全0 的 tensor 和 对角矩阵 简单 ...
def q_sample(self, x_start: torch.Tensor, t: int, noise: torch.Tensor = None) -> torch.Tensor:if noise is None:noise = torch.randn_like(x_start) sqrt_alphas_cumprod_t = extract(self.sqrt_alphas_cumprod, t, x_start.shape)sqrt_one_minus_al...
such as prioritized replay buffers and the like.) To install the library locally, start by cloning the repo: git clone https://github.com/pytorch/rl and don't forget to check out the branch or tag you want to use for the build: git checkout v0.4.0 Go to the directory where you ha...
, 0.]]) >>> torch.where(a > 1, torch.full_like(a, 1), a) # 大于1 的部分直接用1代替, 其他保留原值 tensor([[ 1., -2.], [ 1., 0.]]) # 得到非零元素 torch.nonzero(tensor) # 非零元素的索引 torch.nonzero(tensor == 0) # 零元素的索引 torch.nonzero(tensor).size(0) ...
In PyTorch 2.1 or before, users can use ParallelStyles like PairwiseParallel and specify input/output layout with functions like make_input_replicate_1d or make_output_replicate_1d. And we have default values for _prepare_input and _prepare_output. The UX of Tensor Parallel was like: from to...
Start the learning path Get started with PyTorch on the AI Show Learn the basics of PyTorch, including how to build and deploy a model and how to connect to the strong community of users. Watch the video Learn the basics of PyTorch ...