rand_like (区间[0,1)上的均匀分布、输出张量大小同输入张量) randint (区间[low,high) 上的均匀分布) randint_like(区间[low,high) 上的均匀分布、输出张量大小同输入张量) randn (标准正态分布) randn_like (标准正态分布、输出张量大小同输入张量) randperm (区间[0,n-1]上的随机排列) 参考 随机数种子...
新张量保留参数张量的属性(形状,数据类型),除非显式覆盖。 x_ones = torch.ones_like(x_data)# retains the properties of x_dataprint(f"Ones Tensor: \n{x_ones}\n") x_rand = torch.rand_like(x_data, dtype=torch.float)# overrides the datatype of x_dataprint(f"Random Tensor: \n{x_rand...
通过torch.zeros()构造一个矩阵全为 0,并且通过dtype设置数据类型为 long 我们还可以通过torch.zero_()和torch.zeros_like()将现有矩阵转换为全0矩阵. import torch x = torch.zeros(4, 3, dtype=torch.long) print(x) 1. 2. 3. 通过torch.tensor()直接使用数据,构造一个张量: import torch x = torc...
def reduce(tensor: Tensor, pattern: str, reduction: Reduction, **axes_lengths: int) -> Tensor: """ einops.reduce provides combination of reordering and reduction using reader-friendly notation. Examples for reduce operation: ```python >>> x = np.random.randn(100, 32, 64) # perform max-...
随机试验(random experiment):在相同条件下,对某随机现象进行的大量重复观测。例如抛骰子,观察其点数;抛硬币,看其哪一面向上。 相信大家对于日常生活中的概率这个词已经耳熟能详了,我们接下来定义三种概率。 现在有长度为n且按照时间分布的序列,x1,x2,...,xt−1,xt,...,xn 先验概率(根据以往经验和分析得到...
>>>x=torch.zeros(5,3,dtype=torch.long)>>>print(x)tensor([[0,0,0],[0,0,0],[0,0,0],[0,0,0],[0,0,0]])>>>#从已有列表转化为张量>>>x=torch.tensor([5.5,3])>>>print(x)tensor([5.5000,3.0000])>>>#新建一个与之前tensor一样shape的tensor>>>y=torch.randn_like(x,dtype=...
y = torch.ones_like(x, device=device) z = x + y print(z) print(z.to('cpu', torch.double)) y.cpu().data.numpy() # numpy操作不能在GPU上运行 import numpy as np # N is batch size; D_in is input dimension; # H is hidden dimension; D_out is output dimension. ...
A Tensor library like Numpy, unlike Numpy it has strong GPU support. Lua is a wrapper for Torch (Yes! you need to have a good understanding of Lua), and for that you will need LuaRocks package manager. 1Torch是一个与Numpy类似的张量(Tensor)操作库,与Numpy不同的是Torch对GPU支持的很好,...
更多的随机抽样方法,参见链接: https://pytorch.org/docs/stable/torch.html#random-sampling 二、基本操作、运算 Basic operations 1.tensor的切片、合并、变形、抽取操作 这里简单总结一些重要的tensor基本操作: torch.cat(seq, dim=0, out=None)把一堆tensor丢进去,按照dim指定的维度拼接、堆叠在一起. 比如: ...
I hope that torch.randn_like can take generator=RNG argument as other random generation functions. init_params = [torch.randn_like(param.data, generator=rng) for param in nn.parameters()] I know that the equivalence init_params = [torch...