* (Tensor mean, float std, *, torch.Generator generator, Tensor out) * (float mean, Tensor std, *, torch.Generator generator, Tensor out) * (float mean, float std, tuple of ints size, *, torch.Generator generato
addmm_(beta=1, mat, alpha=1, mat1, mat2) → Tensor addmm()的in-place运算形式 addmv(beta=1, tensor, alpha=1, mat, vec) → Tensor 请查看torch.addmv() addmv_(beta=1, tensor, alpha=1, mat, vec) → Tensor addmv()的in-place运算形式 addr(beta=1, alpha=1, vec1, vec2) → ...
contiguous() → Tensor copy_(src, non_blocking=False) → Tensor cos() → Tensor cos_() → Tensor cosh() → Tensor cosh_() → Tensor cpu() → Tensor cross(other, dim=-1) → Tensor cuda(device=None, non_blocking=False) → Tensor cumprod(dim, dtype=None) → Tensor cumsum(dim, dt...
random.randint(low,high=None,size=None,dtype=int) Return random integers from low (inclusive) to high (exclusive). Return random integers from the “discrete uniform” distribution of the specified dtype in the “half-open” interval [low, high). If high is None (the default), then results...
dtype('float64')# 原数组的数据类型并没有改变>>>arr_2_d_int = arr_2_d.astype('int32') >>>arr_2_d_int.dtype dtype('int32') 其他创建数组的方式 np.ones() 与 np.zeros() >>>np.ones() Traceback (most recent call last): ...
tensor([1e-323], dtype=torch.float64) tensor([ 0.], dtype=torch.float64) >>> torch.set_flush_denormal(False) True >>> torch.tensor([1e-323], dtype=torch.float64) tensor(9.88131e-324 * [ 1.0000], dtype=torch.float64) Creation Ops Note Random sampling creation ops are listed ...
torch.randperm()randperm(n, out=None, dtype=torch.int64)-> LongTensor #torch中没有random.shuffle #y = torch.randperm(n) y是把1到n这些数随机打乱得到的一个数字序列,给定参数n,返回一个从[0, n -1) 的随机整数排列。n (int) – 上边界。(不包含)...
device=None, requires_grad=False, pin_memory=False) -> Tensor Returns a tensor filled with random numbers from a uniform distribution on the interval :math:`[0, 1)` The shape of the tensor is defined by the variable argument :attr:`size`. Args: size (int...): a sequence of integers...
一个空张量tensor可以通过规定其大小来构建: >>> torch.IntTensor(2, 4).zero_() 0 0 0 0 0 0 0 0 [torch.IntTensor of size 2x4] 可以用python的索引和切片来获取和修改一个张量tensor中的内容: >>> x = torch.FloatTensor([[1, 2, 3], [4, 5, 6]]) ...
for batch_features, batch_labels in tensor_loader:print(f"特征形状: {batch_features.shape}, 标签形状: {batch_labels.shape}")break 6、RandomSampler RandomSampler用于从数据集中随机采样元素。在使用随机梯度下降(SGD)等需要随机采样的训练...