PyTorch 笔记（08）

#PyTorch 笔记（08）| 来源: 网络整理| 查看: 265

1. 常用函数

比较函数中有一些是逐元素比较，操作类似逐元素操作，还有一些类似归并操作，常用的比较函数如下表所示。表中第一行的比较操作已经实现了运算符重载，因此可以使用 a>=b，a>b ，a !=b 和 a == b，其返回的结果是一个 ByteTensor，可用来选取元素。

max/min 操作有些特殊，以 max 为例，有以下三种使用情况：

t.max(tensor) ：返回 tensor 中最大的一个数；t.max(tensor，dim) ：指定维上最大数，返回 tensor 和下标；t.max(tensor1，tensor2) ：比较两个 tensor 相比较大的元素； 2. 使用示例

torch.gt、torch.lt、torch.ge、torch.le、torch.eq、torch.ne 的函数参数和返回值是类似的，都如下所示：

Args: input (Tensor): the tensor to compare other (Tensor or float): the tensor or value to compare out (Tensor, optional): the output tensor that must be a BoolTensor

Returns: Tensor: A torch.BoolTensor containing a True at each location where comparison is true

2.1 torch.gt In [1]: import torch as t In [2]: a = t.Tensor([[1,2],[3,4]]) In [3]: a Out[3]: tensor([[1., 2.], [3., 4.]]) In [4]: a.gt(4) Out[4]: tensor([[False, False], [False, False]]) In [7]: a.gt(t.Tensor([[1,1], [3, 3]])) Out[7]: tensor([[False, True], [False, True]]) 2.2 torch.lt

函数参数同 torch.gt。

In [9]: a.lt(4) Out[9]: tensor([[ True, True], [ True, False]]) In [10]: a.lt(t.Tensor([[1,1], [3, 3]])) Out[10]: tensor([[False, False], [False, False]]) In [11]: 2.3 torch.ge In [12]: a.ge(4) Out[12]: tensor([[False, False], [False, True]]) In [13]: a.ge(t.Tensor([[1,1], [3, 3]])) Out[13]: tensor([[True, True], [True, True]]) 2.4 torch.le In [14]: a.le(4) Out[14]: tensor([[True, True], [True, True]]) In [15]: a.le(t.Tensor([[1,1], [3, 3]])) Out[15]: tensor([[ True, False], [ True, False]]) In [16]: 2.5 torch.eq In [16]: a.eq(4) Out[16]: tensor([[False, False], [False, True]]) In [17]: a.eq(t.Tensor([[1,1], [3, 3]])) Out[17]: tensor([[ True, False], [ True, False]]) 2.6 torch.ne In [18]: a.ne(4) Out[18]: tensor([[ True, True], [ True, False]]) In [19]: a.ne(t.Tensor([[1,1], [3, 3]])) Out[19]: tensor([[False, True], [False, True]]) 2.7 torch.topk

函数定义如下：

topk(input, k, dim=None, largest=True, sorted=True, out=None) -> (Tensor, LongTensor)

参数说明：

Returns the :attr:`k` largest elements of the given :attr:`input` tensor along a given dimension. If :attr:`dim` is not given, the last dimension of the `input` is chosen. If :attr:`largest` is ``False`` then the `k` smallest elements are returned. A namedtuple of `(values, indices)` is returned, where the `indices` are the indices of the elements in the original `input` tensor. The boolean option :attr:`sorted` if ``True``, will make sure that the returned `k` elements are themselves sorted Args: input (Tensor): the input tensor. k (int): the k in "top-k" dim (int, optional): the dimension to sort along largest (bool, optional): controls whether to return largest or smallest elements sorted (bool, optional): controls whether to return the elements in sorted order out (tuple, optional): the output tuple of (Tensor, LongTensor) that can be optionally given to be used as output buffers In [21]: a Out[21]: tensor([[1., 2.], [3., 4.]]) In [22]: a.topk(2) Out[22]: torch.return_types.topk( values=tensor([[2., 1.], [4., 3.]]), indices=tensor([[1, 0], [1, 0]])) In [24]: a.topk(1, dim=1) Out[24]: torch.return_types.topk( values=tensor([[2.], [4.]]), indices=tensor([[1], [1]])) In [25]: 2.8 torch.sort

函数定义如下：

sort(input, dim=-1, descending=False, out=None) -> (Tensor, LongTensor)

函数参数如下：

Sorts the elements of the :attr:`input` tensor along a given dimension in ascending order by value. If :attr:`dim` is not given, the last dimension of the `input` is chosen. If :attr:`descending` is ``True`` then the elements are sorted in descending order by value. A namedtuple of (values, indices) is returned, where the `values` are the sorted values and `indices` are the indices of the elements in the original `input` tensor. Args: input (Tensor): the input tensor. dim (int, optional): the dimension to sort along descending (bool, optional): controls the sorting order (ascending or descending) out (tuple, optional): the output tuple of (`Tensor`, `LongTensor`) that can be optionally given to be used as output buffers In [28]: b = t.randn(2,3) In [29]: b Out[29]: tensor([[-0.1936, -1.8862, 0.1491], [ 0.8152, 1.1863, -0.4711]]) In [30]: b.sort() Out[30]: torch.return_types.sort( values=tensor([[-1.8862, -0.1936, 0.1491], [-0.4711, 0.8152, 1.1863]]), indices=tensor([[1, 0, 2], [2, 0, 1]])) In [31]: b.sort(dim=0) Out[31]: torch.return_types.sort( values=tensor([[-0.1936, -1.8862, -0.4711], [ 0.8152, 1.1863, 0.1491]]), indices=tensor([[0, 0, 1], [1, 1, 0]])) In [32]: 2.9 torch.max

函数定义如下：

max(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor)

函数参数如下：

Args: input (Tensor): the input tensor. dim (int): the dimension to reduce. keepdim (bool): whether the output tensor has :attr:`dim` retained or not. Default: ``False``. out (tuple, optional): the result tuple of two output tensors (max, max_indices)

或者：

Args: input (Tensor): the input tensor. other (Tensor): the second input tensor out (Tensor, optional): the output tensor.

示例如下：

In [2]: import torch as t In [4]: a = t.Tensor([[1,2], [3,4]]) In [5]: a Out[5]: tensor([[1., 2.], [3., 4.]]) In [7]: a.max() Out[7]: tensor(4.) In [8]: b = t.Tensor([[2,0], [2,6]]) In [9]: a.max(b) Out[9]: tensor([[2., 2.], [3., 6.]]) In [10]: a.max(dim=0) Out[10]: torch.return_types.max( values=tensor([3., 4.]), indices=tensor([1, 1])) In [11]: a.max(dim=1) Out[11]: torch.return_types.max( values=tensor([2., 4.]), indices=tensor([1, 1])) In [12]: 2.10 torch.min

函数定义如下：

min(input, dim, keepdim=False, out=None) -> (Tensor, LongTensor) min(input, other, out=None) -> Tensor min(input) -> Tensor

函数参数如下：

Args: input (Tensor): the input tensor. dim (int): the dimension to reduce. keepdim (bool): whether the output tensor has :attr:`dim` retained or not. out (tuple, optional): the tuple of two output tensors (min, min_indices)

或者：

Args: input (Tensor): the input tensor. other (Tensor): the second input tensor out (Tensor, optional): the output tensor.

使用示例：

In [13]: a = t.Tensor([[1,2], [3,4]]) In [14]: a Out[14]: tensor([[1., 2.], [3., 4.]]) In [15]: a.min() Out[15]: tensor(1.) In [16]: b = t.Tensor([[2,0], [2,6]]) In [17]: b Out[17]: tensor([[2., 0.], [2., 6.]]) In [18]: a.min(b) Out[18]: tensor([[1., 0.], [2., 4.]]) In [19]: a.min(dim=0) Out[19]: torch.return_types.min( values=tensor([1., 2.]), indices=tensor([0, 0])) In [20]: a.min(dim=1) Out[20]: torch.return_types.min( values=tensor([1., 3.]), indices=tensor([0, 0])) In [21]:

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