如何在pytorch中使用numel函数

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获取tensor中一共包含多少个元素

import torchx = torch.randn(3,3)print("number elements of x is ",x.numel())y = torch.randn(3,10,5)print("number elements of y is ",y.numel())

输出：

number elements of x is 9

number elements of y is 150

27和150分别位x和y中各有多少个元素或变量

补充：pytorch获取张量元素个数numel()的用法

numel就是"number of elements"的简写。

numel()可以直接返回int类型的元素个数

import torch a = torch.randn(1, 2, 3, 4)b = a.numel()print(type(b)) # intprint(b) # 24

通过numel()函数，我们可以迅速查看一个张量到底又多少元素。

补充：pytorch 卷积结构和numel()函数

看代码吧~

from torch import nn class CNN(nn.Module):    def __init__(self, num_channels=1, d=56, s=12, m=4):        super(CNN, self).__init__()        self.first_part = nn.Sequential(            nn.Conv2d(num_channels, d, kernel_size=3, padding=5//2),            nn.Conv2d(num_channels, d, kernel_size=(1,3), padding=5//2),            nn.Conv2d(num_channels, d, kernel_size=(3,1), padding=5//2),            nn.PReLU(d)        )     def forward(self, x):        x = self.first_part(x)        return x model = CNN()for m in model.first_part:    if isinstance(m, nn.Conv2d):        # print('m:',m.weight.data)        print('m:',m.weight.data[0])        print('m:',m.weight.data[0][0])        print('m:',m.weight.data.numel()) #numel() 计算矩阵中元素的个数 结果：m: tensor([[[-0.2822,  0.0128, -0.0244],         [-0.2329,  0.1037,  0.2262],         [ 0.2845, -0.3094,  0.1443]]]) #卷积核大小为3x3m: tensor([[-0.2822,  0.0128, -0.0244],        [-0.2329,  0.1037,  0.2262],        [ 0.2845, -0.3094,  0.1443]]) #卷积核大小为3x3m: 504   # = 56 x (3 x 3)  输出通道数为56，卷积核大小为3x3m: tensor([-0.0335,  0.2945,  0.2512,  0.2770,  0.2071,  0.1133, -0.1883,  0.2738,         0.0805,  0.1339, -0.3000, -0.1911, -0.1760,  0.2855, -0.0234, -0.0843,         0.1815,  0.2357,  0.2758,  0.2689, -0.2477, -0.2528, -0.1447, -0.0903,         0.1870,  0.0945, -0.2786, -0.0419,  0.1577, -0.3100, -0.1335, -0.3162,        -0.1570,  0.3080,  0.0951,  0.1953,  0.1814, -0.1936,  0.1466, -0.2911,        -0.1286,  0.3024,  0.1143, -0.0726, -0.2694, -0.3230,  0.2031, -0.2963,         0.2965,  0.2525, -0.2674,  0.0564, -0.3277,  0.2185, -0.0476,  0.0558]) bias偏置的值m: tensor([[[ 0.5747, -0.3421,  0.2847]]]) 卷积核大小为1x3m: tensor([[ 0.5747, -0.3421,  0.2847]]) 卷积核大小为1x3m: 168 # = 56 x (1 x 3) 输出通道数为56，卷积核大小为1x3m: tensor([ 0.5328, -0.5711, -0.1945,  0.2844,  0.2012, -0.0084,  0.4834, -0.2020,        -0.0941,  0.4683, -0.2386,  0.2781, -0.1812, -0.2990, -0.4652,  0.1228,        -0.0627,  0.3112, -0.2700,  0.0825,  0.4345, -0.0373, -0.3220, -0.5038,        -0.3166, -0.3823,  0.3947, -0.3232,  0.1028,  0.2378,  0.4589,  0.1675,        -0.3112, -0.0905, -0.0705,  0.2763,  0.5433,  0.2768, -0.3804,  0.4855,        -0.4880, -0.4555,  0.4143,  0.5474,  0.3305, -0.0381,  0.2483,  0.5133,        -0.3978,  0.0407,  0.2351,  0.1910, -0.5385,  0.1340,  0.1811, -0.3008]) bias偏置的值m: tensor([[[0.0184],         [0.0981],         [0.1894]]]) 卷积核大小为3x1m: tensor([[0.0184],        [0.0981],        [0.1894]]) 卷积核大小为3x1m: 168 # = 56 x (3 x 1) 输出通道数为56，卷积核大小为3x1m: tensor([-0.2951, -0.4475,  0.1301,  0.4747, -0.0512,  0.2190,  0.3533, -0.1158,         0.2237, -0.1407, -0.4756,  0.1637, -0.4555, -0.2157,  0.0577, -0.3366,        -0.3252,  0.2807,  0.1660,  0.2949, -0.2886, -0.5216,  0.1665,  0.2193,         0.2038, -0.1357,  0.2626,  0.2036,  0.3255,  0.2756,  0.1283, -0.4909,         0.5737, -0.4322, -0.4930, -0.0846,  0.2158,  0.5565,  0.3751, -0.3775,        -0.5096, -0.4520,  0.2246, -0.5367,  0.5531,  0.3372, -0.5593, -0.2780,        -0.5453, -0.2863,  0.5712, -0.2882,  0.4788,  0.3222, -0.4846,  0.2170]) bias偏置的值  '''初始化后'''class CNN(nn.Module):    def __init__(self, num_channels=1, d=56, s=12, m=4):        super(CNN, self).__init__()        self.first_part = nn.Sequential(            nn.Conv2d(num_channels, d, kernel_size=3, padding=5//2),            nn.Conv2d(num_channels, d, kernel_size=(1,3), padding=5//2),            nn.Conv2d(num_channels, d, kernel_size=(3,1), padding=5//2),            nn.PReLU(d)        )        self._initialize_weights()    def _initialize_weights(self):        for m in self.first_part:            if isinstance(m, nn.Conv2d):                nn.init.normal_(m.weight.data, mean=0.0, std=math.sqrt(2/(m.out_channels*m.weight.data[0][0].numel())))                nn.init.zeros_(m.bias.data)     def forward(self, x):        x = self.first_part(x)        return x model = CNN()for m in model.first_part:    if isinstance(m, nn.Conv2d):        # print('m:',m.weight.data)        print('m:',m.weight.data[0])        print('m:',m.weight.data[0][0])        print('m:',m.weight.data.numel()) #numel() 计算矩阵中元素的个数 结果：m: tensor([[[-0.0284, -0.0585,  0.0271],         [ 0.0125,  0.0554,  0.0511],         [-0.0106,  0.0574, -0.0053]]])m: tensor([[-0.0284, -0.0585,  0.0271],        [ 0.0125,  0.0554,  0.0511],        [-0.0106,  0.0574, -0.0053]])m: 504m: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,        0., 0., 0., 0., 0., 0., 0., 0.])m: tensor([[[ 0.0059,  0.0465, -0.0725]]])m: tensor([[ 0.0059,  0.0465, -0.0725]])m: 168m: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,        0., 0., 0., 0., 0., 0., 0., 0.])m: tensor([[[ 0.0599],         [-0.1330],         [ 0.2456]]])m: tensor([[ 0.0599],        [-0.1330],        [ 0.2456]])m: 168m: tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,        0., 0., 0., 0., 0., 0., 0., 0.])

pytorch的优点

1.PyTorch是相当简洁且高效快速的框架；2.设计追求最少的封装；3.设计符合人类思维，它让用户尽可能地专注于实现自己的想法；4.与google的Tensorflow类似，FAIR的支持足以确保PyTorch获得持续的开发更新；5.PyTorch作者亲自维护的论坛 供用户交流和求教问题6.入门简单

以上就是如何在pytorch中使用numel函数，小编相信有部分知识点可能是我们日常工作会见到或用到的。希望你能通过这篇文章学到更多知识。更多详情敬请关注编程网行业资讯频道。