这篇文章主要介绍了pytorch中的努梅尔函数用法说明，具有很好的参考价值，希望对大家有所帮助。一起跟随小编过来看看吧
获取tensor中一共包含多少个元素
进口火炬
x=torch.randn(3，3)
打印( x的数字元素是，x.numel())
y=torch.randn(3，10，5)
打印( y的数字元素是，y.numel())
输出：
x的元素个数是9
y的元素数是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
努梅尔就是"元素数量"的简写。
numel()可以直接返回int类型的元素个数
进口火炬
a=torch.randn(1，2，3，4)
b=a.numel()
打印（类型(b)) # int
打印第24位
通过努梅尔()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~
从火炬进口神经网络
美国有线新闻网；卷积神经网络类(NN .模块):
def __init__(self，num_channels=1，d=56，s=12，m=4):
超级(美国有线电视新闻网，自我).__init__()
self.first_part=nn .顺序（
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 .预备课程(四)
)
向前定义（自身，x):
x=self.first_part(x)
返回x
model=CNN()
对于模型.第一部分中的男：
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：
男：张量([[[-0.2822,0.0128,-0.0244),
[-0.2329, 0.1037, 0.2262],
[ 0.2845, -0.3094, 0.1443]]]) #卷积核大小为3x3
男：张量([[-0.2822,0.0128,-0.0244),
[-0.2329, 0.1037, 0.2262],
[ 0.2845, -0.3094, 0.1443]]) #卷积核大小为3x3
m: 504 #=56 x (3 x 3)输出通道数为56,卷积核大小为3x3
男：张量([-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]]偏差偏置的值
男：张量([[[ 0.5747,-0.3421,0.2847]])卷积核大小为1x3
男：张量([[ 0.5747,-0.3421,0.2847]])卷积核大小为1x3
m: 168 #=56 x (1 x 3)输出通道数为56,卷积核大小为1x3
男：张量([ 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])偏差偏置的值
男：张量([[[0.0184],
[0.0981],
[0.1894]]]) 卷积核大小为3x1
男：张量([[0.0184],
[0.0981],
[0.1894]]) 卷积核大小为3x1
m: 168 #=56 x (3 x 1)输出通道数为56,卷积核大小为3x1
男：张量([-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])偏差偏置的值
初始化后
美国有线新闻网；卷积神经网络类(NN .模块):
def __init__(self，num_channels=1，d=56，s=12，m=4):
超级(美国有线电视新闻网，自我).__init__()
self.first_part=nn .顺序（
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 .预备课程(四)
)
自我。_初始化_权重()
定义_初始化_权重(自身):
对于m in self.first_part:
if isinstance(m，nn .Conv2d):
nn.init.normal_(m.weight.data，mean=0.0，STD=math。sqrt(2/(米输出通道*米重量。数据[0][0]).numel())))))
nn.init.zeros_(m.bias.data)
向前定义（自身，x):
x=self.first_part(x)
返回x
model=CNN()
对于模型.第一部分中的男：
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：
男：张量([[[-0.0284,-0.0585,0.0271),
[ 0.0125, 0.0554, 0.0511],
[-0.0106, 0.0574, -0.0053]]])
男：张量([[-0.0284,-0.0585,0.0271),
[ 0.0125, 0.0554, 0.0511],
[-0.0106, 0.0574, -0.0053]])
男：504
男：张量([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.])
男：张量([[[ 0.0059,0.0465,-0.0725]])
男：张量([[ 0.0059,0.0465,-0.0725]])
男：168
男：张量([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.])
男：张量([[[ 0.0599],
[-0.1330],
[ 0.2456]]])
男：张量([[ 0.0599],
[-0.1330],
[ 0.2456]])
男：168
男：张量([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.])
以上为个人经验，希望能给大家一个参考，也希望大家多多支持我们。如有错误或未考虑完全的地方，望不吝赐教。
