当前位置：首页 > article >正文

torch.nn.和torch.nn.functional.的区别

article 2024/12/25 2:19:44

torch.nn.**

torch.nn.**是一个继承了torch.nn.Module的类，使用前必须先构造对象，然后再调用。如果直接使用则会报错
例如

a = torch.randn(3,4)
print(a)
sigmoid = torch.nn.Sigmoid()
a = sigmoid(a)
print(a)
a = torch.nn.Sigmoid(a)

tensor([[ 0.2462, -2.1680, -1.4064, -0.0268],
        [-0.4800, -0.4670,  1.7318,  0.3498],
        [ 0.0137, -2.1080, -0.0825, -0.1350]])
tensor([[0.5612, 0.1027, 0.1968, 0.4933],
        [0.3823, 0.3853, 0.8496, 0.5866],
        [0.5034, 0.1083, 0.4794, 0.4663]])
        Traceback (most recent call last):
        
Traceback (most recent call last):
  File "C:\文件\Llama\tmp.py", line 8, in <module>
    a = torch.nn.Sigmoid(a)
        ^^^^^^^^^^^^^^^^^^^
  File "C:\Users\90929\AppData\Local\conda\conda\envs\lce\Lib\site-packages\torch\nn\modules\module.py", line 485, in __init__
    raise TypeError(
TypeError: Sigmoid.__init__() takes 1 positional argument but 2 were given

torch.nn.functional.**

torch.nn.functional.**是一个纯数学函数，可以直接使用

a = torch.randn(3,4)
print(a)
a = torch.nn.functional.sigmoid(a)
print(a)

tensor([[-0.1516,  0.5398,  0.3226, -0.4956],
        [-0.2250,  0.6393,  0.4432,  0.4215],
        [-0.5741,  0.0689,  0.3078, -1.5994]])
tensor([[0.4622, 0.6318, 0.5799, 0.3786],
        [0.4440, 0.6546, 0.6090, 0.6039],
        [0.3603, 0.5172, 0.5763, 0.1681]])