Pytorch--Dropout笔记

dropout常常用于抑制过拟合，pytorch也提供了很方便的函数。但是经常不知道dropout的参数p是什么意思。在TensorFlow中p叫做keep_prob,就一直以为pytorch中的p应该就是保留节点数的比例，但是实验结果发现反了，实际上表示的是不保留节点数的比例。看下面的例子：

a = torch.randn(10,1)
>>> tensor([[ 0.0684],
        [-0.2395],
        [ 0.0785],
        [-0.3815],
        [-0.6080],
        [-0.1690],
        [ 1.0285],
        [ 1.1213],
        [ 0.5261],
        [ 1.1664]])

• p=0.5

torch.nn.Dropout(0.5)(a)
>>> tensor([[ 0.0000],  
        [-0.0000],  
        [ 0.0000],  
        [-0.7631],  
        [-0.0000],  
        [-0.0000],  
        [ 0.0000],  
        [ 0.0000],  
        [ 1.0521],  
        [ 2.3328]]) 

• p=0

torch.nn.Dropout(0)(a)
>>> tensor([[ 0.0684],
        [-0.2395],
        [ 0.0785],
        [-0.3815],
        [-0.6080],
        [-0.1690],
        [ 1.0285],
        [ 1.1213],
        [ 0.5261],
        [ 1.1664]]) 

• p=1

torch.nn.Dropout(0)(a)
>>> tensor([[0.],  
        [-0.], 
        [0.],  
        [-0.], 
        [-0.], 
        [-0.], 
        [0.],  
        [0.],  
        [0.],  
        [0.]]) 

MARSGGBO♥原创





2019-3-25

转载于:https://www.cnblogs.com/marsggbo/p/10592643.html