标签:tensor nn float64 dtype Dropout torch output input
Dropout
torch.nn.Dropout(p=0.5, inplace=False)
- p – probability of an element to be zeroed. Default: 0.5
- inplace – If set to
True
, will do this operation in-place. Default:False
训练过程中以概率P随机的将参数置0,其中P为置0的概率,例如P=1表示将网络参数全部置0
During training, randomly zeroes some of the elements of the input tensor with probability
p
using samples from a Bernoulli distribution. Each channel will be zeroed out independently on every forward call.
**注意:**Pytorch文档中给出了一点,输出的参数会以 1 1 − p \frac{1}{1-p} 1−p1进行一个缩放
Furthermore, the outputs are scaled by a factor of 1 1 − p \frac{1}{1-p} 1−p1 during training. This means that during evaluation the module simply computes an identity function.
下面例子展示出在dropout之后,参数变为了原来的 1 1 − p = 2 \frac{1}{1-p} = 2 1−p1=2倍
input = torch.tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]], dtype=torch.float64)
input = torch.unsqueeze(input, 0)
m = nn.Dropout(p = 0.5)
output = m(input)
print("input: ", input)
print("output: ", output)
print("input: ", input)
'''
input:
tensor([[[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]]], dtype=torch.float64)
output:
tensor([[[ 2., 4., 0.],
[ 0., 10., 12.],
[ 0., 16., 0.]]], dtype=torch.float64)
input:
tensor([[[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]]], dtype=torch.float64)
'''
当我们把nn.Dropout
的inplace=True
时,计算的结果就会替换掉原来的输入input
,如下:
input = torch.tensor([[1, 2, 3],
[4, 5, 6],
[7, 8, 9]], dtype=torch.float64)
input = torch.unsqueeze(input, 0)
m = nn.Dropout(p = 0.5, inplace=True)
output = m(input)
print("input: ", input)
print("output: ", output)
print("input: ", input)
'''
input:
tensor([[[1., 2., 3.],
[4., 5., 6.],
[7., 8., 9.]]], dtype=torch.float64)
output:
tensor([[[ 2., 4., 0.],
[ 0., 10., 12.],
[ 0., 16., 0.]]], dtype=torch.float64)
input:
tensor([[[ 2., 4., 0.],
[ 0., 10., 12.],
[ 0., 16., 0.]]], dtype=torch.float64)
'''
标签:tensor,nn,float64,dtype,Dropout,torch,output,input 来源: https://blog.csdn.net/weixin_41978699/article/details/121189603
本站声明: 1. iCode9 技术分享网(下文简称本站)提供的所有内容,仅供技术学习、探讨和分享; 2. 关于本站的所有留言、评论、转载及引用,纯属内容发起人的个人观点,与本站观点和立场无关; 3. 关于本站的所有言论和文字,纯属内容发起人的个人观点,与本站观点和立场无关; 4. 本站文章均是网友提供,不完全保证技术分享内容的完整性、准确性、时效性、风险性和版权归属;如您发现该文章侵犯了您的权益,可联系我们第一时间进行删除; 5. 本站为非盈利性的个人网站,所有内容不会用来进行牟利,也不会利用任何形式的广告来间接获益,纯粹是为了广大技术爱好者提供技术内容和技术思想的分享性交流网站。