*Memos:
- My post explains Dropout Layer.
- My post explains manual_seed().
- My post explains requires_grad.
Dropout() can get the 0D or more D tensor of the zero or more elements randomly zeroed or multiplied from the 0D or more D tensor of zero or more elements as shown below:
*Memos:
- The 1st argument for initialization is
p(Optional-Default:0.5-Type:float): *Memos:- It's the probability of an element to be zeroed.
- It must be
0 <= x <= 1.
- The 2nd argument for initialization is
inplace(Optional-Default:False-Type:bool):- It does in-place operation.
- Keep it
Falsebecause it's problematic withTrue.
- The 1st argument is
input(Required-Type:tensoroffloat): *Memos:- It must be the 0D or more D tensor of zero or more elements.
- The tensor's
requires_gradwhich isFalseby default is not set toTruebyDropout().
import torch from torch import nn tensor1 = torch.tensor([8., -3., 0., 1., 5., -2.]) tensor1.requires_grad # False torch.manual_seed(7) dropout1 = nn.Dropout() tensor2 = dropout1(input=tensor1) tensor2 # tensor([16., -0., 0., 2., 10., -4.]) tensor2.requires_grad # False dropout1 # Dropout(p=0.5, inplace=False) dropout1.p # 0.5 dropout1.inplace # False torch.manual_seed(7) dropout2 = nn.Dropout() dropout2(input=tensor2) # tensor([32., -0., 0., 4., 20., -8.]) torch.manual_seed(7) dropout = nn.Dropout(p=0.5, inplace=False) dropout(input=tensor1) # tensor([16., -0., 0., 2., 10., -4.]) torch.manual_seed(7) dropout = nn.Dropout(p=0.8) dropout(input=tensor1) # tensor([40., -0., 0., 0., 25., -0.]) torch.manual_seed(7) dropout = nn.Dropout(p=0.3) dropout(input=tensor1) # tensor([11.4286, -0.0000, 0.0000, 1.4286, 7.1429, -2.8571]) my_tensor = torch.tensor([[8., -3., 0.], [1., 5., -2.]]) torch.manual_seed(7) dropout = nn.Dropout() dropout(input=my_tensor) # tensor([[16., -0., 0.], # [2., 10., -4.]]) torch.manual_seed(7) dropout = nn.Dropout(p=0.8) dropout(input=my_tensor) # tensor([[40., -0., 0.], # [0., 25., -0.]]) torch.manual_seed(7) dropout = nn.Dropout(p=0.3) dropout(input=my_tensor) # tensor([[11.4286, -0.0000, 0.0000], # [1.4286, 7.1429, -2.8571]]) my_tensor = torch.tensor([[8.], [-3.], [0.], [1.], [5.], [-2.]]) torch.manual_seed(7) dropout = nn.Dropout() dropout(input=my_tensor) # tensor([[16.], [-0.], [0.], [2.], [10.], [-4.]]) torch.manual_seed(7) dropout = nn.Dropout(p=0.8) dropout(input=my_tensor) # tensor([[40.], [-0.], [0.], [0.], [25.], [-0.]]) torch.manual_seed(7) dropout = nn.Dropout(p=0.3) dropout(input=my_tensor) # tensor([[11.4286], [-0.0000], [0.0000], [1.4286], [7.1429], [-2.8571]]) my_tensor = torch.tensor([[[8., -3., 0.], [1., 5., -2.]]]) torch.manual_seed(7) dropout = nn.Dropout() dropout(input=my_tensor) # tensor([[[16., -0., 0.], # [2., 10., -4.]]]) 
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