*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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