37 lines
1.0 KiB
Python
37 lines
1.0 KiB
Python
import time
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import numpy as np
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import torch
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from torch.nn.functional import *
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from torch.utils.data import Dataset, DataLoader
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from torch import nn
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from torchvision import datasets, transforms
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from tqdm import tqdm
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from utils import *
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import ipdb
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class My_Dropout(nn.Module):
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def __init__(self, p, **kwargs):
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super().__init__()
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self.p = p
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self.mask = None
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def forward(self, x:torch.Tensor):
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if self.training:
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self.mask = (torch.rand(x.shape) > self.p).to(dtype=torch.float32, device=x.device)
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return x * self.mask / (1 - self.p)
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else:
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return x
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if __name__ == "__main__":
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my_dropout = My_Dropout(p=0.5)
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nn_dropout = nn.Dropout(p=0.5)
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x = torch.tensor([[1.0, 2.0, 3.0, 4.0, 5.0],
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[6.0, 7.0, 8.0, 9.0, 10.0]])
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print(f"输入：\n{x}")
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output_my_dropout = my_dropout(x)
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output_nn_dropout = nn_dropout(x)
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print(f"My_Dropout输出：\n{output_my_dropout}")
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print(f"nn.Dropout输出：\n{output_nn_dropout}")
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