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This article mainly introduces how to test nn.Dropout in Pytorch, which has a certain reference value, interested friends can refer to, I hope you can learn a lot after reading this article, let the editor take you to understand it.

Conclusion

The nn.Dropout of Pytorch drops different parameters each time it is called, even if it is the same forward.

If the dropout rate size of the dropout used many times in the model is the same, use the same dropout layer.

As shown in the code: import torchimport torch.nn as nnclass MyModel (nn.Module): def _ _ init__ (self): super (MyModel, self). _ _ init__ () self.dropout_1 = nn.Dropout (0.5) self.dropout_2 = nn.Dropout (0.5) def forward (self Input): # print (input) drop_1 = self.dropout_1 (input) print (drop_1) drop_1 = self.dropout_1 (input) print (drop_1) drop_2 = self.dropout_2 (input) print (drop_2) if _ name__ ='_ _ main__': I = torch.rand ((5) 5)) m = MyModel () m.forward (I)

The results are as follows:

*\ python.exe * / model.pytensor ([[0.0000, 0.0914, 0.0000, 1.4095, 0.0000], [0.0000, 0.0000, 0.1726, 1.3800, 0.0000], [1.7651,0.0000,0.0000,0.9421,1.5603], [1.0510,1.7290,0.0000,0.0000,0.8565], [0.0000,0.0000,0.0000,0.0000]) tensor ([0.0000] 0.0000, 0.4722, 1.4095, 0.0000], [0.0416, 0.0000, 0.1726, 1.3800, 1.3193], [0.0000, 0.3401,0.6550,0.0000,0.0000], [1.0510,1.7290,1.5515,0.0000,0.0000], [0.6388,0.0000,0.00001.0122,0.0000]) tensor ([0.0000,0.0000,0.4722,0.00001.2689] [0.0416, 0.0000, 0.0000, 1.3800, 0.0000], [0.0000, 0.0000, 0.6550, 0.0000, 1.5603], [0.0000, 0.0000, 1.5515, 1.4596, 0.0000], [0.0000, 0.0000, 0.0000, 0.0000, 0.0000]]) Process finished with exit code 0 thank you for reading this article carefully I hope the article "how to Test nn.Dropout in Pytorch" shared by the editor will be helpful to you. At the same time, I also hope that you will support us and pay attention to the industry information channel. More related knowledge is waiting for you to learn!

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