1from torch import nn 

2import torchapp as ta 

3from torchapp.examples.logistic_regression import LogisticRegressionApp 

4 

5class MultilayerPerceptronApp(LogisticRegressionApp): 

6 def model( 

7 self, 

8 hidden_layers:int = ta.Param(default=1, tune_min=0, tune_max=4, tune=True, help="The number of hidden layers"), 

9 hidden_size:int = ta.Param(default=64, tune_min=8, tune_max=256, tune=True, tune_log=True, help="The size of the hidden layers"), 

10 hidden_bias:bool = ta.Param(default=True, tune=True, help="Whether or not the hidden layers have bias"), 

11 ) -> nn.Module: 

12 in_features = 1 

13 

14 layer_list = [] 

15 for _ in range(hidden_layers): 

16 layer_list.append(nn.Linear(in_features=in_features, out_features=hidden_size, bias=hidden_bias)) 

17 in_features = hidden_size 

18 

19 # final layer 

20 layer_list.append(nn.Linear(in_features=in_features, out_features=1, bias=True)) 

21 

22 return nn.Sequential(*layer_list) 

23 

24 

25if __name__ == "__main__": 

26 MultilayerPerceptronApp.main()