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import torch
import torch.nn as nn
import torchvision
from monotonenorm import GroupSort, direct_norm
# GroupSort Neural Networks created using monotonenorm package.
#See https://github.com/niklasnolte/MonotoneNorm for more information
class NN_phi(nn.Module):
def __init__(self,dim_input):
super(NN_phi, self).__init__()
self.linear1=direct_norm(torch.nn.Linear(dim_input,16),kind="two-inf")
self.group1=GroupSort(16//2)#GroupSort with a grouping size of 2
self.linear2=direct_norm(torch.nn.Linear(16,32),kind="inf")
self.group2=GroupSort(32//2)
self.linear3=direct_norm(torch.nn.Linear(32,1),kind="inf")
def forward(self, x):
x=self.linear1(x)
x=self.group1(x)
x=self.linear2(x)
x=self.group2(x)
x=self.linear3(x)
return x
class NN_h_RELU(nn.Module):
def __init__(self,dim_input):
super(NN_h_RELU, self).__init__()
self.linear1=torch.nn.Linear(dim_input,16)
self.RELU=torch.nn.ReLU()
self.linear2=torch.nn.Linear(16,32)
self.linear3=torch.nn.Linear(32,1)
def forward(self, x):
x=self.linear1(x)
x=self.RELU(x)
x=self.linear2(x)
x=self.RELU(x)
x=self.linear3(x)
return x
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