models.py

import torch.nn as nn


class MvCLNfcMNIST(nn.Module):
    def __init__(self):
        super(MvCLNfcMNIST, self).__init__()
        self.encoder0 = nn.Sequential(
            nn.Linear(784, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 10),
            nn.BatchNorm1d(10),
            nn.ReLU(True)
        )
        self.encoder1 = nn.Sequential(
            nn.Linear(784, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 10),
            nn.BatchNorm1d(10),
            nn.ReLU(True)
        )

    def forward(self, x0, x1):
        h0 = self.encoder0(x0.view(x0.size()[0], -1))
        h1 = self.encoder1(x1.view(x1.size()[0], -1))
        return h0, h1


class MvCLNfcCaltech(nn.Module):
    def __init__(self):
        super(MvCLNfcCaltech, self).__init__()
        self.encoder0 = nn.Sequential(
            nn.Linear(1984, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 10),
            nn.BatchNorm1d(10),
            nn.ReLU(True)
        )
        self.encoder1 = nn.Sequential(
            nn.Linear(512, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 10),
            nn.BatchNorm1d(10),
            nn.ReLU(True)
        )

    def forward(self, x0, x1):
        h0 = self.encoder0(x0.view(x0.size()[0], -1))
        h1 = self.encoder1(x1.view(x1.size()[0], -1))
        return h0, h1


class MvCLNfcScene(nn.Module):  # 20, 59
    def __init__(self):
        super(MvCLNfcScene, self).__init__()
        self.encoder0 = nn.Sequential(
            nn.Linear(20, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 10),
            nn.BatchNorm1d(10),
            nn.ReLU(True)
        )
        self.encoder1 = nn.Sequential(
            nn.Linear(59, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 10),
            nn.BatchNorm1d(10),
            nn.ReLU(True)
        )

    def forward(self, x0, x1):
        h0 = self.encoder0(x0.view(x0.size()[0], -1))
        h1 = self.encoder1(x1.view(x1.size()[0], -1))
        return h0, h1


class MvCLNfcReuters(nn.Module):
    def __init__(self):
        super(MvCLNfcReuters, self).__init__()
        self.encoder0 = nn.Sequential(
            nn.Linear(10, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 10),
            nn.BatchNorm1d(10),
            nn.ReLU(True)
        )
        self.encoder1 = nn.Sequential(
            nn.Linear(10, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 1024),
            nn.BatchNorm1d(1024),
            nn.ReLU(True),
            nn.Dropout(0.2),
            nn.Linear(1024, 10),
            nn.BatchNorm1d(10),
            nn.ReLU(True)
        )

    def forward(self, x0, x1):
        return self.encoder0(x0.view(x0.size()[0], -1)), self.encoder1(x1.view(x1.size()[0], -1))