models.py

import torch
import torch.nn as nn
import torch.nn.functional as F

class MLP(nn.Module):
    def __init__(self):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(64, 40)
        self.fc2 = nn.Linear(40, 20)
        self.fc3 = nn.Linear(20, 10)

    def forward(self, x):
        out = F.relu(self.fc1(x))
        out = F.relu(self.fc2(out))
        out = self.fc3(out)
        return out

class MLP_R(nn.Module):
    def __init__(self):
        super(MLP_R, self).__init__()
        self.fc1 = nn.Linear(1, 10)
        self.fc2 = nn.Linear(10, 1)
    def forward(self,x):
        out = F.elu(self.fc1(x))
        out = self.fc2(out)
        return out

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 4, 5)
        self.relu1 = nn.RReLU()
        self.pool1 = nn.MaxPool2d(2)
        self.conv2 = nn.Conv2d(4, 8, 5)
        self.relu2 = nn.RReLU()
        self.pool2 = nn.MaxPool2d(2)
        self.fc1 = nn.Linear(128, 10)

    def forward(self, x):
        out = self.conv1(x)
        out = self.relu1(out)
        out = self.pool1(out)
        out = self.conv2(out)
        out = self.relu2(out)
        out = self.pool2(out)
        out = out.view(out.shape[0], -1)
        out = self.fc1(out)
        return out