model.py

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


class Net_mnist(nn.Module):
    def __init__(self):
        super(Net_mnist, self).__init__()
        self.conv1 = nn.Conv2d(1, 20, 5, 1)
        self.conv2 = nn.Conv2d(20, 50, 5, 1)
        self.fc1 = nn.Linear(4*4*50, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = torch.tanh(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = torch.tanh(self.conv2(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 4*4*50)
        x = torch.tanh(self.fc1(x))
        x = self.fc2(x)
        return x


class Net_cifar10(nn.Module):
    def __init__(self):
        super(Net_cifar10, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = self.pool(torch.tanh(self.conv1(x)))
        x = self.pool(torch.tanh(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = torch.tanh(self.fc1(x))
        x = self.fc2(x)
        return x

class Net_cifar100(nn.Module):
    def __init__(self):
        super(Net_cifar100, self).__init__()
        self.conv1 = nn.Conv2d(3, 6, 5)
        self.pool = nn.MaxPool2d(2, 2)
        self.conv2 = nn.Conv2d(6, 16, 5)
        self.fc1 = nn.Linear(16 * 5 * 5, 128)
        self.fc2 = nn.Linear(128, 100)

    def forward(self, x):
        x = self.pool(torch.tanh(self.conv1(x)))
        x = self.pool(torch.tanh(self.conv2(x)))
        x = x.view(-1, 16 * 5 * 5)
        x = torch.tanh(self.fc1(x))
        x = self.fc2(x)
        return x