models.py

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

class Logistic(nn.Module):
    def __init__(self):
        super(Logistic, self).__init__()
        self.layer1 = nn.Linear(28*28, 10)

    def forward(self, x):
        x = self.layer1(x)
        return F.softmax(x, dim=1)

class NeuralNework(nn.Module):
    def __init__(self):
        super(NeuralNework, self).__init__()
        self.layer1 = nn.Linear(28*28, 512)
        self.layer2 = nn.Linear(512, 512)
        self.output = nn.Linear(512, 10)

    def forward(self, x):
        x = F.relu(self.layer1(x))
        x = F.relu(self.layer2(x))
        x = self.output(x)
        return F.softmax(x, dim=1)

class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.conv2_drop = nn.Dropout2d()
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)

    def forward(self, x):
        x = x.view(-1, 1, 28, 28)
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, training=self.training)
        x = self.fc2(x)
        return F.softmax(x, dim=1)

if __name__ == '__main__':
    a = Logistic()
    b = NeuralNework()
    c = CNN()