import torch
import torch.nn as nn


class Discriminator(torch.nn.Module):

    def __init__(self):
        super(Discriminator, self).__init__()
        self.hidden0 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=
            32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.
            MaxPool2d(kernel_size=2, stride=2))
        self.hidden1 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels
            =64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.
            MaxPool2d(kernel_size=2, stride=2))
        self.hidden2 = nn.Sequential(nn.Linear(7 * 7 * 64, 128), nn.ReLU(),
            nn.Dropout(p=0.5))
        self.out = nn.Sequential(nn.Linear(128, 1), nn.Sigmoid())

    def forward(self, x):
        x = self.hidden0(x)
        x = self.hidden1(x)
        x = x.reshape(x.size(0), -1)
        x = self.hidden2(x)
        x = self.out(x)
        return x

c42da916055bf60d4aac55cc3cc08fadb76f444f
<mask token>

<mask token>


class Discriminator(torch.nn.Module):

    def __init__(self):
        super(Discriminator, self).__init__()
        self.hidden0 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=
            32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.
            MaxPool2d(kernel_size=2, stride=2))
        self.hidden1 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels
            =64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.
            MaxPool2d(kernel_size=2, stride=2))
        self.hidden2 = nn.Sequential(nn.Linear(7 * 7 * 64, 128), nn.ReLU(),
            nn.Dropout(p=0.5))
        self.out = nn.Sequential(nn.Linear(128, 1), nn.Sigmoid())
    <mask token>

<mask token>


class Discriminator(torch.nn.Module):

    def __init__(self):
        super(Discriminator, self).__init__()
        self.hidden0 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=
            32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.
            MaxPool2d(kernel_size=2, stride=2))
        self.hidden1 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels
            =64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.
            MaxPool2d(kernel_size=2, stride=2))
        self.hidden2 = nn.Sequential(nn.Linear(7 * 7 * 64, 128), nn.ReLU(),
            nn.Dropout(p=0.5))
        self.out = nn.Sequential(nn.Linear(128, 1), nn.Sigmoid())

    def forward(self, x):
        x = self.hidden0(x)
        x = self.hidden1(x)
        x = x.reshape(x.size(0), -1)
        x = self.hidden2(x)
        x = self.out(x)
        return x

import torch
import torch.nn as nn


class Discriminator(torch.nn.Module):

    def __init__(self):
        super(Discriminator, self).__init__()
        self.hidden0 = nn.Sequential(nn.Conv2d(in_channels=1, out_channels=
            32, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.
            MaxPool2d(kernel_size=2, stride=2))
        self.hidden1 = nn.Sequential(nn.Conv2d(in_channels=32, out_channels
            =64, kernel_size=3, stride=1, padding=1), nn.ReLU(), nn.
            MaxPool2d(kernel_size=2, stride=2))
        self.hidden2 = nn.Sequential(nn.Linear(7 * 7 * 64, 128), nn.ReLU(),
            nn.Dropout(p=0.5))
        self.out = nn.Sequential(nn.Linear(128, 1), nn.Sigmoid())

    def forward(self, x):
        x = self.hidden0(x)
        x = self.hidden1(x)
        x = x.reshape(x.size(0), -1)
        x = self.hidden2(x)
        x = self.out(x)
        return x
