models.py

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

class CNN(nn.Module):
    def __init__(self):
        super(CNN,self).__init__()
        self.conv1 = nn.Conv2d(1, 32, kernel_size=5)
        self.conv2 = nn.Conv2d(32, 32, kernel_size=5)
        self.conv3 = nn.Conv2d(32, 64, kernel_size=5)
        self.fc1 = nn.Linear(3*3*64, 256)
        self.fc2 = nn.Linear(256, 10) # modified
        
    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.relu(F.max_pool2d(self.conv2(x), 2))
        x = F.dropout(x, p=0.5, training = self.training)
        x = F.relu(F.max_pool2d(self.conv3(x), 2))
        x = F.dropout(x, p=0.5, training = self.training)
        x = x.view(x.shape[0], -1)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, p=0.5, training = self.training)
        x = self.fc2(x)
        return x