import torch
import torch.nn as nn

class SimpleCNN(nn.Module):
    def __init__(self, num_classes=10):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=32, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(32, 64, kernel_size=3, padding=1)
        self.conv3 = nn.Conv2d(64, 128, kernel_size=3, padding=1)
        self.pool = nn.MaxPool2d(2, 2)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.5)
        self.fc1 = nn.Linear(128 * 28 * 28, 512)
        self.fc2 = nn.Linear(512, num_classes)

    def forward(self, x):
        x = self.pool(self.relu(self.conv1(x)))
        x = self.pool(self.relu(self.conv2(x)))
        x = self.pool(self.relu(self.conv3(x)))
        x = x.flatten(1)
        x = self.dropout(self.relu(self.fc1(x)))
        return self.fc2(x)

Deep Learning — CNNs, RNNs, Transformers

Deep Learning — CNNs, RNNs, Transformers

Deep Learning — CNNs, RNNs, Transformers