jrtechs-RandomScripts/ML Cookbook/CNNs/BasicCNN.py

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

# CNN architecture definition
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        # convolutional layer
        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
        # max pooling layer
        self.pool = nn.MaxPool2d(2, 2)

    def forward(self, x):
        # add sequence of convolutional and max pooling layers
        x = self.pool(F.relu(self.conv1(x)))
        return x