Machine-Learning-Collection/ML/Pytorch/Basics/pytorch_simple_CNN.py

"""
A simple walkthrough of how to code a convolutional neural network (CNN)
using the PyTorch library. For demonstration we train it on the very
common MNIST dataset of handwritten digits. In this code we go through
how to create the network as well as initialize a loss function, optimizer,
check accuracy and more.

Programmed by Aladdin Persson
* 2020-04-08: Initial coding
* 2021-03-24: More detailed comments and small revision of the code
* 2022-12-19: Small revision of code, checked that it works with latest PyTorch version

"""

# Imports
import torch
import torch.nn.functional as F  # Parameterless functions, like (some) activation functions
import torchvision.datasets as datasets  # Standard datasets
import torchvision.transforms as transforms  # Transformations we can perform on our dataset for augmentation
from torch import optim  # For optimizers like SGD, Adam, etc.
from torch import nn  # All neural network modules
from torch.utils.data import (
    DataLoader,
)  # Gives easier dataset managment by creating mini batches etc.
from tqdm import tqdm  # For nice progress bar!

# Simple CNN
class CNN(nn.Module):
    def __init__(self, in_channels=1, num_classes=10):
        super(CNN, self).__init__()
        self.conv1 = nn.Conv2d(
            in_channels=in_channels,
            out_channels=8,
            kernel_size=3,
            stride=1,
            padding=1,
        )
        self.pool = nn.MaxPool2d(kernel_size=2, stride=2)
        self.conv2 = nn.Conv2d(
            in_channels=8,
            out_channels=16,
            kernel_size=3,
            stride=1,
            padding=1,
        )
        self.fc1 = nn.Linear(16 * 7 * 7, num_classes)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = self.pool(x)
        x = F.relu(self.conv2(x))
        x = self.pool(x)
        x = x.reshape(x.shape[0], -1)
        x = self.fc1(x)
        return x


# Set device
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Hyperparameters
in_channels = 1
num_classes = 10
learning_rate = 3e-4 # karpathy's constant
batch_size = 64
num_epochs = 3

# Load Data
train_dataset = datasets.MNIST(
    root="dataset/", train=True, transform=transforms.ToTensor(), download=True
)
test_dataset = datasets.MNIST(
    root="dataset/", train=False, transform=transforms.ToTensor(), download=True
)
train_loader = DataLoader(dataset=train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(dataset=test_dataset, batch_size=batch_size, shuffle=True)

# Initialize network
model = CNN(in_channels=in_channels, num_classes=num_classes).to(device)

# Loss and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

# Train Network
for epoch in range(num_epochs):
    for batch_idx, (data, targets) in enumerate(tqdm(train_loader)):
        # Get data to cuda if possible
        data = data.to(device=device)
        targets = targets.to(device=device)

        # forward
        scores = model(data)
        loss = criterion(scores, targets)

        # backward
        optimizer.zero_grad()
        loss.backward()

        # gradient descent or adam step
        optimizer.step()

# Check accuracy on training & test to see how good our model
def check_accuracy(loader, model):
    num_correct = 0
    num_samples = 0
    model.eval()

    with torch.no_grad():
        for x, y in loader:
            x = x.to(device=device)
            y = y.to(device=device)

            scores = model(x)
            _, predictions = scores.max(1)
            num_correct += (predictions == y).sum()
            num_samples += predictions.size(0)

    model.train()
    return num_correct / num_samples


print(f"Accuracy on training set: {check_accuracy(train_loader, model)*100:.2f}")
print(f"Accuracy on test set: {check_accuracy(test_loader, model)*100:.2f}")