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git clone https://github.com/AumPauskar/biomed-image-analysis.git
cd mnist/

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import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision
from torchvision import datasets, transforms
from torch.utils.data import DataLoader
import numpy as np

# Load the MNIST dataset
mnist_dataset = torchvision.datasets.MNIST(root='./data', train=True, transform=None, target_transform=None, download=True)

# Get the first image from the dataset
image, label = mnist_dataset[0]

# Check if CUDA is available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Step 2: Load the MNIST dataset
transform = transforms.ToTensor()

train_data = datasets.MNIST(root='./data', train=True, download=True, transform=transform)
test_data = datasets.MNIST(root='./data', train=False, download=True, transform=transform)

train_loader = DataLoader(train_data, batch_size=200, shuffle=True)
test_loader = DataLoader(test_data, batch_size=200, shuffle=False)

# Step 3: Define the CNN model
class ConvNet(nn.Module):
    def __init__(self):
        super(ConvNet, self).__init__()
        self.conv1 = nn.Conv2d(1, 32, 5)
        self.fc1 = nn.Linear(12*12*32, 128)
        self.fc2 = nn.Linear(128, 10)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        x = F.max_pool2d(x, 2, 2)
        x = x.view(-1, 12*12*32)
        x = F.relu(self.fc1(x))
        x = self.fc2(x)
        return x

model = ConvNet().to(device)  # Move the model to GPU

# Step 4: Define the loss function and optimizer
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=0.001)

# Step 5: Train the model
for epoch in range(10):
    for i, (images, labels) in enumerate(train_loader):
        images = images.to(device)  # Move the images to GPU
        labels = labels.to(device)  # Move the labels to GPU

        outputs = model(images)
        loss = criterion(outputs, labels)
        
        optimizer.zero_grad()
        loss.backward()
        optimizer.step()

# Step 6: Evaluate the model
model.eval()
with torch.no_grad():
    correct = 0
    total = 0
    for images, labels in test_loader:
        images = images.to(device)  # Move the images to GPU
        labels = labels.to(device)  # Move the labels to GPU

        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        total += labels.size(0)
        correct += (predicted == labels).sum().item()

    print('Test Accuracy: {} %'.format(100 * correct / total))

# Step 7: Predict on the test data and count the occurrences of each digit
with torch.no_grad():
    all_predicted = []
    for images, labels in test_loader:
        images = images.to(device)  # Move the images to GPU

        outputs = model(images)
        _, predicted = torch.max(outputs.data, 1)
        all_predicted += predicted.tolist()

(unique, counts) = np.unique(all_predicted, return_counts=True)
frequencies = np.asarray((unique, counts)).T
print(frequencies)

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import torch
import torch.nn as nn
import torch.optim as optim
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision.datasets import FashionMNIST

# Define a simple neural network
class SimpleNN(nn.Module):
    def __init__(self):
        super(SimpleNN, self).__init__()
        self.fc = nn.Linear(28 * 28, 10)

    def forward(self, x):
        x = x.view(-1, 28 * 28)
        x = self.fc(x)
        return x

def train(model, train_loader, criterion, optimizer, device):
    model.train()
    for data, target in train_loader:
        data, target = data.to(device), target.to(device)
        optimizer.zero_grad()
        output = model(data)
        loss = criterion(output, target)
        loss.backward()
        optimizer.step()

def count_occurrences(model, test_loader, device):
    model.eval()
    occurrences = torch.zeros(10, dtype=torch.long, device=device)
    with torch.no_grad():
        for data, target in test_loader:
            data, target = data.to(device), target.to(device)
            output = model(data)
            _, predicted = torch.max(output, 1)
            occurrences += torch.bincount(predicted, minlength=10)
    return occurrences


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

    # Define transformations
    transform = transforms.Compose([transforms.ToTensor()])

    # Load MNIST Fashion dataset
    train_dataset = FashionMNIST(root='./data', train=True, download=True, transform=transform)
    test_dataset = FashionMNIST(root='./data', train=False, download=True, transform=transform)

    # Create data loaders
    train_loader = DataLoader(dataset=train_dataset, batch_size=64, shuffle=True)
    test_loader = DataLoader(dataset=test_dataset, batch_size=64, shuffle=False)

    # Initialize the model, criterion, and optimizer
    model = SimpleNN().to(device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(model.parameters(), lr=0.01)

    # Train the model
    for epoch in range(5):  # You can adjust the number of epochs
        train(model, train_loader, criterion, optimizer, device)

    # Count occurrences
    occurrences = count_occurrences(model, test_loader, device)

    # Print the occurrences
    for i, count in enumerate(occurrences):
        print(f"Accessory {i}: {count.item()} occurrences")

if __name__ == "__main__":
    main()