pages/14_MNIST.py

import streamlit as st
import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
import matplotlib.pyplot as plt

# Define the neural network
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.fc1 = nn.Linear(28 * 28, 128)
        self.fc2 = nn.Linear(128, 64)
        self.fc3 = nn.Linear(64, 10)
    
    def forward(self, x):
        x = x.view(-1, 28 * 28)
        x = torch.relu(self.fc1(x))
        x = torch.relu(self.fc2(x))
        x = self.fc3(x)
        return x

# Function to train the model
def train_model(num_epochs):
    # Define transformations
    transform = transforms.Compose([
        transforms.ToTensor(),
        transforms.Normalize((0.5,), (0.5,))
    ])

    # Load datasets
    trainset = torchvision.datasets.MNIST(root='./data', train=True, download=True, transform=transform)
    trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)

    testset = torchvision.datasets.MNIST(root='./data', train=False, download=True, transform=transform)
    testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)

    # Initialize the network, loss function, and optimizer
    net = Net()
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)

    # Track loss over epochs
    loss_values = []

    # Training loop
    for epoch in range(num_epochs):
        running_loss = 0.0
        for i, data in enumerate(trainloader, 0):
            inputs, labels = data
            optimizer.zero_grad()
            outputs = net(inputs)
            loss = criterion(outputs, labels)
            loss.backward()
            optimizer.step()
            running_loss += loss.item()
        
        # Append average loss for this epoch
        loss_values.append(running_loss / len(trainloader))
        st.write(f'Epoch {epoch + 1}, Loss: {running_loss / len(trainloader):.3f}')

    st.write('Finished Training')

    # Plot the loss values
    plt.figure(figsize=(10, 5))
    plt.plot(range(1, num_epochs + 1), loss_values, marker='o')
    plt.title('Training Loss over Epochs')
    plt.xlabel('Epoch')
    plt.ylabel('Loss')
    st.pyplot(plt)

    # Evaluate the network on the test data
    correct = 0
    total = 0
    with torch.no_grad():
        for data in testloader:
            images, labels = data
            outputs = net(images)
            _, predicted = torch.max(outputs.data, 1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()

    st.write(f'Accuracy of the network on the 10000 test images: {100 * correct / total}%')

# Streamlit interface
st.title('MNIST Digit Classification with PyTorch')
num_epochs = st.number_input('Enter number of epochs:', min_value=1, max_value=100, value=10)
if st.button('Run'):
    train_model(num_epochs)