pages/15_CNN.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
import seaborn as sns
from torch.utils.data import DataLoader
from sklearn.metrics import confusion_matrix
import numpy as np

# Device configuration
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

# Streamlit interface
st.title("CNN for Image Classification using CIFAR-10")
st.write("""
This application demonstrates how to build and train a Convolutional Neural Network (CNN) for image classification using the CIFAR-10 dataset. You can adjust hyperparameters, visualize sample images, and see the model's performance.
""")

# Hyperparameters
num_epochs = st.sidebar.slider("Number of epochs", 1, 20, 10)
batch_size = st.sidebar.slider("Batch size", 10, 200, 100, step=10)
learning_rate = st.sidebar.slider("Learning rate", 0.0001, 0.01, 0.001, step=0.0001)

# CIFAR-10 dataset
transform = transforms.Compose(
    [transforms.ToTensor(),
     transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))])

train_dataset = torchvision.datasets.CIFAR10(root='./data', train=True,
                                             download=True, transform=transform)

test_dataset = torchvision.datasets.CIFAR10(root='./data', train=False,
                                            download=True, transform=transform)

train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)

# Display some sample images
st.write("## Sample Images from CIFAR-10 Dataset")
sample_images, sample_labels = next(iter(train_loader))
fig, axes = plt.subplots(1, 6, figsize=(15, 5))
for i in range(6):
    axes[i].imshow(np.transpose(sample_images[i].numpy(), (1, 2, 0)))
    axes[i].set_title(f'Label: {sample_labels[i].item()}')
    axes[i].axis('off')
st.pyplot(fig)

# Class distribution
st.write("## Class Distribution in CIFAR-10 Dataset")
class_names = train_dataset.classes
class_counts = np.bincount([sample_labels[i].item() for i in range(len(sample_labels))])
fig, ax = plt.subplots()
sns.barplot(x=class_names, y=class_counts, ax=ax)
ax.set_ylabel('Count')
ax.set_title('Class Distribution')
st.pyplot(fig)

# Define a Convolutional Neural Network
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.layer1 = nn.Sequential(
            nn.Conv2d(3, 32, kernel_size=3, padding=1),
            nn.BatchNorm2d(32),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=2, stride=2))
        self.layer2 = nn.Sequential(
            nn.Conv2d(32, 64, kernel_size=3),
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.MaxPool2d(2))
        
        # Automatically determine the size of the flattened features after convolution and pooling
        self._to_linear = None
        self.convs(torch.randn(1, 3, 32, 32))
        
        self.fc1 = nn.Linear(self._to_linear, 600)
        self.drop = nn.Dropout2d(0.25)
        self.fc2 = nn.Linear(600, 100)
        self.fc3 = nn.Linear(100, 10)

    def convs(self, x):
        x = self.layer1(x)
        x = self.layer2(x)
        if self._to_linear is None:
            self._to_linear = x.view(x.size(0), -1).shape[1]
        return x
    
    def forward(self, x):
        x = self.convs(x)
        x = x.view(x.size(0), -1)
        x = self.fc1(x)
        x = self.drop(x)
        x = self.fc2(x)
        x = self.fc3(x)
        return x

model = CNN().to(device)

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

# Button to start training
if st.button("Start Training"):
    # Lists to store losses and accuracy
    train_losses = []
    test_losses = []
    test_accuracies = []
    
    # Progress bar
    progress_bar = st.progress(0)

    # Train the model
    total_step = len(train_loader)
    for epoch in range(num_epochs):
        train_loss = 0
        for i, (images, labels) in enumerate(train_loader):
            images = images.to(device)
            labels = labels.to(device)

            # Forward pass
            outputs = model(images)
            loss = criterion(outputs, labels)

            # Backward and optimize
            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            train_loss += loss.item()

        train_loss /= total_step
        train_losses.append(train_loss)
        st.write(f'Epoch [{epoch+1}/{num_epochs}], Loss: {train_loss:.4f}')

        # Test the model
        model.eval()
        with torch.no_grad():
            test_loss = 0
            correct = 0
            total = 0
            all_labels = []
            all_predictions = []
            for images, labels in test_loader:
                images = images.to(device)
                labels = labels.to(device)
                outputs = model(images)
                loss = criterion(outputs, labels)
                test_loss += loss.item()
                _, predicted = torch.max(outputs.data, 1)
                total += labels.size(0)
                correct += (predicted == labels).sum().item()
                all_labels.extend(labels.cpu().numpy())
                all_predictions.extend(predicted.cpu().numpy())

            test_loss /= len(test_loader)
            test_losses.append(test_loss)
            accuracy = 100 * correct / total
            test_accuracies.append(accuracy)
            st.write(f'Test Loss: {test_loss:.4f}, Accuracy: {accuracy:.2f}%')
        model.train()

        # Update progress bar
        progress_bar.progress((epoch + 1) / num_epochs)

    # Plotting the loss and accuracy
    fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(14, 5))
    ax1.plot(range(1, num_epochs + 1), train_losses, label='Train Loss')
    ax1.plot(range(1, num_epochs + 1), test_losses, label='Test Loss')
    ax1.set_xlabel('Epoch')
    ax1.set_ylabel('Loss')
    ax1.set_title('Training and Test Loss')
    ax1.legend()

    ax2.plot(range(1, num_epochs + 1), test_accuracies, label='Test Accuracy')
    ax2.set_xlabel('Epoch')
    ax2.set_ylabel('Accuracy (%)')
    ax2.set_title('Test Accuracy')
    ax2.legend()
    st.pyplot(fig)

    # Confusion Matrix
    cm = confusion_matrix(all_labels, all_predictions)
    fig, ax = plt.subplots(figsize=(10, 10))
    sns.heatmap(cm, annot=True, fmt="d", xticklabels=class_names, yticklabels=class_names, cmap='Blues')
    ax.set_xlabel('Predicted')
    ax.set_ylabel('True')
    ax.set_title('Confusion Matrix')
    st.pyplot(fig)

    # Save the model checkpoint
    torch.save(model.state_dict(), 'cnn_model.pth')
    st.write("Model training completed and saved as 'cnn_model.pth'")