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'")