Delete pages/15_Simple_CNN.py

pages/15_Simple_CNN.py

@@ -1,111 +0,0 @@
-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 numpy as np
-
-# Define the CNN
-class SimpleCNN(nn.Module):
-    def __init__(self):
-        super(SimpleCNN, self).__init__()
-        self.conv1 = nn.Conv2d(3, 16, 3, padding=1)
-        self.conv2 = nn.Conv2d(16, 32, 3, padding=1)
-        self.pool = nn.MaxPool2d(2, 2)
-        self.fc1 = nn.Linear(32 * 8 * 8, 128)
-        self.fc2 = nn.Linear(128, 10)
-    
-    def forward(self, x):
-        x = self.pool(torch.relu(self.conv1(x)))
-        x = self.pool(torch.relu(self.conv2(x)))
-        x = x.view(-1, 32 * 8 * 8)
-        x = torch.relu(self.fc1(x))
-        x = self.fc2(x)
-        return x
-
-# Function to train the model
-def train_model(num_epochs):
-    transform = transforms.Compose([
-        transforms.ToTensor(),
-        transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))
-    ])
-
-    trainset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform)
-    trainloader = torch.utils.data.DataLoader(trainset, batch_size=64, shuffle=True)
-
-    testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=transform)
-    testloader = torch.utils.data.DataLoader(testset, batch_size=64, shuffle=False)
-
-    CIFAR10_CLASSES = [
-        'plane', 'car', 'bird', 'cat', 'deer',
-        'dog', 'frog', 'horse', 'ship', 'truck'
-    ]
-
-    net = SimpleCNN()
-    criterion = nn.CrossEntropyLoss()
-    optimizer = optim.SGD(net.parameters(), lr=0.01, momentum=0.9)
-
-    loss_values = []
-    st.write("Training the model...")
-
-    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()
-        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)
-
-    correct = 0
-    total = 0
-    with torch.no_grad():
-        for data in testloader:
-            images, labels = data
-            outputs = net(images)
-            _, predicted = torch.max(outputs, 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}%')
-
-    # Visualize some test images and their predictions
-    def imshow(img):
-        img = img / 2 + 0.5  # Unnormalize
-        npimg = img.numpy()
-        plt.imshow(np.transpose(npimg, (1, 2, 0)))
-        plt.show()
-
-    dataiter = iter(testloader)
-    images, labels = next(dataiter)
-
-    imshow(torchvision.utils.make_grid(images))
-
-    outputs = net(images)
-    _, predicted = torch.max(outputs, 1)
-
-    st.write('Predicted: ', ' '.join(f'{CIFAR10_CLASSES[predicted[j]]:5s}' for j in range(8)))
-    st.write('Actual:    ', ' '.join(f'{CIFAR10_CLASSES[labels[j]]:5s}' for j in range(8)))
-    st.pyplot()
-
-# Streamlit interface
-st.title('CIFAR-10 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)
-