Delete pages/14_MNIST.py

pages/14_MNIST.py

@@ -1,92 +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
-
-# 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)
-
-