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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)
# Display sample images
display_sample_images(trainloader)
# 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}%')
# Function to display sample images
def display_sample_images(loader):
dataiter = iter(loader)
images, labels = dataiter.next()
images = images[:5]
labels = labels[:5]
fig, axes = plt.subplots(1, 5, figsize=(15, 3))
for i in range(5):
ax = axes[i]
ax.imshow(images[i].numpy().squeeze(), cmap='gray')
ax.set_title(f'Label: {labels[i].item()}')
ax.axis('off')
st.pyplot(fig)
# 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)
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