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import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import gradio as gr
# Synthetic data
torch.manual_seed(0)
X = torch.linspace(0, 1, 100).unsqueeze(1)
y = 2 * X + 3 + 0.1 * torch.randn_like(X)
# Model
class LinearModel(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(1, 1)
def forward(self, x):
return self.linear(x)
# Training loop
def train(optimizer_name):
model = LinearModel()
criterion = nn.MSELoss()
optimizer = {
'SGD': torch.optim.SGD(model.parameters(), lr=0.1),
'Adam': torch.optim.Adam(model.parameters(), lr=0.1),
'RMSProp': torch.optim.RMSprop(model.parameters(), lr=0.1),
'Adagrad': torch.optim.Adagrad(model.parameters(), lr=0.1)
}[optimizer_name]
losses = []
for epoch in range(100):
optimizer.zero_grad()
output = model(X)
loss = criterion(output, y)
loss.backward()
optimizer.step()
losses.append(loss.item())
return losses
# Gradio plot
def plot_optimizer(opt_name):
losses = train(opt_name)
plt.figure(figsize=(6, 4))
plt.plot(losses, label=opt_name)
plt.xlabel('Epoch')
plt.ylabel('Loss')
plt.title(f'{opt_name} Loss Curve')
plt.grid(True)
plt.legend()
return plt.gcf()
demo = gr.Interface(
fn=plot_optimizer,
inputs=gr.Dropdown(['SGD', 'Adam', 'RMSProp', 'Adagrad'], label="Choose Optimizer"),
outputs=gr.Plot(label="Loss Curve"),
title="Optimizer Comparison on Linear Regression",
description="Select an optimizer to visualize its convergence on a simple linear regression task.'SGD':, 'Adam', 'RMSProp':,'Adagrad':"
)
if __name__ == "__main__":
demo.launch()
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