File size: 1,879 Bytes
20d9d57 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 |
import os
import gradio as gr
import matplotlib.pyplot as plt
from transformers import ViTForImageClassification, TrainingArguments, Trainer
from datasets import load_dataset
def finetune_model(epochs, save_at_num_epoch, learning_rate):
# Load the dataset
dataset = load_dataset("imagenet")
# Initialize the model
model = ViTForImageClassification.from_pretrained("google/vit-base-patch16-224")
# Define the training arguments
training_args = TrainingArguments(
output_dir="vit-finetuned",
num_train_epochs=epochs,
save_strategy="steps",
save_steps=save_at_num_epoch,
learning_rate=learning_rate,
)
# Create the trainer and fine-tune the model
trainer = Trainer(model=model, args=training_args, train_dataset=dataset["train"])
train_metrics = trainer.train()
# Save the fine-tuned model
model.save_pretrained("vit-finetuned")
# Plot the loss graph
plt.figure(figsize=(8, 6))
plt.plot(train_metrics.history["loss"])
plt.title("Model Loss")
plt.xlabel("Epoch")
plt.ylabel("Loss")
plt.savefig("loss_graph.png")
return "Fine-tuning complete!"
# Create the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("# Fine-Tune a Model")
with gr.Column():
epochs = gr.Slider(label="Epochs", minimum=1, maximum=10, value=3)
save_at_num_epoch = gr.Slider(label="Save Model Every X Epochs", minimum=1, maximum=epochs, value=1)
learning_rate = gr.Slider(label="Learning Rate", minimum=1e-5, maximum=1e-3, value=2e-5)
run_button = gr.Button("Fine-Tune Model")
status = gr.Textbox(label="Fine-Tuning Status")
loss_graph = gr.Image(label="Loss Graph")
run_button.click(finetune_model, inputs=[epochs, save_at_num_epoch, learning_rate], outputs=[status, loss_graph])
if __name__ == "__main__":
demo.launch() |