How to Display Training Progress in a GUI Progress Bar using TensorFlow Keras

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What will you learn?

In this tutorial, you will master the art of integrating a progress bar into your GUI application. By leveraging TensorFlow Keras, you’ll visualize the training progress of your machine learning model in real-time.

Introduction to the Problem and Solution

When building machine learning models, tracking training progress is crucial for monitoring performance. To tackle this challenge effectively, we can create a custom callback function in TensorFlow Keras. This callback function updates a progress bar during model training, providing users with visual feedback on the training progress.

By incorporating callbacks, we can interact with training events and enhance user experience by offering real-time updates through our GUI interface. This approach not only improves user engagement but also allows for better understanding of the model’s behavior during training.

Code

import tensorflow as tf
from tensorflow import keras
from tkinter import Tk, ttk

# Custom Callback Class for updating progress bar during training
class ProgressBarCallback(keras.callbacks.Callback):
    def __init__(self, progbar):
        super(ProgressBarCallback, self).__init__()
        self.progbar = progbar

    def on_train_batch_end(self, batch, logs=None):
        self.progbar['value'] += 1  # Update progress bar value

# Create GUI window and progress bar widget
root = Tk()
root.title("Model Training Progress")
prog_bar = ttk.Progressbar(root, orient='horizontal', length=300)
prog_bar.pack()

# Define your model and compile it before starting training

# Instantiate custom callback with the created progress bar widget
progress_callback = ProgressBarCallback(prog_bar)

# Start model training with the defined callback
model.fit(x_train, y_train, epochs=10, callbacks=[progress_callback])

root.mainloop()  # Launches the GUI window for displaying progress

# Credits: PythonHelpDesk.com - Providing Python support and guidance.

# Copyright PHD

Explanation

  • Define a custom ProgressBarCallback class inheriting from keras.callbacks.Callback to update the provided progress bar after each batch of data processed during training.
  • Import necessary libraries including TensorFlow (tf) and tkinter (for creating GUI).
  • Initialize ProgressBarCallback instance with tkinter Progressbar.
  • Set up tkinter window and initialize an empty Progressbar.
  • Ensure neural network model is defined and compiled before running code snippet.
  • Commence model training using .fit() method while passing custom callback for UI updates.
    How do I install TensorFlow?

    You can install TensorFlow using pip: pip install tensorflow.

    Can I customize the appearance of my tkinter Progressbar?

    Yes! Customize its color, size or style based on your requirements.

    Is it possible to use other GUI libraries besides tkinter?

    While focusing on tkinter due to simplicity reasons, explore PyQt or PyGTK for advanced features.

    How frequently does the Progress Bar update during training?

    The Progress Bar updates after each batch completion by default but can be customized within Callback class.

    Will integrating a Progress Bar impact my model’s performance?

    No. The overhead from updating a graphical element like a Progress Bar is negligible compared to actual computations involved in neural network operations.

    Conclusion

    Integrating a live-updating Progress Bar in your GUI application offers valuable insights into machine learning models’ progression. Leveraging TensorFlow Keras‘s flexibility through custom callbacks enhances monitoring efficiency and interactivity throughout modeling journeys. For further assistance or similar queries visit PythonHelpDesk.com.

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