TensorFlow.js Visor

 The TensorFlow.js Visor is a built-in debugging and visualization tool for TensorFlow.js. It provides a user-friendly interface for monitoring and visualizing machine learning models, datasets, and their training progress in real time. Visor runs in the browser alongside your TensorFlow.js application.

Key Features of Visor:

1. Visualization of Model Layers:

   • Explore the architecture of your TensorFlow.js model, including layer types, shapes, and parameters.

2. Real-Time Training Metrics:

   • View live updates of loss, accuracy, and other metrics as your model trains.

3. Dataset Inspection:

   • Visualize and examine your dataset, which can help in preprocessing and debugging.

4. Interactive Interface:

   • Allows you to interact with visualizations and tailor the experience to your specific debugging needs.

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How to Use Visor:

1. Import TensorFlow.js Library: Ensure TensorFlow.js is included in your project:

   <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs"></script>
   

2. Activate Visor: Open the visor using the following code:

   tfvis.visor().open();
   

3. Adding Visualizations: You can add visualizations like graphs, histograms, and model summaries using tfvis methods. For example:

   // Visualize a model summary
   const model = tf.sequential({
       layers: [tf.layers.dense({units: 10, inputShape: [1]})]
   });
   tfvis.show.modelSummary({name: 'Model Summary'}, model);
   

4. Training Visualization: Use TensorFlow.js callbacks to track training metrics:

   const metrics = ['loss', 'acc'];
   const container = { name: 'Training Performance' };
   const fitCallbacks = tfvis.show.fitCallbacks(container, metrics);
   
   await model.fit(xs, ys, {
       epochs: 10,
       callbacks: fitCallbacks
   });
   

5. Custom Visualizations: Add your own visualizations or data to the visor using tfvis.render functions.

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Closing the Visor:

You can close the visor with:

tfvis.visor().close();

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Additional Resources:

• Official Documentation: TensorFlow.js Visor
• Demos and Examples: TensorFlow.js GitHub and related repositories have practical implementations showcasing the use of the Visor.

Would you like assistance with a specific example or integration?