Why is visualizing training progress important in TensorFlow?

Here are some common reasons for this, as follows:.

1. Monitoring Performance Metrics: During training, you can track performance metrics such as loss and accuracy to evaluate how well your model is performing on the training and validation datasets.
2. Debugging and Diagnostics: Visualizing training progress allows you to diagnose issues that may arise during training, such as vanishing gradients, exploding gradients, or learning rate decay.
3. Hyperparameter Tuning: Visualizing training progress helps you tune hyperparameters more effectively by observing how changes in hyperparameter learning rate and batch size affect model performance.
4. Model Interpretability: Visualizations such as histograms of weights and biases, activation functions, and feature maps provide insights into how the model transforms input data and makes predictions.
5. Comparing Models: Visualizing training progress allows you to compare multiple models or model variants to determine which one performs better.

Visualization training progress provides insights into how model is learning overtime, hence allowing practioners to monitor performance and gain insights from the training process. We can visualize the training progess using TensorBoard. TensorBoard is a web-based interface that monitors metrics like loss and accuracy, weights and bias and more. In this article, we are going to explore how we can visualize the training progress using TensorBoard.