Setting Up TensorBoard
Explore how to set up TensorBoard in Jupyter Notebooks or Colaboratory to monitor deep learning experiments using JAX and Flax. Learn to log training metrics, visualize model architecture, analyze weights, and optimize hyperparameters for effective model evaluation.
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Tracking machine learning experiments makes understanding and visualizing the model’s performance easy. It also makes it possible to spot any problems in the network. For example, we can quickly spot overfitting by looking at the training and validation charts. We can plot these charts using our favorite charts package, such as Matplotlib. However, we can also use more advanced tools, such as TensorBoard.
TensorBoard is an open-source library that provides tools for experiment tracking in machine learning. We can use TensorBoard for:
- Tracking and visualizing model evaluation metrics, such as accuracy.
- Logging images.
- Visualizing