How do I adjust Keras callbacks to log training progress into a TensorBoard

Question

Can i know How do I adjust Keras callbacks to log training progress into a TensorBoard?

Answer 1

To log training progress into TensorBoard in Keras, use the TensorBoard callback, specifying the log_dir, and run TensorBoard to visualize loss, accuracy, learning rates, and model graphs in real-time.

Here is the code snippet given below:

In the above code we are using the following techniques:

• Logs Training Metrics in Real-Time:

  • Tracks loss, accuracy, and learning rates during training.

• Visualizes Model Graphs (write_graph=True):

  • Displays the computation graph of the Keras model.

• Supports Weight Histograms (histogram_freq=1):

  • Monitors weight distributions and gradients for debugging.

• Image Logging for CNNs (write_images=True):

  • Stores sample images if applicable (useful for vision models).

• Easy Access via Terminal (tensorboard --logdir=logs/fit)

  • Launch TensorBoard locally to monitor training performance interactively

Hence, using Keras TensorBoard callback allows efficient logging and real-time visualization of training progress, aiding in performance monitoring and debugging.