Enable visualizations for TensorBoard.

Inherits From: Callback

Used in the notebooks

Used in the guide Used in the tutorials

TensorBoard is a visualization tool provided with TensorFlow.

This callback logs events for TensorBoard, including:

  • Metrics summary plots
  • Training graph visualization
  • Activation histograms
  • Sampled profiling

When used in Model.evaluate, in addition to epoch summaries, there will be a summary that records evaluation metrics vs Model.optimizer.iterations written. The metric names will be prepended with evaluation, with Model.optimizer.iterations being the step in the visualized TensorBoard.

If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line:

tensorboard --logdir=path_to_your_logs

You can find more information about TensorBoard here.

log_dir the path of the directory where to save the log files to be parsed by TensorBoard. e.g. log_dir = os.path.join(working_dir, 'logs') This directory should not be reused by any other callbacks.
histogram_freq frequency (in epochs) at which to compute activation and weight histograms for the layers of the model. If set to 0, histograms w