|TensorFlow 1 version||View source on GitHub|
Enable visualizations for TensorBoard.
tf.keras.callbacks.TensorBoard( log_dir='logs', histogram_freq=0, write_graph=True, write_images=False, write_steps_per_second=False, update_freq='epoch', profile_batch=2, embeddings_freq=0, embeddings_metadata=None, **kwargs )
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
written. The metric names will be prepended 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:
You can find more information about TensorBoard here.
||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.|
||frequency (in epochs) at which to compute activation and weight histograms for the layers of the model. If set to 0, histograms w|