This callback writes a log for TensorBoard, which allows you to visualize dynamic graphs of your training and test metrics, as well as activation histograms for the different layers in your model.
callback_tensorboard(log_dir = NULL, histogram_freq = 0,
batch_size = 32, write_graph = TRUE, write_grads = FALSE,
write_images = FALSE, embeddings_freq = 0,
embeddings_layer_names = NULL, embeddings_metadata = NULL,
embeddings_data = NULL, update_freq = "epoch")The path of the directory where to save the log files to be
parsed by Tensorboard. The default is NULL, which will use the active
run directory (if available) and otherwise will use "logs".
frequency (in epochs) at which to compute activation histograms for the layers of the model. If set to 0, histograms won't be computed.
size of batch of inputs to feed to the network for histograms computation.
whether to visualize the graph in Tensorboard. The log
file can become quite large when write_graph is set to TRUE
whether to visualize gradient histograms in TensorBoard.
histogram_freq must be greater than 0.
whether to write model weights to visualize as image in Tensorboard.
frequency (in epochs) at which selected embedding layers will be saved.
a list of names of layers to keep eye on. If
NULL or empty list all the embedding layers will be watched.
a named list which maps layer name to a file name in which metadata for this embedding layer is saved. See the details about the metadata file format. In case if the same metadata file is used for all embedding layers, string can be passed.
Data to be embedded at layers specified in
embeddings_layer_names. Array (if the model has a single input) or list
of arrays (if the model has multiple inputs). Learn more about embeddings
'batch' or 'epoch' or integer. When using 'batch', writes
the losses and metrics to TensorBoard after each batch. The same
applies for 'epoch'. If using an integer, let's say 10000,
the callback will write the metrics and losses to TensorBoard every
10000 samples. Note that writing too frequently to TensorBoard
can slow down your training.
TensorBoard is a visualization tool provided with TensorFlow.
You can find more information about TensorBoard here.
When using a backend other than TensorFlow, TensorBoard will still work (if you have TensorFlow installed), but the only feature available will be the display of the losses and metrics plots.
Other callbacks: callback_csv_logger,
callback_early_stopping,
callback_lambda,
callback_learning_rate_scheduler,
callback_model_checkpoint,
callback_progbar_logger,
callback_reduce_lr_on_plateau,
callback_remote_monitor,
callback_terminate_on_naan