callback_tensorboard {keras3} | R Documentation |
Enable visualizations for TensorBoard.
Description
TensorBoard is a visualization tool provided with TensorFlow. A TensorFlow installation is required to use this callback.
This callback logs events for TensorBoard, including:
Metrics summary plots
Training graph visualization
Weight histograms
Sampled profiling
When used in model |> evaluate()
or regular validation
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
or reticulate::py_install()
, you should be able
to launch TensorBoard from the command line:
tensorboard --logdir=path_to_your_logs
or from R with tensorflow::tensorboard()
.
You can find more information about TensorBoard here.
Usage
callback_tensorboard(
log_dir = "logs",
histogram_freq = 0L,
write_graph = TRUE,
write_images = FALSE,
write_steps_per_second = FALSE,
update_freq = "epoch",
profile_batch = 0L,
embeddings_freq = 0L,
embeddings_metadata = NULL
)
Arguments
log_dir |
the path of the directory where to save the log files to be
parsed by TensorBoard. e.g.,
|
histogram_freq |
frequency (in epochs) at which to compute weight histograms for the layers of the model. If set to 0, histograms won't be computed. Validation data (or split) must be specified for histogram visualizations. |
write_graph |
(Not supported at this time)
Whether to visualize the graph in TensorBoard.
Note that the log file can become quite large
when |
write_images |
whether to write model weights to visualize as image in TensorBoard. |
write_steps_per_second |
whether to log the training steps per second into TensorBoard. This supports both epoch and batch frequency logging. |
update_freq |
|
profile_batch |
(Not supported at this time) Profile the batch(es) to sample compute characteristics. profile_batch must be a non-negative integer or a tuple of integers. A pair of positive integers signify a range of batches to profile. By default, profiling is disabled. |
embeddings_freq |
frequency (in epochs) at which embedding layers will be visualized. If set to 0, embeddings won't be visualized. |
embeddings_metadata |
Named list which maps embedding layer names to the filename of a file in which to save metadata for the embedding layer. In case the same metadata file is to be used for all embedding layers, a single filename can be passed. |
Value
A Callback
instance that can be passed to fit.keras.src.models.model.Model()
.
Examples
tensorboard_callback <- callback_tensorboard(log_dir = "./logs") model %>% fit(x_train, y_train, epochs = 2, callbacks = list(tensorboard_callback)) # Then run the tensorboard command to view the visualizations.
Custom batch-level summaries in a subclassed Model:
MyModel <- new_model_class("MyModel", initialize = function() { self$dense <- layer_dense(units = 10) }, call = function(x) { outputs <- x |> self$dense() tf$summary$histogram('outputs', outputs) outputs } ) model <- MyModel() model |> compile(optimizer = 'sgd', loss = 'mse') # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf$summary` contained in `model$call()`, # metrics added in `model |>compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback))
Custom batch-level summaries in a Functional API Model:
my_summary <- function(x) { tf$summary$histogram('x', x) x } inputs <- layer_input(10) outputs <- inputs |> layer_dense(10) |> layer_lambda(my_summary) model <- keras_model(inputs, outputs) model |> compile(optimizer = 'sgd', loss = 'mse') # Make sure to set `update_freq = N` to log a batch-level summary every N # batches. In addition to any `tf.summary` contained in `Model.call`, # metrics added in `Model.compile` will be logged every N batches. tb_callback <- callback_tensorboard(log_dir = './logs', update_freq = 1) model |> fit(x_train, y_train, callbacks = list(tb_callback))
Profiling:
# Profile a single batch, e.g. the 5th batch. tensorboard_callback <- callback_tensorboard( log_dir = './logs', profile_batch = 5) model |> fit(x_train, y_train, epochs = 2, callbacks = list(tensorboard_callback)) # Profile a range of batches, e.g. from 10 to 20. tensorboard_callback <- callback_tensorboard( log_dir = './logs', profile_batch = c(10, 20)) model |> fit(x_train, y_train, epochs = 2, callbacks = list(tensorboard_callback))
See Also
Other callbacks:
Callback()
callback_backup_and_restore()
callback_csv_logger()
callback_early_stopping()
callback_lambda()
callback_learning_rate_scheduler()
callback_model_checkpoint()
callback_reduce_lr_on_plateau()
callback_remote_monitor()
callback_swap_ema_weights()
callback_terminate_on_nan()