tf.keras.callbacks.TensorBoard

Enable visualizations for TensorBoard.

Inherits From: Callback

View aliases

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.keras.callbacks.TensorBoard

tf.keras.callbacks.TensorBoard(
    log_dir='./logs', histogram_freq=0, batch_size=32, write_graph=True,
    write_grads=False, write_images=False, embeddings_freq=0,
    embeddings_layer_names=None, embeddings_metadata=None, embeddings_data=None,
    update_freq='epoch', profile_batch=2
)

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

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.

Arguments
log_dir	the path of the directory where to save the log files to be parsed by TensorBoard.
histogram_freq	frequency (in epochs) at which to compute activation and 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	whether to visualize the graph in TensorBoard. The log file can become quite large when write_graph is set to True.
write_grads	whether to visualize gradient histograms in TensorBoard. histogram_freq must be greater than 0.
batch_size	size of batch of inputs to feed to the network for histograms computation.
write_images	whether to write model weights to visualize as image in TensorBoard.
embeddings_freq	frequency (in epochs) at which selected embedding layers will be saved. If set to 0, embeddings won't be computed. Data to be visualized in TensorBoard's Embedding tab must be passed as embeddings_data.
embeddings_layer_names	a list of names of layers to keep eye on. If None or empty list all the embedding layer will be watched.
embeddings_metadata	a dictionary which maps layer name to a file name in which metadata for this embedding layer is saved. See the details about metadata files format. In case if the same metadata file is used for all embedding layers, string can be passed.
embeddings_data	data to be embedded at layers specified in embeddings_layer_names. Numpy array (if the model has a single input) or list of Numpy arrays (if the model has multiple inputs). Learn more about embeddings
update_freq	'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 1000, the callback will write the metrics and losses to TensorBoard every 1000 samples. Note that writing too frequently to TensorBoard can slow down your training.
profile_batch	Profile the batch to sample compute characteristics. By default, it will profile the second batch. Set profile_batch=0 to disable profiling.

Raises
ValueError	If histogram_freq is set and no validation data is provided.

Eager Compatibility

Using the TensorBoard callback will work when eager execution is enabled, with the restriction that outputting histogram summaries of weights and gradients is not supported. Consequently, histogram_freq will be ignored.

Methods

on_batch_begin

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on_batch_begin(
    batch, logs=None
)

A backwards compatibility alias for on_train_batch_begin.

on_batch_end

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on_batch_end(
    batch, logs=None
)

Writes scalar summaries for metrics on every training batch.

Performs profiling if current batch is in profiler_batches.

on_epoch_begin

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on_epoch_begin(
    epoch, logs=None
)

Add histogram op to Model eval_function callbacks, reset batch count.

on_epoch_end

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on_epoch_end(
    epoch, logs=None
)

Checks if summary ops should run next epoch, logs scalar summaries.

on_predict_batch_begin

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on_predict_batch_begin(
    batch, logs=None
)

Called at the beginning of a batch in predict methods.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Has keys batch and size representing the current batch number and the size of the batch.

on_predict_batch_end

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on_predict_batch_end(
    batch, logs=None
)

Called at the end of a batch in predict methods.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Metric results for this batch.

on_predict_begin

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on_predict_begin(
    logs=None
)

Called at the beginning of prediction.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_predict_end

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on_predict_end(
    logs=None
)

Called at the end of prediction.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_test_batch_begin

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on_test_batch_begin(
    batch, logs=None
)

Called at the beginning of a batch in evaluate methods.

Also called at the beginning of a validation batch in the fit methods, if validation data is provided.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Has keys batch and size representing the current batch number and the size of the batch.

on_test_batch_end

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on_test_batch_end(
    batch, logs=None
)

Called at the end of a batch in evaluate methods.

Also called at the end of a validation batch in the fit methods, if validation data is provided.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Metric results for this batch.

on_test_begin

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on_test_begin(
    logs=None
)

Called at the beginning of evaluation or validation.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_test_end

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on_test_end(
    logs=None
)

Called at the end of evaluation or validation.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_train_batch_begin

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on_train_batch_begin(
    batch, logs=None
)

Called at the beginning of a training batch in fit methods.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Has keys batch and size representing the current batch number and the size of the batch.

on_train_batch_end

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on_train_batch_end(
    batch, logs=None
)

Called at the end of a training batch in fit methods.

Subclasses should override for any actions to run.

Arguments
batch	integer, index of batch within the current epoch.
logs	dict. Metric results for this batch.

on_train_begin

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on_train_begin(
    logs=None
)

Called at the beginning of training.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

on_train_end

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on_train_end(
    logs=None
)

Called at the end of training.

Subclasses should override for any actions to run.

Arguments
logs	dict. Currently no data is passed to this argument for this method but that may change in the future.

set_model

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set_model(
    model
)

Sets Keras model and creates summary ops.

set_params

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set_params(
    params
)