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tf.keras.callbacks.TensorBoard

TensorFlow 2.0 version View source on GitHub

Class TensorBoard

Enable visualizations for TensorBoard.

Inherits From: Callback

Aliases:

  • Class tf.compat.v1.keras.callbacks.TensorBoard

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.

__init__

View source

__init__(
    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
)

Methods

on_batch_begin

View source

on_batch_begin(
    batch,
    logs=None
)

A backwards compatibility alias for on_train_batch_begin.

on_batch_end

View source

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

View source

on_epoch_begin(
    epoch,
    logs=None
)

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

on_epoch_end

View source

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

View source

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

View source

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

View source

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

View source

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

View source

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

View source

on_train_begin(logs=None)

on_train_end

View source

on_train_end(logs=None)

set_model

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set_model(model)

Sets Keras model and creates summary ops.

set_params

View source

set_params(params)