tf.keras.callbacks.TensorBoard

Class TensorBoard

Inherits From: Callback

Defined in tensorflow/python/keras/callbacks.py.

Tensorboard basic visualizations.

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.

TensorBoard is a visualization tool provided with TensorFlow.

If you have installed TensorFlow with pip, you should be able to launch TensorBoard from the command line:

tensorboard --logdir=/full_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.

Raises:

  • ValueError: If histogram_freq is set and no validation data is provided.

Eager Compatibility

Using Tensorboard callback will work while eager execution is enabled, however outputting histogram summaries of weights and gradients is not supported, and thus histogram_freq will be ignored.

__init__

__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'
)

Initialize self. See help(type(self)) for accurate signature.

Methods

tf.keras.callbacks.TensorBoard.on_batch_begin

on_batch_begin(
    batch,
    logs=None
)

tf.keras.callbacks.TensorBoard.on_batch_end

on_batch_end(
    batch,
    logs=None
)

Writes scalar summaries for metrics on every training batch.

tf.keras.callbacks.TensorBoard.on_epoch_begin

on_epoch_begin(
    epoch,
    logs=None
)

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

tf.keras.callbacks.TensorBoard.on_epoch_end

on_epoch_end(
    epoch,
    logs=None
)

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

tf.keras.callbacks.TensorBoard.on_train_batch_begin

on_train_batch_begin(
    batch,
    logs=None
)

tf.keras.callbacks.TensorBoard.on_train_batch_end

on_train_batch_end(
    batch,
    logs=None
)

tf.keras.callbacks.TensorBoard.on_train_begin

on_train_begin(logs=None)

tf.keras.callbacks.TensorBoard.on_train_end

on_train_end(logs=None)

tf.keras.callbacks.TensorBoard.set_model

set_model(model)

Sets Keras model and creates summary ops.

tf.keras.callbacks.TensorBoard.set_params

set_params(params)