tf.compat.v1.keras.callbacks.TensorBoard

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Enable visualizations for TensorBoard.

Inherits From: TensorBoard

tf.compat.v1.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.

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. Here are 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 in this guide.
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.

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

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
)