tf.keras.callbacks.TensorBoard

Enable visualizations for TensorBoard.

Inherits From: Callback

tf.keras.callbacks.TensorBoard(
    log_dir='logs',
    histogram_freq=0,
    write_graph=True,
    write_images=False,
    write_steps_per_second=False,
    update_freq='epoch',
    profile_batch=0,
    embeddings_freq=0,
    embeddings_metadata=None
)

Used in the notebooks

Used in the guide Used in the tutorials

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, 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. e.g., log_dir = os.path.join(working_dir, 'logs'). This directory should not be reused by any other callbacks.
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_graph is set to True.
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 "batch" or "epoch" or integer. When using "epoch", writes the losses and metrics to TensorBoard after every epoch. If using an integer, let's say 1000, all metrics and losses (including custom ones added by Model.compile) will be logged to TensorBoard every 1000 batches. "batch" is a synonym for 1, meaning that they will be written every batch. Note however that writing too frequently to TensorBoard can slow down your training, especially when used with distribution strategies as it will incur additional synchronization overhead. Batch-level summary writing is also available via train_step override. Please see TensorBoard Scalars tutorial # noqa: E501 for more details.
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 Dictionary 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.

Examples:

tensorboard_callback = keras.callbacks.TensorBoard(log_dir="./logs")
model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
# Then run the tensorboard command to view the visualizations.

Custom batch-level summaries in a subclassed Model:

class MyModel(keras.Model):

    def build(self, _):
        self.dense = keras.layers.Dense(10)

    def call(self, x):
        outputs = self.dense(x)
        tf.summary.histogram('outputs', outputs)
        return outputs

model = MyModel()
model.compile('sgd', '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 = keras.callbacks.TensorBoard('./logs', update_freq=1)
model.fit(x_train, y_train, callbacks=[tb_callback])

Custom batch-level summaries in a Functional API Model:

def my_summary(x):
    tf.summary.histogram('x', x)
    return x

inputs = keras.Input(10)
x = keras.layers.Dense(10)(inputs)
outputs = keras.layers.Lambda(my_summary)(x)
model = keras.Model(inputs, outputs)
model.compile('sgd', '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 = keras.callbacks.TensorBoard('./logs', update_freq=1)
model.fit(x_train, y_train, callbacks=[tb_callback])

Profiling:

# Profile a single batch, e.g. the 5th batch.
tensorboard_callback = keras.callbacks.TensorBoard(
    log_dir='./logs', profile_batch=5)
model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])

# Profile a range of batches, e.g. from 10 to 20.
tensorboard_callback = keras.callbacks.TensorBoard(
    log_dir='./logs', profile_batch=(10,20))
model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])

model

summary

Methods

on_batch_begin

View source

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
)

A backwards compatibility alias for on_train_batch_end.

on_epoch_begin

View source

on_epoch_begin(
    epoch, logs=None
)

Called at the start of an epoch.

Subclasses should override for any actions to run. This function should only be called during TRAIN mode.

Args
epoch Integer, index of epoch.
logs Dict. Currently no data is passed to this argument for this method but that may change in the future.

on_epoch_end

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on_epoch_end(
    epoch, logs=None
)

Runs metrics and histogram summaries at epoch end.

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.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Currently no data is passed to this argument for this method but that may change in the future.

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.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Aggregated metric results up until 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.

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

on_predict_end

View source

on_predict_end(
    logs=None
)

Called at the end of prediction.

Subclasses should override for any actions to run.

Args
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.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Currently no data is passed to this argument for this method but that may change in the future.

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.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Aggregated metric results up until 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.

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

on_test_end

View source

on_test_end(
    logs=None
)

Called at the end of evaluation or validation.

Subclasses should override for any actions to run.

Args
logs Dict. Currently the output of the last call to on_test_batch_end() 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.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Currently no data is passed to this argument for this method but that may change in the future.

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.

Note that if the steps_per_execution argument to compile in Model is set to N, this method will only be called every N batches.

Args
batch Integer, index of batch within the current epoch.
logs Dict. Aggregated metric results up until this batch.

on_train_begin

View source

on_train_begin(
    logs=None
)

Called at the beginning of training.

Subclasses should override for any actions to run.

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

on_train_end

View source

on_train_end(
    logs=None
)

Called at the end of training.

Subclasses should override for any actions to run.

Args
logs Dict. Currently the output of the last call to on_epoch_end() 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 writes graph if specified.

set_params

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set_params(
    params
)