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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,
**kwargs
)
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
When used in Model.evaluate
, 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.
Examples:
Basic usage:
tensorboard_callback = tf.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(tf.keras.Model):
def build(self, _):
self.dense = tf.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 = tf.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 = tf.keras.Input(10)
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Lambda(my_summary)(x)
model = tf.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 = tf.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 = tf.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 = tf.keras.callbacks.TensorBoard(
log_dir='./logs', profile_batch=(10,20))
model.fit(x_train, y_train, epochs=2, callbacks=[tensorboard_callback])
Methods
set_model
set_model(
model
)
Sets Keras model and writes graph if specified.
set_params
set_params(
params
)