tf.keras.callbacks.ReduceLROnPlateau

Enable visualizations for TensorBoard.

Inherits From: Callback

tf.keras.callbacks.TensorBoard(
    log_dir='logs', histogram_freq=0, write_graph=True,
    write_images=False, update_freq='epoch', profile_batch=2,
    embeddings_freq=0, embeddings_metadata=None, **kwargs
)

Used in the notebooks

Used in the guide	Used in the tutorials
Training and evaluation with the built-in methods Training Keras models with TensorFlow Cloud	Distributed training with Keras Overfit and underfit Word2Vec Word embeddings TensorBoard Scalars: Logging training metrics in Keras

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_images`	whether to write model weights to visualize as image in TensorBoard.
`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 batches. Note that writing too frequently to TensorBoard can slow down your training.
`profile_batch`	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, it will profile the second batch. Set profile_batch=0 to disable profiling.
`embeddings_freq`	frequency (in epochs) at which embedding layers will be visualized. If set to 0, embeddings won't be visualized.
`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.

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`

View source

set_model(
    model
)

Sets Keras model and writes graph if specified.

`set_params`

View source

set_params(
    params
)

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tf.keras / callbacks / callbacks.ReduceLROnPlateau