Usage of callbacks
A callback is a set of functions to be applied at given stages of the training procedure. You can use callbacks to get a view on internal states and statistics of the model during training. You can pass a list of callbacks (as the keyword argument callbacks) to the .fit() method of the Sequential or Model classes. The relevant methods of the callbacks will then be called at each stage of the training.
Callback
keras.callbacks.Callback()
Abstract base class used to build new callbacks.
Properties
- params: dict. Training parameters (eg. verbosity, batch size, number of epochs...).
- model: instance of
keras.models.Model. Reference of the model being trained.
The logs dictionary that callback methods
take as argument will contain keys for quantities relevant to
the current batch or epoch.
Currently, the .fit() method of the Sequential model class
will include the following quantities in the logs that
it passes to its callbacks:
on_epoch_end: logs include acc and loss, and
optionally include val_loss
(if validation is enabled in fit), and val_acc
(if validation and accuracy monitoring are enabled).
on_batch_begin: logs include size,
the number of samples in the current batch.
on_batch_end: logs include loss, and optionally acc
(if accuracy monitoring is enabled).
BaseLogger
keras.callbacks.BaseLogger(stateful_metrics=None)
Callback that accumulates epoch averages of metrics.
This callback is automatically applied to every Keras model.
Arguments
stateful_metrics: Iterable of string names of metrics that
should not be averaged over an epoch.
Metrics in this list will be logged as-is in on_epoch_end.
All others will be averaged in on_epoch_end.
TerminateOnNaN
keras.callbacks.TerminateOnNaN()
Callback that terminates training when a NaN loss is encountered.
ProgbarLogger
keras.callbacks.ProgbarLogger(count_mode='samples', stateful_metrics=None)
Callback that prints metrics to stdout.
Arguments
- count_mode: One of "steps" or "samples". Whether the progress bar should count samples seen or steps (batches) seen.
- stateful_metrics: Iterable of string names of metrics that should not be averaged over an epoch. Metrics in this list will be logged as-is. All others will be averaged over time (e.g. loss, etc).
Raises
ValueError: In case of invalid count_mode.
History
keras.callbacks.History()
Callback that records events into a History object.
This callback is automatically applied to
every Keras model. The History object
gets returned by the fit method of models.
ModelCheckpoint
keras.callbacks.ModelCheckpoint(filepath, monitor='val_loss', verbose=0, save_best_only=False, save_weights_only=False, mode='auto', period=1)
Save the model after every epoch.
filepath can contain named formatting options,
which will be filled the value of epoch and
keys in logs (passed in on_epoch_end).
For example: if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5,
then the model checkpoints will be saved with the epoch number and
the validation loss in the filename.
Arguments
filepath: string, path to save the model file.
monitor: quantity to monitor.
verbose: verbosity mode, 0 or 1.
save_best_only: if save_best_only=True,
the latest best model according to
the quantity monitored will not be overwritten.
mode: one of {auto, min, max}.
If save_best_only=True, the decision
to overwrite the current save file is made
based on either the maximization or the
minimization of the monitored quantity. For val_acc,
this should be max, for val_loss this should
be min, etc. In auto mode, the direction is
automatically inferred from the name of the monitored quantity.
save_weights_only: if True, then only the model's weights will be
saved (model.save_weights(filepath)), else the full model
is saved (model.save(filepath)).
period: Interval (number of epochs) between checkpoints.
EarlyStopping
keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=0, patience=0, verbose=0, mode='auto', baseline=None, restore_best_weights=False)
Stop training when a monitored quantity has stopped improving.
Arguments
monitor: quantity to be monitored.
min_delta: minimum change in the monitored quantity
to qualify as an improvement, i.e. an absolute
change of less than min_delta, will count as no
improvement.
patience: number of epochs with no improvement
after which training will be stopped.
verbose: verbosity mode.
mode: one of {auto, min, max}. In min mode,
training will stop when the quantity
monitored has stopped decreasing; in max
mode it will stop when the quantity
monitored has stopped increasing; in auto
mode, the direction is automatically inferred
from the name of the monitored quantity.
baseline: Baseline value for the monitored quantity to reach.
Training will stop if the model doesn't show improvement
over the baseline.
restore_best_weights: whether to restore model weights from
the epoch with the best value of the monitored quantity.
If False, the model weights obtained at the last step of
training are used.
RemoteMonitor
keras.callbacks.RemoteMonitor(root='http://localhost:9000', path='/publish/epoch/end/', field='data', headers=None, send_as_json=False)
Callback used to stream events to a server.
Requires the requests library.
Events are sent to root + '/publish/epoch/end/' by default. Calls are
HTTP POST, with a data argument which is a
JSON-encoded dictionary of event data.
If send_as_json is set to True, the content type of the request will be
application/json. Otherwise the serialized JSON will be send within a form
Arguments
root: String; root url of the target server.
path: String; path relative to root to which the events will be sent.
field: String; JSON field under which the data will be stored.
The field is used only if the payload is sent within a form
(i.e. send_as_json is set to False).
headers: Dictionary; optional custom HTTP headers.
send_as_json: Boolean; whether the request should be send as
application/json.
LearningRateScheduler
keras.callbacks.LearningRateScheduler(schedule, verbose=0)
Learning rate scheduler.
Arguments
schedule: a function that takes an epoch index as input (integer, indexed from 0) and current learning rate and returns a new learning rate as output (float). verbose: int. 0: quiet, 1: update messages.
TensorBoard
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')
TensorBoard basic visualizations.
TensorBoard is a visualization tool provided with TensorFlow.
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.
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
When using a backend other than TensorFlow, TensorBoard will still work (if you have TensorFlow installed), but the only feature available will be the display of the losses and metrics plots.
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 10000,
the callback will write the metrics and losses to TensorBoard every
10000 samples. Note that writing too frequently to TensorBoard
can slow down your training.
ReduceLROnPlateau
keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=10, verbose=0, mode='auto', min_delta=0.0001, cooldown=0, min_lr=0)
Reduce learning rate when a metric has stopped improving.
Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced.
Example
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
patience=5, min_lr=0.001)
model.fit(X_train, Y_train, callbacks=[reduce_lr])
Arguments
monitor: quantity to be monitored.
factor: factor by which the learning rate will
be reduced. new_lr = lr * factor
patience: number of epochs with no improvement
after which learning rate will be reduced.
verbose: int. 0: quiet, 1: update messages.
mode: one of {auto, min, max}. In min mode,
lr will be reduced when the quantity
monitored has stopped decreasing; in max
mode it will be reduced when the quantity
monitored has stopped increasing; in auto
mode, the direction is automatically inferred
from the name of the monitored quantity.
min_delta: threshold for measuring the new optimum,
to only focus on significant changes.
cooldown: number of epochs to wait before resuming
normal operation after lr has been reduced.
min_lr: lower bound on the learning rate.
CSVLogger
keras.callbacks.CSVLogger(filename, separator=',', append=False)
Callback that streams epoch results to a csv file.
Supports all values that can be represented as a string, including 1D iterables such as np.ndarray.
Example
csv_logger = CSVLogger('training.log')
model.fit(X_train, Y_train, callbacks=[csv_logger])
Arguments
filename: filename of the csv file, e.g. 'run/log.csv'. separator: string used to separate elements in the csv file. append: True: append if file exists (useful for continuing training). False: overwrite existing file,
LambdaCallback
keras.callbacks.LambdaCallback(on_epoch_begin=None, on_epoch_end=None, on_batch_begin=None, on_batch_end=None, on_train_begin=None, on_train_end=None)
Callback for creating simple, custom callbacks on-the-fly.
This callback is constructed with anonymous functions that will be called at the appropriate time. Note that the callbacks expects positional arguments, as:
on_epoch_beginandon_epoch_endexpect two positional arguments:epoch,logson_batch_beginandon_batch_endexpect two positional arguments:batch,logson_train_beginandon_train_endexpect one positional argument:logs
Arguments
- on_epoch_begin: called at the beginning of every epoch.
- on_epoch_end: called at the end of every epoch.
- on_batch_begin: called at the beginning of every batch.
- on_batch_end: called at the end of every batch.
- on_train_begin: called at the beginning of model training.
- on_train_end: called at the end of model training.
Example
# Print the batch number at the beginning of every batch.
batch_print_callback = LambdaCallback(
on_batch_begin=lambda batch,logs: print(batch))
# Stream the epoch loss to a file in JSON format. The file content
# is not well-formed JSON but rather has a JSON object per line.
import json
json_log = open('loss_log.json', mode='wt', buffering=1)
json_logging_callback = LambdaCallback(
on_epoch_end=lambda epoch, logs: json_log.write(
json.dumps({'epoch': epoch, 'loss': logs['loss']}) + '\n'),
on_train_end=lambda logs: json_log.close()
)
# Terminate some processes after having finished model training.
processes = ...
cleanup_callback = LambdaCallback(
on_train_end=lambda logs: [
p.terminate() for p in processes if p.is_alive()])
model.fit(...,
callbacks=[batch_print_callback,
json_logging_callback,
cleanup_callback])
Create a callback
You can create a custom callback by extending the base class keras.callbacks.Callback. A callback has access to its associated model through the class property self.model.
Here's a simple example saving a list of losses over each batch during training:
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.losses = []
def on_batch_end(self, batch, logs={}):
self.losses.append(logs.get('loss'))
Example: recording loss history
class LossHistory(keras.callbacks.Callback):
def on_train_begin(self, logs={}):
self.losses = []
def on_batch_end(self, batch, logs={}):
self.losses.append(logs.get('loss'))
model = Sequential()
model.add(Dense(10, input_dim=784, kernel_initializer='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
history = LossHistory()
model.fit(x_train, y_train, batch_size=128, epochs=20, verbose=0, callbacks=[history])
print(history.losses)
# outputs
'''
[0.66047596406559383, 0.3547245744908703, ..., 0.25953155204159617, 0.25901699725311789]
'''
Example: model checkpoints
from keras.callbacks import ModelCheckpoint
model = Sequential()
model.add(Dense(10, input_dim=784, kernel_initializer='uniform'))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='rmsprop')
'''
saves the model weights after each epoch if the validation loss decreased
'''
checkpointer = ModelCheckpoint(filepath='/tmp/weights.hdf5', verbose=1, save_best_only=True)
model.fit(x_train, y_train, batch_size=128, epochs=20, verbose=0, validation_data=(X_test, Y_test), callbacks=[checkpointer])