## 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.

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### Callback

    keras.callbacks.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).

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### BaseLogger

    keras.callbacks.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`.

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### TerminateOnNaN

    keras.callbacks.callbacks.TerminateOnNaN()

Callback that terminates training when a NaN loss is encountered.

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### ProgbarLogger

    keras.callbacks.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`.

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### History

    keras.callbacks.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.

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### ModelCheckpoint

    keras.callbacks.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 with the values 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.
- 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)`).
- 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.
- period: Interval (number of epochs) between checkpoints.

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### EarlyStopping

    keras.callbacks.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 that produced the monitored quantity with no improvement after which training will be stopped. Validation quantities may not be produced for every epoch, if the validation frequency (`model.fit(validation_freq=5)`) is greater than one.
- 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.

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### RemoteMonitor

    keras.callbacks.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.

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### LearningRateScheduler

    keras.callbacks.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.

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### ReduceLROnPlateau

    keras.callbacks.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 that produced the monitored quantity with no improvement after which training will be stopped. Validation quantities may not be produced for every epoch, if the validation frequency (`model.fit(validation_freq=5)`) is greater than one.
- 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.

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### CSVLogger

    keras.callbacks.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,

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### LambdaCallback

    keras.callbacks.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_begin` and `on_epoch_end` expect two positional arguments: `epoch`, `logs`
- `on_batch_begin` and `on_batch_end` expect two positional arguments: `batch`, `logs`
- `on_train_begin` and `on_train_end` expect 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])

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### TensorBoard

    keras.callbacks.tensorboard_v1.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.
- batch_size: size of batch of inputs to feed to the network for histograms computation.
- 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.
- 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.

# 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])

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