Overview

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() function. The relevant methods of the callbacks will then be called at each stage of the training.

For example:

library(keras)

# generate dummy training data
data <- matrix(rexp(1000*784), nrow = 1000, ncol = 784)
labels <- matrix(round(runif(1000*10, min = 0, max = 9)), nrow = 1000, ncol = 10)

# create model
model <- keras_model_sequential() 

# add layers and compile
model %>%
  layer_dense(32, input_shape = c(784)) %>%
  layer_activation('relu') %>%
  layer_dense(10) %>%
  layer_activation('softmax') %>% 
  compile(
    loss='binary_crossentropy',
    optimizer = optimizer_sgd(),
    metrics='accuracy'
  )
  
# fit with callbacks
model %>% fit(data, labels, callbacks = list(
  callback_model_checkpoint("checkpoints.h5"),
  callback_reduce_lr_on_plateau(monitor = "val_loss", factor = 0.1)
))

Built in Callbacks

The following built-in callbacks are available as part of Keras:

callback_progbar_logger()

Callback that prints metrics to stdout.

callback_model_checkpoint()

Save the model after every epoch.

callback_early_stopping()

Stop training when a monitored quantity has stopped improving.

callback_remote_monitor()

Callback used to stream events to a server.

callback_learning_rate_scheduler()

Learning rate scheduler.

callback_tensorboard()

TensorBoard basic visualizations

callback_reduce_lr_on_plateau()

Reduce learning rate when a metric has stopped improving.

callback_csv_logger()

Callback that streams epoch results to a csv file

callback_lambda()

Create a custom callback

Custom Callbacks

You can create a custom callback by creating a new R6 class that inherits from the KerasCallback class.

Here’s a simple example saving a list of losses over each batch during training:

library(keras)

# define custom callback class
LossHistory <- R6::R6Class("LossHistory",
  inherit = KerasCallback,
  
  public = list(
    
    losses = NULL,
     
    on_batch_end = function(batch, logs = list()) {
      self$losses <- c(self$losses, logs[["loss"]])
    }
))

# define model
model <- keras_model_sequential() 

# add layers and compile
model %>% 
  layer_dense(units = 10, input_shape = c(784)) %>% 
  layer_activation(activation = 'softmax') %>% 
  compile(
    loss = 'categorical_crossentropy', 
    optimizer = 'rmsprop'
  )

# create history callback object and use it during training
history <- LossHistory$new()
model %>% fit(
  X_train, Y_train,
  batch_size=128, epochs=20, verbose=0,
  callbacks= list(history)
)

# print the accumulated losses
history$losses
[1] 0.6604760 0.3547246 0.2595316 0.2590170 ...

Fields

Custom callback objects have access to the current model and it’s training parameters via the following fields:

self$params

Named list with training parameters (eg. verbosity, batch size, number of epochs…).

self$model

Reference to the Keras model being trained.

Methods

Custom callback objects can implement one or more of the following methods:

on_epoch_begin(epoch, logs)

Called at the beginning of each epoch.

on_epoch_end(epoch, logs)

Called at the end of each epoch.

on_batch_begin(batch, logs)

Called at the beginning of each batch.

on_batch_end(batch, logs)

Called at the end of each batch.

on_train_begin(logs)

Called at the beginning of training.

on_train_end(logs)

Called at the end of training.