# fit_generator

0th

Percentile

##### Fits the model on data yielded batch-by-batch by a generator.

The generator is run in parallel to the model, for efficiency. For instance, this allows you to do real-time data augmentation on images on CPU in parallel to training your model on GPU.

##### Usage
fit_generator(object, generator, steps_per_epoch, epochs = 1,
verbose = getOption("keras.fit_verbose", default = 1), callbacks = NULL,
view_metrics = getOption("keras.view_metrics", default = "auto"),
validation_data = NULL, validation_steps = NULL, class_weight = NULL,
max_queue_size = 10, workers = 1, initial_epoch = 0)
##### Arguments
object

Keras model object

generator

A generator (e.g. like the one provided by flow_images_from_directory() or a custom R generator function).

The output of the generator must be a list of one of these forms:

 - (inputs, targets)
- (inputs, targets, sample_weights)


This list (a single output of the generator) makes a single batch. Therefore, all arrays in this list must have the same length (equal to the size of this batch). Different batches may have different sizes. For example, the last batch of the epoch is commonly smaller than the others, if the size of the dataset is not divisible by the batch size. The generator is expected to loop over its data indefinitely. An epoch finishes when steps_per_epoch batches have been seen by the model.

steps_per_epoch

Total number of steps (batches of samples) to yield from generator before declaring one epoch finished and starting the next epoch. It should typically be equal to the number of samples if your dataset divided by the batch size.

epochs

Integer. Number of epochs to train the model. An epoch is an iteration over the entire data provided, as defined by steps_per_epoch. Note that in conjunction with initial_epoch, epochs is to be understood as "final epoch". The model is not trained for a number of iterations given by epochs, but merely until the epoch of index epochs is reached.

verbose

Verbosity mode (0 = silent, 1 = progress bar, 2 = one line per epoch).

callbacks

List of callbacks to apply during training.

view_metrics

View realtime plot of training metrics (by epoch). The default ("auto") will display the plot when running within RStudio, metrics were specified during model compile(), epochs > 1 and verbose > 0. Use the global keras.view_metrics option to establish a different default.

validation_data

this can be either:

• a generator for the validation data

• a list (inputs, targets)

• a list (inputs, targets, sample_weights). on which to evaluate the loss and any model metrics at the end of each epoch. The model will not be trained on this data.

validation_steps

Only relevant if validation_data is a generator. Total number of steps (batches of samples) to yield from generator before stopping at the end of every epoch. It should typically be equal to the number of samples of your validation dataset divided by the batch size.

class_weight

Optional named list mapping class indices (integer) to a weight (float) value, used for weighting the loss function (during training only). This can be useful to tell the model to "pay more attention" to samples from an under-represented class.

max_queue_size

Maximum size for the generator queue. If unspecified, max_queue_size will default to 10.

workers

Maximum number of threads to use for parallel processing. Note that parallel processing will only be performed for native Keras generators (e.g. flow_images_from_directory()) as R based generators must run on the main thread.

initial_epoch

epoch at which to start training (useful for resuming a previous training run)

##### Value

Training history object (invisibly)

Other model functions: compile, evaluate.keras.engine.training.Model, evaluate_generator, fit, get_config, get_layer, keras_model_sequential, keras_model, multi_gpu_model, pop_layer, predict.keras.engine.training.Model, predict_generator, predict_on_batch, predict_proba, summary.keras.engine.training.Model, train_on_batch