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.
fit_generator(object, generator, steps_per_epoch, epochs = 1, verbose = 1,
callbacks = NULL, validation_data = NULL, validation_steps = NULL,
class_weight = NULL, max_queue_size = 10, initial_epoch = 0)Keras model object
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)
All arrays should contain the same number of samples. The generator is expected
to loop over its data indefinitely. An epoch finishes when steps_per_epoch
batches have been seen by the model.
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 unique samples if your
dataset divided by the batch size.
integer, total number of iterations on the data.
verbosity mode, 0, 1, or 2.
list of callbacks to be called during training.
this can be either:
a generator for the validation data
a list (inputs, targets)
a list (inputs, targets, sample_weights).
Only relevant if validation_data is a generator.
Total number of steps (batches of samples) to yield from generator before
stopping.
dictionary mapping class indices to a weight for the class.
maximum size for the generator queue
epoch at which to start training (useful for resuming a previous training run)
Training history object (invisibly)
Other model functions: compile,
evaluate_generator, evaluate,
fit, get_config,
get_layer,
keras_model_sequential,
keras_model, pop_layer,
predict.keras.engine.training.Model,
predict_generator,
predict_on_batch,
predict_proba,
summary.keras.engine.training.Model,
train_on_batch