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, view_metrics = getOption("keras.view_metrics", default = "auto"), validation_data = NULL, validation_steps = NULL, class_weight = NULL, max_queue_size = 10, initial_epoch = 0)
Keras model object
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
Total number of steps (batches of samples) to yield
integer, total number of iterations on the data.
Verbosity mode (0 = silent, 1 = verbose, 2 = one log line per epoch).
list of callbacks to be called during training.
View realtime plot of training metrics (by epoch). The
this can be either:
Only relevant if
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: