dcase_framework.keras_utils.ProgressPlotterCallback

class dcase_framework.keras_utils.ProgressPlotterCallback(*args, **kwargs)

Keras callback to plot progress during the training process and save final progress into figure. Implements Keras Callback API.

Constructor

Parameters:

epochs : int

Total amount of epochs

metric : str

Metric name

manual_update : bool

Manually update callback, use this to when injecting external metrics Default value True

interactive : bool

Show plot during the training and update with plotting rate Default value True

plotting_rate : int

Plot update rate in seconds Default value 10

save : bool

Save plot on disk, plotting rate applies

filename : str

Filename of figure Default value 1

focus_span : int

Epoch amount to highlight, and show separately in the plot. Default value 10

__init__(*args, **kwargs)

Constructor

Parameters:

epochs : int

Total amount of epochs

metric : str

Metric name

manual_update : bool

Manually update callback, use this to when injecting external metrics Default value True

interactive : bool

Show plot during the training and update with plotting rate Default value True

plotting_rate : int

Plot update rate in seconds Default value 10

save : bool

Save plot on disk, plotting rate applies

filename : str

Filename of figure Default value 1

focus_span : int

Epoch amount to highlight, and show separately in the plot. Default value 10

Methods

__init__(\*args, \*\*kwargs)	Constructor
add_external_metric(metric_label)	Add external metric to be monitored
close()	Manually close progress logging
draw()	Draw plot
get_operator(metric)
on_batch_begin(batch[, logs])
on_batch_end(batch[, logs])
on_epoch_begin(epoch[, logs])
on_epoch_end(epoch[, logs])
on_train_begin([logs])
on_train_end([logs])
set_external_metric_value(metric_label, ...)	Add external metric value
set_model(model)
set_params(params)
update()	Update
update_progress_bar([increase])	Update progress to tqdm progress bar
update_progress_log()	Update progress to logging interface