dcase_util.keras.ProgressPlotterCallback
- class dcase_util.keras.ProgressPlotterCallback(epochs=None, manual_update=False, external_metric_labels=None, metric=None, loss=None, filename=None, plotting_rate=10, interactive=True, save=False, focus_span=10, **kwargs)[source]
Keras callback to plot progress during the training process and save final progress into figure. Implements Keras Callback API.
Constructor
- Parameters
- epochsint
Total amount of epochs Default value None
- metricstr
Metric name Default value None
- manual_updatebool
Manually update callback, use this to when injecting external metrics Default value False
- interactivebool
Show plot during the training and update with plotting rate Default value True
- plotting_rateint
Plot update rate in seconds Default value 10
- savebool
Save plot on disk, plotting rate applies Default value False
- filenamestr
Filename of figure Default value None
- focus_spanint
Epoch amount to highlight, and show separately in the plot. Default value 10
- __init__(epochs=None, manual_update=False, external_metric_labels=None, metric=None, loss=None, filename=None, plotting_rate=10, interactive=True, save=False, focus_span=10, **kwargs)[source]
Constructor
- Parameters
- epochsint
Total amount of epochs Default value None
- metricstr
Metric name Default value None
- manual_updatebool
Manually update callback, use this to when injecting external metrics Default value False
- interactivebool
Show plot during the training and update with plotting rate Default value True
- plotting_rateint
Plot update rate in seconds Default value 10
- savebool
Save plot on disk, plotting rate applies Default value False
- filenamestr
Filename of figure Default value None
- focus_spanint
Epoch amount to highlight, and show separately in the plot. Default value 10
Methods
__init__([epochs, manual_update, ...])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_log()Update progress to logging interface
Attributes
logger