Callbacks

Plotter

pyjet.callbacks.Plotter(monitor, scale='linear', plot_during_train=True, save_to_file=None, block_on_end=True)

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MetricLogger

pyjet.callbacks.MetricLogger(log_fname)

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ReduceLROnPlateau

pyjet.callbacks.ReduceLROnPlateau(optimizer, monitor, monitor_val=True, factor=0.1, patience=10, verbose=0, mode='auto', epsilon=0.0001, cooldown=0, min_lr=0)

Reduce learning rate when a metric has stopped improving. Models often benefit from reducing the learning rate by a factor of 2-10 once learning stagnates. This callback monitors a quantity and if no improvement is seen for a 'patience' number of epochs, the learning rate is reduced. Example

reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.2,
                              patience=5, min_lr=0.001)
model.fit(X_train, Y_train, callbacks=[reduce_lr])

Arguments

optimizer: the pytorch optimizer to modify
monitor: quantity to be monitored.
monitor_val: whether or not to monitor the validation quantity.
factor: factor by which the learning rate will be reduced. new_lr = lr * factor
patience: number of epochs with no improvement after which learning rate will be reduced.
verbose: int. 0: quiet, 1: update messages.
mode: one of {auto, min, max}. In min mode, lr will be reduced when the quantity monitored has stopped decreasing; in max mode it will be reduced when the quantity monitored has stopped increasing; in auto mode, the direction is automatically inferred from the name of the monitored quantity.
epsilon: threshold for measuring the new optimum, to only focus on significant changes.
cooldown: number of epochs to wait before resuming normal operation after lr has been reduced.
min_lr: lower bound on the learning rate.

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LRScheduler

pyjet.callbacks.LRScheduler(optimizer, schedule, verbose=0)

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Callback

pyjet.callbacks.Callback()

Abstract base class used to build new callbacks. Properties

params: dict. Training parameters (eg. verbosity, batch size, number of epochs...).
model: instance of keras.models.Model. Reference of the model being trained. The logs dictionary that callback methods take as argument will contain keys for quantities relevant to the current batch or epoch. Currently, the .fit() method of the Sequential model class will include the following quantities in the logs that it passes to its callbacks:
on_epoch_end: logs include acc and loss, and optionally include val_loss (if validation is enabled in fit), and val_acc (if validation and accuracy monitoring are enabled).
on_batch_begin: logs include size, the number of samples in the current batch.
on_batch_end: logs include loss, and optionally acc (if accuracy monitoring is enabled).

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ModelCheckpoint

pyjet.callbacks.ModelCheckpoint(filepath, monitor, monitor_val=True, verbose=0, save_best_only=False, mode='auto', period=1)

Save the model after every epoch. filepath can contain named formatting options, which will be filled the value of epoch and keys in logs (passed in on_epoch_end). For example: if filepath is weights.{epoch:02d}-{val_loss:.2f}.hdf5, then the model checkpoints will be saved with the epoch number and the validation loss in the filename. Arguments

filepath: string, path to save the model file.
monitor: quantity to monitor.
monitor_val: whether or not to monitor the validation quantity.
verbose: verbosity mode, 0 or 1.
save_best_only: if save_best_only=True, the latest best model according to the quantity monitored will not be overwritten.
mode: one of {auto, min, max}. If save_best_only=True, the decision to overwrite the current save file is made based on either the maximization or the minimization of the monitored quantity. For val_acc, this should be max, for val_loss this should be min, etc. In auto mode, the direction is automatically inferred from the name of the monitored quantity.
save_weights_only: if True, then only the model's weights will be saved (model.save_weights(filepath)), else the full model is saved (model.save(filepath)).
period: Interval (number of epochs) between checkpoints.