inferno.trainers.callbacks.logging package

Submodules

inferno.trainers.callbacks.logging.base module

class inferno.trainers.callbacks.logging.base.Logger(log_directory=None)[source]

Bases: inferno.trainers.callbacks.base.Callback

A special callback for logging.

Loggers are special because they’re required to be serializable, whereas other callbacks have no such guarantees. In this regard, they jointly handled by trainers and the callback engine.

log_directory
set_log_directory(log_directory)[source]

inferno.trainers.callbacks.logging.tensorboard module

class inferno.trainers.callbacks.logging.tensorboard.TaggedImage(array, tag)[source]

Bases: object

class inferno.trainers.callbacks.logging.tensorboard.TensorboardLogger(log_directory=None, log_scalars_every=None, log_images_every=None, send_image_at_batch_indices='all', send_image_at_channel_indices='all', send_volume_at_z_indices='mid')[source]

Bases: inferno.trainers.callbacks.logging.base.Logger

Class to enable logging of training progress to Tensorboard.

Currently supports logging scalars and images.

end_of_training_iteration(**_)[source]
end_of_validation_run(**_)[source]
extract_images_from_batch(batch, base_tag=None, prefix=None)[source]
get_config()[source]
log_histogram(tag, values, step, bins=1000)[source]

Logs the histogram of a list/vector of values.

log_image_or_volume_batch(tag, batch, step=None)[source]
log_images(tag, images, step, image_format='CHW')[source]

Logs a list of images.

log_images_every
log_images_now
log_object(tag, object_, allow_scalar_logging=True, allow_image_logging=True)[source]
log_scalar(tag, value, step)[source]
tag : basestring
Name of the scalar

value step : int

training iteration
log_scalars_every
log_scalars_now
observe_state(key, observe_while='training')[source]
observe_states(keys, observe_while='training')[source]
observe_training_and_validation_state(key)[source]
observe_training_and_validation_states(keys)[source]
writer

Module contents

inferno.trainers.callbacks.logging.get_logger(name)[source]
class inferno.trainers.callbacks.logging.TensorboardLogger(log_directory=None, log_scalars_every=None, log_images_every=None, send_image_at_batch_indices='all', send_image_at_channel_indices='all', send_volume_at_z_indices='mid')[source]

Bases: inferno.trainers.callbacks.logging.base.Logger

Class to enable logging of training progress to Tensorboard.

Currently supports logging scalars and images.

end_of_training_iteration(**_)[source]
end_of_validation_run(**_)[source]
extract_images_from_batch(batch, base_tag=None, prefix=None)[source]
get_config()[source]
log_histogram(tag, values, step, bins=1000)[source]

Logs the histogram of a list/vector of values.

log_image_or_volume_batch(tag, batch, step=None)[source]
log_images(tag, images, step, image_format='CHW')[source]

Logs a list of images.

log_images_every
log_images_now
log_object(tag, object_, allow_scalar_logging=True, allow_image_logging=True)[source]
log_scalar(tag, value, step)[source]
tag : basestring
Name of the scalar

value step : int

training iteration
log_scalars_every
log_scalars_now
observe_state(key, observe_while='training')[source]
observe_states(keys, observe_while='training')[source]
observe_training_and_validation_state(key)[source]
observe_training_and_validation_states(keys)[source]
writer