trw.callbacks.callback_tensorboard_embedding

Module Contents

Classes

CallbackTensorboardEmbedding

This callback records the embedding to be displayed with tensorboard

Functions

get_as_image(images)	Return the images as (N, C, H, W) or None if not an image
keep_small_features(feature_name, feature_value)	Keep only the small features (e.g., len(shape) == 1) for the embedding infos
is_batch_vector(value, batch_size)	Return true if a vector like
add_classification_strings_from_output(dataset_name, split_name, output, datasets_infos, prefix='')	Special classification helper: add the class name (output and output_truth) as a string using the class

Attributes

logger

trw.callbacks.callback_tensorboard_embedding.logger

trw.callbacks.callback_tensorboard_embedding.get_as_image(images)

Return the images as (N, C, H, W) or None if not an image

TODO: smarter image detection!

Parameters

images – the object to check

Returns

None if not an image, or images with format (N, C, H, W)

trw.callbacks.callback_tensorboard_embedding.keep_small_features(feature_name, feature_value)

Keep only the small features (e.g., len(shape) == 1) for the embedding infos

Returns

if True, keep the feature else discard it

trw.callbacks.callback_tensorboard_embedding.is_batch_vector(value, batch_size)

Return true if a vector like :param value: the value to test :param batch_size: the expected size of the batch

trw.callbacks.callback_tensorboard_embedding.add_classification_strings_from_output(dataset_name, split_name, output, datasets_infos, prefix='')

Special classification helper: add the class name (output and output_truth) as a string using the class mapping contained in datasets_infos

Parameters

• dataset_name – the dataset name

• split_name – the split name

• output – the output

• datasets_infos – should contain the mapping

• prefix – the output and output_truth will be prefixed with prefix

Returns

the additional strings in a dictionary

class trw.callbacks.callback_tensorboard_embedding.CallbackTensorboardEmbedding(embedding_name, dataset_name=None, split_name=None, image_name=None, maximum_samples=2000, keep_features_fn=keep_small_features)

Bases: trw.callbacks.callback_tensorboard.CallbackTensorboardBased

This callback records the embedding to be displayed with tensorboard

Note: we must recalculate the embedding as we need to associate a specific input (i.e., we can’t store everything in memory so we need to collect what we need batch by batch)

first_time(self, datasets, options)

__call__(self, options, history, model, losses, outputs, datasets, datasets_infos, callbacks_per_batch, **kwargs)