Python API¶

Estimators¶

`bob.learn.tensorflow.estimators.Logits`(…)	Logits estimator.
`bob.learn.tensorflow.estimators.LogitsCenterLoss`([…])	Logits estimator with center loss.
`bob.learn.tensorflow.estimators.Triplet`([…])	NN estimator for Triplet networks.
`bob.learn.tensorflow.estimators.Siamese`([…])	NN estimator for Siamese Networks.
`bob.learn.tensorflow.estimators.Regressor`(…)	An estimator for regression problems
`bob.learn.tensorflow.estimators.MovingAverageOptimizer`(…)	Creates a callable that can be given to bob.learn.tensorflow.estimators
`bob.learn.tensorflow.estimators.learning_rate_decay_fn`(…)	A simple learning_rate_decay_fn.

Architectures¶

`bob.learn.tensorflow.network.chopra`(inputs)	Class that creates the architecture presented in the paper:
`bob.learn.tensorflow.network.light_cnn9`(inputs)	Creates the graph for the Light CNN-9 in
`bob.learn.tensorflow.network.dummy`(inputs[, …])	Create all the necessary variables for this CNN
`bob.learn.tensorflow.network.mlp`(inputs, …)	An MLP is a representation of a Multi-Layer Perceptron.
`bob.learn.tensorflow.network.mlp_with_batchnorm_and_dropout`(…)
`bob.learn.tensorflow.network.inception_resnet_v2`(inputs)	Creates the Inception Resnet V2 model.
`bob.learn.tensorflow.network.inception_resnet_v1`(inputs)	Creates the Inception Resnet V1 model.
`bob.learn.tensorflow.network.inception_resnet_v2_batch_norm`(inputs)	Creates the Inception Resnet V2 model applying batch not to each Convolutional and FullyConnected layer.
`bob.learn.tensorflow.network.inception_resnet_v1_batch_norm`(inputs)	Creates the Inception Resnet V1 model applying batch not to each Convolutional and FullyConnected layer.
`bob.learn.tensorflow.network.SimpleCNN.slim_architecture`(…)
`bob.learn.tensorflow.network.vgg_19`(inputs)	Oxford Net VGG 19-Layers version E Example from tf-slim
`bob.learn.tensorflow.network.vgg_16`(inputs)	Oxford Net VGG 16-Layers version E Example from tf-slim

Data¶

`bob.learn.tensorflow.dataset.bio.BioGenerator`(…)	A generator class which wraps bob.bio.base databases so that they can be used with tf.data.Dataset.from_generator
`bob.learn.tensorflow.dataset.image.shuffle_data_and_labels_image_augmentation`(…)	Dump random batches from a list of image paths and labels:
`bob.learn.tensorflow.dataset.siamese_image.shuffle_data_and_labels_image_augmentation`(…)	Dump random batches for siamese networks from a list of image paths and labels:
`bob.learn.tensorflow.dataset.triplet_image.shuffle_data_and_labels_image_augmentation`(…)	Dump random batches for triplee networks from a list of image paths and labels:
`bob.learn.tensorflow.dataset.tfrecords.shuffle_data_and_labels_image_augmentation`(…)	Dump random batches from a list of tf-record files and applies some image augmentation
`bob.learn.tensorflow.dataset.tfrecords.shuffle_data_and_labels`(…)	Dump random batches from a list of tf-record files
`bob.learn.tensorflow.dataset.generator.dataset_using_generator`(…)	A generator class which wraps samples so that they can be used with tf.data.Dataset.from_generator
`bob.learn.tensorflow.utils.util.to_channels_last`(image)	Converts the image to channel_last format.
`bob.learn.tensorflow.utils.util.to_channels_first`(image)	Converts the image to channel_first format.

Style Transfer¶

bob.learn.tensorflow.style_transfer.do_style_transfer(…) Trains neural style transfer using the approach presented in:

Losses¶

`bob.learn.tensorflow.loss.mean_cross_entropy_loss`(…)	Simple CrossEntropy loss.
`bob.learn.tensorflow.loss.mean_cross_entropy_center_loss`(…)	Implementation of the CrossEntropy + Center Loss from the paper “A Discriminative Feature Learning Approach for Deep Face Recognition”(http://ydwen.github.io/papers/WenECCV16.pdf)
`bob.learn.tensorflow.loss.contrastive_loss`(…)	Compute the contrastive loss as in
`bob.learn.tensorflow.loss.triplet_loss`(…)	Compute the triplet loss as in
`bob.learn.tensorflow.loss.triplet_average_loss`(…)	Compute the triplet loss as in
`bob.learn.tensorflow.loss.triplet_fisher_loss`(…)
`bob.learn.tensorflow.loss.linear_gram_style_loss`(…)	Implements the style loss from:
`bob.learn.tensorflow.loss.content_loss`(…)	Implements the content loss from:
`bob.learn.tensorflow.loss.denoising_loss`(noise)	Computes the denoising loss as in:
`bob.learn.tensorflow.loss.balanced_softmax_cross_entropy_loss_weights`(labels)	Computes weights that normalizes your loss per class.
`bob.learn.tensorflow.loss.balanced_sigmoid_cross_entropy_loss_weights`(labels)	Computes weights that normalizes your loss per class.

Detailed Information¶

bob.learn.tensorflow.get_config()[source]¶: Returns a string containing the configuration information.

bob.learn.tensorflow.estimators.check_features(features)[source]¶

bob.learn.tensorflow.estimators.get_trainable_variables(extra_checkpoint, mode='train')[source]¶

Given the extra_checkpoint dictionary provided to the estimator, extract the content of “trainable_variables”.

If trainable_variables is not provided, all end points are trainable by default. If trainable_variables==[], all end points are NOT trainable. If trainable_variables contains some end_points, ONLY these endpoints will be trainable.

bob.learn.tensorflow.estimators.extra_checkpoint¶

The extra_checkpoint dictionary provided to the estimator

Type:	dict

bob.learn.tensorflow.estimators.mode¶: The estimator mode. TRAIN, EVAL, and PREDICT. If not TRAIN, None is returned.

Returns:	Returns None if trainable_variables is not in extra_checkpoint; otherwise returns the content of extra_checkpoint .

class bob.learn.tensorflow.estimators.Logits(architecture, optimizer, loss_op, n_classes, config=None, embedding_validation=False, model_dir='', validation_batch_size=None, params=None, extra_checkpoint=None, apply_moving_averages=True, add_histograms=None, vat_loss=None, architecture_has_logits=False, balanced_loss_weight=False, use_sigmoid=False, labels_are_one_hot=False, optimize_loss=<function optimize_loss>, optimize_loss_learning_rate=None)¶

Bases: tensorflow_estimator.python.estimator.estimator.Estimator

Logits estimator.

NN estimator with Cross entropy loss in the hot-encoded layer bob.learn.tensorflow.estimators.Logits.

The architecture function should follow the following pattern:

def my_beautiful_architecture(placeholder, **kwargs):

  end_points = dict()
  graph = convXX(placeholder)
  end_points['conv'] = graph

return graph, end_points

The loss function should follow the following pattern:

def my_beautiful_loss(logits, labels, **kwargs):
  return loss_set_of_ops(logits, labels)

architecture¶: Pointer to a function that builds the graph.

optimizer¶: One of the tensorflow solvers

config¶

n_classes¶: Number of classes of your problem. The logits will be appended in this class

loss_op¶: Pointer to a function that computes the loss.

embedding_validation¶: Run the validation using embeddings?? [default: False]

model_dir¶: Model path

validation_batch_size¶: Size of the batch for validation. This value is used when the validation with embeddings is used. This is a hack.

params¶: Extra params for the model function (please see https://www.tensorflow.org/extend/estimators for more info)

extra_checkpoint¶

In case you want to use other model to initialize some variables. This argument should be in the following format:

extra_checkpoint = {
  "checkpoint_path": <YOUR_CHECKPOINT>,
  "scopes": dict({"<SOURCE_SCOPE>/": "<TARGET_SCOPE>/"}),
  "trainable_variables": [<LIST OF VARIABLES OR SCOPES THAT YOU WANT TO RETRAIN>]
}

Type:	dict

apply_moving_averages¶

Apply exponential moving average in the training variables and in the loss. https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage By default the decay for the variable averages is 0.9999 and for the loss is 0.9

Type:	bool

class bob.learn.tensorflow.estimators.LogitsCenterLoss(architecture=None, optimizer=None, config=None, n_classes=0, embedding_validation=False, model_dir='', alpha=0.9, factor=0.01, validation_batch_size=None, params=None, extra_checkpoint=None, apply_moving_averages=True, optimize_loss=<function optimize_loss>, optimize_loss_learning_rate=None)¶

Bases: tensorflow_estimator.python.estimator.estimator.Estimator

Logits estimator with center loss.

NN estimator with Cross entropy loss in the hot-encoded layer bob.learn.tensorflow.estimators.Logits plus the center loss implemented in: “Wen, Yandong, et al. “A discriminative feature learning approach for deep face recognition.” European Conference on Computer Vision. Springer, Cham, 2016.”

See Logits for the description of parameters.

class bob.learn.tensorflow.estimators.MovingAverageOptimizer(optimizer, **kwargs)¶

Bases: object

Creates a callable that can be given to bob.learn.tensorflow.estimators

This class is useful when you want to have a learning_rate_decay_fn and a moving average optimizer and use bob.learn.tensorflow.estimators

optimizer¶

A tf.train.Optimizer that is created and wrapped with tf.contrib.opt.MovingAverageOptimizer.

Type:	object

Example

>>> import tensorflow as tf
>>> from bob.learn.tensorflow.estimators import MovingAverageOptimizer
>>> optimizer = MovingAverageOptimizer("adam")
>>> actual_optimizer = optimizer(lr=1e-3)
>>> isinstance(actual_optimizer, tf.train.Optimizer)
True
>>> actual_optimizer is optimizer.optimizer
True

class bob.learn.tensorflow.estimators.Regressor(architecture, optimizer=<tensorflow.python.training.adam.AdamOptimizer object>, loss_op=<function mean_squared_error>, label_dimension=1, config=None, model_dir=None, apply_moving_averages=True, add_regularization_losses=True, extra_checkpoint=None, add_histograms=None, optimize_loss=<function optimize_loss>, optimize_loss_learning_rate=None)¶

Bases: tensorflow_estimator.python.estimator.estimator.Estimator

An estimator for regression problems

class bob.learn.tensorflow.estimators.Siamese(architecture=None, optimizer=None, config=None, loss_op=None, model_dir='', validation_batch_size=None, params=None, extra_checkpoint=None, add_histograms=None, add_regularization_losses=True, optimize_loss=<function optimize_loss>, optimize_loss_learning_rate=None)¶

Bases: tensorflow_estimator.python.estimator.estimator.Estimator

NN estimator for Siamese Networks. Proposed in: “Chopra, Sumit, Raia Hadsell, and Yann LeCun. “Learning a similarity metric discriminatively, with application to face verification.” Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on. Vol. 1. IEEE, 2005.”

See Logits for the description of parameters.

class bob.learn.tensorflow.estimators.Triplet(architecture=None, optimizer=None, config=None, loss_op=<function triplet_loss>, model_dir='', validation_batch_size=None, extra_checkpoint=None, optimize_loss=<function optimize_loss>, optimize_loss_learning_rate=None)¶

Bases: tensorflow_estimator.python.estimator.estimator.Estimator

NN estimator for Triplet networks.

Schroff, Florian, Dmitry Kalenichenko, and James Philbin. “Facenet: A unified embedding for face recognition and clustering.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

See Logits for the description of parameters.

bob.learn.tensorflow.estimators.learning_rate_decay_fn(learning_rate, global_step, decay_steps, decay_rate, staircase=False)[source]¶

A simple learning_rate_decay_fn.

To use it with tf.contrib.layer.optimize_loss:

>>> from bob.learn.tensorflow.estimators import learning_rate_decay_fn
>>> from functools import partial
>>> learning_rate_decay_fn = partial(
...     learning_rate_decay_fn,
...     decay_steps=1000,
...     decay_rate=0.9,
...     staircase=True,
... )

bob.learn.tensorflow.dataset.from_hdf5file_to_tensor(filename)[source]¶

bob.learn.tensorflow.dataset.from_filename_to_tensor(filename, extension=None)[source]¶

Read a file and it convert it to tensor.

If the file extension is something that tensorflow understands (.jpg, .bmp, .tif,…), it uses the tf.image.decode_image otherwise it uses bob.io.base.load

bob.learn.tensorflow.dataset.append_image_augmentation(image, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, random_gamma=False, random_crop=False)[source]¶

Append to the current tensor some random image augmentation operation

Parameters

gray_scale:: Convert to gray scale?
output_shape:: If set, will randomly crop the image given the output shape
random_flip:: Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
random_brightness:: Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
random_contrast:: Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
random_saturation:: Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
random_rotate:: Randomly rotate face images between -5 and 5 degrees
per_image_normalization:: Linearly scales image to have zero mean and unit norm.

bob.learn.tensorflow.dataset.arrange_indexes_by_label(input_labels, possible_labels)[source]¶

bob.learn.tensorflow.dataset.triplets_random_generator(input_data, input_labels)[source]¶

Giving a list of samples and a list of labels, it dumps a series of triplets for triple nets.

Parameters

input_data: List of whatever representing the data samples

input_labels: List of the labels (needs to be in EXACT same order as input_data)

bob.learn.tensorflow.dataset.siamease_pairs_generator(input_data, input_labels)[source]¶

Giving a list of samples and a list of labels, it dumps a series of pairs for siamese nets.

Parameters

input_data: List of whatever representing the data samples

input_labels: List of the labels (needs to be in EXACT same order as input_data)

bob.learn.tensorflow.dataset.blocks_tensorflow(images, block_size)[source]¶

Return all non-overlapping blocks of an image using tensorflow operations.

Parameters:

images (tf.Tensor) – The input color images. It is assumed that the image has a shape of [?, H, W, C].
block_size ((int, int)) – A tuple of two integers indicating the block size.

Returns:

blocks (tf.Tensor) – All the blocks in the batch dimension. The output will be of size [?, block_size[0], block_size[1], C].
n_blocks (int) – The number of blocks that was obtained per image.

bob.learn.tensorflow.dataset.tf_repeat(tensor, repeats)[source]¶

Parameters:

tensor – A Tensor. 1-D or higher.
repeats – A list. Number of repeat for each dimension, length must be the same as the number of dimensions in input

Returns:

A Tensor. Has the same type as input. Has the shape of tensor.shape *
repeats

bob.learn.tensorflow.dataset.all_patches(image, label, key, size)[source]¶

Extracts all patches of an image

Parameters:

image – The image should be channels_last format and already batched.
label – The label for the image
key – The key for the image
size ((int, int)) – The height and width of the blocks.

Returns:

blocks – The non-overlapping blocks of size from image and labels and keys are repeated.
label
key

class bob.learn.tensorflow.dataset.generator.Generator(samples, reader, multiple_samples=False, **kwargs)[source]¶

Bases: object

A generator class which wraps samples so that they can be used with tf.data.Dataset.from_generator

epoch¶

The number of epochs that have been passed so far.

Type:	int

multiple_samples¶

If true, it assumes that the bio database’s samples actually contain multiple samples. This is useful for when you want to for example treat video databases as image databases.

Type:	`bool`, optional

reader¶

A callable with the signature of data, label, key = reader(sample) which takes a sample and loads it.

Type:	`object`, optional

samples¶

A list of samples to be given to reader to load the data.

Type:	[`object`]

output_types¶

The types of the returned samples.

Type:	(object, object, object)

output_shapes¶

The shapes of the returned samples.

Type:	`(tf.TensorShape, tf.TensorShape, tf.TensorShape)`

output_types

output_shapes

bob.learn.tensorflow.dataset.generator.dataset_using_generator(*args, **kwargs)[source]¶

A generator class which wraps samples so that they can be used with tf.data.Dataset.from_generator

bob.learn.tensorflow.dataset.generator.samples¶

A list of samples to be given to reader to load the data.

Type:	[`object`]

bob.learn.tensorflow.dataset.generator.reader¶

A callable with the signature of data, label, key = reader(sample) which takes a sample and loads it.

Type:	`object`, optional

bob.learn.tensorflow.dataset.generator.multiple_samples¶

If true, it assumes that the bio database’s samples actually contain multiple samples. This is useful for when you want to for example treat video databases as image databases.

Type:	`bool`, optional

class bob.learn.tensorflow.dataset.bio.BioGenerator(database, biofiles, load_data=None, biofile_to_label=None, multiple_samples=False, **kwargs)[source]¶

Bases: object

A generator class which wraps bob.bio.base databases so that they can be used with tf.data.Dataset.from_generator

biofile_to_label¶

A callable with the signature of label = biofile_to_label(biofile). By default -1 is returned as label.

Type:	`object`, optional

biofiles¶

The list of the bio files .

Type:	[`bob.bio.base.database.BioFile`]

database¶

The database that you want to use.

Type:	`bob.bio.base.database.BioDatabase`

epoch¶

The number of epochs that have been passed so far.

Type:	int

keys¶

The keys of samples obtained by calling biofile.make_path("", "")

Type:	[str]

labels¶

The labels obtained by calling label = biofile_to_label(biofile)

Type:	[int]

load_data¶

A callable with the signature of data = load_data(database, biofile). bob.bio.base.read_original_data is wrapped to be used by default.

Type:	`object`, optional

multiple_samples¶

If true, it assumes that the bio database’s samples actually contain multiple samples. This is useful for when you want to for example treat video databases as image databases.

Type:	`bool`, optional

output_types¶

The types of the returned samples.

Type:	(object, object, object)

output_shapes¶

The shapes of the returned samples.

Type:	`(tf.TensorShape, tf.TensorShape, tf.TensorShape)`

labels

keys

output_types

output_shapes

bob.learn.tensorflow.dataset.image.shuffle_data_and_labels_image_augmentation(filenames, labels, data_shape, data_type, batch_size, epochs=None, buffer_size=1000, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶

Dump random batches from a list of image paths and labels:

The list of files and labels should be in the same order e.g. filenames = [‘class_1_img1’, ‘class_1_img2’, ‘class_2_img1’] labels = [0, 0, 1]

Parameters

filenames:

List containing the path of the images

labels:

List containing the labels (needs to be in EXACT same order as filenames)

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

batch_size:

Size of the batch

epochs:

Number of epochs to be batched

buffer_size:

Size of the shuffle bucket

gray_scale:

Convert to gray scale?

output_shape:

If set, will randomly crop the image given the output shape

random_flip:

Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)

random_brightness:

Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)

random_contrast:

Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)

random_saturation:

Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)

random_rotate:

Randomly rotate face images between -5 and 5 degrees

per_image_normalization:

Linearly scales image to have zero mean and unit norm.

extension:

If None, will load files using tf.image.decode.. if set to hdf5, will load with bob.io.base.load

bob.learn.tensorflow.dataset.image.create_dataset_from_path_augmentation(filenames, labels, data_shape, data_type, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶

Create dataset from a list of tf-record files

Parameters

filenames:

List containing the path of the images

labels:

List containing the labels (needs to be in EXACT same order as filenames)

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

feature:

bob.learn.tensorflow.dataset.image.image_augmentation_parser(filename, label, data_shape, data_type, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶: Parses a single tf.Example into image and label tensors.

bob.learn.tensorflow.dataset.siamese_image.shuffle_data_and_labels_image_augmentation(filenames, labels, data_shape, data_type, batch_size, epochs=None, buffer_size=1000, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶

Dump random batches for siamese networks from a list of image paths and labels:

The list of files and labels should be in the same order e.g. filenames = [‘class_1_img1’, ‘class_1_img2’, ‘class_2_img1’] labels = [0, 0, 1]

The batches returned with tf.Session.run() with be in the following format: data a dictionary containing the keys [‘left’, ‘right’], each one representing one element of the pair and labels which is [0, 1] where 0 is the genuine pair and 1 is the impostor pair.

Parameters

filenames:

List containing the path of the images

labels:

List containing the labels (needs to be in EXACT same order as filenames)

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

batch_size:

Size of the batch

epochs:

Number of epochs to be batched

buffer_size:

Size of the shuffle bucket

gray_scale:

Convert to gray scale?

output_shape:

If set, will randomly crop the image given the output shape

random_flip:

Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)

random_brightness:

Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)

random_contrast:

Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)

random_saturation:

Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)

random_rotate:

Randomly rotate face images between -5 and 5 degrees

per_image_normalization:

Linearly scales image to have zero mean and unit norm.

extension:

If None, will load files using tf.image.decode.. if set to hdf5, will load with bob.io.base.load

bob.learn.tensorflow.dataset.siamese_image.create_dataset_from_path_augmentation(filenames, labels, data_shape, data_type, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶

Create dataset from a list of tf-record files

Parameters

filenames:

List containing the path of the images

labels:

List containing the labels (needs to be in EXACT same order as filenames)

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

batch_size:

Size of the batch

epochs:

Number of epochs to be batched

buffer_size:

Size of the shuffle bucket

gray_scale:

Convert to gray scale?

output_shape:

If set, will randomly crop the image given the output shape

random_flip:

Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)

random_brightness:

Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)

random_contrast:

Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)

random_saturation:

Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)

random_rotate:

Randomly rotate face images between -10 and 10 degrees

per_image_normalization:

Linearly scales image to have zero mean and unit norm.

extension:

If None, will load files using tf.image.decode.. if set to hdf5, will load with bob.io.base.load

bob.learn.tensorflow.dataset.siamese_image.image_augmentation_parser(filename_left, filename_right, label, data_shape, data_type, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶: Parses a single tf.Example into image and label tensors.

bob.learn.tensorflow.dataset.triplet_image.shuffle_data_and_labels_image_augmentation(filenames, labels, data_shape, data_type, batch_size, epochs=None, buffer_size=1000, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶

Dump random batches for triplee networks from a list of image paths and labels:

The list of files and labels should be in the same order e.g. filenames = [‘class_1_img1’, ‘class_1_img2’, ‘class_2_img1’] labels = [0, 0, 1]

The batches returned with tf.Session.run() with be in the following format: data a dictionary containing the keys [‘anchor’, ‘positive’, ‘negative’].

Parameters

filenames:

List containing the path of the images

labels:

List containing the labels (needs to be in EXACT same order as filenames)

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

batch_size:

Size of the batch

epochs:

Number of epochs to be batched

buffer_size:

Size of the shuffle bucket

gray_scale:

Convert to gray scale?

output_shape:

If set, will randomly crop the image given the output shape

random_flip:

Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)

random_brightness:

Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)

random_contrast:

Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)

random_saturation:

Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)

random_rotate:

Randomly rotate face images between -5 and 5 degrees

per_image_normalization:

Linearly scales image to have zero mean and unit norm.

extension:

If None, will load files using tf.image.decode.. if set to hdf5, will load with bob.io.base.load

bob.learn.tensorflow.dataset.triplet_image.create_dataset_from_path_augmentation(filenames, labels, data_shape, data_type=tf.float32, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶

Create dataset from a list of tf-record files

Parameters

filenames:

List containing the path of the images

labels:

List containing the labels (needs to be in EXACT same order as filenames)

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

feature:

bob.learn.tensorflow.dataset.triplet_image.image_augmentation_parser(anchor, positive, negative, data_shape, data_type=tf.float32, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, extension=None)[source]¶: Parses a single tf.Example into image and label tensors.

Utilities for TFRecords

bob.learn.tensorflow.dataset.tfrecords.tfrecord_name_and_json_name(output)[source]¶

bob.learn.tensorflow.dataset.tfrecords.normalize_tfrecords_path(output)[source]¶

bob.learn.tensorflow.dataset.tfrecords.bytes_feature(value)[source]¶

bob.learn.tensorflow.dataset.tfrecords.int64_feature(value)[source]¶

bob.learn.tensorflow.dataset.tfrecords.dataset_to_tfrecord(dataset, output)[source]¶

Writes a tf.data.Dataset into a TFRecord file.

Parameters:	dataset (`tf.data.Dataset`) – The tf.data.Dataset that you want to write into a TFRecord file. output (str) – Path to the TFRecord file. Besides this file, a .json file is also created. This json file is needed when you want to convert the TFRecord file back into a dataset.
Returns:	A tf.Operation that, when run, writes contents of dataset to a file. When running in eager mode, calling this function will write the file. Otherwise, you have to call session.run() on the returned operation.
Return type:	`tf.Operation`

bob.learn.tensorflow.dataset.tfrecords.dataset_from_tfrecord(tfrecord)[source]¶

Reads TFRecords and returns a dataset. The TFRecord file must have been created using the dataset_to_tfrecord function.

Parameters:	tfrecord (str or list) – Path to the TFRecord file. Pass a list if you are sure several tfrecords need the same map function.
Returns:	A dataset that contains the data from the TFRecord file.
Return type:	`tf.data.Dataset`

bob.learn.tensorflow.dataset.tfrecords.write_a_sample(writer, data, label, key, feature=None, size_estimate=False)[source]¶

bob.learn.tensorflow.dataset.tfrecords.example_parser(serialized_example, feature, data_shape, data_type)[source]¶: Parses a single tf.Example into image and label tensors.

bob.learn.tensorflow.dataset.tfrecords.image_augmentation_parser(serialized_example, feature, data_shape, data_type, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, random_gamma=False, random_crop=False)[source]¶: Parses a single tf.Example into image and label tensors.

bob.learn.tensorflow.dataset.tfrecords.read_and_decode(filename_queue, data_shape, data_type=tf.float32, feature=None)[source]¶: Simples parse possible for a tfrecord. It assumes that you have the pair train/data and train/label

bob.learn.tensorflow.dataset.tfrecords.create_dataset_from_records(tfrecord_filenames, data_shape, data_type, feature=None)[source]¶

Create dataset from a list of tf-record files

Parameters

tfrecord_filenames:

List containing the tf-record paths

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

feature:

bob.learn.tensorflow.dataset.tfrecords.create_dataset_from_records_with_augmentation(tfrecord_filenames, data_shape, data_type, feature=None, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, random_gamma=False, random_crop=False)[source]¶

Create dataset from a list of tf-record files

Parameters

tfrecord_filenames:

List containing the tf-record paths

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

feature:

bob.learn.tensorflow.dataset.tfrecords.shuffle_data_and_labels_image_augmentation(tfrecord_filenames, data_shape, data_type, batch_size, epochs=None, buffer_size=1000, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, random_gamma=False, random_crop=False, drop_remainder=False)[source]¶

Dump random batches from a list of tf-record files and applies some image augmentation

Parameters:

tfrecord_filenames – List containing the tf-record paths
data_shape – Samples shape saved in the tf-record
data_type – tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
batch_size – Size of the batch
epochs – Number of epochs to be batched
buffer_size – Size of the shuffle bucket
gray_scale – Convert to gray scale?
output_shape – If set, will randomly crop the image given the output shape
random_flip – Randomly flip an image horizontally (https://www.tensorflow.org/api_docs/python/tf/image/random_flip_left_right)
random_brightness – Adjust the brightness of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_brightness)
random_contrast – Adjust the contrast of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_contrast)
random_saturation – Adjust the saturation of an RGB image by a random factor (https://www.tensorflow.org/api_docs/python/tf/image/random_saturation)
random_rotate – Randomly rotate face images between -5 and 5 degrees
per_image_normalization – Linearly scales image to have zero mean and unit norm.
drop_remainder – If True, the last remaining batch that has smaller size than batch_size will be dropped.

bob.learn.tensorflow.dataset.tfrecords.shuffle_data_and_labels(tfrecord_filenames, data_shape, data_type, batch_size, epochs=None, buffer_size=1000)[source]¶

Dump random batches from a list of tf-record files

Parameters

tfrecord_filenames:

List containing the tf-record paths

data_shape:

Samples shape saved in the tf-record

data_type:

tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)

batch_size:

Size of the batch

epochs:

Number of epochs to be batched

buffer_size:

Size of the shuffle bucket

bob.learn.tensorflow.dataset.tfrecords.batch_data_and_labels(tfrecord_filenames, data_shape, data_type, batch_size, epochs=1)[source]¶

Dump in order batches from a list of tf-record files

Parameters:	tfrecord_filenames – List containing the tf-record paths data_shape – Samples shape saved in the tf-record data_type – tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types) batch_size – Size of the batch epochs – Number of epochs to be batched

bob.learn.tensorflow.dataset.tfrecords.batch_data_and_labels_image_augmentation(tfrecord_filenames, data_shape, data_type, batch_size, epochs=1, gray_scale=False, output_shape=None, random_flip=False, random_brightness=False, random_contrast=False, random_saturation=False, random_rotate=False, per_image_normalization=True, random_gamma=False, random_crop=False, drop_remainder=False)[source]¶

Dump in order batches from a list of tf-record files

Parameters:

tfrecord_filenames – List containing the tf-record paths
data_shape – Samples shape saved in the tf-record
data_type – tf data type(https://www.tensorflow.org/versions/r0.12/resources/dims_types#data_types)
batch_size – Size of the batch
epochs – Number of epochs to be batched
drop_remainder – If True, the last remaining batch that has smaller size than batch_size will be dropped.

bob.learn.tensorflow.dataset.tfrecords.describe_tf_record(tf_record_path, shape, batch_size=1)[source]¶

Describe the number of samples and the number of classes of a tf-record

Parameters:

tf_record_path (str) – Base path containing your tf-record files
shape (tuple) – Shape inside of the tf-record
batch_size (int) – Well, batch size

Returns:

n_samples (int) – Total number of samples
n_classes (int) – Total number of classes

bob.learn.tensorflow.network.chopra(inputs, conv1_kernel_size=[7, 7], conv1_output=15, pooling1_size=[2, 2], conv2_kernel_size=[6, 6], conv2_output=45, pooling2_size=[4, 3], fc1_output=250, seed=10, reuse=False)¶

Class that creates the architecture presented in the paper:

Chopra, Sumit, Raia Hadsell, and Yann LeCun. “Learning a similarity metric discriminatively, with application to face verification.” 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR‘05). Vol. 1. IEEE, 2005.

This is modifield version of the original architecture. It is inspired on https://gitlab.idiap.ch/bob/xfacereclib.cnn/blob/master/lua/network.lua

– C1 : Convolutional, kernel = 7x7 pixels, 15 feature maps

– M2 : MaxPooling, 2x2

– HT : Hard Hyperbolic Tangent

– C3 : Convolutional, kernel = 6x6 pixels, 45 feature maps

– M4 : MaxPooling, 4x3

– HT : Hard Hyperbolic Tangent

– R : Reshaping layer HT 5x5 => 25 (45 times; once for each feature map)

– L5 : Linear 25 => 250

Parameters

conv1_kernel_size:

conv1_output:

pooling1_size:

conv2_kernel_size:

conv2_output:

pooling2_size

fc1_output:

seed:

bob.learn.tensorflow.network.dummy(inputs, reuse=False, mode='train', trainable_variables=None, **kwargs)¶

Create all the necessary variables for this CNN

Parameters:	inputs – reuse – mode – trainable_variables –

bob.learn.tensorflow.network.inception_resnet_v1(inputs, dropout_keep_prob=0.8, bottleneck_layer_size=128, reuse=None, scope='InceptionResnetV1', mode='train', trainable_variables=None, **kwargs)¶

Creates the Inception Resnet V1 model.

Parameters:

inputs – 4-D tensor of size [batch_size, height, width, 3].
num_classes – number of predicted classes.
is_training – whether is training or not.
dropout_keep_prob (float) – the fraction to keep before final layer.
reuse – whether or not the network and its variables should be reused. To be able to reuse ‘scope’ must be given.
scope – Optional variable_scope.
trainable_variables (list) – List of variables to be trainable=True

Returns:

logits – the logits outputs of the model.
end_points – the set of end_points from the inception model.

bob.learn.tensorflow.network.inception_resnet_v1_batch_norm(inputs, dropout_keep_prob=0.8, bottleneck_layer_size=128, reuse=None, scope='InceptionResnetV1', mode='train', trainable_variables=None, weight_decay=1e-05, **kwargs)¶

Creates the Inception Resnet V1 model applying batch not to each Convolutional and FullyConnected layer.

Parameters:

inputs – 4-D tensor of size [batch_size, height, width, 3].
num_classes – number of predicted classes.
is_training – whether is training or not.
dropout_keep_prob (float) – the fraction to keep before final layer.
reuse – whether or not the network and its variables should be reused. To be able to reuse ‘scope’ must be given.
scope – Optional variable_scope.
trainable_variables (list) – List of variables to be trainable=True

Returns:

logits – the logits outputs of the model.
end_points – the set of end_points from the inception model.

bob.learn.tensorflow.network.inception_resnet_v2(inputs, dropout_keep_prob=0.8, bottleneck_layer_size=128, reuse=None, scope='InceptionResnetV2', mode='train', trainable_variables=None, **kwargs)¶

Creates the Inception Resnet V2 model.

Parameters:

inputs – 4-D tensor of size [batch_size, height, width, 3].
num_classes – number of predicted classes.
is_training – whether is training or not.
dropout_keep_prob (float) – the fraction to keep before final layer.
reuse – whether or not the network and its variables should be reused. To be able to reuse ‘scope’ must be given.
scope – Optional variable_scope.
trainable_variables (list) – List of variables to be trainable=True

Returns:

logits – the logits outputs of the model.
end_points – the set of end_points from the inception model.

bob.learn.tensorflow.network.inception_resnet_v2_batch_norm(inputs, dropout_keep_prob=0.8, bottleneck_layer_size=128, reuse=None, scope='InceptionResnetV2', mode='train', trainable_variables=None, weight_decay=5e-05, **kwargs)¶

Creates the Inception Resnet V2 model applying batch not to each Convolutional and FullyConnected layer.

Parameters:

inputs:

4-D tensor of size [batch_size, height, width, 3].

num_classes:

number of predicted classes.

is_training:

whether is training or not.

dropout_keep_prob: float

the fraction to keep before final layer.

reuse:

whether or not the network and its variables should be reused. To be able to reuse ‘scope’ must be given.

scope:

Optional variable_scope.

trainable_variables: list

List of variables to be trainable=True

Returns:

logits:

the logits outputs of the model.

end_points:

the set of end_points from the inception model.

bob.learn.tensorflow.network.light_cnn9(inputs, seed=10, reuse=False, trainable_variables=None, **kwargs)¶

Creates the graph for the Light CNN-9 in

Wu, Xiang, et al. “A light CNN for deep face representation with noisy labels.” arXiv preprint arXiv:1511.02683 (2015).

bob.learn.tensorflow.network.mlp(inputs, output_shape, hidden_layers=[10], hidden_activation=<function tanh>, output_activation=None, seed=10, **kwargs)¶

An MLP is a representation of a Multi-Layer Perceptron.

This implementation is feed-forward and fully-connected. The implementation allows setting a global and the output activation functions. References to fully-connected feed-forward networks: Bishop’s Pattern Recognition and Machine Learning, Chapter 5. Figure 5.1 shows what is programmed.

MLPs normally are multi-layered systems, with 1 or more hidden layers.

Parameters

output_shape: number of neurons in the output.

hidden_layers: list that contains the amount of hidden layers, where each element is the number of neurons

hidden_activation: Activation function of the hidden layers. Possible values can be seen

here.

If you set to None, the activation will be linear.

output_activation: Activation of the output layer. If you set to None, the activation will be linear

seed:

bob.learn.tensorflow.network.mlp_with_batchnorm_and_dropout(inputs, fully_connected_layers, mode='train', trainable_variables=None, **kwargs)[source]¶

bob.learn.tensorflow.network.vgg_16(inputs, reuse=None, mode='train', trainable_variables=None, scope='vgg_16', **kwargs)¶

Oxford Net VGG 16-Layers version E Example from tf-slim

https://raw.githubusercontent.com/tensorflow/models/master/research/slim/nets/vgg.py

Parameters:

inputs: a 4-D tensor of size [batch_size, height, width, 3].

reuse: whether or not the network and its variables should be reused. To be

able to reuse ‘scope’ must be given.

mode:

Estimator mode keys

bob.learn.tensorflow.network.vgg_19(inputs, reuse=None, mode='train', **kwargs)¶

Oxford Net VGG 19-Layers version E Example from tf-slim

https://raw.githubusercontent.com/tensorflow/models/master/research/slim/nets/vgg.py

Parameters:

inputs: a 4-D tensor of size [batch_size, height, width, 3].

reuse: whether or not the network and its variables should be reused. To be

able to reuse ‘scope’ must be given.

mode:

Estimator mode keys

The network using keras (same as new_architecture function below):

from tensorflow.python.keras import *
from tensorflow.python.keras.layers import *
simplecnn = Sequential([
    Conv2D(32,(3,3),padding='same',use_bias=False, input_shape=(28,28,3)),
    BatchNormalization(scale=False),
    Activation('relu'),
    MaxPool2D(padding='same'),
    Conv2D(64,(3,3),padding='same',use_bias=False),
    BatchNormalization(scale=False),
    Activation('relu'),
    MaxPool2D(padding='same'),
    Flatten(),
    Dense(1024, use_bias=False),
    BatchNormalization(scale=False),
    Activation('relu'),
    Dropout(rate=0.4),
    Dense(2),
])
simplecnn.summary()
_________________________________________________________________
Layer (type)                 Output Shape              Param #
=================================================================
conv2d_1 (Conv2D)            (None, 28, 28, 32)        864
_________________________________________________________________
batch_normalization_1 (Batch (None, 28, 28, 32)        96
_________________________________________________________________
activation_1 (Activation)    (None, 28, 28, 32)        0
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 14, 14, 32)        0
_________________________________________________________________
conv2d_2 (Conv2D)            (None, 14, 14, 64)        18432
_________________________________________________________________
batch_normalization_2 (Batch (None, 14, 14, 64)        192
_________________________________________________________________
activation_2 (Activation)    (None, 14, 14, 64)        0
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 7, 7, 64)          0
_________________________________________________________________
flatten_1 (Flatten)          (None, 3136)              0
_________________________________________________________________
dense_1 (Dense)              (None, 1024)              3211264
_________________________________________________________________
batch_normalization_3 (Batch (None, 1024)              3072
_________________________________________________________________
activation_3 (Activation)    (None, 1024)              0
_________________________________________________________________
dropout_1 (Dropout)          (None, 1024)              0
_________________________________________________________________
dense_2 (Dense)              (None, 2)                 2050
=================================================================
Total params: 3,235,970
Trainable params: 3,233,730
Non-trainable params: 2,240
_________________________________________________________________

bob.learn.tensorflow.network.SimpleCNN.architecture(input_layer, mode='train', kernerl_size=(3, 3), n_classes=2, data_format='channels_last', reuse=False, add_batch_norm=False, trainable_variables=None, **kwargs)[source]¶

bob.learn.tensorflow.network.SimpleCNN.base_architecture(input_layer, mode='train', kernerl_size=(3, 3), data_format='channels_last', add_batch_norm=False, trainable_variables=None, use_bias_with_batch_norm=True, **kwargs)[source]¶

bob.learn.tensorflow.network.SimpleCNN.create_conv_layer(inputs, mode, data_format, endpoints, number, filters, kernel_size, pool_size, pool_strides, add_batch_norm=False, trainable_variables=None, use_bias_with_batch_norm=True)[source]¶

bob.learn.tensorflow.network.SimpleCNN.model_fn(features, labels, mode, params=None, config=None)[source]¶: Model function for CNN.

bob.learn.tensorflow.network.SimpleCNN.new_architecture(input_layer, mode='train', kernerl_size=(3, 3), data_format='channels_last', add_batch_norm=True, trainable_variables=None, use_bias_with_batch_norm=False, reuse=False, **kwargs)[source]¶

bob.learn.tensorflow.network.SimpleCNN.slim_architecture(input_layer, mode='train', kernerl_size=(3, 3), data_format='channels_last', add_batch_norm=True, trainable_variables=None, use_bias_with_batch_norm=False, reuse=False, **kwargs)[source]¶

bob.learn.tensorflow.utils.util.keras_channels_index()[source]¶

bob.learn.tensorflow.utils.util.compute_euclidean_distance(x, y)[source]¶: Computes the euclidean distance between two tensorflow variables

bob.learn.tensorflow.utils.util.load_mnist(perc_train=0.9)[source]¶

bob.learn.tensorflow.utils.util.create_mnist_tfrecord(tfrecords_filename, data, labels, n_samples=6000)[source]¶

bob.learn.tensorflow.utils.util.compute_eer(data_train, labels_train, data_validation, labels_validation, n_classes)[source]¶

bob.learn.tensorflow.utils.util.compute_accuracy(data_train, labels_train, data_validation, labels_validation, n_classes)[source]¶

bob.learn.tensorflow.utils.util.debug_embbeding(image, architecture, embbeding_dim=2, feature_layer='fc3')[source]¶

bob.learn.tensorflow.utils.util.pdist(A)[source]¶: Compute a pairwise euclidean distance in the same fashion as in scipy.spation.distance.pdist

bob.learn.tensorflow.utils.util.predict_using_tensors(embedding, labels, num=None)[source]¶: Compute the predictions through exhaustive comparisons between embeddings using tensors

bob.learn.tensorflow.utils.util.compute_embedding_accuracy_tensors(embedding, labels, num=None)[source]¶

Compute the accuracy in a closed-set

Parameters

embeddings: tf.Tensor: Set of embeddings
labels: tf.Tensor: Correspondent labels

bob.learn.tensorflow.utils.util.compute_embedding_accuracy(embedding, labels)[source]¶

Compute the accuracy in a closed-set

Parameters

embeddings: numpy.array: Set of embeddings
labels: numpy.array: Correspondent labels

bob.learn.tensorflow.utils.util.get_available_gpus()[source]¶

Returns the number of GPU devices that are available.

Returns:	The names of available GPU devices.
Return type:	[str]

bob.learn.tensorflow.utils.util.to_channels_last(image)[source]¶

Converts the image to channel_last format. This is the same format as in matplotlib, skimage, and etc.

Parameters:	image (tf.Tensor) – At least a 3 dimensional image. If the dimension is more than 3, the last 3 dimensions are assumed to be [C, H, W].
Returns:	image – The image in […, H, W, C] format.
Return type:	tf.Tensor
Raises:	`ValueError` – If dim of image is less than 3.

bob.learn.tensorflow.utils.util.to_channels_first(image)[source]¶

Converts the image to channel_first format. This is the same format as in bob.io.image and bob.io.video.

Parameters:	image (tf.Tensor) – At least a 3 dimensional image. If the dimension is more than 3, the last 3 dimensions are assumed to be [H, W, C].
Returns:	image – The image in […, C, H, W] format.
Return type:	tf.Tensor
Raises:	`ValueError` – If dim of image is less than 3.

bob.learn.tensorflow.utils.util.to_skimage(image)¶

Converts the image to channel_last format. This is the same format as in matplotlib, skimage, and etc.

Parameters:	image (tf.Tensor) – At least a 3 dimensional image. If the dimension is more than 3, the last 3 dimensions are assumed to be [C, H, W].
Returns:	image – The image in […, H, W, C] format.
Return type:	tf.Tensor
Raises:	`ValueError` – If dim of image is less than 3.

bob.learn.tensorflow.utils.util.to_matplotlib(image)¶

Converts the image to channel_last format. This is the same format as in matplotlib, skimage, and etc.

Parameters:	image (tf.Tensor) – At least a 3 dimensional image. If the dimension is more than 3, the last 3 dimensions are assumed to be [C, H, W].
Returns:	image – The image in […, H, W, C] format.
Return type:	tf.Tensor
Raises:	`ValueError` – If dim of image is less than 3.

bob.learn.tensorflow.utils.util.to_bob(image)¶

Converts the image to channel_first format. This is the same format as in bob.io.image and bob.io.video.

Parameters:	image (tf.Tensor) – At least a 3 dimensional image. If the dimension is more than 3, the last 3 dimensions are assumed to be [H, W, C].
Returns:	image – The image in […, C, H, W] format.
Return type:	tf.Tensor
Raises:	`ValueError` – If dim of image is less than 3.

bob.learn.tensorflow.utils.util.bytes2human(n, format='%(value).1f %(symbol)s', symbols='customary')[source]¶: Convert n bytes into a human readable string based on format. From: https://code.activestate.com/recipes/578019-bytes-to-human-human-to- bytes-converter/ Author: Giampaolo Rodola’ <g.rodola [AT] gmail [DOT] com> License: MIT symbols can be either “customary”, “customary_ext”, “iec” or “iec_ext”, see: http://goo.gl/kTQMs

bob.learn.tensorflow.style_transfer.compute_features(input_image, architecture, checkpoint_dir, target_end_points, preprocess_fn=None)[source]¶

For a given set of end_points, convolve the input image until these points

Parameters:	input_image (`numpy.array`) – Input image in the format WxHxC architecture – Pointer to the architecture function checkpoint_dir (str) – DCNN checkpoint directory end_points (dict) – Dictionary containing the end point tensors preprocess_fn – Pointer to a preprocess function

bob.learn.tensorflow.style_transfer.compute_gram(features)[source]¶

Given a list of features (as numpy.arrays) comput the gram matrices of each pinning the channel as in:

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).

Parameters:	features (`numpy.array`) – Convolved features in the format NxWxHxC

bob.learn.tensorflow.style_transfer.do_style_transfer(content_image, style_images, architecture, checkpoint_dir, scopes, content_end_points, style_end_points, preprocess_fn=None, un_preprocess_fn=None, pure_noise=False, iterations=1000, learning_rate=0.1, content_weight=5.0, style_weight=500.0, denoise_weight=500.0)[source]¶

Trains neural style transfer using the approach presented in:

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).

Parameters:

content_image (numpy.array) – Content image in the Bob format (C x W x H)
style_images (list) – List of numpy.array (Bob format (C x W x H)) that encodes the style
architecture – Point to a function with the base architecture
checkpoint_dir – CNN checkpoint path
scopes – Dictionary containing the mapping scores
content_end_points – List of end_points (from the architecture) for the used to encode the content
style_end_points – List of end_points (from the architecture) for the used to encode the style
preprocess_fn – Preprocess function. Pointer to a function that preprocess the INPUT signal
unpreprocess_fn – Un preprocess function. Pointer to a function that preprocess the OUTPUT signal
pure_noise – If set will save the raw noisy generated image. If not set, the output will be RGB = stylizedYUV.Y, originalYUV.U, originalYUV.V
iterations – Number of iterations to generate the image
learning_rate – Adam learning rate
content_weight – Weight of the content loss
style_weight – Weight of the style loss
denoise_weight – Weight denoising loss

class bob.learn.tensorflow.loss.VATLoss(epsilon=8.0, xi=1e-06, num_power_iterations=1, method='vatent', **kwargs)[source]¶

Bases: object

A class to hold parameters for Virtual Adversarial Training (VAT) Loss and perform it.

epsilon¶

norm length for (virtual) adversarial training

Type:	float

method¶

The method for calculating the loss: vatent for VAT loss + entropy and vat for only VAT loss.

Type:	str

num_power_iterations¶

the number of power iterations

Type:	int

xi¶

small constant for finite difference

Type:	float

virtual_adversarial_loss(features, logits, architecture, mode, name='vat_loss')[source]¶

generate_virtual_adversarial_perturbation(features, logits, architecture, mode)[source]¶

bob.learn.tensorflow.loss.balanced_sigmoid_cross_entropy_loss_weights(labels, dtype='float32')[source]¶

Computes weights that normalizes your loss per class.

Labels must be a batch of binary labels. The function takes labels and computes the weights per batch. Weights will be smaller for the class that have more samples in this batch. This is useful if you unbalanced classes in your dataset or batch.

Parameters:	labels (`tf.Tensor`) – Labels of your current input. The shape must be [batch_size] and values must be either 0 or 1. dtype (`tf.dtype`) – The dtype that weights will have. It should be float. Best is to provide logits.dtype as input.
Returns:	Computed weights that will cancel your dataset imbalance per batch.
Return type:	`tf.Tensor`

Examples

>>> import numpy
>>> import tensorflow as tf
>>> from bob.learn.tensorflow.loss import balanced_sigmoid_cross_entropy_loss_weights
>>> labels = numpy.array([1, 1, 0, 0, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 0, 1, 0,
...                 1, 1, 0, 1, 1, 1, 0, 1, 0, 1], dtype="int32")
>>> sum(labels), len(labels)
(20, 32)
>>> session = tf.Session() # Eager execution is also possible check https://www.tensorflow.org/guide/eager
>>> session.run(balanced_sigmoid_cross_entropy_loss_weights(labels, dtype='float32'))
array([0.8      , 0.8      , 1.3333334, 1.3333334, 1.3333334, 0.8      ,
       0.8      , 1.3333334, 0.8      , 0.8      , 0.8      , 0.8      ,
       0.8      , 0.8      , 1.3333334, 0.8      , 1.3333334, 0.8      ,
       1.3333334, 1.3333334, 0.8      , 1.3333334, 0.8      , 0.8      ,
       1.3333334, 0.8      , 0.8      , 0.8      , 1.3333334, 0.8      ,
       1.3333334, 0.8      ], dtype=float32)

You would use it like this:

>>> #weights = balanced_sigmoid_cross_entropy_loss_weights(labels, dtype=logits.dtype)
>>> #loss = tf.losses.sigmoid_cross_entropy(logits=logits, labels=labels, weights=weights)

bob.learn.tensorflow.loss.balanced_softmax_cross_entropy_loss_weights(labels, dtype='float32')[source]¶

Computes weights that normalizes your loss per class.

Labels must be a batch of one-hot encoded labels. The function takes labels and computes the weights per batch. Weights will be smaller for classes that have more samples in this batch. This is useful if you unbalanced classes in your dataset or batch.

Parameters:	labels (`tf.Tensor`) – Labels of your current input. The shape must be [batch_size, n_classes]. If your labels are not one-hot encoded, you can use `tf.one_hot` to convert them first before giving them to this function. dtype (`tf.dtype`) – The dtype that weights will have. It should be float. Best is to provide logits.dtype as input.
Returns:	Computed weights that will cancel your dataset imbalance per batch.
Return type:	`tf.Tensor`

Examples

>>> import numpy
>>> import tensorflow as tf
>>> from bob.learn.tensorflow.loss import balanced_softmax_cross_entropy_loss_weights
>>> labels = numpy.array([[1, 0, 0],
...                 [1, 0, 0],
...                 [0, 0, 1],
...                 [0, 1, 0],
...                 [0, 0, 1],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [0, 0, 1],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [0, 1, 0],
...                 [1, 0, 0],
...                 [0, 1, 0],
...                 [1, 0, 0],
...                 [0, 0, 1],
...                 [0, 0, 1],
...                 [1, 0, 0],
...                 [0, 0, 1],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [0, 1, 0],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [1, 0, 0],
...                 [0, 1, 0],
...                 [1, 0, 0],
...                 [0, 0, 1],
...                 [1, 0, 0]], dtype="int32")
>>> session = tf.Session() # Eager execution is also possible check https://www.tensorflow.org/guide/eager
>>> session.run(tf.reduce_sum(labels, axis=0))
array([20,  5,  7], dtype=int32)
>>> session.run(balanced_softmax_cross_entropy_loss_weights(labels, dtype='float32'))
array([0.53333336, 0.53333336, 1.5238096 , 2.1333334 , 1.5238096 ,
       0.53333336, 0.53333336, 1.5238096 , 0.53333336, 0.53333336,
       0.53333336, 0.53333336, 0.53333336, 0.53333336, 2.1333334 ,
       0.53333336, 2.1333334 , 0.53333336, 1.5238096 , 1.5238096 ,
       0.53333336, 1.5238096 , 0.53333336, 0.53333336, 2.1333334 ,
       0.53333336, 0.53333336, 0.53333336, 2.1333334 , 0.53333336,
       1.5238096 , 0.53333336], dtype=float32)

You would use it like this:

>>> #weights = balanced_softmax_cross_entropy_loss_weights(labels, dtype=logits.dtype)
>>> #loss = tf.losses.softmax_cross_entropy(logits=logits, labels=labels, weights=weights)

bob.learn.tensorflow.loss.content_loss(noises, content_features)[source]¶

Implements the content loss from:

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).

For a given noise signal \(n\), content image \(c\) and convolved with the DCNN \(\phi\) until the layer \(l\) the content loss is defined as:

\(L(n,c) = \sum_{l=?}^{?}({\phi^l(n) - \phi^l(c)})^2\)

Parameters:	noises (`list`) – A list of tf.Tensor containing all the noises convolved content_features (`list`) – A list of numpy.array containing all the content_features convolved

bob.learn.tensorflow.loss.contrastive_loss(left_embedding, right_embedding, labels, contrastive_margin=2.0)¶

Compute the contrastive loss as in

http://yann.lecun.com/exdb/publis/pdf/hadsell-chopra-lecun-06.pdf

\(L = 0.5 * (1-Y) * D^2 + 0.5 * (Y) * {max(0, margin - D)}^2\)

where, 0 are assign for pairs from the same class and 1 from pairs from different classes.

Parameters

left_feature:: First element of the pair
right_feature:: Second element of the pair
labels:: Label of the pair (0 or 1)
margin:: Contrastive margin

bob.learn.tensorflow.loss.denoising_loss(noise)[source]¶

Computes the denoising loss as in:

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).

Parameters:	noise – Input noise

bob.learn.tensorflow.loss.linear_gram_style_loss(noises, gram_style_features)[source]¶

Implements the style loss from:

Gatys, Leon A., Alexander S. Ecker, and Matthias Bethge. “A neural algorithm of artistic style.” arXiv preprint arXiv:1508.06576 (2015).

For a given noise signal \(n\), content image \(c\) and convolved with the DCNN \(\phi\) until the layer \(l\) the STYLE loss is defined as

\(L(n,c) = \sum_{l=?}^{?}\frac{({\phi^l(n)^T*\phi^l(n) - \phi^l(c)^T*\phi^l(c)})^2}{N*M}\)

Parameters:	noises (`list`) – A list of tf.Tensor containing all the noises convolved gram_style_features (`list`) – A list of numpy.array containing all the content_features convolved

bob.learn.tensorflow.loss.mean_cross_entropy_center_loss(logits, prelogits, labels, n_classes, alpha=0.9, factor=0.01)¶

Implementation of the CrossEntropy + Center Loss from the paper “A Discriminative Feature Learning Approach for Deep Face Recognition”(http://ydwen.github.io/papers/WenECCV16.pdf)

Parameters: logits: prelogits: labels: n_classes: Number of classes of your task alpha: Alpha factor ((1-alpha)*centers-prelogits) factor: Weight factor of the center loss

bob.learn.tensorflow.loss.mean_cross_entropy_loss(logits, labels, add_regularization_losses=True)¶

Simple CrossEntropy loss. Basically it wrapps the function tf.nn.sparse_softmax_cross_entropy_with_logits.

Parameters: logits: labels: add_regularization_losses: Regulize the loss???

bob.learn.tensorflow.loss.triplet_average_loss(anchor_embedding, positive_embedding, negative_embedding, margin=5.0)¶

Compute the triplet loss as in

Schroff, Florian, Dmitry Kalenichenko, and James Philbin. “Facenet: A unified embedding for face recognition and clustering.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

\(L = sum( |f_a - f_p|^2 - |f_a - f_n|^2 + \lambda)\)

Parameters

left_feature:: First element of the pair
right_feature:: Second element of the pair
label:: Label of the pair (0 or 1)
margin:: Contrastive margin

bob.learn.tensorflow.loss.triplet_fisher_loss(anchor_embedding, positive_embedding, negative_embedding)¶

bob.learn.tensorflow.loss.triplet_loss(anchor_embedding, positive_embedding, negative_embedding, margin=5.0)¶

Compute the triplet loss as in

Schroff, Florian, Dmitry Kalenichenko, and James Philbin. “Facenet: A unified embedding for face recognition and clustering.” Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2015.

\(L = sum( |f_a - f_p|^2 - |f_a - f_n|^2 + \lambda)\)

Parameters

left_feature:: First element of the pair
right_feature:: Second element of the pair
label:: Label of the pair (0 or 1)
margin:: Contrastive margin