Functions — github.com/Ximilar-com/tf-metric-learning

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Functions116 in github.com/Ximilar-com/tf-metric-learning

⨍ Functions 116◇ Types & classes 22

↓ 10 callersMethodaugment_image

(self, image)

examples/base.py:71

↓ 9 callersMethodbatch

(self, iterable, n=1)

tf_metric_learning/utils/recall.py:44

↓ 5 callersMethod__init__

(self, input_name=LABELS, **kwargs)

tf_metric_learning/layers/utils.py:48

↓ 5 callersMethodsearch

(self, embedding, include_distances=False, n=20)

tf_metric_learning/utils/index.py:53

↓ 4 callersMethodextract_embeddings

(self, data)

tf_metric_learning/miners/annoy.py:28

↓ 4 callersMethodmine_item

(self, ids, distances, label, operator, index)

tf_metric_learning/miners/annoy.py:35

↓ 4 callersMethodpick_positive

Current implementation is picking random positive sample from same class. You can reimplement/override this mechanism.

examples/base.py:49

↓ 3 callersMethodloss_fn

(self, embeddings_a, embeddings_p, labels)

tf_metric_learning/layers/npair.py:27

↓ 3 callersMethodpick_negative

Pick negative sample.

examples/base.py:56

↓ 3 callersFunctionstore_images

(filename, *images)

examples/base.py:10

↓ 2 callersMethodadd_to_index

(self, i, embedding)

tf_metric_learning/utils/index.py:38

↓ 2 callersMethodbuild

(self, k=5)

tf_metric_learning/utils/index.py:31

↓ 2 callersMethodcreate_index

(self)

tf_metric_learning/utils/index.py:28

↓ 2 callersMethodeuclidean_distance

(self, a, b)

tf_metric_learning/layers/triplet.py:27

↓ 2 callersMethodeuclidean_distance

(self, x, y)

tf_metric_learning/layers/contrastive.py:37

↓ 2 callersMethodget_config

(self)

tf_metric_learning/layers/utils.py:52

↓ 2 callersMethodget_label

(self, index)

tf_metric_learning/utils/index.py:25

↓ 2 callersMethodl2norm

Regularize output of embeddings

tf_metric_learning/layers/npair.py:31

↓ 2 callersFunctionnormalize_images

(images)

examples/soft_triple.py:11

↓ 2 callersMethodsearch_easiest_negative

(self, embedding, label, n=20)

tf_metric_learning/miners/annoy.py:45

↓ 1 callersMethod__init__

(self, weight=1.0, **kwargs)

tf_metric_learning/layers/classification.py:42

↓ 1 callersMethodcompute_data

(self)

tf_metric_learning/utils/recall.py:49

↓ 1 callersMethodcreate_ids

(self, labels)

tf_metric_learning/utils/index.py:22

↓ 1 callersMethodcreate_labels_id_data

(self, train_images, train_labels)

examples/base.py:30

↓ 1 callersMethodcreate_sprite

(self, images)

tf_metric_learning/utils/projector.py:92

↓ 1 callersMethodeuclidean_distance

(self, x, y)

tf_metric_learning/layers/npair.py:38

↓ 1 callersMethodeval_recall

(self, annoy_results, label, recalls)

tf_metric_learning/utils/recall.py:85

↓ 1 callersMethodevaluate

(self, images)

tf_metric_learning/utils/recall.py:65

↓ 1 callersMethodget_config

(self)

tf_metric_learning/layers/classification.py:48

↓ 1 callersMethodget_item_vector

(self, id)

tf_metric_learning/utils/index.py:50

↓ 1 callersFunctionget_version

(rel_path)

setup.py:13

↓ 1 callersMethodloss_fn

Regularize output of embeddings

tf_metric_learning/layers/utils.py:99

↓ 1 callersMethodloss_fn

(self, embeddings, labels, p_lambda=20.0, p_tau=0.2, p_gamma=0.1, p_delta=0.01)

tf_metric_learning/layers/soft_triple.py:34

↓ 1 callersMethodloss_fn

(self, embeddings_a, embeddings_p, embeddings_n)

tf_metric_learning/layers/triplet.py:20

↓ 1 callersMethodloss_fn

(self, embeddings_1, embeddings_2, y_true)

tf_metric_learning/layers/contrastive.py:27

↓ 1 callersMethodloss_fn

(self, embeddings, labels)

tf_metric_learning/layers/proxy_anchor.py:37

↓ 1 callersMethodloss_fn

(self, embeddings, labels)

tf_metric_learning/layers/multisimilarity.py:28

↓ 1 callersMethodon_epoch_end

(self)

examples/base.py:42

↓ 1 callersFunctionread

(rel_path)

setup.py:8

↓ 1 callersMethodregister_embedding

(self)

tf_metric_learning/utils/projector.py:112

↓ 1 callersMethodregularization

(self, p_lambda=20.0, p_tau=0.2, p_gamma=0.1, p_delta=0.01)

tf_metric_learning/layers/soft_triple.py:64

↓ 1 callersMethodreindex

(self, embeddings)

tf_metric_learning/utils/index.py:41

↓ 1 callersMethodsave_labels_tsv

(self, labels)

tf_metric_learning/utils/projector.py:84

↓ 1 callersFunctionscheduler

(epoch)

examples/classification.py:49

↓ 1 callersFunctionscheduler

(epoch)

examples/triplet.py:68

↓ 1 callersFunctionscheduler

(epoch)

examples/contrastive.py:68

↓ 1 callersFunctionscheduler

(epoch)

examples/proxy_anchor.py:37

↓ 1 callersMethodsearch_easiest_positive

(self, embedding, label, n=20)

tf_metric_learning/miners/annoy.py:53

↓ 1 callersMethodsearch_hardest_negative

(self, embedding, label, n=20)

tf_metric_learning/miners/annoy.py:41

↓ 1 callersMethodsearch_hardest_positive

(self, embedding, label, n=20)

tf_metric_learning/miners/annoy.py:49

examples/triplet.py:24

Method__getitem__

(self, idx)

examples/contrastive.py:24

Method__init__

( self, model, data_store, data_search, save_dir=None, eb_size

tf_metric_learning/utils/recall.py:14

Method__init__

Initialize callback for visuallizing embeddings into tensorflow projector. ! Currently due to some bugs in tensorboard, you need to

tf_metric_learning/utils/projector.py:18

Method__init__

(self, eb_size, labels, metric="euclidean", save_dir=None, progress=True, **kwargs)

tf_metric_learning/utils/index.py:11

Method__init__

(self, weight=1.0, **kwargs)

tf_metric_learning/layers/classification.py:11

Method__init__

(self, concat_labels=False, **kwargs)

tf_metric_learning/layers/utils.py:13

Method__init__

(self, **kwargs)

tf_metric_learning/layers/utils.py:37

Method__init__

(self, **kwargs)

tf_metric_learning/layers/utils.py:64

Method__init__

(self, **kwargs)

tf_metric_learning/layers/utils.py:76

Method__init__

(self, weight=0.002, **kwargs)

tf_metric_learning/layers/utils.py:89

Method__init__

(self, reg_lambda=0.0, weight=1.0, **kwargs)

tf_metric_learning/layers/npair.py:16

Method__init__

(self, num_class, num_centers, embeddings_size, weight=1.0, **kwargs)

tf_metric_learning/layers/soft_triple.py:7

Method__init__

(self, margin=0.2, normalize=False, weight=1.0, **kwargs)

tf_metric_learning/layers/triplet.py:8

Method__init__

(self, margin=1.0, normalize=True, crossentropy=False, weight=1.0, **kwargs)

tf_metric_learning/layers/contrastive.py:9

Method__init__

(self, num_class, embeddings_size, margin=0.1, alpha=32.0, weight=1.0, **kwargs)

tf_metric_learning/layers/proxy_anchor.py:7

Method__init__

(self, alpha=2.0, beta=50.0, lamb=1.0, eps=0.1, weight=1.0, mining=False, **kwargs)

tf_metric_learning/layers/multisimilarity.py:12

Method__init__

( self, base_model, eb_size, labels, metric="euclidean", save_

tf_metric_learning/miners/annoy.py:10

Method__init__

(self, base_model, train_images, train_labels, embedding_size, batch_size)

examples/npair.py:24

Method__init__

(self, base_model, train_images, train_labels, embedding_size, batch_size)

examples/base.py:18

Method__init__

(self, base_model, train_images, train_labels, embedding_size, batch_size)

examples/triplet.py:21

Method__init__

(self, base_model, train_images, train_labels, embedding_size, batch_size)

examples/contrastive.py:21

tf_metric_learning/layers/soft_triple.py:15

Methodbuild

(self, input_shape)

tf_metric_learning/layers/proxy_anchor.py:16

Methodcall

Compute the Categorical cross entropy :param y_pred: output from dense layer with softmax where shape is [batch_size, classes]

tf_metric_learning/layers/classification.py:22

Methodcall

Compute the Binary cross entropy :param y_pred: output from dense layer with sigmoid where shape is [batch_size, classes] :pa

tf_metric_learning/layers/classification.py:53

Methodcall

(self, inputs)

tf_metric_learning/layers/utils.py:23

Methodcall

(self, inputs)

tf_metric_learning/layers/utils.py:40

Methodcall

(self, inputs)

tf_metric_learning/layers/utils.py:57

Methodcall

(self, inputs)

tf_metric_learning/layers/utils.py:67

Methodcall

(self, inputs)

tf_metric_learning/layers/utils.py:79

Methodcall

(self, inputs)

tf_metric_learning/layers/utils.py:105

Methodcall

(self, inputs)

tf_metric_learning/layers/npair.py:43

Methodcall

(self, inputs)

tf_metric_learning/layers/soft_triple.py:87

Methodcall

(self, inputs)

tf_metric_learning/layers/triplet.py:30

Methodcall

(self, inputs)

tf_metric_learning/layers/contrastive.py:40

Methodcall

(self, inputs)

tf_metric_learning/layers/proxy_anchor.py:60

Methodcall

(self, inputs)

tf_metric_learning/layers/multisimilarity.py:64

Functionfilter_test

(image, label)

examples/classification.py:41

Functionfilter_train

(image, label)

examples/classification.py:29

Methodget_config

(self)

tf_metric_learning/layers/classification.py:17

Methodget_config

(self)

tf_metric_learning/layers/utils.py:18

Methodget_config

(self)

tf_metric_learning/layers/utils.py:94

Methodget_config

(self)

tf_metric_learning/layers/npair.py:22

Methodget_config

(self)

tf_metric_learning/layers/soft_triple.py:24

Methodget_config

(self)

tf_metric_learning/layers/triplet.py:15

Methodget_config

(self)

tf_metric_learning/layers/contrastive.py:17

Methodget_config

(self)

tf_metric_learning/layers/proxy_anchor.py:25

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