tensorflow-recommenders - v0.7.3

Changed

• The Retrieval task now accepts a list of factorized metrics instead of a single optional metric.

Added

• tfrs.experimental.optimizers.ClippyAdagrad: a new optimizer based on tf.keras.optimizers.Adagrad that is able to improve training stability.
• tfrs.metrics.FactorizedTopK now accepts sample weights which are used to compute weighted top k metrics.

- Python
Published by maciejkula about 3 years ago

tensorflow-recommenders - v0.7.2

• Improved support for using TPUEmbedding under parameter server strategy.

- Python
Published by maciejkula over 3 years ago

tensorflow-recommenders - v0.7.1

Release fixes.

- Python
Published by maciejkula over 3 years ago

tensorflow-recommenders - v0.7.0

[0.7.0][2022-07-07]

A number of changes to make factorized top-K metric computation more accurate and less prone to user error.

Changed

• tfrs.layers.embedding.TPUEmbedding now supports input features with dynamic shape. batch_size argument is deprecated and no longer required.

• tfrs.layers.embedding.TPUEmbedding now supports running on different versions of TPU.

• Pinned TensorFlow to >= 2.9.0 which works with Scann 1.2.7.

• tfrs.tasks.Ranking.call now accepts a compute_batch_metrics argument to allow switching off batch metric computation. Following this change, 'compute_metrics'argument does not impact computation of batch metrics.

Breaking changes

• tfrs.metrics.FactorizedTopK requires the candidate ids for positive candidates to be supplied when using approximate top-K sources. Each top-K layer now has an exact method to broadcast its ability to return exact or approximate top-K results.
• Removed metrics constructor parameter for tfrs.metrics.FactorizedTopK. FactorizedTopK only makes sense with top-k metrics, and this change enforces this.
• Replaced the k constructor argument in tfrs.metrics.FactorizedTopK with ks: a list of k values at which to compute the top k metric.

Changed

• The tfrs.metrics.FactorizedTopK metric can now compute candidate-id based metrics when given the true_candidate_ids argument in its call method.

Added

• The Retrieval task now also accepts a loss_metrics argument.

- Python
Published by maciejkula over 3 years ago

tensorflow-recommenders - v0.6.0

Changed

• Pinned TensorFlow to >= 2.6.0, which works with Scann 1.2.3.

Breaking changes

• TopK layer indexing API changed. Indexing with datasets is now done via the index_from_dataset method. This change reduces the possibility of misaligning embeddings and candidate identifiers when indexing via indeterministic datasets.

- Python
Published by maciejkula over 4 years ago

tensorflow-recommenders - v0.5.2

Fixed

• Fixed error in default arguments to tfrs.experimental.models.Ranking (https://github.com/tensorflow/recommenders/issues/311).
• Fix TPUEmbedding layer to use named parameters.

Added

• Added batch_metrics to tfrs.tasks.Retrieval for measuring how good the model is at picking out the true candidate for a query from other candidates in the batch.
• Added tfrs.experimental.layers.embedding.PartialTPUEmbedding layer, which uses tfrs.layers.embedding.TPUEmbedding for large embedding lookups and tf.keras.layers.Embedding for smaller embedding lookups.

- Python
Published by maciejkula over 4 years ago

tensorflow-recommenders - v0.5.1

Changed

• Supplying incompatibly-shaped candidates and identifiers inputs to factorized_top_k layers will now raise (to prevent issues similar to https://github.com/tensorflow/recommenders/issues/286).

- Python
Published by maciejkula almost 5 years ago

tensorflow-recommenders - v0.5.0

Added

• tfrs.experimental.models.Ranking, an experimental pre-built model for ranking tasks. Can be used as DLRM like model with Dot Product feature interaction or DCN like model with Cross layer.

Changed

• Fixed the bug in tfrs.layers.loss.SamplingProbablityCorrection that logits should subtract the log of item probability.
• tfrs.experimental.optimizers.CompositeOptimizer: an optimizer that composes multiple individual optimizers which can be applied to different subsets of the model's variables.
• tfrs.layers.dcn.Cross and DotInteraction layers have been moved to tfrs.layers.feature_interaction package.

- Python
Published by maciejkula almost 5 years ago

tensorflow-recommenders - v0.4.0

Added

• TopK layers now come with a query_with_exclusions method, allowing certain candidates to be excluded from top-k retrieval.
• TPUEmbedding Keras layer for accelerating embedding lookups for large tables with TPU.

Changed

• factorized_top_k.Streaming layer now accepts a query model, like other factorized_top_k layers.

• Updated ScaNN to 1.2.0, which requires TensorFlow 2.4.x. When not using ScaNN, any TF >= 2.3 is still supported.

- Python
Published by maciejkula about 5 years ago

tensorflow-recommenders - v0.3.2

Changed

• Pinned TensorFlow to >= 2.3 when ScaNN is not being installed. When ScaNN is being installed, we pin on >= 2.3, < 2.4. This allows users to use TFRS on TF 2.4 when they are not using ScaNN.

- Python
Published by maciejkula about 5 years ago

tensorflow-recommenders - v0.3.1

Changed

• Pinned TensorFlow to 2.3.x and ScaNN to 1.1.1 to ensure TF and ScaNN versions are in lockstep.

- Python
Published by maciejkula about 5 years ago

tensorflow-recommenders - v0.3.0

Added

• Deep cross networks: efficient ways of learning feature interactions.
• ScaNN integration: efficient approximate maximum inner product search for fast retrieval.

- Python
Published by maciejkula over 5 years ago

tensorflow-recommenders - v0.2.0

Breaking changes

• Corpus retrieval metrics and layers have been reworked. tfrs.layers.corpus.DatasetTopk has been removed, tfrs.layers.corpus.DatasetIndexedTopK renamed to tfrs.layers.factorized_top_k.Streaming, tfrs.layers.ann.BruteForce renamed to tfrs.layers.factorized_top_k.BruteForce. All top-k retrieval layers (BruteForce, Streaming) now follow a common interface.

Added

• tfrs.tasks.Ranking.call now accepts a compute_metrics argument to allow switching off metric computation.
• tfrs.tasks.Ranking now accepts label and prediction metrics.
• Add metrics setter/getters on tfrs.tasks.Retrieval.

Changed

• Dataset parallelism enabled by default in DatasetTopK and DatasetIndexedTopK layers, bringing over 2x speed-ups to evaluations workloads.
• evaluate_metrics argument to tfrs.tasks.Retrieval.call renamed to compute_metrics.

- Python
Published by maciejkula over 5 years ago

tensorflow-recommenders - Update PyPI readme with fixed links.

- Python
Published by maciejkula over 5 years ago

tensorflow-recommenders - Minor PyPI upload changes.

- Python
Published by maciejkula over 5 years ago

tensorflow-recommenders - Initial release.

- Python
Published by maciejkula over 5 years ago