elliot.recommender.visual_recommenders.DVBPR package

Submodules

elliot.recommender.visual_recommenders.DVBPR.DVBPR module

Module description:

class elliot.recommender.visual_recommenders.DVBPR.DVBPR.DVBPR(data, config, params, *args, **kwargs)

Bases: elliot.recommender.recommender_utils_mixin.RecMixin, elliot.recommender.base_recommender_model.BaseRecommenderModel

Visually-Aware Fashion Recommendation and Design with Generative Image Models

For further details, please refer to the paper

Parameters

• lr – Learning rate

• epochs – Number of epochs

• factors – Number of latent factors

• batch_size – Batch size

• batch_eval – Batch for evaluation

• lambda_1 – Regularization coefficient

• lambda_2 – CNN regularization coefficient

To include the recommendation model, add it to the config file adopting the following pattern:

models:
  DVBPR:
    meta:
      save_recs: True
    lr: 0.0001
    epochs: 50
    factors: 100
    batch_size: 128
    batch_eval: 128
    lambda_1: 0.0001
    lambda_2: 1.0

get_recommendations(k: int = 100)

property name

train()

elliot.recommender.visual_recommenders.DVBPR.DVBPR_model module

Module description:

class elliot.recommender.visual_recommenders.DVBPR.DVBPR_model.DVBPRModel(*args, **kwargs)

Bases: tensorflow.python.keras.engine.training.Model

call(inputs, training=None, mask=None)

get_config()

get_top_k(preds, train_mask, k=100)

predict_item_batch(start, stop, phi)

train_step(batch)

elliot.recommender.visual_recommenders.DVBPR.FeatureExtractor module

class elliot.recommender.visual_recommenders.DVBPR.FeatureExtractor.FeatureExtractor(*args, **kwargs)

Bases: tensorflow.python.keras.engine.training.Model, abc.ABC

call(inputs, training=None, mask=None)

Calls the model on new inputs.

In this case call just reapplies all ops in the graph to the new inputs (e.g. build a new computational graph from the provided inputs).

Parameters

• inputs – A tensor or list of tensors.

• training – Boolean or boolean scalar tensor, indicating whether to run the Network in training mode or inference mode.

• mask – A mask or list of masks. A mask can be either a tensor or None (no mask).

Returns

A tensor if there is a single output, or a list of tensors if there are more than one outputs.

Module contents

Module description: