elliot.recommender.autoencoders.vae package

Submodules

elliot.recommender.autoencoders.vae.multi_vae module

Module description:

class elliot.recommender.autoencoders.vae.multi_vae.MultiVAE(data, config, params, *args, **kwargs)

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

Variational Autoencoders for Collaborative Filtering

For further details, please refer to the paper

Parameters

• intermediate_dim – Number of intermediate dimension

• latent_dim – Number of latent factors

• reg_lambda – Regularization coefficient

• lr – Learning rate

• dropout_pkeep – Dropout probaility

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

models:
  MultiVAE:
    meta:
      save_recs: True
    epochs: 10
    batch_size: 512
    intermediate_dim: 600
    latent_dim: 200
    reg_lambda: 0.01
    lr: 0.001
    dropout_pkeep: 1

property name

train()

elliot.recommender.autoencoders.vae.multi_vae_model module

Module description:

class elliot.recommender.autoencoders.vae.multi_vae_model.Decoder(*args, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

Converts z, the encoded digit vector, back into a readable digit.

call(inputs, **kwargs)

class elliot.recommender.autoencoders.vae.multi_vae_model.Encoder(*args, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

Maps MNIST digits to a triplet (z_mean, z_log_var, z).

call(inputs, training=None)

class elliot.recommender.autoencoders.vae.multi_vae_model.Sampling(*args, **kwargs)

Bases: tensorflow.python.keras.engine.base_layer.Layer

Uses (z_mean, z_log_var) to sample z, the vector encoding a digit.

call(inputs)

class elliot.recommender.autoencoders.vae.multi_vae_model.VariationalAutoEncoder(*args, **kwargs)

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

Combines the encoder and decoder into an end-to-end model for training.

call(inputs, training=None, **kwargs)

get_config()

Returns the config of the layer.

A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.

The config of a layer does not include connectivity information, nor the layer class name. These are handled by Network (one layer of abstraction above).

Returns

Python dictionary.

get_top_k(preds, train_mask, k=100)

predict(inputs, training=False, **kwargs)

Get full predictions on the whole users/items matrix.

Returns

The matrix of predicted values.

train_step(batch, anneal_ph=0.0, **kwargs)

Module contents

Module description: