elliot.recommender.neural.UserAutoRec package¶
Submodules¶
elliot.recommender.neural.UserAutoRec.userautorec module¶
Module description:
-
class
elliot.recommender.neural.UserAutoRec.userautorec.
UserAutoRec
(data, config, params, *args, **kwargs)[source]¶ Bases:
elliot.recommender.recommender_utils_mixin.RecMixin
,elliot.recommender.base_recommender_model.BaseRecommenderModel
AutoRec: Autoencoders Meet Collaborative Filtering (User-based)
For further details, please refer to the paper
- Parameters
hidden_neuron – List of units for each layer
lr – Learning rate
l_w – Regularization coefficient
To include the recommendation model, add it to the config file adopting the following pattern:
models: UserAutoRec: meta: save_recs: True epochs: 10 batch_size: 512 hidden_neuron: 500 lr: 0.0001 l_w: 0.001
-
property
name
¶
elliot.recommender.neural.UserAutoRec.userautorec_model module¶
Module description:
-
class
elliot.recommender.neural.UserAutoRec.userautorec_model.
Decoder
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.base_layer.Layer
-
class
elliot.recommender.neural.UserAutoRec.userautorec_model.
Encoder
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.base_layer.Layer
-
class
elliot.recommender.neural.UserAutoRec.userautorec_model.
UserAutoRecModel
(*args, **kwargs)[source]¶ Bases:
tensorflow.python.keras.engine.training.Model
-
get_config
()[source]¶ 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.
-
Module contents¶
Module description: