Source code for texar.tf.modules.embedders.embedder_base

# Copyright 2018 The Texar Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
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"""
The base embedder class.
"""

import tensorflow as tf

from texar.tf.module_base import ModuleBase
from texar.tf.modules.embedders import embedder_utils
from texar.tf.utils.shapes import shape_list

# pylint: disable=invalid-name

__all__ = [
    "EmbedderBase"
]


[docs]class EmbedderBase(ModuleBase): r"""The base embedder class that all embedder classes inherit. Args: num_embeds (int, optional): The number of embedding elements, e.g., the vocabulary size of a word embedder. hparams (dict or HParams, optional): Embedder hyperparameters. Missing hyperparamerter will be set to default values. See :meth:`default_hparams` for the hyperparameter structure and default values. """ def __init__(self, num_embeds=None, hparams=None): ModuleBase.__init__(self, hparams) self._num_embeds = num_embeds # pylint: disable=attribute-defined-outside-init def _init_parameterized_embedding(self, init_value, num_embeds, hparams): self._embedding = embedder_utils.get_embedding( hparams, init_value, num_embeds, self.variable_scope) if hparams.trainable: self._add_trainable_variable(self._embedding) self._num_embeds = shape_list(self._embedding)[0] self._dim = shape_list(self._embedding)[1:] self._dim_rank = len(self._dim) if self._dim_rank == 1: self._dim = self._dim[0] def _get_dropout_layer(self, hparams, ids_rank=None, dropout_input=None, dropout_strategy=None): r"""Creates dropout layer according to dropout strategy. Called in :meth:`_build`. """ dropout_layer = None st = dropout_strategy st = hparams.dropout_strategy if st is None else st if hparams.dropout_rate > 0.: if st == 'element': noise_shape = None elif st == 'item': assert dropout_input is not None assert ids_rank is not None noise_shape = (shape_list(dropout_input)[:ids_rank] + [1] * self._dim_rank) elif st == 'item_type': noise_shape = [None] + [1] * self._dim_rank # type: ignore else: raise ValueError('Unknown dropout strategy: {}'.format(st)) dropout_layer = tf.layers.Dropout( rate=hparams.dropout_rate, noise_shape=noise_shape) return dropout_layer
[docs] @staticmethod def default_hparams(): r"""Returns a dictionary of hyperparameters with default values. .. code-block:: python { "name": "embedder" } """ return { "name": "embedder" }
@property def num_embeds(self): r"""The number of embedding elements. """ return self._num_embeds