Source code for tensorlayer.layers.embedding

#! /usr/bin/python
# -*- coding: utf-8 -*-

import numpy as np
import tensorflow as tf

import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.layers.core import Layer

# from tensorlayer.layers.core import LayersConfig

__all__ = [
    'OneHot',
    'Word2vecEmbedding',
    'Embedding',
    'AverageEmbedding',
]


[docs]class OneHot(Layer): """ The :class:`OneHot` class is the starting layer of a neural network, see ``tf.one_hot``. Useful link: `https://www.tensorflow.org/api_docs/python/tf/one_hot`. Parameters ---------- depth : None or int If the input indices is rank N, the output will have rank N+1. The new axis is created at dimension `axis` (default: the new axis is appended at the end). on_value : None or number The value to represnt `ON`. If None, it will default to the value 1. off_value : None or number The value to represnt `OFF`. If None, it will default to the value 0. axis : None or int The axis. dtype : None or TensorFlow dtype The data type, None means tf.float32. name : str A unique layer name. Examples --------- >>> import tensorflow as tf >>> import tensorlayer as tl >>> net = tl.layers.Input([32], dtype=tf.int32) >>> onehot = tl.layers.OneHot(depth=8) >>> print(onehot) OneHot(depth=8, name='onehot') >>> tensor = tl.layers.OneHot(depth=8)(net) >>> print(tensor) tf.Tensor([...], shape=(32, 8), dtype=float32) """ def __init__(self, depth=None, on_value=None, off_value=None, axis=None, dtype=None, name=None): #'input'): super(OneHot, self).__init__(name) self.depth = depth self.on_value = on_value self.off_value = off_value self.axis = axis self.dtype = dtype logging.info("OneHotInput %s" % (self.name)) if not self._built: self.build(tuple()) self._built = True if self.depth is None: raise RuntimeError(self.__class__.__name__ + ": depth == None the number of output units is undefined") def __repr__(self): s = ('{classname}(depth={depth}') if self.on_value is not None: s += ', on_value={on_value}' if self.off_value is not None: s += ', off_value={off_value}' if self.axis is not None: s += ', axis={axis}' if self.name is not None: s += ', name=\'{name}\'' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): pass # @tf.function def forward(self, inputs): """ Parameters ---------- inputs : input tensor The inputs are indices. The locations represented by indices in indices take value on_value, while all other locations take value off_value. """ outputs = tf.one_hot( inputs, self.depth, on_value=self.on_value, off_value=self.off_value, axis=self.axis, dtype=self.dtype ) return outputs
[docs]class Word2vecEmbedding(Layer): """ The :class:`Word2vecEmbedding` class is a fully connected layer. For Word Embedding, words are input as integer index. The output is the embedded word vector. The layer integrates NCE loss by default (activate_nce_loss=True). If the NCE loss is activated, in a dynamic model, the computation of nce loss can be turned off in customised forward feeding by setting use_nce_loss=False when the layer is called. The NCE loss can be deactivated by setting activate_nce_loss=False. Parameters ---------- vocabulary_size : int The size of vocabulary, number of words embedding_size : int The number of embedding dimensions num_sampled : int The number of negative examples for NCE loss activate_nce_loss : boolean Whether activate nce loss or not. By default, True If True, the layer will return both outputs of embedding and nce_cost in forward feeding. If False, the layer will only return outputs of embedding. In a dynamic model, the computation of nce loss can be turned off in forward feeding by setting use_nce_loss=False when the layer is called. In a static model, once the model is constructed, the computation of nce loss cannot be changed (always computed or not computed). nce_loss_args : dictionary The arguments for tf.nn.nce_loss() E_init : initializer The initializer for initializing the embedding matrix nce_W_init : initializer The initializer for initializing the nce decoder weight matrix nce_b_init : initializer The initializer for initializing of the nce decoder bias vector name : str A unique layer name Attributes ---------- outputs : Tensor The embedding layer outputs. normalized_embeddings : Tensor Normalized embedding matrix. nce_weights : Tensor The NCE weights only when activate_nce_loss is True. nce_biases: Tensor The NCE biases only when activate_nce_loss is True. Examples -------- Word2Vec With TensorLayer (Example in `examples/text_word_embedding/tutorial_word2vec_basic.py`) >>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size = 8 >>> embedding_size = 50 >>> inputs = tl.layers.Input([batch_size], dtype=tf.int32) >>> labels = tl.layers.Input([batch_size, 1], dtype=tf.int32) >>> emb_net = tl.layers.Word2vecEmbedding( >>> vocabulary_size=10000, >>> embedding_size=embedding_size, >>> num_sampled=100, >>> activate_nce_loss=True, # the nce loss is activated >>> nce_loss_args={}, >>> E_init=tl.initializers.random_uniform(minval=-1.0, maxval=1.0), >>> nce_W_init=tl.initializers.truncated_normal(stddev=float(1.0 / np.sqrt(embedding_size))), >>> nce_b_init=tl.initializers.constant(value=0.0), >>> name='word2vec_layer', >>> ) >>> print(emb_net) Word2vecEmbedding(vocabulary_size=10000, embedding_size=50, num_sampled=100, activate_nce_loss=True, nce_loss_args={}) >>> embed_tensor = emb_net(inputs, use_nce_loss=False) # the nce loss is turned off and no need to provide labels >>> embed_tensor = emb_net([inputs, labels], use_nce_loss=False) # the nce loss is turned off and the labels will be ignored >>> embed_tensor, embed_nce_loss = emb_net([inputs, labels]) # the nce loss is calculated >>> outputs = tl.layers.Dense(n_units=10, name="dense")(embed_tensor) >>> model = tl.models.Model(inputs=[inputs, labels], outputs=[outputs, embed_nce_loss], name="word2vec_model") # a static model >>> out = model([data_x, data_y], is_train=True) # where data_x is inputs and data_y is labels References ---------- `https://www.tensorflow.org/tutorials/representation/word2vec` """ def __init__( self, vocabulary_size, embedding_size, num_sampled=64, activate_nce_loss=True, nce_loss_args=None, E_init=tl.initializers.random_uniform(minval=-1.0, maxval=1.0), nce_W_init=tl.initializers.truncated_normal(stddev=0.03), nce_b_init=tl.initializers.constant(value=0.0), name=None, #'word2vec', ): super(Word2vecEmbedding, self).__init__(name) self.vocabulary_size = vocabulary_size self.embedding_size = embedding_size self.num_sampled = num_sampled self.E_init = E_init self.activate_nce_loss = activate_nce_loss if self.activate_nce_loss: self.nce_loss_args = nce_loss_args self.nce_W_init = nce_W_init self.nce_b_init = nce_b_init if not self._built: self.build(tuple()) self._built = True logging.info("Word2vecEmbedding %s: (%d, %d)" % (self.name, self.vocabulary_size, self.embedding_size)) def __repr__(self): s = ('{classname}(') s += 'vocabulary_size={vocabulary_size}' s += ', embedding_size={embedding_size}' s += ', num_sampled={num_sampled}' s += ', activate_nce_loss={activate_nce_loss}' if self.activate_nce_loss: s += ', nce_loss_args={nce_loss_args}' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): """ Parameters ---------- inputs_shape : tuple the shape of inputs tensor """ # Look up embeddings for inputs. # Note: a row of 'embeddings' is the vector representation of a word. # for the sake of speed, it is better to slice the embedding matrix # instead of transferring a word id to one-hot-format vector and then # multiply by the embedding matrix. # embed is the outputs of the hidden layer (embedding layer), it is a # row vector with 'embedding_size' values. self.embeddings = self._get_weights( "embeddings", shape=(self.vocabulary_size, self.embedding_size), init=self.E_init, ) self.normalized_embeddings = tf.nn.l2_normalize(self.embeddings, 1) if self.activate_nce_loss: # Construct the variables for the NCE loss (i.e. negative sampling) self.nce_weights = self._get_weights( "nce_weights", shape=(self.vocabulary_size, self.embedding_size), init=self.nce_W_init, ) self.nce_biases = self._get_weights( "nce_biases", shape=(self.vocabulary_size, ), init=self.nce_b_init, ) # @tf.function def forward(self, inputs, use_nce_loss=None): """ Parameters ---------- inputs : tensor or list If the nce loss is activated and is used, the argument should be a list of two tensors [inputs, labels]. Otherwise, the argument should be a single tensor which is inputs. use_nce_loss: boolean Whether use NCE loss in this run. If the nce loss is used, the activate_nce_loss should be True when the layer is initialized. By default, same as activate_nce_loss. Outputs: ---------- outputs: tensor nce_cost: tensor The nce_cost is returned only if the nce_loss is used. """ ids = inputs[0] if isinstance(inputs, list) else inputs outputs = tf.nn.embedding_lookup(params=self.embeddings, ids=ids) if use_nce_loss is True and not self.activate_nce_loss: raise AttributeError( "The nce loss is not activated when the %s is initialized. Please set activate_nce_loss=True." % self.__class__.__name__ ) if self.activate_nce_loss and (use_nce_loss is True or use_nce_loss is None): if not isinstance(inputs, list): raise ValueError("If nce loss is used, the labels of inputs must be provided.") nce_cost = tf.reduce_mean( input_tensor=tf.nn.nce_loss( weights=self.nce_weights, biases=self.nce_biases, inputs=outputs, labels=inputs[1], num_sampled=self.num_sampled, num_classes=self.vocabulary_size, **self.nce_loss_args ) ) return outputs, nce_cost return outputs
[docs]class Embedding(Layer): """ The :class:`Embedding` class is a look-up table for word embedding. Word content are accessed using integer indexes, then the output is the embedded word vector. To train a word embedding matrix, you can used :class:`Word2vecEmbedding`. If you have a pre-trained matrix, you can assign the parameters into it. Parameters ---------- vocabulary_size : int The size of vocabulary, number of words. embedding_size : int The number of embedding dimensions. E_init : initializer The initializer for the embedding matrix. E_init_args : dictionary The arguments for embedding matrix initializer. name : str A unique layer name. Attributes ---------- outputs : tensor The embedding layer output is a 3D tensor in the shape: (batch_size, num_steps(num_words), embedding_size). Examples -------- >>> import tensorflow as tf >>> import tensorlayer as tl >>> input = tl.layers.Input([8, 100], dtype=tf.int32) >>> embed = tl.layers.Embedding(vocabulary_size=1000, embedding_size=50, name='embed') >>> print(embed) Embedding(vocabulary_size=1000, embedding_size=50) >>> tensor = embed(input) >>> print(tensor) tf.Tensor([...], shape=(8, 100, 50), dtype=float32) """ def __init__( self, vocabulary_size, embedding_size, E_init=tl.initializers.random_uniform(-0.1, 0.1), name=None, #'embedding', ): super(Embedding, self).__init__(name) self.vocabulary_size = vocabulary_size self.embedding_size = embedding_size self.E_init = E_init if not self._built: self.build(tuple()) self._built = True logging.info("Embedding %s: (%d, %d)" % (self.name, self.vocabulary_size, self.embedding_size)) def __repr__(self): s = ('{classname}(') s += 'vocabulary_size={vocabulary_size}' s += ', embedding_size={embedding_size}' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): """ Parameters ---------- inputs_shape : tuple the shape of inputs tensor """ self.embeddings = self._get_weights( "embeddings", shape=(self.vocabulary_size, self.embedding_size), init=self.E_init, ) # @tf.function def forward(self, inputs): """ Parameters ---------- inputs : Tensor The input of a network. """ outputs = tf.nn.embedding_lookup(params=self.embeddings, ids=inputs) return outputs
[docs]class AverageEmbedding(Layer): """The :class:`AverageEmbedding` averages over embeddings of inputs. This is often used as the input layer for models like DAN[1] and FastText[2]. Parameters ---------- vocabulary_size : int The size of vocabulary. embedding_size : int The dimension of the embedding vectors. pad_value : int The scalar padding value used in inputs, 0 as default. E_init : initializer The initializer of the embedding matrix. name : str A unique layer name. Attributes ---------- outputs : tensor The embedding layer output is a 2D tensor in the shape: (batch_size, embedding_size). References ---------- - [1] Iyyer, M., Manjunatha, V., Boyd-Graber, J., & Daum’e III, H. (2015). Deep Unordered Composition Rivals Syntactic Methods for Text Classification. In Association for Computational Linguistics. - [2] Joulin, A., Grave, E., Bojanowski, P., & Mikolov, T. (2016). `Bag of Tricks for Efficient Text Classification. <http://arxiv.org/abs/1607.01759>`__ Examples --------- >>> import tensorflow as tf >>> import tensorlayer as tl >>> batch_size = 8 >>> length = 5 >>> input = tl.layers.Input([batch_size, length], dtype=tf.int32) >>> avgembed = tl.layers.AverageEmbedding(vocabulary_size=1000, embedding_size=50, name='avg') >>> print(avgembed) AverageEmbedding(vocabulary_size=1000, embedding_size=50, pad_value=0) >>> tensor = avgembed(input) >>> print(tensor) tf.Tensor([...], shape=(8, 50), dtype=float32) """ def __init__( self, vocabulary_size, embedding_size, pad_value=0, E_init=tl.initializers.random_uniform(-0.1, 0.1), name=None, # 'average_embedding', ): super(AverageEmbedding, self).__init__(name) self.vocabulary_size = vocabulary_size self.embedding_size = embedding_size self.pad_value = pad_value self.E_init = E_init if not self._built: self.build(tuple()) self._built = True logging.info("AverageEmbedding %s: (%d, %d)" % (self.name, self.vocabulary_size, self.embedding_size)) def __repr__(self): s = ('{classname}(') s += 'vocabulary_size={vocabulary_size}' s += ', embedding_size={embedding_size}' s += ', pad_value={pad_value}' s += ')' return s.format(classname=self.__class__.__name__, **self.__dict__) def build(self, inputs_shape): """ Parameters ---------- inputs_shape : tuple the shape of inputs tensor. """ # if len(inputs_shape) != 2: # raise ValueError('inputs must be of size (batch_size, sentence_length)') self.embeddings = self._get_weights( "embeddings", shape=(self.vocabulary_size, self.embedding_size), init=self.E_init, ) # @tf.function def forward(self, inputs): """ Parameters ---------- inputs : tensor The network input. For word inputs, please use integer index format, 2D tensor: (batch_size, sentence_length). """ word_embeddings = tf.nn.embedding_lookup( params=self.embeddings, ids=inputs, name='word_embeddings', ) # Zero out embeddings of pad value masks = tf.not_equal(inputs, self.pad_value, name='masks') word_embeddings *= tf.cast(tf.expand_dims(masks, axis=-1), dtype=tf.float32) sum_word_embeddings = tf.reduce_sum(input_tensor=word_embeddings, axis=1) # Count number of non-padding words in each sentence sentence_lengths = tf.math.count_nonzero( masks, axis=1, keepdims=True, dtype=tf.float32, name='sentence_lengths', ) sentence_embeddings = tf.divide( sum_word_embeddings, sentence_lengths + 1e-8, # Add epsilon to avoid dividing by 0 name='sentence_embeddings' ) outputs = sentence_embeddings return outputs