TensorFlow - Incorporation de mots

L'incorporation de mots est le concept de mappage d'objets discrets tels que des mots vers des vecteurs et des nombres réels. C'est important pour les entrées pour l'apprentissage automatique. Le concept comprend des fonctions standard, qui transforment efficacement les objets d'entrée discrets en vecteurs utiles.

L'exemple d'illustration de l'entrée de l'incorporation de mots est comme indiqué ci-dessous -

blue: (0.01359, 0.00075997, 0.24608, ..., -0.2524, 1.0048, 0.06259)
blues: (0.01396, 0.11887, -0.48963, ..., 0.033483, -0.10007, 0.1158)
orange: (-0.24776, -0.12359, 0.20986, ..., 0.079717, 0.23865, -0.014213)
oranges: (-0.35609, 0.21854, 0.080944, ..., -0.35413, 0.38511, -0.070976)

Word2vec

Word2vec est l'approche la plus couramment utilisée pour la technique d'incorporation de mots non supervisée. Il entraîne le modèle de telle manière qu'un mot d'entrée donné prédit le contexte du mot en utilisant des skip-grammes.

TensorFlow permet de nombreuses façons d'implémenter ce type de modèle avec des niveaux croissants de sophistication et d'optimisation et en utilisant des concepts multithreading et des abstractions de plus haut niveau.

import os 
import math 
import numpy as np 
import tensorflow as tf 

from tensorflow.contrib.tensorboard.plugins import projector 
batch_size = 64 
embedding_dimension = 5 
negative_samples = 8 
LOG_DIR = "logs/word2vec_intro" 

digit_to_word_map = {
   1: "One", 
   2: "Two", 
   3: "Three", 
   4: "Four", 
   5: "Five", 
   6: "Six", 
   7: "Seven", 
   8: "Eight", 
   9: "Nine"} 
sentences = [] 

# Create two kinds of sentences - sequences of odd and even digits. 
   for i in range(10000): 
   rand_odd_ints = np.random.choice(range(1, 10, 2), 3) 
      sentences.append(" ".join([digit_to_word_map[r] for r in rand_odd_ints])) 
   rand_even_ints = np.random.choice(range(2, 10, 2), 3) 
      sentences.append(" ".join([digit_to_word_map[r] for r in rand_even_ints])) 
   
# Map words to indices
word2index_map = {} 
index = 0 

for sent in sentences: 
   for word in sent.lower().split(): 
   
   if word not in word2index_map: 
      word2index_map[word] = index 
      index += 1 
index2word_map = {index: word for word, index in word2index_map.items()} 

vocabulary_size = len(index2word_map) 

# Generate skip-gram pairs 
skip_gram_pairs = [] 

for sent in sentences: 
   tokenized_sent = sent.lower().split() 
   
   for i in range(1, len(tokenized_sent)-1):        
      word_context_pair = [[word2index_map[tokenized_sent[i-1]], 
         word2index_map[tokenized_sent[i+1]]], word2index_map[tokenized_sent[i]]] 
      
      skip_gram_pairs.append([word_context_pair[1], word_context_pair[0][0]]) 
      skip_gram_pairs.append([word_context_pair[1], word_context_pair[0][1]]) 

def get_skipgram_batch(batch_size): 
   instance_indices = list(range(len(skip_gram_pairs))) 
      np.random.shuffle(instance_indices)
   batch = instance_indices[:batch_size] 
   x = [skip_gram_pairs[i][0] for i in batch] 
   y = [[skip_gram_pairs[i][1]] for i in batch] 
   return x, y 
   
# batch example 
x_batch, y_batch = get_skipgram_batch(8) 
x_batch 
y_batch 
[index2word_map[word] for word in x_batch] [index2word_map[word[0]] for word in y_batch] 

# Input data, labels train_inputs = tf.placeholder(tf.int32, shape = [batch_size]) 
   train_labels = tf.placeholder(tf.int32, shape = [batch_size, 1]) 

# Embedding lookup table currently only implemented in CPU with 
   tf.name_scope("embeddings"): 
   embeddings = tf.Variable(    
      tf.random_uniform([vocabulary_size, embedding_dimension], -1.0, 1.0), 
         name = 'embedding') 
   # This is essentialy a lookup table 
   embed = tf.nn.embedding_lookup(embeddings, train_inputs) 
   
# Create variables for the NCE loss
nce_weights = tf.Variable(     
   tf.truncated_normal([vocabulary_size, embedding_dimension], stddev = 1.0 / 
      math.sqrt(embedding_dimension))) 
   
nce_biases = tf.Variable(tf.zeros([vocabulary_size])) 

loss = tf.reduce_mean(     
   tf.nn.nce_loss(weights = nce_weights, biases = nce_biases, inputs = embed, 
   labels = train_labels,num_sampled = negative_samples, 
   num_classes = vocabulary_size)) tf.summary.scalar("NCE_loss", loss) 
   
# Learning rate decay 
global_step = tf.Variable(0, trainable = False) 
   learningRate = tf.train.exponential_decay(learning_rate = 0.1, 
   global_step = global_step, decay_steps = 1000, decay_rate = 0.95, staircase = True) 

train_step = tf.train.GradientDescentOptimizer(learningRate).minimize(loss) 
   merged = tf.summary.merge_all() 
with tf.Session() as sess: 
   train_writer = tf.summary.FileWriter(LOG_DIR,    
      graph = tf.get_default_graph()) 
   saver = tf.train.Saver() 
   
   with open(os.path.join(LOG_DIR, 'metadata.tsv'), "w") as metadata: 
      metadata.write('Name\tClass\n') for k, v in index2word_map.items(): 
      metadata.write('%s\t%d\n' % (v, k)) 
   
   config = projector.ProjectorConfig() 
   embedding = config.embeddings.add() embedding.tensor_name = embeddings.name 
   
   # Link this tensor to its metadata file (e.g. labels). 
   embedding.metadata_path = os.path.join(LOG_DIR, 'metadata.tsv') 
      projector.visualize_embeddings(train_writer, config) 
   
   tf.global_variables_initializer().run() 
   
   for step in range(1000): 
      x_batch, y_batch = get_skipgram_batch(batch_size) summary, _ = sess.run(
         [merged, train_step], feed_dict = {train_inputs: x_batch, train_labels: y_batch})
      train_writer.add_summary(summary, step)
      
      if step % 100 == 0:
         saver.save(sess, os.path.join(LOG_DIR, "w2v_model.ckpt"), step)
         loss_value = sess.run(loss, feed_dict = {
            train_inputs: x_batch, train_labels: y_batch})
         print("Loss at %d: %.5f" % (step, loss_value))

   # Normalize embeddings before using
   norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings), 1, keep_dims = True))
   normalized_embeddings = embeddings /
      norm normalized_embeddings_matrix = sess.run(normalized_embeddings)
   
ref_word = normalized_embeddings_matrix[word2index_map["one"]]

cosine_dists = np.dot(normalized_embeddings_matrix, ref_word)
ff = np.argsort(cosine_dists)[::-1][1:10] for f in ff: print(index2word_map[f])
print(cosine_dists[f])

Production

Le code ci-dessus génère la sortie suivante -