Вложение слова – это концепция отображения отдельных объектов, таких как слова, на векторы и действительные числа. Это важно для ввода для машинного обучения. Концепция включает в себя стандартные функции, которые эффективно преобразуют дискретные входные объекты в полезные векторы.
Пример ввода слов встраивания показан ниже:
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 является наиболее распространенным подходом, используемым для неконтролируемой техники встраивания слов. Он обучает модель таким образом, что заданное входное слово предсказывает контекст слова с помощью скип-грамм.
TensorFlow позволяет реализовать множество способов реализации этой модели с повышением уровня сложности и оптимизации, а также с использованием концепций многопоточности и абстракций более высокого уровня.
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])
Приведенный выше код генерирует следующий вывод: