电商网站开发过程是什么,推广整合营销,阿凡达网站建设网,全球速卖通大学Word embedding是从离散对象(如单词)映射到向量和实数的概念#xff0c;可将离散的输入对象有效地转换为有用的向量。
Word embedding的输入如下所示:
blue: (0.01359, 0.00075997, 0.24608, ..., -0.2524, 1.0048, 0.06259)
blues: (0.01396, 0.11887, -0.48963, ..., 0.03…
Word embedding是从离散对象(如单词)映射到向量和实数的概念可将离散的输入对象有效地转换为有用的向量。
Word embedding的输入如下所示:
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是用于无监督最常见方法它以一种方式训练模型即给定的输入单词通过使用跳跃语法来预测单词的上下文。
TensorFlow提供了多种方法来实现这种模型从而提高了复杂性和优化级别并使用了多线程概念和更高级别的抽象。
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 [] # 创建两种句子 - 奇数和偶数序列。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])) # 将单词映射到索引
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) # 生成skip-gram对
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[i1]]], 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 #批处理示例
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] #输入数据标签 train_inputstf.placeholder(tf.int32, shape[batch_size])train_labels tf.placeholder(tf.int32, shape [batch_size, 1]) # 嵌入查找表目前仅在 CPU 中实现tf.name_scope(embeddings): embeddings tf.Variable( tf.random_uniform([vocabulary_size, embedding_dimension], -1.0, 1.0), name embedding) # 这本质上是一个查找表embed tf.nn.embedding_lookup(embeddings, train_inputs) # 为 NCE 损失创建变量
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) # 学习率衰减
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 Class
) for k, v in index2word_map.items(): metadata.write(%s %d% (v, k)) config projector.ProjectorConfig() embedding config.embeddings.add() embedding.tensor_name embeddings.name # 将此张量链接到其元数据文件例如标签。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))# 在使用之前规范化嵌入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])
上面的代码生成以下输出- TensorFlow - 单词嵌入 - 无涯教程网无涯教程网提供Word embedding是从离散对象(如单词)映射到向量和实数的概念可将离散的输入对象有效...https://www.learnfk.com/tensorflow/tensorflow-word-embedding.html
