tesonflow实现word2Vec

Posted 2020-10-15 哈哈哈哈哈哈哈哈哈

word2Vec 是实现从原始语料中学习字词空间向量的预测模型

使用word2Vec的skip_Gram模型

import collections
import math
import os
import random
import zipfile
import numpy as np
import urllib.request
import tensorflow as tf
url = \'http://mattmahoney.net/dc/\'
def maybe_download(filename,expected_bytes):
    "下载数据的压缩文件并核对文件尺寸大小"
    if not os.path.exists(filename):
        filename ,_=urllib.request.urlretrieve(url+filename,filename)
    statinfo = os.stat(filename)
    if statinfo.st_size == expected_bytes:
        print(\'Found and verified\',filename)
    else:
        print(statinfo.st_size)
        raise Exception(
            \'Failed to verify\'+filename +\'.can you get to it with a browser?\'
        )
    return filename
filename = maybe_download(\'text8.zip\',31344016)
def read_data(filename):
    with zipfile.ZipFile(filename) as f:
        "将数据转化为单词列表"
        data = tf.compat.as_str(f.read(f.namelist()[0])).split( )
    return data

words = read_data(filename)
print(\'Data size\',len(words))
"创建词汇表"
vocabulary_size =50000
def build_dataset(words):
    count = [[\'UNK\',-1]]
    "统计单词列表中单词的频数，把前50000的放入字典"
    count.extend(collections.Counter(words).most_common(vocabulary_size-1))
    dictionary = dict()
    for word,_ in count:
        dictionary[word] = len(dictionary)
    data = list()
    unk_count = 0
    """
    不在前50000里面 编码为0
    """
    for word in words:
        if word in dictionary:
            index = dictionary[word]
        else:
            index = 0
            unk_count +=1
        data.append(index)
    count[0][1] = unk_count
    reverse_dictionary = dict(zip(dictionary.values(),dictionary.keys()))
    return data,count,dictionary,reverse_dictionary
data, count,dictionary,reverse_dictionary = build_dataset(words)
del words
print(\'Most common words (+UNK)\',count[:5])
print(\'Sample data\',data[:10],[reverse_dictionary[i] for i in data[:10]])
data_index = 0
def generate_batch(batch_size,num_skips,skip_window):
    """

    :param batch_size:
    :param num_skips:  对每个单词生成多少样本 不大于2*skip_window
    :param skip_window: 滑动窗口步长
    :return: batch
              labels
    """
    global data_index
    assert batch_size %num_skips==0
    assert num_skips <=2*skip_window
    batch = np.ndarray(shape=(batch_size),dtype=np.int32)
    labels = np.ndarray(shape=(batch_size,1),dtype=np.int32)
    span = 2*skip_window+1
    buffer = collections.deque(maxlen=span)
    for _ in range(span):
        buffer.append(data[data_index])
        data_index = (data_index+1)%len(data)
    for i in range(batch_size//num_skips):  # 一块batch里面有包含的目标单词数
        target = skip_window
        target_to_avoid = [skip_window] #需要避免的单词列表
        for j in range(num_skips):
            # 找到可以使用的语境词语
            while target in target_to_avoid:
                target = random.randint(0,span-1)
            target_to_avoid.append(target)
            batch[i*num_skips+j]=buffer[skip_window] #目标词汇
            labels[i*num_skips+j,0] = buffer[target] #语境词汇
        "buffer此时已经填满，后续的数据会覆盖掉前面的数据"
        buffer.append(data[data_index])
        data_index=(data_index+1)%len(data)
    return batch,labels
batch,labels = generate_batch(batch_size=8,num_skips=2,skip_window=1)
for i in range(8):
    print(batch[i],reverse_dictionary[batch[i]],\'->\',labels[i,0],reverse_dictionary[labels[i,0]])
batch_size = 128
embedding_size = 128  #单词转化为稠密词向量的维度
skip_window = 1
num_skips = 2
valid_size = 16     #验证单词数
valid_window = 100   #验证单词数从频数最高的100个单词里面抽取
valid_examples = np.random.choice(valid_window,valid_size,replace=False) #负样本的噪声单词数
num_sampled =64
graph = tf.Graph()
with graph.as_default():
    train_inputs = tf.placeholder(tf.int32,shape=[batch_size])
    train_labels = tf.placeholder(tf.int32,shape=[batch_size,1])
    valid_dataset = tf.constant(valid_examples,dtype = tf.int32)
    with tf.device(\'/cpu:0\'):
        embeddings = tf.Variable(
            tf.random_uniform([vocabulary_size,embedding_size],-1.0,1.0)
        )
        embed = tf.nn.embedding_lookup(embeddings,train_inputs)  #查找输入对应的向量
        nce_weights = tf.Variable(
            tf.truncated_normal([vocabulary_size,embedding_size],
                                stddev=1.0/math.sqrt(embedding_size))
        )
        nce_biases = tf.Variable(tf.zeros([vocabulary_size]))
        loss = tf.reduce_mean(tf.nn.nce_loss(
            weights=nce_weights,
            biases= nce_biases,
            labels=train_labels,
            inputs=embed,
            num_sampled=num_sampled,
            num_classes=vocabulary_size
        ))
        optimizer = tf.train.GradientDescentOptimizer(1.0).minimize(loss)
        norm = tf.sqrt(tf.reduce_sum(tf.square(embeddings),1,keep_dims = True))
        normalized_embeddings=embeddings/norm
        valid_embeddings = tf.nn.embedding_lookup(normalized_embeddings,valid_dataset)
        similarity = tf.matmul(valid_embeddings,normalized_embeddings,transpose_b=True)
        init = tf.global_variables_initializer()
        num_step =100001
        with tf.Session(graph=graph)as session:
            init.run()
            print(\'Initialized\')
            average_loss = 0
            for step in range(num_step):
                batch_inputs,batch_labels=generate_batch(batch_size,num_skips,skip_window)
                feed_dict={train_inputs:batch_inputs,train_labels:batch_labels}
                _,loss_val = session.run([optimizer,loss],feed_dict=feed_dict)
                average_loss+=loss_val
                if step%200==0:
                    if step >0:
                        average_loss /=2000
                print(\'Average loss at step\',step,":",average_loss)
                average_loss=0
                "把验证单词的相关单词与所有单词计算相关性，并输出前8个相似性高的单词"
                if step%10000==0:
                    sim = similarity.eval()
                    for i in range(valid_size):
                        valid_word = reverse_dictionary[valid_examples[i]]
                        top_k = 8
                        nearest = (-sim[i,:]).argsort()[1:top_k+1]
                        log_str = "Nearest to %s:"%valid_word
                        for k in range(top_k):
                            close_word = reverse_dictionary[nearest[k]]
                            log_str= "%s %s,"%(log_str,close_word)
                        print(log_str)
            final_embeddings = normalized_embeddings.eval()

　　使用url下载数据集会出现数据集下载不完整，推荐手动下载数据集 网址为http://mattmahoney.net/dc/text8.zip

       结果如下