基于tensorflow实现word2vec

使用NCE作为损失函数，SGD优化，skipGram模式

# -*- coding: utf-8 -*-"""Created on Sat Jul 22 17:35:12 2017@author: bryan"""import collectionsimport mathimport osimport randomimport zipfileimport numpy as npimport urllibimport tensorflow as tfimport matplotlib.pyplot as pltfrom sklearn.manifold import TSNEurl='http://mattmahoney.net/dc/'def maybe_download(filename,expected_bytes):    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 filenamefilename=maybe_download('text8.zip',31344016)def read_data(filename):    with zipfile.ZipFile(filename) as f:        data=tf.compat.as_str(f.read(f.namelist()[0])).split()    return datawords=read_data(filename)print('Data size',len(words))vocabulary_size=50000def build_dataset(words):    count=[['UNK',-1]]    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    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_dictionarydata,count,dictionary,reverse_dictionary=build_dataset(words)del wordsprint('Most common words (+UNK)',count[:5])print('Sample data ',data[:10],[reverse_dictionary[i] for i in data[:10]])data_index=0def generate_batch(batch_size,num_skips,skip_window):#num_skips 为对每个单词生成多少个样本, skpi_window为单词最远可以联系的距离    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):        target=skip_window        targets_to_avoid=[skip_window]        for j in range(num_skips):            while target in targets_to_avoid:                target=random.randint(0,span-1)            targets_to_avoid.append(target)            batch[i*num_skips+j]=buffer[skip_window]            labels[i*num_skips+j,0]=buffer[target]        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=128embedding_size=128 #生成的向量维度skip_window=1num_skips=2valid_size=16valid_window=100valid_examples=np.random.choice(valid_window,valid_size,replace=False)num_sampled=64    gragh=tf.Graph()with gragh.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)        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_steps=100001with tf.Session(graph=gragh) as session:    init.run()    print("Initialized")    average_loss=0    for step in range(num_steps):        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% 2000==0:            if step>0:                average_loss/=2000            print('Average loss at step',step,':',average_loss)            average_loss=0                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()                def plot_with_labels(low_dim_embs,labels,filename='tsne.png'):    assert low_dim_embs.shape[0] >= len(labels),'More labels than embeddings'    plt.figure(figsize=(18,18))    for i , label in enumerate(labels):        x,y=low_dim_embs[i,:]        plt.scatter(x,y)        plt.annotate(label,                     xy=(x,y),                     xytext=(5,2),                     textcoords='offset points',                     ha='right',                     va='bottom')    plt.savefig(filename)tsne=TSNE(perplexity=30,n_components=2,init='pca',n_iter=5000)plot_only=100low_dim_embs=tsne.fit_transform(final_embeddings[:plot_only,:])labels=[reverse_dictionary[i] for i in range(plot_only)]plot_with_labels(low_dim_embs,labels,'F:\\learning\\tf\\tsne.png')