Word2vec.py

import collections
import math
import os
import random
import zipfile
import numpy as np
import urllib
import tensorflow as tf

url = '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 get file')
    return filename

filename = 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 data

words = read_data(filename)
print ('Data size',len(words))

vocabulary_size = 50000
def 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_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):
    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+1])
        data_index = (data_index+1) % len(data)
    for i in range(batch_size//num_skips):
        target = skip_window
        target_to_avoid = [skip_window]
        for j in range(num_skips):
            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]
            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 = 128
embedding_size = 128
skip_window = 1
num_skips = 2
valid_size = 16
valid_window = 100
valid_examples = np.random.choice(valid_window,valid_size,replace=False)
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)

        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_embeddimhs = embeddings / norm
    valid_embeddings = tf.nn.embedding_lookup(normalized_embeddimhs,valid_dataset)
    similarity = tf.matmul(valid_embeddings,normalized_embeddimhs,transpose_b=True)

    init = tf.global_variables_initializer()



num_steps = 100001
with tf.Session(graph=graph) as session:
    session.run(init)
    print ("Initialized")

    average_loss = 0
    for step in range(num_steps):
        batch_inputs, batch_labels = generate_batch(batch_size,num_steps,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:
            print ("Average loss at step",step,": ",average_loss)
            average_loss = 0