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课件/0411Skip-gram源码讲解/word2vec.c 0 → 100644
// Copyright 2013 Google Inc. All Rights Reserved.
// Copyright 2013 Google Inc. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include <stdio.h>
#include <stdlib.h>
#include <string.h>
#include <math.h>
#include <pthread.h>
#define MAX_STRING 100
#define EXP_TABLE_SIZE 1000
#define MAX_EXP 6
#define MAX_SENTENCE_LENGTH 1000
#define MAX_CODE_LENGTH 40
// 构建的全局变量
const int vocab_hash_size = 30000000; // Maximum 30 * 0.7 = 21M words in the vocabulary
// 定义的浮点数
typedef float real; // Precision of float numbers
// 词的结构体
struct vocab_word {
long long cn; // 出现的次数
int *point; // 从根结点到叶子节点的路径
char *word, *code, codelen;// 分别对应着词，Huffman编码，编码长度
};
char train_file[MAX_STRING], output_file[MAX_STRING];// 训练文件，输出文件
char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING];
struct vocab_word *vocab; // 出现的词的统计
// 初始化参数
int binary = 0, cbow = 1, debug_mode = 2, window = 5, min_count = 5, num_threads = 12, min_reduce = 1;
int *vocab_hash;// 存储词的hash
long long vocab_max_size = 1000, vocab_size = 0, layer1_size = 100;
long long train_words = 0, word_count_actual = 0, iter = 5, file_size = 0, classes = 0;
real alpha = 0.025, starting_alpha, sample = 1e-3;
real *syn0, *syn1, *syn1neg, *expTable;
clock_t start;
int hs = 0, negative = 5;
const int table_size = 1e8;
int *table;
// 生成负采样的概率表
void InitUnigramTable() {
int a, i;
double train_words_pow = 0;
double d1, power = 0.75;
table = (int *)malloc(table_size * sizeof(int));// int --> int
for (a = 0; a < vocab_size; a++) train_words_pow += pow(vocab[a].cn, power);
// 类似轮盘赌生成每个词的概率
i = 0;
d1 = pow(vocab[i].cn, power) / train_words_pow;
for (a = 0; a < table_size; a++) {
table[a] = i;
if (a / (double)table_size > d1) {
i++;
d1 += pow(vocab[i].cn, power) / train_words_pow;
}
if (i >= vocab_size) i = vocab_size - 1;
}
}
// Reads a single word from a file, assuming space + tab + EOL to be word boundaries
// 读取每一个词
void ReadWord(char *word, FILE *fin) {
int a = 0, ch;
while (!feof(fin)) {
ch = fgetc(fin);
if (ch == 13) continue; // 回车，\r
if ((ch == ' ') || (ch == '\t') || (ch == '\n')) {
if (a > 0) {// 当前的词还没结束
if (ch == '\n') ungetc(ch, fin);
break;
}
if (ch == '\n') {
strcpy(word, (char *)"</s>");// 换行符用</s>表示
return;
} else continue;
}
word[a] = ch;
a++;
if (a >= MAX_STRING - 1) a--; // Truncate too long words
}
word[a] = 0;
}
// Returns hash value of a word
// 取词的hash值
int GetWordHash(char *word) {
unsigned long long a, hash = 0;
for (a = 0; a < strlen(word); a++) hash = hash * 257 + word[a];
hash = hash % vocab_hash_size;
return hash;
}
// Returns position of a word in the vocabulary; if the word is not found, returns -1
// 查找词在词库中的位置，若没有查找到则返回-1
int SearchVocab(char *word) {
unsigned int hash = GetWordHash(word);
while (1) {
if (vocab_hash[hash] == -1) return -1;// 不存在该词
if (!strcmp(word, vocab[vocab_hash[hash]].word)) return vocab_hash[hash];// 返回索引值
hash = (hash + 1) % vocab_hash_size;
}
return -1;// 不存在该词
}
// Reads a word and returns its index in the vocabulary
// 返回的是在词库中的位置
int ReadWordIndex(FILE *fin) {
char word[MAX_STRING];
ReadWord(word, fin);
if (feof(fin)) return -1;
return SearchVocab(word);
}
// Adds a word to the vocabulary
// 为词库中增加一个词
int AddWordToVocab(char *word) {
unsigned int hash, length = strlen(word) + 1;// 单词的长度+1
if (length > MAX_STRING) length = MAX_STRING;
vocab[vocab_size].word = (char *)calloc(length, sizeof(char));//开始的位置增加指定的词
strcpy(vocab[vocab_size].word, word);
vocab[vocab_size].cn = 0;
vocab_size++;
// Reallocate memory if needed
if (vocab_size + 2 >= vocab_max_size) {
vocab_max_size += 1000;
vocab = (struct vocab_word *)realloc(vocab, vocab_max_size * sizeof(struct vocab_word));
}
hash = GetWordHash(word);// 对增加的词hash
while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;// hash的碰撞检测
vocab_hash[hash] = vocab_size - 1;// 词的hash值->词的词库中的索引
return vocab_size - 1;
}
// Used later for sorting by word counts
int VocabCompare(const void *a, const void *b) {
return ((struct vocab_word *)b)->cn - ((struct vocab_word *)a)->cn;
}
// Sorts the vocabulary by frequency using word counts
// 根据词出现的频率对词库中的词排序
void SortVocab() {
int a, size;
unsigned int hash;
// Sort the vocabulary and keep </s> at the first position
qsort(&vocab[1], vocab_size - 1, sizeof(struct vocab_word), VocabCompare);
// 排完序后需要重新做hash运算
for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
size = vocab_size;
train_words = 0;
for (a = 0; a < size; a++) {
// Words occuring less than min_count times will be discarded from the vocab
// 根据min_count对低频词的处理
if ((vocab[a].cn < min_count) && (a != 0)) {
vocab_size--;
free(vocab[a].word);
} else {
// Hash will be re-computed, as after the sorting it is not actual
hash=GetWordHash(vocab[a].word);
while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
vocab_hash[hash] = a;
train_words += vocab[a].cn;
}
}
vocab = (struct vocab_word *)realloc(vocab, (vocab_size + 1) * sizeof(struct vocab_word));
// Allocate memory for the binary tree construction
// 为构建huffman树申请空间
for (a = 0; a < vocab_size; a++) {
vocab[a].code = (char *)calloc(MAX_CODE_LENGTH, sizeof(char));
vocab[a].point = (int *)calloc(MAX_CODE_LENGTH, sizeof(int));
}
}
// Reduces the vocabulary by removing infrequent tokens
// 删除频率较小的词
void ReduceVocab() {
int a, b = 0;
unsigned int hash;
// 通过min_reduce控制
for (a = 0; a < vocab_size; a++) if (vocab[a].cn > min_reduce) {
vocab[b].cn = vocab[a].cn;
vocab[b].word = vocab[a].word;
b++;
} else free(vocab[a].word);
vocab_size = b;// 删减后词的个数
// 重新进行hash操作
for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1;
for (a = 0; a < vocab_size; a++) {
// Hash will be re-computed, as it is not actual
hash = GetWordHash(vocab[a].word);
while (vocab_hash[hash] != -1) hash = (hash + 1) % vocab_hash_size;
vocab_hash[hash] = a;
}
fflush(stdout);
min_reduce++;
}
// Create binary Huffman tree using the word counts
// Frequent words will have short uniqe binary codes
// 根据词库中的词频构建Huffman树
void CreateBinaryTree() {
long long a, b, i, min1i, min2i, pos1, pos2, point[MAX_CODE_LENGTH];
char code[MAX_CODE_LENGTH];
// 申请2倍的词的空间，（在这里完全没有必要申请这么多的空间）
long long *count = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));
long long *binary = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));
long long *parent_node = (long long *)calloc(vocab_size * 2 + 1, sizeof(long long));
// 分成两半进行初始化
for (a = 0; a < vocab_size; a++) count[a] = vocab[a].cn;// 前半部分初始化为每个词出现的次数
for (a = vocab_size; a < vocab_size * 2; a++) count[a] = 1e15;// 后半部分初始化为一个固定的常数
// 两个指针：
// pos1指向前半截的尾部
// pos2指向后半截的开始
pos1 = vocab_size - 1;
pos2 = vocab_size;
// Following algorithm constructs the Huffman tree by adding one node at a time
// 每次增加一个节点，构建Huffman树
for (a = 0; a < vocab_size - 1; a++) {
// First, find two smallest nodes 'min1, min2'
// 选择最小的节点min1
if (pos1 >= 0) {
if (count[pos1] < count[pos2]) {
min1i = pos1;
pos1--;
} else {
min1i = pos2;
pos2++;
}
} else {
min1i = pos2;
pos2++;
}
// 选择最小的节点min2
if (pos1 >= 0) {
if (count[pos1] < count[pos2]) {
min2i = pos1;
pos1--;
} else {
min2i = pos2;
pos2++;
}
} else {
min2i = pos2;
pos2++;
}
count[vocab_size + a] = count[min1i] + count[min2i];
// 设置父节点
parent_node[min1i] = vocab_size + a;
parent_node[min2i] = vocab_size + a;
binary[min2i] = 1;// 设置一个子树的编码为1
}
// Now assign binary code to each vocabulary word
// 为每一个词分配二进制编码，即Huffman编码
for (a = 0; a < vocab_size; a++) {// 针对每一个词
b = a;
i = 0;
while (1) {
code[i] = binary[b];// 找到当前的节点的编码
point[i] = b;// 记录从叶子节点到根结点的序列
i++;
b = parent_node[b];// 找到当前节点的父节点
if (b == vocab_size * 2 - 2) break;// 已经找到了根结点，根节点是没有编码的
}
vocab[a].codelen = i;// 词的编码长度
vocab[a].point[0] = vocab_size - 2;// 根结点
for (b = 0; b < i; b++) {
vocab[a].code[i - b - 1] = code[b];// 编码的反转
vocab[a].point[i - b] = point[b] - vocab_size;// 记录的是从根结点到叶子节点的路径
}
}
free(count);
free(binary);
free(parent_node);
}
// 读取输入的文件，并从输入文件中构建词库
void LearnVocabFromTrainFile() {
char word[MAX_STRING];// 存储每一个单词
FILE *fin;
long long a, i;
for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1; // 初始化
fin = fopen(train_file, "rb");
if (fin == NULL) {
printf("ERROR: training data file not found!\n");
exit(1);
}
vocab_size = 0;// 记录文件中的词的个数
AddWordToVocab((char *)"</s>");// 在最开始增加指定的词
// 开始从文本取每一个词
while (1) {
ReadWord(word, fin); // 读取每一个词
if (feof(fin)) break; // 判断文件是否读完
train_words++; // 记录词的个数
if ((debug_mode > 1) && (train_words % 100000 == 0)) {
printf("%lldK%c", train_words / 1000, 13);
fflush(stdout);
}
i = SearchVocab(word);// 查找词在词库中的位置
if (i == -1) {// 没有查找到对应的词
a = AddWordToVocab(word);// 增加词
vocab[a].cn = 1;// 设置词出现的次数为1
} else vocab[i].cn++;// 设置词出现的次数+1
// 根据当前词的个数和设定的hash表的大小，删除低频词
if (vocab_size > vocab_hash_size * 0.7) ReduceVocab();
}
SortVocab();// 根据词出现的频率对词进行排序
if (debug_mode > 0) {
printf("Vocab size: %lld\n", vocab_size);
printf("Words in train file: %lld\n", train_words);
}
file_size = ftell(fin);
fclose(fin);
}
// 保存词库
void SaveVocab() {
long long i;
FILE *fo = fopen(save_vocab_file, "wb");
// 保存词库时，保存的是词库中的词和词出现的次数
for (i = 0; i < vocab_size; i++) fprintf(fo, "%s %lld\n", vocab[i].word, vocab[i].cn);
fclose(fo);
}
void ReadVocab() {
long long a, i = 0;
char c;
char word[MAX_STRING];
FILE *fin = fopen(read_vocab_file, "rb");
if (fin == NULL) {
printf("Vocabulary file not found\n");
exit(1);
}
for (a = 0; a < vocab_hash_size; a++) vocab_hash[a] = -1; // 初始化vocab_hash
vocab_size = 0;
while (1) {
ReadWord(word, fin);
if (feof(fin)) break;
a = AddWordToVocab(word);
fscanf(fin, "%lld%c", &vocab[a].cn, &c);
i++;
}
SortVocab();
if (debug_mode > 0) {
printf("Vocab size: %lld\n", vocab_size);
printf("Words in train file: %lld\n", train_words);
}
fin = fopen(train_file, "rb");
if (fin == NULL) {
printf("ERROR: training data file not found!\n");
exit(1);
}
fseek(fin, 0, SEEK_END);
file_size = ftell(fin);
fclose(fin);
}
// 初始化网络
// 主要分为两个部分：1、对词向量的初始化；2、对映射层到输出层权重的初始化
void InitNet() {
long long a, b;
unsigned long long next_random = 1;
// 为每一个词分配词向量的空间
// 对齐分配内存,posix_memalign函数的用法类似于malloc的用法，最后一个参数的分配的内存的大小
a = posix_memalign((void **)&syn0, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn0 == NULL) {printf("Memory allocation failed\n"); exit(1);}
// 层次softmax的结构
if (hs) {
// 映射层到输出层之间的权重
a = posix_memalign((void **)&syn1, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn1 == NULL) {printf("Memory allocation failed\n"); exit(1);}
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
syn1[a * layer1_size + b] = 0;// 权重初始化为0
}
// 负采样的结构
if (negative>0) {
a = posix_memalign((void **)&syn1neg, 128, (long long)vocab_size * layer1_size * sizeof(real));
if (syn1neg == NULL) {printf("Memory allocation failed\n"); exit(1);}
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++)
syn1neg[a * layer1_size + b] = 0;
}
// 随机初始化
for (a = 0; a < vocab_size; a++) for (b = 0; b < layer1_size; b++) {
next_random = next_random * (unsigned long long)25214903917 + 11;
// 1、与：相当于将数控制在一定范围内
// 2、0xFFFF：65536
// 3、/65536：[0,1]之间
syn0[a * layer1_size + b] = (((next_random & 0xFFFF) / (real)65536) - 0.5) / layer1_size;// 初始化词向量
}
// 构建Huffman树
CreateBinaryTree();
}
void *TrainModelThread(void *id) {
long long a, b, d, cw, word, last_word, sentence_length = 0, sentence_position = 0;
long long word_count = 0, last_word_count = 0, sen[MAX_SENTENCE_LENGTH + 1];
long long l1, l2, c, target, label, local_iter = iter;
unsigned long long next_random = (long long)id;
real f, g;
clock_t now;
// layer1_size为词向量的长度
real *neu1 = (real *)calloc(layer1_size, sizeof(real));// 存储映射层的结果
real *neu1e = (real *)calloc(layer1_size, sizeof(real));// skip-gram中使用到的向量
FILE *fi = fopen(train_file, "rb");
// 利用多线程对训练文件划分，每个线程训练一部分的数据
fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);
// 训练模型的核心部分
while (1) {
// 每处理10000个词重新计算学习率
if (word_count - last_word_count > 10000) {// 每处理10000个词重新计算学习率
word_count_actual += word_count - last_word_count;
last_word_count = word_count;
if ((debug_mode > 1)) {
now=clock();
printf("%cAlpha: %f Progress: %.2f%% Words/thread/sec: %.2fk ", 13, alpha,
word_count_actual / (real)(iter * train_words + 1) * 100,
word_count_actual / ((real)(now - start + 1) / (real)CLOCKS_PER_SEC * 1000));
fflush(stdout);
}
// 重新计算alpha的值
alpha = starting_alpha * (1 - word_count_actual / (real)(iter * train_words + 1));
// 防止学习率过小
if (alpha < starting_alpha * 0.0001) alpha = starting_alpha * 0.0001;
}
// sentence_length=0表示的是当前还没有读取文本
// 开始读取文本，读取词的个数最多为MAX_SENTENCE_LENGTH
if (sentence_length == 0) {
// 需要根据文件指针的位置读取相应的文本
while (1) {
word = ReadWordIndex(fi);// 词在词库中的索引
if (feof(fi)) break;
if (word == -1) continue;// 没有查到该词
word_count++;
if (word == 0) break;
// The subsampling randomly discards frequent words while keeping the ranking same
if (sample > 0) {
real ran = (sqrt(vocab[word].cn / (sample * train_words)) + 1) * (sample * train_words) / vocab[word].cn;
next_random = next_random * (unsigned long long)25214903917 + 11;
if (ran < (next_random & 0xFFFF) / (real)65536) continue;
}
sen[sentence_length] = word;// 存储词在词库中的位置，word代表的是Index
sentence_length++;
if (sentence_length >= MAX_SENTENCE_LENGTH) break;// 达到指定长度
}
sentence_position = 0;// 将待处理的文本指针置0
}
// 当前的线程已经处理完分配给该线程的文本
if (feof(fi) || (word_count > train_words / num_threads)) {// 当前线程已经读完数据
word_count_actual += word_count - last_word_count;
// 当前线程的迭代次数
local_iter--;
if (local_iter == 0) break;// 迭代结束
// 重新置0，准备下一次重新迭代
word_count = 0;
last_word_count = 0;
sentence_length = 0;
// 重置文件指针
fseek(fi, file_size / (long long)num_threads * (long long)id, SEEK_SET);
continue;
}
// sen表示的是当前的线程读取到的每一个词对应在词库中的索引
word = sen[sentence_position];//sentence_position表示的是当前词
if (word == -1) continue;
// 初始化映射层
for (c = 0; c < layer1_size; c++) neu1[c] = 0;// 映射层的结果
for (c = 0; c < layer1_size; c++) neu1e[c] = 0;
// 产生一个0~window-1的随机数
next_random = next_random * (unsigned long long)25214903917 + 11;
b = next_random % window;
// 模型的训练
if (cbow) { // 训练CBOW模型
// in -> hidden
// 输入层到映射层
cw = 0;
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
c = sentence_position - window + a;// sentence_position表示的是当前的位置
// 判断c是否越界
if (c < 0) continue;
if (c >= sentence_length) continue;
last_word = sen[c];// 找到c对应的索引
if (last_word == -1) continue;
for (c = 0; c < layer1_size; c++) neu1[c] += syn0[c + last_word * layer1_size];// 累加
cw++;
}
if (cw) {
for (c = 0; c < layer1_size; c++) neu1[c] /= cw;// 计算均值
// 计算的中心词是word
// 层次Softmax
if (hs) for (d = 0; d < vocab[word].codelen; d++) {// word为当前词
// 计算输出层的输出
f = 0;
l2 = vocab[word].point[d] * layer1_size;// 找到第d个词对应的权重
// Propagate hidden -> output
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1[c + l2];// 映射层到输出层
if (f <= -MAX_EXP) continue;
else if (f >= MAX_EXP) continue;
else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];// Sigmoid结果
// 'g' is the gradient multiplied by the learning rate
g = (1 - vocab[word].code[d] - f) * alpha;
// Propagate errors output -> hidden
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];// 修改映射后的结果
// Learn weights hidden -> output
for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * neu1[c];// 修改映射层到输出层之间的权重
}
// NEGATIVE SAMPLING
// 负采样
if (negative > 0) for (d = 0; d < negative + 1; d++) {
// 标记target和label
if (d == 0) {// 正样本
target = word;
label = 1;
} else {// 选择出负样本
next_random = next_random * (unsigned long long)25214903917 + 11;
target = table[(next_random >> 16) % table_size];// 从table表中选择出负样本
// 重新选择
if (target == 0) target = next_random % (vocab_size - 1) + 1;
if (target == word) continue;
label = 0;
}
l2 = target * layer1_size;
f = 0;
for (c = 0; c < layer1_size; c++) f += neu1[c] * syn1neg[c + l2];// 映射层到输出层
// g
if (f > MAX_EXP) g = (label - 1) * alpha;
else if (f < -MAX_EXP) g = (label - 0) * alpha;
else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * neu1[c];
}
// hidden -> in
// 以上是从映射层到输出层的修改，现在返回修改每一个词向量
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
c = sentence_position - window + a;
if (c < 0) continue;
if (c >= sentence_length) continue;
last_word = sen[c];
if (last_word == -1) continue;
// 利用窗口内的所有词向量的梯度之和来更新
for (c = 0; c < layer1_size; c++) syn0[c + last_word * layer1_size] += neu1e[c];
}
}
} else { //train skip-gram 训练skip-gram模型
for (a = b; a < window * 2 + 1 - b; a++) if (a != window) {
c = sentence_position - window + a;
if (c < 0) continue;
if (c >= sentence_length) continue;
last_word = sen[c];
if (last_word == -1) continue;
l1 = last_word * layer1_size;
for (c = 0; c < layer1_size; c++) neu1e[c] = 0;
// HIERARCHICAL SOFTMAX
if (hs) for (d = 0; d < vocab[word].codelen; d++) {
f = 0;
l2 = vocab[word].point[d] * layer1_size;
// Propagate hidden -> output
// 映射层即为输入层
for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1[c + l2];
if (f <= -MAX_EXP) continue;
else if (f >= MAX_EXP) continue;
else f = expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))];
// 'g' is the gradient multiplied by the learning rate
g = (1 - vocab[word].code[d] - f) * alpha;
// Propagate errors output -> hidden
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1[c + l2];
// Learn weights hidden -> output
for (c = 0; c < layer1_size; c++) syn1[c + l2] += g * syn0[c + l1];
}
// NEGATIVE SAMPLING
if (negative > 0) for (d = 0; d < negative + 1; d++) {
if (d == 0) {
target = word;
label = 1;
} else {
next_random = next_random * (unsigned long long)25214903917 + 11;
target = table[(next_random >> 16) % table_size];
if (target == 0) target = next_random % (vocab_size - 1) + 1;
if (target == word) continue;
label = 0;
}
l2 = target * layer1_size;
f = 0;
for (c = 0; c < layer1_size; c++) f += syn0[c + l1] * syn1neg[c + l2];
if (f > MAX_EXP) g = (label - 1) * alpha;
else if (f < -MAX_EXP) g = (label - 0) * alpha;
else g = (label - expTable[(int)((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) * alpha;
for (c = 0; c < layer1_size; c++) neu1e[c] += g * syn1neg[c + l2];
for (c = 0; c < layer1_size; c++) syn1neg[c + l2] += g * syn0[c + l1];
}
// Learn weights input -> hidden
for (c = 0; c < layer1_size; c++) syn0[c + l1] += neu1e[c];
}
}
// 当已经处理完读入的所有文本，要重新继续往下读文本
sentence_position++;
if (sentence_position >= sentence_length) {
sentence_length = 0;
continue;
}
}
fclose(fi);
free(neu1);
free(neu1e);
pthread_exit(NULL);
}
// 模型训练
void TrainModel() {
long a, b, c, d;
FILE *fo;
pthread_t *pt = (pthread_t *)malloc(num_threads * sizeof(pthread_t));// 多线程
printf("Starting training using file %s\n", train_file);
starting_alpha = alpha;
// 区分是否指定词库
// 若指定词库，则从词库中读入词
// 若不指定词库，则从文件中构建词库
if (read_vocab_file[0] != 0)
ReadVocab();// 指定词库
else
LearnVocabFromTrainFile();// 不指定词库，从文件中构建词库
if (save_vocab_file[0] != 0) SaveVocab();// 判断是否需要保存词库
// 若没有指定输出文件，则退出
if (output_file[0] == 0) return;
InitNet();// 初始化网络
if (negative > 0) InitUnigramTable();// 利用负采样的方法
// 开始训练
start = clock();
for (a = 0; a < num_threads; a++) pthread_create(&pt[a], NULL, TrainModelThread, (void *)a);
for (a = 0; a < num_threads; a++) pthread_join(pt[a], NULL);
// 输出最终的训练结果
fo = fopen(output_file, "wb");
if (classes == 0) {
// Save the word vectors
fprintf(fo, "%lld %lld\n", vocab_size, layer1_size);
for (a = 0; a < vocab_size; a++) {
fprintf(fo, "%s ", vocab[a].word);
if (binary) for (b = 0; b < layer1_size; b++) fwrite(&syn0[a * layer1_size + b], sizeof(real), 1, fo);
else for (b = 0; b < layer1_size; b++) fprintf(fo, "%lf ", syn0[a * layer1_size + b]);
fprintf(fo, "\n");
}
} else {
// Run K-means on the word vectors
int clcn = classes, iter = 10, closeid;
int *centcn = (int *)malloc(classes * sizeof(int));
int *cl = (int *)calloc(vocab_size, sizeof(int));
real closev, x;
real *cent = (real *)calloc(classes * layer1_size, sizeof(real));
for (a = 0; a < vocab_size; a++) cl[a] = a % clcn;
for (a = 0; a < iter; a++) {
for (b = 0; b < clcn * layer1_size; b++) cent[b] = 0;
for (b = 0; b < clcn; b++) centcn[b] = 1;
for (c = 0; c < vocab_size; c++) {
for (d = 0; d < layer1_size; d++) cent[layer1_size * cl[c] + d] += syn0[c * layer1_size + d];
centcn[cl[c]]++;
}
for (b = 0; b < clcn; b++) {
closev = 0;
for (c = 0; c < layer1_size; c++) {
cent[layer1_size * b + c] /= centcn[b];
closev += cent[layer1_size * b + c] * cent[layer1_size * b + c];
}
closev = sqrt(closev);
for (c = 0; c < layer1_size; c++) cent[layer1_size * b + c] /= closev;
}
for (c = 0; c < vocab_size; c++) {
closev = -10;
closeid = 0;
for (d = 0; d < clcn; d++) {
x = 0;
for (b = 0; b < layer1_size; b++) x += cent[layer1_size * d + b] * syn0[c * layer1_size + b];
if (x > closev) {
closev = x;
closeid = d;
}
}
cl[c] = closeid;
}
}
// Save the K-means classes
for (a = 0; a < vocab_size; a++) fprintf(fo, "%s %d\n", vocab[a].word, cl[a]);
free(centcn);
free(cent);
free(cl);
}
fclose(fo);
}
// 解析命令行
int ArgPos(char *str, int argc, char **argv) {
int a;
for (a = 1; a < argc; a++) if (!strcmp(str, argv[a])) {// 查找对应的参数
if (a == argc - 1) {
printf("Argument missing for %s\n", str);
exit(1);
}
return a;// 匹配成功，返回值所在的位置
}
return -1;
}
int main(int argc, char **argv) {
int i;
// 判断参数的个数
if (argc == 1) {
printf("WORD VECTOR estimation toolkit v 0.1c\n\n");
printf("Options:\n");
printf("Parameters for training:\n");
printf("\t-train <file>\n");
printf("\t\tUse text data from <file> to train the model\n");
printf("\t-output <file>\n");
printf("\t\tUse <file> to save the resulting word vectors / word clusters\n");
printf("\t-size <int>\n");
printf("\t\tSet size of word vectors; default is 100\n");
printf("\t-window <int>\n");
printf("\t\tSet max skip length between words; default is 5\n");
printf("\t-sample <float>\n");
printf("\t\tSet threshold for occurrence of words. Those that appear with higher frequency in the training data\n");
printf("\t\twill be randomly down-sampled; default is 1e-3, useful range is (0, 1e-5)\n");
printf("\t-hs <int>\n");
printf("\t\tUse Hierarchical Softmax; default is 0 (not used)\n");
printf("\t-negative <int>\n");
printf("\t\tNumber of negative examples; default is 5, common values are 3 - 10 (0 = not used)\n");
printf("\t-threads <int>\n");
printf("\t\tUse <int> threads (default 12)\n");
printf("\t-iter <int>\n");
printf("\t\tRun more training iterations (default 5)\n");
printf("\t-min-count <int>\n");
printf("\t\tThis will discard words that appear less than <int> times; default is 5\n");
printf("\t-alpha <float>\n");
printf("\t\tSet the starting learning rate; default is 0.025 for skip-gram and 0.05 for CBOW\n");
printf("\t-classes <int>\n");
printf("\t\tOutput word classes rather than word vectors; default number of classes is 0 (vectors are written)\n");
printf("\t-debug <int>\n");
printf("\t\tSet the debug mode (default = 2 = more info during training)\n");
printf("\t-binary <int>\n");
printf("\t\tSave the resulting vectors in binary moded; default is 0 (off)\n");
printf("\t-save-vocab <file>\n");
printf("\t\tThe vocabulary will be saved to <file>\n");
printf("\t-read-vocab <file>\n");
printf("\t\tThe vocabulary will be read from <file>, not constructed from the training data\n");
printf("\t-cbow <int>\n");
printf("\t\tUse the continuous bag of words model; default is 1 (use 0 for skip-gram model)\n");
printf("\nExamples:\n");
printf("./word2vec -train data.txt -output vec.txt -size 200 -window 5 -sample 1e-4 -negative 5 -hs 0 -binary 0 -cbow 1 -iter 3\n\n");
return 0;
}
output_file[0] = 0;// 输出文件
save_vocab_file[0] = 0;// 输出词的文件
read_vocab_file[0] = 0;// 读入指定词的文件
// 解析word2vec所需用到的参数
if ((i = ArgPos((char *)"-size", argc, argv)) > 0) layer1_size = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-train", argc, argv)) > 0) strcpy(train_file, argv[i + 1]);
if ((i = ArgPos((char *)"-save-vocab", argc, argv)) > 0) strcpy(save_vocab_file, argv[i + 1]);
if ((i = ArgPos((char *)"-read-vocab", argc, argv)) > 0) strcpy(read_vocab_file, argv[i + 1]);
if ((i = ArgPos((char *)"-debug", argc, argv)) > 0) debug_mode = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-binary", argc, argv)) > 0) binary = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-cbow", argc, argv)) > 0) cbow = atoi(argv[i + 1]);
if (cbow) alpha = 0.05;
if ((i = ArgPos((char *)"-alpha", argc, argv)) > 0) alpha = atof(argv[i + 1]);
if ((i = ArgPos((char *)"-output", argc, argv)) > 0) strcpy(output_file, argv[i + 1]);
if ((i = ArgPos((char *)"-window", argc, argv)) > 0) window = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-sample", argc, argv)) > 0) sample = atof(argv[i + 1]);
if ((i = ArgPos((char *)"-hs", argc, argv)) > 0) hs = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-negative", argc, argv)) > 0) negative = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-threads", argc, argv)) > 0) num_threads = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-iter", argc, argv)) > 0) iter = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-min-count", argc, argv)) > 0) min_count = atoi(argv[i + 1]);
if ((i = ArgPos((char *)"-classes", argc, argv)) > 0) classes = atoi(argv[i + 1]);
vocab = (struct vocab_word *)calloc(vocab_max_size, sizeof(struct vocab_word));// 存储每一个词的结构体
vocab_hash = (int *)calloc(vocab_hash_size, sizeof(int));// 存储词的hash
expTable = (real *)malloc((EXP_TABLE_SIZE + 1) * sizeof(real));// 申请EXP_TABLE_SIZE+1个空间
// 计算sigmoid值
for (i = 0; i < EXP_TABLE_SIZE; i++) {
expTable[i] = exp((i / (real)EXP_TABLE_SIZE * 2 - 1) * MAX_EXP); // Precompute the exp() table
expTable[i] = expTable[i] / (expTable[i] + 1); // Precompute f(x) = x / (x + 1)
}
// 开始模型训练
TrainModel();// 模型训练
return 0;
}
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