| // 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 <locale.h> |
| #include <stdio.h> |
| #include <stdlib.h> |
| #include <string.h> |
| #include <unistd.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_CC 100 |
| #define MAX_CODE_LENGTH 40 |
| |
| const int vocab_hash_size = 30000000; // Maximum 30 * 0.7 = 21M words in the vocabulary |
| |
| typedef float real; // Precision of float numbers |
| |
| struct vocab_word { |
| long long cn; |
| int *point; |
| char *word, *code, codelen; |
| }; |
| |
| char train_file[MAX_STRING], output_file[MAX_STRING]; |
| char save_vocab_file[MAX_STRING], read_vocab_file[MAX_STRING]; |
| char save_net_file[MAX_STRING], read_net_file[MAX_STRING]; |
| struct vocab_word *vocab; |
| int binary = 0, type = 1, debug_mode = 2, window = 5, min_count = 5, |
| num_threads = 12, min_reduce = 1; |
| int *vocab_hash; |
| long long *threadPos; |
| int *threadIters; |
| 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, *syn1nce, *expTable; |
| real avgWordLength=0; |
| clock_t start; |
| |
| real *syn1_window, *syn1neg_window, *syn1nce_window; |
| int w_offset, window_layer_size; |
| |
| int window_hidden_size = 500; |
| real *syn_window_hidden, *syn_hidden_word, *syn_hidden_word_neg, |
| *syn_hidden_word_nce; |
| |
| int hs = 0, negative = 5; |
| const int table_size = 1e8; |
| int *table; |
| |
| long cc = 0; |
| |
| //constrastive negative sampling |
| char negative_classes_file[MAX_STRING]; |
| int *word_to_group; |
| int *group_to_table; //group_size*table_size |
| int class_number; |
| |
| //nce |
| real* noise_distribution; |
| int nce = 0; |
| |
| //param caps |
| real CAP_VALUE = 50; |
| int cap = 0; |
| |
| void capParam(real* array, int index) { |
| if (array[index] > CAP_VALUE) |
| array[index] = CAP_VALUE; |
| else if (array[index] < -CAP_VALUE) |
| array[index] = -CAP_VALUE; |
| } |
| |
| real hardTanh(real x) { |
| if (x >= 1) { |
| return 1; |
| } else if (x <= -1) { |
| return -1; |
| } else { |
| return x; |
| } |
| } |
| |
| real dHardTanh(real x, real g) { |
| if (x > 1 && g > 0) { |
| return 0; |
| } |
| if (x < -1 && g < 0) { |
| return 0; |
| } |
| return 1; |
| } |
| |
| void InitUnigramTable() { |
| int a, i; |
| long long train_words_pow = 0; |
| real d1, power = 0.75; |
| table = (int *) malloc(table_size * sizeof(int)); |
| for (a = 0; a < vocab_size; a++) |
| train_words_pow += pow(vocab[a].cn, power); |
| i = 0; |
| d1 = pow(vocab[i].cn, power) / (real) train_words_pow; |
| for (a = 0; a < table_size; a++) { |
| table[a] = i; |
| if (a / (real) table_size > d1) { |
| i++; |
| d1 += pow(vocab[i].cn, power) / (real) train_words_pow; |
| } |
| if (i >= vocab_size) |
| i = vocab_size - 1; |
| } |
| |
| noise_distribution = (real *) calloc(vocab_size, sizeof(real)); |
| for (a = 0; a < vocab_size; a++) |
| noise_distribution[a] = pow(vocab[a].cn, power) |
| / (real) train_words_pow; |
| } |
| |
| // Reads a single word from a file, assuming space + tab + EOL to be word boundaries |
| void ReadWord(char *word, FILE *fin) { |
| int a = 0, ch; |
| while (!feof(fin)) { |
| ch = fgetc(fin); |
| if (ch == 13) |
| continue; |
| if ((ch == ' ') || (ch == '\t') || (ch == '\n')) { |
| if (a > 0) { |
| if (ch == '\n') |
| ungetc(ch, fin); |
| break; |
| } |
| if (ch == '\n') { |
| strcpy(word, (char *) "</s>"); |
| 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 |
| 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 |
| int SearchVocab(char *word) { |
| unsigned int hash = GetWordHash(word); |
| while (1) { |
| if (vocab_hash[hash] == -1) |
| return -1; |
| if (!strcmp(word, vocab[vocab_hash[hash]].word)) |
| return vocab_hash[hash]; |
| hash = (hash + 1) % vocab_hash_size; |
| } |
| return -1; |
| } |
| |
| // Reads a word and returns its index in the vocabulary |
| 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 |
| int AddWordToVocab(char *word) { |
| unsigned int hash, length = strlen(word) + 1; |
| if (length > MAX_STRING) |
| length = MAX_STRING; |
| vocab[vocab_size].word = (char *) calloc(length, sizeof(char)); |
| 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); |
| while (vocab_hash[hash] != -1) |
| hash = (hash + 1) % vocab_hash_size; |
| vocab_hash[hash] = vocab_size - 1; |
| 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 |
| 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); |
| for (a = 0; a < vocab_hash_size; a++) |
| vocab_hash[a] = -1; |
| size = vocab_size; |
| train_words = 0; |
| for (a = 0; a < size; a++) { |
| avgWordLength += vocab[a].cn * (strlen(vocab[a].word) + 1); |
| // Words occuring less than min_count times will be discarded from the vocab |
| 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; |
| } |
| } |
| avgWordLength /= train_words; |
| vocab = (struct vocab_word *) realloc(vocab, |
| (vocab_size + 1) * sizeof(struct vocab_word)); |
| // Allocate memory for the binary tree construction |
| 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 |
| void ReduceVocab() { |
| int a, b = 0; |
| unsigned int hash; |
| 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; |
| 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 |
| void CreateBinaryTree() { |
| long long a, b, i, min1i, min2i, pos1, pos2, point[MAX_CODE_LENGTH]; |
| char code[MAX_CODE_LENGTH]; |
| 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)); |
| for (a = 0; a < vocab_size; a++) |
| count[a] = vocab[a].cn; |
| for (a = vocab_size; a < vocab_size * 2; a++) |
| count[a] = 1e15; |
| pos1 = vocab_size - 1; |
| pos2 = vocab_size; |
| // Following algorithm constructs the Huffman tree by adding one node at a time |
| for (a = 0; a < vocab_size - 1; a++) { |
| // First, find two smallest nodes 'min1, min2' |
| if (pos1 >= 0) { |
| if (count[pos1] < count[pos2]) { |
| min1i = pos1; |
| pos1--; |
| } else { |
| min1i = pos2; |
| pos2++; |
| } |
| } else { |
| min1i = pos2; |
| pos2++; |
| } |
| 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]; |
| parent_node[min1i] = vocab_size + a; |
| parent_node[min2i] = vocab_size + a; |
| binary[min2i] = 1; |
| } |
| // Now assign binary code to each vocabulary word |
| for (a = 0; a < vocab_size; a++) { |
| b = a; |
| i = 0; |
| while (1) { |
| code[i] = binary[b]; |
| point[i] = b; |
| i++; |
| b = parent_node[b]; |
| if (b == vocab_size * 2 - 2) |
| break; |
| } |
| vocab[a].codelen = i; |
| vocab[a].point[0] = vocab_size - 2; |
| for (b = 0; b < i; b++) { |
| vocab[a].code[i - b - 1] = code[b]; |
| vocab[a].point[i - b] = point[b] - vocab_size; |
| } |
| } |
| free(count); |
| free(binary); |
| free(parent_node); |
| } |
| |
| void LearnVocabFromTrainFile() { |
| char word[MAX_STRING]; |
| FILE *fin; |
| long long a, i; |
| for (a = 0; a < vocab_hash_size; a++) |
| vocab_hash[a] = -1; |
| fin = fopen(train_file, "rb"); |
| if (fin == NULL) { |
| printf("ERROR: training data file not found!\n"); |
| exit(1); |
| } |
| vocab_size = 0; |
| AddWordToVocab((char *) "</s>"); |
| while (1) { |
| ReadWord(word, fin); |
| if (feof(fin)) |
| break; |
| train_words++; |
| if ((debug_mode > 1) && (train_words % 100000 == 0)) { |
| printf("%lldK%c", train_words / 1000, 13); |
| fflush(stdout); |
| } |
| i = SearchVocab(word); |
| if (i == -1) { |
| a = AddWordToVocab(word); |
| vocab[a].cn = 1; |
| } else |
| vocab[i].cn++; |
| if (vocab_size > vocab_hash_size * 0.7) |
| ReduceVocab(); |
| } |
| SortVocab(); |
| 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); |
| } |
| |
| void SaveVocab() { |
| long long i; |
| FILE *fo = fopen(save_vocab_file, "wb"); |
| 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; |
| vocab_size = 0; |
| while (1) { |
| ReadWord(word, fin); |
| if (feof(fin)) |
| break; |
| a = AddWordToVocab(word); |
| fscanf(fin, "%lld%c", &vocab[a].cn, &c); |
| i++; |
| } |
| fclose(fin); |
| 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); |
| SortVocab(); |
| if (debug_mode > 0) { |
| printf("Vocab size: %'lld\n", vocab_size); |
| printf("Words in vocab's train file: %'lld\n", train_words); |
| printf("Avg. word length in vocab's train file: %.2f\n", avgWordLength); |
| } |
| train_words = file_size / avgWordLength; |
| if(debug_mode > 0) |
| printf("Estimated words in train file: %'lld\n", train_words); |
| } |
| |
| void InitClassUnigramTable() { |
| long long a, c; |
| printf("loading class unigrams \n"); |
| FILE *fin = fopen(negative_classes_file, "rb"); |
| if (fin == NULL) { |
| printf("ERROR: class file not found!\n"); |
| exit(1); |
| } |
| word_to_group = (int *) malloc(vocab_size * sizeof(int)); |
| for (a = 0; a < vocab_size; a++) |
| word_to_group[a] = -1; |
| char class[MAX_STRING]; |
| char prev_class[MAX_STRING]; |
| prev_class[0] = 0; |
| char word[MAX_STRING]; |
| class_number = -1; |
| while (1) { |
| if (feof(fin)) |
| break; |
| ReadWord(class, fin); |
| ReadWord(word, fin); |
| int word_index = SearchVocab(word); |
| if (word_index != -1) { |
| if (strcmp(class, prev_class) != 0) { |
| class_number++; |
| strcpy(prev_class, class); |
| } |
| word_to_group[word_index] = class_number; |
| } |
| ReadWord(word, fin); |
| } |
| class_number++; |
| fclose(fin); |
| |
| group_to_table = (int *) malloc(table_size * class_number * sizeof(int)); |
| long long train_words_pow = 0; |
| real d1, power = 0.75; |
| |
| for (c = 0; c < class_number; c++) { |
| long long offset = c * table_size; |
| train_words_pow = 0; |
| for (a = 0; a < vocab_size; a++) |
| if (word_to_group[a] == c) |
| train_words_pow += pow(vocab[a].cn, power); |
| int i = 0; |
| while (word_to_group[i] != c && i < vocab_size) |
| i++; |
| d1 = pow(vocab[i].cn, power) / (real) train_words_pow; |
| for (a = 0; a < table_size; a++) { |
| //printf("index %lld , word %d\n", a, i); |
| group_to_table[offset + a] = i; |
| if (a / (real) table_size > d1) { |
| i++; |
| while (word_to_group[i] != c && i < vocab_size) |
| i++; |
| d1 += pow(vocab[i].cn, power) / (real) train_words_pow; |
| } |
| if (i >= vocab_size) |
| while (word_to_group[i] != c && i >= 0) |
| i--; |
| } |
| } |
| } |
| |
| void SaveArgs(int argc, char **argv) { |
| unsigned int i; |
| size_t len = 0; |
| char *_all_args, *all_args; |
| char *args_file = strdup(output_file); |
| strcat(args_file, ".args"); |
| FILE *fargs = fopen(args_file, "w"); |
| if (fargs == NULL) { |
| printf("Cannot save args to %s.\n", args_file); |
| return; |
| } |
| |
| for(i=1; i<argc; i++) { |
| len += strlen(argv[i]); |
| } |
| |
| _all_args = all_args = (char *)malloc(len+argc-1); |
| |
| for(i=1; i<argc; i++) { |
| memcpy(_all_args, argv[i], strlen(argv[i])); |
| _all_args += strlen(argv[i])+1; |
| *(_all_args-1) = ' '; |
| } |
| *(_all_args-1) = 0; |
| |
| fprintf(fargs, "%s\n", all_args); |
| fclose(fargs); |
| |
| free(all_args); |
| |
| return; |
| } |
| |
| void SaveNet() { |
| if(type != 3 || negative <= 0) { |
| fprintf(stderr, "save-net only supported for type 3 with negative sampling\n"); |
| return; |
| } |
| |
| FILE *fnet = fopen(save_net_file, "wb"); |
| if (fnet == NULL) { |
| printf("Net parameter file not found\n"); |
| exit(1); |
| } |
| fwrite(syn0, sizeof(real), vocab_size * layer1_size, fnet); |
| fwrite(syn1neg_window, sizeof(real), vocab_size * window_layer_size, fnet); |
| fclose(fnet); |
| } |
| |
| void InitNet() { |
| long long a, b; |
| unsigned long long next_random = 1; |
| long long read; |
| |
| window_layer_size = layer1_size * window * 2; |
| a = posix_memalign((void **) &syn0, 128, |
| (long long) vocab_size * layer1_size * sizeof(real)); |
| if (syn0 == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| |
| if (hs) { |
| a = posix_memalign((void **) &syn1, 128, |
| (long long) vocab_size * layer1_size * sizeof(real)); |
| if (syn1 == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| a = posix_memalign((void **) &syn1_window, 128, |
| (long long) vocab_size * window_layer_size * sizeof(real)); |
| if (syn1_window == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| a = posix_memalign((void **) &syn_hidden_word, 128, |
| (long long) vocab_size * window_hidden_size * sizeof(real)); |
| if (syn_hidden_word == 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; |
| for (a = 0; a < vocab_size; a++) |
| for (b = 0; b < window_layer_size; b++) |
| syn1_window[a * window_layer_size + b] = 0; |
| for (a = 0; a < vocab_size; a++) |
| for (b = 0; b < window_hidden_size; b++) |
| syn_hidden_word[a * window_hidden_size + b] = 0; |
| } |
| if (negative > 0) { |
| if(type == 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; |
| } else if (type == 3) { |
| a = posix_memalign((void **) &syn1neg_window, 128, |
| (long long) vocab_size * window_layer_size * sizeof(real)); |
| if (syn1neg_window == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| for (a = 0; a < vocab_size; a++) |
| for (b = 0; b < window_layer_size; b++) |
| syn1neg_window[a * window_layer_size + b] = 0; |
| } else if (type == 4) { |
| a = posix_memalign((void **) &syn_hidden_word_neg, 128, |
| (long long) vocab_size * window_hidden_size * sizeof(real)); |
| if (syn_hidden_word_neg == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| for (a = 0; a < vocab_size; a++) |
| for (b = 0; b < window_hidden_size; b++) |
| syn_hidden_word_neg[a * window_hidden_size + b] = 0; |
| } |
| } |
| if (nce > 0) { |
| a = posix_memalign((void **) &syn1nce, 128, |
| (long long) vocab_size * layer1_size * sizeof(real)); |
| if (syn1nce == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| a = posix_memalign((void **) &syn1nce_window, 128, |
| (long long) vocab_size * window_layer_size * sizeof(real)); |
| if (syn1nce_window == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| a = posix_memalign((void **) &syn_hidden_word_nce, 128, |
| (long long) vocab_size * window_hidden_size * sizeof(real)); |
| if (syn_hidden_word_nce == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| |
| for (a = 0; a < vocab_size; a++) |
| for (b = 0; b < layer1_size; b++) |
| syn1nce[a * layer1_size + b] = 0; |
| for (a = 0; a < vocab_size; a++) |
| for (b = 0; b < window_layer_size; b++) |
| syn1nce_window[a * window_layer_size + b] = 0; |
| for (a = 0; a < vocab_size; a++) |
| for (b = 0; b < window_hidden_size; b++) |
| syn_hidden_word_nce[a * window_hidden_size + b] = 0; |
| } |
| |
| if(type == 4) { |
| a = posix_memalign((void **) &syn_window_hidden, 128, |
| window_hidden_size * window_layer_size * sizeof(real)); |
| if (syn_window_hidden == NULL) { |
| printf("Memory allocation failed\n"); |
| exit(1); |
| } |
| for (a = 0; a < window_hidden_size * window_layer_size; a++) { |
| next_random = next_random * (unsigned long long) 25214903917 + 11; |
| syn_window_hidden[a] = (((next_random & 0xFFFF) / (real) 65536) |
| - 0.5) / (window_hidden_size * window_layer_size); |
| } |
| } |
| |
| if (read_net_file[0] == 0) { |
| for (a = 0; a < vocab_size; a++) |
| for (b = 0; b < layer1_size; b++) { |
| next_random = next_random * (unsigned long long) 25214903917 |
| + 11; |
| syn0[a * layer1_size + b] = (((next_random & 0xFFFF) |
| / (real) 65536) - 0.5) / layer1_size; |
| } |
| } else if(type == 3 && negative > 0) { |
| FILE *fnet = fopen(read_net_file, "rb"); |
| if (fnet == NULL) { |
| printf("Net parameter file not found\n"); |
| exit(1); |
| } |
| printf("vocab-size: %lld, layer1_size: %lld, window_layer_size %d\n", vocab_size, layer1_size, window_layer_size); |
| read = fread(syn0, sizeof(real), vocab_size * layer1_size, fnet); |
| if(read != vocab_size * layer1_size) { |
| fprintf(stderr, "read-net failed %lld\n", read); |
| exit(-1); |
| } |
| read = fread(syn1neg_window, sizeof(real), vocab_size * window_layer_size, fnet); |
| if(read != (long long) vocab_size * window_layer_size) { |
| fprintf(stderr, "read-net failed, read %lld, expected: %lld\n", read , |
| (long long) sizeof(real) * vocab_size * window_layer_size); |
| exit(-1); |
| } |
| fgetc(fnet); |
| if(!feof(fnet)) { |
| fprintf(stderr, "Remaining bytes in net-file after read-net. File position: %ld\n", ftell(fnet)); |
| exit(-1); |
| } |
| fclose(fnet); |
| } else { |
| fprintf(stderr, "read-net only supported for type 3 with negative sampling\n"); |
| exit(-1); |
| } |
| |
| CreateBinaryTree(); |
| } |
| |
| char *currentDateTime(char *buf, real offset) { |
| time_t t; |
| time(&t); |
| t += (long) offset; |
| struct tm tstruct; |
| tstruct = *localtime(&t); |
| strftime(buf, 80, "%c", &tstruct); |
| return buf; |
| } |
| |
| void *MonitorThread(void *id) { |
| char *timebuf = malloc(80);; |
| int i, n=num_threads; |
| long long sum; |
| sleep(1); |
| while(n > 0) { |
| sleep(1); |
| sum = n = 0; |
| for(i=0; i < num_threads; i++) { |
| if(threadPos[i] >= 0) { |
| sum += (iter - threadIters[i]) * file_size / num_threads + threadPos[i] - (file_size / num_threads) * i; |
| n++; |
| } else { |
| sum += iter * file_size / num_threads; |
| } |
| } |
| if(n == 0) |
| break; |
| real finished_portion = (real) sum / (float) (file_size * iter); |
| long long now = clock(); |
| long long elapsed = (now - start) / CLOCKS_PER_SEC / num_threads; |
| long long ttg = ((1.0 / finished_portion) * (real) elapsed - elapsed) * ((real) num_threads / n) ; |
| |
| printf("\rAlpha: %.3f Done: %.2f%% with %.2fKB/t/s TE: %llds TTG: %llds ETA: %s\033[K", |
| alpha, |
| finished_portion * 100, |
| (float) sum / elapsed / num_threads / 1000, |
| elapsed, |
| ttg, |
| currentDateTime(timebuf, ttg) |
| ); |
| fflush(stdout); |
| } |
| pthread_exit(NULL); |
| } |
| |
| 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; |
| int input_len_1 = layer1_size; |
| int window_offset = -1; |
| if (type == 2 || type == 4) { |
| input_len_1 = window_layer_size; |
| } |
| real *neu1 = (real *) calloc(input_len_1, sizeof(real)); |
| real *neu1e = (real *) calloc(input_len_1, sizeof(real)); |
| threadIters[(long) id] = iter; |
| |
| int input_len_2 = 0; |
| if (type == 4) { |
| input_len_2 = window_hidden_size; |
| } |
| real *neu2 = (real *) calloc(input_len_2, sizeof(real)); |
| real *neu2e = (real *) calloc(input_len_2, sizeof(real)); |
| |
| FILE *fi = fopen(train_file, "rb"); |
| long long start_pos = file_size / (long long) num_threads * (long long) id; |
| long long end_pos = file_size / (long long) num_threads * (long long) (id + 1) -1; |
| long long current_pos = start_pos; |
| long long last_pos = start_pos;; |
| fseek(fi, start_pos, SEEK_SET); |
| while (1) { |
| if ((current_pos - last_pos > 100000)) { |
| word_count_actual += word_count - last_word_count; |
| last_pos = current_pos; |
| last_word_count = word_count; |
| alpha = starting_alpha |
| * (1 - word_count_actual / (real) (iter * train_words + 1)); |
| if (alpha < starting_alpha * 0.0001) |
| alpha = starting_alpha * 0.0001; |
| } |
| if (sentence_length == 0) { |
| while (1) { |
| word = ReadWordIndex(fi); |
| if (feof(fi)) |
| break; |
| if (word == -1) |
| continue; |
| 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) { |
| if(type == 3) // in structured skipgrams |
| word = -2; // keep the window position correct |
| else |
| continue; |
| } |
| } |
| sen[sentence_length] = word; |
| sentence_length++; |
| if (sentence_length >= MAX_SENTENCE_LENGTH) |
| break; |
| } |
| sentence_position = 0; |
| } |
| current_pos = threadPos[(long) id] = ftell(fi); |
| if (feof(fi) || current_pos >= end_pos ) { |
| word_count_actual += word_count - last_word_count; |
| threadIters[(long) id]--; |
| local_iter--; |
| if (local_iter == 0) |
| break; |
| word_count = 0; |
| current_pos = last_pos = start_pos; |
| last_word_count = 0; |
| sentence_length = 0; |
| fseek(fi, start_pos, SEEK_SET); |
| continue; |
| } |
| word = sen[sentence_position]; |
| while (word == -2 && sentence_position<sentence_length) |
| word = sen[++sentence_position]; |
| if (sentence_position>=sentence_length) { |
| sentence_length=0; |
| continue; |
| } |
| if (word < 0) |
| continue; |
| for (c = 0; c < input_len_1; c++) |
| neu1[c] = 0; |
| for (c = 0; c < input_len_1; c++) |
| neu1e[c] = 0; |
| for (c = 0; c < input_len_2; c++) |
| neu2[c] = 0; |
| for (c = 0; c < input_len_2; c++) |
| neu2e[c] = 0; |
| next_random = next_random * (unsigned long long) 25214903917 + 11; |
| b = next_random % window; |
| if (type == 0) { //train the cbow architecture |
| // in -> hidden |
| cw = 0; |
| 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; |
| for (c = 0; c < layer1_size; c++) |
| neu1[c] += syn0[c + last_word * layer1_size]; |
| cw++; |
| } |
| if (cw) { |
| for (c = 0; c < layer1_size; c++) |
| neu1[c] /= cw; |
| if (hs) |
| for (d = 0; d < vocab[word].codelen; d++) { |
| f = 0; |
| l2 = vocab[word].point[d] * layer1_size; |
| // Propagate hidden -> output |
| for (c = 0; c < layer1_size; c++) |
| f += neu1[c] * 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 * neu1[c]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1, c + l2); |
| } |
| // 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; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) % table_size]; |
| next_random = next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word); |
| } else { |
| 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 += neu1[c] * 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 * neu1[c]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1neg, c + l2); |
| } |
| // Noise Contrastive Estimation |
| if (nce > 0) |
| for (d = 0; d < nce + 1; d++) { |
| if (d == 0) { |
| target = word; |
| label = 1; |
| } else { |
| next_random = next_random |
| * (unsigned long long) 25214903917 + 11; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) % table_size]; |
| next_random = next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| } else { |
| 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 += neu1[c] * syn1nce[c + l2]; |
| if (f > MAX_EXP) |
| g = (label - 1) * alpha; |
| else if (f < -MAX_EXP) |
| g = (label - 0) * alpha; |
| else { |
| f = exp(f); |
| g = |
| (label |
| - f |
| / (noise_distribution[target] |
| * nce + f)) * alpha; |
| } |
| for (c = 0; c < layer1_size; c++) |
| neu1e[c] += g * syn1nce[c + l2]; |
| for (c = 0; c < layer1_size; c++) |
| syn1nce[c + l2] += g * neu1[c]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1nce, c + l2); |
| } |
| // hidden -> in |
| 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; |
| for (c = 0; c < layer1_size; c++) |
| syn0[c + last_word * layer1_size] += neu1e[c]; |
| } |
| } |
| } else if (type == 1) { //train skip-gram |
| 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 |
| 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]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1, c + l2); |
| } |
| // 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; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = |
| group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) |
| % table_size]; |
| next_random = |
| next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word); |
| } else { |
| 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]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1neg, c + l2); |
| } |
| //Noise Contrastive Estimation |
| if (nce > 0) |
| for (d = 0; d < nce + 1; d++) { |
| if (d == 0) { |
| target = word; |
| label = 1; |
| } else { |
| next_random = next_random |
| * (unsigned long long) 25214903917 + 11; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = |
| group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) |
| % table_size]; |
| next_random = |
| next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word); |
| } else { |
| 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] * syn1nce[c + l2]; |
| if (f > MAX_EXP) |
| g = (label - 1) * alpha; |
| else if (f < -MAX_EXP) |
| g = (label - 0) * alpha; |
| else { |
| f = exp(f); |
| g = (label |
| - f |
| / (noise_distribution[target] |
| * nce + f)) * alpha; |
| } |
| for (c = 0; c < layer1_size; c++) |
| neu1e[c] += g * syn1nce[c + l2]; |
| for (c = 0; c < layer1_size; c++) |
| syn1nce[c + l2] += g * syn0[c + l1]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1nce, c + l2); |
| } |
| // Learn weights input -> hidden |
| for (c = 0; c < layer1_size; c++) |
| syn0[c + l1] += neu1e[c]; |
| } |
| } else if (type == 2) { //train the cwindow architecture |
| // in -> hidden |
| cw = 0; |
| for (a = 0; a < window * 2 + 1; 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; |
| window_offset = a * layer1_size; |
| if (a > window) |
| window_offset -= layer1_size; |
| for (c = 0; c < layer1_size; c++) |
| neu1[c + window_offset] += syn0[c |
| + last_word * layer1_size]; |
| cw++; |
| } |
| if (cw) { |
| if (hs) |
| for (d = 0; d < vocab[word].codelen; d++) { |
| f = 0; |
| l2 = vocab[word].point[d] * window_layer_size; |
| // Propagate hidden -> output |
| for (c = 0; c < window_layer_size; c++) |
| f += neu1[c] * syn1_window[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 < window_layer_size; c++) |
| neu1e[c] += g * syn1_window[c + l2]; |
| // Learn weights hidden -> output |
| for (c = 0; c < window_layer_size; c++) |
| syn1_window[c + l2] += g * neu1[c]; |
| if (cap == 1) |
| for (c = 0; c < window_layer_size; c++) |
| capParam(syn1_window, c + l2); |
| } |
| // 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; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) % table_size]; |
| next_random = next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word); |
| } else { |
| 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 * window_layer_size; |
| f = 0; |
| for (c = 0; c < window_layer_size; c++) |
| f += neu1[c] * syn1neg_window[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 < window_layer_size; c++) |
| neu1e[c] += g * syn1neg_window[c + l2]; |
| for (c = 0; c < window_layer_size; c++) |
| syn1neg_window[c + l2] += g * neu1[c]; |
| if (cap == 1) |
| for (c = 0; c < window_layer_size; c++) |
| capParam(syn1neg_window, c + l2); |
| } |
| // Noise Contrastive Estimation |
| if (nce > 0) |
| for (d = 0; d < nce + 1; d++) { |
| if (d == 0) { |
| target = word; |
| label = 1; |
| } else { |
| next_random = next_random |
| * (unsigned long long) 25214903917 + 11; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) % table_size]; |
| next_random = next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word); |
| } else { |
| 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 * window_layer_size; |
| f = 0; |
| for (c = 0; c < window_layer_size; c++) |
| f += neu1[c] * syn1nce_window[c + l2]; |
| if (f > MAX_EXP) |
| g = (label - 1) * alpha; |
| else if (f < -MAX_EXP) |
| g = (label - 0) * alpha; |
| else { |
| f = exp(f); |
| g = |
| (label |
| - f |
| / (noise_distribution[target] |
| * nce + f)) * alpha; |
| } |
| for (c = 0; c < window_layer_size; c++) |
| neu1e[c] += g * syn1nce_window[c + l2]; |
| for (c = 0; c < window_layer_size; c++) |
| syn1nce_window[c + l2] += g * neu1[c]; |
| if (cap == 1) |
| for (c = 0; c < window_layer_size; c++) |
| capParam(syn1nce_window, c + l2); |
| } |
| // hidden -> in |
| for (a = 0; a < window * 2 + 1; 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; |
| window_offset = a * layer1_size; |
| if (a > window) |
| window_offset -= layer1_size; |
| for (c = 0; c < layer1_size; c++) |
| syn0[c + last_word * layer1_size] += neu1e[c |
| + window_offset]; |
| } |
| } |
| } else if (type == 3) { //train structured skip-gram |
| for (a = 0; a < window * 2 + 1; 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 < 0) |
| continue; |
| l1 = last_word * layer1_size; |
| window_offset = a * layer1_size; |
| if (a > window) |
| window_offset -= 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] * window_layer_size; |
| // Propagate hidden -> output |
| for (c = 0; c < layer1_size; c++) |
| f += syn0[c + l1] |
| * syn1_window[c + l2 + window_offset]; |
| 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_window[c + l2 + window_offset]; |
| // Learn weights hidden -> output |
| for (c = 0; c < layer1_size; c++) |
| syn1[c + l2 + window_offset] += g |
| * syn0[c + l1]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1, c + l2 + window_offset); |
| } |
| // 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; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = |
| group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) |
| % table_size]; |
| next_random = |
| next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word); |
| } else { |
| 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 * window_layer_size; |
| f = 0; |
| for (c = 0; c < layer1_size; c++) |
| f += |
| syn0[c + l1] |
| * syn1neg_window[c + l2 |
| + window_offset]; |
| 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; |
| if(debug_mode > 2 && ((long long) id) == 0) { |
| printf("negative sampling %lld for input (word) %s (#%lld), target (last word) %s returned %s (#%lld), ", d, vocab[word].word, word, vocab[last_word].word, vocab[target].word, target); |
| printf("label %lld, a %lld, gain %.4f\n", label, a-window, g); |
| } |
| for (c = 0; c < layer1_size; c++) |
| neu1e[c] += |
| g |
| * syn1neg_window[c + l2 |
| + window_offset]; |
| for (c = 0; c < layer1_size; c++) |
| syn1neg_window[c + l2 + window_offset] += g |
| * syn0[c + l1]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1neg_window, |
| c + l2 + window_offset); |
| } |
| // Noise Constrastive Estimation |
| if (nce > 0) |
| for (d = 0; d < nce + 1; d++) { |
| if (d == 0) { |
| target = word; |
| label = 1; |
| } else { |
| next_random = next_random |
| * (unsigned long long) 25214903917 + 11; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = |
| group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) |
| % table_size]; |
| next_random = |
| next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word); |
| } else { |
| 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 * window_layer_size; |
| f = 0; |
| for (c = 0; c < layer1_size; c++) |
| f += |
| syn0[c + l1] |
| * syn1nce_window[c + l2 |
| + window_offset]; |
| if (f > MAX_EXP) |
| g = (label - 1) * alpha; |
| else if (f < -MAX_EXP) |
| g = (label - 0) * alpha; |
| else { |
| f = exp(f); |
| g = (label |
| - f |
| / (noise_distribution[target] |
| * nce + f)) * alpha; |
| } |
| for (c = 0; c < layer1_size; c++) |
| neu1e[c] += |
| g |
| * syn1nce_window[c + l2 |
| + window_offset]; |
| for (c = 0; c < layer1_size; c++) |
| syn1nce_window[c + l2 + window_offset] += g |
| * syn0[c + l1]; |
| if (cap == 1) |
| for (c = 0; c < layer1_size; c++) |
| capParam(syn1nce_window, |
| c + l2 + window_offset); |
| } |
| // Learn weights input -> hidden |
| for (c = 0; c < layer1_size; c++) { |
| syn0[c + l1] += neu1e[c]; |
| if (syn0[c + l1] > 50) |
| syn0[c + l1] = 50; |
| if (syn0[c + l1] < -50) |
| syn0[c + l1] = -50; |
| } |
| } |
| } else if (type == 4) { //training senna |
| // in -> hidden |
| cw = 0; |
| for (a = 0; a < window * 2 + 1; 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; |
| window_offset = a * layer1_size; |
| if (a > window) |
| window_offset -= layer1_size; |
| for (c = 0; c < layer1_size; c++) |
| neu1[c + window_offset] += syn0[c |
| + last_word * layer1_size]; |
| cw++; |
| } |
| if (cw) { |
| for (a = 0; a < window_hidden_size; a++) { |
| c = a * window_layer_size; |
| for (b = 0; b < window_layer_size; b++) { |
| neu2[a] += syn_window_hidden[c + b] * neu1[b]; |
| } |
| } |
| if (hs) |
| for (d = 0; d < vocab[word].codelen; d++) { |
| f = 0; |
| l2 = vocab[word].point[d] * window_hidden_size; |
| // Propagate hidden -> output |
| for (c = 0; c < window_hidden_size; c++) |
| f += hardTanh(neu2[c]) * syn_hidden_word[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 < window_hidden_size; c++) |
| neu2e[c] += dHardTanh(neu2[c], g) * g |
| * syn_hidden_word[c + l2]; |
| // Learn weights hidden -> output |
| for (c = 0; c < window_hidden_size; c++) |
| syn_hidden_word[c + l2] += dHardTanh(neu2[c], g) * g |
| * neu2[c]; |
| } |
| // 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; |
| if (word_to_group != NULL |
| && word_to_group[word] != -1) { |
| target = word; |
| while (target == word) { |
| target = group_to_table[word_to_group[word] |
| * table_size |
| + (next_random >> 16) % table_size]; |
| next_random = next_random |
| * (unsigned long long) 25214903917 |
| + 11; |
| } |
| //printf("negative sampling %lld for word %s returned %s\n", d, vocab[word].word, vocab[target].word); |
| } else { |
| 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 * window_hidden_size; |
| f = 0; |
| for (c = 0; c < window_hidden_size; c++) |
| f += hardTanh(neu2[c]) |
| * syn_hidden_word_neg[c + l2]; |
| if (f > MAX_EXP) |
| g = (label - 1) * alpha / negative; |
| else if (f < -MAX_EXP) |
| g = (label - 0) * alpha / negative; |
| else |
| g = (label |
| - expTable[(int) ((f + MAX_EXP) |
| * (EXP_TABLE_SIZE / MAX_EXP / 2))]) |
| * alpha / negative; |
| for (c = 0; c < window_hidden_size; c++) |
| neu2e[c] += dHardTanh(neu2[c], g) * g |
| * syn_hidden_word_neg[c + l2]; |
| for (c = 0; c < window_hidden_size; c++) |
| syn_hidden_word_neg[c + l2] += dHardTanh(neu2[c], g) |
| * g * neu2[c]; |
| } |
| for (a = 0; a < window_hidden_size; a++) |
| for (b = 0; b < window_layer_size; b++) |
| neu1e[b] += neu2e[a] |
| * syn_window_hidden[a * window_layer_size + b]; |
| for (a = 0; a < window_hidden_size; a++) |
| for (b = 0; b < window_layer_size; b++) |
| syn_window_hidden[a * window_layer_size + b] += neu2e[a] |
| * neu1[b]; |
| // hidden -> in |
| for (a = 0; a < window * 2 + 1; 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; |
| window_offset = a * layer1_size; |
| if (a > window) |
| window_offset -= layer1_size; |
| for (c = 0; c < layer1_size; c++) |
| syn0[c + last_word * layer1_size] += neu1e[c |
| + window_offset]; |
| } |
| } |
| } else { |
| printf("unknown type %i", type); |
| exit(0); |
| } |
| sentence_position++; |
| if (sentence_position >= sentence_length) { |
| sentence_length = 0; |
| continue; |
| } |
| } |
| fclose(fi); |
| free(neu1); |
| free(neu1e); |
| threadPos[(long) id] = -1; |
| pthread_exit(NULL); |
| } |
| |
| void ShowCollocations() { |
| long a, b, c, d, e, window_offset, target, max_target=0, maxmax_target; |
| real f, max_f, maxmax_f; |
| real *target_sums, bestf[MAX_CC], worstbest; |
| long besti[MAX_CC]; |
| int N = 10, bestp[MAX_CC]; |
| a = posix_memalign((void **) &target_sums, 128, vocab_size * sizeof(real)); |
| |
| for (d = cc; d < vocab_size; d++) { |
| for (b = 0; b < vocab_size; b++) |
| target_sums[b]=0; |
| for (b = 0; b < N; b++) |
| bestf[b]=-1; |
| worstbest = -1; |
| |
| maxmax_f = -1; |
| maxmax_target = 0; |
| for (a = window * 2 + 1; a >=0; a--) { |
| if (a != window) { |
| max_f = -1; |
| window_offset = a * layer1_size; |
| if (a > window) |
| window_offset -= layer1_size; |
| for(target = 0; target < vocab_size; target ++) { |
| if(target == d) |
| continue; |
| f = 0; |
| for (c = 0; c < layer1_size; c++) |
| f += syn0[d* layer1_size + c] * syn1neg_window[target * window_layer_size + window_offset + c]; |
| if (f < -MAX_EXP) |
| continue; |
| else if (f > MAX_EXP) |
| continue; |
| else |
| f = expTable[(int) ((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]; |
| if(f > max_f) { |
| max_f = f; |
| max_target = target; |
| } |
| target_sums[target] += (1-target_sums[target]) * f; |
| if(f > worstbest) { |
| for (b = 0; b < N; b++) { |
| if (f > bestf[b]) { |
| for (e = N - 1; e > b; e--) { |
| bestf[e] = bestf[e - 1]; |
| besti[e] = besti[e - 1]; |
| bestp[e] = bestp[e - 1]; |
| } |
| bestf[b] = f; |
| besti[b] = target; |
| bestp[b] = window-a; |
| break; |
| } |
| } |
| worstbest = bestf[N-1]; |
| } |
| } |
| printf("%s (%.2f) ", vocab[max_target].word, max_f); |
| if(max_f > maxmax_f) { |
| maxmax_f = max_f; |
| maxmax_target = max_target; |
| } |
| } else { |
| printf("\x1b[1m%s\x1b[0m ", vocab[d].word); |
| } |
| } |
| max_f = -1; |
| for (b = 0; b < vocab_size; b++) { |
| if(target_sums[b] > max_f) { |
| max_f = target_sums[b]; |
| max_target = b; |
| } |
| } |
| printf(" – max sum: %s (%.2f), max resp.: \x1b[1m%s\x1b[0m (%.2f)\n", |
| vocab[max_target].word, max_f, |
| vocab[maxmax_target].word, maxmax_f); |
| for(b=0; b<N && bestf[b]>-1; b++) |
| printf("%-32s %.2f %d\n", vocab[besti[b]].word, bestf[b], bestp[b]); |
| printf("\n"); |
| } |
| } |
| |
| void TrainModel() { |
| long a, b, c, d; |
| FILE *fo; |
| pthread_t *pt = (pthread_t *) malloc(num_threads * sizeof(pthread_t)); |
| threadPos = malloc(num_threads * sizeof(long long)); |
| threadIters = malloc(num_threads * sizeof(int)); |
| char *timebuf = malloc(80); |
| printf("Starting training using file %s\n", train_file); |
| starting_alpha = alpha; |
| if (read_vocab_file[0] != 0) |
| ReadVocab(); |
| else |
| LearnVocabFromTrainFile(); |
| if (save_vocab_file[0] != 0) |
| SaveVocab(); |
| if (output_file[0] == 0) |
| return; |
| InitNet(); |
| if(cc > 0) |
| ShowCollocations(); |
| if (negative > 0 || nce > 0) |
| InitUnigramTable(); |
| if (negative_classes_file[0] != 0) |
| InitClassUnigramTable(); |
| start = clock(); |
| for (a = 0; a < num_threads; a++) |
| pthread_create(&pt[a], NULL, TrainModelThread, (void *) a); |
| if(debug_mode > 1) |
| pthread_create(&pt[num_threads], NULL, MonitorThread, (void *) a); |
| for (a = 0; a < num_threads; a++) |
| pthread_join(pt[a], NULL); |
| if(debug_mode > 1) { |
| pthread_join(pt[num_threads], NULL); |
| clock_t now = clock(); |
| printf("\nFinished: %s - user: %lds - real: %lds\n", currentDateTime(timebuf, 0), (now-start) / CLOCKS_PER_SEC / num_threads, (now-start) / CLOCKS_PER_SEC); |
| printf("Saving vectors to %s ...", output_file); |
| fflush(stdout); |
| } |
| 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"); |
| } |
| if(debug_mode > 1) |
| fprintf(stderr, "\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); |
| if (save_net_file[0] != 0) |
| SaveNet(); |
| } |
| |
| int ArgPos(char *str, int argc, char **argv) { |
| int a; |
| for (a = 1; a < argc; a++) |
| if (!strcmp(str, argv[a])) { |
| if (a == argc - 1) { |
| printf("Argument missing for %s\n", str); |
| exit(1); |
| } |
| return a; |
| } |
| return -1; |
| } |
| |
| int main(int argc, char **argv) { |
| int i; |
| setlocale(LC_ALL, ""); |
| 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-negative-classes <file>\n"); |
| printf("\t\tNegative classes to sample from\n"); |
| printf("\t-nce <int>\n"); |
| printf( |
| "\t\tNumber of negative examples for nce; default is 0, 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-read-net <file>\n"); |
| printf( |
| "\t\tThe net parameters will be read from <file>, not initialized randomly\n"); |
| printf("\t-save-net <file>\n"); |
| printf("\t\tThe net parameters will be saved to <file>\n"); |
| printf("\t-show-cc <int>\n"); |
| printf("\t\tShow words with their collocators starting from word rank <int>. Depends on -read-vocab and -read-net.\n"); |
| printf("\t-type <int>\n"); |
| printf( |
| "\t\tType of embeddings (0 for cbow, 1 for skipngram, 2 for cwindow, 3 for structured skipngram, 4 for senna type)\n"); |
| printf("\t-cap <int>\n"); |
| printf( |
| "\t\tlimit the parameter values to the range [-50, 50]; default is 0 (off)\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 -type 1 -iter 3\n\n"); |
| return 0; |
| } |
| output_file[0] = 0; |
| save_vocab_file[0] = 0; |
| read_vocab_file[0] = 0; |
| save_net_file[0] = 0; |
| read_net_file[0] = 0; |
| negative_classes_file[0] = 0; |
| 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 *) "-save-net", argc, argv)) > 0) |
| strcpy(save_net_file, argv[i + 1]); |
| if ((i = ArgPos((char *) "-read-net", argc, argv)) > 0) |
| strcpy(read_net_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 *) "-show-cc", argc, argv)) > 0) |
| cc = atoi(argv[i + 1]); |
| if ((i = ArgPos((char *) "-type", argc, argv)) > 0) |
| type = atoi(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 *) "-negative-classes", argc, argv)) > 0) |
| strcpy(negative_classes_file, argv[i + 1]); |
| if ((i = ArgPos((char *) "-nce", argc, argv)) > 0) |
| nce = 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]); |
| if ((i = ArgPos((char *) "-cap", argc, argv)) > 0) |
| cap = atoi(argv[i + 1]); |
| if (type == 0 || type == 2 || type == 4) |
| alpha = 0.05; |
| if ((i = ArgPos((char *) "-alpha", argc, argv)) > 0) |
| alpha = atof(argv[i + 1]); |
| vocab = (struct vocab_word *) calloc(vocab_max_size, |
| sizeof(struct vocab_word)); |
| vocab_hash = (int *) calloc(vocab_hash_size, sizeof(int)); |
| expTable = (real *) malloc((EXP_TABLE_SIZE + 1) * sizeof(real)); |
| 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) |
| } |
| SaveArgs(argc, argv); |
| TrainModel(); |
| return 0; |
| } |
| |