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以前看的国外的一篇文章，用代码解释word2vec训练过程，觉得写的不错，转过来了
原文链接
hashed vocabulary and Huffman tree.frequency (frequency of word), word (string repr of word), path (point in c-version, all parent nodes of word in Huffman tree), code (binary repr of word in Huffman tree)count in the vocabclass HashedVocab(object): HASH_SIZE = 30000000 ## max hash size ## count threshold for a word to be in the vocab - ignore infrequent word MIN_COUNT = 5 @staticmethod def file2ws(fpath): """ file to wordstream: lazily read words from a file as an iterator """ with open(fpath) as fin: word_pattern = re.compile(r‘(.*?)\s‘) mf = mmap.mmap(fin.fileno(), 0, access = mmap.ACCESS_READ) for match in word_pattern.finditer(mf): word = match.group(1) if word: yield word def __init__(self): ## vocab stores vocab_word dict self.vocab = [] ## vocab_hash stores the index of vocab_word in vocab self.vocab_hash = np.empty(HashedVocab.HASH_SIZE, dtype = np.int64) self.vocab_hash.fill(-1) ## house-keeping - total number of word occurences in training set ## it will be used as an estimate of training size later, which ## affect the adjustment of learning rate of the deep structure self.n_train_words = 0 def fit(self, word_stream): """ build hashed_vocab and Huffman tree from word stream the word_stream is usually from reading a word file, e.g., using file2ws """ ## word counter wc = Counter(word_stream) ## total occurances of training words self.n_train_words = sum(wc.values()) ## Sort the words by their counts, filter out infrequent words, ## construct vocab_word (a dict) and put them in self.vocab self.vocab = map(lambda x: dict(zip([‘word‘, ‘count‘], x)), filter(lambda x: x[1] > HashedVocab.MIN_COUNT, wc.most_common(len(wc)))) ## if vocab is already too big for hash, either (1) ignoring more infrequent ## words (as implemented in C by ReduceVocab), (2) making hash size bigger ## here we simply raise an exception for simplicity if len(self.vocab) > HashedVocab.HASH_SIZE * 0.7: raise RuntimeError(‘vocab size too large for hash, increase MIN_COUNT or HASH_SIZE‘) self.build_hash() self.build_huffman_tree() return self def index_of(self, word): """ Get the index of word in vocab by using hash, return -1 if it is NOT there """ word_hash = self.get_word_hash(word) while True: word_index = self.vocab_hash[word_hash] if word_index == -1: return -1 elif word == self.vocab[word_index][‘word‘]: return word_index else: word_hash = (word_hash + 1) % HashedVocab.HASH_SIZE def __getitem__(self, word_index): """ get vocab_word in vocab by its index """ return self.vocab[word_index] def __len__(self): return len(self.vocab) def __iter__(self): return iter(self.vocab) def build_hash(self): self.vocab_hash = np.empty(HashedVocab.HASH_SIZE, dtype = np.int64) self.vocab_hash.fill(-1) for word_index, vocab_word in enumerate(self.vocab): word = vocab_word[‘word‘] word_hash = self.get_word_hash(word) self.add_to_hash(word_hash, word_index) return self def get_word_hash(self, word): word_hash = sum([ord(c)*(257**i) for i, c in zip(range(len(word))[::-1], word)]) word_hash %= HashedVocab.HASH_SIZE return word_hash def add_to_hash(self, word_hash, word_index): while self.vocab_hash[word_hash] != -1: word_hash = (word_hash + 1) % HashedVocab.HASH_SIZE self.vocab_hash[word_hash] = word_index return self def build_huffman_tree(self): """Build the Huffman tree representation for word based on their freq. The vocab_word structure in self.vocab is a dict {word, count, path, code} where vocab_word[‘code‘] is the Huffman coding of word, and vocab_word[‘path‘] will be the path from root to leaf """ ## for a full binary tree with n leaves, n-1 internal nodes will be needed ## for the 2*n-1 long data array (e.g. count and binary), the first n will be ## for the leaf nodes, and the last n-1 will be for the internal nodes vocab_size = len(self) ## workhorse structure for tree construction ## count - the count of words (leaves) and internal nodes (sum of leave counts) count = np.empty(vocab_size * 2 - 1, dtype = np.int64) count.fill(1e15) count[:vocab_size] = [vw[‘count‘] for vw in self.vocab] ## binary - boolean repr for leaves and internal nodes binary = np.zeros(vocab_size*2-1, dtype=np.int64) ## parent_node - storing the path for each node parent_node = np.empty(vocab_size*2-1, dtype = np.int64) ## construct the tree ## DESCRIPTION: iteratively group the two ungrouped nodes (leaf or internal) that ## have the smallest counts ## Since the vocab is sorted in decreasing counts order (first half ) and ## the newly created internal nodes (second half) will be the order of ## increasing counts (the algorithm invariant), so we only need to check ## the two nodes in the middle of array to look for candidates, that is the role ## of min1i and min2i ## start searching for min1i and min2i from the middle of array pos1, pos2 = vocab_size - 1, vocab_size ## construct the vocab_size -1 internal nodes for a in xrange(vocab_size-1): ## min1i if pos1 >= 0: # min1i = pos1 if count[pos1] < count[pos2]: min1i, pos1 = pos1, pos1-1 # min1i = pos2 else: min1i, pos2 = pos2, pos2+1 else: ## running out of leaf nodes min1i, pos2 = pos2, pos2+1 ## min2i if pos1 >= 0: if count[pos1] < count[pos2]: min2i, pos1 = pos1, pos1-1 else: min2i, pos2 = pos2, pos2+1 else: min2i, pos2 = pos2, pos2+1 ## count(parent_node) = count(child1) + count(child2) count[vocab_size + a] = count[min1i] + count[min2i] ## link parent node index parent_node[min1i] = vocab_size + a parent_node[min2i] = vocab_size + a ## binary encoding for min1i is 0 (left), for min2i is 1 (right) binary[min2i] = 1 ## put the built Huffman tree structure in the vocab_word in vocab ## for each leaf node for a in xrange(vocab_size): ## b starting from leaf, along parent_nodes, to the root b = a code, path = [], [] ## traverse along the path to root while True: code.append(binary[b]) path.append(b) b = parent_node[b] ## stop when reaching root if b == vocab_size * 2 - 2: break ## path (or point) is from root to leaf, with an index offset ## -vocab_size self.vocab[a][‘path‘] = [vocab_size - 2] + [p - vocab_size for p in path[::-1]] self.vocab[a][‘code‘] = code[::-1] def inspect_vocab_tree(self): """Draw the built Huffman binary tree for the vocab """ g = nx.DiGraph() vocab_size = len(self) edges = set() for vw in self.vocab: tree_path = [i + vocab_size for i in vw[‘path‘]] tree_path = [i if i >= vocab_size else "%s(%d)" % (self.vocab[i][‘word‘], self.vocab[i][‘count‘]) for i in tree_path] edges.update(zip(tree_path[:-1], tree_path[1:])) g.add_edges_from(edges) pos = nx.graphviz_layout(g, prog = ‘dot‘) nx.draw(g, pos, with_labels = True, arrows = True) return g
class UnigramTable(object): TABLE_SIZE = int(1e8) POWER = 0.75 def __init__(self, hashed_vocab): self.table = np.empty(UnigramTable.TABLE_SIZE, np.int64) vocab = hashed_vocab vocab_size = len(hashed_vocab) ## normalization factor of all word‘s frequency‘s power train_words_pow = sum(math.pow(vw[‘count‘], UnigramTable.POWER) for vw in vocab) ## doing the sampling in the table ## the sampling probability of a unigram is proportional to its ## frequency to a power of POWER (=0.75) ## i marks the index of current word in vocab ## d1 marks the accumulative power-law probability up to the current word ## a / TABLE_SIZE marks the sampling proability up to the current word i = 0 d1 = math.pow(vocab[i][‘count‘], UnigramTable.POWER) / train_words_pow for a in xrange(self.TABLE_SIZE): self.table[a] = i ## compare accumulative sampling prob with power-law accumulative prob ## move to the sampling of next word if they start not matching if a * 1. / UnigramTable.TABLE_SIZE > d1: i += 1 d1 += math.pow(vocab[i][‘count‘], UnigramTable.POWER) / train_words_pow ## put the rest as sampling of the last word (round-off) if i >= vocab_size: i = vocab_size - 1 def __getitem__(self, index): return self.table[index]
The main data structures of CBOW (and for Skip_gram) are syn0, syn1 and syn1neg. All of them can be viewed as feature reprs of the words in the vocab, but the words are combined in different specific ways. For example,
syn0 are the feature repr of words (also the leaf nodes in the Huffman tree) to be learned, and the words that are fired in syn0 are a continuous sequence in a sentence (neighbors in semantic matter).syn1 are the feature repr of internal nodes in the Huffman tree. The words that are fired together are similiar to each other in the way that they are common parents of close words (in terms of similiar frequency) in the Huffman tree. Parents of word nodes in Huffman tree are analogy to prototypes of clusters formed by words.syn1neg are also feature repr of words in the vocab, but those words are selected via negative sampling. The words are sampled based on their frequency.The basic idea is to learn both syn0 and syn1(and/or `syn1neg1). It might be helpful to understand the code in the following way. Again this is my personal understanding of the code, if it is wrong, it has nothing to do with the original project.
neu1 as the sum of the related nodes in layer0 (syn0 vectors). The output f is calcualted as the logistic transformation of inner product between neu1 and syn1.f and the psuedo target binary encoding of the word, we can calucate the cost function and thus gradient. By making the output and pseudo-target close to each other, we enforce that the feature represented as syn0 (based on a word‘s context information in a sentence) can be used to approximate the distribution of the word in terms of the encoding by the Huffman tree. The gradient is calculated as g in the code (actually just part of it, the gradient is g multiplied by the input vector for a logistic regression training).syn0 and syn1 are updated according to the gradient, interleavingly and iteratively. That is, syn1 are updated based on gradient, and all the connected syn0 (neighbors in a sentence); and syn0 are updated based on gradient, and all the connected syn1 (parent nodes of the word in Huffman tree). #### ================= Trained with Negative Sampling ====================syn1neg) are from a sampling of the whole vocabulary, instead of from the Huffman tree.syn0 repr will be very close to itself and ‘orthgonal‘ to other words - kind of like a autoencoder flavor.class CBOW(object): REAL_TYPE = np.float64 def __init__(self, hashed_vocab, unigram_table, layer1_size, learn_hs, learn_negative): """ hashed_vocab: the vocab to build on layer1_size: the size of layer1 of the net, effectively defines the dim of feature space learn_hs: use hierarchical softmax learning learn_negative: use negative sampling learning """ self.vocab = hashed_vocab self.table = unigram_table self.layer1_size = layer1_size self.learn_hs = learn_hs self.learn_negative = learn_negative ## initial value for learning rate, will decrease along learning self.starting_alpha = 0.025 ## a sentence is a bulk of words from train file self.sentence_len = 1000 ## downsampling rate to select words into a sentence self.sampling = 1e-4 ## window defines the neighborhood of a word in the sentence for hs learning self.window = 5 ## negative defines the neighborhood of a word for negative learning self.negative = 5 ## network weights ## syn0 - feature representations of words (leaf nodes) in Huffman tree ## shape = vocab_size x layer1_size self.syn0 = None ## syn1 - feature representations of internal nodes (non-leaf) in Huffman tree ## shape = vocab_size x layer1_size self.syn1 = None ## hidden layer for hs learning ## syn1neg - feature representations of negative-sampled words ## shape = vocab_size x layer1_size self.syn1neg = None ## hidden layer for negative learning def init_net(self): """ """ vocab_size = len(self.vocab) self.syn0 = np.random.uniform(low = -.5 / self.layer1_size, high = .5 / self.layer1_size, size = (vocab_size, self.layer1_size)).astype(CBOW.REAL_TYPE) if self.learn_hs: self.syn1 = np.zeros((vocab_size, self.layer1_size), dtype=CBOW.REAL_TYPE) if self.learn_negative: self.syn1neg = np.zeros((vocab_size, self.layer1_size), dtype = CBOW.REAL_TYPE) def fit(self, train_words): """ train_words: list of training words """ ## initialize net structure self.init_net() ntotal = len(train_words) ## initialize learning parameters alpha = self.starting_alpha next_random = 0 ## read and process words sentence by sentence ## from the train_words nprocessed = 0 while nprocessed < ntotal: ## adjust learning rate based on how many words have ## been trained on alpha = max(self.starting_alpha * (1-nprocessed/(ntotal+1.)), self.starting_alpha * 0.0001) ## refill the sentence sentence = [] while nprocessed < ntotal and len(sentence) < self.sentence_len: ## sampling down the infrequent words word = train_words[nprocessed] word_index = self.vocab.index_of(word) nprocessed += 1 if word_index == -1: continue word_count = self.vocab[word_index][‘count‘] if self.sampling > 0: ran = ( (math.sqrt(word_count / (self.sampling * ntotal)) + 1) * (self.sampling * ntotal) / word_count ) next_random = next_random * 25214903917 + 11 ## down sampling based on word frequency if ran < (next_random & 0xFFFF) / 65536: continue sentence.append(word_index) ## for each word in the preloaded sentence ## pivot is the vocab index of current word to be trained on ## ipivot is the index in the current setence for ipivot, pivot in enumerate(sentence): next_random = next_random * 25214903917 + 11 ## window-b defines the length of the window ## which is the neighborhood size for the current ## word in the sentence b = next_random % self.window ## initialize temp variable ## neu1: the sum of neigbhor vectors in the setence ## neu1e: the gradient vector wrt syn0 ## see the explaination above the code neu1 = np.zeros(self.layer1_size, dtype = self.REAL_TYPE) neu1e = np.zeros(self.layer1_size, dtype = self.REAL_TYPE) ## accumulate sum of neighbor words into neu1 ## neighbors are defined as [ipivot-(window-b), ipivot+(window-b)] left = max(0, ipivot - (self.window-b)) right = min(len(sentence)-1, ipivot+self.window-b) ## all neighborhood index should >= 0 (in vocab) as otherwise ## it won‘t go into the sentence in the first place neighborhood = [sentence[n] for n in range(left, right+1) if sentence[n] >= 0] neu1 = np.sum(self.syn0[neighborhood,:], axis = 0) ## hierarchical softmax learning if self.learn_hs: ## for each output node in layer1 ## which are parent nodes of pivot in Huffman tree ## notice the last element of ‘path‘ is the word itself, ## so exclude it here for parent_index, parent_code in zip(self.vocab[pivot][‘path‘][:-1], self.vocab[pivot][‘code‘]): ## F is the logistic transformation of dot product ## between neu1 and each parent repr in syn1 f = np.dot(neu1, self.syn1[parent_index, :]) ## output out of range if f <= - MAX_EXP or f >= MAX_EXP: continue ## logistic function transformation else: f = EXP_TABLE[int((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))] ## pseduo target is 1 - parent_code g = (1 - parent_code - f) * alpha ## accumulate neu1 to update syn0 later neu1e += g * self.syn1[parent_index] ## update syn1 of current parent self.syn1[parent_index] += g * neu1 ## negative sampling learning ## select one positive and several negative samples if self.learn_negative: for d in range(self.negative + 1): ## make sure to select the current word ## as positive sample if d == 0: target = pivot label = 1 ## select some ‘negative‘ samples randomly else: next_random = next_random * 25214903917 + 11 target = self.table[(next_random >> 16) % self.table.TABLE_SIZE] if (target == pivot): continue ## ignore if it is still positive label = 0 ## this time f is dot product of neu1 with ## syn1neg f = np.dot(neu1, self.syn1neg[target, :]) ## pseudo target is label ## NOTE the differece between hs and negative-sampling ## when dealing with f out of [-MAX_EXP, MAX_EXP] if f > MAX_EXP: g = (label - 1) * alpha elif f < - MAX_EXP: g = (label - 0) * alpha else: g = alpha * (label - EXP_TABLE[int((f + MAX_EXP) * (EXP_TABLE_SIZE / MAX_EXP / 2))]) ## accumulate changes to syn0 to neu1e again neu1e += g * self.syn1neg[target] ## update syn0 after hs and/or negative-sampling learning self.syn0[neighborhood, :] += neu1e
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原文地址：http://www.cnblogs.com/duyang/p/5251773.html
