word2vec.py 文件源码

def train_cbow_pair(model, word, input_word_indices, l1, alpha, learn_vectors=True, learn_hidden=True):
    neu1e = zeros(l1.shape)

    if model.hs:
        l2a = model.syn1[word.point]  # 2d matrix, codelen x layer1_size
        fa = expit(dot(l1, l2a.T))  # propagate hidden -> output
        ga = (1. - word.code - fa) * alpha  # vector of error gradients multiplied by the learning rate
        if learn_hidden:
            model.syn1[word.point] += outer(ga, l1)  # learn hidden -> output
        neu1e += dot(ga, l2a)  # save error

    if model.negative:
        # use this word (label = 1) + `negative` other random words not from this sentence (label = 0)
        word_indices = [word.index]
        while len(word_indices) < model.negative + 1:
            w = model.cum_table.searchsorted(model.random.randint(model.cum_table[-1]))
            if w != word.index:
                word_indices.append(w)
        l2b = model.syn1neg[word_indices]  # 2d matrix, k+1 x layer1_size
        fb = expit(dot(l1, l2b.T))  # propagate hidden -> output
        gb = (model.neg_labels - fb) * alpha  # vector of error gradients multiplied by the learning rate
        if learn_hidden:
            model.syn1neg[word_indices] += outer(gb, l1)  # learn hidden -> output
        neu1e += dot(gb, l2b)  # save error

    if learn_vectors:
        # learn input -> hidden, here for all words in the window separately
        if not model.cbow_mean and input_word_indices:
            neu1e /= len(input_word_indices)
        for i in input_word_indices:
            model.wv.syn0[i] += neu1e * model.syn0_lockf[i]

    return neu1e