培训期间的损失不会减少(Word2Vec，Gensim)

问题描述

每个时期增加model.get_latest_training_loss()会导致什么损失?

What can cause loss from model.get_latest_training_loss() increase on each epoch?

用于培训的代码:

class EpochSaver(CallbackAny2Vec):
    '''Callback to save model after each epoch and show training parameters '''

    def __init__(self, savedir):
        self.savedir = savedir
        self.epoch = 0

        os.makedirs(self.savedir, exist_ok=True)

    def on_epoch_end(self, model):
        savepath = os.path.join(self.savedir, "model_neg{}_epoch.gz".format(self.epoch))
        model.save(savepath)
        print(
            "Epoch saved: {}".format(self.epoch + 1),
            "Start next epoch ... ", sep="\n"
            )
        if os.path.isfile(os.path.join(self.savedir, "model_neg{}_epoch.gz".format(self.epoch - 1))):
            print("Previous model deleted ")
            os.remove(os.path.join(self.savedir, "model_neg{}_epoch.gz".format(self.epoch - 1)))
        self.epoch += 1
        print("Model loss:", model.get_latest_training_loss())

    def train():

        ### Initialize model ###
        print("Start training Word2Vec model")

        workers = multiprocessing.cpu_count()/2

        model = Word2Vec(
            DocIter(),
            size=300, alpha=0.03, min_alpha=0.00025, iter=20,
            min_count=10, hs=0, negative=10, workers=workers,
            window=10, callbacks=[EpochSaver("./checkpoints")],
            compute_loss=True
    )

输出:

从时期(1到20)丢失:

Losses from epochs (1 to 20):

Model loss: 745896.8125
Model loss: 1403872.0
Model loss: 2022238.875
Model loss: 2552509.0
Model loss: 3065454.0
Model loss: 3549122.0
Model loss: 4096209.75
Model loss: 4615430.0
Model loss: 5103492.5
Model loss: 5570137.5
Model loss: 5955891.0
Model loss: 6395258.0
Model loss: 6845765.0
Model loss: 7260698.5
Model loss: 7712688.0
Model loss: 8144109.5
Model loss: 8542560.0
Model loss: 8903244.0
Model loss: 9280568.0
Model loss: 9676936.0

我在做什么错了?

语言阿拉伯语.作为DocIter的输入-带有令牌的列表.

Language arabian.As input from DocIter - list with tokens.

推荐答案

在gensim 3.6.0之前，报告的损失值可能不是很明智，仅将每次调用的计数重置为train()，而不是将每个内部纪元重置.此问题中有一些修复程序:

Up through gensim 3.6.0, the loss value reported may not be very sensible, only resetting the tally each call to train(), rather than each internal epoch. There are some fixes forthcoming in this issue:

https://github.com/RaRe-Technologies/gensim/pull/2135

与此同时，先前值和最新值之间的差异可能更有意义.在这种情况下，您的数据表明，第一个时期总计亏损745896，而最后一个时期(9676936-9280568 =)396,368 –这可能表示希望取得的进展.

In the meantime, the difference between the previous value, and the latest, may be more meaningful. In that case, your data suggest the 1st epoch had a total loss of 745896, while the last had (9676936-9280568=) 396,368 – which may indicate the kind of progress hoped-for.

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