天道酬勤,学无止境

resuming-training

Keras: A loaded checkpoint model to resume a training could decrease the accuracy?

My keras template is generating a checkpoint for every best time of my training. However my internet dropped and when loading my last checkpoint and restarting training from last season (using initial_epoch), the accuracy dropped from 89.1 (loaded model value) to 83.6 in the first season of new training. Is this normal behavior when resuming(restarting) a training? Because when my network fell it was already in the 30th season and there was no drop in accuracy, there was also no significant improvement and so did not generate any new checkpoint, forcing me to come back a few epochs. Thanks in

2022-05-03 06:51:09    分类:问答    keras   floating-accuracy   checkpoint   resuming-training

在分布式张量流中仅恢复零件模型的推荐方法是什么(What's the recommend way of restoring only parts model in distributed tensorflow)

问题 当我们在不同的任务上对模型进行微调时,模型中只有一部分变量从预训练任务中恢复,其他变量作为初始值保留。 正如许多文档所建议的那样(第 1 页第 2 页),当使用本地图进行训练时,在运行全局初始化操作后恢复预训练模型(如果包含 MonitoredSession 或 supervisor,则在“init_fn”中调用恢复)。 但是在分布式情况下, global init op make "model_ready" 在调用恢复模型之前是否返回 true ? 其他非主节点将使用“未准备好”值。 回答1 想办法。 global_variables_initializer 在 facet variable_initializers(global_variables()) 中。 所以我们可以只初始化一些选定的变量并从预训练模型中恢复左边。 “model_ready”将保持为 False,直到所有变量恢复。

2021-11-13 14:26:31    分类:技术分享    tensorflow   resuming-training

What's the recommend way of restoring only parts model in distributed tensorflow

When we finetune a model on a different task, only a part of vars in the model are restored from the pretrained task and others are left as initial values. As many docs recommends(page1 page2), when training with a local graph, restoring the pretrained model after running the global init op(call restoring in "init_fn" if MonitoredSession or supervisor is included). But in the distributed case, does global init op make "model_ready" returns true before the restoring-model called? other non-chief nodes will use the "not ready" values.

2021-11-09 05:50:43    分类:问答    tensorflow   resuming-training

加载训练有素的Keras模型并继续训练(Loading a trained Keras model and continue training)

问题 我想知道是否有可能保存经过部分训练的Keras模型并在再次加载模型后继续进行训练。 这样做的原因是,将来我将拥有更多的训练数据,并且我不想再次对整个模型进行训练。 我正在使用的功能是: #Partly train model model.fit(first_training, first_classes, batch_size=32, nb_epoch=20) #Save partly trained model model.save('partly_trained.h5') #Load partly trained model from keras.models import load_model model = load_model('partly_trained.h5') #Continue training model.fit(second_training, second_classes, batch_size=32, nb_epoch=20) 编辑1:添加了完全正常的示例 对于第10个历元之后的第一个数据集,最后一个历元的损失将为0.0748,准确性为0.9863。 保存,删除和重新加载模型后,第二个数据集上训练的模型的损失和准确性将分别为0.1711和0.9504。 这是由新的训练数据还是由完全重新训练的模型引起的? """ Model by: http:/

2021-04-16 09:38:26    分类:技术分享    python   tensorflow   neural-network   keras   resuming-training

Loading a trained Keras model and continue training

I was wondering if it was possible to save a partly trained Keras model and continue the training after loading the model again. The reason for this is that I will have more training data in the future and I do not want to retrain the whole model again. The functions which I am using are: #Partly train model model.fit(first_training, first_classes, batch_size=32, nb_epoch=20) #Save partly trained model model.save('partly_trained.h5') #Load partly trained model from keras.models import load_model model = load_model('partly_trained.h5') #Continue training model.fit(second_training, second

2021-03-26 18:19:31    分类:问答    python   tensorflow   neural-network   keras   resuming-training