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整合（组合）多个深度学习回归模型，这些模型已经具有辍学层

[英]Ensemble (Combine) multiple deep learning regression models which already have dropout layers

原文 2018-04-04 20:30:27 3 1 python/ tensorflow/ regression/ prediction/ robust

Currently I have multiple trained models for regression task, each model is of the same architecture but while training, I have dropout layer, to improve the performance, is that still possible for me to combine those trained models and calculate the mean of the weights as the combined, new model? 目前，我有多个训练有素的模型用于回归任务，每个模型都具有相同的体系结构，但是在训练时，我具有辍学层以提高性能，我仍然可以将这些训练后的模型结合起来并计算权重的平均值合并的新模型？ I just heard that there is an ensemble prediction method which allow us to do, but I am not sure whether I can still do this because I already have random dropout layer. 我刚刚听说有一种整体预测方法可以使我们做到这一点，但是我不确定是否仍然可以做到这一点，因为我已经有了随机的辍学层。

Any hint is much appreciated! 任何提示，不胜感激！

1 个解决方案

I think the presence of dropout is irrelevant to what you want to do. 我认为辍学与您要执行的操作无关。 Ensembling should work just fine with dropout. 集成应该可以很好地与辍学一起工作。

在深度学习模型中进行投票 - Ensemble with voting in deep learning models

我们可以在不增加分类时间的情况下创建深度学习模型的集合吗？ - Can we create an ensemble of deep learning models without increasing the classification time?

如何将存储在3个数据帧中的3个机器学习模型的结果合并/集合化，并输出1个数据帧并获得大多数人同意的结果？ - How do I combine/ensemble results of 3 machine learning models stored in 3 dataframes and output 1 dataframe with results agreed by majority?

python中如何结合深度学习模型和传统机器学习模型的分类结果 - How to combine the classification results of deep learning models and traditional machine learning models in python

scikit-learn 中的机器学习模型集合 - Ensemble of machine learning models in scikit-learn

用于堆叠集成学习的多项逻辑回归，将 2d 输入到回归中 - Multinomial logistic regression for stacked ensemble learning with 2d input into regression

mxnet：具有共享掩码的多个辍学层 - mxnet: multiple dropout layers with shared mask

Keras 中的检查点深度学习模型 - Checkpoint deep learning models in Keras

有没有办法将多个逻辑回归方程合为一体？ - Is there a way to ensemble multiple logistic regression equations into one?

使用 Keras 将 Dropout 层添加到 Segmentation_Models Resnet34 - Adding Dropout Layers to Segmentation_Models Resnet34 with Keras

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相关问题 在深度学习模型中进行投票 - Ensemble with voting in deep learning models 我们可以在不增加分类时间的情况下创建深度学习模型的集合吗？ - Can we create an ensemble of deep learning models without increasing the classification time? 如何将存储在3个数据帧中的3个机器学习模型的结果合并/集合化，并输出1个数据帧并获得大多数人同意的结果？ - How do I combine/ensemble results of 3 machine learning models stored in 3 dataframes and output 1 dataframe with results agreed by majority? python中如何结合深度学习模型和传统机器学习模型的分类结果 - How to combine the classification results of deep learning models and traditional machine learning models in python scikit-learn 中的机器学习模型集合 - Ensemble of machine learning models in scikit-learn 用于堆叠集成学习的多项逻辑回归，将 2d 输入到回归中 - Multinomial logistic regression for stacked ensemble learning with 2d input into regression mxnet：具有共享掩码的多个辍学层 - mxnet: multiple dropout layers with shared mask Keras 中的检查点深度学习模型 - Checkpoint deep learning models in Keras 有没有办法将多个逻辑回归方程合为一体？ - Is there a way to ensemble multiple logistic regression equations into one? 使用 Keras 将 Dropout 层添加到 Segmentation_Models Resnet34 - Adding Dropout Layers to Segmentation_Models Resnet34 with Keras

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