无法使用model.save保存模型在Keras中遵循multi_gpu_model [英] Can not save model using model.save following multi_gpu_model in Keras

问题描述

在升级到Keras 2.0.9之后，我一直在使用multi_gpu_model实用程序，但是我无法使用

Following the upgrade to Keras 2.0.9, I have been using the multi_gpu_model utility but I can't save my models or best weights using

model.save('path')

我得到的错误是

TypeError:无法腌制模块对象

TypeError: can’t pickle module objects

我怀疑在访问模型对象时存在一些问题.是否可以解决此问题?

I suspect there is some problem gaining access to the model object. Is there a work around this issue?

推荐答案

老实说，最简单的方法实际上是使用

To be honest, the easiest approach to this is to actually examine the multi gpu parallel model using

 parallel_model.summary()

(并行模型只是应用multi_gpu函数之后的模型).这清楚地突出了实际模型(在我看来是倒数第二层-我现在不在电脑旁).然后，您可以使用该层的名称来保存模型.

(The parallel model is simply the model after applying the multi_gpu function). This clearly highlights the actual model (in I think the penultimate layer - I am not at my computer right now). Then you can use the name of this layer to save the model.

 model = parallel_model.get_layer('sequential_1)

通常称为"sequence_1"，但是如果您使用的是已发布的体系结构，则可能是"googlenet"或"alexnet".您将从摘要中看到层的名称.

Often its called sequential_1 but if you are using a published architecture, it may be 'googlenet' or 'alexnet'. You will see the name of the layer from the summary.

然后只需保存即可简单

 model.save()

格言的方法行之有效，但我认为它过大了.

Maxims approach works, but its overkill I think.

Rem:您将需要同时编译模型和并行模型.

Rem: you will need to compile both the model, and the parallel model.

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