Tensorflow Keras内存不足的后果

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我正在使用Keras，在我的GPU（GeForce GTX 970，~4G）上的内存非常有限。因此，我在使用批处理大小设置为高于某个级别的Keras时内存不足（OOM）。降低批量大小我没有这个问题，但Keras输出以下警告：

2019-01-02 09:47:03.173259: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.57GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.211139: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.68GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.268074: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.95GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.685032: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.39GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.732304: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.56GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.850711: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.39GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.879135: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.48GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.963522: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.42GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:03.984897: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.47GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2019-01-02 09:47:04.058733: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.08GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.

这些警告对我这个用户意味着什么？这些性能提升是什么？这是否意味着它只是计算速度更快，或者我甚至可以在更好的验证损失方面获得更好的结果

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在我的设置中，我将Keras与Tensorflow后端一起使用，Tensorflow gpu==1.8.0。

这意味着培训在速度方面会损失一些效率，因为gpu不能用于某些操作。不过，损失的结果不应受到影响

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为了避免此问题，最佳做法是减少批量大小以有效利用可用的GPU内存。

您使用的是

tensorflow

还是

tensorflow GPU

？使用必要的信息编辑问题。因此，应将批量大小减小到不会触发此消息的大小？我个人只在培训一开始就收到了这条信息，然后就不再出现了。