Keras：无法充分利用GPU

Question

运行代码时，每次都会收到此消息：

2018-09-27 19:31:03.353933: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1405] Found device 0 with properties: 
name: GeForce GTX 650 Ti major: 3 minor: 0 memoryClockRate(GHz): 0.941
pciBusID: 0000:01:00.0
totalMemory: 2.00GiB freeMemory: 1.65GiB
2018-09-27 19:31:03.355743: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1484] Adding visible gpu devices: 0
2018-09-27 19:31:04.822514: I tensorflow/core/common_runtime/gpu/gpu_device.cc:965] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-09-27 19:31:04.822895: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971]      0 
2018-09-27 19:31:04.823072: I tensorflow/core/common_runtime/gpu/gpu_device.cc:984] 0:   N 
2018-09-27 19:31:04.823679: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1097] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 1416 MB memory) -> physical GPU (device: 0, name: GeForce GTX 650 Ti, pci bus id: 0000:01:00.0, compute capability: 3.0)
2018-09-27 19:31:12.050251: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 261.79MiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2018-09-27 19:31:17.191146: W tensorflow/core/common_runtime/bfc_allocator.cc:219] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.13GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.

最后两条消息是警告，对我来说似乎很奇怪：我应该有1.65GiB的可用内存，但不能分配一些较小的数量。 我该如何解决？该消息的来源是什么？而且：为什么我的GPU使用率不能超过50％？

这是我开始训练时的样子：

代码本身在my repo中（很难知道我的代码的哪些部分是相关的）。

Answer 1

您似乎没有使用多GPU模型？

例如参见https://www.pyimagesearch.com/2017/10/30/how-to-multi-gpu-training-with-keras-python-and-deep-learning/

Answer 2

您是否尝试过增加批量大小？我从您的代码中看到您使用了add-customer