我想检查带有tensorflow后端的keras是否在gpu上运行良好。我运行了this script，并得到以下输出：
Using TensorFlow backend.
Downloading data from https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz
170500096/170498071 [==============================] - 31s 0us/step
x_train shape: (50000, 32, 32, 3)
50000 train samples
10000 test samples
Using real-time data augmentation.
Epoch 1/100
2018-07-06 15:20:00.130371: I tensorflow/core/platform/cpu_feature_guard.cc:140] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
2018-07-06 15:20:00.209953: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:898] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-07-06 15:20:00.210289: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1356] Found device 0 with properties:
name: GeForce GTX 1050 major: 6 minor: 1 memoryClockRate(GHz): 1.493
pciBusID: 0000:01:00.0
totalMemory: 3.95GiB freeMemory: 113.38MiB
2018-07-06 15:20:00.210305: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1435] Adding visible gpu devices: 0
2018-07-06 15:20:00.408052: I tensorflow/core/common_runtime/gpu/gpu_device.cc:923] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-07-06 15:20:00.408100: I tensorflow/core/common_runtime/gpu/gpu_device.cc:929] 0
2018-07-06 15:20:00.408107: I tensorflow/core/common_runtime/gpu/gpu_device.cc:942] 0: N
2018-07-06 15:20:00.408248: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1053] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 57 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1050, pci bus id: 0000:01:00.0, compute capability: 6.1)
2018-07-06 15:20:00.408744: E tensorflow/stream_executor/cuda/cuda_driver.cc:936] failed to allocate 57.38M (60162048 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY
2018-07-06 15:20:00.683832: E tensorflow/stream_executor/cuda/cuda_blas.cc:462] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-07-06 15:20:00.685728: E tensorflow/stream_executor/cuda/cuda_blas.cc:462] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-07-06 15:20:00.688354: E tensorflow/stream_executor/cuda/cuda_blas.cc:462] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-07-06 15:20:00.689038: E tensorflow/stream_executor/cuda/cuda_blas.cc:462] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-07-06 15:20:00.689718: E tensorflow/stream_executor/cuda/cuda_blas.cc:462] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-07-06 15:20:00.690388: E tensorflow/stream_executor/cuda/cuda_blas.cc:462] failed to create cublas handle: CUBLAS_STATUS_NOT_INITIALIZED
2018-07-06 15:20:00.698165: E tensorflow/stream_executor/cuda/cuda_dnn.cc:455] could not create cudnn handle: CUDNN_STATUS_INTERNAL_ERROR
2018-07-06 15:20:00.698238: F tensorflow/core/kernels/conv_ops.cc:713] Check failed: stream->parent()->GetConvolveAlgorithms( conv_parameters.ShouldIncludeWinogradNonfusedAlgo<T>(), &algorithms)
Aborted (core dumped)
我可以读取totalMemory: 3.95GiB freeMemory: 113.38MiB和failed to allocate 57.38M (60162048 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY。
为什么可用内存很少？我该怎么做才能使the script正常运行并最终享受GPU培训？
OS：Fedora 28
Python 3.6.6
Keras 2.2.0
Tensorflow 1.8.0
[GPU GeForce GTX 1050