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我無法使Keras使用GPU版本的Tensorflow而不是CPU。每次我進口keras它只是說：使用GPU而不是CPU與Keras搭配使用Tensorflow後端的Linux
>>> import keras
Using TensorFlow backend
......這意味着它的工作，但在CPU上，而不是GPU。 我安裝CUDA和cuDNN和使用環境：
conda create -n tensorflow python=3.5 anaconda
我覺得我第一次安裝tensorflow的CPU版本 - 我不記得了，因爲我整天剛開CUDA和cudnn工作。 不管怎樣，我安裝的是GPU版本太多：
pip install --ignore-installed --upgrade \ https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow_gpu-1.1.0-cp35-cp35m-linux_x86_64.whl
，它仍然給出了同樣的消息。我試圖檢查正在使用的設備由下面的代碼：
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
，但我得到這個輸出，說明我使用的設備0，我的GPU：
2017-05-12 02:14:10.746679: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use SSE4.1 instructions, but these are
available on your machine and could speed up CPU computations.
2017-05-12 02:14:10.746735: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use SSE4.2 instructions, but these are
available on your machine and could speed up CPU computations.
2017-05-12 02:14:10.746751: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use AVX instructions, but these are
available on your machine and could speed up CPU computations.
2017-05-12 02:14:10.746764: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use AVX2 instructions, but these are
available on your machine and could speed up CPU computations.
2017-05-12 02:14:10.746777: W
tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow
library wasn't compiled to use FMA instructions, but these are
available on your machine and could speed up CPU computations.
2017-05-12 02:14:10.926330: I
tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:901] successful
NUMA node read from SysFS had negative value (-1), but there must be
at least one NUMA node, so returning NUMA node zero
2017-05-12 02:14:10.926614: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0
with properties:
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.7845
pciBusID 0000:01:00.0
Total memory: 5.93GiB
Free memory: 5.51GiB
2017-05-12 02:14:10.926626: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0
2017-05-12 02:14:10.926629: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y
2017-05-12 02:14:10.926637: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating
TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 6GB,
pci bus id: 0000:01:00.0)
Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX
1060 6GB, pci bus id: 0000:01:00.0
2017-05-12 02:14:10.949871: I
tensorflow/core/common_runtime/direct_session.cc:257] Device mapping:
/job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX
1060 6GB, pci bus id: 0000:01:00.0
我真的跑出來的東西去做。我唯一剩下的就是卸載anaconda並重新安裝所有的東西，因爲我真的花了整整一天的時間使它和keras以及所有東西一起工作（只是沒有GPU），所以我真的不想這樣做。
你能解決這個問題。我面對完全相同的問題？ – rjmessibarca