运行keras脚本时，得到以下输出：
Using TensorFlow backend. 2017-06-14 17:40:44.621761: 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. 2017-06-14 17:40:44.621783: 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. 2017-06-14 17:40:44.621788: 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. 2017-06-14 17:40:44.621791: 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. 2017-06-14 17:40:44.621795: 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. 2017-06-14 17:40:44.721911: 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 2017-06-14 17:40:44.722288: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: name: GeForce GTX 850M major: 5 minor: 0 memoryClockRate (GHz) 0.9015 pciBusID 0000:0a:00.0 Total memory: 3.95GiB Free memory: 3.69GiB 2017-06-14 17:40:44.722302: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 2017-06-14 17:40:44.722307: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y 2017-06-14 17:40:44.722312: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, pci bus id: 0000:0a:00.0)
这是什么意思？我是否正在使用GPU或CPU版本的Tensorflow？
在安装keras之前，我正在使用Tensorflow的GPU版本。
还sudo pip3 list显示tensorflow-gpu(1.1.0)和没有什么像tensorflow-cpu。
sudo pip3 list
tensorflow-gpu(1.1.0)
tensorflow-cpu
运行[此stackoverflow问题]中提到的命令，将得到以下信息：
The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations. 2017-06-14 17:53:31.424793: 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. 2017-06-14 17:53:31.424803: 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. 2017-06-14 17:53:31.424812: 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. 2017-06-14 17:53:31.424820: 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. 2017-06-14 17:53:31.540959: 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 2017-06-14 17:53:31.541359: I tensorflow/core/common_runtime/gpu/gpu_device.cc:887] Found device 0 with properties: name: GeForce GTX 850M major: 5 minor: 0 memoryClockRate (GHz) 0.9015 pciBusID 0000:0a:00.0 Total memory: 3.95GiB Free memory: 128.12MiB 2017-06-14 17:53:31.541407: I tensorflow/core/common_runtime/gpu/gpu_device.cc:908] DMA: 0 2017-06-14 17:53:31.541420: I tensorflow/core/common_runtime/gpu/gpu_device.cc:918] 0: Y 2017-06-14 17:53:31.541441: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 850M, pci bus id: 0000:0a:00.0) 2017-06-14 17:53:31.547902: E tensorflow/stream_executor/cuda/cuda_driver.cc:893] failed to allocate 128.12M (134348800 bytes) from device: CUDA_ERROR_OUT_OF_MEMORY Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 850M, pci bus id: 0000:0a:00.0 2017-06-14 17:53:31.549482: I tensorflow/core/common_runtime/direct_session.cc:257] Device mapping: /job:localhost/replica:0/task:0/gpu:0 -> device: 0, name: GeForce GTX 850M, pci bus id: 0000:0a:00.0
您正在使用GPU版本。您可以列出可用的tensorflow设备（也请检查此问题）：
from tensorflow.python.client import device_lib print(device_lib.list_local_devices()) # list of DeviceAttributes
编辑：
使用tensorflow> =1.4时，您可以运行以下功能：
import tensorflow as tf tf.test.is_gpu_available() # True/False # Or only check for gpu's with cuda support tf.test.is_gpu_available(cuda_only=True)
编辑2：
上面的功能在中已弃用tensorflow > 2.1。相反，您应该使用以下功能：
tensorflow > 2.1
import tensorflow as tf tf.config.list_physical_devices('GPU')
注意：
在您的情况下，cpu和gpu均可用，如果使用tensorflow的cpu版本，则不会列出gpu。在您的情况下，无需设置tensorflow设备（withtf.device("..")），tensorflow将自动选择您的GPU！
withtf.device("..")
此外，您sudo pip3 list清楚地表明您正在使用tensorflow-gpu。如果您拥有tensoflow cpu版本，则名称将类似于tensorflow(1.1.0)。
tensorflow(1.1.0)
检查此问题以获取有关警告的信息。
