我在linux上使用tensorflow后端运行keras。首先，我自己安装了tensorflow GPU版本，并运行以下代码进行检查，发现它在GPU上运行并显示正在运行的GPU，设备映射等。我使用的张量流来自https://storage.googleapis.com/tensorflow/linux/gpu/tensorflow-
0.11.0-cp27-none-linux_x86_64.whl
a = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3], name='a')
b = tf.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[3, 2], name='b')
c = tf.matmul(a, b)
# Creates a session with log_device_placement set to True.
sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
# Runs the op.
print(sess.run(c))
然后，我使用conda install keras安装了keras。我检查了conda list，现在我有两个版本的tensorflow（1.1.0和0.11.0）。我尝试了import tensorflow as tf，结果是：
2017-07-18 16:35:59.569535: 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.
2017-07-18 16:35:59.569629: 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.
2017-07-18 16:35:59.569690: 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.
2017-07-18 16:35:59.569707: 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.
2017-07-18 16:35:59.569731: 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.
Device mapping: no known devices.
2017-07-18 16:35:59.579959: I tensorflow/core/common_runtime/direct_session.cc:257] Device mapping:
MatMul: (MatMul): /job:localhost/replica:0/task:0/cpu:0
2017-07-18 16:36:14.369948: I tensorflow/core/common_runtime/simple_placer.cc:841] MatMul: (MatMul)/job:localhost/replica:0/task:0/cpu:0
b: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-07-18 16:36:14.370051: I tensorflow/core/common_runtime/simple_placer.cc:841] b: (Const)/job:localhost/replica:0/task:0/cpu:0
a: (Const): /job:localhost/replica:0/task:0/cpu:0
2017-07-18 16:36:14.370109: I tensorflow/core/common_runtime/simple_placer.cc:841] a: (Const)/job:localhost/replica:0/task:0/cpu:0
我已经设置了CUDA_VISIBLE_DEVICES，它在安装keras之前有效。这是因为tensorflow版本？安装keras时，我可以选择安装0.11.0而不是1.1.0吗？如果问题是由于张量流没有检测到GPU，我该如何解决这个问题？我在这个link中读到它并且它表示tensorflow将自动在GPU上运行它是否检测到一个。
有可能Keras，取决于更新版本的TensorFlow，导致安装了一个仅CPU的TensorFlow软件包（tensorflow）隐藏了旧版GPU（tensorflow-gpu）。
我会首先升级支持GPU的版本。通常你可以做pip install --upgrade tensorflow-gpu，但你在TensorFlow installation page有特定的Anaconda指令。然后，您可以使用pip uninstall tensorflow卸载仅限CPU的TensorFlow软件包。现在，import tensorflow as tf应该实际导入支持GPU的软件包，正如您所建议的那样，它应该自动检测您的GPU。