标题说明了一切.我正在使用Ubuntu 16.04长期支持.
当我运行keras脚本时,我得到以下输出:
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 …我有以下错误.我正在使用tensorflow的conda安装.我正在努力尝试将它与我的GPU一起使用.
Loaded runtime CuDNN library: 5005 (compatibility version 5000) but source was compiled with 5103 (compatibility version 5100). If using a binary install, upgrade your CuDNN library to match. If building from sources, make sure the library loaded at runtime matches a compatible version specified during compile configuration.
F tensorflow/core/kernels/conv_ops.cc:526] Check failed: stream->parent()->GetConvolveAlgorithms(&algorithms)
Aborted (core dumped)
哪个nvcc返回
/usr/local/cuda-7.5/bin/nvcc
nvcc版本返回
Cuda compilation tools, release 7.5, V7.5.17
我尝试下载CuDNN v5.1并执行以下操作,但它无法正常工作```sudo cp lib*/usr/local/cuda-7.5/lib64/ sudo cp include/cudnn.h/usr/local/cuda- 7.5/include/sudo ldconfig
```
我也试过了另一个文件夹
sudo cp lib* /usr/local/cuda/lib64/ …
我正在尝试在Windows上安装tensorflow。我有python3（3.5.2）和pip3（9.0.1）：
pip3 install --upgrade tensorflow
Collecting tensorflow
Could not find a version that satisfies the requirement tensorflow (from versions: )
No matching distribution found for tensorflow
在这里也发现了这个问题：在pip中找不到tensorflow， 但是没有一个解决方案对我有用。有任何想法吗？
我有一个利用 Keras 和 TensorFlow 构建的对象检测模型的应用程序。我确定我已经安装了 TensorFlow-GPU。当应用程序运行时，我没有看到我的 GPU 按预期得到利用。因此，根据这篇文章/答案，我尝试验证我的 GPU 是否可以被 TensorFlow 使用，但它给出了一个错误，表明我的 GPU 未启用 CUDA（即The requested device appears to be a GPU, but CUDA is not enabled.）：
$ python
Python 3.7.4 (default, Jul 9 2019, 15:11:16)
[GCC 7.4.0] on linux
>>> import tensorflow as tf
>>> with tf.device('/gpu:0'):
... 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, …