TensorFlow not detecting GPU

I installed Cuda-8.0 and Tensorflow GPU version on ubuntu 16.04. It was working fine initally and using GPU. But suddenly it has stopped using GPU. I installed tensorflow through pip and correctly the GPU version as it worked and used GPU initially.

The message I get while importing tensorflow is:

>>> import tensorflow as tf
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:126] Couldn't open CUDA library libcudnn.so.5. LD_LIBRARY_PATH: :/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/local/cuda/lib64:/usr/local/cuda/extras/CUPTI/lib64:/usr/lib/x86_64-linux-gnu
I tensorflow/stream_executor/cuda/cuda_dnn.cc:3517] Unable to load cuDNN DSO
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcufft.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcuda.so.1 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcurand.so.8.0 locally

So clearly it's even able to locate cuda library from LD_LIBRARY_PATH. But when I get following output:

>>> sess = tf.Session(config=tf.ConfigProto(log_device_placement=True))
W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE3 instructions, but these are available on your machine and could speed up CPU computations.
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.
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.
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.
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.
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.
E tensorflow/stream_executor/cuda/cuda_driver.cc:509] failed call to cuInit: CUDA_ERROR_UNKNOWN
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:158] retrieving CUDA diagnostic information for host: naman-pc
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:165] hostname: naman-pc
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:189] libcuda reported version is: 375.39.0
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:363] driver version file contents: """NVRM version: NVIDIA UNIX x86_64 Kernel Module  375.39  Tue Jan 31 20:47:00 PST 2017
GCC version:  gcc version 5.4.0 20160609 (Ubuntu 5.4.0-6ubuntu1~16.04.4) 
"""
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:193] kernel reported version is: 375.39.0
I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:300] kernel version seems to match DSO: 375.39.0
Device mapping: no known devices.
I tensorflow/core/common_runtime/direct_session.cc:257] Device mapping:

So it's not able to locate GPU. nvidia-smi gives following output:

+-----------------------------------------------------------------------------+
| NVIDIA-SMI 375.39                 Driver Version: 375.39                    |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|===============================+======================+======================|
|   0  Graphics Device     Off  | 0000:01:00.0      On |                  N/A |
| 23%   41C    P8    11W / 250W |    337MiB / 11169MiB |      0%      Default |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|=============================================================================|
|    0      1005    G   /usr/lib/xorg/Xorg                             197MiB |
|    0      2032    G   ...s-passed-by-fd --v8-snapshot-passed-by-fd    89MiB |
|    0     30355    G   compiz                                          37MiB |
+-----------------------------------------------------------------------------+

I browsed other links on stackoverflow, but they mostly ask to check LD_LIBRARY_PATH or nvidia-smi. For me both are expected, so not able to understand the issue.

EDIT: I tried installing cudnn 5 and putting it in LD_LIBRARY_PATH also, tensorflow reads it successfully but still the same error on creating session.