Tensorflow 错误。 获取卷积算法失败。 这可能是因为 cuDNN 未能初始化
[英]Tensorflow error. Failed to get convolution algorithm. This is probably because cuDNN failed to initialize
问题描述
I have previously installed Tensorflow on several machines but am stuck installing it on my new laptop with RTX 2060. No matter what combination of versions I try, I get the same error.
我之前已经在几台机器上安装了 Tensorflow，但我一直坚持在我的 RTX 2060 新笔记本电脑上安装它。无论我尝试什么版本组合，我都会遇到同样的错误。 I found similar issues online and it seems like the problem is the version conflict of cudnn and tensorflow.我在网上发现了类似的问题，似乎问题是 cudnn 和 tensorflow 的版本冲突。
Here's my installation and the error.这是我的安装和错误。
Currently, I have Cuda v10.0.130 and cudnn-10.0-windows10-x64-v7.6.0.64 to match the installation of tensorflow, like in the image.目前，我有 Cuda v10.0.130 和 cudnn-10.0-windows10-x64-v7.6.0.64 以匹配 tensorflow 的安装，如图所示。 tf__version__ = 1.13.1. tf__version__ = 1.13.1。 Python version is 3.6. Python 版本为 3.6。 THe cudnn libraries are copied in Cuda installation folder. cudnn 库复制到 Cuda 安装文件夹中。 I also tried with Tensorflow 1.14 and python 3.7 and am getting the same results.我还尝试了 Tensorflow 1.14 和 python 3.7 并且得到了相同的结果。
I'm installing tensorflow with Anaconda conda install tensorflow-gpu我正在安装 tensorflow 和 Anaconda conda conda install tensorflow-gpu
Traceback (most recent call last):
File "<ipython-input-1-c77ea08f5c30>", line 1, in <module>
runfile('C:/Users/mazat/Documents/Python/MVTools/player_detector/player_detector_testing.py', wdir='C:/Users/mazat/Documents/Python/MVTools/player_detector')
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
execfile(filename, namespace)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/mazat/Documents/Python/MVTools/player_detector/player_detector_testing.py", line 399, in <module>
player_detector_run()
File "C:/Users/mazat/Documents/Python/MVTools/player_detector/player_detector_testing.py", line 392, in player_detector_run
glavnaya(dropbox_folder,gamename,mvstatus)
File "C:/Users/mazat/Documents/Python/MVTools/player_detector/player_detector_testing.py", line 247, in glavnaya
__,box1,score = yolo_class.detect_images(im2[ii].astype('uint8'))
File "C:\Users\mazat\Documents\Python\MVTools\player_detector\yolo3\yolo3.py", line 181, in detect_images
K.learning_phase(): 0
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\client\session.py", line 929, in run
run_metadata_ptr)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\client\session.py", line 1152, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\client\session.py", line 1328, in _do_run
run_metadata)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\client\session.py", line 1348, in _do_call
raise type(e)(node_def, op, message)
UnknownError: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[node conv2d_1/convolution (defined at C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\keras\backend\tensorflow_backend.py:3650) ]]
Caused by op 'conv2d_1/convolution', defined at:
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\runpy.py", line 85, in _run_code
exec(code, run_globals)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\spyder_kernels\console\__main__.py", line 11, in <module>
start.main()
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\spyder_kernels\console\start.py", line 318, in main
kernel.start()
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\ipykernel\kernelapp.py", line 563, in start
self.io_loop.start()
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tornado\platform\asyncio.py", line 148, in start
self.asyncio_loop.run_forever()
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\asyncio\base_events.py", line 438, in run_forever
self._run_once()
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\asyncio\base_events.py", line 1451, in _run_once
handle._run()
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\asyncio\events.py", line 145, in _run
self._callback(*self._args)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tornado\ioloop.py", line 690, in <lambda>
lambda f: self._run_callback(functools.partial(callback, future))
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tornado\ioloop.py", line 743, in _run_callback
ret = callback()
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tornado\gen.py", line 787, in inner
self.run()
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tornado\gen.py", line 748, in run
yielded = self.gen.send(value)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\ipykernel\kernelbase.py", line 365, in process_one
yield gen.maybe_future(dispatch(*args))
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tornado\gen.py", line 209, in wrapper
yielded = next(result)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\ipykernel\kernelbase.py", line 272, in dispatch_shell
yield gen.maybe_future(handler(stream, idents, msg))
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tornado\gen.py", line 209, in wrapper
yielded = next(result)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\ipykernel\kernelbase.py", line 542, in execute_request
user_expressions, allow_stdin,
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tornado\gen.py", line 209, in wrapper
yielded = next(result)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\ipykernel\ipkernel.py", line 294, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\ipykernel\zmqshell.py", line 536, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\IPython\core\interactiveshell.py", line 2855, in run_cell
raw_cell, store_history, silent, shell_futures)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\IPython\core\interactiveshell.py", line 2881, in _run_cell
return runner(coro)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\IPython\core\async_helpers.py", line 68, in _pseudo_sync_runner
coro.send(None)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\IPython\core\interactiveshell.py", line 3058, in run_cell_async
interactivity=interactivity, compiler=compiler, result=result)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\IPython\core\interactiveshell.py", line 3249, in run_ast_nodes
if (await self.run_code(code, result, async_=asy)):
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\IPython\core\interactiveshell.py", line 3326, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-1-c77ea08f5c30>", line 1, in <module>
runfile('C:/Users/mazat/Documents/Python/MVTools/player_detector/player_detector_testing.py', wdir='C:/Users/mazat/Documents/Python/MVTools/player_detector')
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
execfile(filename, namespace)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "C:/Users/mazat/Documents/Python/MVTools/player_detector/player_detector_testing.py", line 399, in <module>
player_detector_run()
File "C:/Users/mazat/Documents/Python/MVTools/player_detector/player_detector_testing.py", line 392, in player_detector_run
glavnaya(dropbox_folder,gamename,mvstatus)
File "C:/Users/mazat/Documents/Python/MVTools/player_detector/player_detector_testing.py", line 137, in glavnaya
yolo_class=YOLO(model_name,script_dir, res)
File "C:\Users\mazat\Documents\Python\MVTools\player_detector\yolo3\yolo3.py", line 39, in __init__
self.boxes, self.scores, self.classes = self.generate()
File "C:\Users\mazat\Documents\Python\MVTools\player_detector\yolo3\yolo3.py", line 68, in generate
if is_tiny_version else yolo_body(Input(shape=(None,None,3)), num_anchors//3, num_classes)
File "C:\Users\mazat\Documents\Python\MVTools\player_detector\yolo3\model.py", line 72, in yolo_body
darknet = Model(inputs, darknet_body(inputs))
File "C:\Users\mazat\Documents\Python\MVTools\player_detector\yolo3\model.py", line 48, in darknet_body
x = DarknetConv2D_BN_Leaky(32, (3,3))(x)
File "C:\Users\mazat\Documents\Python\MVTools\player_detector\yolo3\utils.py", line 16, in <lambda>
return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)
File "C:\Users\mazat\Documents\Python\MVTools\player_detector\yolo3\utils.py", line 16, in <lambda>
return reduce(lambda f, g: lambda *a, **kw: g(f(*a, **kw)), funcs)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\keras\engine\base_layer.py", line 457, in __call__
output = self.call(inputs, **kwargs)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\keras\layers\convolutional.py", line 171, in call
dilation_rate=self.dilation_rate)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\keras\backend\tensorflow_backend.py", line 3650, in conv2d
data_format=tf_data_format)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 851, in convolution
return op(input, filter)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 966, in __call__
return self.conv_op(inp, filter)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 591, in __call__
return self.call(inp, filter)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 208, in __call__
name=self.name)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 1026, in conv2d
data_format=data_format, dilations=dilations, name=name)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 788, in _apply_op_helper
op_def=op_def)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\util\deprecation.py", line 507, in new_func
return func(*args, **kwargs)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\framework\ops.py", line 3300, in create_op
op_def=op_def)
File "C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\tensorflow\python\framework\ops.py", line 1801, in __init__
self._traceback = tf_stack.extract_stack()
UnknownError (see above for traceback): Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed above.
[[node conv2d_1/convolution (defined at C:\Users\mazat\Anaconda3\envs\tf_gpu\lib\site-packages\keras\backend\tensorflow_backend.py:3650) ]]
Here's also result of conda list这也是conda list的结果
Same issue if I don't force python or tensorflow versions and install the default tensorflow 1.14 and python 3.7如果我不强制使用 python 或 tensorflow 版本并安装默认 tensorflow 1.14 和 Z23EEEB4347BDD26BDDFC6B77EE，则会出现同样的问题。
(tf_gpu_tds) C:\Users\mazat>conda list
# packages in environment at C:\Users\mazat\Anaconda3\envs\tf_gpu_tds:
#
# Name Version Build Channel
_tflow_select 2.1.0 gpu
absl-py 0.8.0 py37_0
alabaster 0.7.12 py37_0
asn1crypto 0.24.0 py37_0
astor 0.8.0 py37_0
astroid 2.3.1 py37_0
attrs 19.1.0 py37_1
babel 2.7.0 py_0
backcall 0.1.0 py37_0
blas 1.0 mkl
bleach 3.1.0 py37_0
ca-certificates 2019.9.11 hecc5488_0 conda-forge
certifi 2019.9.11 py37_0
cffi 1.12.3 py37h7a1dbc1_0
chardet 3.0.4 py37_1003
cloudpickle 1.2.2 py_0
colorama 0.4.1 py37_0
cryptography 2.7 py37h7a1dbc1_0
cudatoolkit 10.0.130 0
cudnn 7.6.0 cuda10.0_0
cycler 0.10.0 py_1 conda-forge
cytoolz 0.10.0 py37hfa6e2cd_0 conda-forge
dask-core 2.5.0 py_0 conda-forge
decorator 4.4.0 py37_1
defusedxml 0.6.0 py_0
docutils 0.15.2 py37_0
entrypoints 0.3 py37_0
freetype 2.9.1 ha9979f8_1
gast 0.3.2 py_0
grpcio 1.16.1 py37h351948d_1
h5py 2.9.0 py37h5e291fa_0
hdf5 1.10.4 h7ebc959_0
icc_rt 2019.0.0 h0cc432a_1
icu 58.2 ha66f8fd_1
idna 2.8 py37_0
imageio 2.5.0 py37_0 conda-forge
imagesize 1.1.0 py37_0
intel-openmp 2019.4 245
ipykernel 5.1.2 py37h39e3cac_0
ipython 7.8.0 py37h39e3cac_0
ipython_genutils 0.2.0 py37_0
isort 4.3.21 py37_0
jedi 0.15.1 py37_0
jinja2 2.10.1 py37_0
joblib 0.13.2 py37_0
jpeg 9b hb83a4c4_2
jsonschema 3.0.2 py37_0
jupyter_client 5.3.3 py37_1
jupyter_core 4.5.0 py_0
keras-applications 1.0.8 py_0
keras-base 2.2.4 py37_0 anaconda
keras-gpu 2.2.4 0 anaconda
keras-preprocessing 1.1.0 py_1
keyring 18.0.0 py37_0
kiwisolver 1.1.0 py37he980bc4_0 conda-forge
lazy-object-proxy 1.4.2 py37he774522_0
libpng 1.6.37 h2a8f88b_0
libprotobuf 3.9.2 h7bd577a_0
libsodium 1.0.16 h9d3ae62_0
libtiff 4.0.10 hb898794_2
markdown 3.1.1 py37_0
markupsafe 1.1.1 py37he774522_0
matplotlib-base 3.1.1 py37h2852a4a_1 conda-forge
mccabe 0.6.1 py37_1
mistune 0.8.4 py37he774522_0
mkl 2019.4 245
mkl-service 2.3.0 py37hb782905_0
mkl_fft 1.0.14 py37h14836fe_0
mkl_random 1.1.0 py37h675688f_0
nbconvert 5.6.0 py37_1
nbformat 4.4.0 py37_0
networkx 2.3 py_0 conda-forge
numpy 1.16.5 py37h19fb1c0_0
numpy-base 1.16.5 py37hc3f5095_0
numpydoc 0.9.1 py_0
olefile 0.46 py37_0
openssl 1.1.1c hfa6e2cd_0 conda-forge
packaging 19.2 py_0
pandas 0.25.1 py37ha925a31_0 anaconda
pandoc 2.2.3.2 0
pandocfilters 1.4.2 py37_1
parso 0.5.1 py_0
pickleshare 0.7.5 py37_0
pillow 6.1.0 py37hdc69c19_0
pip 19.2.3 py37_0
prompt_toolkit 2.0.9 py37_0
protobuf 3.9.2 py37h33f27b4_0
psutil 5.6.3 py37he774522_0
pycodestyle 2.5.0 py37_0
pycparser 2.19 py37_0
pyflakes 2.1.1 py37_0
pygments 2.4.2 py_0
pylint 2.4.2 py37_0
pyopenssl 19.0.0 py37_0
pyparsing 2.4.2 py_0
pyqt 5.9.2 py37h6538335_2
pyreadline 2.1 py37_1
pyrsistent 0.15.4 py37he774522_0
pysocks 1.7.1 py37_0
python 3.7.4 h5263a28_0
python-dateutil 2.8.0 py37_0
pytz 2019.2 py_0
pywavelets 1.0.3 py37h452e1ab_1 conda-forge
pywin32 223 py37hfa6e2cd_1
pyyaml 5.1.2 py37he774522_0 anaconda
pyzmq 18.1.0 py37ha925a31_0
qt 5.9.7 vc14h73c81de_0
qtawesome 0.6.0 py_0
qtconsole 4.5.5 py_0
qtpy 1.9.0 py_0
requests 2.22.0 py37_0
rope 0.14.0 py_0
scikit-image 0.15.0 py37he350917_2 conda-forge
scikit-learn 0.21.3 py37h6288b17_0
scipy 1.3.1 py37h29ff71c_0
setuptools 41.2.0 py37_0
sip 4.19.8 py37h6538335_0
six 1.12.0 py37_0
snowballstemmer 1.9.1 py_0
sphinx 2.2.0 py_0
sphinxcontrib-applehelp 1.0.1 py_0
sphinxcontrib-devhelp 1.0.1 py_0
sphinxcontrib-htmlhelp 1.0.2 py_0
sphinxcontrib-jsmath 1.0.1 py_0
sphinxcontrib-qthelp 1.0.2 py_0
sphinxcontrib-serializinghtml 1.1.3 py_0
spyder 3.3.6 py37_0
spyder-kernels 0.5.2 py37_0
sqlite 3.29.0 he774522_0
tensorboard 1.14.0 py37he3c9ec2_0
tensorflow 1.14.0 gpu_py37h5512b17_0
tensorflow-base 1.14.0 gpu_py37h55fc52a_0
tensorflow-estimator 1.14.0 py_0
tensorflow-gpu 1.14.0 h0d30ee6_0
termcolor 1.1.0 py37_1
testpath 0.4.2 py37_0
tk 8.6.8 hfa6e2cd_0
toolz 0.10.0 py_0 conda-forge
tornado 6.0.3 py37he774522_0
traitlets 4.3.2 py37_0
urllib3 1.24.2 py37_0
vc 14.1 h0510ff6_4
vs2015_runtime 14.16.27012 hf0eaf9b_0
wcwidth 0.1.7 py37_0
webencodings 0.5.1 py37_1
werkzeug 0.16.0 py_0
wheel 0.33.6 py37_0
win_inet_pton 1.1.0 py37_0
wincertstore 0.2 py37_0
wrapt 1.11.2 py37he774522_0
xz 5.2.4 h2fa13f4_4
yaml 0.1.7 vc14h4cb57cf_1 [vc14] anaconda
zeromq 4.3.1 h33f27b4_3
zlib 1.2.11 h62dcd97_3
zstd 1.3.7 h508b16e_0
2 个解决方案
解决方案1
3 已采纳 2019-10-03 11:24:32
In the end, I got my answer from this github issue .最后，我从这个 github 问题中得到了答案。 After several reinstalls and restarts, these lines of code started making all the difference:经过几次重新安装和重新启动后，这些代码行开始发挥作用：
import tensorflow as tf
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
tf.keras.backend.set_session(tf.Session(config=config))
I'm still not sure what was happening, but I guess my suggestion would be to make sure you restart your computer often enough.我仍然不确定发生了什么，但我想我的建议是确保您经常重新启动计算机。 Now both environments with TF1.13.1 + Python 3.6 and TF1.14+Python 3.7 work for me.现在 TF1.13.1 + Python 3.6 和 TF1.14+Python 3.7 的两个环境都适合我。
Please note that, due to compatibility reasons, the code for newer (>= 2.0) versions of tensorflow is:请注意，由于兼容性原因，tensorflow 的较新 (>= 2.0) 版本的代码为：
import tensorflow as tf
config = tf.compat.v1.ConfigProto()
config.gpu_options.allow_growth = True
tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=config))
解决方案2
0 2019-09-30 19:34:27
Since the compatibility issue is the most probable option as we discussed in the comment sections, I found the tested versions of tensorflow with respect to the CUDA and cuDNN versions.由于兼容性问题是我们在评论部分讨论的最可能的选项，我发现 tensorflow 的测试版本相对于CUDA和cuDNN版本。 You can find it in here .你可以在这里找到它。
Please feel free to update the status of your problem after you setup your environment according to the given link.根据给定的链接设置环境后，请随时更新问题的状态。
I hope it would be resolved.我希望它会得到解决。
EDIT : In case you're in Windows, I'd like to update a new link编辑：如果你在 Windows，我想更新一个新链接
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cuDNN 错误 无法获得卷积算法。 这可能是因为 cuDNN 初始化失败 - cuDNN Error Failed to get convolution algorithm. This is probably because cuDNN failed to initialize
CUDA Tensorflow 版本，nvidia-smi 版本问题。 获取卷积算法失败。 这可能是因为 cuDNN 初始化失败， - CUDA Tensorflow Version ,nvidia-smi version issue. Failed to get convolution algorithm. This is probably because cuDNN failed to initialize,
无法创建 cudnn 句柄：CUDNN_STATUS_ALLOC_FAILED，无法获得卷积算法。 这可能是因为 cuDNN 未能初始化 - Could not create cudnn handle: CUDNN_STATUS_ALLOC_FAILED , Failed to get convolution algorithm. This is probably because cuDNN failed to initialize
(0) 未知：获取卷积算法失败。 这可能是因为 cuDNN 未能初始化 - (0) Unknown: Failed to get convolution algorithm. This is probably because cuDNN failed to initialize
获取卷积算法失败。 这可能是因为 cuDNN 初始化失败，所以尝试查看是否打印了警告日志消息 - Failed to get convolution algorithm. This is probably because cuDNN failed to initialize, so try looking to see if a warning log message was printed
Google Colab 错误：无法获得卷积算法。这可能是因为 cuDNN 初始化失败 - Google Colab Error : Failed to get convolution algorithm.This is probably because cuDNN failed to initialize
CNN error Failed to get convolution algorithm.这可能是因为cuDNN初始化失败， - CNN error Failed to get convolution algorithm.This is probably because cuDNN failed to initialize,
获取卷积算法失败。这可能是因为cuDNN初始化失败。[{node conv2d_1/Conv2D}] - Failed to get convolution algorithm.This is probably because cuDNN failed to initialize.[{node conv2d_1/Conv2D}]
获取卷积算法失败。 在 Tensorflow 图像识别中 - Failed to get convolution algorithm. In Tensorflow Image Recognition
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