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Lorsque j'essaie d'utiliser with tf.device('/gpu:0'): dans mon projet, il renvoie l'erreur indiquée ci-dessous. J'ai installé tensorflow-gpu.tflearn déclenche une erreur lors de l'utilisation gpu
code:
def main():
with tf.device('/gpu:0'):
model = cnn_model.googlenet(WIDTH, HEIGHT, 3, LR, output=8,model_name=MODEL_NAME)
model.fit({'input': batch_x}, {'targets': batch_y}, n_epoch=1,snapshot_step=2500, show_metric=True, run_id=MODEL_NAME)
cnn_model se trouve ici: https://github.com/MrGrimod/gta_self_driving/blob/master/model/cnn_model.py
Erreur:
WARNING:tensorflow:From
C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\util\tf_should_use.py:175:
initialize_all_variables (from tensorflow.python.ops.variables) is
deprecated and will be removed after 2017-03-02.
Instructions for updating:
Use `tf.global_variables_initializer` instead.
hdf5 is not supported on this machine (please install/reinstall h5py for
optimal experience)
curses is not supported on this machine (please install/reinstall curses for
an optimal experience)
Traceback (most recent call last):
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\client\session.py", line 1327, in _do_call
return fn(*args)
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\client\session.py", line 1297, in _run_fn
self._extend_graph()
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\client\session.py", line 1358, in _extend_graph
self._session, graph_def.SerializeToString(), status)
File
"C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\contextlib.py"
, line 88, in __exit__
next(self.gen)
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\framework\errors_impl.py", line 466, in
raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: Node
'init_3/NoOp': Unknown input node '^is_training/Assign'
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "train.py", line 57, in <module>
main()
File "train.py", line 26, in main
model = cnn_model.googlenet(WIDTH, HEIGHT, 3, LR, output=8,
model_name=MODEL_NAME)
File "C:\Users\MrGrimod\Desktop\gta_self_driving\model\cnn_model.py", line
147, in googlenet
max_checkpoints=0, tensorboard_verbose=0,tensorboard_dir='log')
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tflearn\models\dnn.py", line 65, in __init__
best_val_accuracy=best_val_accuracy)
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tflearn\helpers\trainer.py", line 170, in __init__
self.session.run(init)
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\client\session.py", line 895, in run
run_metadata_ptr)
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\client\session.py", line 1124, in _run
feed_dict_tensor, options, run_metadata)
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\client\session.py", line 1321, in _do_run
options, run_metadata)
File "C:\Users\MrGrimod\AppData\Local\Programs\Python\Python36\lib\site-
packages\tensorflow\python\client\session.py", line 1340, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: Node
'init_3/NoOp': Unknown input node '^is_training/Assign'
2017-09-22 09:59:42.453552: W C:\tf_jenkins\home\workspace\rel-
win\M\windows\PY\36\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.
2017-09-22 09:59:42.453565: W C:\tf_jenkins\home\workspace\rel-
win\M\windows\PY\36\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.