Tensorflow OOM sur GPU

je suis en formation de Musique de Données sur un LSTM-RNN dans Tensorflow et rencontré un Problème avec GPU-Allocation de Mémoire dont je ne comprends pas: j'ai rencontré un OOM quand il semble être à peu près assez de VRAM encore disponibles.
Un peu de contexte:
Je travaille sur Ubuntu Gnome 16.04, à l'aide d'un GTX1060 6 GB, processeur Intel Xeon E3-1231V3 et 8GO de RAM.
Alors maintenant, la première partie du message d'erreur qui je peux comprendre, à la et je vais ajouter que tout message d'erreur à la fin encore une fois pour toute personne qui pourrait demander de l'aide:

Je tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Morceaux de
taille 256 total 2.0 KiB j'
tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Morceaux de taille
1280 pour 1,2 KiB j'
tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Morceaux de taille
44288 totalisant 216.2 KiB j'
tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Morceaux de taille
56064 totalisant 273.8 KiB j'
tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Morceaux de taille
154350080 totalisant 588.80 MiB I
tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Morceaux de taille
813400064 totalisant 2.27 GiB j'
tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Morceaux de taille
1612612352 total de 1,50 Gio je
tensorflow/core/common_runtime/bfc_allocator.cc:700] Somme Totale de
-l'utilisation de morceaux de: 4.35 Go je
tensorflow/core/common_runtime/bfc_allocator.cc:702] Statistiques:

Limite: 5484118016

InUse: 4670717952

MaxInUse: 5484118016

NumAllocs: 29

MaxAllocSize: 1612612352

W tensorflow/core/common_runtime/bfc_allocator.cc:274]
*********************___________*__***************************************************xxxxxxxxxxxxxx W tensorflow/core/common_runtime/bfc_allocator.cc:275] a Couru hors de la
mémoire en essayant de répartir 775.72 MiB. Voir les journaux de l'état de la mémoire. W
tensorflow/core/cadre/op_kernel.cc:993] Ressources épuisées: OOM
lors de l'attribution du tenseur de la forme[14525,14000]

Donc, je peux lire qu'il y est un maximum de 5484118016 octets à allouer,
4670717952 octets sont déjà en cours d'utilisation, et un autre 777.72 MO = 775720000 octets sont alloués. 5484118016 octets - 4670717952 octets - 775720000 octets = 37680064 octets selon ma calculatrice.
Donc, il devrait encore être 37MB de gratuit VRAM après l'attribution de l'espace pour le nouveau Tenseur, il veut pousser là. Cela semble également être tout à fait légitime pour moi, comme Tensorflow serait probablement (je suppose?) ne pas essayer d'allouer plus de RAM qu'il est toujours disponible et vient de mettre le reste des données en attente dans la mémoire RAM ou quelque chose.

Maintenant, je suppose qu'il est juste une grosse erreur dans mon raisonnement, mais je serais très reconnaissante si quelqu'un pouvait m'expliquer, ce que cette erreur est. L'évidence de problèmes-la stratégie pour mon problème est juste de faire mes lots un peu plus petit, ayant chacun à environ 1,5 GO probablement tout est trop grand. Encore j'aimerais savoir quel est le véritable problème est là.

edit: j'ai trouvé quelque chose à me dire d'essayer:

config = tf.ConfigProto()
config.gpu_options.allocator_type = 'BFC'
with tf.Session(config = config) as s:

qui ne fonctionne toujours pas, mais comme le tensorflow documentation est dépourvue de toute explication de ce

 gpu_options.allocator_type = 'BFC'

serait, j'aimerais vous demander de vous les gars.

Ajouter le reste du message d'erreur pour toute personne intéressée:

Désolé pour le long copier/coller, mais peut-être que quelqu'un aurait besoin/envie de le voir,

Je vous remercie beaucoup à l'avance,
Leon

(gputensorflow) leon@ljksUbuntu:~/Tensorflow$ python Netzwerk_v0.5.1_gamma.py 
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcublas.so.8.0 locally
I tensorflow/stream_executor/dso_loader.cc:135] successfully opened CUDA library libcudnn.so.5 locally
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
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.
I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:910] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
I tensorflow/core/common_runtime/gpu/gpu_device.cc:885] Found device 0 with properties: 
name: GeForce GTX 1060 6GB
major: 6 minor: 1 memoryClockRate (GHz) 1.7335
pciBusID 0000:01:00.0
Total memory: 5.93GiB
Free memory: 5.40GiB
I tensorflow/core/common_runtime/gpu/gpu_device.cc:906] DMA: 0 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:916] 0:   Y 
I tensorflow/core/common_runtime/gpu/gpu_device.cc:975] Creating TensorFlow device (/gpu:0) -> (device: 0, name: GeForce GTX 1060 6GB, pci bus id: 0000:01:00.0)
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (256):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (512):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1024):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2048):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4096):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8192):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16384):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (32768):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (65536):     Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (131072):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (262144):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (524288):    Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (1048576):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (2097152):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (4194304):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (8388608):   Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (16777216):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (33554432):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (67108864):  Total Chunks: 0, Chunks in use: 0 0B allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (134217728):     Total Chunks: 1, Chunks in use: 0 147.20MiB allocated for chunks. 147.20MiB client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:643] Bin (268435456):     Total Chunks: 1, Chunks in use: 0 628.52MiB allocated for chunks. 0B client-requested for chunks. 0B in use in bin. 0B client-requested in use in bin.
I tensorflow/core/common_runtime/bfc_allocator.cc:660] Bin for 775.72MiB was 256.00MiB, Chunk State: 
I tensorflow/core/common_runtime/bfc_allocator.cc:666]   Size: 628.52MiB | Requested Size: 0B | in_use: 0, prev:   Size: 147.20MiB | Requested Size: 147.20MiB | in_use: 1, next:   Size: 54.8KiB | Requested Size: 54.7KiB | in_use: 1
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10208000000 of size 1280
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10208000500 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10208000600 of size 56064
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1020800e100 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1020800e200 of size 44288
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10208018f00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10208019000 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10208019100 of size 813400064
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102387d1100 of size 56064
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102387dec00 of size 154350080
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10241b11e00 of size 44288
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10241b1cb00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10241b1cc00 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x10241b1cd00 of size 154350080
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102722d4d00 of size 56064
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1027b615a00 of size 44288
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1027b620700 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1027b620800 of size 256
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x1027b620900 of size 813400064
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102abdd8900 of size 813400064
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102dc590900 of size 56064
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102dc59e400 of size 56064
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102dc5abf00 of size 154350080
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102e58df100 of size 154350080
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102eec12300 of size 44288
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102eec1d000 of size 44288
I tensorflow/core/common_runtime/bfc_allocator.cc:678] Chunk at 0x102eec27d00 of size 1612612352
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x1024ae4ff00 of size 659049984
I tensorflow/core/common_runtime/bfc_allocator.cc:687] Free at 0x102722e2800 of size 154350080
I tensorflow/core/common_runtime/bfc_allocator.cc:693]      Summary of in-use Chunks by size: 
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 8 Chunks of size 256 totalling 2.0KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1280 totalling 1.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 44288 totalling 216.2KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 5 Chunks of size 56064 totalling 273.8KiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 4 Chunks of size 154350080 totalling 588.80MiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 3 Chunks of size 813400064 totalling 2.27GiB
I tensorflow/core/common_runtime/bfc_allocator.cc:696] 1 Chunks of size 1612612352 totalling 1.50GiB
I tensorflow/core/common_runtime/bfc_allocator.cc:700] Sum Total of in-use chunks: 4.35GiB
I tensorflow/core/common_runtime/bfc_allocator.cc:702] Stats: 
Limit:                  5484118016
InUse:                  4670717952
MaxInUse:               5484118016
NumAllocs:                      29
MaxAllocSize:           1612612352
W tensorflow/core/common_runtime/bfc_allocator.cc:274] *********************___________*__***************************************************xxxxxxxxxxxxxx
W tensorflow/core/common_runtime/bfc_allocator.cc:275] Ran out of memory trying to allocate 775.72MiB.  See logs for memory state.
W tensorflow/core/framework/op_kernel.cc:993] Resource exhausted: OOM when allocating tensor with shape[14525,14000]
Traceback (most recent call last):
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1022, in _do_call
return fn(*args)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1004, in _run_fn
status, run_metadata)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/contextlib.py", line 66, in __exit__
next(self.gen)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/framework/errors_impl.py", line 469, in raise_exception_on_not_ok_status
pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[14525,14000]
[[Node: rnn/basic_lstm_cell/weights/Initializer/random_uniform = Add[T=DT_FLOAT, _class=["loc:@rnn/basic_lstm_cell/weights"], _device="/job:localhost/replica:0/task:0/gpu:0"](rnn/basic_lstm_cell/weights/Initializer/random_uniform/mul, rnn/basic_lstm_cell/weights/Initializer/random_uniform/min)]]
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "Netzwerk_v0.5.1_gamma.py", line 171, in <module>
session.run(tf.global_variables_initializer())
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 767, in run
run_metadata_ptr)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 965, in _run
feed_dict_string, options, run_metadata)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1015, in _do_run
target_list, options, run_metadata)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/client/session.py", line 1035, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[14525,14000]
[[Node: rnn/basic_lstm_cell/weights/Initializer/random_uniform = Add[T=DT_FLOAT, _class=["loc:@rnn/basic_lstm_cell/weights"], _device="/job:localhost/replica:0/task:0/gpu:0"](rnn/basic_lstm_cell/weights/Initializer/random_uniform/mul, rnn/basic_lstm_cell/weights/Initializer/random_uniform/min)]]
Caused by op 'rnn/basic_lstm_cell/weights/Initializer/random_uniform', defined at:
File "Netzwerk_v0.5.1_gamma.py", line 94, in <module>
initial_state=initial_state, time_major=False)       # time_major = FALSE currently
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py", line 545, in dynamic_rnn
dtype=dtype)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py", line 712, in _dynamic_rnn_loop
swap_memory=swap_memory)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2626, in while_loop
result = context.BuildLoop(cond, body, loop_vars, shape_invariants)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2459, in BuildLoop
pred, body, original_loop_vars, loop_vars, shape_invariants)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/control_flow_ops.py", line 2409, in _BuildLoop
body_result = body(*packed_vars_for_body)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py", line 697, in _time_step
(output, new_state) = call_cell()
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/rnn.py", line 683, in <lambda>
call_cell = lambda: cell(input_t, state)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 179, in __call__
concat = _linear([inputs, h], 4 * self._num_units, True, scope=scope)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/contrib/rnn/python/ops/core_rnn_cell_impl.py", line 747, in _linear
"weights", [total_arg_size, output_size], dtype=dtype)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 988, in get_variable
custom_getter=custom_getter)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 890, in get_variable
custom_getter=custom_getter)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 348, in get_variable
validate_shape=validate_shape)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 333, in _true_getter
caching_device=caching_device, validate_shape=validate_shape)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 684, in _get_single_variable
validate_shape=validate_shape)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 226, in __init__
expected_shape=expected_shape)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/variables.py", line 303, in _init_from_args
initial_value(), name="initial_value", dtype=dtype)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/variable_scope.py", line 673, in <lambda>
shape.as_list(), dtype=dtype, partition_info=partition_info)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/init_ops.py", line 360, in __call__
dtype, seed=self.seed)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/random_ops.py", line 246, in random_uniform
return math_ops.add(rnd * (maxval - minval), minval, name=name)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/ops/gen_math_ops.py", line 73, in add
result = _op_def_lib.apply_op("Add", x=x, y=y, name=name)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/framework/op_def_library.py", line 763, in apply_op
op_def=op_def)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 2395, in create_op
original_op=self._default_original_op, op_def=op_def)
File "/home/leon/anaconda3/envs/gputensorflow/lib/python3.5/site-packages/tensorflow/python/framework/ops.py", line 1264, in __init__
self._traceback = _extract_stack()
ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[14525,14000]
[[Node: rnn/basic_lstm_cell/weights/Initializer/random_uniform = Add[T=DT_FLOAT, _class=["loc:@rnn/basic_lstm_cell/weights"], _device="/job:localhost/replica:0/task:0/gpu:0"](rnn/basic_lstm_cell/weights/Initializer/random_uniform/mul, rnn/basic_lstm_cell/weights/Initializer/random_uniform/min)]]
J'ai rencontré ce problème récemment, et le visage de ressources d'échappement problème dans le milieu de la formation. Et j'ai suivi ce github.com/tensorflow/tensorflow/issues/4735, et s'est attaqué à ce problème par la diminution de la validation de la taille des lots.

OriginalL'auteur LJKS | 2017-02-27