1oglop1 · February 16, 2018 18:20
diff --git a/tensor.txt b/tensor.txt
 (anaserp) C:\Users\win10\serp>serpent train context 1
 c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\h5py\__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.
  from ._conv import register_converters as _register_converters
 Using TensorFlow backend.
 Found 98 images belonging to 4 classes.
 Found 14 images belonging to 4 classes.
 2018-02-16 19:11:30.020571: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
 2018-02-16 19:11:30.264945: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1105] Found device 0 with properties:
 name: GeForce GTX 760 major: 3 minor: 0 memoryClockRate(GHz): 1.0845
 pciBusID: 0000:01:00.0
 totalMemory: 2.00GiB freeMemory: 1.65GiB
 2018-02-16 19:11:30.265189: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\gpu\gpu_device.cc:1195] Creating TensorFlow device (/device:GPU:0) -> (device: 0, name: GeForce GTX 760, pci bus id: 0000:01:00.0, compute capability: 3.0)
 c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\machine_learning\context_classification\context_classifiers\cnn_inception_v3_context_classifier.py:76: UserWarning: The semantics of the Keras 2 argument `steps_per_epoch` is not the same as the Keras 1 argument `samples_per_epoch`. `steps_per_epoch` is the number of batches to draw from the generator at each epoch. Basically steps_per_epoch = samples_per_epoch/batch_size. Similarly `nb_val_samples`->`validation_steps` and `val_samples`->`steps` arguments have changed. Update your method calls accordingly.
  callbacks=callbacks
 c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\machine_learning\context_classification\context_classifiers\cnn_inception_v3_context_classifier.py:76: UserWarning: Update your `fit_generator` call to the Keras 2 API: `fit_generator(<keras.pre..., validation_data=<keras.pre..., class_weight="auto", callbacks=[], steps_per_epoch=3, epochs=1, validation_steps=14)`
  callbacks=callbacks
 Epoch 1/1
 2018-02-16 19:12:49.964758: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.41GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
 1/3 [=========>....................] - ETA: 7s - loss: 1.0510 - acc: 1.00002018-02-16 19:12:50.266525: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:217] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.14GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory is available.
 2018-02-16 19:13:00.589475: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:273] Allocator (GPU_0_bfc) ran out of memory trying to allocate 487.88MiB.  Current allocation summary follows.
 2018-02-16 19:13:00.589832: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (256):       Total Chunks: 614, Chunks in use: 614. 153.5KiB allocated for chunks. 153.5KiB in use in bin. 33.5KiB client-requested in use in bin.
 2018-02-16 19:13:00.591189: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (512):       Total Chunks: 415, Chunks in use: 415. 282.5KiB allocated for chunks. 282.5KiB in use in bin. 262.4KiB client-requested in use in bin.
 2018-02-16 19:13:00.591599: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (1024):      Total Chunks: 136, Chunks in use: 136. 202.0KiB allocated for chunks. 202.0KiB in use in bin. 201.8KiB client-requested in use in bin.
 2018-02-16 19:13:00.592068: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (2048):      Total Chunks: 1, Chunks in use: 1. 3.5KiB allocated for chunks. 3.5KiB in use in bin. 3.4KiB client-requested in use in bin.
 2018-02-16 19:13:00.592509: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (4096):      Total Chunks: 5, Chunks in use: 4. 20.8KiB allocated for chunks. 16.0KiB in use in bin. 16.0KiB client-requested in use in bin.
 2018-02-16 19:13:00.592972: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (8192):      Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
 2018-02-16 19:13:00.593316: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (16384):     Total Chunks: 4, Chunks in use: 4. 72.0KiB allocated for chunks. 72.0KiB in use in bin. 64.0KiB client-requested in use in bin.
 2018-02-16 19:13:00.593816: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (32768):     Total Chunks: 6, Chunks in use: 5. 241.5KiB allocated for chunks. 204.0KiB in use in bin. 164.0KiB client-requested in use in bin.
 2018-02-16 19:13:00.594192: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (65536):     Total Chunks: 12, Chunks in use: 12. 840.0KiB allocated for chunks. 840.0KiB in use in bin. 774.0KiB client-requested in use in bin.
 2018-02-16 19:13:00.595265: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (131072):    Total Chunks: 3, Chunks in use: 3. 648.0KiB allocated for chunks. 648.0KiB in use in bin. 648.0KiB client-requested in use in bin.
 2018-02-16 19:13:00.595560: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (262144):    Total Chunks: 18, Chunks in use: 14. 6.84MiB allocated for chunks. 5.24MiB in use in bin. 5.16MiB client-requested in use in bin.
 2018-02-16 19:13:00.596054: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (524288):    Total Chunks: 39, Chunks in use: 37. 28.24MiB allocated for chunks. 26.73MiB in use in bin. 24.98MiB client-requested in use in bin.
 2018-02-16 19:13:00.596460: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (1048576):   Total Chunks: 16, Chunks in use: 14. 25.95MiB allocated for chunks. 22.70MiB in use in bin. 20.88MiB client-requested in use in bin.
 2018-02-16 19:13:00.596973: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (2097152):   Total Chunks: 7, Chunks in use: 6. 20.59MiB allocated for chunks. 18.09MiB in use in bin. 14.86MiB client-requested in use in bin.
 2018-02-16 19:13:00.597268: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (4194304):   Total Chunks: 3, Chunks in use: 3. 17.72MiB allocated for chunks. 17.72MiB in use in bin. 15.61MiB client-requested in use in bin.
 2018-02-16 19:13:00.597676: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (8388608):   Total Chunks: 4, Chunks in use: 4. 32.00MiB allocated for chunks. 32.00MiB in use in bin. 32.00MiB client-requested in use in bin.
 2018-02-16 19:13:00.598036: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (16777216):  Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
 2018-02-16 19:13:00.598479: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (33554432):  Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
 2018-02-16 19:13:00.598893: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (67108864):  Total Chunks: 0, Chunks in use: 0. 0B allocated for chunks. 0B in use in bin. 0B client-requested in use in bin.
 2018-02-16 19:13:00.599263: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (134217728):         Total Chunks: 2, Chunks in use: 1. 471.88MiB allocated for chunks. 243.94MiB in use in bin. 243.94MiB client-requested in use in bin.
 2018-02-16 19:13:00.599649: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:628] Bin (268435456):         Total Chunks: 2, Chunks in use: 2. 829.61MiB allocated for chunks. 829.61MiB in use in bin. 731.82MiB client-requested in use in bin.
 2018-02-16 19:13:00.602695: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:644] Bin for 487.88MiB was 256.00MiB, Chunk State:
 2018-02-16 19:13:00.603091: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80000 of size 1280
 2018-02-16 19:13:00.603778: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80500 of size 256
 2018-02-16 19:13:00.604185: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80600 of size 256
 2018-02-16 19:13:00.604774: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80700 of size 256
 2018-02-16 19:13:00.605106: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80800 of size 256
 2018-02-16 19:13:00.605833: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80900 of size 256
 2018-02-16 19:13:00.606154: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80A00 of size 256
 2018-02-16 19:13:00.606818: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80B00 of size 256
 2018-02-16 19:13:00.607187: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80C00 of size 256
 2018-02-16 19:13:00.607568: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80D00 of size 256
 2018-02-16 19:13:00.607936: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80E00 of size 256
 2018-02-16 19:13:00.608302: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B80F00 of size 256
 2018-02-16 19:13:00.608783: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81000 of size 256
 2018-02-16 19:13:00.609149: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81100 of size 256
 2018-02-16 19:13:00.609524: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81200 of size 256
 2018-02-16 19:13:00.610026: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81300 of size 256
 2018-02-16 19:13:00.610403: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81400 of size 256
 2018-02-16 19:13:00.613602: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81500 of size 256
 2018-02-16 19:13:00.613979: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81600 of size 256
 2018-02-16 19:13:00.614357: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81700 of size 256
 2018-02-16 19:13:00.614792: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81800 of size 256
 2018-02-16 19:13:00.615266: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81900 of size 256
 2018-02-16 19:13:00.615690: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B81A00 of size 3584
 2018-02-16 19:13:00.616076: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B82800 of size 256
 2018-02-16 19:13:00.616569: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B82900 of size 256
 2018-02-16 19:13:00.616916: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B82A00 of size 256
 2018-02-16 19:13:00.617270: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B82B00 of size 256
 2018-02-16 19:13:00.617832: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B82C00 of size 256
 2018-02-16 19:13:00.618192: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B82D00 of size 256
 2018-02-16 19:13:00.618626: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:662] Chunk at 0000000501B82E00 of size 256
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 2018-02-16 19:13:01.222045: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 00000005025F2B00 of size 331776
 2018-02-16 19:13:01.222372: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 00000005028AE900 of size 4864
 2018-02-16 19:13:01.222720: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 0000000503DBEC00 of size 1638400
 2018-02-16 19:13:01.223058: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 0000000504641500 of size 1769472
 2018-02-16 19:13:01.223407: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 0000000505415F00 of size 38400
 2018-02-16 19:13:01.223756: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 0000000505F21100 of size 393216
 2018-02-16 19:13:01.224080: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 000000050617E400 of size 458752
 2018-02-16 19:13:01.224417: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 00000005064F5900 of size 491520
 2018-02-16 19:13:01.224737: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 000000050682FC00 of size 2621440
 2018-02-16 19:13:01.225061: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 0000000506B82400 of size 860160
 2018-02-16 19:13:01.225394: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 0000000506E63E00 of size 716800
 2018-02-16 19:13:01.225763: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:671] Free at 000000054D2C8F00 of size 239009024
 2018-02-16 19:13:01.226055: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:677]      Summary of in-use Chunks by size:
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 2018-02-16 19:13:01.238597: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:680] 1 Chunks of size 983040 totalling 960.0KiB
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 2018-02-16 19:13:01.244734: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:680] 2 Chunks of size 3670016 totalling 7.00MiB
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 2018-02-16 19:13:01.246583: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:680] 1 Chunks of size 358322176 totalling 341.72MiB
 2018-02-16 19:13:01.246994: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:680] 1 Chunks of size 511582208 totalling 487.88MiB
 2018-02-16 19:13:01.247355: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:684] Sum Total of in-use chunks: 1.17GiB
 2018-02-16 19:13:01.247730: I C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:686] Stats:
 Limit:                  1504939212
 InUse:                  1256605184
 MaxInUse:               1501561344
 NumAllocs:                    2802
 MaxAllocSize:           1356315904

 2018-02-16 19:13:01.248137: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\common_runtime\bfc_allocator.cc:277] ***************************xxxxxx****************************************************_______________
 2018-02-16 19:13:01.248645: W C:\tf_jenkins\workspace\rel-win\M\windows-gpu\PY\36\tensorflow\core\framework\op_kernel.cc:1198] Resource exhausted: OOM when allocating tensor with shape[32,64,197,317] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
 Traceback (most recent call last):
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\client\session.py", line 1350, in _do_call
    return fn(*args)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\client\session.py", line 1329, in _run_fn
    status, run_metadata)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 473, in __exit__
    c_api.TF_GetCode(self.status.status))
 tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[32,64,197,317] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
         [[Node: conv2d_3/convolution = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](activation_2/Relu, conv2d_3/kernel/read)]]
 Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

         [[Node: loss/mul/_3091 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4141_loss/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
 Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.


 During handling of the above exception, another exception occurred:

 Traceback (most recent call last):
  File "c:\users\win10\anaconda3\envs\anaserp\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\win10\Anaconda3\envs\anaserp\Scripts\serpent.exe\__main__.py", line 9, in <module>
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\serpent.py", line 52, in execute
    command_function_mapping[command](*sys.argv[2:])
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\serpent.py", line 389, in train
    train_context(*args)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\serpent.py", line 568, in train_context
    ContextClassifier.executable_train(epochs=int(epochs), validate=argv_is_true(validate), autosave=argv_is_true(autosave))
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\machine_learning\context_classification\context_classifier.py", line 106, in executable_train
    context_classifier.train(epochs=epochs, autosave=autosave, validate=validate)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\machine_learning\context_classification\context_classifiers\cnn_inception_v3_context_classifier.py", line 76, in train
    callbacks=callbacks
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\engine\training.py", line 2244, in fit_generator
    class_weight=class_weight)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\engine\training.py", line 1890, in train_on_batch
    outputs = self.train_function(ins)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\backend\tensorflow_backend.py", line 2475, in __call__
    **self.session_kwargs)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\client\session.py", line 895, in run
    run_metadata_ptr)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\client\session.py", line 1128, in _run
    feed_dict_tensor, options, run_metadata)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\client\session.py", line 1344, in _do_run
    options, run_metadata)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\client\session.py", line 1363, in _do_call
    raise type(e)(node_def, op, message)
 tensorflow.python.framework.errors_impl.ResourceExhaustedError: OOM when allocating tensor with shape[32,64,197,317] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
         [[Node: conv2d_3/convolution = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](activation_2/Relu, conv2d_3/kernel/read)]]
 Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

         [[Node: loss/mul/_3091 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4141_loss/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
 Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.


 Caused by op 'conv2d_3/convolution', defined at:
  File "c:\users\win10\anaconda3\envs\anaserp\lib\runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "C:\Users\win10\Anaconda3\envs\anaserp\Scripts\serpent.exe\__main__.py", line 9, in <module>
    sys.exit(execute())
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\serpent.py", line 52, in execute
    command_function_mapping[command](*sys.argv[2:])
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\serpent.py", line 389, in train
    train_context(*args)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\serpent.py", line 568, in train_context
    ContextClassifier.executable_train(epochs=int(epochs), validate=argv_is_true(validate), autosave=argv_is_true(autosave))
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\machine_learning\context_classification\context_classifier.py", line 106, in executable_train
    context_classifier.train(epochs=epochs, autosave=autosave, validate=validate)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\serpent\machine_learning\context_classification\context_classifiers\cnn_inception_v3_context_classifier.py", line 37, in train
    input_shape=self.input_shape
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\applications\inception_v3.py", line 177, in InceptionV3
    x = conv2d_bn(x, 64, 3, 3)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\applications\inception_v3.py", line 81, in conv2d_bn
    name=conv_name)(x)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\engine\topology.py", line 617, in __call__
    output = self.call(inputs, **kwargs)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\layers\convolutional.py", line 168, in call
    dilation_rate=self.dilation_rate)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\keras\backend\tensorflow_backend.py", line 3332, in conv2d
    data_format=tf_data_format)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 754, in convolution
    return op(input, filter)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 838, in __call__
    return self.conv_op(inp, filter)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 502, in __call__
    return self.call(inp, filter)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\ops\nn_ops.py", line 190, in __call__
    name=self.name)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\ops\gen_nn_ops.py", line 725, in conv2d
    data_format=data_format, dilations=dilations, name=name)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
    op_def=op_def)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\framework\ops.py", line 3160, in create_op
    op_def=op_def)
  File "c:\users\win10\anaconda3\envs\anaserp\lib\site-packages\tensorflow\python\framework\ops.py", line 1625, in __init__
    self._traceback = self._graph._extract_stack()  # pylint: disable=protected-access

 ResourceExhaustedError (see above for traceback): OOM when allocating tensor with shape[32,64,197,317] and type float on /job:localhost/replica:0/task:0/device:GPU:0 by allocator GPU_0_bfc
         [[Node: conv2d_3/convolution = Conv2D[T=DT_FLOAT, data_format="NHWC", dilations=[1, 1, 1, 1], padding="SAME", strides=[1, 1, 1, 1], use_cudnn_on_gpu=true, _device="/job:localhost/replica:0/task:0/device:GPU:0"](activation_2/Relu, conv2d_3/kernel/read)]]
 Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.

         [[Node: loss/mul/_3091 = _Recv[client_terminated=false, recv_device="/job:localhost/replica:0/task:0/device:CPU:0", send_device="/job:localhost/replica:0/task:0/device:GPU:0", send_device_incarnation=1, tensor_name="edge_4141_loss/mul", tensor_type=DT_FLOAT, _device="/job:localhost/replica:0/task:0/device:CPU:0"]()]]
 Hint: If you want to see a list of allocated tensors when OOM happens, add report_tensor_allocations_upon_oom to RunOptions for current allocation info.