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Posted to commits@mxnet.apache.org by GitBox <gi...@apache.org> on 2018/06/22 00:39:47 UTC
[GitHub] vdantu commented on issue #8299: test_cifar10 fails in CI master
build
vdantu commented on issue #8299: test_cifar10 fails in CI master build
URL: https://github.com/apache/incubator-mxnet/issues/8299#issuecomment-399285524
I couldn't reproduce this issue, any suggestions on what I should try? Also, how could I look for last time this test failed?
Tested it on CPU..
Python environment:
---------------------
**Python 2.7.12** (default, Dec 4 2017, 14:50:18)
[GCC 5.4.0 20160609] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Test runs:
----------
(env) :/incubator-mxnet# **MXNET_TEST_COUNT=30000 nosetests --logging-level=DEBUG --verbose -s tests/python/train/test_dtype.py:test_cifar10**
test_dtype.test_cifar10 ... [00:15:56] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar/train.rec, use 1 threads for decoding..
[00:15:56] src/io/iter_image_recordio_2.cc:228: Load mean image from data/cifar/mean.bin
[00:15:56] src/io/iter_image_recordio_2.cc:246: Load mean image from data/cifar/mean.bin completed
[00:15:57] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar/test.rec, use 1 threads for decoding..
[00:15:57] src/io/iter_image_recordio_2.cc:228: Load mean image from data/cifar/mean.bin
[00:15:57] src/io/iter_image_recordio_2.cc:246: Load mean image from data/cifar/mean.bin completed
/incubator-mxnet/incubator-mxnet/python/mxnet/model.py:1004: DeprecationWarning: mxnet.model.FeedForward has been deprecated. Please use mxnet.mod.Module instead.
optimizer=optimizer, initializer=initializer, **kwargs)
/incubator-mxnet/incubator-mxnet/python/mxnet/model.py:591: DeprecationWarning: Calling initializer with init(str, NDArray) has been deprecated.please use init(mx.init.InitDesc(...), NDArray) instead.
self.initializer(k, v)
Start training with [cpu(0)]
Epoch[0] Batch [50] Speed: 2353.29 samples/sec accuracy=0.169531
Epoch[0] Batch [100] Speed: 3413.73 samples/sec accuracy=0.270000
Epoch[0] Batch [150] Speed: 3929.30 samples/sec accuracy=0.323906
Epoch[0] Batch [200] Speed: 5827.04 samples/sec accuracy=0.347656
Epoch[0] Batch [250] Speed: 8605.79 samples/sec accuracy=0.368750
Epoch[0] Batch [300] Speed: 7193.14 samples/sec accuracy=0.380000
Epoch[0] Batch [350] Speed: 9871.67 samples/sec accuracy=0.400156
Epoch[0] Resetting Data Iterator
Epoch[0] Time cost=10.354
Epoch[0] Validation-accuracy=0.409019
final accuracy = 0.409019
Epoch[0] Batch [50] Speed: 6709.95 samples/sec accuracy=0.183517
Epoch[0] Batch [100] Speed: 2762.11 samples/sec accuracy=0.256094
Epoch[0] Batch [150] Speed: 2599.24 samples/sec accuracy=0.316719
Epoch[0] Batch [200] Speed: 4019.71 samples/sec accuracy=0.352344
Epoch[0] Batch [250] Speed: 4482.44 samples/sec accuracy=0.362969
Epoch[0] Batch [300] Speed: 2311.78 samples/sec accuracy=0.378125
^[Epoch[0] Batch [350] Speed: 6287.10 samples/sec accuracy=0.394375
Epoch[0] Train-accuracy=0.390039
Epoch[0] Time cost=13.991
Epoch[0] Validation-accuracy=0.415843
final accuracy = 0.415565
Start training with [cpu(0)]
Epoch[0] Batch [50] Speed: 4131.70 samples/sec accuracy=0.158438
Epoch[0] Batch [100] Speed: 3603.14 samples/sec accuracy=0.255469
Epoch[0] Batch [150] Speed: 3880.94 samples/sec accuracy=0.309688
Epoch[0] Batch [200] Speed: 3466.17 samples/sec accuracy=0.346719
Epoch[0] Batch [250] Speed: 1907.85 samples/sec accuracy=0.365625
Epoch[0] Batch [300] Speed: 13460.59 samples/sec accuracy=0.377344
Epoch[0] Batch [350] Speed: 3278.69 samples/sec accuracy=0.395781
Epoch[0] Resetting Data Iterator
Epoch[0] Time cost=13.526
Epoch[0] Validation-accuracy=0.420985
final accuracy = 0.420985
Epoch[0] Batch [50] Speed: 2937.12 samples/sec accuracy=0.171722
Epoch[0] Batch [100] Speed: 3230.27 samples/sec accuracy=0.271875
Epoch[0] Batch [150] Speed: 7009.35 samples/sec accuracy=0.319844
Epoch[0] Batch [200] Speed: 7694.27 samples/sec accuracy=0.345938
Epoch[0] Batch [250] Speed: 5635.28 samples/sec accuracy=0.365000
Epoch[0] Batch [300] Speed: 3673.31 samples/sec accuracy=0.387344
Epoch[0] Batch [350] Speed: 1934.83 samples/sec accuracy=0.399219
Epoch[0] Train-accuracy=0.394531
Epoch[0] Time cost=13.540
Epoch[0] Validation-accuracy=0.419600
final accuracy = 0.418570
[00:17:02] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar/train.rec, use 1 threads for decoding..
[00:17:03] src/io/iter_image_recordio_2.cc:170: ImageRecordIOParser2: data/cifar/test.rec, use 1 threads for decoding..
Start training with [cpu(0)]
Epoch[0] Batch [50] Speed: 6886.05 samples/sec accuracy=0.157344
Epoch[0] Batch [100] Speed: 2879.89 samples/sec accuracy=0.242969
Epoch[0] Batch [150] Speed: 2601.20 samples/sec accuracy=0.255469
Epoch[0] Batch [200] Speed: 3848.15 samples/sec accuracy=0.284531
Epoch[0] Batch [250] Speed: 5992.76 samples/sec accuracy=0.293438
Epoch[0] Batch [300] Speed: 3103.23 samples/sec accuracy=0.314219
Epoch[0] Batch [350] Speed: 6909.95 samples/sec accuracy=0.332344
Epoch[0] Resetting Data Iterator
Epoch[0] Time cost=13.161
Epoch[0] Validation-accuracy=0.352848
final accuracy = 0.352848
Epoch[0] Batch [50] Speed: 7453.44 samples/sec accuracy=0.161612
Epoch[0] Batch [100] Speed: 4445.21 samples/sec accuracy=0.235469
Epoch[0] Batch [150] Speed: 3297.28 samples/sec accuracy=0.266719
Epoch[0] Batch [200] Speed: 6435.73 samples/sec accuracy=0.292500
Epoch[0] Batch [250] Speed: 9299.93 samples/sec accuracy=0.310625
Epoch[0] Batch [300] Speed: 2478.55 samples/sec accuracy=0.323594
Epoch[0] Batch [350] Speed: 4273.77 samples/sec accuracy=0.324844
Epoch[0] Train-accuracy=0.309180
Epoch[0] Time cost=10.967
Epoch[0] Validation-accuracy=0.358683
final accuracy = 0.358474
ok
----------------------------------------------------------------------
Ran 1 test in 96.476s
OK
----------------------------------------------------------------
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