1、环境：Win7x64、python3.7×64、tensorflow1.14、CPU i5-9400F
2、
3、
3.1、cifar10，没有数据，全新下载，下到默认目录（C:\Users\Administrator\tensorflow_datasets），全过程 控制台输出：（20190903）
"C:\Program Files\Python37\python.exe" E:/Project_Py37/cifar/cifar10/cifar10_input.py
WARNING: Logging before flag parsing goes to stderr.
W0903 08:30:02.193804 4392 lazy_loader.py:50]
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
* https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.
Downloading and preparing dataset cifar10 (162.17 MiB) to C:\Users\Administrator\tensorflow_datasets\cifar10\1.0.2...
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Extraction completed...: 0 file [00:00, ? file/s]C:\Program Files\Python37\lib\site-packages\urllib3\connectionpool.py:851: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.io/en/latest/advanced-usage.html#ssl-warnings
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0 examples [00:00, ? examples/s]2019-09-03 08:40:46.206804: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
Shuffling...: 0%| | 0/10 [00:00<?, ? shard/s]W0903 08:41:16.825804 4392 deprecation.py:323] From C:\Program Files\Python37\lib\site-packages\tensorflow_datasets\core\file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
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W0903 08:41:25.143805 4392 dataset_builder.py:439] Warning: Setting shuffle_files=True because split=TRAIN and shuffle_files=None. This behavior will be deprecated on 2019-08-06, at which point shuffle_files=False will be the default for all splits.
Dataset cifar10 downloaded and prepared to C:\Users\Administrator\tensorflow_datasets\cifar10\1.0.2. Subsequent calls will reuse this data.
W0903 08:41:25.231804 4392 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_input.py:64: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead.
W0903 08:41:25.267804 4392 deprecation.py:323] From C:\Program Files\Python37\lib\site-packages\tensorflow\python\ops\image_ops_impl.py:1514: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
W0903 08:41:25.277804 4392 deprecation.py:323] From E:/Project_Py37/cifar/cifar10/cifar10_input.py:45: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.
W0903 08:41:25.292804 4392 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_input.py:48: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.
Process finished with exit code 03.1.1、当 “C:\Users\Administrator\tensorflow_datasets”中 已经有了 tfrecord文件之后，再次下载的话，输出 类似如下信息：（只要 版本对 就不需要重新下载了）
Downloading and preparing dataset cifar10 (162.17 MiB) to C:\Users\Administrator\tensorflow_datasets\cifar10\1.0.2... Dataset cifar10 downloaded and prepared to C:\Users\Administrator\tensorflow_datasets\cifar10\1.0.2. Subsequent calls will reuse this data.
4、
5、代码 输出
5.1、ZC：代码里面的 注释我去掉了，这样贴的东西可以少一点…
def builder(name, **builder_init_kwargs): name, builder_kwargs = _dataset_name_and_kwargs_from_name_str(name) builder_kwargs.update(builder_init_kwargs) if name in _ABSTRACT_DATASET_REGISTRY: raise DatasetNotFoundError(name, is_abstract=True) if name in _IN_DEVELOPMENT_REGISTRY: raise DatasetNotFoundError(name, in_development=True) if name not in _DATASET_REGISTRY: raise DatasetNotFoundError(name) try: return _DATASET_REGISTRY[name](**builder_kwargs) except BaseException: logging.error("Failed to construct dataset %s", name) raise
其中 打印出 _DATASET_REGISTRY、name、builder_kwargs 的信息，如下：
_DATASET_REGISTRY :
{
'dummy_dataset_shared_generator': <class 'tensorflow_datasets.testing.test_utils.DummyDatasetSharedGenerator'>,
'dummy_mnist': <class 'tensorflow_datasets.testing.test_utils.DummyMnist'>,
'groove': <class 'tensorflow_datasets.audio.groove.Groove'>,
'nsynth': <class 'tensorflow_datasets.audio.nsynth.Nsynth'>,
'abstract_reasoning': <class 'tensorflow_datasets.image.abstract_reasoning.AbstractReasoning'>,
'aflw2k3d': <class 'tensorflow_datasets.image.aflw2k3d.Aflw2k3d'>,
'bigearthnet': <class 'tensorflow_datasets.image.bigearthnet.Bigearthnet'>,
'mnist': <class 'tensorflow_datasets.image.mnist.MNIST'>,
'fashion_mnist': <class 'tensorflow_datasets.image.mnist.FashionMNIST'>,
'kmnist': <class 'tensorflow_datasets.image.mnist.KMNIST'>,
'emnist': <class 'tensorflow_datasets.image.mnist.EMNIST'>,
'binarized_mnist': <class 'tensorflow_datasets.image.binarized_mnist.BinarizedMNIST'>,
'binary_alpha_digits': <class 'tensorflow_datasets.image.binary_alpha_digits.BinaryAlphaDigits'>,
'caltech101': <class 'tensorflow_datasets.image.caltech.Caltech101'>,
'caltech_birds2010': <class 'tensorflow_datasets.image.caltech_birds.CaltechBirds2010'>,
'caltech_birds2011': <class 'tensorflow_datasets.image.caltech_birds.CaltechBirds2011'>,
'cats_vs_dogs': <class 'tensorflow_datasets.image.cats_vs_dogs.CatsVsDogs'>,
'curated_breast_imaging_ddsm': <class 'tensorflow_datasets.image.cbis_ddsm.CuratedBreastImagingDDSM'>,
'celeb_a': <class 'tensorflow_datasets.image.celeba.CelebA'>,
'celeb_a_hq': <class 'tensorflow_datasets.image.celebahq.CelebAHq'>,
'chexpert': <class 'tensorflow_datasets.image.chexpert.Chexpert'>,
'cifar10': <class 'tensorflow_datasets.image.cifar.Cifar10'>,
'cifar100': <class 'tensorflow_datasets.image.cifar.Cifar100'>,
'cifar10_corrupted': <class 'tensorflow_datasets.image.cifar10_corrupted.Cifar10Corrupted'>,
'clevr': <class 'tensorflow_datasets.image.clevr.CLEVR'>,
'coco': <class 'tensorflow_datasets.image.coco.Coco'>,
'coco2014': <class 'tensorflow_datasets.image.coco2014_legacy.Coco2014'>,
'coil100': <class 'tensorflow_datasets.image.coil100.Coil100'>,
'colorectal_histology': <class 'tensorflow_datasets.image.colorectal_histology.ColorectalHistology'>,
'colorectal_histology_large': <class 'tensorflow_datasets.image.colorectal_histology.ColorectalHistologyLarge'>,
'cycle_gan': <class 'tensorflow_datasets.image.cycle_gan.CycleGAN'>,
'deep_weeds': <class 'tensorflow_datasets.image.deep_weeds.DeepWeeds'>,
'diabetic_retinopathy_detection': <class 'tensorflow_datasets.image.diabetic_retinopathy_detection.DiabeticRetinopathyDetection'>,
'downsampled_imagenet': <class 'tensorflow_datasets.image.downsampled_imagenet.DownsampledImagenet'>,
'dsprites': <class 'tensorflow_datasets.image.dsprites.Dsprites'>,
'dtd': <class 'tensorflow_datasets.image.dtd.Dtd'>,
'eurosat': <class 'tensorflow_datasets.image.eurosat.Eurosat'>,
'tf_flowers': <class 'tensorflow_datasets.image.flowers.TFFlowers'>,
'food101': <class 'tensorflow_datasets.image.food101.Food101'>,
'horses_or_humans': <class 'tensorflow_datasets.image.horses_or_humans.HorsesOrHumans'>,
'image_label_folder': <class 'tensorflow_datasets.image.image_folder.ImageLabelFolder'>,
'imagenet2012': <class 'tensorflow_datasets.image.imagenet.Imagenet2012'>,
'imagenet2012_corrupted': <class 'tensorflow_datasets.image.imagenet2012_corrupted.Imagenet2012Corrupted'>,
'kitti': <class 'tensorflow_datasets.image.kitti.Kitti'>,
'lfw': <class 'tensorflow_datasets.image.lfw.LFW'>,
'lsun': <class 'tensorflow_datasets.image.lsun.Lsun'>,
'mnist_corrupted': <class 'tensorflow_datasets.image.mnist_corrupted.MNISTCorrupted'>,
'omniglot': <class 'tensorflow_datasets.image.omniglot.Omniglot'>,
'open_images_v4': <class 'tensorflow_datasets.image.open_images.OpenImagesV4'>,
'oxford_flowers102': <class 'tensorflow_datasets.image.oxford_flowers102.OxfordFlowers102'>,
'oxford_iiit_pet': <class 'tensorflow_datasets.image.oxford_iiit_pet.OxfordIIITPet'>,
'patch_camelyon': <class 'tensorflow_datasets.image.patch_camelyon.PatchCamelyon'>,
'pet_finder': <class 'tensorflow_datasets.image.pet_finder.PetFinder'>,
'quickdraw_bitmap': <class 'tensorflow_datasets.image.quickdraw.QuickdrawBitmap'>,
'resisc45': <class 'tensorflow_datasets.image.resisc45.Resisc45'>,
'rock_paper_scissors': <class 'tensorflow_datasets.image.rock_paper_scissors.RockPaperScissors'>,
'scene_parse150': <class 'tensorflow_datasets.image.scene_parse_150.SceneParse150'>,
'shapes3d': <class 'tensorflow_datasets.image.shapes3d.Shapes3d'>,
'smallnorb': <class 'tensorflow_datasets.image.smallnorb.Smallnorb'>,
'so2sat': <class 'tensorflow_datasets.image.so2sat.So2sat'>,
'stanford_dogs': <class 'tensorflow_datasets.image.stanford_dogs.StanfordDogs'>,
'stanford_online_products': <class 'tensorflow_datasets.image.stanford_online_products.StanfordOnlineProducts'>,
'sun397': <class 'tensorflow_datasets.image.sun.Sun397'>,
'svhn_cropped': <class 'tensorflow_datasets.image.svhn.SvhnCropped'>,
'uc_merced': <class 'tensorflow_datasets.image.uc_merced.UcMerced'>,
'visual_domain_decathlon': <class 'tensorflow_datasets.image.visual_domain_decathlon.VisualDomainDecathlon'>,
'voc2007': <class 'tensorflow_datasets.image.voc.Voc2007'>,
'amazon_us_reviews': <class 'tensorflow_datasets.structured.amazon_us_reviews.AmazonUSReviews'>,
'higgs': <class 'tensorflow_datasets.structured.higgs.Higgs'>,
'iris': <class 'tensorflow_datasets.structured.iris.Iris'>,
'rock_you': <class 'tensorflow_datasets.structured.rock_you.RockYou'>,
'titanic': <class 'tensorflow_datasets.structured.titanic.Titanic'>,
'cnn_dailymail': <class 'tensorflow_datasets.text.cnn_dailymail.CnnDailymail'>,
'definite_pronoun_resolution': <class 'tensorflow_datasets.text.definite_pronoun_resolution.DefinitePronounResolution'>,
'gap': <class 'tensorflow_datasets.text.gap.Gap'>,
'glue': <class 'tensorflow_datasets.text.glue.Glue'>,
'imdb_reviews': <class 'tensorflow_datasets.text.imdb.IMDBReviews'>,
'lm1b': <class 'tensorflow_datasets.text.lm1b.Lm1b'>,
'multi_nli': <class 'tensorflow_datasets.text.multi_nli.MultiNLI'>,
'snli': <class 'tensorflow_datasets.text.snli.Snli'>,
'squad': <class 'tensorflow_datasets.text.squad.Squad'>,
'super_glue': <class 'tensorflow_datasets.text.super_glue.SuperGlue'>,
'trivia_qa': <class 'tensorflow_datasets.text.trivia_qa.TriviaQA'>,
'wikipedia': <class 'tensorflow_datasets.text.wikipedia.Wikipedia'>,
'xnli': <class 'tensorflow_datasets.text.xnli.Xnli'>,
'flores': <class 'tensorflow_datasets.translate.flores.Flores'>,
'para_crawl': <class 'tensorflow_datasets.translate.para_crawl.ParaCrawl'>,
'ted_hrlr_translate': <class 'tensorflow_datasets.translate.ted_hrlr.TedHrlrTranslate'>,
'ted_multi_translate': <class 'tensorflow_datasets.translate.ted_multi.TedMultiTranslate'>,
'wmt_translate': <class 'tensorflow_datasets.translate.wmt.WmtTranslate'>,
'wmt14_translate': <class 'tensorflow_datasets.translate.wmt14.Wmt14Translate'>,
'wmt15_translate': <class 'tensorflow_datasets.translate.wmt15.Wmt15Translate'>,
'wmt16_translate': <class 'tensorflow_datasets.translate.wmt16.Wmt16Translate'>,
'wmt17_translate': <class 'tensorflow_datasets.translate.wmt17.Wmt17Translate'>,
'wmt18_translate': <class 'tensorflow_datasets.translate.wmt18.Wmt18Translate'>,
'wmt19_translate': <class 'tensorflow_datasets.translate.wmt19.Wmt19Translate'>,
'wmt_t2t_translate': <class 'tensorflow_datasets.translate.wmt_t2t.WmtT2tTranslate'>,
'bair_robot_pushing_small': <class 'tensorflow_datasets.video.bair_robot_pushing.BairRobotPushingSmall'>,
'moving_mnist': <class 'tensorflow_datasets.video.moving_mnist.MovingMnist'>,
'starcraft_video': <class 'tensorflow_datasets.video.starcraft.StarcraftVideo'>,
'ucf101': <class 'tensorflow_datasets.video.ucf101.Ucf101'>}
name : cifar10
builder_kwargs : {'data_dir': '~\\tensorflow_datasets'}
6、
6.1、cifar100，没有数据，全新下载，下到默认目录（C:\Users\Administrator\tensorflow_datasets），全过程 控制台输出：（20190904）
"C:\Program Files\Python37\python.exe" E:/Project_Py37/cifar/cifar10/cifar10_input.py
WARNING: Logging before flag parsing goes to stderr.
W0904 08:07:41.751157 6092 lazy_loader.py:50]
The TensorFlow contrib module will not be included in TensorFlow 2.0.
For more information, please see:
* https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md
* https://github.com/tensorflow/addons
* https://github.com/tensorflow/io (for I/O related ops)
If you depend on functionality not listed there, please file an issue.
W0904 08:07:42.109957 6092 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_input.py:117: The name tf.Session is deprecated. Please use tf.compat.v1.Session instead.
2019-09-04 08:07:42.109957: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2
W0904 08:07:42.109957 6092 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_input.py:118: The name tf.global_variables_initializer is deprecated. Please use tf.compat.v1.global_variables_initializer instead.
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Dl Completed...: 0%| | 0/1 [01:17<?, ? url/s]
Dl Size...: 38%|███▊ | 61/160 [01:17<01:42, 1.04s/ MiB]
Extraction completed...: 0 file [01:17, ? file/s]
Dl Completed...: 0%| | 0/1 [01:18<?, ? url/s]
Dl Size...: 39%|███▉ | 62/160 [01:18<01:38, 1.00s/ MiB]
Extraction completed...: 0 file [01:18, ? file/s]
Dl Completed...: 0%| | 0/1 [01:19<?, ? url/s]
Dl Size...: 39%|███▉ | 63/160 [01:19<01:33, 1.04 MiB/s]
Extraction completed...: 0 file [01:19, ? file/s]
Dl Completed...: 0%| | 0/1 [01:20<?, ? url/s]
Dl Size...: 40%|████ | 64/160 [01:20<01:31, 1.05 MiB/s]
Extraction completed...: 0 file [01:20, ? file/s]
Dl Completed...: 0%| | 0/1 [01:21<?, ? url/s]
Dl Size...: 41%|████ | 65/160 [01:21<01:43, 1.09s/ MiB]
Extraction completed...: 0 file [01:21, ? file/s]
Dl Completed...: 0%| | 0/1 [01:23<?, ? url/s]
Dl Size...: 41%|████▏ | 66/160 [01:23<01:54, 1.22s/ MiB]
Extraction completed...: 0 file [01:23, ? file/s]
Dl Completed...: 0%| | 0/1 [01:25<?, ? url/s]
Dl Size...: 42%|████▏ | 67/160 [01:25<02:14, 1.44s/ MiB]
Extraction completed...: 0 file [01:25, ? file/s]
Dl Completed...: 0%| | 0/1 [01:27<?, ? url/s]
Dl Size...: 42%|████▎ | 68/160 [01:27<02:44, 1.79s/ MiB]
Extraction completed...: 0 file [01:27, ? file/s]
Dl Completed...: 0%| | 0/1 [01:29<?, ? url/s]
Dl Size...: 43%|████▎ | 69/160 [01:29<02:32, 1.67s/ MiB]
Extraction completed...: 0 file [01:29, ? file/s]
Dl Completed...: 0%| | 0/1 [01:30<?, ? url/s]
Dl Size...: 44%|████▍ | 70/160 [01:30<02:15, 1.51s/ MiB]
Extraction completed...: 0 file [01:30, ? file/s]
Dl Completed...: 0%| | 0/1 [01:31<?, ? url/s]
Dl Size...: 44%|████▍ | 71/160 [01:31<01:58, 1.33s/ MiB]
Extraction completed...: 0 file [01:31, ? file/s]
Dl Completed...: 0%| | 0/1 [01:32<?, ? url/s]
Dl Size...: 45%|████▌ | 72/160 [01:32<01:46, 1.21s/ MiB]
Extraction completed...: 0 file [01:32, ? file/s]
Dl Completed...: 0%| | 0/1 [01:33<?, ? url/s]
Dl Size...: 46%|████▌ | 73/160 [01:33<01:37, 1.12s/ MiB]
Extraction completed...: 0 file [01:33, ? file/s]
Dl Completed...: 0%| | 0/1 [01:33<?, ? url/s]
Dl Size...: 46%|████▋ | 74/160 [01:33<01:30, 1.06s/ MiB]
Extraction completed...: 0 file [01:33, ? file/s]
Dl Completed...: 0%| | 0/1 [01:34<?, ? url/s]
Dl Size...: 47%|████▋ | 75/160 [01:34<01:26, 1.02s/ MiB]
Extraction completed...: 0 file [01:34, ? file/s]
Dl Completed...: 0%| | 0/1 [01:35<?, ? url/s]
Dl Size...: 48%|████▊ | 76/160 [01:35<01:22, 1.01 MiB/s]
Extraction completed...: 0 file [01:35, ? file/s]
Dl Completed...: 0%| | 0/1 [01:36<?, ? url/s]
Dl Size...: 48%|████▊ | 77/160 [01:36<01:19, 1.04 MiB/s]
Extraction completed...: 0 file [01:36, ? file/s]
Dl Completed...: 0%| | 0/1 [01:37<?, ? url/s]
Dl Size...: 49%|████▉ | 78/160 [01:37<01:17, 1.06 MiB/s]
Extraction completed...: 0 file [01:37, ? file/s]
Dl Completed...: 0%| | 0/1 [01:38<?, ? url/s]
Dl Size...: 49%|████▉ | 79/160 [01:38<01:15, 1.07 MiB/s]
Extraction completed...: 0 file [01:38, ? file/s]
Dl Completed...: 0%| | 0/1 [01:39<?, ? url/s]
Dl Size...: 50%|█████ | 80/160 [01:39<01:14, 1.07 MiB/s]
Extraction completed...: 0 file [01:39, ? file/s]
Dl Completed...: 0%| | 0/1 [01:40<?, ? url/s]
Dl Size...: 51%|█████ | 81/160 [01:40<01:12, 1.08 MiB/s]
Extraction completed...: 0 file [01:40, ? file/s]
Dl Completed...: 0%| | 0/1 [01:41<?, ? url/s]
Dl Size...: 51%|█████▏ | 82/160 [01:41<01:12, 1.08 MiB/s]
Extraction completed...: 0 file [01:41, ? file/s]
Dl Completed...: 0%| | 0/1 [01:42<?, ? url/s]
Dl Size...: 52%|█████▏ | 83/160 [01:42<01:16, 1.00 MiB/s]
Extraction completed...: 0 file [01:42, ? file/s]
Dl Completed...: 0%| | 0/1 [01:43<?, ? url/s]
Dl Size...: 52%|█████▎ | 84/160 [01:43<01:19, 1.04s/ MiB]
Extraction completed...: 0 file [01:43, ? file/s]
Dl Completed...: 0%| | 0/1 [01:44<?, ? url/s]
Dl Size...: 53%|█████▎ | 85/160 [01:44<01:17, 1.04s/ MiB]
Extraction completed...: 0 file [01:44, ? file/s]
Dl Completed...: 0%| | 0/1 [01:45<?, ? url/s]
Dl Size...: 54%|█████▍ | 86/160 [01:45<01:14, 1.00s/ MiB]
Extraction completed...: 0 file [01:45, ? file/s]
Dl Completed...: 0%| | 0/1 [01:46<?, ? url/s]
Dl Size...: 54%|█████▍ | 87/160 [01:46<01:11, 1.03 MiB/s]
Extraction completed...: 0 file [01:46, ? file/s]
Dl Completed...: 0%| | 0/1 [01:47<?, ? url/s]
Dl Size...: 55%|█████▌ | 88/160 [01:47<01:08, 1.05 MiB/s]
Extraction completed...: 0 file [01:47, ? file/s]
Dl Completed...: 0%| | 0/1 [01:48<?, ? url/s]
Dl Size...: 56%|█████▌ | 89/160 [01:48<01:06, 1.06 MiB/s]
Extraction completed...: 0 file [01:48, ? file/s]
Dl Completed...: 0%| | 0/1 [01:49<?, ? url/s]
Dl Size...: 56%|█████▋ | 90/160 [01:49<01:12, 1.03s/ MiB]
Extraction completed...: 0 file [01:49, ? file/s]
Dl Completed...: 0%| | 0/1 [01:50<?, ? url/s]
Dl Size...: 57%|█████▋ | 91/160 [01:50<01:13, 1.06s/ MiB]
Extraction completed...: 0 file [01:50, ? file/s]
Dl Completed...: 0%| | 0/1 [01:51<?, ? url/s]
Dl Size...: 57%|█████▊ | 92/160 [01:51<01:12, 1.06s/ MiB]
Extraction completed...: 0 file [01:51, ? file/s]
Dl Completed...: 0%| | 0/1 [01:52<?, ? url/s]
Dl Size...: 58%|█████▊ | 93/160 [01:52<01:11, 1.06s/ MiB]
Extraction completed...: 0 file [01:52, ? file/s]
Dl Completed...: 0%| | 0/1 [01:53<?, ? url/s]
Dl Size...: 59%|█████▉ | 94/160 [01:53<01:10, 1.07s/ MiB]
Extraction completed...: 0 file [01:53, ? file/s]
Dl Completed...: 0%| | 0/1 [01:54<?, ? url/s]
Dl Size...: 59%|█████▉ | 95/160 [01:54<01:09, 1.07s/ MiB]
Extraction completed...: 0 file [01:54, ? file/s]
Dl Completed...: 0%| | 0/1 [01:55<?, ? url/s]
Dl Size...: 60%|██████ | 96/160 [01:55<01:07, 1.06s/ MiB]
Extraction completed...: 0 file [01:55, ? file/s]
Dl Completed...: 0%| | 0/1 [01:56<?, ? url/s]
Dl Size...: 61%|██████ | 97/160 [01:56<01:05, 1.04s/ MiB]
Extraction completed...: 0 file [01:56, ? file/s]
Dl Completed...: 0%| | 0/1 [01:57<?, ? url/s]
Dl Size...: 61%|██████▏ | 98/160 [01:57<01:04, 1.05s/ MiB]
Extraction completed...: 0 file [01:57, ? file/s]
Dl Completed...: 0%| | 0/1 [01:59<?, ? url/s]
Dl Size...: 62%|██████▏ | 99/160 [01:59<01:05, 1.08s/ MiB]
Extraction completed...: 0 file [01:59, ? file/s]
Dl Completed...: 0%| | 0/1 [02:00<?, ? url/s]
Dl Size...: 62%|██████▎ | 100/160 [02:00<01:04, 1.07s/ MiB]
Extraction completed...: 0 file [02:00, ? file/s]
Dl Completed...: 0%| | 0/1 [02:01<?, ? url/s]
Dl Size...: 63%|██████▎ | 101/160 [02:01<01:01, 1.04s/ MiB]
Extraction completed...: 0 file [02:01, ? file/s]
Dl Completed...: 0%| | 0/1 [02:02<?, ? url/s]
Dl Size...: 64%|██████▍ | 102/160 [02:02<00:57, 1.00 MiB/s]
Extraction completed...: 0 file [02:02, ? file/s]
Dl Completed...: 0%| | 0/1 [02:02<?, ? url/s]
Dl Size...: 64%|██████▍ | 103/160 [02:02<00:55, 1.03 MiB/s]
Extraction completed...: 0 file [02:02, ? file/s]
Dl Completed...: 0%| | 0/1 [02:03<?, ? url/s]
Dl Size...: 65%|██████▌ | 104/160 [02:03<00:53, 1.05 MiB/s]
Extraction completed...: 0 file [02:03, ? file/s]
Dl Completed...: 0%| | 0/1 [02:04<?, ? url/s]
Dl Size...: 66%|██████▌ | 105/160 [02:04<00:51, 1.06 MiB/s]
Extraction completed...: 0 file [02:04, ? file/s]
Dl Completed...: 0%| | 0/1 [02:05<?, ? url/s]
Dl Size...: 66%|██████▋ | 106/160 [02:05<00:50, 1.07 MiB/s]
Extraction completed...: 0 file [02:05, ? file/s]
Dl Completed...: 0%| | 0/1 [02:07<?, ? url/s]
Dl Size...: 67%|██████▋ | 107/160 [02:07<01:01, 1.16s/ MiB]
Extraction completed...: 0 file [02:07, ? file/s]
Dl Completed...: 0%| | 0/1 [02:08<?, ? url/s]
Dl Size...: 68%|██████▊ | 108/160 [02:08<01:03, 1.23s/ MiB]
Extraction completed...: 0 file [02:08, ? file/s]
Dl Completed...: 0%| | 0/1 [02:10<?, ? url/s]
Dl Size...: 68%|██████▊ | 109/160 [02:10<01:04, 1.27s/ MiB]
Extraction completed...: 0 file [02:10, ? file/s]
Dl Completed...: 0%| | 0/1 [02:11<?, ? url/s]
Dl Size...: 69%|██████▉ | 110/160 [02:11<01:04, 1.30s/ MiB]
Extraction completed...: 0 file [02:11, ? file/s]
Dl Completed...: 0%| | 0/1 [02:12<?, ? url/s]
Dl Size...: 69%|██████▉ | 111/160 [02:12<01:04, 1.32s/ MiB]
Extraction completed...: 0 file [02:12, ? file/s]
Dl Completed...: 0%| | 0/1 [02:14<?, ? url/s]
Dl Size...: 70%|███████ | 112/160 [02:14<01:03, 1.32s/ MiB]
Extraction completed...: 0 file [02:14, ? file/s]
Dl Completed...: 0%| | 0/1 [02:15<?, ? url/s]
Dl Size...: 71%|███████ | 113/160 [02:15<01:00, 1.28s/ MiB]
Extraction completed...: 0 file [02:15, ? file/s]
Dl Completed...: 0%| | 0/1 [02:16<?, ? url/s]
Dl Size...: 71%|███████▏ | 114/160 [02:16<00:55, 1.20s/ MiB]
Extraction completed...: 0 file [02:16, ? file/s]
Dl Completed...: 0%| | 0/1 [02:17<?, ? url/s]
Dl Size...: 72%|███████▏ | 115/160 [02:17<00:52, 1.16s/ MiB]
Extraction completed...: 0 file [02:17, ? file/s]
Dl Completed...: 0%| | 0/1 [02:18<?, ? url/s]
Dl Size...: 72%|███████▎ | 116/160 [02:18<00:49, 1.12s/ MiB]
Extraction completed...: 0 file [02:18, ? file/s]
Dl Completed...: 0%| | 0/1 [02:19<?, ? url/s]
Dl Size...: 73%|███████▎ | 117/160 [02:19<00:45, 1.06s/ MiB]
Extraction completed...: 0 file [02:19, ? file/s]
Dl Completed...: 0%| | 0/1 [02:20<?, ? url/s]
Dl Size...: 74%|███████▍ | 118/160 [02:20<00:42, 1.02s/ MiB]
Extraction completed...: 0 file [02:20, ? file/s]
Dl Completed...: 0%| | 0/1 [02:21<?, ? url/s]
Dl Size...: 74%|███████▍ | 119/160 [02:21<00:41, 1.00s/ MiB]
Extraction completed...: 0 file [02:21, ? file/s]
Dl Completed...: 0%| | 0/1 [02:22<?, ? url/s]
Dl Size...: 75%|███████▌ | 120/160 [02:22<00:39, 1.03 MiB/s]
Extraction completed...: 0 file [02:22, ? file/s]
Dl Completed...: 0%| | 0/1 [02:23<?, ? url/s]
Dl Size...: 76%|███████▌ | 121/160 [02:23<00:36, 1.06 MiB/s]
Extraction completed...: 0 file [02:23, ? file/s]
Dl Completed...: 0%| | 0/1 [02:23<?, ? url/s]
Dl Size...: 76%|███████▋ | 122/160 [02:23<00:35, 1.08 MiB/s]
Extraction completed...: 0 file [02:23, ? file/s]
Dl Completed...: 0%| | 0/1 [02:24<?, ? url/s]
Dl Size...: 77%|███████▋ | 123/160 [02:24<00:34, 1.07 MiB/s]
Extraction completed...: 0 file [02:24, ? file/s]
Dl Completed...: 0%| | 0/1 [02:27<?, ? url/s]
Dl Size...: 78%|███████▊ | 124/160 [02:27<00:48, 1.36s/ MiB]
Extraction completed...: 0 file [02:27, ? file/s]
Dl Completed...: 0%| | 0/1 [02:32<?, ? url/s]
Dl Size...: 78%|███████▊ | 125/160 [02:32<01:24, 2.41s/ MiB]
Extraction completed...: 0 file [02:32, ? file/s]
Dl Completed...: 0%| | 0/1 [02:36<?, ? url/s]
Dl Size...: 79%|███████▉ | 126/160 [02:36<01:46, 3.14s/ MiB]
Extraction completed...: 0 file [02:36, ? file/s]
Dl Completed...: 0%| | 0/1 [02:40<?, ? url/s]
Dl Size...: 79%|███████▉ | 127/160 [02:40<01:46, 3.22s/ MiB]
Extraction completed...: 0 file [02:40, ? file/s]
Dl Completed...: 0%| | 0/1 [02:42<?, ? url/s]
Dl Size...: 80%|████████ | 128/160 [02:42<01:29, 2.80s/ MiB]
Extraction completed...: 0 file [02:42, ? file/s]
Dl Completed...: 0%| | 0/1 [02:43<?, ? url/s]
Dl Size...: 81%|████████ | 129/160 [02:43<01:15, 2.45s/ MiB]
Extraction completed...: 0 file [02:43, ? file/s]
Dl Completed...: 0%| | 0/1 [02:45<?, ? url/s]
Dl Size...: 81%|████████▏ | 130/160 [02:45<01:05, 2.18s/ MiB]
Extraction completed...: 0 file [02:45, ? file/s]
Dl Completed...: 0%| | 0/1 [02:46<?, ? url/s]
Dl Size...: 82%|████████▏ | 131/160 [02:46<00:56, 1.94s/ MiB]
Extraction completed...: 0 file [02:46, ? file/s]
Dl Completed...: 0%| | 0/1 [02:48<?, ? url/s]
Dl Size...: 82%|████████▎ | 132/160 [02:48<00:52, 1.87s/ MiB]
Extraction completed...: 0 file [02:48, ? file/s]
Dl Completed...: 0%| | 0/1 [02:50<?, ? url/s]
Dl Size...: 83%|████████▎ | 133/160 [02:50<00:47, 1.77s/ MiB]
Extraction completed...: 0 file [02:50, ? file/s]
Dl Completed...: 0%| | 0/1 [02:51<?, ? url/s]
Dl Size...: 84%|████████▍ | 134/160 [02:51<00:42, 1.64s/ MiB]
Extraction completed...: 0 file [02:51, ? file/s]
Dl Completed...: 0%| | 0/1 [02:52<?, ? url/s]
Dl Size...: 84%|████████▍ | 135/160 [02:52<00:38, 1.54s/ MiB]
Extraction completed...: 0 file [02:52, ? file/s]
Dl Completed...: 0%| | 0/1 [02:54<?, ? url/s]
Dl Size...: 85%|████████▌ | 136/160 [02:54<00:35, 1.48s/ MiB]
Extraction completed...: 0 file [02:54, ? file/s]
Dl Completed...: 0%| | 0/1 [02:55<?, ? url/s]
Dl Size...: 86%|████████▌ | 137/160 [02:55<00:32, 1.40s/ MiB]
Extraction completed...: 0 file [02:55, ? file/s]
Dl Completed...: 0%| | 0/1 [02:56<?, ? url/s]
Dl Size...: 86%|████████▋ | 138/160 [02:56<00:28, 1.30s/ MiB]
Extraction completed...: 0 file [02:56, ? file/s]
Dl Completed...: 0%| | 0/1 [02:57<?, ? url/s]
Dl Size...: 87%|████████▋ | 139/160 [02:57<00:24, 1.19s/ MiB]
Extraction completed...: 0 file [02:57, ? file/s]
Dl Completed...: 0%| | 0/1 [02:58<?, ? url/s]
Dl Size...: 88%|████████▊ | 140/160 [02:58<00:21, 1.09s/ MiB]
Extraction completed...: 0 file [02:58, ? file/s]
Dl Completed...: 0%| | 0/1 [02:58<?, ? url/s]
Dl Size...: 88%|████████▊ | 141/160 [02:58<00:19, 1.02s/ MiB]
Extraction completed...: 0 file [02:58, ? file/s]
Dl Completed...: 0%| | 0/1 [02:59<?, ? url/s]
Dl Size...: 89%|████████▉ | 142/160 [02:59<00:17, 1.02 MiB/s]
Extraction completed...: 0 file [02:59, ? file/s]
Dl Completed...: 0%| | 0/1 [03:01<?, ? url/s]
Dl Size...: 89%|████████▉ | 143/160 [03:01<00:17, 1.04s/ MiB]
Extraction completed...: 0 file [03:01, ? file/s]
Dl Completed...: 0%| | 0/1 [03:01<?, ? url/s]
Dl Size...: 90%|█████████ | 144/160 [03:01<00:15, 1.00 MiB/s]
Extraction completed...: 0 file [03:01, ? file/s]
Dl Completed...: 0%| | 0/1 [03:02<?, ? url/s]
Dl Size...: 91%|█████████ | 145/160 [03:02<00:15, 1.01s/ MiB]
Extraction completed...: 0 file [03:02, ? file/s]
Dl Completed...: 0%| | 0/1 [03:03<?, ? url/s]
Dl Size...: 91%|█████████▏| 146/160 [03:03<00:13, 1.04 MiB/s]
Extraction completed...: 0 file [03:03, ? file/s]
Dl Completed...: 0%| | 0/1 [03:04<?, ? url/s]
Dl Size...: 92%|█████████▏| 147/160 [03:04<00:12, 1.06 MiB/s]
Extraction completed...: 0 file [03:04, ? file/s]
Dl Completed...: 0%| | 0/1 [03:05<?, ? url/s]
Dl Size...: 92%|█████████▎| 148/160 [03:05<00:11, 1.07 MiB/s]
Extraction completed...: 0 file [03:05, ? file/s]
Dl Completed...: 0%| | 0/1 [03:06<?, ? url/s]
Dl Size...: 93%|█████████▎| 149/160 [03:06<00:10, 1.08 MiB/s]
Extraction completed...: 0 file [03:06, ? file/s]
Dl Completed...: 0%| | 0/1 [03:07<?, ? url/s]
Dl Size...: 94%|█████████▍| 150/160 [03:07<00:09, 1.09 MiB/s]
Extraction completed...: 0 file [03:07, ? file/s]
Dl Completed...: 0%| | 0/1 [03:08<?, ? url/s]
Dl Size...: 94%|█████████▍| 151/160 [03:08<00:08, 1.08 MiB/s]
Extraction completed...: 0 file [03:08, ? file/s]
Dl Completed...: 0%| | 0/1 [03:09<?, ? url/s]
Dl Size...: 95%|█████████▌| 152/160 [03:09<00:07, 1.09 MiB/s]
Extraction completed...: 0 file [03:09, ? file/s]
Dl Completed...: 0%| | 0/1 [03:10<?, ? url/s]
Dl Size...: 96%|█████████▌| 153/160 [03:10<00:06, 1.04 MiB/s]
Extraction completed...: 0 file [03:10, ? file/s]
Dl Completed...: 0%| | 0/1 [03:11<?, ? url/s]
Dl Size...: 96%|█████████▋| 154/160 [03:11<00:06, 1.15s/ MiB]
Extraction completed...: 0 file [03:11, ? file/s]
Dl Completed...: 0%| | 0/1 [03:13<?, ? url/s]
Dl Size...: 97%|█████████▋| 155/160 [03:13<00:06, 1.28s/ MiB]
Extraction completed...: 0 file [03:13, ? file/s]
Dl Completed...: 0%| | 0/1 [03:15<?, ? url/s]
Dl Size...: 98%|█████████▊| 156/160 [03:15<00:05, 1.39s/ MiB]
Extraction completed...: 0 file [03:15, ? file/s]
Dl Completed...: 0%| | 0/1 [03:16<?, ? url/s]
Dl Size...: 98%|█████████▊| 157/160 [03:16<00:04, 1.38s/ MiB]
Extraction completed...: 0 file [03:16, ? file/s]
Dl Completed...: 0%| | 0/1 [03:18<?, ? url/s]
Dl Size...: 99%|█████████▉| 158/160 [03:18<00:02, 1.45s/ MiB]
Extraction completed...: 0 file [03:18, ? file/s]
Dl Completed...: 0%| | 0/1 [03:19<?, ? url/s]
Dl Size...: 99%|█████████▉| 159/160 [03:19<00:01, 1.50s/ MiB]
Extraction completed...: 0 file [03:19, ? file/s]
Dl Completed...: 0%| | 0/1 [03:21<?, ? url/s]
Dl Size...: 100%|██████████| 160/160 [03:21<00:00, 1.57s/ MiB]
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Shuffling...: 0%| | 0/10 [00:00<?, ? shard/s]W0904 08:11:40.826758 6092 deprecation.py:323] From C:\Program Files\Python37\lib\site-packages\tensorflow_datasets\core\file_format_adapter.py:209: tf_record_iterator (from tensorflow.python.lib.io.tf_record) is deprecated and will be removed in a future version.
Instructions for updating:
Use eager execution and:
`tf.data.TFRecordDataset(path)`
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W0904 08:11:49.859158 6092 dataset_builder.py:439] Warning: Setting shuffle_files=True because split=TRAIN and shuffle_files=None. This behavior will be deprecated on 2019-08-06, at which point shuffle_files=False will be the default for all splits.
Dataset cifar100 downloaded and prepared to C:\Users\Administrator\tensorflow_datasets\cifar100\1.3.1. Subsequent calls will reuse this data.
W0904 08:11:49.952758 6092 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_input.py:65: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead.
W0904 08:11:49.983958 6092 deprecation.py:323] From C:\Program Files\Python37\lib\site-packages\tensorflow\python\ops\image_ops_impl.py:1514: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Deprecated in favor of operator or tf.math.divide.
W0904 08:11:49.999558 6092 deprecation.py:323] From E:/Project_Py37/cifar/cifar10/cifar10_input.py:46: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`.
W0904 08:11:50.015158 6092 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_input.py:49: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead.
labels1 :
Tensor("IteratorGetNext:1", shape=(?,), dtype=int64)
sess.run(labels1) :
[97 72 29 55 41 77 19 43 94 31 17 66 87 25 82 90 29 67 1 39 68 63 83 98
36 76 75 98 83 67 9 35 13 18 56 33 10 41 21 84 9 87 75 75 19 54 36 70
8 25 58 94 32 58 90 51 40 23 28 25 82 86 51 61 98 82 30 48 93 83 34 73
26 50 62 98 1 40 60 48 23 47 14 18 7 89 11 78 7 75 34 66 92 45 65 3
41 89 70 40 4 18 74 37 39 97 48 8 5 90 45 92 36 30 70 79 26 96 49 84
15 81 72 31 92 89 80 70]
images1 :
Tensor("IteratorGetNext:0", shape=(?, 24, 24, 3), dtype=float32)
images1.shape :
(?, 24, 24, 3)
sess.run(images1).shape :
(128, 24, 24, 3)
Process finished with exit code 0
7、
8、cifar10 训练过程。20190903下班开始，到20190904上班，貌似 这个训练的速度很慢啊… 我没改训练相关的代码，是不是有哪些地方可以改进？
ZC：CPU占用 基本保持 99%左右，这是我 自己手动停止的程序
ZC：看到 下面 每10step，差不多就要耗时4+分钟，代码中 max_steps的值为100000，那么就要 10000个4分钟（方便点按4分钟计算）–>40000分钟–> 666.666667小时–> 27.777778天，那就要 差不多 一个月了… 处理方法？升级机子？用GPU？修改训练代码（还要保证准确率啊）？
"C:\Program Files\Python37\python.exe" E:/Project_Py37/cifar/cifar10/cifar10_train.py WARNING: Logging before flag parsing goes to stderr. W0903 17:01:04.763609 3520 lazy_loader.py:50] The TensorFlow contrib module will not be included in TensorFlow 2.0. For more information, please see: * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md * https://github.com/tensorflow/addons * https://github.com/tensorflow/io (for I/O related ops) If you depend on functionality not listed there, please file an issue. W0903 17:01:05.115609 3520 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_train.py:127: The name tf.app.run is deprecated. Please use tf.compat.v1.app.run instead. W0903 17:01:05.116609 3520 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_train.py:120: The name tf.gfile.Exists is deprecated. Please use tf.io.gfile.exists instead. W0903 17:01:05.116609 3520 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_train.py:121: The name tf.gfile.DeleteRecursively is deprecated. Please use tf.io.gfile.rmtree instead. W0903 17:01:05.116609 3520 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_train.py:122: The name tf.gfile.MakeDirs is deprecated. Please use tf.io.gfile.makedirs instead. W0903 17:01:05.117609 3520 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_train.py:62: The name tf.train.get_or_create_global_step is deprecated. Please use tf.compat.v1.train.get_or_create_global_step instead. main(1) : ./tmp/cifar10_train I0903 17:01:05.120609 3520 dataset_builder.py:184] Overwrite dataset info from restored data version. I0903 17:01:05.122608 3520 dataset_builder.py:253] Reusing dataset cifar10 (C:\Users\Administrator\tensorflow_datasets\cifar10\1.0.2) I0903 17:01:05.122608 3520 dataset_builder.py:399] Constructing tf.data.Dataset for split train, from C:\Users\Administrator\tensorflow_datasets\cifar10\1.0.2 W0903 17:01:05.122608 3520 dataset_builder.py:439] Warning: Setting shuffle_files=True because split=TRAIN and shuffle_files=None. This behavior will be deprecated on 2019-08-06, at which point shuffle_files=False will be the default for all splits. W0903 17:01:05.187609 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10_input.py:65: The name tf.random_crop is deprecated. Please use tf.image.random_crop instead. W0903 17:01:05.219609 3520 deprecation.py:323] From C:\Program Files\Python37\lib\site-packages\tensorflow\python\ops\image_ops_impl.py:1514: div (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version. Instructions for updating: Deprecated in favor of operator or tf.math.divide. W0903 17:01:05.228609 3520 deprecation.py:323] From E:\Project_Py37\cifar\cifar10\cifar10_input.py:46: DatasetV1.make_one_shot_iterator (from tensorflow.python.data.ops.dataset_ops) is deprecated and will be removed in a future version. Instructions for updating: Use `for ... in dataset:` to iterate over a dataset. If using `tf.estimator`, return the `Dataset` object directly from your input function. As a last resort, you can use `tf.compat.v1.data.make_one_shot_iterator(dataset)`. W0903 17:01:05.240608 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10_input.py:49: The name tf.summary.image is deprecated. Please use tf.compat.v1.summary.image instead. W0903 17:01:05.242608 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:178: The name tf.variable_scope is deprecated. Please use tf.compat.v1.variable_scope instead. W0903 17:01:05.242608 3520 deprecation.py:506] From E:\Project_Py37\cifar\cifar10\cifar10.py:126: calling TruncatedNormal.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor W0903 17:01:05.242608 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:102: The name tf.get_variable is deprecated. Please use tf.compat.v1.get_variable instead. W0903 17:01:05.250608 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:85: The name tf.summary.histogram is deprecated. Please use tf.compat.v1.summary.histogram instead. W0903 17:01:05.251609 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:86: The name tf.summary.scalar is deprecated. Please use tf.compat.v1.summary.scalar instead. W0903 17:01:05.262609 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:190: The name tf.nn.max_pool is deprecated. Please use tf.nn.max_pool2d instead. W0903 17:01:05.293608 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:129: The name tf.add_to_collection is deprecated. Please use tf.compat.v1.add_to_collection instead. W0903 17:01:05.350608 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:270: The name tf.get_collection is deprecated. Please use tf.compat.v1.get_collection instead. W0903 17:01:05.350608 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:318: The name tf.train.exponential_decay is deprecated. Please use tf.compat.v1.train.exponential_decay instead. I0903 17:01:05.373208 3520 summary_op_util.py:66] Summary name local3/weight_loss (raw) is illegal; using local3/weight_loss__raw_ instead. I0903 17:01:05.373208 3520 summary_op_util.py:66] Summary name local4/weight_loss (raw) is illegal; using local4/weight_loss__raw_ instead. I0903 17:01:05.373208 3520 summary_op_util.py:66] Summary name cross_entropy (raw) is illegal; using cross_entropy__raw_ instead. I0903 17:01:05.373208 3520 summary_op_util.py:66] Summary name total_loss (raw) is illegal; using total_loss__raw_ instead. W0903 17:01:05.373208 3520 deprecation_wrapper.py:119] From E:\Project_Py37\cifar\cifar10\cifar10.py:330: The name tf.train.GradientDescentOptimizer is deprecated. Please use tf.compat.v1.train.GradientDescentOptimizer instead. W0903 17:01:05.467408 3520 deprecation.py:323] From C:\Program Files\Python37\lib\site-packages\tensorflow\python\training\moving_averages.py:433: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. W0903 17:01:05.611208 3520 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_train.py:81: The name tf.train.SessionRunHook is deprecated. Please use tf.estimator.SessionRunHook instead. W0903 17:01:05.611208 3520 deprecation_wrapper.py:119] From E:/Project_Py37/cifar/cifar10/cifar10_train.py:107: The name tf.train.MonitoredTrainingSession is deprecated. Please use tf.compat.v1.train.MonitoredTrainingSession instead. I0903 17:01:05.611208 3520 basic_session_run_hooks.py:541] Create CheckpointSaverHook. W0903 17:01:05.810209 3520 deprecation.py:323] From C:\Program Files\Python37\lib\site-packages\tensorflow\python\ops\array_ops.py:1354: add_dispatch_support.<locals>.wrapper (from tensorflow.python.ops.array_ops) is deprecated and will be removed in a future version. Instructions for updating: Use tf.where in 2.0, which has the same broadcast rule as np.where I0903 17:01:05.862209 3520 monitored_session.py:240] Graph was finalized. 2019-09-03 17:01:05.863209: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 I0903 17:01:05.951209 3520 session_manager.py:500] Running local_init_op. I0903 17:01:05.961209 3520 session_manager.py:502] Done running local_init_op. I0903 17:01:06.295208 3520 basic_session_run_hooks.py:606] Saving checkpoints for 0 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 17:01:33.559809: step 0, loss = 4.68 (46.1 examples/sec; 2.777 sec/batch) 2019-09-03 17:06:00.632410: step 10, loss = 4.59 (4.8 examples/sec; 26.707 sec/batch) 2019-09-03 17:10:33.134212: step 20, loss = 4.48 (4.7 examples/sec; 27.250 sec/batch) I0903 17:11:28.001412 3520 basic_session_run_hooks.py:606] Saving checkpoints for 23 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 17:15:07.789814: step 30, loss = 4.39 (4.7 examples/sec; 27.466 sec/batch) 2019-09-03 17:19:44.396016: step 40, loss = 4.20 (4.6 examples/sec; 27.661 sec/batch) I0903 17:21:34.979816 3520 basic_session_run_hooks.py:606] Saving checkpoints for 45 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 17:24:21.915417: step 50, loss = 4.28 (4.6 examples/sec; 27.752 sec/batch) 2019-09-03 17:29:00.172619: step 60, loss = 4.24 (4.6 examples/sec; 27.826 sec/batch) I0903 17:31:47.024620 3520 basic_session_run_hooks.py:606] Saving checkpoints for 67 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 17:33:39.251021: step 70, loss = 4.14 (4.6 examples/sec; 27.908 sec/batch) 2019-09-03 17:38:17.761823: step 80, loss = 4.18 (4.6 examples/sec; 27.851 sec/batch) I0903 17:42:01.403424 3520 basic_session_run_hooks.py:606] Saving checkpoints for 89 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 17:42:58.203025: step 90, loss = 4.08 (4.6 examples/sec; 28.044 sec/batch) I0903 17:47:38.394626 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0361685 2019-09-03 17:47:38.831426: step 100, loss = 4.02 (4.6 examples/sec; 28.063 sec/batch) I0903 17:52:18.321028 3520 basic_session_run_hooks.py:606] Saving checkpoints for 111 into ./tmp/cifar10_train\model.ckpt. W0903 17:52:18.539428 3520 deprecation.py:323] From C:\Program Files\Python37\lib\site-packages\tensorflow\python\training\saver.py:960: remove_checkpoint (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version. Instructions for updating: Use standard file APIs to delete files with this prefix. 2019-09-03 17:52:18.679828: step 110, loss = 4.06 (4.6 examples/sec; 27.985 sec/batch) 2019-09-03 17:56:58.434630: step 120, loss = 3.97 (4.6 examples/sec; 27.975 sec/batch) 2019-09-03 18:01:38.793432: step 130, loss = 4.00 (4.6 examples/sec; 28.036 sec/batch) I0903 18:02:35.000232 3520 basic_session_run_hooks.py:606] Saving checkpoints for 133 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 18:06:19.767034: step 140, loss = 3.96 (4.6 examples/sec; 28.097 sec/batch) 2019-09-03 18:11:00.177035: step 150, loss = 3.88 (4.6 examples/sec; 28.041 sec/batch) I0903 18:12:52.122636 3520 basic_session_run_hooks.py:606] Saving checkpoints for 155 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 18:15:40.680637: step 160, loss = 3.96 (4.6 examples/sec; 28.050 sec/batch) 2019-09-03 18:20:21.527439: step 170, loss = 3.96 (4.6 examples/sec; 28.085 sec/batch) I0903 18:23:09.913840 3520 basic_session_run_hooks.py:606] Saving checkpoints for 177 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 18:25:03.076241: step 180, loss = 3.81 (4.5 examples/sec; 28.155 sec/batch) 2019-09-03 18:29:44.063443: step 190, loss = 3.83 (4.6 examples/sec; 28.099 sec/batch) I0903 18:33:28.831244 3520 basic_session_run_hooks.py:606] Saving checkpoints for 199 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 18:34:25.820044: step 200, loss = 3.82 (4.5 examples/sec; 28.176 sec/batch) I0903 18:34:25.820044 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0356196 2019-09-03 18:39:06.978846: step 210, loss = 3.74 (4.6 examples/sec; 28.116 sec/batch) I0903 18:43:47.950448 3520 basic_session_run_hooks.py:606] Saving checkpoints for 221 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 18:43:48.792848: step 220, loss = 3.73 (4.5 examples/sec; 28.181 sec/batch) 2019-09-03 18:48:29.982850: step 230, loss = 3.71 (4.6 examples/sec; 28.119 sec/batch) 2019-09-03 18:53:10.892052: step 240, loss = 3.67 (4.6 examples/sec; 28.091 sec/batch) I0903 18:54:07.052052 3520 basic_session_run_hooks.py:606] Saving checkpoints for 243 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 18:57:52.628053: step 250, loss = 3.77 (4.5 examples/sec; 28.174 sec/batch) 2019-09-03 19:02:33.374655: step 260, loss = 3.62 (4.6 examples/sec; 28.075 sec/batch) I0903 19:04:25.771056 3520 basic_session_run_hooks.py:606] Saving checkpoints for 265 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 19:07:15.031057: step 270, loss = 3.66 (4.5 examples/sec; 28.166 sec/batch) 2019-09-03 19:11:55.784259: step 280, loss = 3.72 (4.6 examples/sec; 28.075 sec/batch) I0903 19:14:44.248660 3520 basic_session_run_hooks.py:606] Saving checkpoints for 287 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 19:16:37.021061: step 290, loss = 3.68 (4.6 examples/sec; 28.124 sec/batch) I0903 19:21:17.789862 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0355623 2019-09-03 19:21:18.304662: step 300, loss = 3.36 (4.6 examples/sec; 28.128 sec/batch) I0903 19:25:03.209864 3520 basic_session_run_hooks.py:606] Saving checkpoints for 309 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 19:25:59.697464: step 310, loss = 3.52 (4.5 examples/sec; 28.139 sec/batch) 2019-09-03 19:30:40.216666: step 320, loss = 3.55 (4.6 examples/sec; 28.052 sec/batch) I0903 19:35:20.828468 3520 basic_session_run_hooks.py:606] Saving checkpoints for 331 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 19:35:21.733268: step 330, loss = 3.53 (4.5 examples/sec; 28.152 sec/batch) 2019-09-03 19:40:01.659670: step 340, loss = 3.38 (4.6 examples/sec; 27.993 sec/batch) 2019-09-03 19:44:41.929271: step 350, loss = 3.37 (4.6 examples/sec; 28.027 sec/batch) I0903 19:45:37.933272 3520 basic_session_run_hooks.py:606] Saving checkpoints for 353 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 19:49:22.760473: step 360, loss = 3.36 (4.6 examples/sec; 28.083 sec/batch) 2019-09-03 19:54:03.201675: step 370, loss = 3.26 (4.6 examples/sec; 28.044 sec/batch) I0903 19:55:55.630876 3520 basic_session_run_hooks.py:606] Saving checkpoints for 375 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 19:58:44.859677: step 380, loss = 3.28 (4.5 examples/sec; 28.166 sec/batch) 2019-09-03 20:03:25.464679: step 390, loss = 3.30 (4.6 examples/sec; 28.061 sec/batch) I0903 20:06:13.765280 3520 basic_session_run_hooks.py:606] Saving checkpoints for 397 into ./tmp/cifar10_train\model.ckpt. I0903 20:08:06.756080 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0356003 2019-09-03 20:08:06.756080: step 400, loss = 3.46 (4.6 examples/sec; 28.129 sec/batch) 2019-09-03 20:12:47.181682: step 410, loss = 3.34 (4.6 examples/sec; 28.043 sec/batch) I0903 20:16:31.416084 3520 basic_session_run_hooks.py:606] Saving checkpoints for 419 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 20:17:28.137684: step 420, loss = 3.11 (4.6 examples/sec; 28.096 sec/batch) 2019-09-03 20:22:08.376086: step 430, loss = 3.26 (4.6 examples/sec; 28.024 sec/batch) I0903 20:26:48.864088 3520 basic_session_run_hooks.py:606] Saving checkpoints for 441 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 20:26:49.737688: step 440, loss = 3.24 (4.5 examples/sec; 28.136 sec/batch) 2019-09-03 20:31:30.386689: step 450, loss = 3.16 (4.6 examples/sec; 28.065 sec/batch) 2019-09-03 20:36:10.937091: step 460, loss = 3.33 (4.6 examples/sec; 28.055 sec/batch) I0903 20:37:06.691492 3520 basic_session_run_hooks.py:606] Saving checkpoints for 463 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 20:40:51.159893: step 470, loss = 3.14 (4.6 examples/sec; 28.022 sec/batch) 2019-09-03 20:45:31.211095: step 480, loss = 3.37 (4.6 examples/sec; 28.005 sec/batch) I0903 20:47:22.829095 3520 basic_session_run_hooks.py:606] Saving checkpoints for 485 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 20:50:11.231097: step 490, loss = 3.01 (4.6 examples/sec; 28.002 sec/batch) I0903 20:54:51.282298 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0356566 2019-09-03 20:54:51.812698: step 500, loss = 3.00 (4.6 examples/sec; 28.058 sec/batch) I0903 20:57:39.465899 3520 basic_session_run_hooks.py:606] Saving checkpoints for 507 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 20:59:31.489500: step 510, loss = 3.28 (4.6 examples/sec; 27.968 sec/batch) 2019-09-03 21:04:11.330902: step 520, loss = 3.46 (4.6 examples/sec; 27.984 sec/batch) I0903 21:07:54.941303 3520 basic_session_run_hooks.py:606] Saving checkpoints for 529 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 21:08:51.509904: step 530, loss = 2.99 (4.6 examples/sec; 28.018 sec/batch) 2019-09-03 21:13:30.999506: step 540, loss = 3.04 (4.6 examples/sec; 27.949 sec/batch) I0903 21:18:10.520307 3520 basic_session_run_hooks.py:606] Saving checkpoints for 551 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 21:18:11.347107: step 550, loss = 3.13 (4.6 examples/sec; 28.035 sec/batch) 2019-09-03 21:22:50.992709: step 560, loss = 3.13 (4.6 examples/sec; 27.965 sec/batch) 2019-09-03 21:27:30.435511: step 570, loss = 2.99 (4.6 examples/sec; 27.944 sec/batch) I0903 21:28:26.595511 3520 basic_session_run_hooks.py:606] Saving checkpoints for 573 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 21:32:11.034713: step 580, loss = 2.98 (4.6 examples/sec; 28.060 sec/batch) 2019-09-03 21:36:50.978715: step 590, loss = 3.24 (4.6 examples/sec; 27.994 sec/batch) I0903 21:38:42.877515 3520 basic_session_run_hooks.py:606] Saving checkpoints for 595 into ./tmp/cifar10_train\model.ckpt. I0903 21:41:31.529116 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0357111 2019-09-03 21:41:31.575916: step 600, loss = 2.94 (4.6 examples/sec; 28.060 sec/batch) 2019-09-03 21:46:11.268318: step 610, loss = 2.89 (4.6 examples/sec; 27.969 sec/batch) I0903 21:48:59.233519 3520 basic_session_run_hooks.py:606] Saving checkpoints for 617 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 21:50:51.569120: step 620, loss = 2.95 (4.6 examples/sec; 28.030 sec/batch) 2019-09-03 21:55:31.043122: step 630, loss = 3.00 (4.6 examples/sec; 27.947 sec/batch) I0903 21:59:15.137123 3520 basic_session_run_hooks.py:606] Saving checkpoints for 639 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 22:00:11.858724: step 640, loss = 3.14 (4.6 examples/sec; 28.082 sec/batch) 2019-09-03 22:04:51.063925: step 650, loss = 3.06 (4.6 examples/sec; 27.921 sec/batch) I0903 22:09:30.773927 3520 basic_session_run_hooks.py:606] Saving checkpoints for 661 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 22:09:31.678727: step 660, loss = 2.79 (4.6 examples/sec; 28.061 sec/batch) 2019-09-03 22:14:11.261929: step 670, loss = 2.70 (4.6 examples/sec; 27.958 sec/batch) 2019-09-03 22:18:50.799331: step 680, loss = 2.83 (4.6 examples/sec; 27.954 sec/batch) I0903 22:19:46.943731 3520 basic_session_run_hooks.py:606] Saving checkpoints for 683 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 22:23:31.302932: step 690, loss = 2.98 (4.6 examples/sec; 28.050 sec/batch) I0903 22:28:10.979734 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0357213 2019-09-03 22:28:11.510134: step 700, loss = 3.04 (4.6 examples/sec; 28.021 sec/batch) I0903 22:30:03.050135 3520 basic_session_run_hooks.py:606] Saving checkpoints for 705 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 22:32:51.191936: step 710, loss = 2.73 (4.6 examples/sec; 27.968 sec/batch) 2019-09-03 22:37:30.619138: step 720, loss = 3.09 (4.6 examples/sec; 27.943 sec/batch) I0903 22:40:18.053939 3520 basic_session_run_hooks.py:606] Saving checkpoints for 727 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 22:42:10.701540: step 730, loss = 2.78 (4.6 examples/sec; 28.008 sec/batch) 2019-09-03 22:46:49.769941: step 740, loss = 2.71 (4.6 examples/sec; 27.907 sec/batch) I0903 22:50:32.662743 3520 basic_session_run_hooks.py:606] Saving checkpoints for 749 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 22:51:29.306343: step 750, loss = 3.05 (4.6 examples/sec; 27.954 sec/batch) 2019-09-03 22:56:08.047145: step 760, loss = 2.59 (4.6 examples/sec; 27.874 sec/batch) I0903 23:00:46.959547 3520 basic_session_run_hooks.py:606] Saving checkpoints for 771 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 23:00:47.770747: step 770, loss = 2.48 (4.6 examples/sec; 27.972 sec/batch) 2019-09-03 23:05:27.002749: step 780, loss = 2.69 (4.6 examples/sec; 27.923 sec/batch) 2019-09-03 23:10:06.258350: step 790, loss = 2.73 (4.6 examples/sec; 27.926 sec/batch) I0903 23:11:02.121951 3520 basic_session_run_hooks.py:606] Saving checkpoints for 793 into ./tmp/cifar10_train\model.ckpt. I0903 23:14:46.200352 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0357754 2019-09-03 23:14:46.247152: step 800, loss = 2.72 (4.6 examples/sec; 27.999 sec/batch) 2019-09-03 23:19:25.315554: step 810, loss = 2.91 (4.6 examples/sec; 27.907 sec/batch) I0903 23:21:16.902355 3520 basic_session_run_hooks.py:606] Saving checkpoints for 815 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 23:24:04.789556: step 820, loss = 2.87 (4.6 examples/sec; 27.947 sec/batch) 2019-09-03 23:28:43.533358: step 830, loss = 2.57 (4.6 examples/sec; 27.874 sec/batch) I0903 23:31:30.992959 3520 basic_session_run_hooks.py:606] Saving checkpoints for 837 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 23:33:23.127159: step 840, loss = 2.73 (4.6 examples/sec; 27.959 sec/batch) 2019-09-03 23:38:02.117561: step 850, loss = 2.59 (4.6 examples/sec; 27.899 sec/batch) I0903 23:41:45.634362 3520 basic_session_run_hooks.py:606] Saving checkpoints for 859 into ./tmp/cifar10_train\model.ckpt. 2019-09-03 23:42:42.075163: step 860, loss = 2.67 (4.6 examples/sec; 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I0904 03:07:00.590042 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0359334 2019-09-04 03:07:00.590042: step 1300, loss = 2.16 (4.6 examples/sec; 27.838 sec/batch) 2019-09-04 03:11:38.441643: step 1310, loss = 2.13 (4.6 examples/sec; 27.785 sec/batch) I0904 03:16:16.355645 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1321 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 03:16:17.182445: step 1320, loss = 2.09 (4.6 examples/sec; 27.874 sec/batch) 2019-09-04 03:20:54.566047: step 1330, loss = 2.13 (4.6 examples/sec; 27.738 sec/batch) 2019-09-04 03:25:32.152449: step 1340, loss = 2.14 (4.6 examples/sec; 27.759 sec/batch) I0904 03:26:27.782049 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1343 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 03:30:10.955650: step 1350, loss = 2.35 (4.6 examples/sec; 27.880 sec/batch) 2019-09-04 03:34:48.907852: step 1360, loss = 2.09 (4.6 examples/sec; 27.795 sec/batch) I0904 03:36:40.089053 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1365 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 03:39:27.695454: step 1370, loss = 2.14 (4.6 examples/sec; 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27.732 sec/batch) 2019-09-04 04:16:30.364068: step 1450, loss = 1.88 (4.6 examples/sec; 27.732 sec/batch) I0904 04:17:25.744069 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1453 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 04:21:08.059670: step 1460, loss = 1.84 (4.6 examples/sec; 27.770 sec/batch) 2019-09-04 04:25:45.131272: step 1470, loss = 2.13 (4.6 examples/sec; 27.707 sec/batch) I0904 04:27:36.078472 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1475 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 04:30:23.107674: step 1480, loss = 1.97 (4.6 examples/sec; 27.798 sec/batch) 2019-09-04 04:35:00.045875: step 1490, loss = 1.95 (4.6 examples/sec; 27.694 sec/batch) I0904 04:37:46.435477 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1497 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 04:39:38.225077: step 1500, loss = 1.93 (4.6 examples/sec; 27.818 sec/batch) I0904 04:39:38.225077 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0360184 2019-09-04 04:44:15.780279: step 1510, loss = 2.29 (4.6 examples/sec; 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27.807 sec/batch) I0904 05:25:55.855895 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0360019 2019-09-04 05:25:56.417495: step 1600, loss = 1.81 (4.6 examples/sec; 27.790 sec/batch) I0904 05:28:42.604296 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1607 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 05:30:34.471897: step 1610, loss = 1.96 (4.6 examples/sec; 27.805 sec/batch) 2019-09-04 05:35:11.756299: step 1620, loss = 1.80 (4.6 examples/sec; 27.728 sec/batch) I0904 05:38:53.557100 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1629 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 05:39:49.654700: step 1630, loss = 1.91 (4.6 examples/sec; 27.790 sec/batch) 2019-09-04 05:44:26.601502: step 1640, loss = 1.92 (4.6 examples/sec; 27.695 sec/batch) I0904 05:49:03.610704 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1651 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 05:49:04.437504: step 1650, loss = 1.84 (4.6 examples/sec; 27.784 sec/batch) 2019-09-04 05:53:41.337506: step 1660, loss = 2.00 (4.6 examples/sec; 27.690 sec/batch) 2019-09-04 05:58:18.393507: step 1670, loss = 1.73 (4.6 examples/sec; 27.706 sec/batch) I0904 05:59:13.960708 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1673 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 06:02:56.860509: step 1680, loss = 1.73 (4.6 examples/sec; 27.847 sec/batch) 2019-09-04 06:07:34.696511: step 1690, loss = 1.85 (4.6 examples/sec; 27.784 sec/batch) I0904 06:09:25.924511 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1695 into ./tmp/cifar10_train\model.ckpt. I0904 06:12:13.218913 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0360054 2019-09-04 06:12:13.250113: step 1700, loss = 1.90 (4.6 examples/sec; 27.855 sec/batch) 2019-09-04 06:16:50.290515: step 1710, loss = 1.83 (4.6 examples/sec; 27.704 sec/batch) I0904 06:19:36.477316 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1717 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 06:21:28.360516: step 1720, loss = 1.90 (4.6 examples/sec; 27.807 sec/batch) 2019-09-04 06:26:05.091918: step 1730, loss = 2.02 (4.6 examples/sec; 27.673 sec/batch) I0904 06:29:46.502720 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1739 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 06:30:42.569120: step 1740, loss = 1.65 (4.6 examples/sec; 27.748 sec/batch) 2019-09-04 06:35:21.255522: step 1750, loss = 1.84 (4.6 examples/sec; 27.869 sec/batch) I0904 06:39:59.356724 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1761 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 06:39:59.746723: step 1760, loss = 1.76 (4.6 examples/sec; 27.849 sec/batch) 2019-09-04 06:44:38.347125: step 1770, loss = 1.92 (4.6 examples/sec; 27.860 sec/batch) 2019-09-04 06:49:16.947527: step 1780, loss = 1.73 (4.6 examples/sec; 27.860 sec/batch) I0904 06:50:12.639527 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1783 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 06:53:56.265529: step 1790, loss = 1.71 (4.6 examples/sec; 27.932 sec/batch) I0904 06:58:34.803530 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0359507 2019-09-04 06:58:35.333931: step 1800, loss = 1.55 (4.6 examples/sec; 27.907 sec/batch) I0904 07:00:26.234331 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1805 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 07:03:13.367132: step 1810, loss = 1.75 (4.6 examples/sec; 27.803 sec/batch) 2019-09-04 07:07:51.125134: step 1820, loss = 1.91 (4.6 examples/sec; 27.776 sec/batch) I0904 07:10:37.405535 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1827 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 07:12:28.851936: step 1830, loss = 1.85 (4.6 examples/sec; 27.773 sec/batch) 2019-09-04 07:17:07.655138: step 1840, loss = 1.59 (4.6 examples/sec; 27.880 sec/batch) I0904 07:20:51.015939 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1849 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 07:21:47.300739: step 1850, loss = 1.72 (4.6 examples/sec; 27.965 sec/batch) 2019-09-04 07:26:26.649941: step 1860, loss = 1.75 (4.6 examples/sec; 27.935 sec/batch) I0904 07:31:06.128943 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1871 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 07:31:06.924543: step 1870, loss = 1.71 (4.6 examples/sec; 28.027 sec/batch) 2019-09-04 07:35:46.576145: step 1880, loss = 1.90 (4.6 examples/sec; 27.965 sec/batch) 2019-09-04 07:40:26.362147: step 1890, loss = 1.78 (4.6 examples/sec; 27.979 sec/batch) I0904 07:41:22.241347 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1893 into ./tmp/cifar10_train\model.ckpt. I0904 07:45:06.257348 3520 basic_session_run_hooks.py:692] global_step/sec: 0.0358236 2019-09-04 07:45:06.304148: step 1900, loss = 1.63 (4.6 examples/sec; 27.994 sec/batch) 2019-09-04 07:49:45.700150: step 1910, loss = 1.62 (4.6 examples/sec; 27.940 sec/batch) I0904 07:51:37.630151 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1915 into ./tmp/cifar10_train\model.ckpt. 2019-09-04 07:54:25.673352: step 1920, loss = 1.67 (4.6 examples/sec; 27.997 sec/batch) 2019-09-04 07:59:05.396754: step 1930, loss = 1.80 (4.6 examples/sec; 27.972 sec/batch) I0904 08:01:53.514355 3520 basic_session_run_hooks.py:606] Saving checkpoints for 1937 into ./tmp/cifar10_train\model.ckpt. Process finished with exit code -1
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