ResNet18_v2 under the directory of /mxnet/example/gluon #15962

CynthiaProtector · 2019-08-21T14:40:12Z

CynthiaProtector
Aug 21, 2019

I tried to train ResNet18_v2 with CIFAR-10, the initial learning rate is 0.1, and decrease to 0.01 and 0.001 at the 80 epoch and 160 epoch (The image_classification.py doest not implement the lr_scheduler function and I add that myself). Even training on a single node with 4 GPUs, the final test accuracy is around 81%, why is that?

Answered by CynthiaProtector

Sep 20, 2019

The other reason is that no data augmentation is applied in the training process, therefore, the final test accuracy is around 81% because of the limited training dataset. When the data augmentation is applied, the test accuracy is improved.

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mxnet-label-bot · 2019-08-21T14:40:16Z

mxnet-label-bot
Aug 21, 2019

Hey, this is the MXNet Label Bot.
Thank you for submitting the issue! I will try and suggest some labels so that the appropriate MXNet community members can help resolve it.
Here are my recommended labels: Gluon, Example

0 replies

CynthiaProtector · 2019-08-21T15:43:24Z

CynthiaProtector
Aug 21, 2019
Author

The following is a sample of the training log:

2019-08-21 12:23:33,543 INFO Namespace(batch_norm=False, batch_size=32, dataset='cifar10', epochs=200, kvstore='device', log_interval=50, lr=0.1, lr_factor=0.1, lr_step_epochs='80,120', mode='symbolic', model='resnet18_v2', momentum=0.9, num_examples=50000, num_gpus=4, num_workers=1, optimizer='sgd', seed=123, train_data='', use_pretrained=False, use_thumbnail=False, val_data='', wd=0.0001)
2019-08-21 12:23:49,109 INFO =============== Changing the global learning rate =================
2019-08-21 12:23:49,109 INFO ===================================================================
2019-08-21 12:24:33,668 INFO Epoch[0] Batch [50] Speed: 590.59 samples/sec accuracy=0.211397
2019-08-21 12:24:44,679 INFO Epoch[0] Batch [100] Speed: 581.25 samples/sec accuracy=0.270625
2019-08-21 12:24:55,786 INFO Epoch[0] Batch [150] Speed: 576.24 samples/sec accuracy=0.315156
2019-08-21 12:25:06,760 INFO Epoch[0] Batch [200] Speed: 583.23 samples/sec accuracy=0.362812
2019-08-21 12:25:17,796 INFO Epoch[0] Batch [250] Speed: 579.94 samples/sec accuracy=0.377812
2019-08-21 12:25:28,772 INFO Epoch[0] Batch [300] Speed: 583.12 samples/sec accuracy=0.413906
2019-08-21 12:25:39,790 INFO Epoch[0] Batch [350] Speed: 580.83 samples/sec accuracy=0.422500
2019-08-21 12:25:48,608 INFO Epoch[0] Train-accuracy=0.441992
2019-08-21 12:25:48,609 INFO Epoch[0] Time cost=86.588
2019-08-21 12:25:49,821 INFO Saved checkpoint to "image-classifier-resnet18_v2-0001.params"
2019-08-21 12:25:53,679 INFO Epoch[0] Validation-accuracy=0.453125
2019-08-21 12:26:04,903 INFO Epoch[1] Batch [50] Speed: 578.84 samples/sec accuracy=0.452053
2019-08-21 12:26:15,890 INFO Epoch[1] Batch [100] Speed: 582.55 samples/sec accuracy=0.472500
2019-08-21 12:26:26,949 INFO Epoch[1] Batch [150] Speed: 578.72 samples/sec accuracy=0.477187
2019-08-21 12:26:37,900 INFO Epoch[1] Batch [200] Speed: 584.44 samples/sec accuracy=0.494531
2019-08-21 12:26:48,954 INFO Epoch[1] Batch [250] Speed: 579.02 samples/sec accuracy=0.490000
2019-08-21 12:26:59,934 INFO Epoch[1] Batch [300] Speed: 582.87 samples/sec accuracy=0.517656
2019-08-21 12:27:10,943 INFO Epoch[1] Batch [350] Speed: 581.35 samples/sec accuracy=0.525781
2019-08-21 12:27:19,730 INFO Epoch[1] Train-accuracy=0.521484
2019-08-21 12:27:19,731 INFO Epoch[1] Time cost=86.046
2019-08-21 12:27:20,175 INFO Saved checkpoint to "image-classifier-resnet18_v2-0002.params"
2019-08-21 12:27:23,861 INFO Epoch[1] Validation-accuracy=0.521134
2019-08-21 12:27:35,069 INFO Epoch[2] Batch [50] Speed: 579.56 samples/sec accuracy=0.540135
2019-08-21 12:27:46,034 INFO Epoch[2] Batch [100] Speed: 583.65 samples/sec accuracy=0.557656
2019-08-21 12:27:57,058 INFO Epoch[2] Batch [150] Speed: 580.57 samples/sec accuracy=0.567344
2019-08-21 12:28:08,046 INFO Epoch[2] Batch [200] Speed: 582.47 samples/sec accuracy=0.559375
2019-08-21 12:28:19,084 INFO Epoch[2] Batch [250] Speed: 579.87 samples/sec accuracy=0.576562
2019-08-21 12:28:30,016 INFO Epoch[2] Batch [300] Speed: 585.44 samples/sec accuracy=0.587656
2019-08-21 12:28:40,986 INFO Epoch[2] Batch [350] Speed: 583.43 samples/sec accuracy=0.602812
2019-08-21 12:28:49,554 INFO Epoch[2] Train-accuracy=0.589944
2019-08-21 12:28:49,554 INFO Epoch[2] Time cost=85.688
2019-08-21 12:28:50,124 INFO Saved checkpoint to "image-classifier-resnet18_v2-0003.params"
2019-08-21 12:28:53,842 INFO Epoch[2] Validation-accuracy=0.566106
2019-08-21 12:29:05,046 INFO Epoch[3] Batch [50] Speed: 580.29 samples/sec accuracy=0.600184
2019-08-21 12:29:16,037 INFO Epoch[3] Batch [100] Speed: 582.32 samples/sec accuracy=0.619375
2019-08-21 12:29:27,059 INFO Epoch[3] Batch [150] Speed: 580.66 samples/sec accuracy=0.617812
2019-08-21 12:29:38,037 INFO Epoch[3] Batch [200] Speed: 583.01 samples/sec accuracy=0.627188
2019-08-21 12:29:49,074 INFO Epoch[3] Batch [250] Speed: 579.88 samples/sec accuracy=0.632656
2019-08-21 12:30:00,027 INFO Epoch[3] Batch [300] Speed: 584.34 samples/sec accuracy=0.638437
2019-08-21 12:30:10,987 INFO Epoch[3] Batch [350] Speed: 583.99 samples/sec accuracy=0.653594
2019-08-21 12:30:19,793 INFO Epoch[3] Train-accuracy=0.651758
2019-08-21 12:30:19,793 INFO Epoch[3] Time cost=85.945
2019-08-21 12:30:20,306 INFO Saved checkpoint to "image-classifier-resnet18_v2-0004.params"
2019-08-21 12:30:23,991 INFO Epoch[3] Validation-accuracy=0.615685
2019-08-21 12:30:35,210 INFO Epoch[4] Batch [50] Speed: 578.68 samples/sec accuracy=0.652880
2019-08-21 12:30:46,182 INFO Epoch[4] Batch [100] Speed: 583.32 samples/sec accuracy=0.667656
2019-08-21 12:30:57,237 INFO Epoch[4] Batch [150] Speed: 578.96 samples/sec accuracy=0.671406
2019-08-21 12:31:08,167 INFO Epoch[4] Batch [200] Speed: 585.56 samples/sec accuracy=0.665781
2019-08-21 12:31:19,217 INFO Epoch[4] Batch [250] Speed: 579.22 samples/sec accuracy=0.677031
2019-08-21 12:31:30,184 INFO Epoch[4] Batch [300] Speed: 583.57 samples/sec accuracy=0.675781
2019-08-21 12:31:41,163 INFO Epoch[4] Batch [350] Speed: 582.96 samples/sec accuracy=0.685312
2019-08-21 12:31:49,925 INFO Epoch[4] Train-accuracy=0.679688
2019-08-21 12:31:49,925 INFO Epoch[4] Time cost=85.929
2019-08-21 12:31:50,364 INFO Saved checkpoint to "image-classifier-resnet18_v2-0005.params"
2019-08-21 12:31:54,047 INFO Epoch[4] Validation-accuracy=0.648438
2019-08-21 12:32:05,266 INFO Epoch[5] Batch [50] Speed: 578.44 samples/sec accuracy=0.688725
2019-08-21 12:32:16,198 INFO Epoch[5] Batch [100] Speed: 585.44 samples/sec accuracy=0.692187
2019-08-21 12:32:27,238 INFO Epoch[5] Batch [150] Speed: 579.71 samples/sec accuracy=0.697812
2019-08-21 12:32:38,180 INFO Epoch[5] Batch [200] Speed: 584.93 samples/sec accuracy=0.703594
2019-08-21 12:32:49,205 INFO Epoch[5] Batch [250] Speed: 580.51 samples/sec accuracy=0.711719
2019-08-21 12:33:00,140 INFO Epoch[5] Batch [300] Speed: 585.31 samples/sec accuracy=0.715938
2019-08-21 12:33:11,119 INFO Epoch[5] Batch [350] Speed: 582.95 samples/sec accuracy=0.713125
2019-08-21 12:33:19,656 INFO Epoch[5] Train-accuracy=0.712340
2019-08-21 12:33:19,656 INFO Epoch[5] Time cost=85.601
2019-08-21 12:33:20,204 INFO Saved checkpoint to "image-classifier-resnet18_v2-0006.params"
2019-08-21 12:33:23,899 INFO Epoch[5] Validation-accuracy=0.670373
2019-08-21 12:33:35,099 INFO Epoch[6] Batch [50] Speed: 580.23 samples/sec accuracy=0.709252
2019-08-21 12:33:46,060 INFO Epoch[6] Batch [100] Speed: 583.89 samples/sec accuracy=0.719531
2019-08-21 12:33:57,063 INFO Epoch[6] Batch [150] Speed: 581.67 samples/sec accuracy=0.724219
2019-08-21 12:34:08,034 INFO Epoch[6] Batch [200] Speed: 583.38 samples/sec accuracy=0.724844
2019-08-21 12:34:19,036 INFO Epoch[6] Batch [250] Speed: 581.72 samples/sec accuracy=0.734219
2019-08-21 12:34:30,006 INFO Epoch[6] Batch [300] Speed: 583.47 samples/sec accuracy=0.738281
2019-08-21 12:34:41,003 INFO Epoch[6] Batch [350] Speed: 581.97 samples/sec accuracy=0.742344
2019-08-21 12:34:49,735 INFO Epoch[6] Train-accuracy=0.732812
2019-08-21 12:34:49,735 INFO Epoch[6] Time cost=85.831
2019-08-21 12:34:50,240 INFO Saved checkpoint to "image-classifier-resnet18_v2-0007.params"
2019-08-21 12:34:53,930 INFO Epoch[6] Validation-accuracy=0.687901
2019-08-21 12:35:05,096 INFO Epoch[7] Batch [50] Speed: 583.06 samples/sec accuracy=0.716912
2019-08-21 12:35:16,046 INFO Epoch[7] Batch [100] Speed: 584.47 samples/sec accuracy=0.729375
2019-08-21 12:35:27,088 INFO Epoch[7] Batch [150] Speed: 579.63 samples/sec accuracy=0.742188
2019-08-21 12:35:38,018 INFO Epoch[7] Batch [200] Speed: 585.56 samples/sec accuracy=0.745625
2019-08-21 12:35:49,040 INFO Epoch[7] Batch [250] Speed: 580.69 samples/sec accuracy=0.764062
2019-08-21 12:35:59,982 INFO Epoch[7] Batch [300] Speed: 584.91 samples/sec accuracy=0.761719
2019-08-21 12:36:10,947 INFO Epoch[7] Batch [350] Speed: 583.71 samples/sec accuracy=0.760000
2019-08-21 12:36:19,387 INFO Epoch[7] Train-accuracy=0.760817
2019-08-21 12:36:19,388 INFO Epoch[7] Time cost=85.454
2019-08-21 12:36:19,901 INFO Saved checkpoint to "image-classifier-resnet18_v2-0008.params"
2019-08-21 12:36:23,504 INFO Epoch[7] Validation-accuracy=0.708734
2019-08-21 12:36:34,802 INFO Epoch[8] Batch [50] Speed: 579.03 samples/sec accuracy=0.751685
2019-08-21 12:36:45,745 INFO Epoch[8] Batch [100] Speed: 584.87 samples/sec accuracy=0.763125
2019-08-21 12:36:56,801 INFO Epoch[8] Batch [150] Speed: 578.91 samples/sec accuracy=0.756250
2019-08-21 12:37:07,758 INFO Epoch[8] Batch [200] Speed: 584.10 samples/sec accuracy=0.768437
2019-08-21 12:37:18,763 INFO Epoch[8] Batch [250] Speed: 581.62 samples/sec accuracy=0.769219
2019-08-21 12:37:29,701 INFO Epoch[8] Batch [300] Speed: 585.11 samples/sec accuracy=0.770781
2019-08-21 12:37:40,623 INFO Epoch[8] Batch [350] Speed: 585.97 samples/sec accuracy=0.774844
2019-08-21 12:37:49,407 INFO Epoch[8] Train-accuracy=0.778906
2019-08-21 12:37:49,407 INFO Epoch[8] Time cost=85.892
2019-08-21 12:37:50,179 INFO Saved checkpoint to "image-classifier-resnet18_v2-0009.params"
2019-08-21 12:37:54,031 INFO Epoch[8] Validation-accuracy=0.716871
2019-08-21 12:38:05,232 INFO Epoch[9] Batch [50] Speed: 580.40 samples/sec accuracy=0.768382
2019-08-21 12:38:16,176 INFO Epoch[9] Batch [100] Speed: 584.83 samples/sec accuracy=0.778594
2019-08-21 12:38:27,198 INFO Epoch[9] Batch [150] Speed: 580.69 samples/sec accuracy=0.767188
2019-08-21 12:38:38,144 INFO Epoch[9] Batch [200] Speed: 584.70 samples/sec accuracy=0.776094
2019-08-21 12:38:49,195 INFO Epoch[9] Batch [250] Speed: 579.16 samples/sec accuracy=0.791094
2019-08-21 12:39:00,144 INFO Epoch[9] Batch [300] Speed: 584.51 samples/sec accuracy=0.790312
2019-08-21 12:39:11,131 INFO Epoch[9] Batch [350] Speed: 582.58 samples/sec accuracy=0.793281
2019-08-21 12:39:19,856 INFO Epoch[9] Train-accuracy=0.789258
2019-08-21 12:39:19,856 INFO Epoch[9] Time cost=85.819
2019-08-21 12:39:20,397 INFO Saved checkpoint to "image-classifier-resnet18_v2-0010.params"
2019-08-21 12:39:24,108 INFO Epoch[9] Validation-accuracy=0.724760
2019-08-21 12:39:35,289 INFO Epoch[10] Batch [50] Speed: 582.10 samples/sec accuracy=0.783548
2019-08-21 12:39:46,202 INFO Epoch[10] Batch [100] Speed: 586.50 samples/sec accuracy=0.792500
2019-08-21 12:39:57,223 INFO Epoch[10] Batch [150] Speed: 580.72 samples/sec accuracy=0.788281
2019-08-21 12:40:08,162 INFO Epoch[10] Batch [200] Speed: 585.05 samples/sec accuracy=0.789062
2019-08-21 12:40:19,202 INFO Epoch[10] Batch [250] Speed: 579.74 samples/sec accuracy=0.806250
2019-08-21 12:40:30,149 INFO Epoch[10] Batch [300] Speed: 584.66 samples/sec accuracy=0.806875
2019-08-21 12:40:41,089 INFO Epoch[10] Batch [350] Speed: 585.04 samples/sec accuracy=0.811406
2019-08-21 12:40:49,628 INFO Epoch[10] Train-accuracy=0.803486
2019-08-21 12:40:49,629 INFO Epoch[10] Time cost=85.516
2019-08-21 12:40:50,167 INFO Saved checkpoint to "image-classifier-resnet18_v2-0011.params"
2019-08-21 12:40:53,845 INFO Epoch[10] Validation-accuracy=0.724259
2019-08-21 12:41:05,046 INFO Epoch[11] Batch [50] Speed: 579.67 samples/sec accuracy=0.800398
2019-08-21 12:41:15,972 INFO Epoch[11] Batch [100] Speed: 585.76 samples/sec accuracy=0.801406
2019-08-21 12:41:26,993 INFO Epoch[11] Batch [150] Speed: 580.72 samples/sec accuracy=0.801250
2019-08-21 12:41:37,929 INFO Epoch[11] Batch [200] Speed: 585.26 samples/sec accuracy=0.807813
2019-08-21 12:41:48,959 INFO Epoch[11] Batch [250] Speed: 580.22 samples/sec accuracy=0.818281
2019-08-21 12:41:59,929 INFO Epoch[11] Batch [300] Speed: 583.51 samples/sec accuracy=0.816875
2019-08-21 12:42:10,861 INFO Epoch[11] Batch [350] Speed: 585.44 samples/sec accuracy=0.815781
2019-08-21 12:42:19,618 INFO Epoch[11] Train-accuracy=0.818750
2019-08-21 12:42:19,618 INFO Epoch[11] Time cost=85.767
2019-08-21 12:42:20,101 INFO Saved checkpoint to "image-classifier-resnet18_v2-0012.params"
2019-08-21 12:42:23,786 INFO Epoch[11] Validation-accuracy=0.731971
2019-08-21 12:42:35,004 INFO Epoch[12] Batch [50] Speed: 579.01 samples/sec accuracy=0.811734
2019-08-21 12:42:45,924 INFO Epoch[12] Batch [100] Speed: 586.10 samples/sec accuracy=0.816406
2019-08-21 12:42:56,958 INFO Epoch[12] Batch [150] Speed: 580.03 samples/sec accuracy=0.811250
2019-08-21 12:43:07,893 INFO Epoch[12] Batch [200] Speed: 585.31 samples/sec accuracy=0.817031
2019-08-21 12:43:18,902 INFO Epoch[12] Batch [250] Speed: 581.37 samples/sec accuracy=0.827344
2019-08-21 12:43:29,849 INFO Epoch[12] Batch [300] Speed: 584.66 samples/sec accuracy=0.825937
2019-08-21 12:43:40,806 INFO Epoch[12] Batch [350] Speed: 584.12 samples/sec accuracy=0.831250
2019-08-21 12:43:49,553 INFO Epoch[12] Train-accuracy=0.831055
2019-08-21 12:43:49,553 INFO Epoch[12] Time cost=85.763
2019-08-21 12:43:50,034 INFO Saved checkpoint to "image-classifier-resnet18_v2-0013.params"
2019-08-21 12:43:53,737 INFO Epoch[12] Validation-accuracy=0.727364
2019-08-21 12:44:04,941 INFO Epoch[13] Batch [50] Speed: 580.42 samples/sec accuracy=0.823683
2019-08-21 12:44:15,890 INFO Epoch[13] Batch [100] Speed: 584.51 samples/sec accuracy=0.825937
2019-08-21 12:44:26,919 INFO Epoch[13] Batch [150] Speed: 580.34 samples/sec accuracy=0.822969
2019-08-21 12:44:37,847 INFO Epoch[13] Batch [200] Speed: 585.64 samples/sec accuracy=0.837031
2019-08-21 12:44:48,868 INFO Epoch[13] Batch [250] Speed: 580.75 samples/sec accuracy=0.839063
2019-08-21 12:44:59,788 INFO Epoch[13] Batch [300] Speed: 586.11 samples/sec accuracy=0.840000
2019-08-21 12:45:10,742 INFO Epoch[13] Batch [350] Speed: 584.25 samples/sec accuracy=0.835156
2019-08-21 12:45:19,295 INFO Epoch[13] Train-accuracy=0.828726
2019-08-21 12:45:19,295 INFO Epoch[13] Time cost=85.553
2019-08-21 12:45:19,764 INFO Saved checkpoint to "image-classifier-resnet18_v2-0014.params"
2019-08-21 12:45:23,450 INFO Epoch[13] Validation-accuracy=0.736579
2019-08-21 12:45:34,647 INFO Epoch[14] Batch [50] Speed: 581.00 samples/sec accuracy=0.839001
2019-08-21 12:45:45,589 INFO Epoch[14] Batch [100] Speed: 584.92 samples/sec accuracy=0.834219
2019-08-21 12:45:56,616 INFO Epoch[14] Batch [150] Speed: 580.39 samples/sec accuracy=0.828906
2019-08-21 12:46:07,526 INFO Epoch[14] Batch [200] Speed: 586.61 samples/sec accuracy=0.840469
2019-08-21 12:46:18,562 INFO Epoch[14] Batch [250] Speed: 579.95 samples/sec accuracy=0.848125
2019-08-21 12:46:29,527 INFO Epoch[14] Batch [300] Speed: 583.71 samples/sec accuracy=0.850938
2019-08-21 12:46:40,491 INFO Epoch[14] Batch [350] Speed: 583.75 samples/sec accuracy=0.853437
2019-08-21 12:46:49,256 INFO Epoch[14] Train-accuracy=0.848047
2019-08-21 12:46:49,256 INFO Epoch[14] Time cost=85.802
2019-08-21 12:46:49,740 INFO Saved checkpoint to "image-classifier-resnet18_v2-0015.params"
2019-08-21 12:46:53,400 INFO Epoch[14] Validation-accuracy=0.726262
2019-08-21 12:47:04,611 INFO Epoch[15] Batch [50] Speed: 580.19 samples/sec accuracy=0.840839
2019-08-21 12:47:15,528 INFO Epoch[15] Batch [100] Speed: 586.26 samples/sec accuracy=0.834531
2019-08-21 12:47:26,574 INFO Epoch[15] Batch [150] Speed: 579.42 samples/sec accuracy=0.843281
2019-08-21 12:47:37,525 INFO Epoch[15] Batch [200] Speed: 584.46 samples/sec accuracy=0.847187
2019-08-21 12:47:48,546 INFO Epoch[15] Batch [250] Speed: 580.70 samples/sec accuracy=0.851094
2019-08-21 12:47:59,502 INFO Epoch[15] Batch [300] Speed: 584.16 samples/sec accuracy=0.862344
2019-08-21 12:48:10,448 INFO Epoch[15] Batch [350] Speed: 584.73 samples/sec accuracy=0.846094
2019-08-21 12:48:18,906 INFO Epoch[15] Train-accuracy=0.855569
2019-08-21 12:48:18,906 INFO Epoch[15] Time cost=85.500
2019-08-21 12:48:19,532 INFO Saved checkpoint to "image-classifier-resnet18_v2-0016.params"
2019-08-21 12:48:23,131 INFO Epoch[15] Validation-accuracy=0.742588
2019-08-21 12:48:34,383 INFO Epoch[16] Batch [50] Speed: 582.89 samples/sec accuracy=0.855239
2019-08-21 12:48:45,333 INFO Epoch[16] Batch [100] Speed: 584.49 samples/sec accuracy=0.847500
2019-08-21 12:48:56,372 INFO Epoch[16] Batch [150] Speed: 579.81 samples/sec accuracy=0.845000
2019-08-21 12:49:07,323 INFO Epoch[16] Batch [200] Speed: 584.42 samples/sec accuracy=0.857187
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2019-08-21 12:49:40,270 INFO Epoch[16] Batch [350] Speed: 585.13 samples/sec accuracy=0.865781
2019-08-21 12:49:49,038 INFO Epoch[16] Train-accuracy=0.857617
2019-08-21 12:49:49,038 INFO Epoch[16] Time cost=85.898
2019-08-21 12:49:49,510 INFO Saved checkpoint to "image-classifier-resnet18_v2-0017.params"
2019-08-21 12:49:53,309 INFO Epoch[16] Validation-accuracy=0.740704
2019-08-21 12:50:04,502 INFO Epoch[17] Batch [50] Speed: 580.50 samples/sec accuracy=0.860600
2019-08-21 12:50:15,471 INFO Epoch[17] Batch [100] Speed: 583.53 samples/sec accuracy=0.855000
2019-08-21 12:50:26,486 INFO Epoch[17] Batch [150] Speed: 581.02 samples/sec accuracy=0.861719
2019-08-21 12:50:37,433 INFO Epoch[17] Batch [200] Speed: 584.64 samples/sec accuracy=0.864219
2019-08-21 12:50:48,476 INFO Epoch[17] Batch [250] Speed: 579.57 samples/sec accuracy=0.864219
2019-08-21 12:50:59,417 INFO Epoch[17] Batch [300] Speed: 584.98 samples/sec accuracy=0.865156
2019-08-21 12:51:10,373 INFO Epoch[17] Batch [350] Speed: 584.19 samples/sec accuracy=0.875469
2019-08-21 12:51:19,146 INFO Epoch[17] Train-accuracy=0.863477
2019-08-21 12:51:19,146 INFO Epoch[17] Time cost=85.832
2019-08-21 12:51:19,609 INFO Saved checkpoint to "image-classifier-resnet18_v2-0018.params"
2019-08-21 12:51:23,293 INFO Epoch[17] Validation-accuracy=0.733874
2019-08-21 12:51:34,479 INFO Epoch[18] Batch [50] Speed: 580.58 samples/sec accuracy=0.865349
2019-08-21 12:51:45,434 INFO Epoch[18] Batch [100] Speed: 584.26 samples/sec accuracy=0.869844
2019-08-21 12:51:56,451 INFO Epoch[18] Batch [150] Speed: 580.92 samples/sec accuracy=0.862344
2019-08-21 12:52:07,423 INFO Epoch[18] Batch [200] Speed: 583.29 samples/sec accuracy=0.868125
2019-08-21 12:52:18,443 INFO Epoch[18] Batch [250] Speed: 580.81 samples/sec accuracy=0.876250
2019-08-21 12:52:29,393 INFO Epoch[18] Batch [300] Speed: 584.46 samples/sec accuracy=0.875625
2019-08-21 12:52:40,370 INFO Epoch[18] Batch [350] Speed: 583.08 samples/sec accuracy=0.877344
2019-08-21 12:52:48,875 INFO Epoch[18] Train-accuracy=0.868590
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2019-08-21 14:24:19,812 INFO Epoch[79] Validation-accuracy=0.763922
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2019-08-21 15:24:04,587 INFO Epoch[119] Batch [350] Speed: 582.41 samples/sec accuracy=1.000000
2019-08-21 15:24:13,021 INFO Epoch[119] Train-accuracy=1.000000
2019-08-21 15:24:13,021 INFO Epoch[119] Time cost=85.445
2019-08-21 15:24:13,516 INFO Saved checkpoint to "image-classifier-resnet18_v2-0120.params"
2019-08-21 15:24:17,140 INFO Epoch[119] Validation-accuracy=0.810497
2019-08-21 15:24:17,227 INFO Update[46876]: Change learning rate to 1.00000e-03
2019-08-21 15:24:28,413 INFO Epoch[120] Batch [50] Speed: 582.99 samples/sec accuracy=1.000000
2019-08-21 15:24:39,364 INFO Epoch[120] Batch [100] Speed: 584.45 samples/sec accuracy=1.000000
2019-08-21 15:24:50,322 INFO Epoch[120] Batch [150] Speed: 584.06 samples/sec accuracy=1.000000
2019-08-21 15:25:01,296 INFO Epoch[120] Batch [200] Speed: 583.25 samples/sec accuracy=1.000000
2019-08-21 15:25:12,305 INFO Epoch[120] Batch [250] Speed: 581.35 samples/sec accuracy=1.000000
2019-08-21 15:25:23,248 INFO Epoch[120] Batch [300] Speed: 584.86 samples/sec accuracy=1.000000
2019-08-21 15:25:34,161 INFO Epoch[120] Batch [350] Speed: 586.45 samples/sec accuracy=1.000000
2019-08-21 15:25:42,945 INFO Epoch[120] Train-accuracy=1.000000
2019-08-21 15:25:42,945 INFO Epoch[120] Time cost=85.793
2019-08-21 15:25:43,405 INFO Saved checkpoint to "image-classifier-resnet18_v2-0121.params"
2019-08-21 15:25:47,191 INFO Epoch[120] Validation-accuracy=0.811313
2019-08-21 15:25:58,395 INFO Epoch[121] Batch [50] Speed: 579.68 samples/sec accuracy=1.000000
2019-08-21 15:26:09,312 INFO Epoch[121] Batch [100] Speed: 586.24 samples/sec accuracy=1.000000
2019-08-21 15:26:20,311 INFO Epoch[121] Batch [150] Speed: 581.91 samples/sec accuracy=1.000000
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2019-08-21 15:26:42,258 INFO Epoch[121] Batch [250] Speed: 580.33 samples/sec accuracy=1.000000
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2019-08-21 15:27:04,179 INFO Epoch[121] Batch [350] Speed: 583.70 samples/sec accuracy=1.000000
2019-08-21 15:27:12,942 INFO Epoch[121] Train-accuracy=1.000000
2019-08-21 15:27:12,942 INFO Epoch[121] Time cost=85.745
2019-08-21 15:27:13,446 INFO Saved checkpoint to "image-classifier-resnet18_v2-0122.params"
2019-08-21 15:27:17,130 INFO Epoch[121] Validation-accuracy=0.809696
2019-08-21 15:27:28,333 INFO Epoch[122] Batch [50] Speed: 579.49 samples/sec accuracy=1.000000
2019-08-21 15:27:39,235 INFO Epoch[122] Batch [100] Speed: 587.09 samples/sec accuracy=1.000000
2019-08-21 15:27:50,280 INFO Epoch[122] Batch [150] Speed: 579.42 samples/sec accuracy=1.000000
2019-08-21 15:28:01,210 INFO Epoch[122] Batch [200] Speed: 585.58 samples/sec accuracy=1.000000
2019-08-21 15:28:12,214 INFO Epoch[122] Batch [250] Speed: 581.64 samples/sec accuracy=1.000000
2019-08-21 15:28:23,151 INFO Epoch[122] Batch [300] Speed: 585.18 samples/sec accuracy=1.000000
2019-08-21 15:28:34,108 INFO Epoch[122] Batch [350] Speed: 584.09 samples/sec accuracy=1.000000
2019-08-21 15:28:42,642 INFO Epoch[122] Train-accuracy=1.000000
2019-08-21 15:28:42,642 INFO Epoch[122] Time cost=85.507
2019-08-21 15:28:43,123 INFO Saved checkpoint to "image-classifier-resnet18_v2-0123.params"
2019-08-21 15:28:46,802 INFO Epoch[122] Validation-accuracy=0.810497
2019-08-21 15:28:57,993 INFO Epoch[123] Batch [50] Speed: 580.61 samples/sec accuracy=1.000000
2019-08-21 15:29:08,914 INFO Epoch[123] Batch [100] Speed: 586.02 samples/sec accuracy=1.000000
2019-08-21 15:29:19,958 INFO Epoch[123] Batch [150] Speed: 579.49 samples/sec accuracy=1.000000
2019-08-21 15:29:30,868 INFO Epoch[123] Batch [200] Speed: 586.67 samples/sec accuracy=1.000000
2019-08-21 15:29:41,904 INFO Epoch[123] Batch [250] Speed: 579.92 samples/sec accuracy=1.000000
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2019-08-21 15:30:03,772 INFO Epoch[123] Batch [350] Speed: 586.51 samples/sec accuracy=1.000000
2019-08-21 15:30:12,574 INFO Epoch[123] Train-accuracy=1.000000
2019-08-21 15:30:12,574 INFO Epoch[123] Time cost=85.767

0 replies

frankfliu · 2019-08-21T15:58:20Z

frankfliu
Aug 21, 2019

@mxnet-label-bot add [training, question]

0 replies

zachgk · 2019-09-19T21:26:10Z

zachgk
Sep 19, 2019
Collaborator

Your training accuracy is going to 1.0 so your model is probably overfitting the dataset (https://www.d2l.ai/chapter_multilayer-perceptrons/underfit-overfit.html)

0 replies

CynthiaProtector · 2019-09-20T00:31:30Z

CynthiaProtector
Sep 20, 2019
Author

The other reason is that no data augmentation is applied in the training process, therefore, the final test accuracy is around 81% because of the limited training dataset. When the data augmentation is applied, the test accuracy is improved.

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