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plog.txt
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plog.txt
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05/13/2019 04:54:41 AM predict logging started
05/13/2019 04:54:41 AM checkpoint: vgg19_checkpoint.pth top_k: 5 cat_to_name.json use_gpu: True
05/13/2019 04:54:41 AM The flower to be predicted is: image_path: ./flowers/test/95/image_07470.jpg
05/13/2019 04:54:49 AM with the following trained model: vgg19 25088 102 4096 epochs: 1
05/13/2019 04:55:01 AM [ 0.5138377 0.07287035 0.05391807 0.05369947 0.03466265] ['95', '84', '78', '88', '24'] bougainvillea
05/13/2019 04:55:01 AM image_path: ./flowers/test/95/image_07470.jpg probs: [ 0.5138377 0.07287035 0.05391807 0.05369947 0.03466265] classes: ['95', '84', '78', '88', '24'] flower_name: bougainvillea
05/13/2019 04:55:48 AM Test_accuracy: 71.92% #Correct: 589 Total: 819
05/13/2019 04:55:48 AM Training time_elapsed: 0m 47s
05/13/2019 05:23:07 AM predict logging started
05/13/2019 05:23:07 AM checkpoint: densenet161checkpoint.pth top_k: 5 cat_to_name.json use_gpu: True
05/13/2019 05:23:07 AM The flower to be predicted is: image_path: ./flowers/test/43/image_02435.jpg
05/13/2019 05:23:20 AM with the following trained model: densenet161 2208 102 4096 epochs: 4
05/13/2019 05:23:21 AM [ 0.78713262 0.16304828 0.03613681 0.00372377 0.00160664] ['43', '68', '36', '93', '76'] sword lily
05/13/2019 05:23:21 AM image_path: ./flowers/test/43/image_02435.jpg probs: [ 0.78713262 0.16304828 0.03613681 0.00372377 0.00160664] classes: ['43', '68', '36', '93', '76'] flower_name: sword lily
05/13/2019 05:24:09 AM Test_accuracy: 94.02% #Correct: 770 Total: 819
05/13/2019 05:24:09 AM Training time_elapsed: 0m 49s
05/13/2019 05:24:33 AM predict logging started
05/13/2019 05:24:33 AM checkpoint: densenet161checkpoint.pth top_k: 5 cat_to_name.json use_gpu: True
05/13/2019 05:24:33 AM The flower to be predicted is: image_path: ./flowers/test/1/image_06752.jpg
05/13/2019 05:24:38 AM with the following trained model: densenet161 2208 102 4096 epochs: 4
05/13/2019 05:24:39 AM [ 0.34037384 0.22975056 0.1833526 0.1172583 0.05755446] ['1', '67', '93', '34', '86'] pink primrose
05/13/2019 05:24:39 AM image_path: ./flowers/test/1/image_06752.jpg probs: [ 0.34037384 0.22975056 0.1833526 0.1172583 0.05755446] classes: ['1', '67', '93', '34', '86'] flower_name: pink primrose
05/13/2019 05:25:28 AM Test_accuracy: 94.02% #Correct: 770 Total: 819
05/13/2019 05:25:28 AM Training time_elapsed: 0m 49s
05/13/2019 05:27:29 AM predict logging started
05/13/2019 05:27:29 AM checkpoint: densenet121_checkpoint.pth top_k: 5 cat_to_name.json use_gpu: False
05/13/2019 05:27:29 AM The flower to be predicted is: image_path: ./flowers/test/1/image_06752.jpg
05/13/2019 05:27:34 AM with the following trained model: densenet121 1024 102 4096 epochs: 10
05/13/2019 05:27:35 AM [ 9.56950605e-01 2.68212110e-02 1.42777078e-02 7.06269871e-04
6.15072902e-04] ['1', '67', '19', '55', '69'] pink primrose
05/13/2019 05:27:35 AM image_path: ./flowers/test/1/image_06752.jpg probs: [ 9.56950605e-01 2.68212110e-02 1.42777078e-02 7.06269871e-04
6.15072902e-04] classes: ['1', '67', '19', '55', '69'] flower_name: pink primrose
05/13/2019 05:27:35 AM Training time_elapsed: 0m 0s
05/13/2019 05:39:51 AM predict logging started
05/13/2019 05:39:51 AM checkpoint: densenet161checkpoint.pth top_k: 5 cat_to_name.json use_gpu: False
05/13/2019 05:39:51 AM The flower to be predicted is: image_path: ./flowers/test/95/image_07573.jpg
05/13/2019 05:39:57 AM with the following trained model: densenet161 2208 102 4096 epochs: 4
05/13/2019 05:39:58 AM [ 9.95905221e-01 1.19204528e-03 7.71214953e-04 7.69451086e-04
3.17286002e-04] ['95', '89', '19', '97', '43'] bougainvillea
05/13/2019 05:39:58 AM image_path: ./flowers/test/95/image_07573.jpg probs: [ 9.95905221e-01 1.19204528e-03 7.71214953e-04 7.69451086e-04
05/13/2019 05:42:26 AM predict logging started
05/13/2019 05:42:26 AM checkpoint: vgg16_checkpoint.pth top_k: 5 cat_to_name.json use_gpu: False
05/13/2019 05:42:26 AM The flower to be predicted is: image_path: ./flowers/test/85/image_04814.jpg
05/13/2019 05:42:37 AM with the following trained model: vgg16 25088 102 4096 epochs: 1
05/13/2019 05:42:37 AM [ 0.41802704 0.10769656 0.05648354 0.05605945 0.04793549] ['88', '43', '4', '73', '87'] cyclamen
05/13/2019 05:42:37 AM image_path: ./flowers/test/85/image_04814.jpg probs: [ 0.41802704 0.10769656 0.05648354 0.05605945 0.04793549] classes: ['88', '43', '4', '73', '87'] flower_name: cyclamen
05/13/2019 05:42:37 AM Accuracy Testing time_elapsed: 0m 0s
05/13/2019 05:43:04 AM predict logging started
05/13/2019 05:43:04 AM checkpoint: vgg16_checkpoint.pth top_k: 5 cat_to_name.json use_gpu: False
05/13/2019 05:43:04 AM The flower to be predicted is: image_path: ./flowers/test/74/image_01209.jpg
05/13/2019 05:43:10 AM with the following trained model: vgg16 25088 102 4096 epochs: 1
05/13/2019 05:43:10 AM [ 0.34556937 0.2367534 0.13801578 0.03752066 0.03382921] ['74', '88', '95', '43', '78'] rose
05/13/2019 05:43:10 AM image_path: ./flowers/test/74/image_01209.jpg probs: [ 0.34556937 0.2367534 0.13801578 0.03752066 0.03382921] classes: ['74', '88', '95', '43', '78'] flower_name: rose
05/13/2019 05:43:52 AM Test_accuracy: 71.43% #Correct: 585 Total: 819
05/13/2019 05:43:52 AM Accuracy Testing time_elapsed: 0m 42s
05/13/2019 05:44:05 AM predict logging started
05/13/2019 05:44:05 AM checkpoint: alexnetcheckpoint.pth top_k: 5 cat_to_name.json use_gpu: False
05/13/2019 05:44:05 AM The flower to be predicted is: image_path: ./flowers/test/29/image_04137.jpg
05/13/2019 05:44:11 AM with the following trained model: alexnet 9216 102 4096 epochs: 15
05/13/2019 05:44:11 AM [ 1.00000000e+00 6.35238195e-10 3.75698604e-13 1.21126674e-13
5.86536367e-14] ['29', '14', '13', '51', '77'] artichoke
05/13/2019 05:44:11 AM image_path: ./flowers/test/29/image_04137.jpg probs: [ 1.00000000e+00 6.35238195e-10 3.75698604e-13 1.21126674e-13
5.86536367e-14] classes: ['29', '14', '13', '51', '77'] flower_name: artichoke
05/13/2019 05:44:32 AM Test_accuracy: 85.35% #Correct: 699 Total: 819
05/13/2019 05:44:32 AM Accuracy Testing time_elapsed: 0m 21s
05/13/2019 05:45:01 AM predict logging started
05/13/2019 05:45:01 AM checkpoint: alexnetcheckpoint.pth top_k: 5 cat_to_name.json use_gpu: False
05/13/2019 05:45:01 AM The flower to be predicted is: image_path: ./flowers/test/1/image_06752.jpg
05/13/2019 05:45:06 AM with the following trained model: alexnet 9216 102 4096 epochs: 15
05/13/2019 05:45:06 AM [ 0.58722192 0.30926263 0.08742899 0.00393038 0.00181095] ['19', '86', '1', '93', '52'] balloon flower
05/13/2019 05:45:06 AM image_path: ./flowers/test/1/image_06752.jpg probs: [ 0.58722192 0.30926263 0.08742899 0.00393038 0.00181095] classes: ['19', '86', '1', '93', '52'] flower_name: balloon flower
05/13/2019 05:45:46 AM Test_accuracy: 85.35% #Correct: 699 Total: 819
05/13/2019 05:45:46 AM Accuracy Testing time_elapsed: 0m 21s
05/13/2019 05:45:56 AM predict logging started
05/13/2019 05:45:56 AM checkpoint: alexnetcheckpoint.pth top_k: 5 cat_to_name.json use_gpu: True
05/13/2019 05:45:56 AM The flower to be predicted is: image_path: ./flowers/test/74/image_01254.jpg
05/13/2019 05:46:01 AM with the following trained model: alexnet 9216 102 4096 epochs: 15
05/13/2019 05:46:01 AM [ 9.97884154e-01 1.21472299e-03 8.47363786e-04 2.83674472e-05
1.16221981e-05] ['74', '43', '96', '95', '31'] rose
05/13/2019 05:46:01 AM image_path: ./flowers/test/74/image_01254.jpg probs: [ 9.97884154e-01 1.21472299e-03 8.47363786e-04 2.83674472e-05
1.16221981e-05] classes: ['74', '43', '96', '95', '31'] flower_name: rose
05/13/2019 05:46:23 AM Test_accuracy: 85.35% #Correct: 699 Total: 819
05/13/2019 05:46:23 AM Accuracy Testing time_elapsed: 0m 21s