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ReportC.txt
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ReportC.txt
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N training set length 8136
N testing set length 1017
N validation set length 1017
S training set length 13740
S testing set length 1717
S validation set length 1719
V training set length 20505
V testing set length 2563
V validation set length 2564
F training set length 6616
F testing set length 827
F validation set length 827
Q training set length 8308
Q testing set length 1038
Q validation set length 1040
2018-08-31 09:13:50.276231: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA
Start Training...Testing each 500 steps, Saving each 1000 steps
Iteration 1/100000:loss 1.610119
Accuracy=23.40%
Accuracy_N=75.28%
Accuracy_S=0.00%
Accuracy_V=45.79%
Accuracy_F=0.00%
Accuracy_Q=0.93%
Confusion Matrix:
[[67 0 21 1 0]
[83 0 14 1 0]
[49 0 49 8 1]
[28 0 70 0 1]
[72 1 29 4 1]]
Sensity_N=22.41%
Sensity_S=0.00%
Sensity_V=26.78%
Sensity_F=0.00%
Sensity_Q=33.33%
======================================
Iteration 100/100000:loss 0.585953
Iteration 200/100000:loss 0.398528
Iteration 300/100000:loss 0.374634
Iteration 400/100000:loss 0.399275
Iteration 500/100000:loss 0.283323
Accuracy=91.00%
Accuracy_N=91.30%
Accuracy_S=85.00%
Accuracy_V=92.93%
Accuracy_F=89.52%
Accuracy_Q=96.15%
Confusion Matrix:
[[ 84 3 0 2 3]
[ 10 85 1 1 3]
[ 5 0 92 2 0]
[ 5 1 3 94 2]
[ 2 0 1 1 100]]
Sensity_N=79.25%
Sensity_S=95.51%
Sensity_V=94.85%
Sensity_F=94.00%
Sensity_Q=92.59%
======================================
Iteration 600/100000:loss 0.301508
Iteration 700/100000:loss 0.267846
Iteration 800/100000:loss 0.244497
Iteration 900/100000:loss 0.226801
Iteration 1000/100000:loss 0.225553
Accuracy=93.40%
Accuracy_N=94.38%
Accuracy_S=90.38%
Accuracy_V=96.19%
Accuracy_F=87.25%
Accuracy_Q=99.00%
Confusion Matrix:
[[ 84 3 0 0 2]
[ 9 94 0 0 1]
[ 2 0 101 2 0]
[ 4 1 4 89 4]
[ 0 0 0 1 99]]
Sensity_N=84.85%
Sensity_S=95.92%
Sensity_V=96.19%
Sensity_F=96.74%
Sensity_Q=93.40%
======================================
Save model to path: /Users/languilin/Desktop/WorkSpacePrivate/GitHubGrayLand/ECG-Arrhythmia/TrainingModels/ModelC.ckpt
Iteration 1100/100000:loss 0.222098
Iteration 1200/100000:loss 0.168571
Iteration 1300/100000:loss 0.207118
Iteration 1400/100000:loss 0.177274
Iteration 1500/100000:loss 0.141902
Accuracy=93.40%
Accuracy_N=90.57%
Accuracy_S=95.15%
Accuracy_V=93.27%
Accuracy_F=89.13%
Accuracy_Q=98.95%
Confusion Matrix:
[[96 5 1 3 1]
[ 4 98 1 0 0]
[ 2 1 97 3 1]
[ 7 1 0 82 2]
[ 0 0 1 0 94]]
Sensity_N=88.07%
Sensity_S=93.33%
Sensity_V=97.00%
Sensity_F=93.18%
Sensity_Q=95.92%
======================================
Iteration 1600/100000:loss 0.170335
Iteration 1700/100000:loss 0.186575
Iteration 1800/100000:loss 0.135338
Iteration 1900/100000:loss 0.128350
Iteration 2000/100000:loss 0.110450
Accuracy=93.80%
Accuracy_N=93.81%
Accuracy_S=90.70%
Accuracy_V=91.75%
Accuracy_F=95.33%
Accuracy_Q=96.91%
Confusion Matrix:
[[106 6 1 0 0]
[ 7 78 1 0 0]
[ 1 0 89 7 0]
[ 2 2 1 102 0]
[ 0 2 1 0 94]]
Sensity_N=91.38%
Sensity_S=88.64%
Sensity_V=95.70%
Sensity_F=93.58%
Sensity_Q=100.00%
======================================
Save model to path: /Users/languilin/Desktop/WorkSpacePrivate/GitHubGrayLand/ECG-Arrhythmia/TrainingModels/ModelC.ckpt
Iteration 2100/100000:loss 0.114692
Iteration 2200/100000:loss 0.098530
Iteration 2300/100000:loss 0.071617
Iteration 2400/100000:loss 0.140422
Iteration 2500/100000:loss 0.063150
Accuracy=95.80%
Accuracy_N=97.32%
Accuracy_S=91.51%
Accuracy_V=94.90%
Accuracy_F=96.74%
Accuracy_Q=98.91%
Confusion Matrix:
[[109 2 0 1 0]
[ 7 97 1 1 0]
[ 0 1 93 3 1]
[ 2 0 1 89 0]
[ 0 0 1 0 91]]
Sensity_N=92.37%
Sensity_S=97.00%
Sensity_V=96.88%
Sensity_F=94.68%
Sensity_Q=98.91%
======================================
Iteration 2600/100000:loss 0.089410
Iteration 2700/100000:loss 0.067864
Iteration 2800/100000:loss 0.121898
Iteration 2900/100000:loss 0.141311
Iteration 3000/100000:loss 0.089876
Accuracy=96.40%
Accuracy_N=97.12%
Accuracy_S=93.48%
Accuracy_V=95.24%
Accuracy_F=97.85%
Accuracy_Q=98.11%
Confusion Matrix:
[[101 1 0 1 1]
[ 5 86 1 0 0]
[ 1 1 100 2 1]
[ 2 0 0 91 0]
[ 0 0 2 0 104]]
Sensity_N=92.66%
Sensity_S=97.73%
Sensity_V=97.09%
Sensity_F=96.81%
Sensity_Q=98.11%
======================================
Save model to path: /Users/languilin/Desktop/WorkSpacePrivate/GitHubGrayLand/ECG-Arrhythmia/TrainingModels/ModelC.ckpt
Iteration 3100/100000:loss 0.075161
Iteration 3200/100000:loss 0.116702
Iteration 3300/100000:loss 0.083704
Iteration 3400/100000:loss 0.087697
Iteration 3500/100000:loss 0.088388
Accuracy=96.00%
Accuracy_N=99.03%
Accuracy_S=96.30%
Accuracy_V=93.27%
Accuracy_F=91.75%
Accuracy_Q=100.00%
Confusion Matrix:
[[102 0 1 0 0]
[ 3 104 0 1 0]
[ 3 0 97 4 0]
[ 4 1 3 89 0]
[ 0 0 0 0 88]]
Sensity_N=91.07%
Sensity_S=99.05%
Sensity_V=96.04%
Sensity_F=94.68%
Sensity_Q=100.00%
======================================
...
Iteration 8500/100000:loss 0.019590
Accuracy=98.40%
Accuracy_N=96.26%
Accuracy_S=98.02%
Accuracy_V=97.59%
Accuracy_F=100.00%
Accuracy_Q=100.00%
Confusion Matrix:
[[103 1 0 2 1]
[ 1 99 0 1 0]
[ 0 2 81 0 0]
[ 0 0 0 107 0]
[ 0 0 0 0 102]]
Sensity_N=99.04%
Sensity_S=97.06%
Sensity_V=100.00%
Sensity_F=97.27%
Sensity_Q=99.03%
======================================
...
Iteration 12000/100000:loss 0.032258
Accuracy=98.40%
Accuracy_N=96.15%
Accuracy_S=99.15%
Accuracy_V=97.92%
Accuracy_F=100.00%
Accuracy_Q=98.89%
Confusion Matrix:
[[100 2 1 0 1]
[ 1 117 0 0 0]
[ 0 0 94 1 1]
[ 0 0 0 92 0]
[ 1 0 0 0 89]]
Sensity_N=98.04%
Sensity_S=98.32%
Sensity_V=98.95%
Sensity_F=98.92%
Sensity_Q=97.80%
======================================
Save model to path: /Users/languilin/Desktop/WorkSpacePrivate/GitHubGrayLand/ECG-Arrhythmia/TrainingModels/ModelC.ckpt