What does the training accuracy plot of my convolution neural network (CNN) show?

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Khadija Al Jabri
Khadija Al Jabri el 14 de Dic. de 2017
Editada: Khadija Al Jabri el 14 de Dic. de 2017
Hello everybody
the result of my CNN is shown in the picture attached. I'm wondering about the accuracy why it goes down and up during the training? is it normal or it should grow gradually? and what is the possible error that may I have on my net (or parameters)!! Additionally, whatever I change the training options; the test accuracy does not exceed 42% !!!
if true
Training on single CPU.
|=========================================================================================|
| Epoch | Iteration | Time Elapsed | Mini-batch | Mini-batch | Base Learning|
| | | (seconds) | Loss | Accuracy | Rate |
|=========================================================================================|
| 1 | 1 | 6.67 | 1.6277 | 0.00% | 1.00e-04 |
| 1 | 20 | 128.30 | 1.6677 | 25.00% | 1.00e-04 |
| 1 | 40 | 253.77 | 1.6505 | 50.00% | 1.00e-04 |
| 1 | 60 | 381.41 | 0.9331 | 87.50% | 1.00e-04 |
| 1 | 80 | 505.31 | 1.0754 | 25.00% | 1.00e-04 |
| 2 | 100 | 629.83 | 1.6579 | 12.50% | 1.00e-04 |
| 2 | 120 | 758.84 | 1.3724 | 62.50% | 1.00e-04 |
| 2 | 140 | 884.09 | 1.1539 | 50.00% | 1.00e-04 |
| 2 | 160 | 1028.53 | 1.1311 | 37.50% | 1.00e-04 |
| 2 | 180 | 1154.14 | 1.4353 | 37.50% | 1.00e-04 |
| 3 | 200 | 1277.55 | 0.9360 | 50.00% | 1.00e-04 |
| 3 | 220 | 1401.44 | 0.9559 | 50.00% | 1.00e-04 |
| 3 | 240 | 1525.49 | 1.6097 | 25.00% | 1.00e-04 |
| 3 | 260 | 1649.96 | 0.9116 | 62.50% | 1.00e-04 |
| 3 | 280 | 1774.19 | 1.0897 | 37.50% | 1.00e-04 |
| 4 | 300 | 1898.34 | 1.4818 | 12.50% | 1.00e-04 |
| 4 | 320 | 2022.42 | 1.1853 | 50.00% | 1.00e-04 |
| 4 | 340 | 2146.87 | 0.9665 | 62.50% | 1.00e-04 |
| 4 | 360 | 2272.24 | 1.1143 | 37.50% | 1.00e-04 |
| 4 | 380 | 2396.43 | 1.1264 | 37.50% | 1.00e-04 |
| 5 | 400 | 2522.21 | 1.5471 | 50.00% | 1.00e-04 |
| 5 | 420 | 2646.45 | 1.3815 | 50.00% | 1.00e-04 |
| 5 | 440 | 2776.98 | 0.7213 | 87.50% | 1.00e-04 |
| 5 | 460 | 2906.50 | 0.8455 | 87.50% | 1.00e-04 |
| 6 | 480 | 3033.40 | 1.7557 | 12.50% | 1.00e-04 |
| 6 | 500 | 3159.12 | 1.1510 | 50.00% | 1.00e-04 |
| 6 | 520 | 3290.33 | 1.0716 | 62.50% | 1.00e-04 |
| 6 | 540 | 3419.24 | 1.2187 | 37.50% | 1.00e-04 |
| 6 | 560 | 3545.82 | 1.3443 | 37.50% | 1.00e-04 |
| 7 | 580 | 3671.92 | 0.9136 | 50.00% | 1.00e-04 |
| 7 | 600 | 3796.45 | 0.8985 | 62.50% | 1.00e-04 |
| 7 | 620 | 3920.45 | 1.4416 | 37.50% | 1.00e-04 |
| 7 | 640 | 4051.54 | 0.9950 | 75.00% | 1.00e-04 |
| 7 | 660 | 4191.68 | 0.8132 | 75.00% | 1.00e-04 |
| 8 | 680 | 4328.36 | 1.3569 | 25.00% | 1.00e-04 |
| 8 | 700 | 4463.55 | 1.1009 | 50.00% | 1.00e-04 |
| 8 | 720 | 4593.56 | 1.0073 | 62.50% | 1.00e-04 |
| 8 | 740 | 4718.89 | 1.0589 | 50.00% | 1.00e-04 |
| 8 | 760 | 4843.50 | 0.9829 | 50.00% | 1.00e-04 |
| 9 | 780 | 4965.23 | 1.2858 | 62.50% | 1.00e-04 |
| 9 | 800 | 5086.95 | 1.4522 | 50.00% | 1.00e-04 |
| 9 | 820 | 5207.89 | 0.4955 | 100.00% | 1.00e-04 |
| 9 | 840 | 5328.95 | 0.7283 | 100.00% | 1.00e-04 |
| 10 | 860 | 5450.18 | 1.6487 | 37.50% | 1.00e-04 |
| 10 | 880 | 5570.79 | 0.8402 | 75.00% | 1.00e-04 |
| 10 | 900 | 5692.05 | 0.8969 | 62.50% | 1.00e-04 |
| 10 | 920 | 5812.29 | 1.1199 | 37.50% | 1.00e-04 |
| 10 | 940 | 5932.70 | 1.0859 | 50.00% | 1.00e-04 |
| 11 | 960 | 6053.34 | 0.7106 | 62.50% | 1.00e-04 |
| 11 | 980 | 6173.80 | 0.8470 | 50.00% | 1.00e-04 |
| 11 | 1000 | 6295.36 | 1.3543 | 25.00% | 1.00e-04 |
| 11 | 1020 | 6415.40 | 1.0594 | 50.00% | 1.00e-04 |
| 11 | 1040 | 6537.31 | 0.4968 | 75.00% | 1.00e-04 |
| 12 | 1060 | 6659.25 | 1.0452 | 50.00% | 1.00e-04 |
| 12 | 1080 | 6780.46 | 0.8746 | 62.50% | 1.00e-04 |
| 12 | 1100 | 6900.97 | 1.1169 | 50.00% | 1.00e-04 |
| 12 | 1120 | 7022.03 | 0.9600 | 50.00% | 1.00e-04 |
| 12 | 1140 | 7144.63 | 0.8063 | 50.00% | 1.00e-04 |
| 13 | 1160 | 7266.01 | 1.0481 | 75.00% | 1.00e-04 |
| 13 | 1180 | 7385.75 | 1.3504 | 50.00% | 1.00e-04 |
| 13 | 1200 | 7505.62 | 0.3157 | 100.00% | 1.00e-04 |
| 13 | 1220 | 7627.16 | 0.6529 | 87.50% | 1.00e-04 |
| 14 | 1240 | 7749.26 | 1.1844 | 62.50% | 1.00e-04 |
| 14 | 1260 | 7874.78 | 0.6447 | 75.00% | 1.00e-04 |
| 14 | 1280 | 7994.68 | 0.7824 | 62.50% | 1.00e-04 |
| 14 | 1300 | 8114.98 | 0.9300 | 62.50% | 1.00e-04 |
| 14 | 1320 | 8237.20 | 0.8984 | 62.50% | 1.00e-04 |
| 15 | 1340 | 8359.44 | 0.4070 | 75.00% | 1.00e-04 |
| 15 | 1360 | 8481.32 | 1.0424 | 62.50% | 1.00e-04 |
| 15 | 1380 | 8601.45 | 0.8956 | 50.00% | 1.00e-04 |
| 15 | 1400 | 8722.41 | 0.9647 | 62.50% | 1.00e-04 |
| 15 | 1420 | 8844.57 | 0.2415 | 100.00% | 1.00e-04 |
| 15 | 1425 | 8874.94 | 0.6794 | 62.50% | 1.00e-04 |
|=========================================================================================|
accuracy =
0.3787
end

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