---
# System prepended metadata

title: 深度學習

---

# 深度學習
# a)Method descriptions:
    架設cnn網路並預測手勢
# b)Source code explanations:
    在程式碼內
# c)實驗成果:
Epoch {1}
-------------------------------


Warning (from warnings module):
  File "C:\Users\TW-Recruit\Desktop\深度\reg_benjamin.py", line 65
    return self.logsoftmax(out)
UserWarning: Implicit dimension choice for log_softmax has been deprecated. Change the call to include dim=X as an argument.
loss: 0.065235 [    0/  540]
loss: 3.464860 [   30/  540]
loss: 5.801835 [   60/  540]
loss: 6.929997 [   90/  540]
loss: 8.269307 [  120/  540]
loss: 0.029482 [  150/  540]
loss: 0.027458 [  180/  540]
loss: 2.933991 [  210/  540]
loss: 0.101763 [  240/  540]
loss: 2.714937 [  270/  540]
loss: 0.061495 [  300/  540]
loss: 5.872705 [  330/  540]
loss: 0.012855 [  360/  540]
loss: 2.500899 [  390/  540]
loss: 6.737005 [  420/  540]
loss: 5.976598 [  450/  540]
loss: 3.504619 [  480/  540]
loss: 7.985864 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {2}
-------------------------------

loss: 4.753898 [    0/  540]
loss: 0.018671 [   30/  540]
loss: 3.410061 [   60/  540]
loss: 0.035747 [   90/  540]
loss: 0.047177 [  120/  540]
loss: 0.025933 [  150/  540]
loss: 0.111312 [  180/  540]
loss: 3.929190 [  210/  540]
loss: 5.559365 [  240/  540]
loss: 2.515308 [  270/  540]
loss: 3.223219 [  300/  540]
loss: 3.876003 [  330/  540]
loss: 3.214012 [  360/  540]
loss: 4.303778 [  390/  540]
loss: 3.509511 [  420/  540]
loss: 0.029173 [  450/  540]
loss: 7.044427 [  480/  540]
loss: 3.585757 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {3}
-------------------------------

loss: 0.024747 [    0/  540]
loss: 5.028611 [   30/  540]
loss: 0.021400 [   60/  540]
loss: 5.498536 [   90/  540]
loss: 3.509511 [  120/  540]
loss: 0.007420 [  150/  540]
loss: 5.240959 [  180/  540]
loss: 2.243244 [  210/  540]
loss: 2.651394 [  240/  540]
loss: 5.364343 [  270/  540]
loss: 4.068261 [  300/  540]
loss: 6.352154 [  330/  540]
loss: 4.598450 [  360/  540]
loss: 0.008729 [  390/  540]
loss: 4.538314 [  420/  540]
loss: 0.049507 [  450/  540]
loss: 4.117374 [  480/  540]
loss: 5.559365 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {4}
-------------------------------

loss: 3.225171 [    0/  540]
loss: 4.171563 [   30/  540]
loss: 0.027249 [   60/  540]
loss: 2.741087 [   90/  540]
loss: 5.107327 [  120/  540]
loss: 4.053771 [  150/  540]
loss: 0.085870 [  180/  540]
loss: 0.019637 [  210/  540]
loss: 3.394980 [  240/  540]
loss: 3.182096 [  270/  540]
loss: 0.030831 [  300/  540]
loss: 5.398232 [  330/  540]
loss: 0.048474 [  360/  540]
loss: 0.025428 [  390/  540]
loss: 4.476646 [  420/  540]
loss: 5.801835 [  450/  540]
loss: 0.030398 [  480/  540]
loss: 0.024315 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {5}
-------------------------------

loss: 5.471781 [    0/  540]
loss: 6.473519 [   30/  540]
loss: 0.038137 [   60/  540]
loss: 2.714937 [   90/  540]
loss: 2.441057 [  120/  540]
loss: 2.658318 [  150/  540]
loss: 3.598749 [  180/  540]
loss: 5.231395 [  210/  540]
loss: 0.076402 [  240/  540]
loss: 4.265241 [  270/  540]
loss: 2.676218 [  300/  540]
loss: 3.642813 [  330/  540]
loss: 6.154784 [  360/  540]
loss: 0.028266 [  390/  540]
loss: 0.026811 [  420/  540]
loss: 6.068089 [  450/  540]
loss: 0.042659 [  480/  540]
loss: 0.059371 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {6}
-------------------------------

loss: 0.036411 [    0/  540]
loss: 3.888846 [   30/  540]
loss: 3.585757 [   60/  540]
loss: 5.112121 [   90/  540]
loss: 3.980577 [  120/  540]
loss: 5.711401 [  150/  540]
loss: 0.089743 [  180/  540]
loss: 4.249712 [  210/  540]
loss: 0.024166 [  240/  540]
loss: 0.028800 [  270/  540]
loss: 0.020797 [  300/  540]
loss: 0.054998 [  330/  540]
loss: 4.393620 [  360/  540]
loss: 4.587889 [  390/  540]
loss: 0.072655 [  420/  540]
loss: 8.086723 [  450/  540]
loss: 0.030410 [  480/  540]
loss: 3.054413 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {7}
-------------------------------

loss: 0.054998 [    0/  540]
loss: 2.606077 [   30/  540]
loss: 5.646023 [   60/  540]
loss: 4.988642 [   90/  540]
loss: 0.128827 [  120/  540]
loss: 0.039510 [  150/  540]
loss: 0.072655 [  180/  540]
loss: 5.276203 [  210/  540]
loss: 2.894662 [  240/  540]
loss: 4.393620 [  270/  540]
loss: 3.238527 [  300/  540]
loss: 4.781704 [  330/  540]
loss: 4.825458 [  360/  540]
loss: 0.010187 [  390/  540]
loss: 2.737911 [  420/  540]
loss: 5.169029 [  450/  540]
loss: 0.030363 [  480/  540]
loss: 4.576992 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {8}
-------------------------------

loss: 2.916328 [    0/  540]
loss: 3.882244 [   30/  540]
loss: 0.057494 [   60/  540]
loss: 3.049276 [   90/  540]
loss: 0.004427 [  120/  540]
loss: 0.059371 [  150/  540]
loss: 2.646603 [  180/  540]
loss: 2.645606 [  210/  540]
loss: 4.145267 [  240/  540]
loss: 6.411925 [  270/  540]
loss: 0.094388 [  300/  540]
loss: 0.013396 [  330/  540]
loss: 6.015685 [  360/  540]
loss: 3.841204 [  390/  540]
loss: 2.720193 [  420/  540]
loss: 5.908076 [  450/  540]
loss: 3.238726 [  480/  540]
loss: 3.039895 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {9}
-------------------------------

loss: 0.021725 [    0/  540]
loss: 3.692283 [   30/  540]
loss: 4.298891 [   60/  540]
loss: 4.587889 [   90/  540]
loss: 2.676075 [  120/  540]
loss: 3.858973 [  150/  540]
loss: 5.422315 [  180/  540]
loss: 4.377876 [  210/  540]
loss: 5.509836 [  240/  540]
loss: 3.464860 [  270/  540]
loss: 0.037129 [  300/  540]
loss: 3.509511 [  330/  540]
loss: 0.012486 [  360/  540]
loss: 0.019883 [  390/  540]
loss: 0.060805 [  420/  540]
loss: 0.131757 [  450/  540]
loss: 5.028611 [  480/  540]
loss: 2.916328 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Epoch {10}
-------------------------------

loss: 0.023889 [    0/  540]
loss: 3.642813 [   30/  540]
loss: 4.305746 [   60/  540]
loss: 2.803155 [   90/  540]
loss: 6.737005 [  120/  540]
loss: 0.019637 [  150/  540]
loss: 0.056732 [  180/  540]
loss: 5.102562 [  210/  540]
loss: 0.010071 [  240/  540]
loss: 0.038178 [  270/  540]
loss: 2.954634 [  300/  540]
loss: 0.034217 [  330/  540]
loss: 5.945993 [  360/  540]
loss: 0.042593 [  390/  540]
loss: 4.317887 [  420/  540]
loss: 6.785609 [  450/  540]
loss: 2.676075 [  480/  540]
loss: 5.811920 [  510/  540]
Accuracy: 33.3%, Avg loss: 3.201234 

Done!
# d)Discussions on the results:
這次作業做的很失敗，很多部分都需要學習與debug，但是沒有把握好時間努力製作，最後也沒能做出來accuracy超過35%以上的成果
# e)Concluding remarks:
這次最主要遇到的問題在於資料讀取方式，解決方式是在跟同學討論與看老師的網站其他兩位先交作業的同學，才發現到的解決方法。也多看了好幾次老師的影片，讓CNN網路的結構更清晰的了解，希望下一個作業能夠成功完成。
