我想在python中實現卷積神經網絡。架構如下:
INPUT->[Convolution->Sigmoid->Pooling]->[Convolution->Sigmoid->Pooling]->Fully Connected Layer-> Hidden Layer->Ouput
。
輸入形狀:28個* 28個
過濾器/加權形狀,用於卷積層1:20 * 1 * 5 * 5
過濾器/加權形狀,用於卷積二層:40 * 20 * 5 * 5
激活函數:乙狀結腸( 1 /(1 + e^-x))神經網絡與乙狀結腸激活產生所有1's
由於過濾器/砝碼的大形狀,在COnvolutional Layer 2中應用點積時,得到的輸出值接近20或更高,隨後導致sigmoid激活函數值後的輸出全部爲1。
輸出的卷積層1:在卷積二層
[ 0.75810452 0.79819809 0.70897314 0.50897858 0.02901152 0.98447587
0.99995668 0.99999814 0.99912627 0.7885211 0.87708188 0.76611807]
...
...
輸出:
上convlayer2應用乙狀結腸之後[ 19.88641441 20.11005634 20.04984707 20.19106394 19.93096274
20.1585536 19.84757161 19.79030395]
...
...
輸出:
[ 1. 1. 1. 1. 1. 1. 1. 1.]
...
...
[ 1. 1. 1. 0.99999 1. 1. 1. 1.]
我發現這個類似的問題論壇:Neural Network sigmoid function。我沒有犯下蒂姆答案中指出的錯誤。 但我無法弄清楚的是:
最後,即使有這些變化,與所有正權仍然可能會產生全1的輸出完全連接的神經網絡。您可以包含與抑制節點相對應的負權重,或顯着減少連通性(例如,第n層中的節點連接到第n + 1層中的節點的概率爲0.1)。
在convlayer2上應用sigmoid後,我應該正常化輸出嗎?或嘗試其他的東西?
編輯: 輸入數據:
[[ 3. 0. 0. 3. 7. 3. 0. 3. 0. 11. 0. 0.
3. 0. 0. 3. 8. 0. 0. 3. 0. 0. 0. 2.
0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 1. 5. 0. 12. 0.
16. 0. 0. 4. 0. 2. 8. 3. 0. 4. 8. 0.
0. 0. 0. 0.]
[ 0. 0. 2. 0. 0. 0. 1. 2. 1. 12. 0. 8.
0. 0. 6. 0. 11. 0. 0. 6. 7. 2. 0. 0.
0. 0. 0. 0.]
[ 0. 1. 3. 0. 0. 2. 3. 0. 0. 0. 12. 0.
0. 23. 0. 0. 0. 0. 11. 3. 0. 0. 4. 0.
0. 0. 0. 0.]
[ 0. 1. 1. 0. 0. 2. 0. 0. 6. 0. 25. 27.
136. 135. 188. 89. 84. 25. 0. 0. 3. 1. 0. 0.
0. 0. 0. 0.]
[ 4. 0. 0. 0. 0. 0. 0. 0. 3. 88. 247. 236.
255. 249. 250. 227. 240. 136. 37. 1. 0. 2. 2. 0.
0. 0. 0. 0.]
[ 2. 0. 0. 3. 0. 0. 4. 27. 193. 251. 253. 255.
255. 255. 255. 240. 254. 255. 213. 89. 0. 0. 14. 1.
0. 0. 0. 0.]
[ 0. 0. 0. 6. 0. 0. 18. 56. 246. 255. 253. 243.
251. 255. 245. 255. 255. 254. 255. 231. 119. 7. 0. 5.
0. 0. 0. 0.]
[ 4. 0. 0. 12. 13. 0. 65. 190. 246. 255. 255. 251.
255. 109. 88. 199. 255. 247. 250. 255. 234. 92. 0. 0.
0. 0. 0. 0.]
[ 0. 10. 1. 0. 0. 18. 163. 248. 255. 235. 216. 150.
128. 45. 6. 8. 22. 212. 255. 255. 252. 172. 0. 15.
0. 0. 0. 0.]
[ 0. 1. 4. 5. 0. 0. 187. 255. 254. 94. 57. 7.
1. 0. 6. 0. 0. 139. 242. 255. 255. 218. 62. 0.
0. 0. 0. 0.]
[ 5. 2. 0. 0. 11. 56. 252. 235. 253. 20. 5. 2.
5. 1. 0. 1. 2. 0. 97. 249. 248. 249. 166. 8.
0. 0. 0. 0.]
[ 0. 0. 2. 0. 0. 70. 255. 255. 245. 25. 10. 0.
0. 1. 0. 4. 10. 0. 10. 255. 246. 250. 155. 0.
0. 0. 0. 0.]
[ 2. 0. 7. 12. 0. 87. 226. 255. 184. 0. 3. 0.
10. 5. 0. 0. 0. 9. 0. 183. 251. 255. 222. 15.
0. 0. 0. 0.]
[ 0. 5. 1. 0. 19. 230. 255. 243. 255. 35. 2. 0.
0. 0. 0. 9. 8. 0. 0. 70. 245. 242. 255. 14.
0. 0. 0. 0.]
[ 0. 4. 3. 0. 19. 251. 239. 255. 247. 30. 1. 0.
4. 4. 14. 0. 0. 2. 0. 47. 255. 255. 247. 21.
0. 0. 0. 0.]
[ 6. 0. 2. 2. 0. 173. 247. 252. 250. 28. 10. 0.
0. 8. 0. 0. 0. 8. 0. 67. 249. 255. 255. 12.
0. 0. 0. 0.]
[ 0. 0. 6. 3. 0. 88. 255. 251. 255. 188. 21. 0.
15. 0. 8. 2. 16. 0. 35. 200. 247. 251. 134. 4.
0. 0. 0. 0.]
[ 0. 3. 3. 1. 0. 11. 211. 247. 249. 255. 189. 76.
0. 0. 4. 0. 2. 0. 169. 255. 255. 247. 47. 0.
0. 0. 0. 0.]
[ 0. 6. 0. 0. 2. 0. 59. 205. 255. 240. 255. 182.
41. 56. 28. 33. 42. 239. 246. 251. 238. 157. 0. 1.
0. 0. 0. 0.]
[ 2. 1. 0. 0. 2. 10. 0. 104. 239. 255. 240. 255.
253. 247. 237. 255. 255. 250. 255. 239. 255. 100. 0. 1.
0. 0. 0. 0.]
[ 1. 0. 3. 0. 0. 7. 0. 4. 114. 255. 255. 255.
255. 247. 249. 253. 251. 254. 237. 251. 89. 0. 0. 1.
0. 0. 0. 0.]
[ 0. 0. 9. 0. 0. 1. 13. 0. 14. 167. 255. 246.
253. 255. 255. 254. 242. 255. 244. 61. 0. 19. 0. 1.
0. 0. 0. 0.]
[ 2. 1. 7. 0. 0. 4. 0. 14. 0. 27. 61. 143.
255. 255. 252. 255. 149. 21. 6. 16. 0. 0. 7. 0.
0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.]
[ 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.
0. 0. 0. 0.]]
權重convlayer 1:
[[[-0.01216923 -0.00584966 0.04876327 0.04628595 0.05644253]
[-0.03813031 -0.0304277 0.05728934 -0.01358741 -0.02875361]
[ 0.04929296 0.05958448 0.05497736 0.04699187 -0.04964543]
[ 0.01874465 0.05793848 0.03988833 -0.02355133 -0.05672331]
[ 0.03986748 -0.06098319 0.01299825 -0.00239702 -0.01750711]]]
[[[-0.02474246 0.0423619 -0.02130952 0.00718671 0.02677802]
[ 0.04151089 0.04336411 -0.03549197 -0.01935773 0.04035303]
[ 0.01466489 -0.01117737 0.0081063 0.01310948 0.01900553]
[-0.01723775 0.0148552 -0.03563556 -0.04108806 0.01764391]
[ 0.03932499 -0.00911049 0.00443425 -0.0388128 0.01646769]]
...........
...........
砝碼在convlayer 2:
[[-0.02894977 -0.00163836 0.0416469 -0.00195158 0.03194728]
[ 0.02618844 -0.00961595 -0.03348994 0.04460359 0.03113144]
[ 0.04166139 -0.02487885 0.02173471 -0.00147136 0.00803713]
[ 0.02262536 -0.03310476 -0.00949261 -0.0450313 0.03128755]
[-0.01181284 0.00558957 -0.02410718 0.01706195 0.01151338]]
[[ 0.04118888 -0.01306432 -0.01013332 0.03423443 0.03135569]
[ 0.00471491 0.02169717 0.00583819 -0.02421325 -0.01708062]
[-0.01244262 -0.00934037 0.00605259 -0.03825137 -0.00606101]
[-0.01699741 0.01311037 0.0307442 0.04153474 -0.00470464]
[-0.02592571 -0.01203504 0.04052782 0.03150989 0.02740532]]
.........
.........
的權重使用澤維爾初始化初始化:
n_in=28*28
n_out = 24*24
w_bound = numpy.sqrt(6./float(n_in+n_out))
filters = numpy.random.uniform(-w_bound,w_bound,(40,20, 5,5))
這是之前還是之後的一些訓練?你如何初始化網絡?你的數據是什麼樣子的,是正常化的,是正面的還是負面的? – Andnp
@Andnp訓練前;這發生在第一次迭代之後;我使用Xavier初始化初始化權重;輸入數據是圖像轉換成像素,範圍[0,255];不包含負值並且不被標準化。我應該將輸入標準化爲[-1,1] - 爲什麼?我已經編輯了需要的細節問題。 – uttejh