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  1. name: "VGG_ILSVRC_16_layers"
  2. input: "data"
  3. input_dim: 10
  4. input_dim: 3
  5. input_dim: 224
  6. input_dim: 224
  7. layers {
  8. bottom: "data"
  9. top: "conv1_1"
  10. name: "conv1_1"
  11. type: CONVOLUTION
  12. convolution_param {
  13. num_output: 64
  14. pad: 1
  15. kernel_size: 3
  16. }
  17. }
  18. layers {
  19. bottom: "conv1_1"
  20. top: "conv1_1"
  21. name: "relu1_1"
  22. type: RELU
  23. }
  24. layers {
  25. bottom: "conv1_1"
  26. top: "conv1_2"
  27. name: "conv1_2"
  28. type: CONVOLUTION
  29. convolution_param {
  30. num_output: 64
  31. pad: 1
  32. kernel_size: 3
  33. }
  34. }
  35. layers {
  36. bottom: "conv1_2"
  37. top: "conv1_2"
  38. name: "relu1_2"
  39. type: RELU
  40. }
  41. layers {
  42. bottom: "conv1_2"
  43. top: "pool1"
  44. name: "pool1"
  45. type: POOLING
  46. pooling_param {
  47. pool: MAX
  48. kernel_size: 2
  49. stride: 2
  50. }
  51. }
  52. layers {
  53. bottom: "pool1"
  54. top: "conv2_1"
  55. name: "conv2_1"
  56. type: CONVOLUTION
  57. convolution_param {
  58. num_output: 128
  59. pad: 1
  60. kernel_size: 3
  61. }
  62. }
  63. layers {
  64. bottom: "conv2_1"
  65. top: "conv2_1"
  66. name: "relu2_1"
  67. type: RELU
  68. }
  69. layers {
  70. bottom: "conv2_1"
  71. top: "conv2_2"
  72. name: "conv2_2"
  73. type: CONVOLUTION
  74. convolution_param {
  75. num_output: 128
  76. pad: 1
  77. kernel_size: 3
  78. }
  79. }
  80. layers {
  81. bottom: "conv2_2"
  82. top: "conv2_2"
  83. name: "relu2_2"
  84. type: RELU
  85. }
  86. layers {
  87. bottom: "conv2_2"
  88. top: "pool2"
  89. name: "pool2"
  90. type: POOLING
  91. pooling_param {
  92. pool: MAX
  93. kernel_size: 2
  94. stride: 2
  95. }
  96. }
  97. layers {
  98. bottom: "pool2"
  99. top: "conv3_1"
  100. name: "conv3_1"
  101. type: CONVOLUTION
  102. convolution_param {
  103. num_output: 256
  104. pad: 1
  105. kernel_size: 3
  106. }
  107. }
  108. layers {
  109. bottom: "conv3_1"
  110. top: "conv3_1"
  111. name: "relu3_1"
  112. type: RELU
  113. }
  114. layers {
  115. bottom: "conv3_1"
  116. top: "conv3_2"
  117. name: "conv3_2"
  118. type: CONVOLUTION
  119. convolution_param {
  120. num_output: 256
  121. pad: 1
  122. kernel_size: 3
  123. }
  124. }
  125. layers {
  126. bottom: "conv3_2"
  127. top: "conv3_2"
  128. name: "relu3_2"
  129. type: RELU
  130. }
  131. layers {
  132. bottom: "conv3_2"
  133. top: "conv3_3"
  134. name: "conv3_3"
  135. type: CONVOLUTION
  136. convolution_param {
  137. num_output: 256
  138. pad: 1
  139. kernel_size: 3
  140. }
  141. }
  142. layers {
  143. bottom: "conv3_3"
  144. top: "conv3_3"
  145. name: "relu3_3"
  146. type: RELU
  147. }
  148. layers {
  149. bottom: "conv3_3"
  150. top: "pool3"
  151. name: "pool3"
  152. type: POOLING
  153. pooling_param {
  154. pool: MAX
  155. kernel_size: 2
  156. stride: 2
  157. }
  158. }
  159. layers {
  160. bottom: "pool3"
  161. top: "conv4_1"
  162. name: "conv4_1"
  163. type: CONVOLUTION
  164. convolution_param {
  165. num_output: 512
  166. pad: 1
  167. kernel_size: 3
  168. }
  169. }
  170. layers {
  171. bottom: "conv4_1"
  172. top: "conv4_1"
  173. name: "relu4_1"
  174. type: RELU
  175. }
  176. layers {
  177. bottom: "conv4_1"
  178. top: "conv4_2"
  179. name: "conv4_2"
  180. type: CONVOLUTION
  181. convolution_param {
  182. num_output: 512
  183. pad: 1
  184. kernel_size: 3
  185. }
  186. }
  187. layers {
  188. bottom: "conv4_2"
  189. top: "conv4_2"
  190. name: "relu4_2"
  191. type: RELU
  192. }
  193. layers {
  194. bottom: "conv4_2"
  195. top: "conv4_3"
  196. name: "conv4_3"
  197. type: CONVOLUTION
  198. convolution_param {
  199. num_output: 512
  200. pad: 1
  201. kernel_size: 3
  202. }
  203. }
  204. layers {
  205. bottom: "conv4_3"
  206. top: "conv4_3"
  207. name: "relu4_3"
  208. type: RELU
  209. }
  210. layers {
  211. bottom: "conv4_3"
  212. top: "pool4"
  213. name: "pool4"
  214. type: POOLING
  215. pooling_param {
  216. pool: MAX
  217. kernel_size: 2
  218. stride: 2
  219. }
  220. }
  221. layers {
  222. bottom: "pool4"
  223. top: "conv5_1"
  224. name: "conv5_1"
  225. type: CONVOLUTION
  226. convolution_param {
  227. num_output: 512
  228. pad: 1
  229. kernel_size: 3
  230. }
  231. }
  232. layers {
  233. bottom: "conv5_1"
  234. top: "conv5_1"
  235. name: "relu5_1"
  236. type: RELU
  237. }
  238. layers {
  239. bottom: "conv5_1"
  240. top: "conv5_2"
  241. name: "conv5_2"
  242. type: CONVOLUTION
  243. convolution_param {
  244. num_output: 512
  245. pad: 1
  246. kernel_size: 3
  247. }
  248. }
  249. layers {
  250. bottom: "conv5_2"
  251. top: "conv5_2"
  252. name: "relu5_2"
  253. type: RELU
  254. }
  255. layers {
  256. bottom: "conv5_2"
  257. top: "conv5_3"
  258. name: "conv5_3"
  259. type: CONVOLUTION
  260. convolution_param {
  261. num_output: 512
  262. pad: 1
  263. kernel_size: 3
  264. }
  265. }
  266. layers {
  267. bottom: "conv5_3"
  268. top: "conv5_3"
  269. name: "relu5_3"
  270. type: RELU
  271. }
  272. layers {
  273. bottom: "conv5_3"
  274. top: "pool5"
  275. name: "pool5"
  276. type: POOLING
  277. pooling_param {
  278. pool: MAX
  279. kernel_size: 2
  280. stride: 2
  281. }
  282. }
  283. layers {
  284. bottom: "pool5"
  285. top: "fc6"
  286. name: "fc6"
  287. type: INNER_PRODUCT
  288. inner_product_param {
  289. num_output: 4096
  290. }
  291. }
  292. layers {
  293. bottom: "fc6"
  294. top: "fc6"
  295. name: "relu6"
  296. type: RELU
  297. }
  298. layers {
  299. bottom: "fc6"
  300. top: "fc6"
  301. name: "drop6"
  302. type: DROPOUT
  303. dropout_param {
  304. dropout_ratio: 0.5
  305. }
  306. }
  307. layers {
  308. bottom: "fc6"
  309. top: "fc7"
  310. name: "fc7"
  311. type: INNER_PRODUCT
  312. inner_product_param {
  313. num_output: 4096
  314. }
  315. }
  316. layers {
  317. bottom: "fc7"
  318. top: "fc7"
  319. name: "relu7"
  320. type: RELU
  321. }
  322. layers {
  323. bottom: "fc7"
  324. top: "fc7"
  325. name: "drop7"
  326. type: DROPOUT
  327. dropout_param {
  328. dropout_ratio: 0.5
  329. }
  330. }
  331. layers {
  332. bottom: "fc7"
  333. top: "fc8"
  334. name: "fc8"
  335. type: INNER_PRODUCT
  336. inner_product_param {
  337. num_output: 1000
  338. }
  339. }
  340. layers {
  341. bottom: "fc8"
  342. top: "prob"
  343. name: "prob"
  344. type: SOFTMAX
  345. }
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