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  1. name: "asl-resnet-depth3-width46-cifar10"
  2. layer {
  3. name: "data"
  4. type: "Data"
  5. top: "data"
  6. top: "label"
  7. include {
  8. phase: TRAIN
  9. }
  10. transform_param {
  11. mirror: true
  12. crop_size: 32
  13. mean_value: 0.0
  14. }
  15. data_param {
  16. source: "data_result/train"
  17. batch_size: 128
  18. backend: LMDB
  19. }
  20. }
  21. layer {
  22. name: "data"
  23. type: "Data"
  24. top: "data"
  25. top: "label"
  26. include {
  27. phase: TEST
  28. }
  29. transform_param {
  30. mean_value: 0.0
  31. }
  32. data_param {
  33. source: "data_result/test"
  34. batch_size: 100
  35. backend: LMDB
  36. }
  37. }
  38. layer {
  39. name: "first_conv"
  40. type: "Convolution"
  41. bottom: "data"
  42. top: "first_conv"
  43. param {
  44. lr_mult: 1.0
  45. decay_mult: 1.0
  46. }
  47. convolution_param {
  48. num_output: 46
  49. bias_term: false
  50. pad: 1
  51. kernel_size: 3
  52. group: 1
  53. stride: 1
  54. weight_filler {
  55. type: "msra"
  56. }
  57. }
  58. }
  59. layer {
  60. name: "group0_block0_bn0"
  61. type: "BatchNorm"
  62. bottom: "first_conv"
  63. top: "group0_block0_bn0"
  64. param {
  65. lr_mult: 0.0
  66. decay_mult: 0.0
  67. }
  68. param {
  69. lr_mult: 0.0
  70. decay_mult: 0.0
  71. }
  72. param {
  73. lr_mult: 0.0
  74. decay_mult: 0.0
  75. }
  76. batch_norm_param {
  77. moving_average_fraction: 0.8999999761581421
  78. }
  79. }
  80. layer {
  81. name: "group0_block0_scale0"
  82. type: "Scale"
  83. bottom: "group0_block0_bn0"
  84. top: "group0_block0_bn0"
  85. param {
  86. lr_mult: 1.0
  87. decay_mult: 1.0
  88. }
  89. param {
  90. lr_mult: 1.0
  91. decay_mult: 0.0
  92. }
  93. scale_param {
  94. bias_term: true
  95. }
  96. }
  97. layer {
  98. name: "group0_block0_relu0"
  99. type: "ReLU"
  100. bottom: "group0_block0_bn0"
  101. top: "group0_block0_bn0"
  102. }
  103. layer {
  104. name: "group0_block0_conv0"
  105. type: "Convolution"
  106. bottom: "group0_block0_bn0"
  107. top: "group0_block0_conv0"
  108. param {
  109. lr_mult: 1.0
  110. decay_mult: 1.0
  111. }
  112. convolution_param {
  113. num_output: 46
  114. bias_term: false
  115. pad: 0
  116. kernel_size: 1
  117. group: 1
  118. stride: 1
  119. weight_filler {
  120. type: "msra"
  121. }
  122. }
  123. }
  124. layer {
  125. name: "group0_block0_bn1"
  126. type: "BatchNorm"
  127. bottom: "group0_block0_conv0"
  128. top: "group0_block0_conv0"
  129. param {
  130. lr_mult: 0.0
  131. decay_mult: 0.0
  132. }
  133. param {
  134. lr_mult: 0.0
  135. decay_mult: 0.0
  136. }
  137. param {
  138. lr_mult: 0.0
  139. decay_mult: 0.0
  140. }
  141. batch_norm_param {
  142. moving_average_fraction: 0.8999999761581421
  143. }
  144. }
  145. layer {
  146. name: "group0_block0_scale1"
  147. type: "Scale"
  148. bottom: "group0_block0_conv0"
  149. top: "group0_block0_conv0"
  150. param {
  151. lr_mult: 1.0
  152. decay_mult: 1.0
  153. }
  154. param {
  155. lr_mult: 1.0
  156. decay_mult: 0.0
  157. }
  158. scale_param {
  159. bias_term: true
  160. }
  161. }
  162. layer {
  163. name: "group0_block0_relu1"
  164. type: "ReLU"
  165. bottom: "group0_block0_conv0"
  166. top: "group0_block0_conv0"
  167. }
  168. layer {
  169. name: "group0_block0_asl"
  170. type: "ActiveShift"
  171. bottom: "group0_block0_conv0"
  172. top: "group0_block0_asl"
  173. param {
  174. lr_mult: 1.0
  175. decay_mult: 0.0
  176. }
  177. asl_param {
  178. pad: 0
  179. stride: 1
  180. shift_filler {
  181. type: "uniform"
  182. min: -1.0
  183. max: 1.0
  184. }
  185. normalize: true
  186. }
  187. }
  188. layer {
  189. name: "group0_block0_conv1"
  190. type: "Convolution"
  191. bottom: "group0_block0_asl"
  192. top: "group0_block0_conv1"
  193. param {
  194. lr_mult: 1.0
  195. decay_mult: 1.0
  196. }
  197. convolution_param {
  198. num_output: 46
  199. bias_term: false
  200. pad: 0
  201. kernel_size: 1
  202. group: 1
  203. stride: 1
  204. weight_filler {
  205. type: "msra"
  206. }
  207. }
  208. }
  209. layer {
  210. name: "group0_block0_sum"
  211. type: "Eltwise"
  212. bottom: "first_conv"
  213. bottom: "group0_block0_conv1"
  214. top: "group0_block0_sum"
  215. eltwise_param {
  216. operation: SUM
  217. }
  218. }
  219. layer {
  220. name: "group0_block1_bn0"
  221. type: "BatchNorm"
  222. bottom: "group0_block0_sum"
  223. top: "group0_block1_bn0"
  224. param {
  225. lr_mult: 0.0
  226. decay_mult: 0.0
  227. }
  228. param {
  229. lr_mult: 0.0
  230. decay_mult: 0.0
  231. }
  232. param {
  233. lr_mult: 0.0
  234. decay_mult: 0.0
  235. }
  236. batch_norm_param {
  237. moving_average_fraction: 0.8999999761581421
  238. }
  239. }
  240. layer {
  241. name: "group0_block1_scale0"
  242. type: "Scale"
  243. bottom: "group0_block1_bn0"
  244. top: "group0_block1_bn0"
  245. param {
  246. lr_mult: 1.0
  247. decay_mult: 1.0
  248. }
  249. param {
  250. lr_mult: 1.0
  251. decay_mult: 0.0
  252. }
  253. scale_param {
  254. bias_term: true
  255. }
  256. }
  257. layer {
  258. name: "group0_block1_relu0"
  259. type: "ReLU"
  260. bottom: "group0_block1_bn0"
  261. top: "group0_block1_bn0"
  262. }
  263. layer {
  264. name: "group0_block1_conv0"
  265. type: "Convolution"
  266. bottom: "group0_block1_bn0"
  267. top: "group0_block1_conv0"
  268. param {
  269. lr_mult: 1.0
  270. decay_mult: 1.0
  271. }
  272. convolution_param {
  273. num_output: 46
  274. bias_term: false
  275. pad: 0
  276. kernel_size: 1
  277. group: 1
  278. stride: 1
  279. weight_filler {
  280. type: "msra"
  281. }
  282. }
  283. }
  284. layer {
  285. name: "group0_block1_bn1"
  286. type: "BatchNorm"
  287. bottom: "group0_block1_conv0"
  288. top: "group0_block1_conv0"
  289. param {
  290. lr_mult: 0.0
  291. decay_mult: 0.0
  292. }
  293. param {
  294. lr_mult: 0.0
  295. decay_mult: 0.0
  296. }
  297. param {
  298. lr_mult: 0.0
  299. decay_mult: 0.0
  300. }
  301. batch_norm_param {
  302. moving_average_fraction: 0.8999999761581421
  303. }
  304. }
  305. layer {
  306. name: "group0_block1_scale1"
  307. type: "Scale"
  308. bottom: "group0_block1_conv0"
  309. top: "group0_block1_conv0"
  310. param {
  311. lr_mult: 1.0
  312. decay_mult: 1.0
  313. }
  314. param {
  315. lr_mult: 1.0
  316. decay_mult: 0.0
  317. }
  318. scale_param {
  319. bias_term: true
  320. }
  321. }
  322. layer {
  323. name: "group0_block1_relu1"
  324. type: "ReLU"
  325. bottom: "group0_block1_conv0"
  326. top: "group0_block1_conv0"
  327. }
  328. layer {
  329. name: "group0_block1_asl"
  330. type: "ActiveShift"
  331. bottom: "group0_block1_conv0"
  332. top: "group0_block1_asl"
  333. param {
  334. lr_mult: 1.0
  335. decay_mult: 0.0
  336. }
  337. asl_param {
  338. pad: 0
  339. stride: 1
  340. shift_filler {
  341. type: "uniform"
  342. min: -1.0
  343. max: 1.0
  344. }
  345. normalize: true
  346. }
  347. }
  348. layer {
  349. name: "group0_block1_conv1"
  350. type: "Convolution"
  351. bottom: "group0_block1_asl"
  352. top: "group0_block1_conv1"
  353. param {
  354. lr_mult: 1.0
  355. decay_mult: 1.0
  356. }
  357. convolution_param {
  358. num_output: 46
  359. bias_term: false
  360. pad: 0
  361. kernel_size: 1
  362. group: 1
  363. stride: 1
  364. weight_filler {
  365. type: "msra"
  366. }
  367. }
  368. }
  369. layer {
  370. name: "group0_block1_sum"
  371. type: "Eltwise"
  372. bottom: "group0_block0_sum"
  373. bottom: "group0_block1_conv1"
  374. top: "group0_block1_sum"
  375. eltwise_param {
  376. operation: SUM
  377. }
  378. }
  379. layer {
  380. name: "group0_block2_bn0"
  381. type: "BatchNorm"
  382. bottom: "group0_block1_sum"
  383. top: "group0_block2_bn0"
  384. param {
  385. lr_mult: 0.0
  386. decay_mult: 0.0
  387. }
  388. param {
  389. lr_mult: 0.0
  390. decay_mult: 0.0
  391. }
  392. param {
  393. lr_mult: 0.0
  394. decay_mult: 0.0
  395. }
  396. batch_norm_param {
  397. moving_average_fraction: 0.8999999761581421
  398. }
  399. }
  400. layer {
  401. name: "group0_block2_scale0"
  402. type: "Scale"
  403. bottom: "group0_block2_bn0"
  404. top: "group0_block2_bn0"
  405. param {
  406. lr_mult: 1.0
  407. decay_mult: 1.0
  408. }
  409. param {
  410. lr_mult: 1.0
  411. decay_mult: 0.0
  412. }
  413. scale_param {
  414. bias_term: true
  415. }
  416. }
  417. layer {
  418. name: "group0_block2_relu0"
  419. type: "ReLU"
  420. bottom: "group0_block2_bn0"
  421. top: "group0_block2_bn0"
  422. }
  423. layer {
  424. name: "group0_block2_conv0"
  425. type: "Convolution"
  426. bottom: "group0_block2_bn0"
  427. top: "group0_block2_conv0"
  428. param {
  429. lr_mult: 1.0
  430. decay_mult: 1.0
  431. }
  432. convolution_param {
  433. num_output: 46
  434. bias_term: false
  435. pad: 0
  436. kernel_size: 1
  437. group: 1
  438. stride: 1
  439. weight_filler {
  440. type: "msra"
  441. }
  442. }
  443. }
  444. layer {
  445. name: "group0_block2_bn1"
  446. type: "BatchNorm"
  447. bottom: "group0_block2_conv0"
  448. top: "group0_block2_conv0"
  449. param {
  450. lr_mult: 0.0
  451. decay_mult: 0.0
  452. }
  453. param {
  454. lr_mult: 0.0
  455. decay_mult: 0.0
  456. }
  457. param {
  458. lr_mult: 0.0
  459. decay_mult: 0.0
  460. }
  461. batch_norm_param {
  462. moving_average_fraction: 0.8999999761581421
  463. }
  464. }
  465. layer {
  466. name: "group0_block2_scale1"
  467. type: "Scale"
  468. bottom: "group0_block2_conv0"
  469. top: "group0_block2_conv0"
  470. param {
  471. lr_mult: 1.0
  472. decay_mult: 1.0
  473. }
  474. param {
  475. lr_mult: 1.0
  476. decay_mult: 0.0
  477. }
  478. scale_param {
  479. bias_term: true
  480. }
  481. }
  482. layer {
  483. name: "group0_block2_relu1"
  484. type: "ReLU"
  485. bottom: "group0_block2_conv0"
  486. top: "group0_block2_conv0"
  487. }
  488. layer {
  489. name: "group0_block2_asl"
  490. type: "ActiveShift"
  491. bottom: "group0_block2_conv0"
  492. top: "group0_block2_asl"
  493. param {
  494. lr_mult: 1.0
  495. decay_mult: 0.0
  496. }
  497. asl_param {
  498. pad: 0
  499. stride: 1
  500. shift_filler {
  501. type: "uniform"
  502. min: -1.0
  503. max: 1.0
  504. }
  505. normalize: true
  506. }
  507. }
  508. layer {
  509. name: "group0_block2_conv1"
  510. type: "Convolution"
  511. bottom: "group0_block2_asl"
  512. top: "group0_block2_conv1"
  513. param {
  514. lr_mult: 1.0
  515. decay_mult: 1.0
  516. }
  517. convolution_param {
  518. num_output: 46
  519. bias_term: false
  520. pad: 0
  521. kernel_size: 1
  522. group: 1
  523. stride: 1
  524. weight_filler {
  525. type: "msra"
  526. }
  527. }
  528. }
  529. layer {
  530. name: "group0_block2_sum"
  531. type: "Eltwise"
  532. bottom: "group0_block1_sum"
  533. bottom: "group0_block2_conv1"
  534. top: "group0_block2_sum"
  535. eltwise_param {
  536. operation: SUM
  537. }
  538. }
  539. layer {
  540. name: "group1_block0_bn0"
  541. type: "BatchNorm"
  542. bottom: "group0_block2_sum"
  543. top: "group1_block0_bn0"
  544. param {
  545. lr_mult: 0.0
  546. decay_mult: 0.0
  547. }
  548. param {
  549. lr_mult: 0.0
  550. decay_mult: 0.0
  551. }
  552. param {
  553. lr_mult: 0.0
  554. decay_mult: 0.0
  555. }
  556. batch_norm_param {
  557. moving_average_fraction: 0.8999999761581421
  558. }
  559. }
  560. layer {
  561. name: "group1_block0_scale0"
  562. type: "Scale"
  563. bottom: "group1_block0_bn0"
  564. top: "group1_block0_bn0"
  565. param {
  566. lr_mult: 1.0
  567. decay_mult: 1.0
  568. }
  569. param {
  570. lr_mult: 1.0
  571. decay_mult: 0.0
  572. }
  573. scale_param {
  574. bias_term: true
  575. }
  576. }
  577. layer {
  578. name: "group1_block0_relu0"
  579. type: "ReLU"
  580. bottom: "group1_block0_bn0"
  581. top: "group1_block0_bn0"
  582. }
  583. layer {
  584. name: "group1_block0_conv0"
  585. type: "Convolution"
  586. bottom: "group1_block0_bn0"
  587. top: "group1_block0_conv0"
  588. param {
  589. lr_mult: 1.0
  590. decay_mult: 1.0
  591. }
  592. convolution_param {
  593. num_output: 92
  594. bias_term: false
  595. pad: 0
  596. kernel_size: 1
  597. group: 1
  598. stride: 1
  599. weight_filler {
  600. type: "msra"
  601. }
  602. }
  603. }
  604. layer {
  605. name: "group1_block0_bn1"
  606. type: "BatchNorm"
  607. bottom: "group1_block0_conv0"
  608. top: "group1_block0_conv0"
  609. param {
  610. lr_mult: 0.0
  611. decay_mult: 0.0
  612. }
  613. param {
  614. lr_mult: 0.0
  615. decay_mult: 0.0
  616. }
  617. param {
  618. lr_mult: 0.0
  619. decay_mult: 0.0
  620. }
  621. batch_norm_param {
  622. moving_average_fraction: 0.8999999761581421
  623. }
  624. }
  625. layer {
  626. name: "group1_block0_scale1"
  627. type: "Scale"
  628. bottom: "group1_block0_conv0"
  629. top: "group1_block0_conv0"
  630. param {
  631. lr_mult: 1.0
  632. decay_mult: 1.0
  633. }
  634. param {
  635. lr_mult: 1.0
  636. decay_mult: 0.0
  637. }
  638. scale_param {
  639. bias_term: true
  640. }
  641. }
  642. layer {
  643. name: "group1_block0_relu1"
  644. type: "ReLU"
  645. bottom: "group1_block0_conv0"
  646. top: "group1_block0_conv0"
  647. }
  648. layer {
  649. name: "group1_block0_asl"
  650. type: "ActiveShift"
  651. bottom: "group1_block0_conv0"
  652. top: "group1_block0_asl"
  653. param {
  654. lr_mult: 1.0
  655. decay_mult: 0.0
  656. }
  657. asl_param {
  658. pad: 0
  659. stride: 2
  660. shift_filler {
  661. type: "uniform"
  662. min: -1.0
  663. max: 1.0
  664. }
  665. normalize: true
  666. }
  667. }
  668. layer {
  669. name: "group1_block0_conv1"
  670. type: "Convolution"
  671. bottom: "group1_block0_asl"
  672. top: "group1_block0_conv1"
  673. param {
  674. lr_mult: 1.0
  675. decay_mult: 1.0
  676. }
  677. convolution_param {
  678. num_output: 92
  679. bias_term: false
  680. pad: 0
  681. kernel_size: 1
  682. group: 1
  683. stride: 1
  684. weight_filler {
  685. type: "msra"
  686. }
  687. }
  688. }
  689. layer {
  690. name: "group1_block0_proj"
  691. type: "Convolution"
  692. bottom: "group1_block0_bn0"
  693. top: "group1_block0_proj"
  694. param {
  695. lr_mult: 1.0
  696. decay_mult: 1.0
  697. }
  698. param {
  699. lr_mult: 2.0
  700. decay_mult: 0.0
  701. }
  702. convolution_param {
  703. num_output: 92
  704. pad: 0
  705. kernel_size: 1
  706. group: 1
  707. stride: 2
  708. weight_filler {
  709. type: "msra"
  710. }
  711. bias_filler {
  712. type: "constant"
  713. value: 0.0
  714. }
  715. }
  716. }
  717. layer {
  718. name: "group1_block0_sum"
  719. type: "Eltwise"
  720. bottom: "group1_block0_proj"
  721. bottom: "group1_block0_conv1"
  722. top: "group1_block0_sum"
  723. eltwise_param {
  724. operation: SUM
  725. }
  726. }
  727. layer {
  728. name: "group1_block1_bn0"
  729. type: "BatchNorm"
  730. bottom: "group1_block0_sum"
  731. top: "group1_block1_bn0"
  732. param {
  733. lr_mult: 0.0
  734. decay_mult: 0.0
  735. }
  736. param {
  737. lr_mult: 0.0
  738. decay_mult: 0.0
  739. }
  740. param {
  741. lr_mult: 0.0
  742. decay_mult: 0.0
  743. }
  744. batch_norm_param {
  745. moving_average_fraction: 0.8999999761581421
  746. }
  747. }
  748. layer {
  749. name: "group1_block1_scale0"
  750. type: "Scale"
  751. bottom: "group1_block1_bn0"
  752. top: "group1_block1_bn0"
  753. param {
  754. lr_mult: 1.0
  755. decay_mult: 1.0
  756. }
  757. param {
  758. lr_mult: 1.0
  759. decay_mult: 0.0
  760. }
  761. scale_param {
  762. bias_term: true
  763. }
  764. }
  765. layer {
  766. name: "group1_block1_relu0"
  767. type: "ReLU"
  768. bottom: "group1_block1_bn0"
  769. top: "group1_block1_bn0"
  770. }
  771. layer {
  772. name: "group1_block1_conv0"
  773. type: "Convolution"
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  1497. }
  1498. layer {
  1499. name: "group2_block2_relu1"
  1500. type: "ReLU"
  1501. bottom: "group2_block2_conv0"
  1502. top: "group2_block2_conv0"
  1503. }
  1504. layer {
  1505. name: "group2_block2_asl"
  1506. type: "ActiveShift"
  1507. bottom: "group2_block2_conv0"
  1508. top: "group2_block2_asl"
  1509. param {
  1510. lr_mult: 1.0
  1511. decay_mult: 0.0
  1512. }
  1513. asl_param {
  1514. pad: 0
  1515. stride: 1
  1516. shift_filler {
  1517. type: "uniform"
  1518. min: -1.0
  1519. max: 1.0
  1520. }
  1521. normalize: true
  1522. }
  1523. }
  1524. layer {
  1525. name: "group2_block2_conv1"
  1526. type: "Convolution"
  1527. bottom: "group2_block2_asl"
  1528. top: "group2_block2_conv1"
  1529. param {
  1530. lr_mult: 1.0
  1531. decay_mult: 1.0
  1532. }
  1533. convolution_param {
  1534. num_output: 184
  1535. bias_term: false
  1536. pad: 0
  1537. kernel_size: 1
  1538. group: 1
  1539. stride: 1
  1540. weight_filler {
  1541. type: "msra"
  1542. }
  1543. }
  1544. }
  1545. layer {
  1546. name: "group2_block2_sum"
  1547. type: "Eltwise"
  1548. bottom: "group2_block1_sum"
  1549. bottom: "group2_block2_conv1"
  1550. top: "group2_block2_sum"
  1551. eltwise_param {
  1552. operation: SUM
  1553. }
  1554. }
  1555. layer {
  1556. name: "last_bn"
  1557. type: "BatchNorm"
  1558. bottom: "group2_block2_sum"
  1559. top: "group2_block2_sum"
  1560. param {
  1561. lr_mult: 0.0
  1562. decay_mult: 0.0
  1563. }
  1564. param {
  1565. lr_mult: 0.0
  1566. decay_mult: 0.0
  1567. }
  1568. param {
  1569. lr_mult: 0.0
  1570. decay_mult: 0.0
  1571. }
  1572. batch_norm_param {
  1573. moving_average_fraction: 0.8999999761581421
  1574. }
  1575. }
  1576. layer {
  1577. name: "last_scale"
  1578. type: "Scale"
  1579. bottom: "group2_block2_sum"
  1580. top: "group2_block2_sum"
  1581. param {
  1582. lr_mult: 1.0
  1583. decay_mult: 1.0
  1584. }
  1585. param {
  1586. lr_mult: 1.0
  1587. decay_mult: 0.0
  1588. }
  1589. scale_param {
  1590. bias_term: true
  1591. }
  1592. }
  1593. layer {
  1594. name: "last_relu"
  1595. type: "ReLU"
  1596. bottom: "group2_block2_sum"
  1597. top: "group2_block2_sum"
  1598. }
  1599. layer {
  1600. name: "global_avg_pool"
  1601. type: "Pooling"
  1602. bottom: "group2_block2_sum"
  1603. top: "global_avg_pool"
  1604. pooling_param {
  1605. pool: AVE
  1606. global_pooling: true
  1607. }
  1608. }
  1609. layer {
  1610. name: "fc"
  1611. type: "InnerProduct"
  1612. bottom: "global_avg_pool"
  1613. top: "fc"
  1614. param {
  1615. lr_mult: 1.0
  1616. decay_mult: 1.0
  1617. }
  1618. param {
  1619. lr_mult: 2.0
  1620. decay_mult: 0.0
  1621. }
  1622. inner_product_param {
  1623. num_output: 10
  1624. weight_filler {
  1625. type: "msra"
  1626. }
  1627. bias_filler {
  1628. type: "constant"
  1629. value: 0.0
  1630. }
  1631. }
  1632. }
  1633. layer {
  1634. name: "loss"
  1635. type: "SoftmaxWithLoss"
  1636. bottom: "fc"
  1637. bottom: "label"
  1638. top: "loss"
  1639. }
  1640. layer {
  1641. name: "softmax"
  1642. type: "Softmax"
  1643. bottom: "fc"
  1644. top: "softmax"
  1645. }
  1646. layer {
  1647. name: "Accuracy"
  1648. type: "Accuracy"
  1649. bottom: "softmax"
  1650. bottom: "label"
  1651. top: "Accuracy"
  1652. }
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