caffe 利用VGG训练自己的数据

写这个是因为有童鞋在跑VGG的时候遇到各种问题，供参考一下。

网络结构

以VGG16为例，自己跑的细胞数据

solver.prototxt：

net: "/media/dl/source/Experiment/cell/test/vgg/vgg16.prototxt"
test_iter: 42
test_interval: 1000
base_lr: 0.0001
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 200
max_iter: 200000
momentum: 0.9
weight_decay: 0.0005
snapshot: 100000
snapshot_prefix: "/media/dl/source/Experiment/cell/test/vgg/vgg"
solver_mode: GPU

vgg16.prototxt：

注意，这里的数据层我是用的“ImageData”格式，也就是没有转为LMDB，直接导入图片进去的，因为我用的服务器，为了方便。如果为了更高效，还是使用LMDB数据库的形式。使用LMDB数据库形式的数据层我也写了下，放在这个prototxt后面作为补充。

另外，注意修改最后一个全连接层的num_output为自己的类别数。并修改该层的名字，如我改为了“cellfc8”，是为了finetune vgg时重新训练该层，不使用该层的预训练参数。

  1 name: "VGG16"
  2 layer {
  3   name: "data"
  4   type: "ImageData"
  5   top: "data"
  6   top: "label"
  7   include {
  8     phase: TRAIN
  9   }
 10   # transform_param {
 11   #   mirror: true
 12   #   crop_size: 224
 13   #   mean_file: "data/ilsvrc12_shrt_256/imagenet_mean.binaryproto"
 14   # }
 15 
 16   image_data_param {
 17     source: "/media/dl/source/Experiment/cell/data/trainnew2_resize/trainnew.txt"
 18     batch_size: 20
 19     shuffle:true
 20     #is_color: false 
 21     new_height: 224
 22     new_width: 224
 23   }
 24 }
 25 layer {
 26   name: "data"
 27   type: "ImageData"
 28   top: "data"
 29   top: "label"
 30   include {
 31     phase: TEST
 32   }
 33   # transform_param {
 34   #   mirror: false
 35   #   crop_size: 224
 36   #   mean_file: "data/ilsvrc12_shrt_256/imagenet_mean.binaryproto"
 37   # }
 38 
 39   image_data_param {
 40     source: "/media/dl/source/Experiment/cell/data/val2_resize/valnew.txt"
 41     batch_size: 50
 42     #is_color: false
 43     new_height: 224
 44     new_width: 224
 45   }
 46 }
 47 layer {
 48   bottom: "data"
 49   top: "conv1_1"
 50   name: "conv1_1"
 51   type: "Convolution"
 52   param {
 53     lr_mult: 1
 54     decay_mult: 1
 55   }
 56   param {
 57     lr_mult: 2
 58     decay_mult: 0
 59   }
 60   convolution_param {
 61     num_output: 64
 62     pad: 1
 63     kernel_size: 3
 64     weight_filler {
 65       type: "gaussian"
 66       std: 0.01
 67     }
 68     bias_filler {
 69       type: "constant"
 70       value: 0
 71     }
 72   }
 73 }
 74 layer {
 75   bottom: "conv1_1"
 76   top: "conv1_1"
 77   name: "relu1_1"
 78   type: "ReLU"
 79 }
 80 layer {
 81   bottom: "conv1_1"
 82   top: "conv1_2"
 83   name: "conv1_2"
 84   type: "Convolution"
 85   param {
 86     lr_mult: 1
 87     decay_mult: 1
 88   }
 89   param {
 90     lr_mult: 2
 91     decay_mult: 0
 92   }
 93   convolution_param {
 94     num_output: 64
 95     pad: 1
 96     kernel_size: 3
 97     weight_filler {
 98       type: "gaussian"
 99       std: 0.01
100     }
101     bias_filler {
102       type: "constant"
103       value: 0
104     }
105   }
106 }
107 layer {
108   bottom: "conv1_2"
109   top: "conv1_2"
110   name: "relu1_2"
111   type: "ReLU"
112 }
113 layer {
114   bottom: "conv1_2"
115   top: "pool1"
116   name: "pool1"
117   type: "Pooling"
118   pooling_param {
119     pool: MAX
120     kernel_size: 2
121     stride: 2
122   }
123 }
124 layer {
125   bottom: "pool1"
126   top: "conv2_1"
127   name: "conv2_1"
128   type: "Convolution"
129   param {
130     lr_mult: 1
131     decay_mult: 1
132   }
133   param {
134     lr_mult: 2
135     decay_mult: 0
136   }
137   convolution_param {
138     num_output: 128
139     pad: 1
140     kernel_size: 3
141     weight_filler {
142       type: "gaussian"
143       std: 0.01
144     }
145     bias_filler {
146       type: "constant"
147       value: 0
148     }
149   }
150 }
151 layer {
152   bottom: "conv2_1"
153   top: "conv2_1"
154   name: "relu2_1"
155   type: "ReLU"
156 }
157 layer {
158   bottom: "conv2_1"
159   top: "conv2_2"
160   name: "conv2_2"
161   type: "Convolution"
162   param {
163     lr_mult: 1
164     decay_mult: 1
165   }
166   param {
167     lr_mult: 2
168     decay_mult: 0
169   }
170   convolution_param {
171     num_output: 128
172     pad: 1
173     kernel_size: 3
174     weight_filler {
175       type: "gaussian"
176       std: 0.01
177     }
178     bias_filler {
179       type: "constant"
180       value: 0
181     }
182   }
183 }
184 layer {
185   bottom: "conv2_2"
186   top: "conv2_2"
187   name: "relu2_2"
188   type: "ReLU"
189 }
190 layer {
191   bottom: "conv2_2"
192   top: "pool2"
193   name: "pool2"
194   type: "Pooling"
195   pooling_param {
196     pool: MAX
197     kernel_size: 2
198     stride: 2
199   }
200 }
201 layer {
202   bottom: "pool2"
203   top: "conv3_1"
204   name: "conv3_1"
205   type: "Convolution"
206   param {
207     lr_mult: 1
208     decay_mult: 1
209   }
210   param {
211     lr_mult: 2
212     decay_mult: 0
213   }
214   convolution_param {
215     num_output: 256
216     pad: 1
217     kernel_size: 3
218     weight_filler {
219       type: "gaussian"
220       std: 0.01
221     }
222     bias_filler {
223       type: "constant"
224       value: 0
225     }
226   }
227 }
228 layer {
229   bottom: "conv3_1"
230   top: "conv3_1"
231   name: "relu3_1"
232   type: "ReLU"
233 }
234 layer {
235   bottom: "conv3_1"
236   top: "conv3_2"
237   name: "conv3_2"
238   type: "Convolution"
239   param {
240     lr_mult: 1
241     decay_mult: 1
242   }
243   param {
244     lr_mult: 2
245     decay_mult: 0
246   }
247   convolution_param {
248     num_output: 256
249     pad: 1
250     kernel_size: 3
251     weight_filler {
252       type: "gaussian"
253       std: 0.01
254     }
255     bias_filler {
256       type: "constant"
257       value: 0
258     }
259   }
260 }
261 layer {
262   bottom: "conv3_2"
263   top: "conv3_2"
264   name: "relu3_2"
265   type: "ReLU"
266 }
267 layer {
268   bottom: "conv3_2"
269   top: "conv3_3"
270   name: "conv3_3"
271   type: "Convolution"
272   param {
273     lr_mult: 1
274     decay_mult: 1
275   }
276   param {
277     lr_mult: 2
278     decay_mult: 0
279   }
280   convolution_param {
281     num_output: 256
282     pad: 1
283     kernel_size: 3
284     weight_filler {
285       type: "gaussian"
286       std: 0.01
287     }
288     bias_filler {
289       type: "constant"
290       value: 0
291     }
292   }
293 }
294 layer {
295   bottom: "conv3_3"
296   top: "conv3_3"
297   name: "relu3_3"
298   type: "ReLU"
299 }
300 layer {
301   bottom: "conv3_3"
302   top: "pool3"
303   name: "pool3"
304   type: "Pooling"
305   pooling_param {
306     pool: MAX
307     kernel_size: 2
308     stride: 2
309   }
310 }
311 layer {
312   bottom: "pool3"
313   top: "conv4_1"
314   name: "conv4_1"
315   type: "Convolution"
316   param {
317     lr_mult: 1
318     decay_mult: 1
319   }
320   param {
321     lr_mult: 2
322     decay_mult: 0
323   }
324   convolution_param {
325     num_output: 512
326     pad: 1
327     kernel_size: 3
328     weight_filler {
329       type: "gaussian"
330       std: 0.01
331     }
332     bias_filler {
333       type: "constant"
334       value: 0
335     }
336   }
337 }
338 layer {
339   bottom: "conv4_1"
340   top: "conv4_1"
341   name: "relu4_1"
342   type: "ReLU"
343 }
344 layer {
345   bottom: "conv4_1"
346   top: "conv4_2"
347   name: "conv4_2"
348   type: "Convolution"
349   param {
350     lr_mult: 1
351     decay_mult: 1
352   }
353   param {
354     lr_mult: 2
355     decay_mult: 0
356   }
357   convolution_param {
358     num_output: 512
359     pad: 1
360     kernel_size: 3
361     weight_filler {
362       type: "gaussian"
363       std: 0.01
364     }
365     bias_filler {
366       type: "constant"
367       value: 0
368     }
369   }
370 }
371 layer {
372   bottom: "conv4_2"
373   top: "conv4_2"
374   name: "relu4_2"
375   type: "ReLU"
376 }
377 layer {
378   bottom: "conv4_2"
379   top: "conv4_3"
380   name: "conv4_3"
381   type: "Convolution"
382   param {
383     lr_mult: 1
384     decay_mult: 1
385   }
386   param {
387     lr_mult: 2
388     decay_mult: 0
389   }
390   convolution_param {
391     num_output: 512
392     pad: 1
393     kernel_size: 3
394     weight_filler {
395       type: "gaussian"
396       std: 0.01
397     }
398     bias_filler {
399       type: "constant"
400       value: 0
401     }
402   }
403 }
404 layer {
405   bottom: "conv4_3"
406   top: "conv4_3"
407   name: "relu4_3"
408   type: "ReLU"
409 }
410 layer {
411   bottom: "conv4_3"
412   top: "pool4"
413   name: "pool4"
414   type: "Pooling"
415   pooling_param {
416     pool: MAX
417     kernel_size: 2
418     stride: 2
419   }
420 }
421 layer {
422   bottom: "pool4"
423   top: "conv5_1"
424   name: "conv5_1"
425   type: "Convolution"
426   param {
427     lr_mult: 1
428     decay_mult: 1
429   }
430   param {
431     lr_mult: 2
432     decay_mult: 0
433   }
434   convolution_param {
435     num_output: 512
436     pad: 1
437     kernel_size: 3
438     weight_filler {
439       type: "gaussian"
440       std: 0.01
441     }
442     bias_filler {
443       type: "constant"
444       value: 0
445     }
446   }
447 }
448 layer {
449   bottom: "conv5_1"
450   top: "conv5_1"
451   name: "relu5_1"
452   type: "ReLU"
453 }
454 layer {
455   bottom: "conv5_1"
456   top: "conv5_2"
457   name: "conv5_2"
458   type: "Convolution"
459   param {
460     lr_mult: 1
461     decay_mult: 1
462   }
463   param {
464     lr_mult: 2
465     decay_mult: 0
466   }
467   convolution_param {
468     num_output: 512
469     pad: 1
470     kernel_size: 3
471     weight_filler {
472       type: "gaussian"
473       std: 0.01
474     }
475     bias_filler {
476       type: "constant"
477       value: 0
478     }
479   }
480 }
481 layer {
482   bottom: "conv5_2"
483   top: "conv5_2"
484   name: "relu5_2"
485   type: "ReLU"
486 }
487 layer {
488   bottom: "conv5_2"
489   top: "conv5_3"
490   name: "conv5_3"
491   type: "Convolution"
492   param {
493     lr_mult: 1
494     decay_mult: 1
495   }
496   param {
497     lr_mult: 2
498     decay_mult: 0
499   }
500   convolution_param {
501     num_output: 512
502     pad: 1
503     kernel_size: 3
504     weight_filler {
505       type: "gaussian"
506       std: 0.01
507     }
508     bias_filler {
509       type: "constant"
510       value: 0
511     }
512   }
513 }
514 layer {
515   bottom: "conv5_3"
516   top: "conv5_3"
517   name: "relu5_3"
518   type: "ReLU"
519 }
520 layer {
521   bottom: "conv5_3"
522   top: "pool5"
523   name: "pool5"
524   type: "Pooling"
525   pooling_param {
526     pool: MAX
527     kernel_size: 2
528     stride: 2
529   }
530 }
531 layer {
532   bottom: "pool5"
533   top: "fc6"
534   name: "fc6"
535   type: "InnerProduct"
536   param {
537     lr_mult: 1
538     decay_mult: 1
539   }
540   param {
541     lr_mult: 2
542     decay_mult: 0
543   }
544   inner_product_param {
545     num_output: 4096
546     weight_filler {
547       type: "gaussian"
548       std: 0.005
549     }
550     bias_filler {
551       type: "constant"
552       value: 0.1
553     }
554   }
555 }
556 layer {
557   bottom: "fc6"
558   top: "fc6"
559   name: "relu6"
560   type: "ReLU"
561 }
562 layer {
563   bottom: "fc6"
564   top: "fc6"
565   name: "drop6"
566   type: "Dropout"
567   dropout_param {
568     dropout_ratio: 0.5
569   }
570 }
571 layer {
572   bottom: "fc6"
573   top: "fc7"
574   name: "fc7"
575   type: "InnerProduct"
576   param {
577     lr_mult: 1
578     decay_mult: 1
579   }
580   param {
581     lr_mult: 2
582     decay_mult: 0
583   }
584   inner_product_param {
585     num_output: 4096
586     weight_filler {
587       type: "gaussian"
588       std: 0.005
589     }
590     bias_filler {
591       type: "constant"
592       value: 0.1
593     }
594   }
595 }
596 layer {
597   bottom: "fc7"
598   top: "fc7"
599   name: "relu7"
600   type: "ReLU"
601 }
602 layer {
603   bottom: "fc7"
604   top: "fc7"
605   name: "drop7"
606   type: "Dropout"
607   dropout_param {
608     dropout_ratio: 0.5
609   }
610 }
611 layer {
612   bottom: "fc7"
613   top: "fc8"
614   name: "cellfc8"
615   type: "InnerProduct"
616   param {
617     lr_mult: 1
618     decay_mult: 1
619   }
620   param {
621     lr_mult: 2
622     decay_mult: 0
623   }
624   inner_product_param {
625     num_output: 7 #改为自己的类别数
626     weight_filler {
627       type: "gaussian"
628       std: 0.005
629     }
630     bias_filler {
631       type: "constant"
632       value: 0.1
633     }
634   }
635 }
636 layer {
637   name: "accuracy_at_1"
638   type: "Accuracy"
639   bottom: "fc8"
640   bottom: "label"
641   top: "accuracy_at_1"
642   accuracy_param {
643     top_k: 1
644   }
645   include {
646     phase: TEST
647   }
648 }
649 layer {
650   name: "accuracy_at_5"
651   type: "Accuracy"
652   bottom: "fc8"
653   bottom: "label"
654   top: "accuracy_at_5"
655   accuracy_param {
656     top_k: 5
657   }
658   include {
659     phase: TEST
660   }
661 }
662 layer {
663   bottom: "fc8"
664   bottom: "label"
665   top: "loss"
666   name: "loss"
667   type: "SoftmaxWithLoss"
668 }

如果使用LMDB数据库形式，将前面的数据层改为：

 1 name: "vgg"
 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: 224
13 #如果图片大于224，则使用crop的方式，小于则使用下面的new_height和new_width
14    # new_height: 224
15     #new_width: 224
16     mean_file: "vggface/face_mean.binaryproto"
17   }
18   data_param {
19     source: "vggface/face_train_lmdb"
20     batch_size: 20
21     backend: LMDB
22   }
23 }
24 layer {
25   name: "data"
26   type: "Data"
27   top: "data"
28   top: "label"
29   include {
30     phase: TEST
31   }
32   transform_param {
33     mirror: false
34     crop_size: 224
35 #如果图片大于224，则使用crop的方式，小于则使用下面的new_height和new_width
36    # new_height: 224
37     #new_width: 224
38     mean_file: "vggface/face_mean.binaryproto"
39   }
40   data_param {
41     source: "vggface/face_val_lmdb"
42     batch_size: 20
43     backend: LMDB
44   }
45 }

训练

放一个shell命令：

#!/usr/bin/env sh

TOOLS=/home/dl/caffe-jonlong/build/tools

$TOOLS/caffe train \
  -solver=/media/dl/source/Experiment/cell/test/vgg/solver.prototxt \
  -weights=/media/dl/source/Experiment/cell/test/vgg/VGG_ILSVRC_16_layers.caffemodel \
  -gpu=all \

预训练模型VGG_ILSVRC_16_layers.caffemodel的下载地址为

http://www.robots.ox.ac.uk/~vgg/software/very_deep/caffe/VGG_ILSVRC_16_layers.caffemodel

本站文章如无特殊说明，均为本站原创，如若转载，请注明出处：caffe 利用VGG训练自己的数据 - Python技术站