Method 2

name: "AlexNet"
  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: 227
 13     mean_file: "data/threats/aa_mean.binaryproto"
 14   }
 15   data_param {
 16     source: "examples/threats/aa_train_lmdb"
 17     batch_size: 256
 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     mirror: false
 31     crop_size: 227
 32     mean_file: "data/threats/aa_mean.binaryproto"
 33   }
 34   data_param {
 35     source: "examples/threats/aa_val_lmdb"
 36     batch_size: 50
 37     backend: LMDB
 38   }
 39 }
 40 layer {
 41   name: "conv1"
 42   type: "Convolution"
 43   bottom: "data"
 44   top: "conv1"
 45   param {
 46     lr_mult: 0.01
 47     decay_mult: 1
 48   }
 49   param {
 50     lr_mult: 0.01
 51     decay_mult: 0
 52   }
 53   convolution_param {
 54     num_output: 96
 55     kernel_size: 11
stride: 4
 57     weight_filler {
 58       type: "gaussian"
 59       std: 0.01
 60     }
 61     bias_filler {
 62       type: "constant"
 63       value: 0
 64     }
 65   }
 66 }
 67 layer {
 68   name: "relu1"
 69   type: "ReLU"
 70   bottom: "conv1"
 71   top: "conv1"
 72 }
 73 layer {
 74   name: "norm1"
 75   type: "LRN"
 76   bottom: "conv1"
 77   top: "norm1"
 78   lrn_param {
 79     local_size: 5
 80     alpha: 0.0001
 81     beta: 0.75
 82   }
 83 }
 84 layer {
 85   name: "pool1"
 86   type: "Pooling"
 87   bottom: "norm1"
 88   top: "pool1"
 89   pooling_param {
 90     pool: MAX
 91     kernel_size: 3
 92     stride: 2
 93   }
 94 }
 95 layer {
 96   name: "conv2"
 97   type: "Convolution"
 98   bottom: "pool1"
 99   top: "conv2"
100   param {
101     lr_mult: 0.01
102     decay_mult: 1
103   }
104   param {
105     lr_mult: 0.01
106     decay_mult: 0
107   }
108   convolution_param {
num_output: 256
110     pad: 2
111     kernel_size: 5
112     group: 2
113     weight_filler {
114       type: "gaussian"
115       std: 0.01
116     }
117     bias_filler {
118       type: "constant"
119       value: 0.1
120     }
121   }
122 }
123 layer {
124   name: "relu2"
125   type: "ReLU"
126   bottom: "conv2"
127   top: "conv2"
128 }
129 layer {
130   name: "norm2"
131   type: "LRN"
132   bottom: "conv2"
133   top: "norm2"
134   lrn_param {
135     local_size: 5
136     alpha: 0.0001
137     beta: 0.75
138   }
139 }
140 layer {
141   name: "pool2"
142   type: "Pooling"
143   bottom: "norm2"
144   top: "pool2"
145   pooling_param {
146     pool: MAX
147     kernel_size: 3
148     stride: 2
149   }
150 }
151 layer {
152   name: "conv3"
153   type: "Convolution"
154   bottom: "pool2"
155   top: "conv3"
156   param {
157     lr_mult: 0.01
158     decay_mult: 1
159   }
160   param {
161     lr_mult: 0.01
decay_mult: 0
163   }
164   convolution_param {
165     num_output: 384
166     pad: 1
167     kernel_size: 3
168     weight_filler {
169       type: "gaussian"
170       std: 0.01
171     }
172     bias_filler {
173       type: "constant"
174       value: 0
175     }
176   }
177 }
178 layer {
179   name: "relu3"
180   type: "ReLU"
181   bottom: "conv3"
182   top: "conv3"
183 }
184 layer {
185   name: "conv4"
186   type: "Convolution"
187   bottom: "conv3"
188   top: "conv4"
189   param {
190     lr_mult: 0.01
191     decay_mult: 1
192   }
193   param {
194     lr_mult: 0.02
195     decay_mult: 0
196   }
197   convolution_param {
198     num_output: 384
199     pad: 1
200     kernel_size: 3
201     group: 2
202     weight_filler {
203       type: "gaussian"
204       std: 0.01
205     }
206     bias_filler {
207       type: "constant"
208       value: 0.1
209     }
210   }
211 }
212 layer {
213   name: "relu4"
214   type: "ReLU"
bottom: "conv4"
216   top: "conv4"
217 }
218 layer {
219   name: "conv5"
220   type: "Convolution"
221   bottom: "conv4"
222   top: "conv5"
223   param {
224     lr_mult: 0.1
225     decay_mult: 1
226   }
227   param {
228     lr_mult: 0.2
229     decay_mult: 0
230   }
231   convolution_param {
232     num_output: 256
233     pad: 1
234     kernel_size: 3
235     group: 2
236     weight_filler {
237       type: "gaussian"
238       std: 0.01
239     }
240     bias_filler {
241       type: "constant"
242       value: 0.1
243     }
244   }
245 }
246 layer {
247   name: "relu5"
248   type: "ReLU"
249   bottom: "conv5"
250   top: "conv5"
251 }
252 layer {
253   name: "pool5"
254   type: "Pooling"
255   bottom: "conv5"
256   top: "pool5"
257   pooling_param {
258     pool: MAX
259     kernel_size: 3
260     stride: 2
261   }
262 }
263 layer {
264   name: "fc6"
265   type: "InnerProduct"
266   bottom: "pool5"
267   top: "fc6"
param {
269     lr_mult: 1
270     decay_mult: 1
271   }
272   param {
273     lr_mult: 2
274     decay_mult: 0
275   }
276   inner_product_param {
277     num_output: 4096
278     weight_filler {
279       type: "gaussian"
280       std: 0.005
281     }
282     bias_filler {
283       type: "constant"
284       value: 0.1
285     }
286   }
287 }
288 layer {
289   name: "relu6"
290   type: "ReLU"
291   bottom: "fc6"
292   top: "fc6"
293 }
294 layer {
295   name: "drop6"
296   type: "Dropout"
297   bottom: "fc6"
298   top: "fc6"
299   dropout_param {
300     dropout_ratio: 0.5
301   }
302 }
303 layer {
304   name: "fc7"
305   type: "InnerProduct"
306   bottom: "fc6"
307   top: "fc7"
308   param {
309     lr_mult: 1
310     decay_mult: 1
311   }
312   param {
313     lr_mult: 2
314     decay_mult: 0
315   }
316   inner_product_param {
317     num_output: 4096
318     weight_filler {
319       type: "gaussian"
320       std: 0.005
}
322     bias_filler {
323       type: "constant"
324       value: 0.1
325     }
326   }
327 }
328 layer {
329   name: "relu7"
330   type: "ReLU"
331   bottom: "fc7"
332   top: "fc7"
333 }
334 layer {
335   name: "drop7"
336   type: "Dropout"
337   bottom: "fc7"
338   top: "fc7"
339   dropout_param {
340     dropout_ratio: 0.5
341   }
342 }
343 layer {
344   name: "fc8_threats"
345   type: "InnerProduct"
346   bottom: "fc7"
347   top: "fc8_threats"
348   param {
349     lr_mult: 10
350     decay_mult: 1
351   }
352   param {
353     lr_mult: 20
354     decay_mult: 0
355   }
356   inner_product_param {
357     num_output: 4
358     weight_filler {
359       type: "gaussian"
360       std: 0.01
361     }
362     bias_filler {
363       type: "constant"
364       value: 0
365     }
366   }
367 }
368
369 layer {
370   name: "accuracy"
371   type: "Accuracy"
372   bottom: "fc8_threats"
373   bottom: "label"
top: "accuracy"
375   include {
376     phase: TEST
377   }
378 }
379 layer {
380   name: "loss"
381   type: "SoftmaxWithLoss"
382   bottom: "fc8_threats"
383   bottom: "label"
384   top: "loss"
385 }