Caffe: Точность застряла на 0 - PullRequest
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Caffe: Точность застряла на 0

0 голосов
/ 23 ноября 2018

Я пытаюсь обучить AlexNet данными мультикласса lmdb.Входные слои: 

layer { 
 name: "data" 
 type: "Data"
 top:  "data" 
 data_param { 
   source: "/home/gal/caffe-1.0/models/mixDCNN/hdf5/TrainImage" 
   backend: LMDB 
   batch_size: 64 
 } 
 transform_param { 
    crop_size: 227 
    mean_file: "/home/gal/caffe-1.0/models/mixDCNN/hdf5/TrainImage/TrainImage.binaryproto"
    mirror: true 
  } 
  include: { phase: TRAIN } 
} 
layer {
  name: "labels"
  type: "Data"
  top: "labels"
  include {
    phase: TRAIN
  }
  data_param {
    source: "/home/gal/caffe-1.0/models/mixDCNN/hdf5/TrainLabel" 
    batch_size: 64
    backend: LMDB
  }
}
layer { 
 name: "data" 
 type: "Data"
 top:  "data" 
 data_param { 
   source: "/home/gal/caffe-1.0/models/mixDCNN/hdf5/ValImage"
   backend: LMDB 
   batch_size: 64
 } 
 transform_param { 
    crop_size: 227 
    mean_file: "/home/gal/caffe-1.0/models/mixDCNN/hdf5/ValImage/ValImage.binaryproto"
    mirror: false 
  } 
  include: { phase: TEST } 
} 
layer {
  name: "labels"
  type: "Data"
  top: "labels"
  include {
    phase: TEST
  }
  data_param {
    source: "/home/gal/caffe-1.0/models/mixDCNN/hdf5/ValLabel" 
    batch_size: 64
    backend: LMDB
  }
}
layer {
  name: "slice"
  type: "Slice"
  bottom: "labels"
  top: "class"
  top: "subclass"
  top: "color"
  slice_param {
    axis: 1
    slice_point: 1
    slice_point: 2
  }
}

И выходные слои: 

layer {
  name: "fc8_1_class"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8_1_class"
  param {
    lr_mult: 5
    decay_mult: 1
  }
  param {
    lr_mult: 10
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "fc8_2_subclass"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8_2_subclass"
  param {
    lr_mult: 5
    decay_mult: 1
  }
  param {
    lr_mult: 10
    decay_mult: 0
  }
  inner_product_param {
    num_output: 15
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "fc8_3_color"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8_3_color"
  param {
    lr_mult: 5
    decay_mult: 1
  }
  param {
    lr_mult: 10
    decay_mult: 0
  }
  inner_product_param {
    num_output: 8
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "accuracy_class"
  type: "Accuracy"
  bottom: "fc8_1_class"
  bottom: "class"
  top: "accuracy_class"
  include: { phase: TEST }
}
layer {
  name: "loss_class"
  type: "SoftmaxWithLoss"
  bottom: "fc8_1_class"
  bottom: "class"
  top: "loss_class"
  loss_weight: 0.5
}
layer {
  name: "accuracy_subclass"
  type: "Accuracy"
  bottom: "fc8_2_subclass"
  bottom: "subclass"
  top: "accuracy_subclass"
  include: { phase: TEST }
}
layer {
  name: "loss_subclass"
  type: "SoftmaxWithLoss"
  bottom: "fc8_2_subclass"
  bottom: "subclass"
  top: "loss_subclass"
  loss_weight: 0.5
}
layer {
  name: "accuracy_color"
  type: "Accuracy"
  bottom: "fc8_3_color"
  bottom: "color"
  top: "accuracy_color"
  include: { phase: TEST }
}
layer {
  name: "loss_color"
  type: "SoftmaxWithLoss"
  bottom: "fc8_3_color"
  bottom: "color"
  top: "loss_color"
  loss_weight: 0.5
}

«Класс» - это доминирующий класс с 2 метками, который дает хорошую точность, с другой стороны, «Подкласс»"имеет 15 меток и дает плохие результаты," Цвет "дает мне точность = 0 независимо от того, что я пробовал.Это часть вывода поезда: 

I1123 10:47:52.258751  3392 solver.cpp:273] Learning Rate Policy: step
I1123 10:47:52.422647  3392 solver.cpp:218] Iteration 0 (7.96684e-08 iter/s, 0.163777s/100 iters), loss = 3.50787
I1123 10:47:52.422716  3392 solver.cpp:237]     Train net output #0: loss_class = 1.83086 (* 0.5 = 0.915429 loss)
I1123 10:47:52.422742  3392 solver.cpp:237]     Train net output #1: loss_color = 2.39675 (* 0.5 = 1.19837 loss)
I1123 10:47:52.422768  3392 solver.cpp:237]     Train net output #2: loss_subclass = 2.78814 (* 0.5 = 1.39407 loss)
I1123 10:47:52.422803  3392 sgd_solver.cpp:105] Iteration 0, lr = 0.0001
I1123 10:48:01.282282  3392 solver.cpp:330] Iteration 100, Testing net (#0)
I1123 10:48:01.554111  3392 blocking_queue.cpp:49] Waiting for data
I1123 10:48:03.717224  3406 data_layer.cpp:73] Restarting data prefetching from start.
I1123 10:48:03.884392  3405 data_layer.cpp:73] Restarting data prefetching from start.
I1123 10:48:04.193727  3392 solver.cpp:397]     Test net output #0: accuracy_class = 0.958594
I1123 10:48:04.193784  3392 solver.cpp:397]     Test net output #1: accuracy_color = 0
I1123 10:48:04.193802  3392 solver.cpp:397]     Test net output #2: accuracy_subclass = 0.495313
I1123 10:48:04.193841  3392 solver.cpp:397]     Test net output #3: loss_class = 0.143002 (* 0.5 = 0.0715012 loss)
I1123 10:48:04.193871  3392 solver.cpp:397]     Test net output #4: loss_color = 1.53319 (* 0.5 = 0.766597 loss)
I1123 10:48:04.193894  3392 solver.cpp:397]     Test net output #5: loss_subclass = 1.40099 (* 0.5 = 0.700494 loss)
I1123 10:48:04.290772  3392 solver.cpp:218] Iteration 100 (8.42592 iter/s, 11.8681s/100 iters), loss = 1.50249
I1123 10:48:04.302697  3392 solver.cpp:237]     Train net output #0: loss_class = 0.145322 (* 0.5 = 0.0726612 loss)
I1123 10:48:04.302740  3392 solver.cpp:237]     Train net output #1: loss_color = 1.44309 (* 0.5 = 0.721547 loss)
I1123 10:48:04.302767  3392 solver.cpp:237]     Train net output #2: loss_subclass = 1.41657 (* 0.5 = 0.708286 loss)
I1123 10:48:04.302788  3392 sgd_solver.cpp:105] Iteration 100, lr = 0.0001
I1123 10:48:08.030251  3404 data_layer.cpp:73] Restarting data prefetching from start.
I1123 10:48:08.077759  3403 data_layer.cpp:73] Restarting data prefetching from start.
I1123 10:48:13.195049  3392 solver.cpp:330] Iteration 200, Testing net (#0)
I1123 10:48:15.287199  3406 data_layer.cpp:73] Restarting data prefetching from start.
I1123 10:48:15.466104  3405 data_layer.cpp:73] Restarting data prefetching from start.
I1123 10:48:16.056556  3392 solver.cpp:397]     Test net output #0: accuracy_class = 0.958594
I1123 10:48:16.056615  3392 solver.cpp:397]     Test net output #1: accuracy_color = 0
I1123 10:48:16.056633  3392 solver.cpp:397]     Test net output #2: accuracy_subclass = 0.520703
I1123 10:48:16.056658  3392 solver.cpp:397]     Test net output #3: loss_class = 0.11685 (* 0.5 = 0.0584251 loss)
I1123 10:48:16.056679  3392 solver.cpp:397]     Test net output #4: loss_color = 1.53322 (* 0.5 = 0.766609 loss)
I1123 10:48:16.056707  3392 solver.cpp:397]     Test net output #5: loss_subclass = 1.42879 (* 0.5 = 0.714395 loss)
I1123 10:48:16.151207  3392 solver.cpp:218] Iteration 200 (8.43993 iter/s, 11.8484s/100 iters), loss = 1.74936
I1123 10:48:16.163187  3392 solver.cpp:237]     Train net output #0: loss_class = 0.253774 (* 0.5 = 0.126887 loss)
I1123 10:48:16.163224  3392 solver.cpp:237]     Train net output #1: loss_color = 1.69651 (* 0.5 = 0.848255 loss)
I1123 10:48:16.163246  3392 solver.cpp:237]     Train net output #2: loss_subclass = 1.54843 (* 0.5 = 0.774216 loss)
I1123 10:48:16.163265  3392 sgd_solver.cpp:105] Iteration 200, lr = 0.0001

Данные содержат около 9000 изображений для поезда и еще 2000 для проверки, метки имеют следующую форму (начинается с 0): 

/media/gal/USB/MAFAT/crops/train/img/15036.png 0 4 18 
/media/gal/USB/MAFAT/crops/train/img/31658.png 1 7 17
/media/gal/USB/MAFAT/crops/train/img/26971.png 0 4 17
/media/gal/USB/MAFAT/crops/train/img/19301.png 0 4 20
/media/gal/USB/MAFAT/crops/train/img/35906.png 1 7 17
/media/gal/USB/MAFAT/crops/train/img/24690.png 1 11 23
/media/gal/USB/MAFAT/crops/train/img/27442.png 0 9 24
/media/gal/USB/MAFAT/crops/train/img/26689.png 1 11 23
/media/gal/USB/MAFAT/crops/train/img/38403.png 0 4 19
/media/gal/USB/MAFAT/crops/train/img/37141.png 0 4 19
/media/gal/USB/MAFAT/crops/train/img/16961.png 0 4 21
/media/gal/USB/MAFAT/crops/train/img/23952.png 1 16 24

Это файл решателя: 

net: "/home/gal/caffe-1.0/models/mixDCNN/hdf5/AlexNetMulti.prototxt"  
test_initialization: false
test_iter: 40
test_interval: 100
base_lr: 0.0001
momentum: 0.9
gamma: 0.1  
lr_policy: "step"    
stepsize: 1600   # drop the learning rate every .... iterations
max_iter: 4800
display: 100
weight_decay: 0.0005
snapshot: 300000
snapshot_prefix: "/home/gal/caffe-1.0/models/mixDCNN/hdf5/snapshot/GoogleNet_class"
solver_mode: GPU

Почему я продолжаю получать точность 0 в классе "color"?

1 Ответ

0 голосов
/ 26 ноября 2018

Вы устанавливаете num_output: 8 для своего слоя "fc8_3_color", то есть вы пытаетесь предсказать «цвет» с помощью 8 дискретных классов 0..7.Однако, глядя на ваши данные, метки для цветов варьируются от 17 до 24 - это не может работать.метки ДОЛЖНЫ быть от 0..7, если вы используете слои "SoftmaxWithLoss" и "Accuracy".

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