machine-learning - 当我训练我的caffe模型时，损失总是保持很大的值，并且准确性没有提高。

Question

这是我训练自己的模型时得到的结果

I0510 20:53:16.677439  3591 solver.cpp:337] Iteration 0, Testing net (#0)
I0510 20:57:20.822933  3591 solver.cpp:404]     Test net output #0: accuracy = 3.78788e-05
I0510 20:57:20.823001  3591 solver.cpp:404]     Test net output #1: loss = 9.27223 (* 1 = 9.27223 loss)
I0510 20:57:21.423084  3591 solver.cpp:228] Iteration 0, loss = 9.29181
I0510 20:57:21.423110  3591 solver.cpp:244]     Train net output #0: loss = 9.29181 (* 1 = 9.29181 loss)
I0510 20:57:21.423120  3591 sgd_solver.cpp:106] Iteration 0, lr = 0.001
I0510 21:06:57.498831  3591 solver.cpp:337] Iteration 1000, Testing net (#0)
I0510 21:10:59.477396  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00186553
I0510 21:10:59.477463  3591 solver.cpp:404]     Test net output #1: loss = 8.86572 (* 1 = 8.86572 loss)
I0510 21:20:35.828510  3591 solver.cpp:337] Iteration 2000, Testing net (#0)
I0510 21:24:42.838196  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00144886
I0510 21:24:42.838245  3591 solver.cpp:404]     Test net output #1: loss = 8.83859 (* 1 = 8.83859 loss)
I0510 21:24:43.412120  3591 solver.cpp:228] Iteration 2000, loss = 8.81461
I0510 21:24:43.412145  3591 solver.cpp:244]     Train net output #0: loss = 8.81461 (* 1 = 8.81461 loss)
I0510 21:24:43.412150  3591 sgd_solver.cpp:106] Iteration 2000, lr = 0.001
I0510 21:38:50.990823  3591 solver.cpp:337] Iteration 3000, Testing net (#0)
I0510 21:42:52.918418  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00140152
I0510 21:42:52.918493  3591 solver.cpp:404]     Test net output #1: loss = 8.81789 (* 1 = 8.81789 loss)
I0510 22:00:09.519151  3591 solver.cpp:337] Iteration 4000, Testing net (#0)
I0510 22:09:13.918016  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00149621
I0510 22:09:13.918102  3591 solver.cpp:404]     Test net output #1: loss = 8.80909 (* 1 = 8.80909 loss)
I0510 22:09:15.127683  3591 solver.cpp:228] Iteration 4000, loss = 8.8597
I0510 22:09:15.127722  3591 solver.cpp:244]     Train net output #0: loss = 8.8597 (* 1 = 8.8597 loss)
I0510 22:09:15.127729  3591 sgd_solver.cpp:106] Iteration 4000, lr = 0.001
I0510 22:28:39.320019  3591 solver.cpp:337] Iteration 5000, Testing net (#0)
I0510 22:37:43.847064  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00118371
I0510 22:37:43.847173  3591 solver.cpp:404]     Test net output #1: loss = 8.80527 (* 1 = 8.80527 loss)
I0510 23:58:17.120088  3591 solver.cpp:454] Snapshotting to binary proto file /home/wang/caffe-master/examples/NN2_iter_10000.caffemodel
I0510 23:58:17.238307  3591 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/wang/caffe-master/examples/NN2_iter_10000.solverstate
I0510 23:58:17.491825  3591 solver.cpp:337] Iteration 10000, Testing net (#0)
I0511 00:02:19.412715  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00186553
I0511 00:02:19.412762  3591 solver.cpp:404]     Test net output #1: loss = 8.79114 (* 1 = 8.79114 loss)
I0511 00:02:19.986547  3591 solver.cpp:228] Iteration 10000, loss = 8.83457
I0511 00:02:19.986570  3591 solver.cpp:244]     Train net output #0: loss = 8.83457 (* 1 = 8.83457 loss)
I0511 00:02:19.986578  3591 sgd_solver.cpp:106] Iteration 10000, lr = 0.001
I0511 00:11:55.546052  3591 solver.cpp:337] Iteration 11000, Testing net (#0)
I0511 00:15:57.490486  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00164773
I0511 00:15:57.490532  3591 solver.cpp:404]     Test net output #1: loss = 8.78702 (* 1 = 8.78702 loss)
I0511 00:25:33.666496  3591 solver.cpp:337] Iteration 12000, Testing net (#0)
I0511 00:29:35.603062  3591 solver.cpp:404]     Test net output #0: accuracy = 0.0016572
I0511 00:29:35.603109  3591 solver.cpp:404]     Test net output #1: loss = 8.7848 (* 1 = 8.7848 loss)
I0511 00:29:36.177078  3591 solver.cpp:228] Iteration 12000, loss = 9.00561
I0511 00:29:36.177105  3591 solver.cpp:244]     Train net output #0: loss = 9.00561 (* 1 = 9.00561 loss)
I0511 00:29:36.177114  3591 sgd_solver.cpp:106] Iteration 12000, lr = 0.001
I0511 00:39:11.729369  3591 solver.cpp:337] Iteration 13000, Testing net (#0)
I0511 00:43:13.678067  3591 solver.cpp:404]     Test net output #0: accuracy = 0.001875
I0511 00:43:13.678113  3591 solver.cpp:404]     Test net output #1: loss = 8.78359 (* 1 = 8.78359 loss)
I0511 00:52:49.851985  3591 solver.cpp:337] Iteration 14000, Testing net (#0)
I0511 00:56:51.767343  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00154356
I0511 00:56:51.767390  3591 solver.cpp:404]     Test net output #1: loss = 8.77998 (* 1 = 8.77998 loss)
I0511 00:56:52.341564  3591 solver.cpp:228] Iteration 14000, loss = 8.83385
I0511 00:56:52.341591  3591 solver.cpp:244]     Train net output #0: loss = 8.83385 (* 1 = 8.83385 loss)
I0511 00:56:52.341598  3591 sgd_solver.cpp:106] Iteration 14000, lr = 0.001
I0511 02:14:38.224290  3591 solver.cpp:454] Snapshotting to binary proto file /home/wang/caffe-master/examples/NN2_iter_20000.caffemodel
I0511 02:14:38.735008  3591 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/wang/caffe-master/examples/NN2_iter_20000.solverstate
I0511 02:14:38.805809  3591 solver.cpp:337] Iteration 20000, Testing net (#0)
I0511 02:18:40.681993  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00179924
I0511 02:18:40.682086  3591 solver.cpp:404]     Test net output #1: loss = 8.78129 (* 1 = 8.78129 loss)
I0511 02:18:41.255969  3591 solver.cpp:228] Iteration 20000, loss = 8.82502
I0511 02:18:41.255995  3591 solver.cpp:244]     Train net output #0: loss = 8.82502 (* 1 = 8.82502 loss)
I0511 02:18:41.256001  3591 sgd_solver.cpp:106] Iteration 20000, lr = 0.001
I0511 04:30:58.924096  3591 solver.cpp:454] Snapshotting to binary proto file /home/wang/caffe-master/examples/NN2_iter_30000.caffemodel
I0511 04:31:00.742739  3591 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/wang/caffe-master/examples/NN2_iter_30000.solverstate
I0511 04:31:01.151980  3591 solver.cpp:337] Iteration 30000, Testing net (#0)
I0511 04:35:03.075263  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00186553
I0511 04:35:03.075307  3591 solver.cpp:404]     Test net output #1: loss = 8.77867 (* 1 = 8.77867 loss)
I0511 04:35:03.649479  3591 solver.cpp:228] Iteration 30000, loss = 8.82915
I0511 04:35:03.649507  3591 solver.cpp:244]     Train net output #0: loss = 8.82915 (* 1 = 8.82915 loss)
I0511 04:35:03.649513  3591 sgd_solver.cpp:106] Iteration 30000, lr = 0.001
I0511 07:55:36.848265  3591 solver.cpp:337] Iteration 45000, Testing net (#0)
I0511 07:59:38.834043  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00179924
I0511 07:59:38.834095  3591 solver.cpp:404]     Test net output #1: loss = 8.77432 (* 1 = 8.77432 loss)
I0511 09:03:48.141854  3591 solver.cpp:454] Snapshotting to binary proto file /home/wang/caffe-master/examples/NN2_iter_50000.caffemodel
I0511 09:03:49.736464  3591 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/wang/caffe-master/examples/NN2_iter_50000.solverstate
I0511 09:03:49.797582  3591 solver.cpp:337] Iteration 50000, Testing net (#0)
I0511 09:07:51.777150  3591 solver.cpp:404]     Test net output #0: accuracy = 0.001875
I0511 09:07:51.777207  3591 solver.cpp:404]     Test net output #1: loss = 8.77058 (* 1 = 8.77058 loss)
I0511 09:07:52.351323  3591 solver.cpp:228] Iteration 50000, loss = 9.11435
I0511 09:07:52.351351  3591 solver.cpp:244]     Train net output #0: loss = 9.11435 (* 1 = 9.11435 loss)
I0511 09:07:52.351357  3591 sgd_solver.cpp:106] Iteration 50000, lr = 0.001
I0511 09:17:28.188742  3591 solver.cpp:337] Iteration 51000, Testing net (#0)
I0511 09:21:30.200623  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00186553
I0511 09:21:30.200716  3591 solver.cpp:404]     Test net output #1: loss = 8.77026 (* 1 = 8.77026 loss)
I0511 09:31:06.596501  3591 solver.cpp:337] Iteration 52000, Testing net (#0)
I0511 09:35:08.580215  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00182765
I0511 09:35:08.580313  3591 solver.cpp:404]     Test net output #1: loss = 8.76917 (* 1 = 8.76917 loss)
I0511 09:35:09.154428  3591 solver.cpp:228] Iteration 52000, loss = 8.89758
I0511 09:35:09.154453  3591 solver.cpp:244]     Train net output #0: loss = 8.89758 (* 1 = 8.89758 loss)
I0511 09:35:09.154459  3591 sgd_solver.cpp:106] Iteration 52000, lr = 0.001
I0511 09:44:44.906309  3591 solver.cpp:337] Iteration 53000, Testing net (#0)
I0511 09:48:46.866353  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00185606
I0511 09:48:46.866430  3591 solver.cpp:404]     Test net output #1: loss = 8.7708 (* 1 = 8.7708 loss)
I0511 09:58:23.097244  3591 solver.cpp:337] Iteration 54000, Testing net (#0)
I0511 10:02:25.056555  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00192235
I0511 10:02:25.056605  3591 solver.cpp:404]     Test net output #1: loss = 8.76884 (* 1 = 8.76884 loss)
I0511 10:02:25.630312  3591 solver.cpp:228] Iteration 54000, loss = 8.90552
I0511 10:02:25.630337  3591 solver.cpp:244]     Train net output #0: loss = 8.90552 (* 1 = 8.90552 loss)
I0511 10:02:25.630342  3591 sgd_solver.cpp:106] Iteration 54000, lr = 0.001
I0511 14:44:51.563555  3591 solver.cpp:337] Iteration 75000, Testing net (#0)
I0511 14:48:53.573640  3591 solver.cpp:404]     Test net output #0: accuracy = 0.0016572
I0511 14:48:53.573724  3591 solver.cpp:404]     Test net output #1: loss = 8.76967 (* 1 = 8.76967 loss)
I0511 14:58:30.080453  3591 solver.cpp:337] Iteration 76000, Testing net (#0)
I0511 15:02:32.076011  3591 solver.cpp:404]     Test net output #0: accuracy = 0.001875
I0511 15:02:32.076077  3591 solver.cpp:404]     Test net output #1: loss = 8.7695 (* 1 = 8.7695 loss)
I0511 15:02:32.650342  3591 solver.cpp:228] Iteration 76000, loss = 9.0084
I0511 15:02:32.650367  3591 solver.cpp:244]     Train net output #0: loss = 9.0084 (* 1 = 9.0084 loss)
I0511 15:02:32.650373  3591 sgd_solver.cpp:106] Iteration 76000, lr = 0.001
I0511 15:12:08.597450  3591 solver.cpp:337] Iteration 77000, Testing net (#0)
I0511 15:16:10.636613  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00181818
I0511 15:16:10.636693  3591 solver.cpp:404]     Test net output #1: loss = 8.76889 (* 1 = 8.76889 loss)
I0511 15:25:47.167667  3591 solver.cpp:337] Iteration 78000, Testing net (#0)
I0511 15:29:49.204596  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00185606
I0511 15:29:49.204649  3591 solver.cpp:404]     Test net output #1: loss = 8.77059 (* 1 = 8.77059 loss)
I0511 15:29:49.779094  3591 solver.cpp:228] Iteration 78000, loss = 8.73139
I0511 15:29:49.779119  3591 solver.cpp:244]     Train net output #0: loss = 8.73139 (* 1 = 8.73139 loss)
I0511 15:29:49.779124  3591 sgd_solver.cpp:106] Iteration 78000, lr = 0.001
I0511 15:39:25.730358  3591 solver.cpp:337] Iteration 79000, Testing net (#0)
I0511 15:43:27.756417  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00192235
I0511 15:43:27.756485  3591 solver.cpp:404]     Test net output #1: loss = 8.76846 (* 1 = 8.76846 loss)
I0511 15:53:04.419961  3591 solver.cpp:454] Snapshotting to binary proto file /home/wang/caffe-master/examples/NN2_iter_80000.caffemodel
I0511 15:53:06.138357  3591 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/wang/caffe-master/examples/NN2_iter_80000.solverstate
I0511 15:53:06.519551  3591 solver.cpp:337] Iteration 80000, Testing net (#0)
I0511 15:57:08.719681  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00164773
I0511 15:57:08.719737  3591 solver.cpp:404]     Test net output #1: loss = 8.77126 (* 1 = 8.77126 loss)
I0511 15:57:09.294163  3591 solver.cpp:228] Iteration 80000, loss = 8.56576
I0511 15:57:09.294188  3591 solver.cpp:244]     Train net output #0: loss = 8.56576 (* 1 = 8.56576 loss)
I0511 15:57:09.294193  3591 sgd_solver.cpp:106] Iteration 80000, lr = 0.001
I0511 17:01:19.190099  3591 solver.cpp:337] Iteration 85000, Testing net (#0)
I0511 17:05:21.148668  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00185606
I0511 17:05:21.148733  3591 solver.cpp:404]     Test net output #1: loss = 8.77196 (* 1 = 8.77196 loss)
I0511 17:14:57.670343  3591 solver.cpp:337] Iteration 86000, Testing net (#0)
I0511 17:18:59.659850  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00181818
I0511 17:18:59.659907  3591 solver.cpp:404]     Test net output #1: loss = 8.77126 (* 1 = 8.77126 loss)
I0511 17:19:00.234335  3591 solver.cpp:228] Iteration 86000, loss = 8.72875
I0511 17:19:00.234359  3591 solver.cpp:244]     Train net output #0: loss = 8.72875 (* 1 = 8.72875 loss)
I0511 17:19:00.234364  3591 sgd_solver.cpp:106] Iteration 86000, lr = 0.001
I0511 17:28:36.196920  3591 solver.cpp:337] Iteration 87000, Testing net (#0)
I0511 17:32:38.181174  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00181818
I0511 17:32:38.181231  3591 solver.cpp:404]     Test net output #1: loss = 8.771 (* 1 = 8.771 loss)
I0511 17:42:14.658293  3591 solver.cpp:337] Iteration 88000, Testing net (#0)
I0511 17:46:16.614358  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00188447
I0511 17:46:16.614415  3591 solver.cpp:404]     Test net output #1: loss = 8.76964 (* 1 = 8.76964 loss)
I0511 17:46:17.188212  3591 solver.cpp:228] Iteration 88000, loss = 8.80409
I0511 17:46:17.188233  3591 solver.cpp:244]     Train net output #0: loss = 8.80409 (* 1 = 8.80409 loss)
I0511 17:46:17.188240  3591 sgd_solver.cpp:106] Iteration 88000, lr = 0.001
I0511 17:55:53.358322  3591 solver.cpp:337] Iteration 89000, Testing net (#0)
I0511 17:59:55.305763  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00186553
I0511 17:59:55.305868  3591 solver.cpp:404]     Test net output #1: loss = 8.76909 (* 1 = 8.76909 loss)
I0511 18:09:31.658655  3591 solver.cpp:454] Snapshotting to binary proto file /home/wang/caffe-master/examples/NN2_iter_90000.caffemodel
I0511 18:09:33.138741  3591 sgd_solver.cpp:273] Snapshotting solver state to binary proto file /home/wang/caffe-master/examples/NN2_iter_90000.solverstate
I0511 18:09:33.691995  3591 solver.cpp:337] Iteration 90000, Testing net (#0)
I0511 18:13:35.626065  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00168561
I0511 18:13:35.626148  3591 solver.cpp:404]     Test net output #1: loss = 8.76973 (* 1 = 8.76973 loss)
I0511 18:13:36.200448  3591 solver.cpp:228] Iteration 90000, loss = 8.97326
I0511 18:13:36.200469  3591 solver.cpp:244]     Train net output #0: loss = 8.97326 (* 1 = 8.97326 loss)
I0511 18:13:36.200474  3591 sgd_solver.cpp:106] Iteration 90000, lr = 0.001
I0511 19:31:23.715662  3591 solver.cpp:337] Iteration 96000, Testing net (#0)
I0511 19:35:25.677780  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00188447
I0511 19:35:25.677836  3591 solver.cpp:404]     Test net output #1: loss = 8.7695 (* 1 = 8.7695 loss)
I0511 19:35:26.251850  3591 solver.cpp:228] Iteration 96000, loss = 8.74232
I0511 19:35:26.251875  3591 solver.cpp:244]     Train net output #0: loss = 8.74232 (* 1 = 8.74232 loss)
I0511 19:35:26.251880  3591 sgd_solver.cpp:106] Iteration 96000, lr = 0.001
I0511 19:45:02.057610  3591 solver.cpp:337] Iteration 97000, Testing net (#0)
I0511 19:49:04.029269  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00188447
I0511 19:49:04.029357  3591 solver.cpp:404]     Test net output #1: loss = 8.77655 (* 1 = 8.77655 loss)
I0511 19:58:40.265120  3591 solver.cpp:337] Iteration 98000, Testing net (#0)
I0511 20:02:42.182787  3591 solver.cpp:404]     Test net output #0: accuracy = 0.00183712
I0511 20:02:42.182859  3591 solver.cpp:404]     Test net output #1: loss = 8.77069 (* 1 = 8.77069 loss)
I0511 20:02:42.756922  3591 solver.cpp:228] Iteration 98000, loss = 8.61745
I0511 20:02:42.756944  3591 solver.cpp:244]     Train net output #0: loss = 8.61745 (* 1 = 8.61745 loss)

由于代码字符数的限制，我不得不删除日志中的一些行。不过，这并不重要。正如您所看到的，“Iteration 98000”和“Iteration 0”之间没有区别。对于这种情况我真的很困惑。

这是我的模型的架构

name: "NN2"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    mean_file :"/home/jiayi-wei/caffe/examples/NN2/image_train_mean.binaryproto"  
  data_param {
    source: "/home/jiayi-wei/caffe/examples/NN2/img_train_lmdb"
    batch_size: 30
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    mean_file :"/home/jiayi-wei/caffe/examples/NN2/image_train_mean.binaryproto"
  data_param {
    source: "/home/jiayi-wei/caffe/examples/NN2/img_val_lmdb"
    batch_size: 11
    backend: LMDB
  }
}
#first layers
layer {
  name: "conv11"
  type: "Convolution"
  bottom: "data"
  top: "conv11"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu11" 
  type: "ReLU"
  bottom: "conv11"
  top: "conv11"
}
layer {
  name: "conv12"
  type: "Convolution"
  bottom: "conv11"
  top: "conv12"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu12" 
  type: "ReLU"
  bottom: "conv12"
  top: "conv12"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv12"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
#second layers
layer {
  name: "conv21"
  type: "Convolution"
  bottom: "pool1"
  top: "conv21"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu21" 
  type: "ReLU"
  bottom: "conv21"
  top: "conv21"
}
layer {
  name: "conv22"
  type: "Convolution"
  bottom: "conv21"
  top: "conv22"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu22" 
  type: "ReLU"
  bottom: "conv22"
  top: "conv22"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv22"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
#third layers
layer {
  name: "conv31"
  type: "Convolution"
  bottom: "pool2"
  top: "conv31"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad:1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu31" 
  type: "ReLU"
  bottom: "conv31"
  top: "conv31"
}
layer {
  name: "conv32"
  type: "Convolution"
  bottom: "conv31"
  top: "conv32"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad:1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu32" 
  type: "ReLU"
  bottom: "conv32"
  top: "conv32"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv32"
  top: "pool3"
  pooling_param {
    pool: MAX
    pad:1
    kernel_size: 2
    stride: 2
  }
}
#fourth layer
layer {
  name: "conv41"
  type: "Convolution"
  bottom: "pool3"
  top: "conv41"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad:1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu41" 
  type: "ReLU"
  bottom: "conv41"
  top: "conv41"
}
layer {
  name: "conv42"
  type: "Convolution"
  bottom: "conv41"
  top: "conv42"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad:1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu42" 
  type: "ReLU"
  bottom: "conv42"
  top: "conv42"
}
layer {
  name: "conv43"
  type: "Convolution"
  bottom: "conv42"
  top: "conv43"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad:1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu43" 
  type: "ReLU"
  bottom: "conv43"
  top: "conv43"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv43"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
#fiveth layer
layer {
  name: "conv51"
  type: "Convolution"
  bottom: "pool4"
  top: "conv51"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad:1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu51" 
  type: "ReLU"
  bottom: "conv51"
  top: "conv51"
}
layer {
  name: "conv52"
  type: "Convolution"
  bottom: "conv51"
  top: "conv52"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad:1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu52" 
  type: "ReLU"
  bottom: "conv52"
  top: "conv52"
}
layer {
  name: "conv53"
  type: "Convolution"
  bottom: "conv52"
  top: "conv53"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad:1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv53"
  top: "pool5"
  pooling_param {
    pool: AVE
    pad:1
    kernel_size: 2
    stride: 2
  }
}

#drop_Fc
layer {
  name: "dropout"
  type: "Dropout"
  bottom: "pool5"
  top: "pool5"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output:1000
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}

layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output:10575
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc7"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "SoftMax"
  type: "SoftmaxWithLoss"
  bottom: "fc7"
  bottom: "label"
  top: "SoftMax"
}

以下是我的求解器。我已将base_lr更改为“0.001”

net: "train_val.prototxt"
test_iter: 10000
test_interval: 1000
base_lr: ０．００１
lr_policy: "step"
gamma: 0.1
stepsize: 100000
display: 20
max_iter: 450000
momentum: 0.9
weight_decay: 0.0005
snapshot: 10000
snapshot_prefix: "/home/jiayi-wei/caffe/examples/NN2"
solver_mode: GPU

我尝试更改一些参数，并且我已经尝试从具有三个“转换”层的 block 中减少“转换”层。然而结果总是如图所示。

请告诉我如何解决这个问题？谢谢

Answer 1

您的base_lr似乎很高。从 base_lr0.001 开始，当您在数千次迭代中不再看到准确性提高时，继续将其减少 10 倍。

注意:这只是一个经验法则，可能并不适用于所有情况。