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Question

我正在尝试通过使用Circles（标签：＆＃34; 1＆＃34;）在黑白图像上训练AlexNet来学习Caffe。 Rectangles（标签：＆＃34; 0＆＃34;）。我使用了1800张训练图像（900个圆圈和900个矩形）。例如：

我的train_val.prototxt看起来像这样：

name: "AlexNet"
layer {
   name: "data"
   type: "Data"
   top: "data"
   top: "label"
   include {
      phase: TRAIN
   }
   data_param {
      source: "newlmdb"
      batch_size: 100
      backend: LMDB
   }
}
layer {
   name: "data"
   type: "Data"
   top: "data"
   top: "label"
   include {
      phase: TEST
   }
   data_param {
      source: "newvallmdb"
      batch_size: 50
      backend: LMDB
   }
}
layer {
   name: "conv1"
   type: "Convolution"
   bottom: "data"
   top: "conv1"
   param {
      lr_mult: 1
       decay_mult: 1
   }
   param {
      lr_mult: 2
      decay_mult: 0
   }
   convolution_param {
      num_output: 96
      kernel_size: 11
      stride: 4
      weight_filler {
         type: "gaussian"
         std: 0.01
      }
      bias_filler {
         type: "constant"
         value: 0
      }
   }
}
layer {
   name: "relu1"
   type: "ReLU"
   bottom: "conv1"
   top: "conv1"
}
layer {
   name: "norm1"
   type: "LRN"
   bottom: "conv1"
   top: "norm1"
   lrn_param {
      local_size: 5
      alpha: 0.0001
      beta: 0.75
   }
}
layer {
   name: "pool1"
   type: "Pooling"
   bottom: "norm1"
   top: "pool1"
   pooling_param {
      pool: MAX
      kernel_size: 3
      stride: 2
   }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
   name: "relu2"
   type: "ReLU"
   bottom: "conv2"
   top: "conv2"
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "conv2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "norm2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

    layer {
      name: "relu5"
      type: "ReLU"
      bottom: "conv5"
      top: "conv5"
    }
    layer {
      name: "pool5"
      type: "Pooling"
      bottom: "conv5"
      top: "pool5"
      pooling_param {
        pool: MAX
        kernel_size: 3
        stride: 

2
  }
}
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: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
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: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc8"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "label"
  top: "loss"
}

我的solver.prototxt看起来像这样：

net: "train_val.prototxt"
test_iter: 200
test_interval: 200
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 50
display: 20
max_iter: 500
momentum: 0.9
weight_decay: 0.0005
snapshot: 100
snapshot_prefix: "training"
solver_mode: GPU

虽然我得到了这个输出：

I1018 10:13:04.936286  7404 solver.cpp:330] Iteration 0, Testing net (#0)
I1018 10:13:06.262091  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:07.556700  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:11.440527  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:12.267205  7404 solver.cpp:397]     Test net output #0: accuracy = 0.94
I1018 10:13:12.267205  7404 solver.cpp:397]     Test net output #1: loss = 0.104804 (* 1 = 0.104804 loss)
I1018 10:13:12.594758  7404 solver.cpp:218] Iteration 0 (-9.63533e-42 iter/s, 7.69215s/20 iters), loss = 0.873365
I1018 10:13:12.594758  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:13:12.594758  7404 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I1018 10:13:15.807883  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:17.305263  7404 solver.cpp:218] Iteration 20 (4.25024 iter/s, 4.70562s/20 iters), loss = 0.873365
I1018 10:13:17.305263  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:13:17.305263  7404 sgd_solver.cpp:105] Iteration 20, lr = 0.01
I1018 10:13:20.019263  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:21.984572  7404 solver.cpp:218] Iteration 40 (4.26967 iter/s, 4.6842s/20 iters), loss = 0.873365
I1018 10:13:21.984572  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:13:21.984572  7404 sgd_solver.cpp:105] Iteration 40, lr = 0.01
I1018 10:13:24.246239  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:26.695078  7404 solver.cpp:218] Iteration 60 (4.25863 iter/s, 4.69634s/20 iters), loss = 0.873365
I1018 10:13:26.695078  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:13:26.695078  7404 sgd_solver.cpp:105] Iteration 60, lr = 0.001
I1018 10:13:28.426422  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:31.421181  7404 solver.cpp:218] Iteration 80 (4.22339 iter/s, 4.73554s/20 iters), loss = 0.873365
I1018 10:13:31.421181  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:13:31.421181  7404 sgd_solver.cpp:105] Iteration 80, lr = 0.001
I1018 10:13:32.731387  7748 data_layer.cpp:73] Restarting data prefetching from start.
[I 10:13:32.934 NotebookApp] Saving file at /Untitled2.ipynb
I1018 10:13:35.788537  7404 solver.cpp:447] Snapshotting to binary proto file training_iter_100.caffemodel
I1018 10:13:37.317111  7404 sgd_solver.cpp:273] Snapshotting solver state to binary proto file training_iter_100.solverstate
I1018 10:13:38.081399  7404 solver.cpp:218] Iteration 100 (3.00631 iter/s, 6.65267s/20 iters), loss = 0
I1018 10:13:38.081399  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:13:38.081399  7404 sgd_solver.cpp:105] Iteration 100, lr = 0.0001
I1018 10:13:38.908077  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:42.791904  7404 solver.cpp:218] Iteration 120 (4.23481 iter/s, 4.72276s/20 iters), loss = 0
I1018 10:13:42.807502  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:13:42.807502  7404 sgd_solver.cpp:105] Iteration 120, lr = 0.0001
I1018 10:13:43.088260  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:47.393225  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:47.549202  7404 solver.cpp:218] Iteration 140 (4.21716 iter/s, 4.74253s/20 iters), loss = 0
I1018 10:13:47.549202  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:13:47.549202  7404 sgd_solver.cpp:105] Iteration 140, lr = 0.0001
I1018 10:13:51.635800  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:52.290904  7404 solver.cpp:218] Iteration 160 (4.21268 iter/s, 4.74757s/20 iters), loss = 0
I1018 10:13:52.290904  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:13:52.290904  7404 sgd_solver.cpp:105] Iteration 160, lr = 1e-05
I1018 10:13:56.003156  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:13:57.048202  7404 solver.cpp:218] Iteration 180 (4.20926 iter/s, 4.75142s/20 iters), loss = 0.873365
I1018 10:13:57.048202  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:13:57.048202  7404 sgd_solver.cpp:105] Iteration 180, lr = 1e-05
I1018 10:14:00.214535  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:01.431155  7404 solver.cpp:447] Snapshotting to binary proto file training_iter_200.caffemodel
I1018 10:14:03.053316  7404 sgd_solver.cpp:273] Snapshotting solver state to binary proto file training_iter_200.solverstate
I1018 10:14:03.552443  7404 solver.cpp:330] Iteration 200, Testing net (#0)
I1018 10:14:04.082764  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:05.439764  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:10.727385  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:10.789775  7404 blocking_queue.cpp:49] Waiting for data
I1018 10:14:10.961350  7404 solver.cpp:397]     Test net output #0: accuracy = 0.94
I1018 10:14:10.961350  7404 solver.cpp:397]     Test net output #1: loss = 0.104804 (* 1 = 0.104804 loss)
I1018 10:14:11.179718  7404 solver.cpp:218] Iteration 200 (1.41459 iter/s, 14.1384s/20 iters), loss = 0.873365
I1018 10:14:11.179718  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:14:11.179718  7404 sgd_solver.cpp:105] Iteration 200, lr = 1e-06
I1018 10:14:13.846925  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:15.952615  7404 solver.cpp:218] Iteration 220 (4.19673 iter/s, 4.76562s/20 iters), loss = 0.873365
I1018 10:14:15.952615  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:14:15.952615  7404 sgd_solver.cpp:105] Iteration 220, lr = 1e-06
I1018 10:14:18.198683  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:20.709913  7404 solver.cpp:218] Iteration 240 (4.19817 iter/s, 4.76398s/20 iters), loss = 0.873365
I1018 10:14:20.709913  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:14:20.709913  7404 sgd_solver.cpp:105] Iteration 240, lr = 1e-06
I1018 10:14:22.441257  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:25.498407  7404 solver.cpp:218] Iteration 260 (4.18243 iter/s, 4.78191s/20 iters), loss = 0.873365
I1018 10:14:25.498407  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:14:25.498407  7404 sgd_solver.cpp:105] Iteration 260, lr = 1e-07
I1018 10:14:26.761821  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:30.271303  7404 solver.cpp:218] Iteration 280 (4.18629 iter/s, 4.7775s/20 iters), loss = 0
I1018 10:14:30.271303  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:14:30.271303  7404 sgd_solver.cpp:105] Iteration 280, lr = 1e-07
I1018 10:14:31.129176  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:34.701050  7404 solver.cpp:447] Snapshotting to binary proto file training_iter_300.caffemodel
I1018 10:14:36.136039  7404 sgd_solver.cpp:273] Snapshotting solver state to binary proto file training_iter_300.solverstate
I1018 10:14:36.931521  7404 solver.cpp:218] Iteration 300 (3.00228 iter/s, 6.66161s/20 iters), loss = 0
I1018 10:14:36.931521  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:14:36.931521  7404 sgd_solver.cpp:105] Iteration 300, lr = 1e-08
I1018 10:14:37.337061  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:41.595233  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:41.688819  7404 solver.cpp:218] Iteration 320 (4.20513 iter/s, 4.7561s/20 iters), loss = 0
I1018 10:14:41.688819  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:14:41.688819  7404 sgd_solver.cpp:105] Iteration 320, lr = 1e-08
I1018 10:14:45.884600  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:46.461715  7404 solver.cpp:218] Iteration 340 (4.19496 iter/s, 4.76763s/20 iters), loss = 0
I1018 10:14:46.461715  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:14:46.461715  7404 sgd_solver.cpp:105] Iteration 340, lr = 1e-08
I1018 10:14:50.111598  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:51.234639  7404 solver.cpp:218] Iteration 360 (4.1858 iter/s, 4.77806s/20 iters), loss = 0.873365
I1018 10:14:51.234639  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:14:51.234639  7404 sgd_solver.cpp:105] Iteration 360, lr = 1e-09
I1018 10:14:54.478982  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:14:56.007566  7404 solver.cpp:218] Iteration 380 (4.19437 iter/s, 4.76829s/20 iters), loss = 0.873365
I1018 10:14:56.007566  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:14:56.007566  7404 sgd_solver.cpp:105] Iteration 380, lr = 1e-09
I1018 10:14:58.705986  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:00.421743  7404 solver.cpp:447] Snapshotting to binary proto file training_iter_400.caffemodel
I1018 10:15:01.903534  7404 sgd_solver.cpp:273] Snapshotting solver state to binary proto file training_iter_400.solverstate
I1018 10:15:02.371469  7404 solver.cpp:330] Iteration 400, Testing net (#0)
I1018 10:15:03.478912  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:04.820323  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:06.146136  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:07.471949  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:08.813360  7792 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:09.796021  7404 solver.cpp:397]     Test net output #0: accuracy = 0.95
I1018 10:15:09.796021  7404 solver.cpp:397]     Test net output #1: loss = 0.0873365 (* 1 = 0.0873365 loss)
I1018 10:15:10.014390  7404 solver.cpp:218] Iteration 400 (1.4278 iter/s, 14.0076s/20 iters), loss = 0.873365
I1018 10:15:10.014390  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:15:10.014390  7404 sgd_solver.cpp:105] Iteration 400, lr = 1e-10
I1018 10:15:12.291669  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:14.787317  7404 solver.cpp:218] Iteration 420 (4.18883 iter/s, 4.7746s/20 iters), loss = 0.873365
I1018 10:15:14.787317  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:15:14.787317  7404 sgd_solver.cpp:105] Iteration 420, lr = 1e-10
I1018 10:15:16.582064  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:19.545646  7404 solver.cpp:218] Iteration 440 (4.20273 iter/s, 4.75881s/20 iters), loss = 0.873365
I1018 10:15:19.545646  7404 solver.cpp:237]     Train net output #0: loss = 0.873365 (* 1 = 0.873365 loss)
I1018 10:15:19.545646  7404 sgd_solver.cpp:105] Iteration 440, lr = 1e-10
I1018 10:15:20.824666  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:24.334172  7404 solver.cpp:218] Iteration 460 (4.18022 iter/s, 4.78443s/20 iters), loss = 0
I1018 10:15:24.334172  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:15:24.334172  7404 sgd_solver.cpp:105] Iteration 460, lr = 1e-11
I1018 10:15:25.114061  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:29.107098  7404 solver.cpp:218] Iteration 480 (4.18678 iter/s, 4.77694s/20 iters), loss = 0
I1018 10:15:29.107098  7404 solver.cpp:237]     Train net output #0: loss = 0 (* 1 = 0 loss)
I1018 10:15:29.107098  7404 sgd_solver.cpp:105] Iteration 480, lr = 1e-11
I1018 10:15:29.497043  7748 data_layer.cpp:73] Restarting data prefetching from start.
I1018 10:15:33.505677  7404 solver.cpp:447] Snapshotting to binary proto file training_iter_500.caffemodel
I1018 10:15:35.112251  7404 sgd_solver.cpp:273] Snapshotting solver state to binary proto file training_iter_500.solverstate
I1018 10:15:35.751760  7404 solver.cpp:310] Iteration 500, loss = 0
I1018 10:15:35.751760  7404 solver.cpp:315] Optimization Done.

正如你所看到的，损失是常数0.873365或0而我不知道为什么。当我使用以下代码测试图像时，我总是得到零：

img = caffe.io.load_image('val/img911.png', color=False)
grayimg = img[:,:,0]
gi = np.reshape(grayimg, (260,260,1))

net = caffe.Net('deploy.prototxt',
                'training_iter_500.caffemodel',
                caffe.TEST)

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
transformer.set_raw_scale('data', 255.0)

net.blobs['data'].reshape(1,1,260,260)
net.blobs['data'].data[...] = transformer.preprocess('data', gi)

out = net.forward()

print out['prob'].argmax()

要创建LMDB文件，我使用了这个脚本：

import numpy as np
import lmdb
import caffe
import cv2

N = 1800

X = np.zeros((N, 1, 260, 260), dtype=np.uint8)
y = np.zeros(N, dtype=np.int64)
map_size = X.nbytes * 10

file = open("train.txt", "r") 
files =  file.readlines() 
print(len(files))

for i in range(0,len(files)):
    line = files[i]
    img_path = line.split()[0]
    label = line.split()[1]
    img = cv2.imread(img_path, cv2.IMREAD_GRAYSCALE)
    X[i]=img

env = lmdb.open('newlmdb', map_size=map_size)

with env.begin(write=True) as txn:
    # txn is a Transaction object
    for i in range(N):
        datum = caffe.proto.caffe_pb2.Datum()
        datum.channels = X.shape[1]
        datum.height = X.shape[2]
        datum.width = X.shape[3]
        datum.data = X[i].tobytes()  # or .tostring() if numpy < 1.9
        datum.label = int(y[i])
        y[i]=label

这是我的代码中的错误还是我选择网络参数不好？

修改

我编辑了数据层以获得零均值输入：

layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 260
    mean_file: "formen_mean.binaryproto"
  }
  data_param {
    source: "newlmdb"
    batch_size: 10
    backend: LMDB
  }
}

将训练图像的数量增加到10000并将图像测试为1000，对我的数据进行洗牌并编辑我的求解器.prototxt：

net: "train_val.prototxt"
test_iter: 20
test_interval: 50
base_lr: 0.01
lr_policy: "step"
gamma: 0.1
stepsize: 50
display: 20
max_iter: 1000
momentum: 0.9
weight_decay: 0.0005
snapshot: 200
debug_info: true
snapshot_prefix: "training"
solver_mode: GPU

在调试信息中的某个时刻发生了以下情况：

I1018 14:21:16.238169  5540 net.cpp:619]     [Backward] Layer drop6, bottom blob fc6 diff: 2.64904e-05
I1018 14:21:16.238169  5540 net.cpp:619]     [Backward] Layer relu6, bottom blob fc6 diff: 1.33896e-05
I1018 14:21:16.269316  5540 net.cpp:619]     [Backward] Layer fc6, bottom blob pool2 diff: 8.48778e-06
I1018 14:21:16.269316  5540 net.cpp:630]     [Backward] Layer fc6, param blob 0 diff: 0.000181272
I1018 14:21:16.269316  5540 net.cpp:630]     [Backward] Layer fc6, param blob 1 diff: 0.000133896
I1018 14:21:16.269316  5540 net.cpp:619]     [Backward] Layer pool2, bottom blob norm2 diff: 1.82455e-06
I1018 14:21:16.269316  5540 net.cpp:619]     [Backward] Layer norm2, bottom blob conv2 diff: 1.82354e-06
I1018 14:21:16.269316  5540 net.cpp:619]     [Backward] Layer relu2, bottom blob conv2 diff: 1.41858e-06
I1018 14:21:16.284889  5540 net.cpp:619]     [Backward] Layer conv2, bottom blob pool1 diff: 1.989e-06
I1018 14:21:16.284889  5540 net.cpp:630]     [Backward] Layer conv2, param blob 0 diff: 0.00600851
I1018 14:21:16.284889  5540 net.cpp:630]     [Backward] Layer conv2, param blob 1 diff: 0.00107259
I1018 14:21:16.284889  5540 net.cpp:619]     [Backward] Layer pool1, bottom blob norm1 diff: 4.57322e-07
I1018 14:21:16.284889  5540 net.cpp:619]     [Backward] Layer norm1, bottom blob conv1 diff: 4.54691e-07
I1018 14:21:16.284889  5540 net.cpp:619]     [Backward] Layer relu1, bottom blob conv1 diff: 2.18649e-07
I1018 14:21:16.284889  5540 net.cpp:630]     [Backward] Layer conv1, param blob 0 diff: 0.0333731
I1018 14:21:16.284889  5540 net.cpp:630]     [Backward] Layer conv1, param blob 1 diff: 0.000384605
E1018 14:21:16.331610  5540 net.cpp:719]     [Backward] All net params (data, diff): L1 norm = (1.0116e+06, 55724.3); L2 norm = (80.218, 24.0218)
I1018 14:21:16.331610  5540 solver.cpp:218] Iteration 0 (0 iter/s, 1.69776s/20 iters), loss = 8.73365
I1018 14:21:16.331610  5540 solver.cpp:237]     Train net output #0: loss = 8.73365 (* 1 = 8.73365 loss)
I1018 14:21:16.331610  5540 sgd_solver.cpp:105] Iteration 0, lr = 0.01
I1018 14:21:19.726611  5540 net.cpp:591]     [Forward] Layer data, top blob data data: 44.8563
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer data, top blob label data: 1
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer conv1, top blob conv1 data: nan
I1018 14:21:19.742184  5540 net.cpp:603]     [Forward] Layer conv1, param blob 0 data: nan
I1018 14:21:19.742184  5540 net.cpp:603]     [Forward] Layer conv1, param blob 1 data: nan
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer relu1, top blob conv1 data: nan
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer norm1, top blob norm1 data: nan
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer pool1, top blob pool1 data: inf
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer conv2, top blob conv2 data: nan
I1018 14:21:19.742184  5540 net.cpp:603]     [Forward] Layer conv2, param blob 0 data: nan
I1018 14:21:19.742184  5540 net.cpp:603]     [Forward] Layer conv2, param blob 1 data: nan
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer relu2, top blob conv2 data: nan
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer norm2, top blob norm2 data: nan
I1018 14:21:19.742184  5540 net.cpp:591]     [Forward] Layer pool2, top blob pool2 data: inf

所以我将base_lr减少到了0.0001。但是在稍后的某个时刻，梯度会下降到零：

I1018 14:24:40.919765  5500 net.cpp:591]     [Forward] Layer loss, top blob loss data: 0
I1018 14:24:40.919765  5500 net.cpp:619]     [Backward] Layer loss, bottom blob fc8 diff: 0
I1018 14:24:40.919765  5500 net.cpp:619]     [Backward] Layer fc8, bottom blob fc7 diff: 0
I1018 14:24:40.919765  5500 net.cpp:630]     [Backward] Layer fc8, param blob 0 diff: 0
I1018 14:24:40.919765  5500 net.cpp:630]     [Backward] Layer fc8, param blob 1 diff: 0
I1018 14:24:40.919765  5500 net.cpp:619]     [Backward] Layer drop7, bottom blob fc7 diff: 0
I1018 14:24:40.919765  5500 net.cpp:619]     [Backward] Layer relu7, bottom blob fc7 diff: 0
I1018 14:24:40.919765  5500 net.cpp:619]     [Backward] Layer fc7, bottom blob fc6 diff: 0
I1018 14:24:40.919765  5500 net.cpp:630]     [Backward] Layer fc7, param blob 0 diff: 0
I1018 14:24:40.919765  5500 net.cpp:630]     [Backward] Layer fc7, param blob 1 diff: 0
I1018 14:24:40.919765  5500 net.cpp:619]     [Backward] Layer drop6, bottom blob fc6 diff: 0
I1018 14:24:40.919765  5500 net.cpp:619]     [Backward] Layer relu6, bottom blob fc6 diff: 0
I1018 14:24:40.936337  5500 net.cpp:619]     [Backward] Layer fc6, bottom blob pool2 diff: 0
I1018 14:24:40.936337  5500 net.cpp:630]     [Backward] Layer fc6, param blob 0 diff: 0
I1018 14:24:40.936337  5500 net.cpp:630]     [Backward] Layer fc6, param blob 1 diff: 0
I1018 14:24:40.936337  5500 net.cpp:619]     [Backward] Layer pool2, bottom blob norm2 diff: 0
I1018 14:24:40.951910  5500 net.cpp:619]     [Backward] Layer norm2, bottom blob conv2 diff: 0
I1018 14:24:40.967483  5500 net.cpp:619]     [Backward] Layer relu2, bottom blob conv2 diff: 0
I1018 14:24:40.967483  5500 net.cpp:619]     [Backward] Layer conv2, bottom blob pool1 diff: 0
I1018 14:24:40.967483  5500 net.cpp:630]     [Backward] Layer conv2, param blob 0 diff: 0
I1018 14:24:40.967483  5500 net.cpp:630]     [Backward] Layer conv2, param blob 1 diff: 0
I1018 14:24:40.967483  5500 net.cpp:619]     [Backward] Layer pool1, bottom blob norm1 diff: 0
I1018 14:24:40.967483  5500 net.cpp:619]     [Backward] Layer norm1, bottom blob conv1 diff: 0
I1018 14:24:40.967483  5500 net.cpp:619]     [Backward] Layer relu1, bottom blob conv1 diff: 0

Answer 1

我不知道为什么你的网不学习。但是您可能需要考虑以下几点：

您的测试阶段：测试batch_size为50，test_iter为200表示您正在验证50*200=10,000示例。由于您总共只有1,800个示例 - 这个大test_iter值的含义是什么？有关此问题的详细信息，请查看this thread。
您似乎正在“按原样”使用图像，这意味着您的输入值范围为[0..255]。从网络的输入中减去平均值是很常见的，这样你就可以得到网络的零均值输入。
考虑一下你的训练debug info：你的渐变消失了吗？你有没有“活跃”的图层（例如，顶部带有"ReLU"的所有负值的图层实际上是不活动的）。
获取恒定损失值表示无论输入如何，您的图层都只预测一个标签，请考虑shuffling您的数据集。

Caffe Net没有训练（训练时损失不会改变）

修改

1 个答案: