ubuntu - 使用 caffe 运行神经网络时出错-6ren

ubuntu - 使用 caffe 运行神经网络时出错

转载作者：行者123 更新时间：2023-12-04 18:56:11

我构建了一个 caffe 网络 + 求解器(用于二进制分类)，当我运行代码(并尝试训练网络)时，我看到了这个错误:

I0914 20:03:01.362612  4024 solver.cpp:280] Learning Rate Policy: step
I0914 20:03:01.367985  4024 solver.cpp:337] Iteration 0, Testing net (#0)
I0914 20:03:01.368085  4024 net.cpp:693] Ignoring source layer train_database
I0914 20:03:04.568979  4024 solver.cpp:404]     Test net output #0: accuracy = 0.07575
I0914 20:03:04.569093  4024 solver.cpp:404]     Test net output #1: loss = 2.20947 (* 1 = 2.20947 loss)
I0914 20:03:04.610549  4024 solver.cpp:228] Iteration 0, loss = 2.31814
I0914 20:03:04.610666  4024 solver.cpp:244]     Train net output #0: loss = 2.31814 (* 1 = 2.31814 loss)
*** Aborted at 1473872584 (unix time) try "date -d @1473872584" if you are using GNU date ***
PC: @     0x7f6870b62c52 caffe::SGDSolver<>::GetLearningRate()
*** SIGFPE (@0x7f6870b62c52) received by PID 4024 (TID 0x7f6871004a40) from PID 1890987090; stack trace: ***
    @     0x7f686f6bbcb0 (unknown)
    @     0x7f6870b62c52 caffe::SGDSolver<>::GetLearningRate()
    @     0x7f6870b62e44 caffe::SGDSolver<>::ApplyUpdate()
    @     0x7f6870b8e2fc caffe::Solver<>::Step()
    @     0x7f6870b8eb09 caffe::Solver<>::Solve()
    @           0x40821d train()
    @           0x40589c main
    @     0x7f686f6a6f45 (unknown)
    @           0x40610b (unknown)
    @                0x0 (unknown)
Floating point exception (core dumped)

我搜索了很多， main solutions that I've found 是:
1.重新编译caffe文件。尝试过 make clean -> make all -> make test -> make runtest2.更改linux使用的驱动程序。我使用红色并更改为绿色(注意:我在我的 caffe 中使用 CPU，它在 makeconfig 文件中提到):

所有这些都没有帮助，我仍然无法运行我的网络。

有人有想法吗？非常感谢，无论如何:)

这是完整的日志:

/home/roishik/anaconda2/bin/python /home/roishik/Desktop/Thesis/Code/cafe_cnn/third/code/run_network.py
I0914 20:03:01.142490  4024 caffe.cpp:210] Use CPU.
I0914 20:03:01.142940  4024 solver.cpp:48] Initializing solver from parameters: 
test_iter: 400
test_interval: 400
base_lr: 0.001
display: 50
max_iter: 40000
lr_policy: "step"
gamma: 0.1
momentum: 0.9
weight_decay: 0.0005
snapshot: 5000
snapshot_prefix: "/home/roishik/Desktop/Thesis/Code/cafe_cnn/third/caffe_models/my_new/snapshots"
solver_mode: CPU
net: "/home/roishik/Desktop/Thesis/Code/cafe_cnn/third/caffe_models/my_new/fc_net_ver1.prototxt"
train_state {
  level: 0
  stage: ""
}
I0914 20:03:01.143082  4024 solver.cpp:91] Creating training net from net file: /home/roishik/Desktop/Thesis/Code/cafe_cnn/third/caffe_models/my_new/fc_net_ver1.prototxt
I0914 20:03:01.143712  4024 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer validation_database
I0914 20:03:01.143754  4024 net.cpp:322] The NetState phase (0) differed from the phase (1) specified by a rule in layer accuracy
I0914 20:03:01.143913  4024 net.cpp:58] Initializing net from parameters: 
name: "fc2Net"
state {
  phase: TRAIN
  level: 0
  stage: ""
}
layer {
  name: "train_database"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mean_file: "/home/roishik/Desktop/Thesis/Code/cafe_cnn/third/input/mean.binaryproto"
  }
  data_param {
    source: "/home/roishik/Desktop/Thesis/Code/cafe_cnn/third/input/train_lmdb"
    batch_size: 200
    backend: LMDB
  }
}
layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "data"
  top: "fc1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "fc1"
  top: "fc1"
}
layer {
  name: "fc2"
  type: "InnerProduct"
  bottom: "fc1"
  top: "fc2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "fc2"
  top: "fc2"
}
layer {
  name: "fc3"
  type: "InnerProduct"
  bottom: "fc2"
  top: "fc3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc3"
  bottom: "label"
  top: "loss"
}
I0914 20:03:01.144016  4024 layer_factory.hpp:77] Creating layer train_database
I0914 20:03:01.144811  4024 net.cpp:100] Creating Layer train_database
I0914 20:03:01.144846  4024 net.cpp:408] train_database -> data
I0914 20:03:01.144909  4024 net.cpp:408] train_database -> label
I0914 20:03:01.144951  4024 data_transformer.cpp:25] Loading mean file from: /home/roishik/Desktop/Thesis/Code/cafe_cnn/third/input/mean.binaryproto
I0914 20:03:01.153393  4035 db_lmdb.cpp:35] Opened lmdb /home/roishik/Desktop/Thesis/Code/cafe_cnn/third/input/train_lmdb
I0914 20:03:01.153481  4024 data_layer.cpp:41] output data size: 200,1,32,32
I0914 20:03:01.154615  4024 net.cpp:150] Setting up train_database
I0914 20:03:01.154670  4024 net.cpp:157] Top shape: 200 1 32 32 (204800)
I0914 20:03:01.154693  4024 net.cpp:157] Top shape: 200 (200)
I0914 20:03:01.154712  4024 net.cpp:165] Memory required for data: 820000
I0914 20:03:01.154742  4024 layer_factory.hpp:77] Creating layer fc1
I0914 20:03:01.154781  4024 net.cpp:100] Creating Layer fc1
I0914 20:03:01.154804  4024 net.cpp:434] fc1 <- data
I0914 20:03:01.154837  4024 net.cpp:408] fc1 -> fc1
I0914 20:03:01.159675  4036 blocking_queue.cpp:50] Waiting for data
I0914 20:03:01.215118  4024 net.cpp:150] Setting up fc1
I0914 20:03:01.215214  4024 net.cpp:157] Top shape: 200 1024 (204800)
I0914 20:03:01.215237  4024 net.cpp:165] Memory required for data: 1639200
I0914 20:03:01.215306  4024 layer_factory.hpp:77] Creating layer relu1
I0914 20:03:01.215342  4024 net.cpp:100] Creating Layer relu1
I0914 20:03:01.215363  4024 net.cpp:434] relu1 <- fc1
I0914 20:03:01.215387  4024 net.cpp:395] relu1 -> fc1 (in-place)
I0914 20:03:01.215417  4024 net.cpp:150] Setting up relu1
I0914 20:03:01.215440  4024 net.cpp:157] Top shape: 200 1024 (204800)
I0914 20:03:01.215459  4024 net.cpp:165] Memory required for data: 2458400
I0914 20:03:01.215478  4024 layer_factory.hpp:77] Creating layer fc2
I0914 20:03:01.215504  4024 net.cpp:100] Creating Layer fc2
I0914 20:03:01.215524  4024 net.cpp:434] fc2 <- fc1
I0914 20:03:01.215549  4024 net.cpp:408] fc2 -> fc2
I0914 20:03:01.264021  4024 net.cpp:150] Setting up fc2
I0914 20:03:01.264062  4024 net.cpp:157] Top shape: 200 1024 (204800)
I0914 20:03:01.264072  4024 net.cpp:165] Memory required for data: 3277600
I0914 20:03:01.264097  4024 layer_factory.hpp:77] Creating layer relu2
I0914 20:03:01.264118  4024 net.cpp:100] Creating Layer relu2
I0914 20:03:01.264129  4024 net.cpp:434] relu2 <- fc2
I0914 20:03:01.264143  4024 net.cpp:395] relu2 -> fc2 (in-place)
I0914 20:03:01.264166  4024 net.cpp:150] Setting up relu2
I0914 20:03:01.264181  4024 net.cpp:157] Top shape: 200 1024 (204800)
I0914 20:03:01.264190  4024 net.cpp:165] Memory required for data: 4096800
I0914 20:03:01.264201  4024 layer_factory.hpp:77] Creating layer fc3
I0914 20:03:01.264219  4024 net.cpp:100] Creating Layer fc3
I0914 20:03:01.264230  4024 net.cpp:434] fc3 <- fc2
I0914 20:03:01.264245  4024 net.cpp:408] fc3 -> fc3
I0914 20:03:01.264389  4024 net.cpp:150] Setting up fc3
I0914 20:03:01.264407  4024 net.cpp:157] Top shape: 200 2 (400)
I0914 20:03:01.264416  4024 net.cpp:165] Memory required for data: 4098400
I0914 20:03:01.264434  4024 layer_factory.hpp:77] Creating layer loss
I0914 20:03:01.264447  4024 net.cpp:100] Creating Layer loss
I0914 20:03:01.264459  4024 net.cpp:434] loss <- fc3
I0914 20:03:01.264469  4024 net.cpp:434] loss <- label
I0914 20:03:01.264487  4024 net.cpp:408] loss -> loss
I0914 20:03:01.264513  4024 layer_factory.hpp:77] Creating layer loss
I0914 20:03:01.264544  4024 net.cpp:150] Setting up loss
I0914 20:03:01.264559  4024 net.cpp:157] Top shape: (1)
I0914 20:03:01.264569  4024 net.cpp:160]     with loss weight 1
I0914 20:03:01.264595  4024 net.cpp:165] Memory required for data: 4098404
I0914 20:03:01.264606  4024 net.cpp:226] loss needs backward computation.
I0914 20:03:01.264617  4024 net.cpp:226] fc3 needs backward computation.
I0914 20:03:01.264626  4024 net.cpp:226] relu2 needs backward computation.
I0914 20:03:01.264636  4024 net.cpp:226] fc2 needs backward computation.
I0914 20:03:01.264647  4024 net.cpp:226] relu1 needs backward computation.
I0914 20:03:01.264655  4024 net.cpp:226] fc1 needs backward computation.
I0914 20:03:01.264667  4024 net.cpp:228] train_database does not need backward computation.
I0914 20:03:01.264675  4024 net.cpp:270] This network produces output loss
I0914 20:03:01.264695  4024 net.cpp:283] Network initialization done.
I0914 20:03:01.265384  4024 solver.cpp:181] Creating test net (#0) specified by net file: /home/roishik/Desktop/Thesis/Code/cafe_cnn/third/caffe_models/my_new/fc_net_ver1.prototxt
I0914 20:03:01.265435  4024 net.cpp:322] The NetState phase (1) differed from the phase (0) specified by a rule in layer train_database
I0914 20:03:01.265606  4024 net.cpp:58] Initializing net from parameters: 
name: "fc2Net"
state {
  phase: TEST
}
layer {
  name: "validation_database"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mean_file: "/home/roishik/Desktop/Thesis/Code/cafe_cnn/second/input/mean.binaryproto"
  }
  data_param {
    source: "/home/roishik/Desktop/Thesis/Code/cafe_cnn/second/input/validation_lmdb"
    batch_size: 40
    backend: LMDB
  }
}
layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "data"
  top: "fc1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "fc1"
  top: "fc1"
}
layer {
  name: "fc2"
  type: "InnerProduct"
  bottom: "fc1"
  top: "fc2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 1024
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "fc2"
  top: "fc2"
}
layer {
  name: "fc3"
  type: "InnerProduct"
  bottom: "fc2"
  top: "fc3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc3"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc3"
  bottom: "label"
  top: "loss"
}
I0914 20:03:01.265750  4024 layer_factory.hpp:77] Creating layer validation_database
I0914 20:03:01.265878  4024 net.cpp:100] Creating Layer validation_database
I0914 20:03:01.265897  4024 net.cpp:408] validation_database -> data
I0914 20:03:01.265918  4024 net.cpp:408] validation_database -> label
I0914 20:03:01.265936  4024 data_transformer.cpp:25] Loading mean file from: /home/roishik/Desktop/Thesis/Code/cafe_cnn/second/input/mean.binaryproto
I0914 20:03:01.266034  4037 db_lmdb.cpp:35] Opened lmdb /home/roishik/Desktop/Thesis/Code/cafe_cnn/second/input/validation_lmdb
I0914 20:03:01.266098  4024 data_layer.cpp:41] output data size: 40,1,32,32
I0914 20:03:01.266295  4024 net.cpp:150] Setting up validation_database
I0914 20:03:01.266315  4024 net.cpp:157] Top shape: 40 1 32 32 (40960)
I0914 20:03:01.266330  4024 net.cpp:157] Top shape: 40 (40)
I0914 20:03:01.266340  4024 net.cpp:165] Memory required for data: 164000
I0914 20:03:01.266350  4024 layer_factory.hpp:77] Creating layer label_validation_database_1_split
I0914 20:03:01.266386  4024 net.cpp:100] Creating Layer label_validation_database_1_split
I0914 20:03:01.266404  4024 net.cpp:434] label_validation_database_1_split <- label
I0914 20:03:01.266422  4024 net.cpp:408] label_validation_database_1_split -> label_validation_database_1_split_0
I0914 20:03:01.266443  4024 net.cpp:408] label_validation_database_1_split -> label_validation_database_1_split_1
I0914 20:03:01.266464  4024 net.cpp:150] Setting up label_validation_database_1_split
I0914 20:03:01.266480  4024 net.cpp:157] Top shape: 40 (40)
I0914 20:03:01.266494  4024 net.cpp:157] Top shape: 40 (40)
I0914 20:03:01.266505  4024 net.cpp:165] Memory required for data: 164320
I0914 20:03:01.266515  4024 layer_factory.hpp:77] Creating layer fc1
I0914 20:03:01.266531  4024 net.cpp:100] Creating Layer fc1
I0914 20:03:01.266543  4024 net.cpp:434] fc1 <- data
I0914 20:03:01.266558  4024 net.cpp:408] fc1 -> fc1
I0914 20:03:01.320364  4024 net.cpp:150] Setting up fc1
I0914 20:03:01.320461  4024 net.cpp:157] Top shape: 40 1024 (40960)
I0914 20:03:01.320489  4024 net.cpp:165] Memory required for data: 328160
I0914 20:03:01.320533  4024 layer_factory.hpp:77] Creating layer relu1
I0914 20:03:01.320571  4024 net.cpp:100] Creating Layer relu1
I0914 20:03:01.320597  4024 net.cpp:434] relu1 <- fc1
I0914 20:03:01.320627  4024 net.cpp:395] relu1 -> fc1 (in-place)
I0914 20:03:01.320652  4024 net.cpp:150] Setting up relu1
I0914 20:03:01.320667  4024 net.cpp:157] Top shape: 40 1024 (40960)
I0914 20:03:01.320678  4024 net.cpp:165] Memory required for data: 492000
I0914 20:03:01.320689  4024 layer_factory.hpp:77] Creating layer fc2
I0914 20:03:01.320709  4024 net.cpp:100] Creating Layer fc2
I0914 20:03:01.320719  4024 net.cpp:434] fc2 <- fc1
I0914 20:03:01.320734  4024 net.cpp:408] fc2 -> fc2
I0914 20:03:01.361732  4024 net.cpp:150] Setting up fc2
I0914 20:03:01.361766  4024 net.cpp:157] Top shape: 40 1024 (40960)
I0914 20:03:01.361802  4024 net.cpp:165] Memory required for data: 655840
I0914 20:03:01.361821  4024 layer_factory.hpp:77] Creating layer relu2
I0914 20:03:01.361837  4024 net.cpp:100] Creating Layer relu2
I0914 20:03:01.361845  4024 net.cpp:434] relu2 <- fc2
I0914 20:03:01.361852  4024 net.cpp:395] relu2 -> fc2 (in-place)
I0914 20:03:01.361866  4024 net.cpp:150] Setting up relu2
I0914 20:03:01.361872  4024 net.cpp:157] Top shape: 40 1024 (40960)
I0914 20:03:01.361877  4024 net.cpp:165] Memory required for data: 819680
I0914 20:03:01.361881  4024 layer_factory.hpp:77] Creating layer fc3
I0914 20:03:01.361892  4024 net.cpp:100] Creating Layer fc3
I0914 20:03:01.361901  4024 net.cpp:434] fc3 <- fc2
I0914 20:03:01.361909  4024 net.cpp:408] fc3 -> fc3
I0914 20:03:01.362009  4024 net.cpp:150] Setting up fc3
I0914 20:03:01.362017  4024 net.cpp:157] Top shape: 40 2 (80)
I0914 20:03:01.362022  4024 net.cpp:165] Memory required for data: 820000
I0914 20:03:01.362032  4024 layer_factory.hpp:77] Creating layer fc3_fc3_0_split
I0914 20:03:01.362041  4024 net.cpp:100] Creating Layer fc3_fc3_0_split
I0914 20:03:01.362046  4024 net.cpp:434] fc3_fc3_0_split <- fc3
I0914 20:03:01.362053  4024 net.cpp:408] fc3_fc3_0_split -> fc3_fc3_0_split_0
I0914 20:03:01.362062  4024 net.cpp:408] fc3_fc3_0_split -> fc3_fc3_0_split_1
I0914 20:03:01.362073  4024 net.cpp:150] Setting up fc3_fc3_0_split
I0914 20:03:01.362082  4024 net.cpp:157] Top shape: 40 2 (80)
I0914 20:03:01.362088  4024 net.cpp:157] Top shape: 40 2 (80)
I0914 20:03:01.362093  4024 net.cpp:165] Memory required for data: 820640
I0914 20:03:01.362097  4024 layer_factory.hpp:77] Creating layer accuracy
I0914 20:03:01.362120  4024 net.cpp:100] Creating Layer accuracy
I0914 20:03:01.362128  4024 net.cpp:434] accuracy <- fc3_fc3_0_split_0
I0914 20:03:01.362134  4024 net.cpp:434] accuracy <- label_validation_database_1_split_0
I0914 20:03:01.362141  4024 net.cpp:408] accuracy -> accuracy
I0914 20:03:01.362152  4024 net.cpp:150] Setting up accuracy
I0914 20:03:01.362159  4024 net.cpp:157] Top shape: (1)
I0914 20:03:01.362164  4024 net.cpp:165] Memory required for data: 820644
I0914 20:03:01.362169  4024 layer_factory.hpp:77] Creating layer loss
I0914 20:03:01.362176  4024 net.cpp:100] Creating Layer loss
I0914 20:03:01.362181  4024 net.cpp:434] loss <- fc3_fc3_0_split_1
I0914 20:03:01.362187  4024 net.cpp:434] loss <- label_validation_database_1_split_1
I0914 20:03:01.362193  4024 net.cpp:408] loss -> loss
I0914 20:03:01.362226  4024 layer_factory.hpp:77] Creating layer loss
I0914 20:03:01.362251  4024 net.cpp:150] Setting up loss
I0914 20:03:01.362265  4024 net.cpp:157] Top shape: (1)
I0914 20:03:01.362277  4024 net.cpp:160]     with loss weight 1
I0914 20:03:01.362298  4024 net.cpp:165] Memory required for data: 820648
I0914 20:03:01.362311  4024 net.cpp:226] loss needs backward computation.
I0914 20:03:01.362323  4024 net.cpp:228] accuracy does not need backward computation.
I0914 20:03:01.362336  4024 net.cpp:226] fc3_fc3_0_split needs backward computation.
I0914 20:03:01.362347  4024 net.cpp:226] fc3 needs backward computation.
I0914 20:03:01.362360  4024 net.cpp:226] relu2 needs backward computation.
I0914 20:03:01.362370  4024 net.cpp:226] fc2 needs backward computation.
I0914 20:03:01.362381  4024 net.cpp:226] relu1 needs backward computation.
I0914 20:03:01.362392  4024 net.cpp:226] fc1 needs backward computation.
I0914 20:03:01.362403  4024 net.cpp:228] label_validation_database_1_split does not need backward computation.
I0914 20:03:01.362416  4024 net.cpp:228] validation_database does not need backward computation.
I0914 20:03:01.362426  4024 net.cpp:270] This network produces output accuracy
I0914 20:03:01.362438  4024 net.cpp:270] This network produces output loss
I0914 20:03:01.362460  4024 net.cpp:283] Network initialization done.
I0914 20:03:01.362552  4024 solver.cpp:60] Solver scaffolding done.
I0914 20:03:01.362591  4024 caffe.cpp:251] Starting Optimization
I0914 20:03:01.362601  4024 solver.cpp:279] Solving fc2Net
I0914 20:03:01.362612  4024 solver.cpp:280] Learning Rate Policy: step
I0914 20:03:01.367985  4024 solver.cpp:337] Iteration 0, Testing net (#0)
I0914 20:03:01.368085  4024 net.cpp:693] Ignoring source layer train_database
I0914 20:03:04.568979  4024 solver.cpp:404]     Test net output #0: accuracy = 0.07575
I0914 20:03:04.569093  4024 solver.cpp:404]     Test net output #1: loss = 2.20947 (* 1 = 2.20947 loss)
I0914 20:03:04.610549  4024 solver.cpp:228] Iteration 0, loss = 2.31814
I0914 20:03:04.610666  4024 solver.cpp:244]     Train net output #0: loss = 2.31814 (* 1 = 2.31814 loss)
*** Aborted at 1473872584 (unix time) try "date -d @1473872584" if you are using GNU date ***
PC: @     0x7f6870b62c52 caffe::SGDSolver<>::GetLearningRate()
*** SIGFPE (@0x7f6870b62c52) received by PID 4024 (TID 0x7f6871004a40) from PID 1890987090; stack trace: ***
    @     0x7f686f6bbcb0 (unknown)
    @     0x7f6870b62c52 caffe::SGDSolver<>::GetLearningRate()
    @     0x7f6870b62e44 caffe::SGDSolver<>::ApplyUpdate()
    @     0x7f6870b8e2fc caffe::Solver<>::Step()
    @     0x7f6870b8eb09 caffe::Solver<>::Solve()
    @           0x40821d train()
    @           0x40589c main
    @     0x7f686f6a6f45 (unknown)
    @           0x40610b (unknown)
    @                0x0 (unknown)
Floating point exception (core dumped)
Done!

最佳答案

查看您的错误消息:您得到了 SIGFPE信号。这表明您获得了 arithmetic error .此外，导致此错误的函数是评估学习率的函数。

似乎您没有在 'solver.prototxt' 中正确配置学习率策略。

关于ubuntu - 使用 caffe 运行神经网络时出错，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/39496365/

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ubuntu - 使用 caffe 运行神经网络时出错