Floating point caffe中的浮点异常

我试着通过一些例子来学习caffe。我正在使用HDF5格式的数据集，在运行caffe列车时出现以下错误：

I1023 17:44:06.023900  3718 layer_factory.hpp:76] Creating layer fkp
I1023 17:44:06.023936  3718 net.cpp:106] Creating Layer fkp
I1023 17:44:06.023977  3718 net.cpp:411] fkp -> data
I1023 17:44:06.024009  3718 net.cpp:411] fkp -> label
I1023 17:44:06.024039  3718 hdf5_data_layer.cpp:79] Loading list of HDF5 filenames from: train.txt
I1023 17:44:06.024081  3718 hdf5_data_layer.cpp:93] Number of HDF5 files: 1
I1023 17:44:06.025254  3718 hdf5.cpp:32] Datatype class: H5T_FLOAT
Floating point exception (core dumped)

我的网：

name: "FKPReg"

layers {
  name: "fkp"
  top: "data"
  top: "label"
  type: HDF5_DATA
  hdf5_data_param {
   source: "train.txt"
   batch_size: 64
  }
    include: { phase: TRAIN }

}

layers {
  name: "data"
  type: HDF5_DATA
  top: "data"
  top: "label"
  hdf5_data_param {
    source: "validate.txt"
    batch_size: 64

  }
  include: { phase: TEST }
}

layers {
  name: "conv1"
  type: CONVOLUTION
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 32
    kernel_size: 11
    stride: 2

    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

layers {
  name: "relu2"
  type: RELU
  bottom: "conv1"
  top: "conv1"
}

layers {
  name: "pool1"
  type: POOLING
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  name: "conv2"
  type: CONVOLUTION
  bottom: "pool1"
  top: "conv2"
  convolution_param {
    num_output: 64
    pad: 2
    kernel_size: 7
    group: 2

    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

layers {
  name: "relu2"
  type: RELU
  bottom: "conv2"
  top: "conv2"
}

layers {
  name: "pool2"
  type: POOLING
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layers {
  name: "norm2"
  type: LRN
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    norm_region: WITHIN_CHANNEL
    local_size: 3
    alpha: 5e-05
    beta: 0.75
  }
}

layers {
  name: "conv3"
  type: CONVOLUTION
  bottom: "norm2"
  top: "conv3"
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 5

    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

layers {
  name: "relu3"
  type: RELU
  bottom: "conv3"
  top: "conv3"
}

layers {
  name: "conv4"
  type: CONVOLUTION
  bottom: "conv3"
  top: "conv4"
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 5

    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

layers {
  name: "relu4"
  type: RELU
  bottom: "conv4"
  top: "conv4"
}

layers {
  name: "conv5"
  type: CONVOLUTION
  bottom: "conv4"
  top: "conv5"
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 5

    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

layers {
  name: "relu5"
  type: RELU
  bottom: "conv5"
  top: "conv5"
}

layers {
  name: "pool5"
  type: POOLING
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 4
    stride: 2
  }
}

layers {
  name: "drop0"
  type: DROPOUT
  bottom: "pool5"
  top: "pool5"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layers {
  name: "ip1"
  type: INNER_PRODUCT
  bottom: "pool5"
  top: "ip1"
  inner_product_param {
    num_output: 100

    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

layers {
  name: "relu4"
  type: RELU
  bottom: "ip1"
  top: "ip1"
}

layers {
  name: "drop1"
  type: DROPOUT
  bottom: "ip1"
  top: "ip1"
  dropout_param {
    dropout_ratio: 0.5
  }
}

layers {
  name: "ip2"
  type: INNER_PRODUCT
  bottom: "ip1"
  top: "ip2"
  inner_product_param {
    num_output: 30

    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}

layers {
  name: "relu22"
  type: RELU
  bottom: "ip2"
  top: "ip2"
}

layers {
  name: "loss"
  type: EUCLIDEAN_LOSS
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}

---

有人能帮我解决这个错误吗？

你能显示训练hdf5数据文件的

h5ls

输出吗？你有什么问题吗？我在我的网络上也看到了。没有修复。。。相反，我将hdf5改为lmdb数据类型，不再面临同样的问题。。