Error using Euclidean Loss

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Atena Nguyen

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Jul 22, 2017, 3:59:48 AM7/22/17

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Hi caffe users,

I got struggle using Euclidean loss using pycaffe for very simple network. I use python to generate the protpotxt without any problem, but the network could not ininitalize with.

Any suggestion?

Thank you

I got this error using IDE pycharm

I0722 16:56:51.209270  9132 net.cpp:84] Creating Layer conv0
I0722 16:56:51.209270  9132 net.cpp:406] conv0 <- data
I0722 16:56:51.209270  9132 net.cpp:380] conv0 -> conv0
I0722 16:56:51.538775  9132 net.cpp:122] Setting up conv0
I0722 16:56:51.538775  9132 net.cpp:129] Top shape: 100 16 32 32 (1638400)
I0722 16:56:51.538775  9132 net.cpp:137] Memory required for data: 7372800
I0722 16:56:51.538775  9132 l*** Check failure stack trace: ***

The prototxt file is

layer {
  name: "data"
  type: "HDF5Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  hdf5_data_param {
    source: "Indept32/train_hdf5_list.txt"
    batch_size: 100
    shuffle: true
  }
}
layer {
  name: "conv0"
  type: "Convolution"
  bottom: "data"
  top: "conv0"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu0"
  type: "ReLU"
  bottom: "conv0"
  top: "conv0"
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "conv0"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 16
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "score"
  type: "Convolution"
  bottom: "conv2"
  top: "score"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 2
    pad: 0
    kernel_size: 1
  }
}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "score"
  bottom: "label"
  top: "loss"
}

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