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path: root/data/lenet.prototxt
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name: "LeNet"
layers {
  layer {
    name: "mnist"
    type: "data"
    source: "mnist-train-leveldb"
    batchsize: 64
    scale: 0.00390625
  }
  top: "data"
  top: "label"
}
layers {
  layer {
    name: "conv1"
    type: "conv"
    num_output: 20
    kernelsize: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    blobs_lr: 1.
    blobs_lr: 2.
  }
  bottom: "data"
  top: "conv1"
}
layers {
  layer {
    name: "pool1"
    type: "pool"
    kernelsize: 2
    stride: 2
    pool: MAX
  }
  bottom: "conv1"
  top: "pool1"
}
layers {
  layer {
    name: "conv2"
    type: "conv"
    num_output: 50
    kernelsize: 5
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    blobs_lr: 1.
    blobs_lr: 2.
  }
  bottom: "pool1"
  top: "conv2"
}
layers {
  layer {
    name: "pool2"
    type: "pool"
    kernelsize: 2
    stride: 2
    pool: MAX
  }
  bottom: "conv2"
  top: "pool2"
}
layers {
  layer {
    name: "ip1"
    type: "innerproduct"
    num_output: 500
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    blobs_lr: 1.
    blobs_lr: 2.
  }
  bottom: "pool2"
  top: "ip1"
}
layers {
  layer {
    name: "relu1"
    type: "relu"
  }
  bottom: "ip1"
  top: "ip1"
}
layers {
  layer {
    name: "ip2"
    type: "innerproduct"
    num_output: 10
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
    }
    blobs_lr: 1.
    blobs_lr: 2.
  }
  bottom: "ip1"
  top: "ip2"
}
layers {
  layer {
    name: "prob"
    type: "softmax_loss"
  }
  bottom: "ip2"
  bottom: "label"
}