train_val.prototxt

gistfile1.txt

name: "LeNet-HWGQ"
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "../mnist_train_lmdb"
    batch_size: 64
    backend: LMDB
  }
}
layer {
  name: "mnist"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    scale: 0.00390625
  }
  data_param {
    source: "../mnist_test_lmdb"
    batch_size: 100
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "BinaryConvolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 20
    kernel_size: 5
    bias_term: false
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
  }
  binary_convolution_param {
    use_alpha: true
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "bn1"
  type: "BatchNorm"
  bottom: "pool1"
  top: "pool1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  batch_norm_param {
    moving_average_fraction: 0.95
  }
}
layer {
  name: "qt1"
  type: "Quant"
  bottom: "pool1"
  top: "qt1"
  quant_param {
    forward_func: "hwgq"
    backward_func: "relu"
    centers: 0.538 centers: 1.076 centers: 1.614
    clip_thr: 1.614
  }
}
layer {
  name: "conv2"
  type: "BinaryConvolution"
  bottom: "qt1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 50
    kernel_size: 5
    bias_term: false
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
  }
  binary_convolution_param {
    use_alpha: true
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "bn2"
  type: "BatchNorm"
  bottom: "pool2"
  top: "pool2"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  batch_norm_param {
    moving_average_fraction: 0.95
  }
}
layer {
  name: "qt2"
  type: "Quant"
  bottom: "pool2"
  top: "qt2"
  quant_param {
    forward_func: "hwgq"
    backward_func: "relu"
    centers: 0.538 centers: 1.076 centers: 1.614
    clip_thr: 1.614
  }
}
layer {
  name: "ip1"
  type: "BinaryInnerProduct"
  bottom: "qt2"
  top: "ip1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  inner_product_param {
    num_output: 500
    bias_term: false
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
  }
  binary_inner_product_param {
    use_alpha: true
  }  
}
layer {
  name: "bn3"
  type: "BatchNorm"
  bottom: "ip1"
  top: "ip1"
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  param {
    lr_mult: 0
  }
  batch_norm_param {
    moving_average_fraction: 0.95
  }
}
layer {
  name: "qt3"
  type: "Quant"
  bottom: "ip1"
  top: "qt3"
  quant_param {
    forward_func: "hwgq"
    backward_func: "relu"
    centers: 0.538 centers: 1.076 centers: 1.614
    clip_thr: 1.614
  }
}
layer {
  name: "ip2"
  type: "BinaryInnerProduct"
  bottom: "qt3"
  top: "ip2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  inner_product_param {
    num_output: 10
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  binary_inner_product_param {
    use_alpha: true
  }  
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "ip2"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "ip2"
  bottom: "label"
  top: "loss"
}