RDCCNN-bn_deploy.prototxt

name: "RDCCNN"
print_net_info: false
input: "data"
input_dim: 1
input_dim: 3
input_dim: 112
input_dim: 96

############## CNN Architecture ###############
layer {
  name: "conv1_data"
  type: "Convolution"
  bottom: "data"
  top: "conv1_data"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 24
    kernel_size: 7
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "batchNorm_conv1_data"
  type: "BatchNorm"
  bottom: "conv1_data"
  top: "batchNorm_conv1_data"
  batch_norm_param {
    use_global_stats: false
  }
}
layer {
  name: "Scale1"
  type: "Scale"
  bottom: "batchNorm_conv1_data"
  top: "Scale1"
  scale_param {
    filler {
      type: "constant"
      value: -1.0
    }
  }
}
layer {
  name: "Concat1"
  type: "Concat"
  bottom: "Scale1"
  bottom: "batchNorm_conv1_data"
  top: "Concat1"
}
layer {
  name: "ReLU1"
  type: "ReLU"
  bottom: "Concat1"
  top: "Concat1"
}
layer {
  name: "pool1_data"
  type: "Pooling"
  bottom: "Concat1"
  top: "pool1_data"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "conv2_data"
  type: "Convolution"
  bottom: "pool1_data"
  top: "conv2_data"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    kernel_size: 5
    stride: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "batchNorm_conv2_data"
  type: "BatchNorm"
  bottom: "conv2_data"
  top: "batchNorm_conv2_data"
  batch_norm_param {
    use_global_stats: false
  }
}
layer {
  name: "scale_data"
  type: "Scale"
  bottom: "batchNorm_conv2_data"
  top: "scale_data"
  scale_param {
    filler {
      type: "constant"
      value: -1.0
    }
  }
}
layer {
  name: "concat_data"
  type: "Concat"
  bottom: "scale_data"
  bottom: "batchNorm_conv2_data"
  top: "concat_data"
}
layer {
  name: "relu_data"
  type: "ReLU"
  bottom: "concat_data"
  top: "concat_data"
}
layer {
  name: "pool2_data"
  type: "Pooling"
  bottom: "concat_data"
  top: "pool2_data"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "flat_data"
  type: "Flatten"
  bottom: "pool2_data"
  top: "flat_data"
  flatten_param {
    axis: 1
  }
}

layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "flat_data"
  top: "fc1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  inner_product_param {
    num_output: 160
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}

layer{
   name: "fc1_bn"
   type: "BatchNorm"
   bottom: "fc1"
   top: "fc1"
   batch_norm_param{
    use_global_stats: true
    }
  
}

layer {
  name: "l2"
  type: "L2Norm"
  bottom: "fc1"
  top: "l2"
}

layer {
  name: "silence"
  type: "Silence"
  bottom: "l2"
}