facex-landmark-net-7conv-s2.prototxt, netscope: http://ethereon.github.io/netscope/#/gist/d0bf2a3d66e152c77327edb88d32d645

facex-landmark-net-7conv-s2.prototxt

name: "Facex-Lanamark-Net-7Conv-s2"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 48
input_dim: 48
##################################
layer {
  name: "conv0"
  type: "Convolution"
  bottom: "data"
  top: "conv0"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
    num_output: 16
    kernel_size: 3
    stride: 2
    pad: 1
     weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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: 1
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 1
    pad: 1
     weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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: 1
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 2
    pad: 1
     weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 64
	kernel_size: 3
    pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 128
	kernel_size: 3
    stride: 2
    pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 128
	kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}

layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv5"
  top: "conv6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  convolution_param {
	num_output: 128
	kernel_size: 3
    stride: 2
    pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
}
layer {
  name: "fc1"
  type: "InnerProduct"
  bottom: "conv6"
  top: "fc1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  inner_product_param {
	#kernel_size: 3
	num_output: 256
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}

layer {
  name: "drop5"
  type: "Dropout"
  bottom: "fc1"
  top: "fc1"
  dropout_param {
    dropout_ratio: 0.25
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "fc1"
  top: "fc1"
}

layer {
  name: "fc2"
  type: "InnerProduct"
  bottom: "fc1"
  top: "fc2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 1
  }
  inner_product_param {
  	#kernel_size: 1
	num_output: 10
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}