# Enter your network definition here.
# Use Shift+Enter to update the visualization.
# Enter your network definition here.
# Use Shift+Enter to update the visualization.
# Enter your network definition here.
# Use Shift+Enter to update the visualization.
name: "RNet"
input: "data"
input_dim: 6
input_dim: 1
input_dim: 32
input_dim: 512


##########################
######################
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 1
    pad:1
     weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu1"
  type: "PReLU"
  bottom: "conv1"
  top: "conv1"
  propagate_down: true
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu2"
  type: "PReLU"
  bottom: "conv2"
  top: "conv2"
  propagate_down: true
}


##################################
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu3"
  type: "PReLU"
  bottom: "conv3"
  top: "conv3"
  propagate_down: true
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
###############################

###############################
# conv4
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu4"
  type: "PReLU"
  bottom: "conv4"
  top: "conv4"
  propagate_down: true
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}



#### conv5
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 4
    stride: 1
    pad_w: 1
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu5"
  type: "PReLU"
  bottom: "conv5"
  top: "conv5"
  propagate_down: true
}



#### tail
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv5"
  top: "conv6"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 81
    kernel_size: 1
    stride: 1
    pad: 0
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prelu6"
  type: "PReLU"
  bottom: "conv6"
  top: "conv6_prelu"
  propagate_down: true
}


### class ###
layer {
  name: "prelu6_1"
  type: "PReLU"
  bottom: "conv6"
  top: "conv6_prelu_1"
  propagate_down: true
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6_prelu_1"
  top: "conv6_relu"
 
}
layer {
  name: "conv1d1"
  type: "Convolution"
  bottom: "conv6_relu"
  top: "conv1d1_conv"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
  
  	num_output: 16
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 7
    
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1d1"
  type: "ReLU"
  bottom: "conv1d1_conv"
  top: "conv1d1_relu"
 
}

layer {
  name: "pool1d1"
  type: "Pooling"
  bottom: "conv1d1_relu"
  top: "conv1d1"
  pooling_param {
    pool: MAX
     kernel_h: 1
    kernel_w: 3
    stride_w: 3
    stride_h: 1
  }
}

#conv1d2
layer {
  name: "conv1d2"
  type: "Convolution"
  bottom: "conv1d1"
  top: "conv1d2_conv"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
  
  	num_output: 16
    stride: 1
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 7
    
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1d2"
  type: "ReLU"
  bottom: "conv1d2_conv"
  top: "conv1d2_relu"

}
layer {
  name: "pool1d2"
  type: "Pooling"
  bottom: "conv1d2_relu"
  top: "conv1d2"
  pooling_param {
    pool: MAX
     kernel_h: 1
    kernel_w: 3
    stride_w: 3
    stride_h: 1
  }
}

#fc1
layer {
  name: "classifier1"
  type: "InnerProduct"
  bottom: "conv1d2"
  top: "classifier1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 32
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relufc1"
  type: "ReLU"
  bottom: "classifier1"
  top: "classifier1"
}

layer {
  name: "dropfc1"
  type: "Dropout"
  bottom: "classifier1"
  top: "classifier1"
  dropout_param {
    dropout_ratio: 0.2
  }
}


#fc3
layer {
  name: "classifier3"
  type: "InnerProduct"
  bottom: "classifier1"
  top: "classifier3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}