# Enter your network definition here.
# Use Shift+Enter to update the visualization.
name: "LatinNet"
input: "data"
input_dim: 12
input_dim: 48
input_dim: 1
input_dim: 50


### class ###
layer {
  name: "conv1D1"
  type: "Convolution"
  bottom: "data"
  top: "conv1D1_conv"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
  
  	num_output: 32
    stride: 1
    pad_h: 0
    pad_w: 1
    kernel_h: 1
    kernel_w: 3
    
    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: 2
    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: 64
    stride: 1
    pad_h: 0
    pad_w: 1
    kernel_h: 1
    kernel_w: 3
    
    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: 2
    stride_h: 1
  }
}

#conv1d3
layer {
  name: "conv1D3"
  type: "Convolution"
  bottom: "conv1D2"
  top: "conv1D3_conv"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
  
  	num_output: 64
    stride: 2
    pad_h: 0
    pad_w: 1
    kernel_h: 1
    kernel_w: 3
    
    weight_filler {
      type: "xavier"
	}
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1D3"
  type: "ReLU"
  bottom: "conv1D3_conv"
  top: "conv1D3_relu"

}
layer {
  name: "pool1D3"
  type: "Pooling"
  bottom: "conv1D3_relu"
  top: "conv1D3"
  pooling_param {
    pool: MAX
     kernel_h: 1
    kernel_w: 3
    stride_w: 2
    stride_h: 1
  }
}




#fc1
layer {
  name: "classifier1"
  type: "InnerProduct"
  bottom: "conv1D3"
  top: "classifier1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 64
    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
# #  }
# #}


#fc2
layer {
  name: "classifier2"
  type: "InnerProduct"
  bottom: "classifier1"
  top: "classifier2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 6
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
# #layer {
# #  name: "relufc2"
# #  type: "ReLU"
# #  bottom: "classifier2"
# #  top: "classifier2"
# #  propagate_down: true
# #}

# #layer {
# #  name: "dropfc2"
# #  type: "Dropout"
# #  bottom: "classifier2"
# #  top: "classifier2"
# #  dropout_param {
# #    dropout_ratio: 0.2
# #  }
# #}