name: "densenet"

input: "data"
input_dim: 1
input_dim: 3
input_dim: 32
input_dim: 280


layer {
  bottom: "data"
    top: "conv1"
    name: "conv1"
    type: "Convolution"
	param {
		lr_mult: 1
		decay_mult: 1
    }
    convolution_param {
    num_output: 64
      kernel_size: 5
      pad: 2
      stride: 2
	  weight_filler { type: "msra"}
	  bias_filler { type: "constant" value: 0 }
      }
}


# DenseBlock 1
layer {
  name: "DenseBlock1"
  type: "DenseBlock"
  bottom: "conv1"
  top: "DenseBlock1"
  denseblock_param {
    numTransition: 8
    initChannel: 64
    growthRate: 8
    Filter_Filler {
      type: "msra"
    }
    BN_Scaler_Filler {
      type: "constant"
      value: 1
    }
    BN_Bias_Filler {
      type: "constant"
      value: 0
    }
    use_dropout: false
    dropout_amount: 0.2
  }
}
layer {
  name: "BatchNorm1"
  type: "BatchNorm"
  bottom: "DenseBlock1"
  top: "BatchNorm1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale1"
  type: "Scale"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU1"
  type: "ReLU"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
}
layer {
  name: "Convolution2"
  type: "Convolution"
  bottom: "BatchNorm1"
  top: "Convolution2"
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "Dropout1"
  type: "Dropout"
  bottom: "Convolution2"
  top: "Dropout1"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Pooling1"
  type: "Pooling"
  bottom: "Dropout1"
  top: "Pooling1"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}

# DenseBlock 2
layer {
  name: "DenseBlock2"
  type: "DenseBlock"
  bottom: "Pooling1"
  top: "DenseBlock2"
  denseblock_param {
    numTransition: 8
    initChannel: 64
    growthRate: 8
    Filter_Filler {
      type: "msra"
    }
    BN_Scaler_Filler {
      type: "constant"
      value: 1
    }
    BN_Bias_Filler {
      type: "constant"
      value: 0
    }
    use_dropout: false
    dropout_amount: 0.2
  }
}
layer {
  name: "BatchNorm2"
  type: "BatchNorm"
  bottom: "DenseBlock2"
  top: "BatchNorm2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale2"
  type: "Scale"
  bottom: "BatchNorm2"
  top: "BatchNorm2"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU2"
  type: "ReLU"
  bottom: "BatchNorm2"
  top: "BatchNorm2"
}



layer {
  name: "Convolution3"
  type: "Convolution"
  bottom: "BatchNorm2"
  top: "Convolution3"
  convolution_param {
    num_output: 192
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "Dropout2"
  type: "Dropout"
  bottom: "Convolution3"
  top: "Convolution3"
  dropout_param {
    dropout_ratio: 0.2
  }
}
layer {
  name: "Pooling2"
  type: "Pooling"
  bottom: "Convolution3"
  top: "Pooling2"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}

# DenseBlock 3
layer {
  name: "DenseBlock3"
  type: "DenseBlock"
  bottom: "Pooling2"
  top: "DenseBlock3"
  denseblock_param {
    numTransition: 8
    initChannel: 64
    growthRate: 8
    Filter_Filler {
      type: "msra"
    }
    BN_Scaler_Filler {
      type: "constant"
      value: 1
    }
    BN_Bias_Filler {
      type: "constant"
      value: 0
    }
    use_dropout: false
    dropout_amount: 0.2
  }
}
layer {
  name: "BatchNorm3"
  type: "BatchNorm"
  bottom: "DenseBlock3"
  top: "BatchNorm3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "Scale3"
  type: "Scale"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "ReLU3"
  type: "ReLU"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
}


layer {
  name: "pool5_ave"
  type: "Pooling"
  bottom: "BatchNorm3"
  top: "pool5_ave"
  pooling_param {
    pool: AVE
    kernel_w: 1
	kernel_h: 4
    stride_w: 1
	stride_h: 1
  }
}

layer {
  name: "pool5_ave_transpose"
  top: "pool5_ave_transpose"
  bottom: "pool5_ave"
  type: "Transpose"
  transpose_param {
    dim: 3
    dim: 2
    dim: 0
    dim: 1
  }
}

layer {
  name: "blstm_input"
  type: "Reshape"
  bottom: "pool5_ave_transpose"
  top: "blstm_input"
  reshape_param {
    shape { dim: -1 }
    axis: 1
    num_axes: 2
  }
}

#===================blstm layer 1============================
#======lstm1===================
layer {
  name: "lstm1"
  type: "Lstm"
  bottom: "blstm_input"
  top: "lstm1"
  lstm_param {
    num_output: 256
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}

# =====lstm1_reverse===================
layer {
  name: "lstm1-reverse1"
  type: "Reverse"
  bottom: "blstm_input"
  top: "rlstm1_input"
  reverse_param {
    axis: 0
  }
}
layer {
  name: "rlstm1"
  type: "Lstm"
  bottom: "rlstm1_input"
  top: "rlstm1-output"
  lstm_param {
    num_output: 256
	weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
   }
}
layer {
  name: "lstm1-reverse2"
  type: "Reverse"
  bottom: "rlstm1-output"
  top: "rlstm1"
  reverse_param {
    axis: 0
  }
}


# merge lstm1 and rlstm1
layer {
  name: "blstm1"
  type: "Eltwise"
  bottom: "lstm1"
  bottom: "rlstm1"
  bottom: "blstm_input"
  top: "blstm1"
  eltwise_param {
    operation: SUM
  }
}


 

#===================blstm layer 2============================
#======lstm2===================
layer {
  name: "lstm2"
  type: "Lstm"
  bottom: "blstm1"
  top: "lstm2"
  lstm_param {
    num_output: 256
    weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
  }
}

# =====lstm2_reverse===================
layer {
  name: "lstm2-reverse1"
  type: "Reverse"
  bottom: "blstm1"
  top: "rlstm2_input"
  reverse_param {
    axis: 0
  }
}

layer {
  name: "rlstm2"
  type: "Lstm"
  bottom: "rlstm2_input"
  top: "rlstm2-output"
  lstm_param {
    num_output: 256
	weight_filler {
      type: "gaussian"
      std: 0.1
    }
    bias_filler {
      type: "constant"
    }
   }
}
layer {
  name: "lstm2-reverse2"
  type: "Reverse"
  bottom: "rlstm2-output"
  top: "rlstm2"
  reverse_param {
    axis: 0
  }
}

# merge lstm2 and rlstm2
layer {
  name: "blstm2"
  type: "Eltwise"
  bottom: "lstm2"
  bottom: "rlstm2"
  bottom: "blstm1"
  bottom: "blstm_input"
  top: "blstm2"
  eltwise_param {
    operation: SUM
  }
}

layer {
  name: "fc1x_69"
  type: "InnerProduct"
  bottom: "blstm2"
  top: "fc1x"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    axis: 2
    num_output: 69
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}


layer {
	name: "result"
	type: "CTCGreedyDecoder"
	bottom: "fc1x"
	top: "result"
}