train_test_BCBN_fastk24L250.prototxt

layer {
  name: "Data1"
  type: "Data"
  top: "Data1"
  top: "Data2"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 32 
  }
  data_param {
    source: "/home/tl486/git_TCcaffe/caffe_multiGPU/caffe/examples/cifar10/cifar10_train_lmdb_padding"
    batch_size: 32
    backend: LMDB
  }
}
layer {
  name: "Data1"
  type: "Data"
  top: "Data1"
  top: "Data2"
  include {
    phase: TEST
  }
  data_param {
    source: "/home/tl486/git_TCcaffe/caffe_multiGPU/caffe/examples/cifar10/cifar10_test_lmdb_nopadding"
    batch_size: 50
    backend: LMDB
  }
}
layer {
  name: "Convolution1"
  type: "Convolution"
  bottom: "Data1"
  top: "Convolution1"
  convolution_param {
    num_output: 48
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "DenseBlock1"
  type: "DenseBlock"
  bottom: "Convolution1"
  top: "DenseBlock1"
  denseblock_param {
    numTransition: 41
    initChannel: 48
    growthRate: 24
    Filter_Filler {
      type: "msra"
    }
    BN_Scaler_Filler {
      type: "constant"
      value: 1
    }
    BN_Bias_Filler {
      type: "constant"
      value: 0
    }
    use_dropout: false
    use_BC: true
    BC_ultra_space_efficient: true
    workspace_MB: 8
  }
}
layer {
  name: "BatchNorm1"
  type: "BatchNorm"
  bottom: "DenseBlock1"
  top: "BatchNorm1"
  batch_norm_param {
    moving_average_fraction : 0.1
    scale_filler {
      type: "constant"
      value: 1
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: CUDNN
  }  
}
layer {
  name: "ReLU1"
  type: "ReLU"
  bottom: "BatchNorm1"
  top: "BatchNorm1"
}
layer {
  name: "Convolution2"
  type: "Convolution"
  bottom: "BatchNorm1"
  top: "Convolution2"
  convolution_param {
    num_output: 516
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Pooling1"
  type: "Pooling"
  bottom: "Convolution2"
  top: "Pooling1"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "DenseBlock2"
  type: "DenseBlock"
  bottom: "Pooling1"
  top: "DenseBlock2"
  denseblock_param {
    numTransition: 41
    initChannel: 516
    growthRate: 24
    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
    use_BC: true
    BC_ultra_space_efficient: true
    workspace_MB: 8
  }
}
layer {
  name: "BatchNorm2"
  type: "BatchNorm"
  bottom: "DenseBlock2"
  top: "BatchNorm2"
  batch_norm_param {
    moving_average_fraction : 0.1
    scale_filler {
      type: "constant"
      value: 1
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: CUDNN
  } 
}
layer {
  name: "ReLU2"
  type: "ReLU"
  bottom: "BatchNorm2"
  top: "BatchNorm2"
}
layer {
  name: "Convolution3"
  type: "Convolution"
  bottom: "BatchNorm2"
  top: "Convolution3"
  convolution_param {
    num_output: 750
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
    }
  }
}
layer {
  name: "Pooling2"
  type: "Pooling"
  bottom: "Convolution3"
  top: "Pooling2"
  pooling_param {
    pool: AVE
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "DenseBlock3"
  type: "DenseBlock"
  bottom: "Pooling2"
  top: "DenseBlock3"
  denseblock_param {
    numTransition: 41
    initChannel: 750
    growthRate: 24
    Filter_Filler {
      type: "msra"
    }
    BN_Scaler_Filler {
      type: "constant"
      value: 1
    }
    BN_Bias_Filler {
      type: "constant"
      value: 0
    }
    use_dropout: false
    use_BC: true
    BC_ultra_space_efficient: true
    workspace_MB: 8
  }
}
layer {
  name: "BatchNorm3"
  type: "BatchNorm"
  bottom: "DenseBlock3"
  top: "BatchNorm3"
  batch_norm_param {
    moving_average_fraction : 0.1
    scale_filler {
      type: "constant"
      value: 1
    }
    bias_filler {
      type: "constant"
      value: 0
    }
    engine: CUDNN
  } 
}
layer {
  name: "ReLU3"
  type: "ReLU"
  bottom: "BatchNorm3"
  top: "BatchNorm3"
}
layer {
  name: "Pooling3"
  type: "Pooling"
  bottom: "BatchNorm3"
  top: "Pooling3"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "InnerProduct1"
  type: "InnerProduct"
  bottom: "Pooling3"
  top: "InnerProduct1"
  inner_product_param {
    num_output: 10
    bias_term: false
    weight_filler {
      type: "xavier"
    }
  }
}
layer {
  name: "SoftmaxWithLoss1"
  type: "SoftmaxWithLoss"
  bottom: "InnerProduct1"
  bottom: "Data2"
  top: "SoftmaxWithLoss1"
}
layer {
  name: "Accuracy1"
  type: "Accuracy"
  bottom: "InnerProduct1"
  bottom: "Data2"
  top: "Accuracy1"
  accuracy_param {
    use_log: true
  } 
}