caffe hdf5,多标签数据的训练prototxt,例子,需要一个slice层

				版权声明:本文为jiarenyf原创文章,未经允许也可以转载,但是附上链接……					https://blog.csdn.net/u011762313/article/details/48851015				</div>
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  • Caffe中HDF5Data用于处理多标签数据,例子如下:
  • name: "LeNet"
    
    ###for data and labels
    
    layer {
      name: "data"
      type: "HDF5Data"
      top: "data"
      top: "labels"
      include {
        phase: TRAIN
      }
      hdf5_data_param {
        source: "list_train.txt"
        batch_size: 100
      }
    }
    layer {
      name: "data"
      type: "HDF5Data"
      top: "data"
      top: "labels"
      include {
        phase: TEST
      }
      hdf5_data_param {
        source: "list_test.txt"
        batch_size: 100
      }
    }
    layer {
      name: "slicers"
      type: "Slice"
      bottom: "labels"
      top: "label_1"
      top: "label_2"
      slice_param {
        axis: 1
        slice_point: 1
      }
    }
    
    ### for all
    
    layer {
      name: "conv_all"
      type: "Convolution"
      bottom: "data"
      top: "conv_all"
      param {
        lr_mult: 1
      }
      param {
        lr_mult: 2
      }
      convolution_param {
        num_output: 50
        kernel_size: 5
        stride: 1
        weight_filler {
          type: "xavier"
        }
        bias_filler {
          type: "constant"
        }
      }
    }
    layer {
      name: "relu_all"
      type: "ReLU"
      bottom: "conv_all"
      top: "conv_all"
    }
    layer {
      name: "pool_all"
      type: "Pooling"
      bottom: "conv_all"
      top: "pool_all"
      pooling_param {
        pool: MAX
        kernel_size: 2
        stride: 2
      }
    }
    
    ### for kind_1
    
    layer {
      name: "ip1"
      type: "InnerProduct"
      bottom: "pool_all"
      top: "ip1"
      param {
        lr_mult: 1
      }
      param {
        lr_mult: 2
      }
      inner_product_param {
        num_output: 2
        weight_filler {
          type: "xavier"
        }
        bias_filler {
          type: "constant"
        }
      }
    }
    layer {
      name: "accuracy1"
      type: "Accuracy"
      bottom: "ip1"
      bottom: "label_1"
      top: "accuracy1"
      include {
        phase: TEST
      }
    }
    layer {
      name: "loss_1"
      type: "SoftmaxWithLoss"
      bottom: "ip1"
      bottom: "label_1"
      top: "loss_1"
    }
    
    ###for kind_2
    
    layer {
      name: "ip2"
      type: "InnerProduct"
      bottom: "pool_all"
      top: "ip2"
      param {
        lr_mult: 1
      }
      param {
        lr_mult: 2
      }
      inner_product_param {
        num_output: 3
        weight_filler {
          type: "xavier"
        }
        bias_filler {
          type: "constant"
        }
      }
    }
    layer {
      name: "accuracy2"
      type: "Accuracy"
      bottom: "ip2"
      bottom: "label_2"
      top: "accuracy2"
      include {
        phase: TEST
      }
    }
    layer {
      name: "loss_2"
      type: "SoftmaxWithLoss"
      bottom: "ip2"
      bottom: "label_2"
      top: "loss_2"
    }
    
    
      
      
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    				版权声明:本文为jiarenyf原创文章,未经允许也可以转载,但是附上链接……					https://blog.csdn.net/u011762313/article/details/48851015				</div>
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    						<ul>
    

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