Caffe中HDF5Data例子

• 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" } 

• 注：如何生成hdf5文件，详见：生成hdf5文件用于多标签训练
• 注：Hdf5Data详见：HDF5 Input
• 注：Slice详见：Slicing

• 最终网络结构如下图：
  
• 注：Caffe学习：使用pycaffe绘制网络结构