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【caffe Layer】代码中文注释
src/caffe/proto/caffe.proto 中LayerParameter部分
1 // NOTE 2 // Update the next available ID when you add a new LayerParameter field. 3 // 如果增加一个新的LayerParameter域,需要更新下一个可用的ID 4 // LayerParameter next available layer-specific ID: 147 (last added: recurrent_param) 5 message LayerParameter { 6 optional string name = 1; // the layer name 名称 7 optional string type = 2; // the layer type 类型 8 repeated string bottom = 3; // the name of each bottom blob 输入的Bottom Blob的名称 9 repeated string top = 4; // the name of each top blob 输出的Top Blob名称 10 11 // The train / test phase for computation.当前阶段TRAIN或TEST 12 optional Phase phase = 10; 13 14 // The amount of weight to assign each top blob in the objective. 15 // Each layer assigns a default value, usually of either 0 or 1, 16 // to each top blob. 17 // 为每个输出Top Blob分配对损失函数的权重,每个Layer都有默认值,0表示不参与计算,1表示参与损失函数计算 18 repeated float loss_weight = 5; 19 20 // Specifies training parameters (multipliers on global learning constants, 21 // and the name and other settings used for weight sharing). 22 // 指定训练参数(例如相对全局学习常熟的缩放因子,以及用于权值共享的名称或其他设置) 23 repeated ParamSpec param = 6; 24 25 // The blobs containing the numeric parameters of the layer. 26 // 承载该曾数值参数的Blob 27 repeated BlobProto blobs = 7; 28 29 // Specifies whether to backpropagate to each bottom. If unspecified, 30 // Caffe will automatically infer whether each input needs backpropagation 31 // to compute parameter gradients. If set to true for some inputs, 32 // backpropagation to those inputs is forced; if set false for some inputs, 33 // backpropagation to those inputs is skipped. 34 // 是否对Bottom Blob进行反向传播过程。该字段维度应与Bottom Blob个数一致。 35 // The size must be either 0 or equal to the number of bottoms. 36 repeated bool propagate_down = 11; 37 38 // Rules controlling whether and when a layer is included in the network, 39 // based on the current NetState. You may specify a non-zero number of rules 40 // to include OR exclude, but not both. If no include or exclude rules are 41 // specified, the layer is always included. If the current NetState meets 42 // ANY (i.e., one or more) of the specified rules, the layer is 43 // included/excluded. 44 // 控制某个层在某个时刻是否包含在网络中(基于当前的NetState) 45 // 可以为include或exclude指定非零值(不能同时) 46 // 如果没有规则,该层一直包含在网络中 47 // 如果当前的NetState满足一定条件,那么该层被包含或被排斥 48 repeated NetStateRule include = 8; 49 repeated NetStateRule exclude = 9; 50 51 // Parameters for data pre-processing. 数据预处理参数 52 optional TransformationParameter transform_param = 100; 53 54 // Parameters shared by loss layers. 所有损失层共享的参数 55 optional LossParameter loss_param = 101; 56 57 // Layer type-specific parameters.特定类型层参数 58 // 注意:一些层实现时可能有多于一种计算引擎,这些层通过选择引擎类型和引擎参数来实现。 59 // 默认引擎是在编译阶段由引擎开关设置的 60 // Note: certain layers may have more than one computational engine 61 // for their implementation. These layers include an Engine type and 62 // engine parameter for selecting the implementation. 63 // The default for the engine is set by the ENGINE switch at compile-time. 64 optional AccuracyParameter accuracy_param = 102; 65 optional ArgMaxParameter argmax_param = 103; 66 optional BatchNormParameter batch_norm_param = 139; 67 optional BiasParameter bias_param = 141; 68 optional ConcatParameter concat_param = 104; 69 optional ContrastiveLossParameter contrastive_loss_param = 105; 70 optional ConvolutionParameter convolution_param = 106; 71 optional CropParameter crop_param = 144; 72 optional DataParameter data_param = 107; 73 optional DropoutParameter dropout_param = 108; 74 optional DummyDataParameter dummy_data_param = 109; 75 optional EltwiseParameter eltwise_param = 110; 76 optional ELUParameter elu_param = 140; 77 optional EmbedParameter embed_param = 137; 78 optional ExpParameter exp_param = 111; 79 optional FlattenParameter flatten_param = 135; 80 optional HDF5DataParameter hdf5_data_param = 112; 81 optional HDF5OutputParameter hdf5_output_param = 113; 82 optional HingeLossParameter hinge_loss_param = 114; 83 optional ImageDataParameter image_data_param = 115; 84 optional InfogainLossParameter infogain_loss_param = 116; 85 optional InnerProductParameter inner_product_param = 117; 86 optional InputParameter input_param = 143; 87 optional LogParameter log_param = 134; 88 optional LRNParameter lrn_param = 118; 89 optional MemoryDataParameter memory_data_param = 119; 90 optional MVNParameter mvn_param = 120; 91 optional ParameterParameter parameter_param = 145; 92 optional PoolingParameter pooling_param = 121; 93 optional PowerParameter power_param = 122; 94 optional PReLUParameter prelu_param = 131; 95 optional PythonParameter python_param = 130; 96 optional RecurrentParameter recurrent_param = 146; 97 optional ReductionParameter reduction_param = 136; 98 optional ReLUParameter relu_param = 123; 99 optional ReshapeParameter reshape_param = 133; 100 optional ScaleParameter scale_param = 142; 101 optional SigmoidParameter sigmoid_param = 124; 102 optional SoftmaxParameter softmax_param = 125; 103 optional SPPParameter spp_param = 132; 104 optional SliceParameter slice_param = 126; 105 optional TanHParameter tanh_param = 127; 106 optional ThresholdParameter threshold_param = 128; 107 optional TileParameter tile_param = 138; 108 optional WindowDataParameter window_data_param = 129; 109 }
摘抄参考赵永科《深度学习 21天实战caffe》
【caffe Layer】代码中文注释
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