标签：gns any inf sof red lin mvn 数据 info
Layer时caffe的基本单元，至少有一个输入Blob（Bottom Blob）和一个输出Blob（Top Blob），有的Layer带有权值（Weight）和偏置项（Bias），有两个运算方向：前向传播（Forward）和反向传播（Backward），其中前向传播会对输入Blob进行某种处理（有权值和偏置项的Layer会对输入进行处理），得到输出Blob；而反向传播计算则对输出Blob的diff进行处理，得到输入Blob的diff（有权值和偏置项的Layer可能会计算权值Blob、偏置项Blob的diff）。
数据结构描述
// NOTE
// Update the next available ID when you add a new LayerParameter field.
//
// LayerParameter next available layer-specific ID: 147 (last added: recurrent_param)
message LayerParameter {
optional string name = 1; // the layer name
optional string type = 2; // the layer type
repeated string bottom = 3; // the name of each bottom blob
repeated string top = 4; // the name of each top blob
//当前阶段（TRAIN或TEST） // The train / test phase for computation.
optional Phase phase = 10;
//为每个Top Blob分配为对损失函数的权重，每个Layer都有默认值，要么为0，表示不参与目标函数计算；要么为1，表示参与损失函数计算
// The amount of weight to assign each top blob in the objective.
// Each layer assigns a default value, usually of either 0 or 1,
// to each top blob.
repeated float loss_weight = 5;
// Specifies training parameters (multipliers on global learning constants,
// and the name and other settings used for weight sharing).
repeated ParamSpec param = 6; //指定训练参数（例如相对全局学习常数的缩放因子，以及用于权值共享的名称或其他设置）
// The blobs containing the numeric parameters of the layer.
repeated BlobProto blobs = 7; //承载了该层数值参数的Blob
// Specifies whether to backpropagate to each bottom. If unspecified,
// Caffe will automatically infer whether each input needs backpropagation
// to compute parameter gradients. If set to true for some inputs,
// backpropagation to those inputs is forced; if set false for some inputs,
// backpropagation to those inputs is skipped.
//
// The size must be either 0 or equal to the number of bottoms.
repeated bool propagate_down = 11; //是否对Bottom Blob进行反向传播过程。该字段的维度应与Bottom Blob的个数一致
// Rules controlling whether and when a layer is included in the network,
// based on the current NetState. You may specify a non-zero number of rules
// to include OR exclude, but not both. If no include or exclude rules are
// specified, the layer is always included. If the current NetState meets
// ANY (i.e., one or more) of the specified rules, the layer is
// included/excluded.
//控制某个层在某个时刻是否包含在网络中，相当于当前NetState。你可以为include或exclude（不要同时）指定非零值。如果没有任何规则，那么该层会被包含或排斥
repeated NetStateRule include = 8;
repeated NetStateRule exclude = 9;
// Parameters for data pre-processing.
//数据预处理参数
optional TransformationParameter transform_param = 100;
// Parameters shared by loss layers.
//所有损失层共享的参数
optional LossParameter loss_param = 101;
// Layer type-specific parameters.
//
// Note: certain layers may have more than one computational engine
// for their implementation. These layers include an Engine type and
// engine parameter for selecting the implementation.
// The default for the engine is set by the ENGINE switch at compile-time.
//指定类型层的参数。注意一些层实现时可能有多于一种的计算引擎，这些层包括一个引擎类型和引擎参数来选择实现。默认引擎是在编译阶段由引擎开关设置的
optional AccuracyParameter accuracy_param = 102;
optional ArgMaxParameter argmax_param = 103;
optional BatchNormParameter batch_norm_param = 139;
optional BiasParameter bias_param = 141;
optional ConcatParameter concat_param = 104;
optional ContrastiveLossParameter contrastive_loss_param = 105;
optional ConvolutionParameter convolution_param = 106;
optional CropParameter crop_param = 144;
optional DataParameter data_param = 107;
optional DropoutParameter dropout_param = 108;
optional DummyDataParameter dummy_data_param = 109;
optional EltwiseParameter eltwise_param = 110;
optional ELUParameter elu_param = 140;
optional EmbedParameter embed_param = 137;
optional ExpParameter exp_param = 111;
optional FlattenParameter flatten_param = 135;
optional HDF5DataParameter hdf5_data_param = 112;
optional HDF5OutputParameter hdf5_output_param = 113;
optional HingeLossParameter hinge_loss_param = 114;
optional ImageDataParameter image_data_param = 115;
optional InfogainLossParameter infogain_loss_param = 116;
optional InnerProductParameter inner_product_param = 117;
optional InputParameter input_param = 143;
optional LogParameter log_param = 134;
optional LRNParameter lrn_param = 118;
optional MemoryDataParameter memory_data_param = 119;
optional MVNParameter mvn_param = 120;
optional ParameterParameter parameter_param = 145;
optional PoolingParameter pooling_param = 121;
optional PowerParameter power_param = 122;
optional PReLUParameter prelu_param = 131;
optional PythonParameter python_param = 130;
optional RecurrentParameter recurrent_param = 146;
optional ReductionParameter reduction_param = 136;
optional ReLUParameter relu_param = 123;
optional ReshapeParameter reshape_param = 133;
optional ScaleParameter scale_param = 142;
optional SigmoidParameter sigmoid_param = 124;
optional SoftmaxParameter softmax_param = 125;
optional SPPParameter spp_param = 132;
optional SliceParameter slice_param = 126;
optional TanHParameter tanh_param = 127;
optional ThresholdParameter threshold_param = 128;
optional TileParameter tile_param = 138;
optional WindowDataParameter window_data_param = 129;
}
标签：gns any inf sof red lin mvn 数据 info
原文地址：http://www.cnblogs.com/mengmengmiaomiao/p/7745149.html
