文章作者：Tyan 博客：noahsnail.com | | 简书
caffe.proto是caffe数据结构定义的主要文件，本文主要是在caffe.proto代码的基础上加上了部分中文注释，其中的内容与caffe的prototxt文件中的结构相对应。
// syntax用来指定protobuf的版本 syntax = "proto2"; // package可以看作C++中的namespace，与Caffe C++代码中的namespace caffe对应 // package用来避免名称冲突 package caffe; // 在消息定义中，每个字段都有唯一的一个数字标识符。这些标识符是用来在消息的二进制格式中识别各个字段的，一旦开始使用就不能够再改变。 // 注：[1,15]之内的标识号在编码的时候会占用一个字节。[16,2047]之内的标识号则占用2个字节。所以应该为那些频繁出现的消息元素保留 [1,15]之内的标识号。 // required：一个格式良好的消息一定要含有一个这种字段，表示该值是必须要设置的。 // optional：消息格式中该字段可以有0个或1个值（不超过1个）。 // repeated：在一个格式良好的消息中，这种字段可以重复任意多次（包括0次）。重复的值的顺序会被保留，表示该值可以重复，相当于Java中的List。 // Specifies the shape (dimensions) of a Blob. // 指定Blob的shape，4-D shape message BlobShape { //数据块形状定义为Num * Channel * Height * Wight, 原因在于caffe基于容器的多维嵌套来实现高维数据的封装, 即vector。 repeated int64 dim = 1 [packed = true]; } // Blob数据块，包括Blob shape，数据和微分 message BlobProto { // Blob的shape, 即numpy中的shape optional BlobShape shape = 7; // Blob的数据部分 repeated float data = 5 [packed = true]; // Blob的微分部分 repeated float diff = 6 [packed = true]; // Blob中的数据部分(double类型) repeated double double_data = 8 [packed = true]; // Blob的微分部分(double类型) repeated double double_diff = 9 [packed = true]; // 4D dimensions -- deprecated. Use "shape" instead. // Blob的4个维度，已被Blob shape代替 // Blob中数据的个数(例如卷积核的个数) optional int32 num = 1 [default = 0]; // Blob中数据的通道数 optional int32 channels = 2 [default = 0]; // Blob中数据的高度 optional int32 height = 3 [default = 0]; // Blob中数据的宽度 optional int32 width = 4 [default = 0]; } // The BlobProtoVector is simply a way to pass multiple blobproto instances // around. // BlobProtoVector, 用来保存多个BlobProb对象的Vector message BlobProtoVector { repeated BlobProto blobs = 1; } //图像数据, channel-图像通道数, height-高度, width-宽度, data-图像像素数据, label-图像标签, float_data-图像浮点型数据(0-1之间), encoded-图像编码方式 message Datum { // 图像的通道数 optional int32 channels = 1; // 图像的高度 optional int32 height = 2; // 图像的宽度 optional int32 width = 3; // the actual image data, in bytes // 实际的图像数据，以字节形式(uint8)表示 optional bytes data = 4; // 图像对应的标签，必须为整形 optional int32 label = 5; // Optionally, the datum could also hold float data. // 可选表示，图像数据表示为float数据，即0-255归一化到0-1之间 repeated float float_data = 6; // If true data contains an encoded image that need to be decoded // encoded为true表示图像采用压缩表示，需要解码 optional bool encoded = 7 [default = false]; } // Filler参数, filler主要对网络权重进行初始化 // Filler类型分为常量初始化（constant）、高斯分布初始化（gaussian）、positive_unitball初始化、均匀分布初始化（uniform）、xavier初始化、msra初始化、双线性初始化（bilinear） message FillerParameter { // The filler type. // Filler的类型 optional string type = 1 [default = 'constant']; // 常量初始化的值 optional float value = 2 [default = 0]; // the value in constant filler // 均匀分布初始化中的最小值 optional float min = 3 [default = 0]; // the min value in uniform filler // 均匀分布初始化中的最大值 optional float max = 4 [default = 1]; // the max value in uniform filler // 高斯分布初始化中的均值 optional float mean = 5 [default = 0]; // the mean value in Gaussian filler // 高斯分布初始化中的标准差 optional float std = 6 [default = 1]; // the std value in Gaussian filler // The expected number of non-zero output weights for a given input in // Gaussian filler -- the default -1 means don't perform sparsification. // 在高斯分布初始化中给定输入及权重，期望输出非0值，默认值-1表示不进行稀疏化 optional int32 sparse = 7 [default = -1]; // Normalize the filler variance by fan_in, fan_out, or their average. // Applies to 'xavier' and 'msra' fillers. // 通过fan_in, fan_out或average来归一化filler方差，主要应用到'xavier'和'msra' filler中 enum VarianceNorm { FAN_IN = 0; FAN_OUT = 1; AVERAGE = 2; } // 定义filler方差归一化，默认为FAN_IN optional VarianceNorm variance_norm = 8 [default = FAN_IN]; } //神经网络参数 message NetParameter { // 神经网络名字 optional string name = 1; // consider giving the network a name // DEPRECATED. See InputParameter. The input blobs to the network. // 已废弃。网络的输入部分，具体请看InputParameter。 repeated string input = 3; // DEPRECATED. See InputParameter. The shape of the input blobs. // 已废弃。输入blob的shape，具体请看InputParameter。 repeated BlobShape input_shape = 8; // 4D input dimensions -- deprecated. Use "input_shape" instead. // If specified, for each input blob there should be four // values specifying the num, channels, height and width of the input blob. // Thus, there should be a total of (4 * #input) numbers. // 已废弃。用input_shape代替。 repeated int32 input_dim = 4; // Whether the network will force every layer to carry out backward operation. // If set False, then whether to carry out backward is determined // automatically according to the net structure and learning rates. // 网络中是否每一层都执行反向传播的标志，如果设为false，反向传播会根据网络结构和学习率自动进行。 optional bool force_backward = 5 [default = false]; // The current "state" of the network, including the phase, level, and stage. // Some layers may be included/excluded depending on this state and the states // specified in the layers' include and exclude fields. // 网络的当前状态，包括phase, level和stage，(phase应该是对应prototxt文件中的TRAIN,TEST) // 某些层是否included/excluded依赖于层中include，exclue字段指定的state。 optional NetState state = 6; // Print debugging information about results while running Net::Forward, // Net::Backward, and Net::Update. // 在执行Net::Forward,Net::Backward, Net::Update时是否打印调试信息。 optional bool debug_info = 7 [default = false]; // The layers that make up the net. Each of their configurations, including // connectivity and behavior, is specified as a LayerParameter. // 构成网络的layer，每一个layer的配置，包括连接性和行为都在LayerParameter中指定。 repeated LayerParameter layer = 100; // ID 100 so layers are printed last. // DEPRECATED: use 'layer' instead. // 已废弃，用layer代替。 repeated V1LayerParameter layers = 2; } // NOTE // Update the next available ID when you add a new SolverParameter field. // 注意：当你添加一个新的SolverParameter字段时，要更新下一个可获取的ID // SolverParameter next available ID: 41 (last added: type) // Solver参数 message SolverParameter { // // Specifying the train and test networks // // Exactly one train net must be specified using one of the following fields: // train_net_param, train_net, net_param, net // One or more test nets may be specified using any of the following fields: // test_net_param, test_net, net_param, net // If more than one test net field is specified (e.g., both net and // test_net are specified), they will be evaluated in the field order given // above: (1) test_net_param, (2) test_net, (3) net_param/net. // A test_iter must be specified for each test_net. // A test_level and/or a test_stage may also be specified for each test_net. // // Proto filename for the train net, possibly combined with one or more test nets. // 训练网络的prototxt文件名，可能结合一个或多个测试网络 optional string net = 24; // Inline train net param, possibly combined with one or more test nets. // 内联训练网络参数，可能结合一个或多个测试网络 optional NetParameter net_param = 25; // 训练网络的proto文件名 optional string train_net = 1; // Proto filename for the train net. // 测试网络的proto文件名 repeated string test_net = 2; // Proto filenames for the test nets. // 内联训练网络参数 optional NetParameter train_net_param = 21; // Inline train net params. // 内联测试网络参数 repeated NetParameter test_net_param = 22; // Inline test net params. // The states for the train/test nets. Must be unspecified or specified once per net. // By default, all states will have solver = true; // train_state will have phase = TRAIN, // and all test_state's will have phase = TEST. // Other defaults are set according to the NetState defaults. // train/test网络的状态，必须不指定或每个网络指定一次 // 默认情况下，所有的状态都有solver = true，train_state的phase = TRAIN，其它默认情况根据NetState默认值设定。 // train网络的状态，必须不指定或每个网络指定一次 optional NetState train_state = 26; // test网络的状态，必须不指定或每个网络指定一次 repeated NetState test_state = 27; // The number of iterations for each test net. // 每个测试网络的迭代次数，即测试数据的迭代次数，测试数据总数=测试迭代次数*测试数据的batch_size。 repeated int32 test_iter = 3; // The number of iterations between two testing phases. // 两次测试间隔的迭代次数，即训练数据迭代多少次进行一次测试。 optional int32 test_interval = 4 [default = 0]; // 测试数据的loss，默认情况下不计算 optional bool test_compute_loss = 19 [default = false]; // If true, run an initial test pass before the first iteration, // ensuring memory availability and printing the starting value of the loss. // 如果为true，在第一次迭代之前进行一次初始测试，从而确保内存可用性并输出初始损失值。 optional bool test_initialization = 32 [default = true]; // 基本学习率 optional float base_lr = 5; // The base learning rate // the number of iterations between displaying info. If display = 0, no info will be displayed. // 执行多少次迭代显示一次信息，如果display = 0，不输出信息。 optional int32 display = 6; // Display the loss averaged over the last average_loss iterations // 输出的平均损失是之前多少次迭代的平均损失。 optional int32 average_loss = 33 [default = 1]; // 训练的最大迭代次数 optional int32 max_iter = 7; // the maximum number of iterations // accumulate gradients over `iter_size` x `batch_size` instances // 累积`iter_size` x `batch_size`个实例的梯度 optional int32 iter_size = 36 [default = 1]; // The learning rate decay policy. The currently implemented learning rate // policies are as follows: // - fixed: always return base_lr. // - step: return base_lr * gamma ^ (floor(iter / step)) // - exp: return base_lr * gamma ^ iter // - inv: return base_lr * (1 + gamma * iter) ^ (- power) // - multistep: similar to step but it allows non uniform steps defined by // stepvalue // - poly: the effective learning rate follows a polynomial decay, to be // zero by the max_iter. return base_lr (1 - iter/max_iter) ^ (power) // - sigmoid: the effective learning rate follows a sigmod decay // return base_lr ( 1/(1 + exp(-gamma * (iter - stepsize)))) // // where base_lr, max_iter, gamma, step, stepvalue and power are defined // in the solver parameter protocol buffer, and iter is the current iteration. // 学习率的变化策略 optional string lr_policy = 8; // 学习率的计算参数 optional float gamma = 9; // The parameter to compute the learning rate. // 学习率的计算参数 optional float power = 10; // The parameter to compute the learning rate. // 动量参数 optional float momentum = 11; // The momentum value. // 权重衰减，权重衰减主要影响神经网络的正则项，具体可参考Caffe文档 optional float weight_decay = 12; // The weight decay. // regularization types supported: L1 and L2, controlled by weight_decay // 正则化类型支持L1和L2，受weight_decay控制。 optional string regularization_type = 29 [default = "L2"]; // the stepsize for learning rate policy "step" // 学习率方案为step时的参数 optional int32 stepsize = 13; // the stepsize for learning rate policy "multistep" // 学习率方案为multistep时的参数 repeated int32 stepvalue = 34; // Set clip_gradients to >= 0 to clip parameter gradients to that L2 norm, // whenever their actual L2 norm is larger. // 设置clip_gradients >= 0可以削减L2范数的梯度，当真实L2范数的梯度大于clip_gradients，将L2范数的梯度设为clip_gradients optional float clip_gradients = 35 [default = -1]; // snapshot的间隔，即迭代多少次保存一次snapshot optional int32 snapshot = 14 [default = 0]; // The snapshot interval // snapshot的前缀 optional string snapshot_prefix = 15; // The prefix for the snapshot. // whether to snapshot diff in the results or not. Snapshotting diff will help // debugging but the final protocol buffer size will be much larger. // 是否在结果中保存snapshot的差分，snapshot diff有助于调试，但snapshot的文件会更大。 optional bool snapshot_diff = 16 [default = false]; // snapshot的保存格式（hdf5,binaryproto）。 enum SnapshotFormat { HDF5 = 0; BINARYPROTO = 1; } // snapshot默认保存为BINARYPROTO。 optional SnapshotFormat snapshot_format = 37 [default = BINARYPROTO]; // the mode solver will use: 0 for CPU and 1 for GPU. Use GPU in default. // 求解神经网络的方式，0 CPU, 1 GPU。默认使用GPU enum SolverMode { CPU = 0; GPU = 1; } // 求解神经网络的模式，0 CPU, 1 GPU。默认使用GPU optional SolverMode solver_mode = 17 [default = GPU]; // the device_id will that be used in GPU mode. Use device_id = 0 in default. // device_id是GPU模式下GPU的ID。 optional int32 device_id = 18 [default = 0]; // If non-negative, the seed with which the Solver will initialize the Caffe // random number generator -- useful for reproducible results. Otherwise, // (and by default) initialize using a seed derived from the system clock. // 如果是非负值，seed用来初始化Caffe的随机数生成器，对于再见结果是很有用的，默认情况下，seed的是从系统时钟获取。 optional int64 random_seed = 20 [default = -1]; // type of the solver // 神经网络求解的类型, 默认为SGD optional string type = 40 [default = "SGD"]; // numerical stability for RMSProp, AdaGrad and AdaDelta and Adam // RMSProp, AdaGrad, AdaDelta, Adam求解类型的参数 optional float delta = 31 [default = 1e-8]; // parameters for the Adam solver // Adam求解类型的参数 optional float momentum2 = 39 [default = 0.999]; // RMSProp decay value // MeanSquare(t) = rms_decay*MeanSquare(t-1) + (1-rms_decay)*SquareGradient(t) // RMSProp类型的衰减值 optional float rms_decay = 38 [default = 0.99]; // If true, print information about the state of the net that may help with // debugging learning problems. // 如果设为true，会输出网络的状态信息，有助于调试 optional bool debug_info = 23 [default = false]; // If false, don't save a snapshot after training finishes. // 如果设为false，不保存训练结束的snapshot。 optional bool snapshot_after_train = 28 [default = true]; // DEPRECATED: old solver enum types, use string instead // 已废弃，使用string代替 enum SolverType { SGD = 0; NESTEROV = 1; ADAGRAD = 2; RMSPROP = 3; ADADELTA = 4; ADAM = 5; } // DEPRECATED: use type instead of solver_type // 已废弃：使用type代替 optional SolverType solver_type = 30 [default = SGD]; } // A message that stores the solver snapshots // 保存solver snapshots message SolverState { // 当前的迭代次数 optional int32 iter = 1; // The current iteration // 保存学习到的网络 optional string learned_net = 2; // The file that stores the learned net. // sgd的求解历史 repeated BlobProto history = 3; // The history for sgd solvers // 学习的当前step optional int32 current_step = 4 [default = 0]; // The current step for learning rate } // 定义phase enum Phase { TRAIN = 0; TEST = 1; } // 网络状态 message NetState { // 属于哪个phase optional Phase phase = 1 [default = TEST]; optional int32 level = 2 [default = 0]; repeated string stage = 3; } // 网络状态分类 message NetStateRule { // Set phase to require the NetState have a particular phase (TRAIN or TEST) // to meet this rule. // 设置phase optional Phase phase = 1; // Set the minimum and/or maximum levels in which the layer should be used. // Leave undefined to meet the rule regardless of level. // 设置layer的level optional int32 min_level = 2; optional int32 max_level = 3; // Customizable sets of stages to include or exclude. // The net must have ALL of the specified stages and NONE of the specified // "not_stage"s to meet the rule. // (Use multiple NetStateRules to specify conjunctions of stages.) // 可定制的stage集合 repeated string stage = 4; repeated string not_stage = 5; } // Specifies training parameters (multipliers on global learning constants, // and the name and other settings used for weight sharing). // 指定训练参数及名称以及权重共享的其它设置 message ParamSpec { // The names of the parameter blobs -- useful for sharing parameters among // layers, but never required otherwise. To share a parameter between two // layers, give it a (non-empty) name. // 两个layer之间进行参数共享的blob名字 optional string name = 1; // Whether to require shared weights to have the same shape, or just the same // count -- defaults to STRICT if unspecified. // 参数共享时是否需要具有相同的shape，默认情况下需要有相同的shape optional DimCheckMode share_mode = 2; // 参数共享时的维度检查 enum DimCheckMode { // STRICT (default) requires that num, channels, height, width each match. STRICT = 0; // PERMISSIVE requires only the count (num*channels*height*width) to match. PERMISSIVE = 1; } // The multiplier on the global learning rate for this parameter. // 学习率参数, learning rate = base_lr * lr_mult optional float lr_mult = 3 [default = 1.0]; // The multiplier on the global weight decay for this parameter. // 权重衰减参数, weight = weight_decay * decay_mult optional float decay_mult = 4 [default = 1.0]; } // NOTE // Update the next available ID when you add a new LayerParameter field. // LayerParameter next available layer-specific ID: 147 (last added: recurrent_param) // 注意：当你添加一个新的LayerParameter字段时，要更新下一个可获取的ID message LayerParameter { // layer名称 optional string name = 1; // the layer name // layer类型 optional string type = 2; // the layer type // layer的输入 repeated string bottom = 3; // the name of each bottom blob // layer的输出 repeated string top = 4; // the name of each top blob // The train / test phase for computation. // layer用在train/test phase optional Phase phase = 10; // 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. // layer对最终的loss损失值的贡献率 repeated float loss_weight = 5; // Specifies training parameters (multipliers on global learning constants, // and the name and other settings used for weight sharing). // 指定训练参数 repeated ParamSpec param = 6; // The blobs containing the numeric parameters of the layer. // layer的blobs repeated BlobProto blobs = 7; // 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. // 指定反向传播是否传播到每一个bottom，如果不指定，caffe会自动检查推断是否每一个输入都需要反向传播来计算梯度。如果一些输入设为true, // 则这些layer强制进行反向传播，如果设为false，这些layer将跳过反向传播。 repeated bool propagate_down = 11; // 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layer included/excluded 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. // loss layer的参数共享 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. // 特定layer的参数 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; } // Message that stores parameters used to apply transformation to the data layer's data // 用来进行数据层（图像）变换的参数 message TransformationParameter { // For data pre-processing, we can do simple scaling and subtracting the // data mean, if provided. Note that the mean subtraction is always carried // out before scaling. // 像素归一化，归一化之前会减去均值 optional float scale = 1 [default = 1]; // Specify if we want to randomly mirror data. // 图像进行随机mirror操作 optional bool mirror = 2 [default = false]; // Specify if we would like to randomly crop an image. // 图像随机crop操作 optional uint32 crop_size = 3 [default = 0]; // mean_file and mean_value cannot be specified at the same time // 图像的均值文件 optional string mean_file = 4; // if specified can be repeated once (would subtract it from all the channels) // or can be repeated the same number of times as channels // (would subtract them from the corresponding channel) // 图像的均值，手动指定，通常是三个 repeated float mean_value = 5; // Force the decoded image to have 3 color channels. // 强制图像必须有三个颜色通道 optional bool force_color = 6 [default = false]; // Force the decoded image to have 1 color channels. // 强制图像为灰度图像 optional bool force_gray = 7 [default = false]; } // Message that stores parameters shared by loss layers // loss层参数 message LossParameter { // If specified, ignore instances with the given label. // 如果指定，则label等于ignore_label的样本将不参与Loss计算，并且反向传播时梯度直接置0。 optional int32 ignore_label = 1; // How to normalize the loss for loss layers that aggregate across batches, // spatial dimensions, or other dimensions. Currently only implemented in // SoftmaxWithLoss and SigmoidCrossEntropyLoss layers. // 指定loss归一化的方式 enum NormalizationMode { // Divide by the number of examples in the batch times spatial dimensions. // Outputs that receive the ignore label will NOT be ignored in computing // the normalization factor. // 所有样本都参与计算，包括ignore label FULL = 0; // Divide by the total number of output locations that do not take the // ignore_label. If ignore_label is not set, this behaves like FULL. // 所有样本都参与计算，不包括ignore label VALID = 1; // Divide by the batch size. // 除以给定的batch size。 BATCH_SIZE = 2; // Do not normalize the loss. // 不归一化loss NONE = 3; } // For historical reasons, the default normalization for // SigmoidCrossEntropyLoss is BATCH_SIZE and *not* VALID. // loss归一化方式 optional NormalizationMode normalization = 3 [default = VALID]; // Deprecated. Ignored if normalization is specified. If normalization // is not specified, then setting this to false will be equivalent to // normalization = BATCH_SIZE to be consistent with previous behavior. // 已废弃。Loss会除以参与计算的样本总数；否则Loss等于直接求和 optional bool normalize = 2; } // Messages that store parameters used by individual layer types follow, in // alphabetical order. // accuracy层参数 message AccuracyParameter { // When computing accuracy, count as correct by comparing the true label to // the top k scoring classes. By default, only compare to the top scoring // class (i.e. argmax). // 计算前top-k的准确率，默认计算top-1准确率 optional uint32 top_k = 1 [default = 1]; // The "label" axis of the prediction blob, whose argmax corresponds to the // predicted label -- may be negative to index from the end (e.g., -1 for the // last axis). For example, if axis == 1 and the predictions are // (N x C x H x W), the label blob is expected to contain N*H*W ground truth // labels with integer values in {0, 1, ..., C-1}. // 指定在哪个维度上计算label optional int32 axis = 2 [default = 1]; // If specified, ignore instances with the given label. // 如果指定，则忽略给定标签的实例 optional int32 ignore_label = 3; } // 标签最大化参数，标签最大化即确定概率最大的label message ArgMaxParameter { // If true produce pairs (argmax, maxval) // 如果为真，则生成(argmax, maxval) optional bool out_max_val = 1 [default = false]; // 类别的top-k optional uint32 top_k = 2 [default = 1]; // The axis along which to maximise -- may be negative to index from the // end (e.g., -1 for the last axis). // By default ArgMaxLayer maximizes over the flattened trailing dimensions // for each index of the first / num dimension. // 根据axis进行标签最大化 optional int32 axis = 3; } // 参数拼接，在deconv的prototxt文件中见过 message ConcatParameter { // The axis along which to concatenate -- may be negative to index from the // end (e.g., -1 for the last axis). Other axes must have the // same dimension for all the bottom blobs. // By default, ConcatLayer concatenates blobs along the "channels" axis (1). // 参数拼接时的维度，按axis进行拼接 optional int32 axis = 2 [default = 1]; // DEPRECATED: alias for "axis" -- does not support negative indexing. // 已废弃。与axis一样。 optional uint32 concat_dim = 1 [default = 1]; } // batch norm层的相关参数, batch norm layer通常配与scale layer一起使用，具体用法可参考Resnet结构 message BatchNormParameter { // If false, accumulate global mean/variance values via a moving average. If // true, use those accumulated values instead of computing mean/variance // across the batch. // 如果设为false，累计全部的mean/variance，如果为true，使用累计值代替batch上mean/variance的计算 // true是使用了caffe内部的均值和方差，false是使用了每个Batch里的数据的均值和方差 optional bool use_global_stats = 1; // How much does the moving average decay each iteration? // 每次迭代平均值衰减比例 optional float moving_average_fraction = 2 [default = .999]; // Small value to add to the variance estimate so that we don't divide by // zero. // variance估计时为了使除数不为0，需要加上eps optional float eps = 3 [default = 1e-5]; } // bias层参数，没找到实际的应用例子 message BiasParameter { // The first axis of bottom[0] (the first input Blob) along which to apply // bottom[1] (the second input Blob). May be negative to index from the end // (e.g., -1 for the last axis). // // For example, if bottom[0] is 4D with shape 100x3x40x60, the output // top[0] will have the same shape, and bottom[1] may have any of the // following shapes (for the given value of axis): // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60 // (axis == 1 == -3) 3; 3x40; 3x40x60 // (axis == 2 == -2) 40; 40x60 // (axis == 3 == -1) 60 // Furthermore, bottom[1] may have the empty shape (regardless of the value of // "axis") -- a scalar bias. optional int32 axis = 1 [default = 1]; // (num_axes is ignored unless just one bottom is given and the bias is // a learned parameter of the layer. Otherwise, num_axes is determined by the // number of axes by the second bottom.) // The number of axes of the input (bottom[0]) covered by the bias // parameter, or -1 to cover all axes of bottom[0] starting from `axis`. // Set num_axes := 0, to add a zero-axis Blob: a scalar. optional int32 num_axes = 2 [default = 1]; // (filler is ignored unless just one bottom is given and the bias is // a learned parameter of the layer.) // The initialization for the learned bias parameter. // Default is the zero (0) initialization, resulting in the BiasLayer // initially performing the identity operation. optional FillerParameter filler = 3; } // 对比损失层，siamese network中使用了对比损失 message ContrastiveLossParameter { // margin for dissimilar pair // 不相似的样本对的距离保持在margin以上 optional float margin = 1 [default = 1.0]; // The first implementation of this cost did not exactly match the cost of // Hadsell et al 2006 -- using (margin - d^2) instead of (margin - d)^2. // legacy_version = false (the default) uses (margin - d)^2 as proposed in the // Hadsell paper. New models should probably use this version. // legacy_version = true uses (margin - d^2). This is kept to support / // reproduce existing models and results // 第一版对比损失没有完全按论文写，如果为false，则按照论文原来的公式计算 optional bool legacy_version = 2 [default = false]; } // 卷积层参数 message ConvolutionParameter { // 输出数据的个数 optional uint32 num_output = 1; // The number of outputs for the layer // 是否有偏置项 optional bool bias_term = 2 [default = true]; // whether to have bias terms // Pad, kernel size, and stride are all given as a single value for equal // dimensions in all spatial dimensions, or once per spatial dimension. // 卷积padding的大小 repeated uint32 pad = 3; // The padding size; defaults to 0 // 卷积核的大小 repeated uint32 kernel_size = 4; // The kernel size // 卷积的步长 repeated uint32 stride = 6; // The stride; defaults to 1 // Factor used to dilate the kernel, (implicitly) zero-filling the resulting // holes. (Kernel dilation is sometimes referred to by its use in the // algorithme à trous from Holschneider et al. 1987.) // 卷积膨胀，在卷积的时候可以skip一定长度的像素 repeated uint32 dilation = 18; // The dilation; defaults to 1 // For 2D convolution only, the *_h and *_w versions may also be used to // specify both spatial dimensions. // padding, kernel, stride的宽度和高度 optional uint32 pad_h = 9 [default = 0]; // The padding height (2D only) optional uint32 pad_w = 10 [default = 0]; // The padding width (2D only) optional uint32 kernel_h = 11; // The kernel height (2D only) optional uint32 kernel_w = 12; // The kernel width (2D only) optional uint32 stride_h = 13; // The stride height (2D only) optional uint32 stride_w = 14; // The stride width (2D only) // 来自于AlexNet论文 optional uint32 group = 5 [default = 1]; // The group size for group conv // 权重初始化 optional FillerParameter weight_filler = 7; // The filler for the weight // 偏置初始化 optional FillerParameter bias_filler = 8; // The filler for the bias enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } // 卷积的方式的选择，default是正常的卷积，caffe是矩阵乘法的卷积，cudnn是cuda库流并行式的卷积 optional Engine engine = 15 [default = DEFAULT]; // The axis to interpret as "channels" when performing convolution. // Preceding dimensions are treated as independent inputs; // succeeding dimensions are treated as "spatial". // With (N, C, H, W) inputs, and axis == 1 (the default), we perform // N independent 2D convolutions, sliding C-channel (or (C/g)-channels, for // groups g>1) filters across the spatial axes (H, W) of the input. // With (N, C, D, H, W) inputs, and axis == 1, we perform // N independent 3D convolutions, sliding (C/g)-channels // filters across the spatial axes (D, H, W) of the input. // 通道channel所在的维度 optional int32 axis = 16 [default = 1]; // Whether to force use of the general ND convolution, even if a specific // implementation for blobs of the appropriate number of spatial dimensions // is available. (Currently, there is only a 2D-specific convolution // implementation; for input blobs with num_axes != 2, this option is // ignored and the ND implementation will be used.) // 如果输入数据维度等于2，则执行通用的ND卷积，否则正常执行卷积 optional bool force_nd_im2col = 17 [default = false]; } // 图像裁剪参数 message CropParameter { // To crop, elements of the first bottom are selected to fit the dimensions // of the second, reference bottom. The crop is configured by // - the crop `axis` to pick the dimensions for cropping // - the crop `offset` to set the shift for all/each dimension // to align the cropped bottom with the reference bottom. // All dimensions up to but excluding `axis` are preserved, while // the dimensions including and trailing `axis` are cropped. // If only one `offset` is set, then all dimensions are offset by this amount. // Otherwise, the number of offsets must equal the number of cropped axes to // shift the crop in each dimension accordingly. // Note: standard dimensions are N,C,H,W so the default is a spatial crop, // and `axis` may be negative to index from the end (e.g., -1 for the last // axis). // axis是在哪个维度上进行裁剪，会裁剪轴2及之后的所有轴 optional int32 axis = 1 [default = 2]; // offset设置是每个维度进行裁剪时的偏移量 repeated uint32 offset = 2; } // 数据层参数 message DataParameter { enum DB { LEVELDB = 0; LMDB = 1; } // Specify the data source. // 设定数据源路径 optional string source = 1; // Specify the batch size. // 指定一次处理的图片数量 optional uint32 batch_size = 4; // The rand_skip variable is for the data layer to skip a few data points // to avoid all asynchronous sgd clients to start at the same point. The skip // point would be set as rand_skip * rand(0,1). Note that rand_skip should not // be larger than the number of keys in the database. // DEPRECATED. Each solver accesses a different subset of the database. // rand_skip跳过指定的数据点，避免异步的sgd从同一个数据点开始 optional uint32 rand_skip = 7 [default = 0]; // 使用的数据库类型，LMDB or LEVELDB optional DB backend = 8 [default = LEVELDB]; // DEPRECATED. See TransformationParameter. For data pre-processing, we can do // simple scaling and subtracting the data mean, if provided. Note that the // mean subtraction is always carried out before scaling. // 已废弃。图像归一化，在TransformationParameter中。 optional float scale = 2 [default = 1]; // 已废弃。均值文件，在TransformationParameter中。 optional string mean_file = 3; // DEPRECATED. See TransformationParameter. Specify if we would like to randomly // crop an image. // 已废弃。图像裁剪，在TransformationParameter中。 optional uint32 crop_size = 5 [default = 0]; // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror // data. // 已废弃。图像翻转，在TransformationParameter中。 optional bool mirror = 6 [default = false]; // Force the encoded image to have 3 color channels // 强制图像数据有三个颜色通道 optional bool force_encoded_color = 9 [default = false]; // Prefetch queue (Number of batches to prefetch to host memory, increase if // data access bandwidth varies). // 预先拉取batch的数目 optional uint32 prefetch = 10 [default = 4]; } // dropout层参数 message DropoutParameter { // 为了避免过拟合，参数随机失活的比例 optional float dropout_ratio = 1 [default = 0.5]; // dropout ratio } // DummyDataLayer fills any number of arbitrarily shaped blobs with random // (or constant) data generated by "Fillers" (see "message FillerParameter"). // DummyData层的参数 message DummyDataParameter { // This layer produces N >= 1 top blobs. DummyDataParameter must specify 1 or N // shape fields, and 0, 1 or N data_fillers. // If 0 data_fillers are specified, ConstantFiller with a value of 0 is used. // If 1 data_filler is specified, it is applied to all top blobs. If N are // specified, the ith is applied to the ith top blob. // blob数据的生成方式 repeated FillerParameter data_filler = 1; // 数据的维度 repeated BlobShape shape = 6; // 4D dimensions -- deprecated. Use "shape" instead. // 已废弃。使用shape代替。 repeated uint32 num = 2; repeated uint32 channels = 3; repeated uint32 height = 4; repeated uint32 width = 5; } //Eltwise层的参数 message EltwiseParameter { // 操作的类型 enum EltwiseOp { PROD = 0; SUM = 1; MAX = 2; } // 数据操作分三种：点乘，相加，取最大值 optional EltwiseOp operation = 1 [default = SUM]; // element-wise operation // SUM操作时各个blob对应的系数 repeated float coeff = 2; // blob-wise coefficient for SUM operation // Whether to use an asymptotically slower (for >2 inputs) but stabler method // of computing the gradient for the PROD operation. (No effect for SUM op.) // 在进行PROD操作，即乘法时是否使用异步操作来计算梯度，更慢但更稳定。 optional bool stable_prod_grad = 3 [default = true]; } // Message that stores parameters used by ELULayer // ELU层的参数，具体看论文 message ELUParameter { // Described in: // Clevert, D.-A., Unterthiner, T., & Hochreiter, S. (2015). Fast and Accurate // Deep Network Learning by Exponential Linear Units (ELUs). arXiv optional float alpha = 1 [default = 1]; } // Message that stores parameters used by EmbedLayer // Embed层的参数，主要用于LSTM等翻译网络 message EmbedParameter { // Embed层的输出 optional uint32 num_output = 1; // The number of outputs for the layer // The input is given as integers to be interpreted as one-hot // vector indices with dimension num_input. Hence num_input should be // 1 greater than the maximum possible input value. // Embed层的输入 optional uint32 input_dim = 2; // 是否使用偏置项 optional bool bias_term = 3 [default = true]; // Whether to use a bias term // 权重生成 optional FillerParameter weight_filler = 4; // The filler for the weight // 偏置生成 optional FillerParameter bias_filler = 5; // The filler for the bias } // Message that stores parameters used by ExpLayer // Exp层的参数，即指数层参数 message ExpParameter { // ExpLayer computes outputs y = base ^ (shift + scale * x), for base > 0. // Or if base is set to the default (-1), base is set to e, // so y = exp(shift + scale * x). // 指数层的计算是y = base ^ (shift + scale * x)，下面分别是公式中的三个参数 optional float base = 1 [default = -1.0]; optional float scale = 2 [default = 1.0]; optional float shift = 3 [default = 0.0]; } // Message that stores parameters used by FlattenLayer // Flatten层的参数，主要是按某个轴展开（平铺），mnist demo的mnist_autoencode就使用了Flatten层 message FlattenParameter { // The first axis to flatten: all preceding axes are retained in the output. // May be negative to index from the end (e.g., -1 for the last axis). // 从哪一层开始展开 optional int32 axis = 1 [default = 1]; // The last axis to flatten: all following axes are retained in the output. // May be negative to index from the end (e.g., the default -1 for the last // axis). // 展开到哪一层结束 optional int32 end_axis = 2 [default = -1]; } // Message that stores parameters used by HDF5DataLayer // HDF5数据层的参数 message HDF5DataParameter { // Specify the data source. // HDF5层输入数据的数据源 optional string source = 1; // Specify the batch size. // 训练的batch_size optional uint32 batch_size = 2; // Specify whether to shuffle the data. // If shuffle == true, the ordering of the HDF5 files is shuffled, // and the ordering of data within any given HDF5 file is shuffled, // but data between different files are not interleaved; all of a file's // data are output (in a random order) before moving onto another file. // 是否对HDF5的输入数据进行shuffle optional bool shuffle = 3 [default = false]; } // HDF5输出层参数 message HDF5OutputParameter { // 输出的HDF5文件的文件名 optional string file_name = 1; } // HingeLoss层参数 message HingeLossParameter { enum Norm { L1 = 1; L2 = 2; } // Specify the Norm to use L1 or L2 // 指定HingeLoss的类型 optional Norm norm = 1 [default = L1]; } // ImageData层参数，网络中直接输入原图 message ImageDataParameter { // Specify the data source. // 描述图像路径及标签的文件 optional string source = 1; // Specify the batch size. // 训练的batch size optional uint32 batch_size = 4 [default = 1]; // The rand_skip variable is for the data layer to skip a few data points // to avoid all asynchronous sgd clients to start at the same point. The skip // point would be set as rand_skip * rand(0,1). Note that rand_skip should not // be larger than the number of keys in the database. // rand_skip跳过指定的数据点，避免异步的sgd从同一个数据点开始，与Data层中是一样的 optional uint32 rand_skip = 7 [default = 0]; // Whether or not ImageLayer should shuffle the list of files at every epoch. // 是否对图像顺序进行shuffle optional bool shuffle = 8 [default = false]; // It will also resize images if new_height or new_width are not zero. // 图像resize的高度 optional uint32 new_height = 9 [default = 0]; // 图像resize的宽度 optional uint32 new_width = 10 [default = 0]; // Specify if the images are color or gray // 指定图像彩色图像还是灰度图像，默认彩色 optional bool is_color = 11 [default = true]; // DEPRECATED. See TransformationParameter. For data pre-processing, we can do // simple scaling and subtracting the data mean, if provided. Note that the // mean subtraction is always carried out before scaling. // 已废弃。参考TransformationParameter中的scale optional float scale = 2 [default = 1]; // 指定均值文件 optional string mean_file = 3; // DEPRECATED. See TransformationParameter. Specify if we would like to randomly // crop an image. // 已废弃。参考TransformationParameter中的crop_size optional uint32 crop_size = 5 [default = 0]; // DEPRECATED. See TransformationParameter. Specify if we want to randomly mirror // data. // 已废弃，参考TransformationParameter的mirror。 optional bool mirror = 6 [default = false]; // 不太清楚root_folder具体是什么 optional string root_folder = 12 [default = ""]; } // 信息增益损失层参数 message InfogainLossParameter { // Specify the infogain matrix source. // 指定存储信息增益矩阵的源文件 optional string source = 1; } // InnerProduct层的参数 message InnerProductParameter { // InnerProduct层的输出 optional uint32 num_output = 1; // The number of outputs for the layer // 是否有偏置项 optional bool bias_term = 2 [default = true]; // whether to have bias terms // 权重初始化，随机生成 optional FillerParameter weight_filler = 3; // The filler for the weight // 偏置初始化，随机生成 optional FillerParameter bias_filler = 4; // The filler for the bias // The first axis to be lumped into a single inner product computation; // all preceding axes are retained in the output. // May be negative to index from the end (e.g., -1 for the last axis). // 从某一维度开始进行内积计算，前面的维度保留 optional int32 axis = 5 [default = 1]; // Specify whether to transpose the weight matrix or not. // If transpose == true, any operations will be performed on the transpose // of the weight matrix. The weight matrix itself is not going to be transposed // but rather the transfer flag of operations will be toggled accordingly. // 是否对权重矩阵进行转置 optional bool transpose = 6 [default = false]; } // Input参数，caffe网络部署时会用到 message InputParameter { // This layer produces N >= 1 top blob(s) to be assigned manually. // Define N shapes to set a shape for each top. // Define 1 shape to set the same shape for every top. // Define no shape to defer to reshaping manually. // 输入数据的shape repeated BlobShape shape = 1; } // Message that stores parameters used by LogLayer // Log层参数，对数据进行Log运算 message LogParameter { // LogLayer computes outputs y = log_base(shift + scale * x), for base > 0. // Or if base is set to the default (-1), base is set to e, // so y = ln(shift + scale * x) = log_e(shift + scale * x) // Log层计算公式为y = log_base(shift + scale * x)，下面分别是公式中的三个参数 optional float base = 1 [default = -1.0]; optional float scale = 2 [default = 1.0]; optional float shift = 3 [default = 0.0]; } // Message that stores parameters used by LRNLayer // LRN层的参数，局部归一化，AlexNet中的LRN message LRNParameter { // 如果是跨通道LRN，则表示求和的通道数；如果是在通道内LRN，则表示求和的正方形区域长度。 optional uint32 local_size = 1 [default = 5]; // 归一化公式中的参数 optional float alpha = 2 [default = 1.]; optional float beta = 3 [default = 0.75]; enum NormRegion { ACROSS_CHANNELS = 0; WITHIN_CHANNEL = 1; } // 归一化的区域，分为通道内和跨通道两种 optional NormRegion norm_region = 4 [default = ACROSS_CHANNELS]; optional float k = 5 [default = 1.]; enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } // 与前面的engine是一样的 optional Engine engine = 6 [default = DEFAULT]; } // 内存数据层参数 message MemoryDataParameter { // 训练的batch_size optional uint32 batch_size = 1; // 图像通道数 optional uint32 channels = 2; // 图像高度 optional uint32 height = 3; // 图像宽度 optional uint32 width = 4; } // mean-variance normalization层参数 message MVNParameter { // This parameter can be set to false to normalize mean only // 是否对方差进行归一化 optional bool normalize_variance = 1 [default = true]; // This parameter can be set to true to perform DNN-like MVN // 是否进行跨通道的MVN optional bool across_channels = 2 [default = false]; // Epsilon for not dividing by zero while normalizing variance // 避免除数为0，与前面的一样 optional float eps = 3 [default = 1e-9]; } // 参数层参数 message ParameterParameter { // 用户自己定义的shape optional BlobShape shape = 1; } // 池化层参数 message PoolingParameter { enum PoolMethod { MAX = 0; AVE = 1; STOCHASTIC = 2; } // 池化的方式 optional PoolMethod pool = 1 [default = MAX]; // The pooling method // Pad, kernel size, and stride are all given as a single value for equal // dimensions in height and width or as Y, X pairs. // padding的大小 optional uint32 pad = 4 [default = 0]; // The padding size (equal in Y, X) // padding的高度 optional uint32 pad_h = 9 [default = 0]; // The padding height // padding的宽度 optional uint32 pad_w = 10 [default = 0]; // The padding width // 池化的核大小 optional uint32 kernel_size = 2; // The kernel size (square) // 核高度 optional uint32 kernel_h = 5; // The kernel height // 核宽度 optional uint32 kernel_w = 6; // The kernel width // 池化的步长 optional uint32 stride = 3 [default = 1]; // The stride (equal in Y, X) // 步长的高度 optional uint32 stride_h = 7; // The stride height // 步长的宽度 optional uint32 stride_w = 8; // The stride width enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } // 执行池化操作的类型，与前面的一样 optional Engine engine = 11 [default = DEFAULT]; // If global_pooling then it will pool over the size of the bottom by doing // kernel_h = bottom->height and kernel_w = bottom->width // global_pooling是对多个通道进行pooling，例如从三通道pooling为单通道 optional bool global_pooling = 12 [default = false]; } // Power层参数 message PowerParameter { // PowerLayer computes outputs y = (shift + scale * x) ^ power. // Power的计算公式为y = (shift + scale * x) ^ power，下面是公式中的参数 optional float power = 1 [default = 1.0]; optional float scale = 2 [default = 1.0]; optional float shift = 3 [default = 0.0]; } // python layer参数，在faster rcnn中有应用 message PythonParameter { // python模块名称 optional string module = 1; // python模块中层的名字，即类名 optional string layer = 2; // This value is set to the attribute `param_str` of the `PythonLayer` object // in Python before calling the `setup()` method. This could be a number, // string, dictionary in Python dict format, JSON, etc. You may parse this // string in `setup` method and use it in `forward` and `backward`. // 可以用来设置参数，key-value形式，可以参考faster rcnn中模型的train.prototxt optional string param_str = 3 [default = '']; // Whether this PythonLayer is shared among worker solvers during data parallelism. // If true, each worker solver sequentially run forward from this layer. // This value should be set true if you are using it as a data layer. // 是否需要在并行时共享layer optional bool share_in_parallel = 4 [default = false]; } // Message that stores parameters used by RecurrentLayer // Recurrent层参数 message RecurrentParameter { // The dimension of the output (and usually hidden state) representation -- // must be explicitly set to non-zero. // Recurrent层的输出——必须非零 optional uint32 num_output = 1 [default = 0]; // 权重初始化，随机生成初始化 optional FillerParameter weight_filler = 2; // The filler for the weight // 偏置初始化，随机生成 optional FillerParameter bias_filler = 3; // The filler for the bias // Whether to enable displaying debug_info in the unrolled recurrent net. // 是否输出调试信息 optional bool debug_info = 4 [default = false]; // Whether to add as additional inputs (bottoms) the initial hidden state // blobs, and add as additional outputs (tops) the final timestep hidden state // blobs. The number of additional bottom/top blobs required depends on the // recurrent architecture -- e.g., 1 for RNNs, 2 for LSTMs. // 是否添加额外的输入 optional bool expose_hidden = 5 [default = false]; } // Message that stores parameters used by ReductionLayer // Reduction层参数 message ReductionParameter { enum ReductionOp { SUM = 1; ASUM = 2; SUMSQ = 3; MEAN = 4; } // 通过reduction操作来将数据减少到一维，可以通过上面的四种方式 optional ReductionOp operation = 1 [default = SUM]; // reduction operation // The first axis to reduce to a scalar -- may be negative to index from the // end (e.g., -1 for the last axis). // (Currently, only reduction along ALL "tail" axes is supported; reduction // of axis M through N, where N < num_axes - 1, is unsupported.) // Suppose we have an n-axis bottom Blob with shape: // (d0, d1, d2, ..., d(m-1), dm, d(m+1), ..., d(n-1)). // If axis == m, the output Blob will have shape // (d0, d1, d2, ..., d(m-1)), // and the ReductionOp operation is performed (d0 * d1 * d2 * ... * d(m-1)) // times, each including (dm * d(m+1) * ... * d(n-1)) individual data. // If axis == 0 (the default), the output Blob always has the empty shape // (count 1), performing reduction across the entire input -- // often useful for creating new loss functions. // 在哪个轴上执行reduction操作 optional int32 axis = 2 [default = 0]; // 输出系数 optional float coeff = 3 [default = 1.0]; // coefficient for output } // Message that stores parameters used by ReLULayer // ReLU层参数 message ReLUParameter { // Allow non-zero slope for negative inputs to speed up optimization // Described in: // Maas, A. L., Hannun, A. Y., & Ng, A. Y. (2013). Rectifier nonlinearities // improve neural network acoustic models. In ICML Workshop on Deep Learning // for Audio, Speech, and Language Processing. // ReLUU操作的阈值 optional float negative_slope = 1 [default = 0]; enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } // 执行ReLU操作的类型，与前面的一样 optional Engine engine = 2 [default = DEFAULT]; } // Reshape层参数，与numpy中的Reshape作用是一样的 message ReshapeParameter { // Specify the output dimensions. If some of the dimensions are set to 0, // the corresponding dimension from the bottom layer is used (unchanged). // Exactly one dimension may be set to -1, in which case its value is // inferred from the count of the bottom blob and the remaining dimensions. // For example, suppose we want to reshape a 2D blob "input" with shape 2 x 8: // // layer { // type: "Reshape" bottom: "input" top: "output" // reshape_param { ... } // } // // If "input" is 2D with shape 2 x 8, then the following reshape_param // specifications are all equivalent, producing a 3D blob "output" with shape // 2 x 2 x 4: // // reshape_param { shape { dim: 2 dim: 2 dim: 4 } } // reshape_param { shape { dim: 0 dim: 2 dim: 4 } } // reshape_param { shape { dim: 0 dim: 2 dim: -1 } } // reshape_param { shape { dim: 0 dim:-1 dim: 4 } } // reshape之后输出的维度 optional BlobShape shape = 1; // axis and num_axes control the portion of the bottom blob's shape that are // replaced by (included in) the reshape. By default (axis == 0 and // num_axes == -1), the entire bottom blob shape is included in the reshape, // and hence the shape field must specify the entire output shape. // // axis may be non-zero to retain some portion of the beginning of the input // shape (and may be negative to index from the end; e.g., -1 to begin the // reshape after the last axis, including nothing in the reshape, // -2 to include only the last axis, etc.). // // For example, suppose "input" is a 2D blob with shape 2 x 8. // Then the following ReshapeLayer specifications are all equivalent, // producing a blob "output" with shape 2 x 2 x 4: // // reshape_param { shape { dim: 2 dim: 2 dim: 4 } } // reshape_param { shape { dim: 2 dim: 4 } axis: 1 } // reshape_param { shape { dim: 2 dim: 4 } axis: -3 } // // num_axes specifies the extent of the reshape. // If num_axes >= 0 (and axis >= 0), the reshape will be performed only on // input axes in the range [axis, axis+num_axes]. // num_axes may also be -1, the default, to include all remaining axes // (starting from axis). // // For example, suppose "input" is a 2D blob with shape 2 x 8. // Then the following ReshapeLayer specifications are equivalent, // producing a blob "output" with shape 1 x 2 x 8. // // reshape_param { shape { dim: 1 dim: 2 dim: 8 } } // reshape_param { shape { dim: 1 dim: 2 } num_axes: 1 } // reshape_param { shape { dim: 1 } num_axes: 0 } // // On the other hand, these would produce output blob shape 2 x 1 x 8: // // reshape_param { shape { dim: 2 dim: 1 dim: 8 } } // reshape_param { shape { dim: 1 } axis: 1 num_axes: 0 } optional int32 axis = 2 [default = 0]; optional int32 num_axes = 3 [default = -1]; } // Scale层参数，与batch norm layer配合使用，可参考Resnet结构 message ScaleParameter { // The first axis of bottom[0] (the first input Blob) along which to apply // bottom[1] (the second input Blob). May be negative to index from the end // (e.g., -1 for the last axis). // // For example, if bottom[0] is 4D with shape 100x3x40x60, the output // top[0] will have the same shape, and bottom[1] may have any of the // following shapes (for the given value of axis): // (axis == 0 == -4) 100; 100x3; 100x3x40; 100x3x40x60 // (axis == 1 == -3) 3; 3x40; 3x40x60 // (axis == 2 == -2) 40; 40x60 // (axis == 3 == -1) 60 // Furthermore, bottom[1] may have the empty shape (regardless of the value of // "axis") -- a scalar multiplier. optional int32 axis = 1 [default = 1]; // (num_axes is ignored unless just one bottom is given and the scale is // a learned parameter of the layer. Otherwise, num_axes is determined by the // number of axes by the second bottom.) // The number of axes of the input (bottom[0]) covered by the scale // parameter, or -1 to cover all axes of bottom[0] starting from `axis`. // Set num_axes := 0, to multiply with a zero-axis Blob: a scalar. optional int32 num_axes = 2 [default = 1]; // (filler is ignored unless just one bottom is given and the scale is // a learned parameter of the layer.) // The initialization for the learned scale parameter. // Default is the unit (1) initialization, resulting in the ScaleLayer // initially performing the identity operation. optional FillerParameter filler = 3; // Whether to also learn a bias (equivalent to a ScaleLayer+BiasLayer, but // may be more efficient). Initialized with bias_filler (defaults to 0). // 是否使用偏置项 optional bool bias_term = 4 [default = false]; // 偏置项初始化 optional FillerParameter bias_filler = 5; } // Sigmoid层参数 message SigmoidParameter { enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } // 使用哪种sigmoid实现 optional Engine engine = 1 [default = DEFAULT]; } // Slice层参数 message SliceParameter { // The axis along which to slice -- may be negative to index from the end // (e.g., -1 for the last axis). // By default, SliceLayer concatenates blobs along the "channels" axis (1). // 在哪个维度上进行拆分 optional int32 axis = 3 [default = 1]; // 指定拆分点 repeated uint32 slice_point = 2; // DEPRECATED: alias for "axis" -- does not support negative indexing. // 已废弃。 optional uint32 slice_dim = 1 [default = 1]; } // Message that stores parameters used by SoftmaxLayer, SoftmaxWithLossLayer // Softmax层参数 message SoftmaxParameter { enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } // 使用哪种softmax实现 optional Engine engine = 1 [default = DEFAULT]; // The axis along which to perform the softmax -- may be negative to index // from the end (e.g., -1 for the last axis). // Any other axes will be evaluated as independent softmaxes. // 在哪个维度上进行softmax optional int32 axis = 2 [default = 1]; } // TanH层参数 message TanHParameter { enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } // 执行tanh激活函数的类型 optional Engine engine = 1 [default = DEFAULT]; } // Message that stores parameters used by TileLayer // Tile层参数，扩大某一维度 message TileParameter { // The index of the axis to tile. // 扩大哪个维度 optional int32 axis = 1 [default = 1]; // The number of copies (tiles) of the blob to output. // 创建多少个副本 optional int32 tiles = 2; } // Message that stores parameters used by ThresholdLayer // Threshold层参数，主要用来测试输入是否超过阈值 message ThresholdParameter { // 设置阈值 optional float threshold = 1 [default = 0]; // Strictly positive values } // WindowData层参数 message WindowDataParameter { // Specify the data source. // 指定数据源 optional string source = 1; // For data pre-processing, we can do simple scaling and subtracting the // data mean, if provided. Note that the mean subtraction is always carried // out before scaling. // 是否归一化 optional float scale = 2 [default = 1]; // 图像均值文件 optional string mean_file = 3; // Specify the batch size. // 训练的batch_size optional uint32 batch_size = 4; // Specify if we would like to randomly crop an image. // 是否随机crop optional uint32 crop_size = 5 [default = 0]; // Specify if we want to randomly mirror data. // 是否随机mirror optional bool mirror = 6 [default = false]; // Foreground (object) overlap threshold // 前景重叠阈值 optional float fg_threshold = 7 [default = 0.5]; // Background (non-object) overlap threshold // 背景重叠阈值 optional float bg_threshold = 8 [default = 0.5]; // Fraction of batch that should be foreground objects // 前景比例 optional float fg_fraction = 9 [default = 0.25]; // Amount of contextual padding to add around a window // (used only by the window_data_layer) // 是否padding optional uint32 context_pad = 10 [default = 0]; // Mode for cropping out a detection window // warp: cropped window is warped to a fixed size and aspect ratio // square: the tightest square around the window is cropped // crop的方式 optional string crop_mode = 11 [default = "warp"]; // cache_images: will load all images in memory for faster access // 是否缓存图像，即将图像都转入内存 optional bool cache_images = 12 [default = false]; // append root_folder to locate images // 图像文件的根目录 optional string root_folder = 13 [default = ""]; } // SPP层参数，SPP是spatial pyramid pooling，空间金字塔池化，具体可参考何凯明论文Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition message SPPParameter { enum PoolMethod { MAX = 0; AVE = 1; STOCHASTIC = 2; } // 空间金字塔高度 optional uint32 pyramid_height = 1; // 池化方法 optional PoolMethod pool = 2 [default = MAX]; // The pooling method enum Engine { DEFAULT = 0; CAFFE = 1; CUDNN = 2; } // 执行SPP的方式 optional Engine engine = 6 [default = DEFAULT]; } // DEPRECATED: use LayerParameter. // 已废弃，使用LayerParameter。 message V1LayerParameter { repeated string bottom = 2; repeated string top = 3; optional string name = 4; repeated NetStateRule include = 32; repeated NetStateRule exclude = 33; enum LayerType { NONE = 0; ABSVAL = 35; ACCURACY = 1; ARGMAX = 30; BNLL = 2; CONCAT = 3; CONTRASTIVE_LOSS = 37; CONVOLUTION = 4; DATA = 5; DECONVOLUTION = 39; DROPOUT = 6; DUMMY_DATA = 32; EUCLIDEAN_LOSS = 7; ELTWISE = 25; EXP = 38; FLATTEN = 8; HDF5_DATA = 9; HDF5_OUTPUT = 10; HINGE_LOSS = 28; IM2COL = 11; IMAGE_DATA = 12; INFOGAIN_LOSS = 13; INNER_PRODUCT = 14; LRN = 15; MEMORY_DATA = 29; MULTINOMIAL_LOGISTIC_LOSS = 16; MVN = 34; POOLING = 17; POWER = 26; RELU = 18; SIGMOID = 19; SIGMOID_CROSS_ENTROPY_LOSS = 27; SILENCE = 36; SOFTMAX = 20; SOFTMAX_LOSS = 21; SPLIT = 22; SLICE = 33; TANH = 23; WINDOW_DATA = 24; THRESHOLD = 31; } optional LayerType type = 5; repeated BlobProto blobs = 6; repeated string param = 1001; repeated DimCheckMode blob_share_mode = 1002; enum DimCheckMode { STRICT = 0; PERMISSIVE = 1; } repeated float blobs_lr = 7; repeated float weight_decay = 8; repeated float loss_weight = 35; optional AccuracyParameter accuracy_param = 27; optional ArgMaxParameter argmax_param = 23; optional ConcatParameter concat_param = 9; optional ContrastiveLossParameter contrastive_loss_param = 40; optional ConvolutionParameter convolution_param = 10; optional DataParameter data_param = 11; optional DropoutParameter dropout_param = 12; optional DummyDataParameter dummy_data_param = 26; optional EltwiseParameter eltwise_param = 24; optional ExpParameter exp_param = 41; optional HDF5DataParameter hdf5_data_param = 13; optional HDF5OutputParameter hdf5_output_param = 14; optional HingeLossParameter hinge_loss_param = 29; optional ImageDataParameter image_data_param = 15; optional InfogainLossParameter infogain_loss_param = 16; optional InnerProductParameter inner_product_param = 17; optional LRNParameter lrn_param = 18; optional MemoryDataParameter memory_data_param = 22; optional MVNParameter mvn_param = 34; optional PoolingParameter pooling_param = 19; optional PowerParameter power_param = 21; optional ReLUParameter relu_param = 30; optional SigmoidParameter sigmoid_param = 38; optional SoftmaxParameter softmax_param = 39; optional SliceParameter slice_param = 31; optional TanHParameter tanh_param = 37; optional ThresholdParameter threshold_param = 25; optional WindowDataParameter window_data_param = 20; optional TransformationParameter transform_param = 36; optional LossParameter loss_param = 42; optional V0LayerParameter layer = 1; } // DEPRECATED: V0LayerParameter is the old way of specifying layer parameters // in Caffe. We keep this message type around for legacy support. // 已废弃。 message V0LayerParameter { optional string name = 1; // the layer name optional string type = 2; // the string to specify the layer type // Parameters to specify layers with inner products. optional uint32 num_output = 3; // The number of outputs for the layer optional bool biasterm = 4 [default = true]; // whether to have bias terms optional FillerParameter weight_filler = 5; // The filler for the weight optional FillerParameter bias_filler = 6; // The filler for the bias optional uint32 pad = 7 [default = 0]; // The padding size optional uint32 kernelsize = 8; // The kernel size optional uint32 group = 9 [default = 1]; // The group size for group conv optional uint32 stride = 10 [default = 1]; // The stride enum PoolMethod { MAX = 0; AVE = 1; STOCHASTIC = 2; } optional PoolMethod pool = 11 [default = MAX]; // The pooling method optional float dropout_ratio = 12 [default = 0.5]; // dropout ratio optional uint32 local_size = 13 [default = 5]; // for local response norm optional float alpha = 14 [default = 1.]; // for local response norm optional float beta = 15 [default = 0.75]; // for local response norm optional float k = 22 [default = 1.]; // For data layers, specify the data source optional string source = 16; // For data pre-processing, we can do simple scaling and subtracting the // data mean, if provided. Note that the mean subtraction is always carried // out before scaling. optional float scale = 17 [default = 1]; optional string meanfile = 18; // For data layers, specify the batch size. optional uint32 batchsize = 19; // For data layers, specify if we would like to randomly crop an image. optional uint32 cropsize = 20 [default = 0]; // For data layers, specify if we want to randomly mirror data. optional bool mirror = 21 [default = false]; // The blobs containing the numeric parameters of the layer repeated BlobProto blobs = 50; // The ratio that is multiplied on the global learning rate. If you want to // set the learning ratio for one blob, you need to set it for all blobs. repeated float blobs_lr = 51; // The weight decay that is multiplied on the global weight decay. repeated float weight_decay = 52; // The rand_skip variable is for the data layer to skip a few data points // to avoid all asynchronous sgd clients to start at the same point. The skip // point would be set as rand_skip * rand(0,1). Note that rand_skip should not // be larger than the number of keys in the database. optional uint32 rand_skip = 53 [default = 0]; // Fields related to detection (det_*) // foreground (object) overlap threshold optional float det_fg_threshold = 54 [default = 0.5]; // background (non-object) overlap threshold optional float det_bg_threshold = 55 [default = 0.5]; // Fraction of batch that should be foreground objects optional float det_fg_fraction = 56 [default = 0.25]; // optional bool OBSOLETE_can_clobber = 57 [default = true]; // Amount of contextual padding to add around a window // (used only by the window_data_layer) optional uint32 det_context_pad = 58 [default = 0]; // Mode for cropping out a detection window // warp: cropped window is warped to a fixed size and aspect ratio // square: the tightest square around the window is cropped optional string det_crop_mode = 59 [default = "warp"]; // For ReshapeLayer, one needs to specify the new dimensions. optional int32 new_num = 60 [default = 0]; optional int32 new_channels = 61 [default = 0]; optional int32 new_height = 62 [default = 0]; optional int32 new_width = 63 [default = 0]; // Whether or not ImageLayer should shuffle the list of files at every epoch. // It will also resize images if new_height or new_width are not zero. optional bool shuffle_images = 64 [default = false]; // For ConcatLayer, one needs to specify the dimension for concatenation, and // the other dimensions must be the same for all the bottom blobs. // By default it will concatenate blobs along the channels dimension. optional uint32 concat_dim = 65 [default = 1]; optional HDF5OutputParameter hdf5_output_param = 1001; } // PReLU层参数，ReLU的进化版本 message PReLUParameter { // Parametric ReLU described in K. He et al, Delving Deep into Rectifiers: // Surpassing Human-Level Performance on ImageNet Classification, 2015. // Initial value of a_i. Default is a_i=0.25 for all i. // 参数初始化 optional FillerParameter filler = 1; // Whether or not slope parameters are shared across channels. // 是否在各通道共享参数 optional bool channel_shared = 2 [default = false]; }