怎么在Caffe中配置每一个层的结构

发布网友发布时间：2022-03-27 13:50

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热心网友时间：2022-03-27 15:19

何Caffe配置每层结构近刚电脑装Caffe由于神经中国络同层结构同类型层同参数所根据Caffe官中国说明文档做简单总结 1. Vision Layers 1.1 卷积层(Convolution) 类型：CONVOLUTION 例 layers { name: "conv1" type: CONVOLUTION bottom: "data" top: "conv1" blobs_lr: 1 # learning rate multiplier for the filters blobs_lr: 2 # learning rate multiplier for the biases weight_decay: 1 # weight decay multiplier for the filters weight_decay: 0 # weight decay multiplier for the biases convolution_param { num_output: 96 # learn 96 filters kernel_size: 11 # each filter is 11x11 stride: 4 # step 4 pixels between each filter application weight_filler { type: "gaussian" # initialize the filters from a Gaussian std: 0.01 # distribution with stdev 0.01 (default mean: 0) } bias_filler { type: "constant" # initialize the biases to zero (0) value: 0 } } } blobs_lr: 习率调整参数面例设置权重习率运行求解器给习率同偏置习率权重两倍 weight_decay：卷积层重要参数必须参数： num_output (c_o)：滤器数 kernel_size (or kernel_h and kernel_w)：滤器选参数： weight_filler [default type: 'constant' value: 0]：参数初始化 bias_filler：偏置初始化 bias_term [default true]：指定否否启偏置项 pad (or pad_h and pad_w) [default 0]：指定输入每边加少像素 stride (or stride_h and stride_w) [default 1]：指定滤器步 group (g) [default 1]: If g > 1, we restrict the connectivityof each filter to a subset of the input. Specifically, the input and outputchannels are separated into g groups, and the ith output group channels will beonly connected to the ith input group channels. 通卷积变化：输入：n * c_i * h_i * w_i 输：n * c_o * h_o * w_o其h_o = (h_i + 2 * pad_h - kernel_h) /stride_h + 1w_o通同计算 1.2 池化层（Pooling）类型：POOLING 例 layers { name: "pool1" type: POOLING bottom: "conv1" top: "pool1" pooling_param { pool: MAX kernel_size: 3 # pool over a 3x3 region stride: 2 # step two pixels (in the bottom blob) between pooling regions } } 卷积层重要参数必需参数： kernel_size (or kernel_h and kernel_w)：滤器选参数： pool [default MAX]：pooling目前MAX, AVE, STOCHASTIC三种 pad (or pad_h and pad_w) [default 0]：指定输入每遍加少像素 stride (or stride_h and stride_w) [default1]：指定滤器步通池化变化：输入：n * c_i * h_i * w_i 输：n * c_o * h_o * w_o其h_o = (h_i + 2 * pad_h - kernel_h) /stride_h + 1w_o通同计算 1.3 Local Response Normalization (LRN) 类型：LRN Local ResponseNormalization局部输入区域进行归化（激a加归化权重（母部）新激b）两种同形式种输入区域相邻channels（cross channel LRN）另种同channel内空间区域（within channel LRN）计算公式：每输入除选参数： local_size [default 5]：于cross channel LRN需要求邻近channel数量；于within channel LRN需要求空间区域边 alpha [default 1]：scaling参数 beta [default 5]：指数 norm_region [default ACROSS_CHANNELS]: 选择哪种LRNACROSS_CHANNELS 或者WITHIN_CHANNEL 2. Loss Layers 深度习通化输目标Loss驱习 2.1 Softmax 类型: SOFTMAX_LOSS 2.2 Sum-of-Squares / Euclidean 类型: EUCLIDEAN_LOSS 2.3 Hinge / Margin 类型: HINGE_LOSS 例： # L1 Norm layers { name: "loss" type: HINGE_LOSS bottom: "pred" bottom: "label" } # L2 Norm layers { name: "loss" type: HINGE_LOSS bottom: "pred" bottom: "label" top: "loss" hinge_loss_param { norm: L2 } } 选参数： norm [default L1]: 选择L1或者 L2范数输入： n * c * h * wPredictions n * 1 * 1 * 1Labels 输 1 * 1 * 1 * 1Computed Loss 2.4 Sigmoid Cross-Entropy 类型：SIGMOID_CROSS_ENTROPY_LOSS 2.5 Infogain 类型：INFOGAIN_LOSS 2.6 Accuracy and Top-k 类型：ACCURACY 用计算输目标确率事实loss且没backward步 3. 激励层（Activation / Neuron Layers）般说激励层element-wise操作输入输相同般情况非线性函数 3.1 ReLU / Rectified-Linear and Leaky-ReLU 类型: RELU 例: layers { name: "relu1" type: RELU bottom: "conv1" top: "conv1" } 选参数： negative_slope [default 0]:指定输入值于零输 ReLU目前使用做激励函数主要其收敛更快并且能保持同效标准ReLU函数max(x, 0)般x > 0输xx <= 0输negative_slopeRELU层支持in-place计算意味着bottom输输入相同避免内存消耗 3.2 Sigmoid 类型: SIGMOID 例: layers { name: "encode1neuron" bottom: "encode1" top: "encode1neuron" type: SIGMOID } SIGMOID 层通 sigmoid(x) 计算每输入x输函数图 3.3 TanH / Hyperbolic Tangent 类型: TANH 例: layers { name: "encode1neuron" bottom: "encode1" top: "encode1neuron" type: SIGMOID } TANH层通 tanh(x) 计算每输入x输函数图 3.3 Absolute Value 类型: ABSVAL 例: layers { name: "layer" bottom: "in" top: "out" type: ABSVAL } ABSVAL层通 abs(x) 计算每输入x输 3.4 Power 类型: POWER 例： layers { name: "layer" bottom: "in" top: "out" type: POWER power_param { power: 1 scale: 1 shift: 0 } } 选参数： power [default 1] scale [default 1] shift [default 0] POWER层通 (shift + scale * x) ^ power计算每输入x输 3.5 BNLL 类型: BNLL 例： layers { name: "layer" bottom: "in" top: "out" type: BNLL } BNLL (binomial normal log likelihood) 层通 log(1 + exp(x)) 计算每输入x输 4. 数据层（Data Layers）数据通数据层进入Caffe数据层整中国络底部数据自高效数据库（LevelDB 或者 LMDB）直接自内存追求高效性HDF5或者般图像格式硬盘读取数据 4.1 Database 类型：DATA 必须参数： source:包含数据目录名称 batch_size:处理输入数量选参数： rand_skip:始候输入跳数值异步随机梯度降（SGD）候非用 backend [default LEVELDB]: 选择使用 LEVELDB 或者 LMDB 4.2 In-Memory 类型: MEMORY_DATA 必需参数： batch_size, channels, height, width: 指定内存读取数据 The memory data layer reads data directly from memory, without copying it. In order to use it, one must call MemoryDataLayer::Reset (from C++) or Net.set_input_arrays (from Python) in order to specify a source of contiguous data (as 4D row major array), which is read one batch-sized chunk at a time. 4.3 HDF5 Input 类型: HDF5_DATA 必要参数： source:需要读取文件名 batch_size：处理输入数量 4.4 HDF5 Output 类型: HDF5_OUTPUT 必要参数： file_name: 输文件名 HDF5作用节其层输入blobs写硬盘 4.5 Images 类型: IMAGE_DATA 必要参数： source: text文件名字每行给张图片文件名label batch_size: batch图片数量选参数： rand_skip：始候输入跳数值异步随机梯度降（SGD）候非用 shuffle [default false] new_height, new_width: 所图像resize 4.6 Windows 类型：WINDOW_DATA 4.7 Dummy 类型：DUMMY_DATA Dummy 层用于development debugging具体参数DummyDataParameter 5. 般层（Common Layers） 5.1 全连接层Inner Proct 类型：INNER_PRODUCT 例： layers { name: "fc8" type: INNER_PRODUCT blobs_lr: 1 # learning rate multiplier for the filters blobs_lr: 2 # learning rate multiplier for the biases weight_decay: 1 # weight decay multiplier for the filters weight_decay: 0 # weight decay multiplier for the biases inner_proct_param { num_output: 1000 weight_filler { type: "gaussian" std: 0.01 } bias_filler { type: "constant" value: 0 } } bottom: "fc7" top: "fc8" } 必要参数： num_output (c_o)：滤器数选参数： weight_filler [default type: 'constant' value: 0]：参数初始化 bias_filler：偏置初始化 bias_term [default true]：指定否否启偏置项通全连接层变化：输入：n * c_i * h_i * w_i 输：n * c_o * 1 *1 5.2 Splitting 类型：SPLIT Splitting层输入blob离输blobs用需要blob输入输层候 5.3 Flattening 类型：FLATTEN Flattening输入n * c * h * w变简单向量其 n * (c*h*w) * 1 * 1 5.4 Concatenation 类型：CONCAT 例： layers { name: "concat" bottom: "in1" bottom: "in2" top: "out" type: CONCAT concat_param { concat_dim: 1 } } 选参数： concat_dim [default 1]：0代表链接num1代表链接channels 通全连接层变化：输入：1K每blobn_i * c_i * h * w 输： concat_dim = 0: (n_1 + n_2 + ... + n_K) *c_1 * h * w需要保证所输入c_i 相同 concat_dim = 1: n_1 * (c_1 + c_2 + ... +c_K) * h * w需要保证所输入n_i 相同通Concatenation层blobs链接blob 5.5 Slicing The SLICE layer is a utility layer that slices an input layer to multiple output layers along a given dimension (currently num or channel only) with given slice indices. 5.6 Elementwise Operations 类型：ELTWISE 5.7 Argmax 类型：ARGMAX 5.8 Softmax 类型：SOFTMAX 5.9 Mean-Variance Normalization 类型：MVN 6. 参考 Caffe