当我们在caffe中开发一个新的层，例如
补充：实际上，我从Github上下载了某人的实现中的层文件：https://github.com/farmingyard/ShuffleNet。其中包括
在proto文件的末尾，然后我在caffe根目录中调用
然后执行
new_layer.cpp、new_layer.cu和new.layer.hpp，我们需要重新编译caffe。是否有可能只编译这些新层文件，就像更新原始库一样，还是必须重新编译整个库？能否给我一些提示？补充：实际上，我从Github上下载了某人的实现中的层文件：https://github.com/farmingyard/ShuffleNet。其中包括
.cpp、.cu和.hpp文件。我将.cpp和.cu放在caffe/src/caffe/layers目录下，并将.hpp放在caffe/include/caffe/layers/目录下，然后在caffe.proto中添加如下内容：message LayerParameter {
...
optional ShuffleChannelParameter shuffle_channel_param = 164;
}
另外：
message ShuffleChannelParameter {
optional uint32 group = 1[default = 1]; // The number of group
}
在proto文件的末尾，然后我在caffe根目录中调用
make clean，接着调用make all，没有错误，并检查到有行数。CXX src/caffe/layers/shuffle_channel_layer.cpp
and
NVCC src/caffe/layers/shuffle_channel_layer.cu
and（我不知道这是否意味着proto文件已重新编译）
CXX .build_release/src/caffe/proto/caffe.pb.cc
然后执行
make proto，会得到以下结果：make: Nothing to be done for proto
然后我运行了make pycaffe命令，没有错误提示，编译成功。随后我使用了作者提供的一个示例网络的prototxt文件来使用新层，但接下来出现了以下错误提示：
Message type "caffe.LayerParameter" has no field named "shuffle_channel_param".
完整的LayerParameter：
// 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
// The train / test phase for computation.
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.
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;
// The blobs containing the numeric parameters of the layer.
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.
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.
repeated NetStateRule include = 8;
repeated NetStateRule exclude = 9;
// Parameters for data pre-processing.
optional TransformationParameter transform_param = 100;
// Parameters shared by loss layers.
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.
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 ROIPoolingParameter roi_pooling_param = 8266711;
optional ScaleParameter scale_param = 142;
optional SigmoidParameter sigmoid_param = 124;
optional SmoothL1LossParameter smooth_l1_loss_param = 8266712;
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;
optional ShuffleChannelParameter shuffle_channel_param = 164;
}