深度学习之Caffe完全掌握：添加新的网络层(训练非图像纯数据)

深度学习之Caffe完全掌握：添加新的网络层

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什么是caffe

Caffe，全称Convolutional Architecture for Fast Feature Embedding。是一种常用的深度学习框架，在视频、图像处理方面应用较多。作者是贾扬清，加州大学伯克利的ph.D。Caffe用C++编写，但可以用python调用。

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关于caffe的使用

你完全可以把python看作它的UI，并不涉及算法具体实现。
你完全可以把prototxt文件看作它的配置，只是模型和任务流程的一种文本描述。
你完全可以把.caffemodel文件视为出产物，模型实体。
（很勉强的排比修辞手法）（1分）

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下载示例程序

root@master:# git clone https://github.com/cbelth/irisCaffe.gitroot@master:# cd irisCaffe/irisroot@master:# python iris_tuto.py... ...

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关于配置环境

注意必须配置好关于pycaffe接口的地址，
我在iris_tuto.py中添加了：

import syssys.path.append("/download/caffe/python/")import caffe

就可以使用其接口了

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我们先看看现在的网络结构

其结构文件如下：

name: "IrisNet"layer {  name: "iris"  type: "HDF5Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  hdf5_data_param {    source: "iris_train_data.txt"    batch_size: 1  }}layer {  name: "iris"  type: "HDF5Data"  top: "data"  top: "label"  include {    phase: TEST  }  hdf5_data_param {    source: "iris_test_data.txt"    batch_size: 1  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "data"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 50    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "ip1"  top: "ip1"}layer {  name: "drop1"  type: "Dropout"  bottom: "ip1"  top: "ip1"  dropout_param {    dropout_ratio: 0.5  }}layer {  name: "ip2"  type: "InnerProduct"  bottom: "ip1"  top: "ip2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 50    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "drop2"  type: "Dropout"  bottom: "ip2"  top: "ip2"  dropout_param {    dropout_ratio: 0.4  }}layer {  name: "ip3"  type: "InnerProduct"  bottom: "ip2"  top: "ip3"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "drop3"  type: "Dropout"  bottom: "ip3"  top: "ip3"  dropout_param {    dropout_ratio: 0.3  }}layer {  name: "loss"  type: "SigmoidCrossEntropyLoss"   # type: "EuclideanLoss"   # type: "HingeLoss"    bottom: "ip3"  bottom: "label"  top: "loss"}

现在我想在ip1层后添加一层新的名为”newLayer”的新层，其结构和ip1一样，那么，就改写为：

name: "IrisNet"layer {  name: "iris"  type: "HDF5Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  hdf5_data_param {    source: "iris_train_data.txt"    batch_size: 1  }}layer {  name: "iris"  type: "HDF5Data"  top: "data"  top: "label"  include {    phase: TEST  }  hdf5_data_param {    source: "iris_test_data.txt"    batch_size: 1  }}layer {  name: "ip1"  type: "InnerProduct"  bottom: "data"  top: "ip1"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 50    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "ip1"  top: "ip1"}layer {  name: "drop1"  type: "Dropout"  bottom: "ip1"  top: "ip1"  dropout_param {    dropout_ratio: 0.5  }}layer {  name: "newLayer"  type: "InnerProduct"  bottom: "ip1"  top: "newLayer"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 50    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "relu_newLayer"  type: "ReLU"  bottom: "newLayer"  top: "newLayer"}layer {  name: "drop_newLayer"  type: "Dropout"  bottom: "newLayer"  top: "newLayer"  dropout_param {    dropout_ratio: 0.5  }}layer {  name: "ip2"  type: "InnerProduct"  bottom: "newLayer"  top: "ip2"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 50    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "drop2"  type: "Dropout"  bottom: "ip2"  top: "ip2"  dropout_param {    dropout_ratio: 0.4  }}layer {  name: "ip3"  type: "InnerProduct"  bottom: "ip2"  top: "ip3"  param {    lr_mult: 1  }  param {    lr_mult: 2  }  inner_product_param {    num_output: 3    weight_filler {      type: "xavier"    }    bias_filler {      type: "constant"    }  }}layer {  name: "drop3"  type: "Dropout"  bottom: "ip3"  top: "ip3"  dropout_param {    dropout_ratio: 0.3  }}layer {  name: "loss"  type: "SigmoidCrossEntropyLoss"   # type: "EuclideanLoss"   # type: "HingeLoss"    bottom: "ip3"  bottom: "label"  top: "loss"}

网络模型结构：

然后运行：

root@master:/App/Caffe_Iris/iris# python iris_tuto.py I1217 21:32:06.172330  6814 layer_factory.hpp:77] Creating layer irisI1217 21:32:06.172345  6814 net.cpp:84] Creating Layer irisI1217 21:32:06.172353  6814 net.cpp:380] iris -> dataI1217 21:32:06.172364  6814 net.cpp:380] iris -> labelI1217 21:32:06.172392  6814 hdf5_data_layer.cpp:80] Loading list of HDF5 filenames from: iris_test_data.txtI1217 21:32:06.172417  6814 hdf5_data_layer.cpp:94] Number of HDF5 files: 1I1217 21:32:06.173000  6814 net.cpp:122] Setting up irisI1217 21:32:06.173017  6814 net.cpp:129] Top shape: 1 1 1 4 (4)I1217 21:32:06.173024  6814 net.cpp:129] Top shape: 1 3 (3)I1217 21:32:06.173028  6814 net.cpp:137] Memory required for data: 28I1217 21:32:06.173033  6814 layer_factory.hpp:77] Creating layer ip1I1217 21:32:06.173075  6814 net.cpp:84] Creating Layer ip1... ...