工厂模式-CaffeNet训练

来源：互联网发布：方正综艺简体下载 mac 编辑：程序博客网时间：2024/05/30 04:30

参考链接：http://blog.csdn.net/lingerlanlan/article/details/32329761

RNN神经网络：http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/detection.ipynb

官方链接：http://nbviewer.ipython.org/github/BVLC/caffe/blob/master/examples/classification.ipynb

参考链接：http://suanfazu.com/t/caffe-shen-du-xue-xi-kuang-jia-shang-shou-jiao-cheng/281/3

模型定义中有一点比较容易被误解，信号在有向图中是自下而上流动的，并不是自上而下。

层的结构定义如下：

1 name:层名称 2 type:层类型 3 top:出口 4 bottom:入口

Each layer type defines three critical computations: setup, forward, andbackward.

Setup: initialize the layer and its connections once at model initialization.
Forward: given input from bottom compute the output and send to the top.
Backward: given the gradient w.r.t. the top output compute the gradient w.r.t. to the input and send to the bottom. A layer with parameters computes the gradient w.r.t. to its parameters and stores it internally.

/home/wishchin/caffe-master/examples/hdf5_classification/train_val2.prototxt

name: "LogisticRegressionNet"layer {  name: "data"  type: "HDF5Data"  top: "data"  top: "label"  include {    phase: TRAIN  }  hdf5_data_param {    source: "hdf5_classification/data/train.txt"    batch_size: 10  }}layer {  name: "data"  type: "HDF5Data"  top: "data"  top: "label"  include {    phase: TEST  }  hdf5_data_param {    source: "hdf5_classification/data/test.txt"    batch_size: 10  }}layer {  name: "fc1"  type: "InnerProduct"  bottom: "data"  top: "fc1"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 40    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "relu1"  type: "ReLU"  bottom: "fc1"  top: "fc1"}layer {  name: "fc2"  type: "InnerProduct"  bottom: "fc1"  top: "fc2"  param {    lr_mult: 1    decay_mult: 1  }  param {    lr_mult: 2    decay_mult: 0  }  inner_product_param {    num_output: 2    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0    }  }}layer {  name: "loss"  type: "SoftmaxWithLoss"  bottom: "fc2"  bottom: "label"  top: "loss"}layer {  name: "accuracy"  type: "Accuracy"  bottom: "fc2"  bottom: "label"  top: "accuracy"  include {    phase: TEST  }}

关于参数与结果的关系：多次训练效果一直在0.7，后来改动了全链接层的初始化参数。高斯分布的标准差由0.001改为0.0001，就是调小了。我的结果有点相似。

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