deep learning实践经验总结2--准确率再次提升，到达0.8，再来总结一下

deep learning实践经验总结2

最近拿caffe来做图片分类，遇到不少问题，同时也吸取不少教训和获得不少经验。

这次拿大摆裙和一步裙做分类，

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。

然后效果很明显，准确率高了，权重图画出来后，也看得出是有意义的了，部分权重图是人的轮廓或者裙子的轮廓。

先看看图片：

大摆裙

    

一步裙

    

然后找一些响应图看一下，当然我这里展示的是一些效果好的响应图。

大摆裙

     

一步裙

     

一些权重图：

    

    

    

这是网络的结构参数：

name: "CIFAR10_full_train"layers { layer {   name: "cifar"   type: "data"   #source: "/home/linger/linger/testfile/crop_train_db"   #source: "/home/linger/linger/testfile/collar_train_db"   source: "/home/linger/linger/testfile/skirt_train_db"   #source: "/home/linger/linger/testfile/pattern_train_db"   meanfile: "/home/linger/linger/testfile/skirt_train_mean.binaryproto"   #cropsize: 200   batchsize: 20 } top: "data" top: "label"}layers {  layer {    name: "conv1"    type: "conv"    num_output: 16    kernelsize: 5    stride:1     weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 1.    weight_decay: 0.001    weight_decay: 0.  }  bottom: "data"  top: "conv1"}layers {  layer {    name: "relu1"    type: "relu"  }  bottom: "conv1"  top: "conv1"}layers {  layer {    name: "pool1"    type: "pool"    pool: MAX    kernelsize: 2    stride:1   }  bottom: "conv1"  top: "pool1"}layers {  layer {    name: "conv2"    type: "conv"    num_output: 16    group: 2    kernelsize: 5    stride:1     weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 1.    weight_decay: 0.001    weight_decay: 0.  }  bottom: "pool1"  top: "conv2"}layers {  layer {    name: "relu2"    type: "relu"  }  bottom: "conv2"  top: "conv2"}layers {  layer {    name: "pool2"    type: "pool"    pool: MAX    kernelsize: 2    stride: 1  }  bottom: "conv2"  top: "pool2"}layers {  layer {    name: "ip1"    type: "innerproduct"    num_output: 100    weight_filler {      type: "gaussian"      std: 0.0001    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 1.    weight_decay: 0.001    weight_decay: 0.  }  bottom: "pool2"  top: "ip1"}layers {  layer {    name: "ip2"    type: "innerproduct"    num_output: 2    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 1.    weight_decay: 0.001    weight_decay: 0.  }  bottom: "ip1"  top: "ip2"}#-----------------------output------------------------layers { layer {   name: "loss"   type: "softmax_loss" } bottom: "ip2" bottom: "label"}

name: "CIFAR10_full_test"layers { layer {   name: "cifar"   type: "data"   #source: "/home/linger/linger/testfile/collar_test_db"   #source: "/home/linger/linger/testfile/crop_test_db"   source: "/home/linger/linger/testfile/skirt_test_db"   #source: "/home/linger/linger/testfile/pattern_test_db"   meanfile: "/home/linger/linger/testfile/skirt_test_mean.binaryproto"   #cropsize: 200   batchsize: 10 } top: "data" top: "label"}layers {  layer {    name: "conv1"    type: "conv"    num_output: 16    kernelsize: 5    stride:1     weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 1.    weight_decay: 0.001    weight_decay: 0.  }  bottom: "data"  top: "conv1"}layers {  layer {    name: "relu1"    type: "relu"  }  bottom: "conv1"  top: "conv1"}layers {  layer {    name: "pool1"    type: "pool"    pool: MAX    kernelsize: 2    stride:1   }  bottom: "conv1"  top: "pool1"}layers {  layer {    name: "conv2"    type: "conv"    num_output: 16    group: 2    kernelsize: 5    stride:1     weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 1.    weight_decay: 0.001    weight_decay: 0.  }  bottom: "pool1"  top: "conv2"}layers {  layer {    name: "relu2"    type: "relu"  }  bottom: "conv2"  top: "conv2"}layers {  layer {    name: "pool2"    type: "pool"    pool: MAX    kernelsize: 2    stride: 1  }  bottom: "conv2"  top: "pool2"}layers {  layer {    name: "ip1"    type: "innerproduct"    num_output: 100    weight_filler {      type: "gaussian"      std: 0.0001    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 1.    weight_decay: 0.001    weight_decay: 0.  }  bottom: "pool2"  top: "ip1"}layers {  layer {    name: "ip2"    type: "innerproduct"    num_output: 2    weight_filler {      type: "gaussian"      std: 0.01    }    bias_filler {      type: "constant"      value: 0.    }    blobs_lr: 1.    blobs_lr: 1.    weight_decay: 0.001    weight_decay: 0.  }  bottom: "ip1"  top: "ip2"}#-----------------------output------------------------layers { layer {   name: "prob"   type: "softmax" } bottom: "ip2" top: "prob"}layers {  layer {    name: "accuracy"    type: "accuracy"  }  bottom: "prob"  bottom: "label"  top: "accuracy"}

# reduce learning rate after 120 epochs (60000 iters) by factor 0f 10# then another factor of 10 after 10 more epochs (5000 iters)# The training protocol buffer definitiontrain_net: "cifar10_full_train.prototxt"# The testing protocol buffer definitiontest_net: "cifar10_full_test.prototxt"# test_iter specifies how many forward passes the test should carry out.# In the case of CIFAR10, we have test batch size 100 and 100 test iterations,# covering the full 10,000 testing images.test_iter: 20# Carry out testing every 1000 training iterations.test_interval: 100# The base learning rate, momentum and the weight decay of the network.base_lr: 0.00001momentum: 0.9weight_decay: 0.004# The learning rate policylr_policy: "fixed"# Display every 200 iterationsdisplay: 20# The maximum number of iterationsmax_iter: 60000# snapshot intermediate resultssnapshot: 1000snapshot_prefix: "cifar10_full"# solver mode: 0 for CPU and 1 for GPUsolver_mode: 1

真的是多玩数据，才会对数据形成一种感觉啊。

下次玩3类的。敬请期待！