caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

时间：2021-06-29 20:17:04

我是分2类 我的训练和测试集在http://blog.csdn.net/wd1603926823/article/details/51732849已经转化为leveldb格式  

首先，我的Imagenet_solver.prototxt文件：

name: "CaffeNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "E:/Keras_Code/examples/imagenet/meanforme.binaryproto"
  }
  data_param {
    source: "E:/Keras_Code/examples/imagenet/train_leveldb"
    batch_size: 8
    backend: LEVELDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 227
    mean_file: "E:/Keras_Code/examples/imagenet/meanforme_test.binaryproto"
  }
  data_param {
    source: "E:/Keras_Code/examples/imagenet/test_leveldb"
    batch_size: 1
    backend: LEVELDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  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: "accuracy"
  type: "Accuracy"
  bottom: "fc8"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "label"
  top: "loss"
}
下面是Imagenet_solver.prototxt文件： 

net: "E:/Keras_Code/examples/imagenet/Imagenet_train_test.prototxt"
test_iter: 24
test_interval: 200

base_lr: 0.001
momentum: 0.9
weight_decay: 0.0005


lr_policy: "inv"
gamma: 0.001
power: 0.75

display:20

max_iter: 2000

snapshot: 400
snapshot_prefix: "E:/Keras_Code/examples/imagenet/mine"

solver_mode: GPU
然后我的label都是从0开始的   如：
caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决
然后在python接口下：accuracy_loss_img.py 训练并画出accuracy 

from pylab import *
import matplotlib.pyplot as plt
import caffe
caffe.set_device(0)
caffe.set_mode_gpu()
solver = caffe.SGDSolver('E:\Keras_Code\examples\imagenet\Imagenet_solver.prototxt')
niter = 2000
display_iter = 400
test_iter = 24
test_interval = 200
# train loss
train_loss = zeros(ceil(niter * 1.0 / display_iter))
# test loss
test_loss = zeros(ceil(niter * 1.0 / test_interval))
# test accuracy
test_acc = zeros(ceil(niter * 1.0 / test_interval))
# iteration 0
solver.step(1)
_train_loss = 0; _test_loss = 0; _accuracy = 0

for it in range(niter):
    solver.step(1)
    _train_loss += solver.net.blobs['loss'].data
    if it % display_iter == 0:
        train_loss[it // display_iter] = _train_loss / display_iter
        _train_loss = 0
    if it % test_interval == 0:
        for test_it in range(test_iter):
            solver.test_nets[0].forward()
            _test_loss += solver.test_nets[0].blobs['loss'].data
#_accuracy += solver.test_nets[0].blobs['accuracy'].data
        test_loss[it / test_interval] = _test_loss / test_iter
        test_acc[it / test_interval] = _accuracy / test_iter
        _test_loss = 0
        _accuracy = 0
print '\nplot the train loss and test accuracy\n'
_, ax1 = plt.subplots()
ax2 = ax1.twinx()

ax1.plot(display_iter * arange(len(train_loss)), train_loss, 'g')
ax1.plot(test_interval * arange(len(test_loss)), test_loss, 'y')
ax2.plot(test_interval * arange(len(test_acc)), test_acc, 'r')
ax1.set_xlabel('iteration')
ax1.set_ylabel('loss')
ax2.set_ylabel('accuracy')
plt.show()


可是accuracy始终为0  而cmd下 明明闪过accuracy=0.78 和accuracy=0.29等之类的数 。。。

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

可是caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决红线一直是0  即使我把

_accuracy += solver.test_nets[0].blobs['accuracy'].data
即使把这句不注释掉 运行出来也是一直为0的红线  好奇怪的。。。？？？为什么  这是第一个问题？！

然后就是对新图片进行预测分类：

那么首先要有自己的deploy.prototxt文件：我的是mydeploy.prototxt：我不知道我这个deploy根据我的Imagenet_train_test.prototxt改写的对不对在dim:10这里：

name: "CaffeNet"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } }  #话说这第一个dim为什么是10啊 10代表的是什么？？第二个问题
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  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: "prob"
  type: "Softmax"
  bottom: "fc8"
  top: "prob"
}
然后就是预测分类 先说下我的类别名txt：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

然后就是预测分类的脚本：

#coding=utf-8
#http://www.cnblogs.com/denny402/p/5685909.html
#http://www.cnblogs.com/NanShan2016/p/5532589.html
import caffe
import numpy as np

deploy='E:/Keras_Code/examples/imagenet/mydeploy.prototxt'   
caffe_model='E:/Keras_Code/examples/imagenet/mine_iter_800.caffemodel'  
img='E:/Keras_Code/examples/imagenet/test13.bmp'  
labels_filename = 'E:/Keras_Code/examples/imagenet/mylabel.txt'  
mean_file='E:/pyCaffe_GPU/python/caffe/imagenet/ilsvrc_2012_mean.npy'
net = caffe.Net(deploy,caffe_model,caffe.TEST) 

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})  
transformer.set_transpose('data', (2,0,1))   
transformer.set_mean('data', np.load(mean_file).mean(1).mean(1))   
transformer.set_raw_scale('data', 255)   
transformer.set_channel_swap('data', (2,1,0))   
im=caffe.io.load_image(img)                  
net.blobs['data'].data[...] = transformer.preprocess('data',im)     

out = net.forward()
labels = np.loadtxt(labels_filename, str, delimiter='\t')   
#print net.blobs
prob= net.blobs['prob'].data[0].flatten() 
print 'frequency belong to 2 classes:', prob
order=prob.argsort()[0]  
print order
#print labels
#print 'the class is:',labels[order]  


###########################################################
#http://www.cnblogs.com/denny402/category/759199.html
#http://www.bubuko.com/infodetail-1019584.html
#http://www.docin.com/p-871820922.html
#http://blog.csdn.net/hjimce/article/details/48972877
可是结果：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决输出的类别标号应该是0啊  怎么是1呢？？这是第3个问题 因为它明明概率是：97%  3%  那么应该属于前一类啊 标号应该为0啊 怎么是1？？？
的确 如@风翼冰舟  所说 标号对应的是【1 0】：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决这样貌似就得到了我想要的分类结果。

但我还是疑惑 allorder中为什么存储的是[1 0]而不是[0 1]呢????

哦 我懂了  刚刚我也去查了argsort这个函数：如果x表示属于每个类别的概率  labelss表示我的标签  如果三类 那么labelss=[0 1 2] 然后：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决人为一下就可以看到属于第0类 因为概率是0.7最大   我这里改成了按降序排列  而不是升序  因为升序如果类别不同label=exam[0]这里就要改不同 2类的话这里如@风翼冰舟所说要改成1  因为后者为概率最大的类别号 如果3类的话要改成2  4类的话改成3 不方便  索性就改成降序  不管你是有几类 最大的标号始终为0  这样方便  我试了下  证明正确：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决再看：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决正确的   

再比如：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决所以这样改更好 弹性更大。

不管几类都是正确的 根据概率来的：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决OK啦！

////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

读取一个文件夹下的一些图片：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

预测这个文件夹下所有图片的分类结果：并写进一个class_result.txt的文件中：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决只是我的模型准确率不好 都分到一个类中去了！

其中这有2个脚本文件：

第一个是myclassify1img.py：

import caffe
import numpy as np
def classify1img(caffe_model,deploy,mean_file,labels_filename,img):
    net = caffe.Net(deploy,caffe_model,caffe.TEST) 
    transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})  
    transformer.set_transpose('data', (2,0,1))  
    transformer.set_mean('data', np.load(mean_file).mean(1).mean(1))   
    transformer.set_raw_scale('data', 255)   
    transformer.set_channel_swap('data', (2,1,0))   
    im=caffe.io.load_image(img)    
    net.blobs['data'].data[...] = transformer.preprocess('data',im)
    out = net.forward()
    labels = np.loadtxt(labels_filename, str, delimiter='\t')  
    prob= net.blobs['prob'].data[0].flatten() 
    order=np.argsort(-prob)[0]
    #print 'class is:',labels[order]
    myclass=labels[order]
    return myclass
第二个是： 

#http://www.111cn.net/phper/python/47504.htm
import os,glob
from myclassify1img import classify1img
import caffe
dir_path='E:/Keras_Code/examples/imagenet/forclassify/'   
imgsextension=['bmp']
os.chdir(dir_path)
imglist=[]
for extension in imgsextension:
extension='*.'+extension
imglist+=[os.path.realpath(e) for e in glob.glob(extension)]
caffe_model='E:/Keras_Code/examples/imagenet/mine_iter_800.caffemodel'  
deploy='E:/Keras_Code/examples/imagenet/mydeploy.prototxt'   
mean_file='E:/pyCaffe_GPU/python/caffe/imagenet/ilsvrc_2012_mean.npy'
labels_filename = 'E:/Keras_Code/examples/imagenet/mylabel.txt'  
class_file='E:/Keras_Code/examples/imagenet/class_result.txt'
f=open(class_file,'w')
for file in imglist:
img=file        
myclass=classify1img(caffe_model,deploy,mean_file,labels_filename,img)  
#print myclass
f.write(myclass+'\n')
f.close()
就行啦！

///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

遇到一个问题 我想把图片显示出来：

import numpy as np
import matplotlib.pyplot as plt
image='E:/Keras_Code/examples/imagenet/test00.bmp'  
plt.imshow(image)
plt.axis('off')
可是总是报错：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决为什么呢？？可是我换用ipython上运行：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决图片就可以显示出来  为什么Notepad++上面运行就不行呢？？？

就剩下第一个和这个问题待解决了。。。

///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

刚刚看到对新图片预测分类时 要用自己的mean文件  据说是要用新图片减去训练集的mean.binaryproto ：之前我是用的pycaffe自带的ilsvrc_2012_mean.npy  而做自己的图片还是不能用它的 因为我看到它是[3，256，256]而我自己的图片不是这样 所以用它的均值不对的：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决这样就行了！和那个npy等价！

比如上面我的待预测的新图片都放在forclassify文件夹下面 比如有10张  只是我不知道这上面预测时到底是用训练集计算得到的那个meanforme.binaryproto还是应该首先对这些新图片也就是forclassify文件夹下的图片计算出一个新mean_new.binaryproto 然后用这个mean_new.binaryproto？？这两个mean到底应该用哪个  有人说用第一个也就是训练集的mean文件  对吗？？？这是第三个问题。

///////////////////////马上要十一了 七天假  哈哈///////////////////////////////////////////////////////////////////////////////////////////////////

按照http://www.07net01.com/2016/05/1502443.html 用自己训练好的模型提取全连接层的特征并保存：

#http://www.cnblogs.com/louyihang-loves-baiyan/p/5078746.html
#http://www.cnblogs.com/louyihang-loves-baiyan/p/5134671.html
import numpy as np
import matplotlib.pyplot as plt
import caffe
from useless_test import vis_square
from PIL import Image
import scipy.io as sio 
caffe.set_mode_gpu()
caffe.set_device(0)

deployPrototxt ='E:/Keras_Code/examples/imagenet/mydeploy.prototxt'
modelFile ='E:/Keras_Code/examples/imagenet/mine_iter_800.caffemodel'  
mean_file='E:/Keras_Code/examples/imagenet/meanforme.binaryproto'
net = caffe.Net(deployPrototxt, modelFile,caffe.TEST)
dir_path='E:/Keras_Code/examples/imagenet/forclassify/'   
#myimglist=readImageList(dir_path)

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})  
transformer.set_transpose('data', (2,0,1))    
mean_blob = caffe.proto.caffe_pb2.BlobProto()
mean_blob.ParseFromString(open(mean_file, 'rb').read())
mean_npy = caffe.io.blobproto_to_array(mean_blob)
b2=mean_npy[0,:,:,:]
transformer.set_mean('data',b2.mean(1).mean(1))  
transformer.set_raw_scale('data', 255)   
transformer.set_channel_swap('data', (2,1,0))  
net.blobs['data'].reshape(1,3,227,227)

for layer_name, blob in net.blobs.iteritems():  
    print layer_name + '\t' + str(blob.data.shape)

image='E:/Keras_Code/examples/imagenet/test00.bmp'  
net.blobs['data'].data[...] = transformer.preprocess('data',caffe.io.load_image(image))
net.forward()
plt.subplot(4, 1, 1)
plt.imshow(Image.open(image))
plt.axis('off')
#conv1
plt.subplot(4, 1, 2)  
feat_conv= net.blobs['conv1'].data[0, :36]
vis_square(feat_conv)

feat_fc= net.blobs['fc6'].data[0]
print feat_fc.shape
#print 'full connect feature:',type(feat_fc)

plt.subplot(4, 1, 3)  
plt.plot(feat_fc.flat)  
plt.subplot(4, 1, 4)  
_ = plt.hist(feat_fc.flat[feat_fc.flat > 0], bins=100) 

feat_fc.shape=(4096,1)
row_feat_fc=np.transpose(feat_fc)
#print row_feat_fc.shape
#np.savetxt("E:/Keras_Code/examples/imagenet/savefeatures.txt",row_feat_fc)
sio.savemat("E:/Keras_Code/examples/imagenet/savefeatures.mat", {'feature':row_feat_fc})

#http://www.07net01.com/2016/05/1502443.html
#filters = net.params['conv1'][0].data
#with open('FirstLayerFilter.pickle','wb') as f:
#    pickle.dump(filters,f)
#vis_square(filters.transpose(0, 2, 3, 1))
#http://blog.csdn.net/thy_2014/article/details/51659300

#with open('FirstLayerOutput.pickle','wb') as f:
#    pickle.dump(feat,f)
#pool = net.blobs['pool1'].data[0,:36]
#with open('pool1.pickle','wb') as f:
#    pickle.dump(pool,f)
#vis_square(pool,padval=1)
#vis_square(pool)
#http://nbviewer.jupyter.org/github/BVLC/caffe/blob/master/examples/00-classification.ipynb
caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决第一张是我要提取特征的原图  第二张是36个小图 即卷积层的输出 第三个是fc6层特征 caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决保存为mat  ！

//////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

上面是提取一张图片特征并保存  现在是提取一个文件夹下的所有图片并保存：

#extract many images in one folder
import os
import numpy as np
import matplotlib.pyplot as plt
import caffe
from useless_test import vis_square
from PIL import Image
import scipy.io as sio 
caffe.set_mode_gpu()
caffe.set_device(0)

deployPrototxt ='E:/Keras_Code/examples/imagenet/mydeploy.prototxt'
modelFile ='E:/Keras_Code/examples/imagenet/mine_iter_800.caffemodel'  
mean_file='E:/Keras_Code/examples/imagenet/meanforme.binaryproto'
net = caffe.Net(deployPrototxt, modelFile,caffe.TEST)

transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})  
transformer.set_transpose('data', (2,0,1))    
mean_blob = caffe.proto.caffe_pb2.BlobProto()
mean_blob.ParseFromString(open(mean_file, 'rb').read())
mean_npy = caffe.io.blobproto_to_array(mean_blob)
b2=mean_npy[0,:,:,:]
transformer.set_mean('data',b2.mean(1).mean(1))  
transformer.set_raw_scale('data', 255)   
transformer.set_channel_swap('data', (2,1,0))  
net.blobs['data'].reshape(1,3,227,227)
dir_path='E:/Keras_Code/examples/imagenet/forclassify'
filelists=os.listdir(dir_path)
#print filelists

#dim=4096  changed by you
features = np.empty((len(filelists),4096),dtype="float32")

for ind,everyimg in enumerate(filelists):
    theimg='E:/Keras_Code/examples/imagenet/forclassify/'+everyimg
    net.blobs['data'].data[...] = transformer.preprocess('data',caffe.io.load_image(theimg))
    net.forward()
    feat_fc= net.blobs['fc6'].data[0]
    feat_fc.shape=(4096,1)
    row_feat_fc=np.transpose(feat_fc)
    features[ind,:]=row_feat_fc

#np.savetxt("E:/Keras_Code/examples/imagenet/savefeatures.txt",row_feat_fc)
sio.savemat("E:/Keras_Code/examples/imagenet/imgsfeatures.mat", {'feature':features})
caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决OK！我的那个文件夹下只有8张图片！

///////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

我想设计自己的层，不用自带model里面的层，设计自己的层就得会计算每层的输入输出：

http://www.myexception.cn/other/1828071.html  按照这个：

我要学着计算这个  就以上面那个模型为例好了   它的层设置是这样子：

name: "CaffeNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 227
    mean_file: "examples/imagenet/meanforme.binaryproto"
  }
  data_param {
    source: "examples/imagenet/train_leveldb"
    batch_size: 8
    backend: LEVELDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 227
    mean_file: "examples/imagenet/meanforme.binaryproto"
  }
  data_param {
    source: "examples/imagenet/test_leveldb"
    batch_size: 1
    backend: LEVELDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "fc6"
  type: "InnerProduct"
  bottom: "pool5"
  top: "fc6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6"
  top: "fc6"
}
layer {
  name: "drop6"
  type: "Dropout"
  bottom: "fc6"
  top: "fc6"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc7"
  type: "InnerProduct"
  bottom: "fc6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7"
  top: "fc7"
}
layer {
  name: "drop7"
  type: "Dropout"
  bottom: "fc7"
  top: "fc7"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc8"
  type: "InnerProduct"
  bottom: "fc7"
  top: "fc8"
  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: "accuracy"
  type: "Accuracy"
  bottom: "fc8"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TRAIN
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc8"
  bottom: "label"
  top: "loss"
}
它的实际每层  我输出来看到是这样：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决
data的1L是不是batchsize？为什么是1呢？是指测试集的batchsize吗？为什么不是训练集的batchsize：8呢？  输入是3X227X227的 ......经过计算conv1后http://www.myexception.cn/other/1828071.html  可以得到96（num_output: 96）和55X55  但是 这个10？是怎么来的 ？默认的吗？.......计算经过pool1后，我按照公式计算出来是96X27X27......再经过norm1(不变)，依旧是96X27X27.......然后是经过conv2，num_output: 256 默认步长stride是1哦  所以又计算出来是 256X27X27.....再经过pool2后，又计算出来的确是256X13X13.....后面的几层我都会按照这样计算了。

那么问题是 那个data的1是train还是test的batchsize或者什么？ 为什么会在conv1变成10？是不是train和test不能写在同一个prototxt中？？？？

哦 我明白了  后面的10是这个文件中设置的：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

data中的1是 在预测分类和提取特征时设置的：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决所以出来是1！ 按道理来说   是不是我该该成和test的batchsize一样大小才对 ？？

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决是将deploy中的dim改成测试集的batchsize的大小吧     还是改下面那个data的reshape的第一维数据成测试集的batchsize大小？ 我觉得是改deploy中的  像上面那样。。。。吧？

和别人讨论后，我觉得应该这样理解 ：因为自己的deploy.prototxt文件主要用在2个地方：提取每层的特征和对新图片预测分类   

提取特征时.py中：net.blobs['data'].reshape(1,3,227,227)   这句中的1的确代表batchsize   但代表的是要提取多少张图片一次性  的batchsize

预测分类时deploy中：input_param { shape: { dim: 10 dim: 3 dim: 227 dim: 227 } } 这个10也的确是代表batchsize  但代表的是一次性预测多少张图片的类别  的batchsize

个人觉得和我们训练集测试集训练模型的那个测试集的batchsize没太大关系     是这样吗？？？？？？？？？？？？

/////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////////

别人给了我猫的图片12000张 狗的图片12000张  我试试这个二分类   ：我拿98张猫和94张狗做测试集   剩余的都是训练集   ：

一、首先对训练集生成标签、测试集生成对应标签：

#include<iostream>
#include<string>
int main()
{
FILE *fid1;
fid1 = fopen("test_labels9.27.txt", "w");
int label0 = 0, label1 = 1;
char name[15], label[2], black[] = "\r\n";
memset(name, 0, sizeof(name));
for (int i = 0; i < 98; ++i)
{

itoa(i, name, 10);
strcat(name, ".jpg ");
itoa(label0, label, 10);

strcat(name, label);
printf("%s", name);
fwrite(name, sizeof(name), 1, fid1);
fwrite(black, strlen(black), 1, fid1);
memset(name, 0, sizeof(name));
memset(label, 0, sizeof(label));
}
for (int i =98; i < 192; ++i)
{

itoa(i, name, 10);
strcat(name, ".jpg ");
itoa(label1, label, 10);
strcat(name, label);
printf("%s", name);
fwrite(name, sizeof(name), 1, fid1);
fwrite(black, strlen(black), 1, fid1);
memset(name, 0, sizeof(name));
memset(label, 0, sizeof(label));
}
fclose(fid1);
return 0;
}

二、将训练测试集转为lmdb格式

E:\pyCaffe_GPU\Build\x64\Release\convert_imageset.exe  E:\pyCaffe_GPU\Build\x64\Release\mytest\ E:\pyCaffe_GPU\Build\x64\Release\mytest\test_labels9.27.txt  E:\pyCaffe_GPU\examples\imagenet\test_lmdb_catdog -backend=lmdb  
pause
<img src="http://img.blog.csdn.net/20160929103509301" alt="" />

训练集也是这样：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

三、生成训练测试集的mean文件：

E:\pyCaffe_GPU\Build\x64\Release\compute_image_mean.exe  E:\pyCaffe_GPU\examples\imagenet\train_lmdb_catdog E:\pyCaffe_GPU\examples\imagenet\mean_catdog_train.binaryproto 
pause

测试集也是这样

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

四、写那两个prototxt文件：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

name: "CaffeNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 300
    mean_file: "examples/imagenet/mean_catdog_train.binaryproto"
  }
  data_param {
    source: "examples/imagenet/train_lmdb_catdog"
    batch_size: 20
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 300
    mean_file: "examples/imagenet/mean_catdog_test.binaryproto"
  }
  data_param {
    source: "examples/imagenet/test_lmdb_catdog"
    batch_size: 5
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 102
    kernel_size: 3
    stride: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 204
    kernel_size: 5
stride: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 300
    kernel_size: 3
stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 5
    stride: 3
  }
}
layer {
  name: "fc4"
  type: "InnerProduct"
  bottom: "pool3"
  top: "fc4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2050
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "fc4"
  top: "fc4"
}
layer {
  name: "drop4"
  type: "Dropout"
  bottom: "fc4"
  top: "fc4"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc5"
  type: "InnerProduct"
  bottom: "fc4"
  top: "fc5"
  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: "accuracy"
  type: "Accuracy"
  bottom: "fc5"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TRAIN
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc5"
  bottom: "label"
  top: "loss"
}
solver.prototxt: 

net: "examples/imagenet/my_traintest.prototxt"
test_iter: 40
test_interval: 50

base_lr: 0.001
momentum: 0.9
weight_decay: 0.0005


lr_policy: "inv"
gamma: 0.001
power: 0.75

display: 10

max_iter: 20000

snapshot: 1000
snapshot_prefix: "examples/imagenet/catdog"

solver_mode: GPU
开始我的batchsize设置为50 竟然报错说 out of memory  后来改小了

五、写train.bat进行训练：

.\Build\x64\Release\caffe.exe train --solver=.\examples\imagenet\my_solver.prototxt  
pause

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

等。。。。。。。。。。。。。。。。

六、结果：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

2万多图片  这个准确率 可以了！



/////////////////////////////////////////////分界线/////////////////////////////////////////////////////////////////////////////////////////

用第一个模型  就是caffe自带的一个较复杂的模型  预测1016张图片的类别：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决这个模型  

结果需要时间：caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决平均27ms每张！

我乱编了一个较简短的模型 如下：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决 可是时间却长些？

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决每张平均46ms！ 为什么呢 好奇怪？
/////////////////////////////////////////////////////////////今天下班就可以去嗨了哈哈////////////////////////////////////////////////////////////////////////////////////


////////////////////////////////////////////////////七天假哈哈/////////////////////////////////////////////////嘴巴都笑烂了////////////////////////////哈哈////////////


对我自己的图片重新来训练：

一、转换lmdb：

E:\pyCaffe_GPU\Build\x64\Release\convert_imageset.exe  E:\pyCaffe_GPU\Build\x64\Release\test_mine\ E:\pyCaffe_GPU\Build\x64\Release\test_mine\test_labels.txt  E:\pyCaffe_GPU\examples\imagenet\test_lmdb_mine --shuffle -backend=lmdb  
pause
caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决
二、算mean 

E:\pyCaffe_GPU\Build\x64\Release\compute_image_mean.exe  E:\pyCaffe_GPU\examples\imagenet\train_lmdb_mine E:\pyCaffe_GPU\examples\imagenet\mean_mine_train.binaryproto 
pause
caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

三、prototxt文件：

name: "CaffeNet"
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
    crop_size: 300
    mean_file: "E:/pyCaffe_GPU/examples/imagenet/mean_mine_train.binaryproto"
  }
  data_param {
    source: "E:/pyCaffe_GPU/examples/imagenet/train_lmdb_mine/"
    batch_size: 10
    backend: LMDB
  }
}
layer {
  name: "data"
  type: "Data"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    crop_size: 300
    mean_file: "E:/pyCaffe_GPU/examples/imagenet/mean_mine_test.binaryproto"
  }
  data_param {
    source: "E:/pyCaffe_GPU/examples/imagenet/test_lmdb_mine/"
    batch_size: 1
    backend: LMDB
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 102
    kernel_size: 3
    stride: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "pool1"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "norm1"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 204
    kernel_size: 5
stride: 3
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 5
    stride: 1
  }
}
layer {
  name: "norm2"
  type: "LRN"
  bottom: "pool2"
  top: "norm2"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "norm2"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 300
    kernel_size: 3
stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 5
    stride: 3
  }
}
layer {
  name: "fc4"
  type: "InnerProduct"
  bottom: "pool3"
  top: "fc4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 2050
    weight_filler {
      type: "gaussian"
      std: 0.005
    }
    bias_filler {
      type: "constant"
      value: 1
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "fc4"
  top: "fc4"
}
layer {
  name: "drop4"
  type: "Dropout"
  bottom: "fc4"
  top: "fc4"
  dropout_param {
    dropout_ratio: 0.5
  }
}
layer {
  name: "fc5"
  type: "InnerProduct"
  bottom: "fc4"
  top: "fc5"
  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: "accuracy"
  type: "Accuracy"
  bottom: "fc5"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TRAIN
  }
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc5"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "fc5"
  bottom: "label"
  top: "loss"
}
四、train.bat训练结果 并画曲线图：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决


http://download.csdn.net/detail/wd1603926823/9671251   plot.py

这是1600次的图  等它执行完20000次我再看。。。
caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决快10000次了。。。caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

大功告成！

五、用最后一次的模型提取特征：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决

六、用最后一次的模型预测类别：

caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决我只拿了8张图片进行预测。。。。。因为我没有新样本了。

七、将类别结果保存：

#http://www.111cn.net/phper/python/47504.htm
import time
start=time.clock()
import os
from myclassify1img import classify1img
import caffe
caffe.set_mode_gpu()
caffe.set_device(0)

dir_path='E:/Keras_Code/examples/imagenet/forclassify/'   
'''
imgsextension=['bmp']
os.chdir(dir_path)
imglist2=[]
for extension in imgsextension:
extension='*.'+extension
imglist2+=[os.path.realpath(e) for e in glob.glob(extension)]
print imglist2 
'''

imglist=[]
for path,d,filelist in os.walk(dir_path):
    filelist.sort(key= lambda x:int(x[:-4]))
    for filename in filelist: 
        mypath =dir_path + filename
        #print filename
        #print mypath
        #imglist+=[mypath]
        imglist.append(mypath)
#print imglist

net_file='E:\pyCaffe_GPU\examples\imagenet\mydeploy_mine.prototxt'
caffe_model='E:\pyCaffe_GPU\examples\imagenet\mine_iter_20000.caffemodel'   
mean_file='E:\pyCaffe_GPU\examples\imagenet\mean_mine_train.binaryproto'
class_result='E:\Keras_Code\examples\imagenet\class_result.txt'

net = caffe.Net(net_file,caffe_model,caffe.TEST)
transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape})
transformer.set_transpose('data', (2,0,1))
#transformer.set_mean('data', np.load(mean_file).mean(1).mean(1))
mean_blob = caffe.proto.caffe_pb2.BlobProto()
mean_blob.ParseFromString(open(mean_file, 'rb').read())
mean_npy = caffe.io.blobproto_to_array(mean_blob)
b2=mean_npy[0,:,:,:]
transformer.set_mean('data',b2.mean(1).mean(1))  
transformer.set_raw_scale('data', 255)
transformer.set_channel_swap('data', (2,1,0)) # if using RGB instead if BGR
#img=caffe.io.load_image('E:/Keras_Code/examples/imagenet/forclassify/test13.bmp')
imagenet_labels_filename = 'E:/Keras_Code/examples/imagenet/mylabel.txt' 

allclass=[]
for file in imglist:
img=file    
myclass=classify1img(imagenet_labels_filename,net,transformer,img)  
#f.write(myclass+'\n')
allclass.append(myclass)

f=open(class_result,'w')
for ind in range(len(filelist)):
    f.write(filelist[ind]+' '+allclass[ind]+'\n')
f.close()

finish=time.clock()
print "read: %f s"%(finish - start)
按我文件夹本来的顺序  caffe在画accuracy曲线图及对某文件夹下多张图片预测分类时的问题解决可以了！



////////////////////////////////////////////还有一个小时就要放七天假了 内心澎湃/////////////

caffe整个流程终于全部完了  而且准确了  哈哈

训练模型、预测分类、提取特征  

终于都搞完了

谢谢很多人的帮助


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