ubuntu下caffe手写数字识别问题、python接口测试

下面这串代码mnist.py主要是生成一些相关的文件如train.prototxt/test.prototxt、solver.prototxt等 感谢两位博主

 

http://www.cnblogs.com/denny402/p/5684431.html
http://blog.csdn.net/houwenbin1986/article/details/52956101#

 

 

# -*- coding: utf-8 -*-
import sys
sys.path.append('/home/xhj/caffe/python') #导入caffe路径
import caffe
from caffe import layers as L,params as P,proto,to_proto
#设定文件的保存路径
root='/home/xhj/caffe/examples/mnist/trainList/mnist/'                           #根目录
train_list=root+'mnist/train/train.txt'     #训练图片列表
test_list=root+'mnist/test/test.txt'        #测试图片列表
train_proto=root+'mnist/train.prototxt'     #训练配置文件
test_proto=root+'mnist/test.prototxt'       #测试配置文件
solver_proto=root+'mnist/solver.prototxt'   #参数文件

#编写一个函数，生成配置文件prototxt
def Lenet(img_list,batch_size,include_acc=False):
    #第一层，数据输入层，以ImageData格式输入
    data, label = L.ImageData(source=img_list, batch_size=batch_size, ntop=2,root_folder=root,
        transform_param=dict(scale= 0.00390625))
    #第二层：卷积层
    conv1=L.Convolution(data, kernel_size=5, stride=1,num_output=20, pad=0,weight_filler=dict(type='xavier'))
    #池化层
    pool1=L.Pooling(conv1, pool=P.Pooling.MAX, kernel_size=2, stride=2)
    #卷积层
    conv2=L.Convolution(pool1, kernel_size=5, stride=1,num_output=50, pad=0,weight_filler=dict(type='xavier'))
    #池化层
    pool2=L.Pooling(conv2, pool=P.Pooling.MAX, kernel_size=2, stride=2)
    #全连接层
    fc3=L.InnerProduct(pool2, num_output=500,weight_filler=dict(type='xavier'))
    #激活函数层
    relu3=L.ReLU(fc3, in_place=True)
    #全连接层
    fc4 = L.InnerProduct(relu3, num_output=10,weight_filler=dict(type='xavier'))
    #softmax层
    loss = L.SoftmaxWithLoss(fc4, label)

    if include_acc:             # test阶段需要有accuracy层
        acc = L.Accuracy(fc4, label)
        return to_proto(loss, acc)
    else:
        return to_proto(loss)

def write_net():
    #写入train.prototxt
    with open(train_proto, 'w') as f:
        f.write(str(Lenet(train_list,batch_size=64)))

    #写入test.prototxt
    with open(test_proto, 'w') as f:
        f.write(str(Lenet(test_list,batch_size=100, include_acc=True)))

#编写一个函数，生成参数文件
def gen_solver(solver_file,train_net,test_net):
    s=proto.caffe_pb2.SolverParameter()
    s.train_net =train_net
    s.test_net.append(test_net)
    s.test_interval = 938    #60000/64，测试间隔参数：训练完一次所有的图片，进行一次测试
    s.test_iter.append(500)  #50000/100 测试迭代次数，需要迭代500次，才完成一次所有数据的测试
    s.max_iter = 9380       #10 epochs , 938*10，最大训练次数
    s.base_lr = 0.01    #基础学习率
    s.momentum = 0.9    #动量
    s.weight_decay = 5e-4  #权值衰减项
    s.lr_policy = 'step'   #学习率变化规则
    s.stepsize=3000         #学习率变化频率
    s.gamma = 0.1          #学习率变化指数
    s.display = 20         #屏幕显示间隔
    s.snapshot = 938       #保存caffemodel的间隔
    s.snapshot_prefix = root+'mnist/lenet'   #caffemodel前缀
    s.type ='SGD'         #优化算法
    s.solver_mode = proto.caffe_pb2.SolverParameter.GPU    #加速
    #写入solver.prototxt
    with open(solver_file, 'w') as f:
        f.write(str(s))

def training(solver_proto):
    #caffe.set_device(0)
    #caffe.set_mode_gpu()
    caffe.set_mode_cpu()
    solver = caffe.SGDSolver(solver_proto)
    solver.solve()

if __name__ == '__main__':
    write_net()
    gen_solver(solver_proto,train_proto,test_proto)
    training(solver_proto)

 

2、训练好模型后，生成识别用的网络模型mkdeploy.py

 

# -*- coding: utf-8 -*- import sys sys.path.append('/home/xhj/caffe/python') import caffe from caffe import layers as L,params as P,to_proto root = '/home/xhj/caffe/examples/mnist/trainList/mnist/' deploy = root+'mnist/deploy.prototxt' #文件保存路径 def create_deploy(): #少了第一层，data层 conv1 = L.Convolution(bottom='data', kernel_size=5, stride=1,num_output=20, pad=0,weight_filler=dict(type='xavier')) pool1 = L.Pooling(conv1, pool=P.Pooling.MAX, kernel_size=2, stride=2) conv2 = L.Convolution(pool1, kernel_size=5, stride=1,num_output=50, pad=0,weight_filler=dict(type='xavier')) pool2 = L.Pooling(conv2, pool=P.Pooling.MAX, kernel_size=2, stride=2) fc3 = L.InnerProduct(pool2, num_output=500,weight_filler=dict(type='xavier')) relu3 = L.ReLU(fc3, in_place=True) fc4 = L.InnerProduct(relu3, num_output=10,weight_filler=dict(type='xavier')) #最后没有accuracy层，但有一个Softmax层 prob = L.Softmax(fc4) return to_proto(prob) def write_deploy(): with open(deploy, 'w') as f: f.write('name:"Lenet"\n') f.write('input:"data"\n') f.write('input_dim:1\n') f.write('input_dim:3\n') f.write('input_dim:28\n') f.write('input_dim:28\n') f.write(str(create_deploy())) if __name__ == '__main__': write_deploy()

然后测试自己的手写代码

#coding=utf-8 import caffe import numpy as np root = 'D:/MyWorks/caffe-windows-master/examples/lenet5/' #根目录 deploy = root + 'mnist/deploy.prototxt' #deploy文件 caffe_model = root + 'mnist/lenet_iter_9380.caffemodel' #训练好的 caffemodel img = root + 'mnist/test/9/00479.png' #随机找的一张待测图片 labels_filename = root + 'mnist/test/labels.txt' #类别名称文件，将数字标签转换回类别名称 net = caffe.Net(deploy,caffe_model,caffe.TEST) #加载model和network #图片预处理设置 transformer = caffe.io.Transformer({'data': net.blobs['data'].data.shape}) #设定图片的shape格式(1,3,28,28) transformer.set_transpose('data', (2,0,1)) #改变维度的顺序，由原始图片(28,28,3)变为(3,28,28) #transformer.set_mean('data', np.load(mean_file).mean(1).mean(1)) #减去均值，前面训练模型时没有减均值，这儿就不用 transformer.set_raw_scale('data', 255) # 缩放到【0，255】之间 transformer.set_channel_swap('data', (2,1,0)) #交换通道，将图片由RGB变为BGR im = caffe.io.load_image(img) #加载图片 net.blobs['data'].data[...] = transformer.preprocess('data',im) #执行上面设置的图片预处理操作，并将图片载入到blob中 #执行测试 out = net.forward() labels = np.loadtxt(labels_filename, str, delimiter='\t') #读取类别名称文件 prob= net.blobs['Softmax1'].data[0].flatten() #取出最后一层（Softmax）属于某个类别的概率值，并打印 print prob order=prob.argsort()[-1] #将概率值排序，取出最大值所在的序号 print 'the class is:',labels[order] #将该序号转换成对应的类别名称，并打印