Caffe卷积神经网络CNN

1、卷积神经网络 Convolutional Neural NetworkSisansiy2 神经网络12, , ., jjnj单层结构重权参数通过训练把各权重估计出来，即可构造出一个模糊人脸数学模型Sisansiy3 BP神经网络多层结构特点：1 数据正向传播，计算输出层误差2 误差反向传播，逐层修正各层权重3 采用梯度下降法加快收敛速度Sisansiy3.1 梯度下降法选取第k个样本：两层结构Sisansiy3.1梯度下降法计算隐层各神经元的输入和输出：Sisansiy3.1梯度下降法计算误差函数对输出层各神经元重权偏导数：Sisansiy3.1梯度下降法计算各神经元重权偏移量，并更新输出层重

2、权：Sisansiy3.2 误差反向传播利用上一层结果，计算误差函数对隐层各神经元重权的偏导数：Sisansiy3.2 误差反向传播计算各神经元重权偏移量，并更新隐层重权：Sisansiy4 卷积神经网络特点：1 继承BP神经网络的优点2 权值共享3 加入卷积层4 加入池化层多层结构Sisansiy4.11 局部权值共享 1000 x1000的图像，有1百万个隐层神经元，那么他们全连接的话将产生1012个权重参数。 假如局部感受区域是10 x10，那么1百万个隐层神经元就只产生108个权重参数。Sisansiy4.12 卷积权值共享 100种滤波器，每种大小为10 x10，即产生100个权重参

3、数。每种滤波器都共享100个权重参数，共产生100 x100=1万个参数。 隐层的参数个数和隐层的神经元个数无关，只和滤波器的大小和滤波器种类的多少有关。Sisansiy4.2 卷积层与池化层 LeNet-5是一种典型的用来识别数字的卷积网络，共有7层，每层都包含可训练参数，输入图像大小为32x32。 C1层是一个卷积层，由6个特征图Feature Map构成。特征图中每个神经元与输入中5x5的邻域相连。特征图的大小为28x28，C1有156个可训练参数，共156*(28*28)=122,304个连接。LeNet-5Sisansiy4.2 卷积层与池化层 输入图像相应的邻域与可训练的5x5特征

4、块fx卷积后，加偏置求和，生成卷积层特征图Cx。 Cx中每2x2邻域像素加权相加，再乘以一个可训练的权重Wx+1，再加上一个可训练偏置bx+1，最后通过非线性限幅函数得到压缩层特征图Sx+1。Sisansiy5 Caffe开源库特点：特点：1 C+/CUDA框架2 提供Matlab和Python接口3 高效稳定，网络设计简单4 CPU与GPU无缝链接Sisansiy5 ImageNet ILSVRC2012Data The validation and test data for this competition will consist of 150,000 photographs, col

5、lected from flickr and other search engines, hand labeled with the presence or absence of 1000 object categories. The 1000 object categories contain both internal nodes and leaf nodes of ImageNet, but do not overlap with each other. A random subset of 50,000 of the images with labels will be release

6、d as validation data included in the development kit along with a list of the 1000 categories. The remaining images will be used for evaluation and will be released without labels at test time. The training data, the subset of ImageNet containing the 1000 categories and 1.2 million images, will be p

7、ackaged for easy downloading. The validation and test data for this competition are not contained in the ImageNet training data (we will remove any duplicates).Sisansiy5 AlexNetSisansiy5 AlexNetname: “AlexNetlayer name: data layer name: conv layer name: pool more layers layer name: loss LeNet AlexNe

8、t 2012 5个卷积池化层，3个全连接层，大约5千万个可调参数top blobbottom blobSisansiyModels from the BMVC-2014 Models from the BMVC-2014 paper Return of the Devil in the paper Return of the Devil in the Details: Delving Deep into Details: Delving Deep into Convolutional NetsConvolutional NetsSisansiyImageNet测试结果Sisansiy6 训练自己的数据 DBconvert_imageset.exeImage_meancompute_image_mean.exeTraincaffe.exeProtocalSisansiy6 命令调用格式compute_image_mean.exe -backend=数据库类型