用CNN用来对自己的数据集分类

cnn === This is a matlab-code implementation of convolutional neural network. Functionality --- * supported layertypes : 'conv', 'sigmoid', 'maxpool', 'meanpool', 'relu', 'tanh', 'softmax', 'stack2line', 'softsign' * supported loss function : 'crossEntropy' * supported training method : 'SGD' * supported computing device : 'GPU', 'CPUonly' * debug tools : deconvnet, display\_training, gradent\_check * supported demo dataset : 'MNIST', 'GENKI-R2009a' Usage --- The structure of convolutional neural network is conv pool [conv pool] stack2line ['nonlinear'] [] means optional, and can be replicated for many times. Layer --- ### conv ### implement convolution computing. To make codes flexible, I do not implemente non-linear functions after convlution. You can add a layer to complete the non-linear instead. To use 'conv' layer, you should specify the following parameters: **filterDim** **numFilters** **nonlineartype** If the inputs has multimaps, then you may specify the connection table between the input maps and the output maps: **conn\_matrix** If you don't specify the connection table, then each output map is connected to all input maps. ### pool/pool ### 'maxpool' and 'meanpool' are both pooling layer. To use pooling layer, the following parameters should be specified: **poolDim** **pooltypes** ### relu/tanh/sigmoid/softmax/softsign ### These four types of layers mainly do the non-linear function to the input. y = max(0,x) y = tanh(x) y = 1/exp(-x) y = softmax(x) y = x/(1+abs(x)) To use them, the following parameters should be specified: **size** Besides, the softmax layer is usually used as output layer. ### stack2line ### After convlution and pooling, the multi-dimention "outputs" usually are converted to a vector to be used as the inputs of the densely connected non-linear layers. And stack2line layer is to indicate this converting. Training Method --- ### SGD ### Computing Device --- ### GPU ### ### CPUonly ### Debug Tools --- ### deconvnet ### ### display\_training ### ### gradient\_check ### Dataset --- ### MNIST ### ### GENKI-R2009a ###