This is a matlab-code implementation of convolutional neural network.

Notes: This repo was deprecated. I suggest you use other deep learning tools, such as caffe, mxnet, tensorflow. They are far more easy to use.

• supported layertypes : 'conv', 'sigmoid', 'maxpool', 'meanpool', 'relu', 'tanh', 'softmax', 'stack2line', 'softsign'
• supported loss function : 'crossEntropy'
• supported training method : 'SGD'
• debug tools : deconvnet, display_training, gradent_check
• supported demo dataset : 'MNIST', 'GENKI-R2009a'

The structure of convolutional neural network is conv pool [conv pool] stack2line ['nonlinear'] [] means optional, and can be replicated for many times.

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.

'maxpool' and 'meanpool' are both pooling layer. To use pooling layer, the following parameters should be specified: poolDim pooltypes

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.

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.