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cnn

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.

Functionality

• 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'

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

CPUonly

Debug Tools

deconvnet

display_training

gradient_check

Dataset

MNIST

GENKI-R2009a