Deep neural network frame (C++ / OpenCV).
To run this code, you should have
- a cifar-10 dataset( put "cifar-10-batches-bin" where this .md file is, you can get it from HERE, make sure to download the binary version which suitable for C programs);
- OpenCV 3.0.
- cmake
##Compile & Run
- Compile:
cmake .
make
- Run:
./cnn3
##Updates
- 3-channels images supported.
- Add Dropout;
- Local Response Normalization supported.
- Use log files dig deeper.
- Use second order derivative back-prop to alter learning rate.
- Jul 1: version 3.1.0 released
- NEW: Jul.13 OpenCV 3.0 supported, use dft to accelerate convolution
##Layer Config Description
- For each layer, there is a layer_name, a layer_type, and a output_format.
- There are currently 2 output formats: matrix (cv::Mat, CV_64FC1), and image (vector of cv::Mat, CV_64FC3).
####Input Layer
- batch size: the training process is using mini-batch stochastic gradient descent.
####Convolutional Layer
- kernel size: size of kernels for convolution calculation.
- kernel amount: amount of kernels for convolution calculation.
- combine map: amount of combine feature map, details can be found in Notes on Convolutional Neural Networks.
- weight decay: weight decay for convolutional kernels.
- padding: padding before doing convolution.
- stride: stride when doing convolution (For "VALID" type of convolution, result size = (image_size + 2 * padding - kernel_size) / stride + 1).
####Fully Connected Layer
- num hidden neurons: size of fully connected layer.
- weight decay: weight decay for fully connected layer.
####Softmax Layer
- num classes: output size of softmax layer.
- weight decay: weight decay for softmax layer.
####Non-linearity Layer
- method: sigmoid/tanh/relu/leaky_relu.
####Pooling Layer
- method: max/mean/stochastic.
- overlap: if use overlap pooling.
- window size: window size when using overlap pooling.
- stride: pooling stride.
####Local Response Normalization Layer
- alpha, beta, k, n: see ImageNet Classification with Deep Convolutional Neural Networks.
####Dropout Layer
- dropout rate: percentage of zeros when generating Bernoulli matrix.
####Combine Layer
- for implementing GoogLeNet, TODO...
####Branch Layer
- for implementing GoogLeNet, TODO...
##Structure and Algorithm See my several posts about CNNs at my tech-blog.
##TODO *combine layer *branch layer
##GPU Version There's also a GPU version of this code which I used nVidia CUDA..
Copyright (c) 2014 Xingdi (Eric) Yuan
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