##UNFINISHED Deep neural network framework (C/C++/CUDA).
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);
- nVidia graphic card which supports nVidia CUDA
- for running network with pre-trained network, you should put pre-trained files into "config" folder, there is a demo config folder which is named "pre-trained-conf", you can rename it to "config" and replace the current "config" folder.
##Compile & Run add this project into nVidia nsight, add curand and cufft into path.
##Updates
- 0.1.0: Aug.5, the first version released.
- 0.1.1: Aug.10, remove hostData in Mat, only use device memory, for speed up.
- 0.1.1: Aug.11, add functions that save matrices and configs into .txt files.
- 0.1.1: Aug.12, add functions that read network from .txt files.
##Data Structures ####Mat
- Similar with Mat in OpenCV, has memory in both CPU and GPU, use it to do most of the calculations.
####cpuMat
- Has memory only in CPU, use it to read dataset, and do pre-processing (unless your GPU memory is huge...).
####vector3f
- Similar with Scalar in OpenCV, has 3 float space, which corresponses 3 channels. For example, the sum of a 3-channal Mat is a vector3f.
####vector2i
- Similar with vector3f, but has 2 int space, use it to represent 2-d position, or size.
##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 single channel Mat, and image (vector of 3-channel Mat).
####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.
##CPU Version There's also a CPU version of this code which I used OpenCV as the linear algebra library..
##TODO
- combine layer
- branch layer
- stochastic pooling
Copyright (c) 2015 Xingdi (Eric) Yuan
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