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Added main.cpp
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‎.gitignore

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*.exe
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*.out
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*.app
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#
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*.vcxproj.user
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*.aps
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# Folder
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.vs
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.vscode
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_Output
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x64
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build

‎Common.props

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‎README.md

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# SimpleCudaNeuralNet
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Concise neural network with C++ and Cuda
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# SimpleCudaNeuralNet
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This is a simple code for self-studying both neural network and Cuda.
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## Status
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### Layers
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* Fully connected layer
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### Activation
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* Relu activation function
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### Loss functions
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* Cost : Sum of Squares
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### Learning
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* Adam
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‎SimpleCudaNn.sln

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‎SimpleCudaNn.vcxproj

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‎main.cpp

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#include "ffCudaNn.h"
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int main()
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{
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ff::CudaNn nn;
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nn.AddFc(1000, 1000);
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nn.AddReluFc(1000, 500);
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nn.AddReluFc(500, 10);
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nn.AddSumOfSquares();
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ff::CudaTensor x(1000, 64);
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ff::CudaTensor y(10, 64);
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x.Random();
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y.Random();
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const ff::CudaTensor* yPred = nullptr;
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for (int i = 0; i < 10000; ++i)
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{
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yPred = nn.Forward(&x);
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if (nullptr == yPred)
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{
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printf("Error: at Forward()\n");
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return -1;
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}
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nn.Backward(&y);
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nn.UpdateWs(0.00001);
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ff::CudaTensor* __yPred = const_cast<ff::CudaTensor*>(yPred);
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__yPred->Pull();
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double loss = 0.0;
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for (int r = 0; r < yPred->_d1; ++r)
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{
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for (int c = 0; c < yPred->_d0; ++c)
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{
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int index = c + r * yPred->_d0;
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double diff = yPred->_data[index] - y._data[index];
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loss += (diff * diff);
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}
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}
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printf("loss: %f\n", loss);
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}
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return 0;
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}

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