Difference between revisions of "A-Team"
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=====Neural Network===== | =====Neural Network===== | ||
======Sebastian's findings====== | ======Sebastian's findings====== | ||
− | I found a simple neural network that takes a MNIST data set and preforms training on batches of data. For a quick illustration MNIST is a numerical data set that contains a many written numbers as well as the corresponding numerical value; between 0 and 9. | + | I found a simple [https://gist.github.com/sbugrov/7f373f0e4788f8e076b8efa2abfd227a.js link title]neural network that takes a MNIST data set and preforms training on batches of data. For a quick illustration MNIST is a numerical data set that contains a many written numbers as well as the corresponding numerical value; between 0 and 9. |
[[File:MnistExamples.png]] | [[File:MnistExamples.png]] | ||
Revision as of 15:17, 7 March 2019
Contents
Back Propagation Acceleration
Team Members
- Sebastian Djurovic, Team Lead and Developer
- Henry Leung, Developer and Quality Control
- ...
Progress
Assignment 1
Our group decided to profile a couple of different solutions, the first being a simple neural network and ray tracing solution, in order to determine the best project to generate a solution for.
Neural Network
Sebastian's findings
I found a simple link titleneural network that takes a MNIST data set and preforms training on batches of data. For a quick illustration MNIST is a numerical data set that contains a many written numbers as well as the corresponding numerical value; between 0 and 9.
Flat profile: Each sample counts as 0.01 seconds. % cumulative self self total time seconds seconds calls ns/call ns/call name 97.94 982.46 982.46 dot(std::vector<float, std::allocator<float> > const&, std::vector<float, std::allocator<float> > const&, int, int, int) 1.45 997.05 14.58 transpose(float*, int, int) 0.15 998.56 1.51 operator-(std::vector<float, std::allocator<float> > const&, std::vector<float, std::allocator<float> > const&) 0.15 1000.06 1.50 relu(std::vector<float, std::allocator<float> > const&) 0.15 1001.55 1.49 operator*(float, std::vector<float, std::allocator<float> > const&) 0.07 1002.27 0.72 519195026 1.39 1.39 void std::vector<float, std::allocator<float> >::emplace_back<float>(float&&) 0.06 1002.91 0.63 operator*(std::vector<float, std::allocator<float> > const&, std::vector<float, std::allocator<float> > const&) 0.05 1003.37 0.46 reluPrime(std::vector<float, std::allocator<float> > const&) 0.02 1003.62 0.25 softmax(std::vector<float, std::allocator<float> > const&, int) 0.01 1003.75 0.13 operator/(std::vector<float, std::allocator<float> > const&, float) 0.01 1003.87 0.12 442679 271.35 271.35 void std::vector<float, std::allocator<float> >::_M_emplace_back_aux<float>(float&&) 0.01 1003.96 0.09 13107321 6.87 6.87 void std::vector<float, std::allocator<float> >::_M_emplace_back_aux<float const&>(float const&) 0.01 1004.02 0.06 split(std::string const&, char) 0.01 1004.08 0.06 462000 130.00 130.00 void std::vector<std::string, std::allocator<std::string> >::_M_emplace_back_aux<std::string const&>(std::string const&) 0.00 1004.11 0.03 std::vector<std::string, std::allocator<std::string> >::~vector() 0.00 1004.12 0.01 random_vector(int) 0.00 1004.12 0.00 3 0.00 0.00 std::vector<float, std::allocator<float> >::vector(unsigned long, std::allocator<float> const&) 0.00 1004.12 0.00 1 0.00 0.00 _GLOBAL__sub_I__Z5printRKSt6vectorIfSaIfEEii