{"push_id":882538555,"size":7,"distinct_size":7,"ref":"refs/heads/master","head":"5cb5a1a18c1670c8f4314c349071612613e7b076","before":"f1eb95166522577b900f4e6fac2beb864d2feea4","commits":[{"sha":"eb2551bc65062205c3a839e2f08f668ea0932285","author":{"email":"e990a86a96c912d3a8bdf23042ae80cad6295061@gmail.com","name":"matthieudelaro"},"message":"First version of the network. Many improvements to do. Success rate evolves from about 0.25 to 0.70 after about 5 epochs, and does not improve afterward. Takes almost 50 seconds per epoch.","distinct":true,"url":"https://api.github.com/repos/matthieudelaro/pyNet/commits/eb2551bc65062205c3a839e2f08f668ea0932285"},{"sha":"9f88e94a73fa7ff8caf05a96e35ae514b1b4e237","author":{"email":"e990a86a96c912d3a8bdf23042ae80cad6295061@gmail.com","name":"matthieudelaro"},"message":"and it takes 3.4s to process the mini data set, with a success rate of 0.225","distinct":true,"url":"https://api.github.com/repos/matthieudelaro/pyNet/commits/9f88e94a73fa7ff8caf05a96e35ae514b1b4e237"},{"sha":"5f540eb8f8f0b6ea3ae47cacf023f33b4cf3d019","author":{"email":"e990a86a96c912d3a8bdf23042ae80cad6295061@gmail.com","name":"matthieudelaro"},"message":"Allocate dWs, dbs, zss and ass only once, instead of for each forward-backward pass => faster : mini dataset processed in 2.5s instead of 3.4s","distinct":true,"url":"https://api.github.com/repos/matthieudelaro/pyNet/commits/5f540eb8f8f0b6ea3ae47cacf023f33b4cf3d019"},{"sha":"a02dccc244a474408fd1935573b7a3ec8219cb93","author":{"email":"e990a86a96c912d3a8bdf23042ae80cad6295061@gmail.com","name":"matthieudelaro"},"message":"Implement learning rate decay","distinct":true,"url":"https://api.github.com/repos/matthieudelaro/pyNet/commits/a02dccc244a474408fd1935573b7a3ec8219cb93"},{"sha":"ae54e44cfe486865e3df4da27771f294afca81ce","author":{"email":"e990a86a96c912d3a8bdf23042ae80cad6295061@gmail.com","name":"matthieudelaro"},"message":"Express pixels from 0 to 1, instead of 0 to 255. Change learning rate. Use vectorized version of ReLU instead of Sigmoid. Result: up to 94% success rate on complete dataset, instead of 60%. 30 epochs in 11 minutes.","distinct":true,"url":"https://api.github.com/repos/matthieudelaro/pyNet/commits/ae54e44cfe486865e3df4da27771f294afca81ce"},{"sha":"4ce7d05d2ae2122b81ea9280555738fc2ed179bf","author":{"email":"e990a86a96c912d3a8bdf23042ae80cad6295061@gmail.com","name":"matthieudelaro"},"message":"Implement LeakyReLU. Success rate : 91% after 4 epochs, 96% after 13 epochs. Dropout implemented but disabled because it decreases the success rate.","distinct":true,"url":"https://api.github.com/repos/matthieudelaro/pyNet/commits/4ce7d05d2ae2122b81ea9280555738fc2ed179bf"},{"sha":"5cb5a1a18c1670c8f4314c349071612613e7b076","author":{"email":"e990a86a96c912d3a8bdf23042ae80cad6295061@gmail.com","name":"matthieudelaro"},"message":"Clarify code, improve logs, create script to generate the dataset from original MNIST dataset","distinct":true,"url":"https://api.github.com/repos/matthieudelaro/pyNet/commits/5cb5a1a18c1670c8f4314c349071612613e7b076"}]}