3 ms·

I managed to get to 0.01 loss from only x1/x2, using 3 hidden layers, L1 regularization, a bit of added noise, and some patience: http://i.imgur.com/Y3zKpJF.png

• aab0 11y ago
  Yes, noise & regularization seem to be key here. I've gotten a 2-layer with 7/8 neurons down to 0.06 and dropping but only with noise & l1: http://playground.tensorflow.org/#activation=relu&regularization=L2&batchSize=6&dataset=spiral&regDataset=reg-plane&learningRate=0.01&regularizationRate=0.03&noise=10&networkShape=8,7&seed=0.52682&showTestData=false&discretize=false&percTrainData=50&x=true&y=true&xTimesY=false&xSquared=false&ySquared=false&cosX=false&sinX=false&cosY=false&sinY=false&collectStats=false&problem=classification&noise_hide=false http://playground.tensorflow.org/#activation=relu&regulariza... Final loss of 0.051. Interestingly, increasing noise from 10 to 15 destroys performance, loss of 0.47.