{
"tag"       : "nonlinear2dBP",
"brief"     : "Non-linear but separable data; 2nd thesis illustration.",
"task"      : "classification",
"algorithm" : "MLP_learn",

"comment" : "Emulated single-objective multistart
             backpropagation. With only two hidden neurons, it is easy
             to spot the problems: (1) dependence on initial
             conditions, (2) getting stuck in local minima. A large
             array of multistarts helps to get a very good final
             outcome very soon in this simple 2D case.",

"_TODO" : "Implement logging/tracing.",

"MLP_learn":
{
  "datafile"  : "2dv.data",
  "task"      : "classification",
  "evolution" : {
    "evol_obj"  : "mse",
    "pop_size"  : 50,
    "max_iter"  : 200,
    "evol_oper" : "none"
  },
  "init" : {
    "init_arch"    : "2-2-2",
    "init_weights" : "uniform(-1,1)"
  },
  "improvement" : {
    "imp_obj"   : "mse",
    "imp_oper"  : "backprop",
    "imp_steps" : 10,
    "imp_inilen": 0.6
  }
}
}