cos_reconsE_nce.yaml

!obj:pylearn2.scripts.train.Train {
    "dataset": &training_data !obj:pylearn2.datasets.cos_dataset.CosDataset {},
    "model": !obj:pylearn2.models.normalized_ebm.NormalizedEBM {
                "init_logZ" : 0.,
                "learn_logZ" : 1,
                "logZ_lr_scale" : .001,
                "ebm": !obj:galatea.models.febm.FEBM {
                "energy_function": !obj:galatea.energy_functions.scratch.recons_model_1 {
                        "nvis" : 2,
                        "nhid" : 400,
                        "irange"  : 1.5,
                        "init_bias_hid" : 0.0,

                        "init_vis_prec" : 5.,
                        "vis_prec_lr_scale" : .001,
                        "learn_vis_prec" : 1.,

                        "init_delta" : 0.0
                        }
                       }
                },
    "algorithm": !obj:pylearn2.training_algorithms.sgd.SGD {
               "learning_rate" : .001,
               "batch_size" : 1,
               "batches_per_iter" : 1000,
               "monitoring_batches" : 10,
               "monitoring_dataset" : !obj:pylearn2.datasets.cos_dataset.CosDataset {},
               "cost" : !obj:pylearn2.costs.ebm_estimation.NCE {
                        "noise" : !obj:pylearn2.distributions.mnd.fit {
                                "dataset" : *training_data,
                                "n_samples" : 1000
                                }
                
               }
        },
    "save_path": "cos_reconsE_nce.pkl"
}