NN_static_MLSE.yaml

!obj:pylearn2.train.Train {
    dataset: &train !obj:pylearn2.datasets.csv_dataset.CSVDataset {
        path: 'train.csv'
    },
    model: !obj:pylearn2.models.mlp.MLP {
        layers: [
                 !obj:pylearn2.models.mlp.RectifiedLinear {
                     layer_name: 'h0',
                     dim: 150,
                     sparse_init: 1,
                     use_bias: True
                 },
                 !obj:pylearn2.models.mlp.Linear {
                     layer_name: 'y',
                     sparse_init: 1,
                     dim: 1,
                     loss_function: 'MLSE'
                 }
                ],
        nvis: 29,
    },
    algorithm: !obj:pylearn2.training_algorithms.sgd.SGD {
        batch_size: 10,
        learning_rate: .002,
        learning_rule: !obj:pylearn2.training_algorithms.learning_rule.Momentum {
            init_momentum: .05
        },
        monitoring_dataset:
            {
                'train' : *train,
                'valid' : !obj:pylearn2.datasets.csv_dataset.CSVDataset {
                                path: 'valid.csv'
                            },
            },
        termination_criterion: !obj:pylearn2.termination_criteria.EpochCounter {
            max_epochs: 150
        }
    },
    extensions: [
        !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
            channel_name: "valid_objective",
            save_path: "best.pkl"
        }
    ],
    save_path: "softmax_regression.pkl",
    save_freq: 1
}