How can I train my pylearn2 neural network on multiple target variables?

I have a functioning neural network pylearn2

that loads data from csv

and predicts a continuous target variable. How can I change it to predict several different target variables?

I am using Caggle African dataset .

And built this functioning mlp file:

!obj:pylearn2.train.Train {
dataset: &train !obj:pylearn2.datasets.csv_dataset.CSVDataset {
    path: 'C:\Users\POWELWE\Git\pylearn2\pylearn2\datasets\soil\training_CA.csv',
    task: 'regression',
    start: 0,
    stop: 1024,
    expect_headers: True,
    num_outputs: 1
},
model: !obj:pylearn2.models.mlp.MLP {
    layers : [
        !obj:pylearn2.models.mlp.RectifiedLinear {
            layer_name: 'h0',
            dim: 200,
            irange: .05,
            max_col_norm: 2.
        },
        !obj:pylearn2.models.mlp.RectifiedLinear {
            layer_name: 'h1',
            dim: 200,
            irange: .05,
            max_col_norm: 2.
        },
        !obj:pylearn2.models.mlp.LinearGaussian {
            init_bias: !obj:pylearn2.models.mlp.mean_of_targets {
                dataset: *train },
            init_beta: !obj:pylearn2.models.mlp.beta_from_targets {
                dataset: *train },
            min_beta: 1.,
            max_beta: 100.,
            beta_lr_scale: 1.,
            dim: 1,
            layer_name: 'y',
            irange: .005
        }
    ],
    nvis: 3594,
},
algorithm: !obj:pylearn2.training_algorithms.bgd.BGD {
    line_search_mode: 'exhaustive',
    batch_size: 1024,
    conjugate: 1,
    reset_conjugate: 0,
    reset_alpha: 0,
    updates_per_batch: 10,
    monitoring_dataset:
        {
            'train' : *train,
            'valid' : !obj:pylearn2.datasets.csv_dataset.CSVDataset {
                path: 'C:\Users\POWELWE\Git\pylearn2\pylearn2\datasets\soil\training_CA.csv',
                task: 'regression',
                start: 1024,
                stop: 1156,
                expect_headers: True,
            }
        },
    termination_criterion: !obj:pylearn2.termination_criteria.MonitorBased {
        channel_name: "valid_y_mse",
        prop_decrease: 0.,
        N: 100
    },
},
extensions: [
    !obj:pylearn2.train_extensions.best_params.MonitorBasedSaveBest {
         channel_name: 'valid_y_mse',
         save_path: "${PYLEARN2_TRAIN_FILE_FULL_STEM}_best.pkl"
    },
],
save_path: "mlp.pkl",
save_freq: 1

      

        
        
        
      

    

}

To predict a single target variable, I removed all of the target variables except from the dataset Ca

and moved them to the first column. When I run the following command in the console ipython

, it works for this single variable:

%run 'C:\Users\POWELWE\Git\pylearn2\pylearn2\scripts\train.py' mlp.yaml

      

        
        
        
      

    

I would like to include other 4 target variables ( P

, pH

, SOC

, Sand

), but do not know how I can set up my model for the training of these additional targets. I am guessing that I need to perform some manipulation with num_outputs

, dim

or nvis

, but have not succeeded in my attempts. This is a preliminary draft for one with many other target variables, so it is important that I train using a single net and not create a new net for each target variable.