Shrinking the size to make example run faster

    regression_metric = Metric(metrics.mean_squared_error, verbosity=verbosity,

                               task_averager=np.mean, mode="regression")

    tensorflow_model = RobustMultitaskRegressor(

        n_tasks, n_features, self.model_dir, layer_sizes=[100], dropouts=[0.],

        n_tasks, n_features, self.model_dir, layer_sizes=[50],

        bypass_layer_sizes=[10], dropouts=[0.],

        learning_rate=0.003, weight_init_stddevs=[.1],

        batch_size=n_samples, verbosity=verbosity)

    model = TensorflowModel(tensorflow_model, self.model_dir)

    # Fit trained model

    model.fit(dataset, nb_epoch=75)

    model.fit(dataset, nb_epoch=25)

    model.save()

    # Eval model on train

    transformers = []

    evaluator = Evaluator(model, dataset, transformers, verbosity=verbosity)

    scores = evaluator.compute_model_performance([regression_metric])

    ############################################### DEBUG

    print("scores[regression_metric.name]")

    print(scores[regression_metric.name])

    ############################################### DEBUG

    assert scores[regression_metric.name] < .5

    assert scores[regression_metric.name] < .15