<commit_before>package hex.schemas;

import hex.deeplearning.DeepLearning;
import hex.deeplearning.DeepLearningParameters;
import water.api.API;
import water.api.FrameV3.ColSpecifierV3;
import water.api.KeyV3.ModelKeyV3;
import water.api.ModelParametersSchema;

public class DeepLearningV3 extends ModelBuilderSchema<DeepLearning,DeepLearningV3,DeepLearningV3.DeepLearningParametersV3> {

  public static final class DeepLearningParametersV3 extends ModelParametersSchema<DeepLearningParameters, DeepLearningParametersV3> {

    // Determines the order of parameters in the GUI
    static public String[] own_fields = new String[] {
//        "n_folds",
//        "keep_cross_validation_splits",
        "response_column",
        "balance_classes",
        "class_sampling_factors",
        "max_after_balance_size",
        "max_confusion_matrix_size",
        "max_hit_ratio_k",
        "checkpoint",
        "overwrite_with_best_model",
        "use_all_factor_levels",
        "activation",
        "hidden",
        "epochs",
        "train_samples_per_iteration",
        "target_ratio_comm_to_comp",
        "seed",
        "adaptive_rate",
        "rho",
        "epsilon",
        "rate",
        "rate_annealing",
        "rate_decay",
        "momentum_start",
        "momentum_ramp",
        "momentum_stable",
        "nesterov_accelerated_gradient",
        "input_dropout_ratio",
        "hidden_dropout_ratios",
        "l1",
        "l2",
        "max_w2",
        "initial_weight_distribution",
        "initial_weight_scale",
        "loss",
        "score_interval",
        "score_training_samples",
        "score_validation_samples",
        "score_duty_cycle",
        "classification_stop",
        "regression_stop",
        "score_validation_sampling",
        "diagnostics",
        "fast_mode",
        "force_load_balance",
        "variable_importances",
        "replicate_training_data",
        "single_node_mode",
        "shuffle_training_data",
        "missing_values_handling",
        "quiet_mode",
        "autoencoder",
        "sparse",
        "col_major",
        "average_activation",
        "sparsity_beta",
        "max_categorical_features",
        "reproducible",
        "export_weights_and_biases",
        "elastic_averaging",
        "elastic_averaging_moving_rate",
        "elastic_averaging_regularization"
    };

    /// Supervised params
    // TODO: pass these as a new helper class that contains frame and vec; right now we have no automagic way to<commit_msg>Remove elastic averaging parameters from Flow.<commit_after>package hex.schemas;

import hex.deeplearning.DeepLearning;
import hex.deeplearning.DeepLearningParameters;
import water.api.API;
import water.api.FrameV3.ColSpecifierV3;
import water.api.KeyV3.ModelKeyV3;
import water.api.ModelParametersSchema;

public class DeepLearningV3 extends ModelBuilderSchema<DeepLearning,DeepLearningV3,DeepLearningV3.DeepLearningParametersV3> {

  public static final class DeepLearningParametersV3 extends ModelParametersSchema<DeepLearningParameters, DeepLearningParametersV3> {

    // Determines the order of parameters in the GUI
    static public String[] own_fields = new String[] {
//        "n_folds",
//        "keep_cross_validation_splits",
        "response_column",
        "balance_classes",
        "class_sampling_factors",
        "max_after_balance_size",
        "max_confusion_matrix_size",
        "max_hit_ratio_k",
        "checkpoint",
        "overwrite_with_best_model",
        "use_all_factor_levels",
        "activation",
        "hidden",
        "epochs",
        "train_samples_per_iteration",
        "target_ratio_comm_to_comp",
        "seed",
        "adaptive_rate",
        "rho",
        "epsilon",
        "rate",
        "rate_annealing",
        "rate_decay",
        "momentum_start",
        "momentum_ramp",
        "momentum_stable",
        "nesterov_accelerated_gradient",
        "input_dropout_ratio",
        "hidden_dropout_ratios",
        "l1",
        "l2",
        "max_w2",
        "initial_weight_distribution",
        "initial_weight_scale",
        "loss",
        "score_interval",
        "score_training_samples",
        "score_validation_samples",
        "score_duty_cycle",
        "classification_stop",
        "regression_stop",
        "score_validation_sampling",
        "diagnostics",
        "fast_mode",
        "force_load_balance",
        "variable_importances",
        "replicate_training_data",
        "single_node_mode",
        "shuffle_training_data",
        "missing_values_handling",
        "quiet_mode",
        "autoencoder",
        "sparse",
        "col_major",
        "average_activation",
        "sparsity_beta",
        "max_categorical_features",
        "reproducible",
        "export_weights_and_biases",
//        "elastic_averaging",
//        "elastic_averaging_moving_rate",
//        "elastic_averaging_regularization"
    };

    /// Supervised params
    // TODO: pass these as a new helper class that contains frame and vec; right now we have no automagic way to
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