public static class DeepLearningModel.DeepLearningParameters
extends hex.Model.Parameters
Modifier and Type  Class and Description 

static class 
DeepLearningModel.DeepLearningParameters.Activation
Activation functions

static class 
DeepLearningModel.DeepLearningParameters.ClassSamplingMethod 
static class 
DeepLearningModel.DeepLearningParameters.InitialWeightDistribution 
static class 
DeepLearningModel.DeepLearningParameters.Loss
Loss functions
Absolute, Quadratic, Huber, Quantile for regression
Quadratic, ModifiedHuber or CrossEntropy for classification

static class 
DeepLearningModel.DeepLearningParameters.MissingValuesHandling 
Modifier and Type  Field and Description 

DeepLearningModel.DeepLearningParameters.Activation 
_activation
The activation function (nonlinearity) to be used the neurons in the hidden layers.

boolean 
_adaptive_rate
The implemented adaptive learning rate algorithm (ADADELTA) automatically
combines the benefits of learning rate annealing and momentum
training to avoid slow convergence.

boolean 
_autoencoder 
double 
_average_activation 
double 
_classification_stop
The stopping criteria in terms of classification error (1accuracy) on the
training data scoring dataset.

boolean 
_col_major 
boolean 
_diagnostics
Gather diagnostics for hidden layers, such as mean and RMS values of learning
rate, momentum, weights and biases.

boolean 
_elastic_averaging 
double 
_elastic_averaging_moving_rate 
double 
_elastic_averaging_regularization 
double 
_epochs
The number of passes over the training dataset to be carried out.

double 
_epsilon
The second of two hyper parameters for adaptive learning rate (ADADELTA).

boolean 
_export_weights_and_biases 
boolean 
_fast_mode
Enable fast mode (minor approximation in backpropagation), should not affect results significantly.

boolean 
_force_load_balance
Increase training speed on small datasets by splitting it into many chunks
to allow utilization of all cores.

int[] 
_hidden
The number and size of each hidden layer in the model.

double[] 
_hidden_dropout_ratios
A fraction of the inputs for each hidden layer to be omitted from training in order
to improve generalization.

water.Key[] 
_initial_biases
Frame keys for initial bias vectors

DeepLearningModel.DeepLearningParameters.InitialWeightDistribution 
_initial_weight_distribution
The distribution from which initial weights are to be drawn.

double 
_initial_weight_scale
The scale of the distribution function for Uniform or Normal distributions.

water.Key[] 
_initial_weights
Frame keys for initial weight matrices

double 
_input_dropout_ratio
A fraction of the features for each training row to be omitted from training in order
to improve generalization (dimension sampling).

double 
_l1
A regularization method that constrains the absolute value of the weights and
has the net effect of dropping some weights (setting them to zero) from a model
to reduce complexity and avoid overfitting.

double 
_l2
A regularization method that constrains the sum of the squared
weights.

DeepLearningModel.DeepLearningParameters.Loss 
_loss
The loss (error) function to be minimized by the model.

int 
_max_categorical_features
Max.

float 
_max_w2
A maximum on the sum of the squared incoming weights into
any one neuron.

int 
_mini_batch_size 
DeepLearningModel.DeepLearningParameters.MissingValuesHandling 
_missing_values_handling 
double 
_momentum_ramp
The momentum_ramp parameter controls the amount of learning for which momentum increases
(assuming momentum_stable is larger than momentum_start).

double 
_momentum_stable
The momentum_stable parameter controls the final momentum value reached after momentum_ramp training samples.

double 
_momentum_start
The momentum_start parameter controls the amount of momentum at the beginning of training.

boolean 
_nesterov_accelerated_gradient
The Nesterov accelerated gradient descent method is a modification to
traditional gradient descent for convex functions.

boolean 
_overwrite_with_best_model
If enabled, store the best model under the destination key of this model at the end of training.

boolean 
_quiet_mode
Enable quiet mode for less output to standard output.

double 
_rate
When adaptive learning rate is disabled, the magnitude of the weight
updates are determined by the user specified learning rate
(potentially annealed), and are a function of the difference
between the predicted value and the target value.

double 
_rate_annealing
Learning rate annealing reduces the learning rate to "freeze" into
local minima in the optimization landscape.

double 
_rate_decay
The learning rate decay parameter controls the change of learning rate across layers.

double 
_regression_stop
The stopping criteria in terms of regression error (MSE) on the training
data scoring dataset.

boolean 
_replicate_training_data
Replicate the entire training dataset onto every node for faster training on small datasets.

boolean 
_reproducible
Force reproducibility on small data (will be slow  only uses 1 thread)

double 
_rho
The first of two hyper parameters for adaptive learning rate (ADADELTA).

double 
_score_duty_cycle
Maximum fraction of wall clock time spent on model scoring on training and validation samples,
and on diagnostics such as computation of feature importances (i.e., not on training).

double 
_score_interval
The minimum time (in seconds) to elapse between model scoring.

long 
_score_training_samples
The number of training dataset points to be used for scoring.

long 
_score_validation_samples
The number of validation dataset points to be used for scoring.

DeepLearningModel.DeepLearningParameters.ClassSamplingMethod 
_score_validation_sampling
Method used to sample the validation dataset for scoring, see Score Validation Samples above.

boolean 
_shuffle_training_data
Enable shuffling of training data (on each node).

boolean 
_single_node_mode
Run on a single node for finetuning of model parameters.

boolean 
_sparse 
double 
_sparsity_beta 
boolean 
_standardize
If enabled, automatically standardize the data.

double 
_target_ratio_comm_to_comp 
long 
_train_samples_per_iteration
The number of training data rows to be processed per iteration.

boolean 
_use_all_factor_levels 
boolean 
_variable_importances
Whether to compute variable importances for input features.

_auto_rebalance, _balance_classes, _categorical_encoding, _checkpoint, _class_sampling_factors, _custom_metric_func, _distribution, _fold_assignment, _fold_column, _huber_alpha, _ignore_const_cols, _ignored_columns, _is_cv_model, _keep_cross_validation_fold_assignment, _keep_cross_validation_predictions, _max_after_balance_size, _max_categorical_levels, _max_confusion_matrix_size, _max_runtime_secs, _nfolds, _offset_column, _parallelize_cross_validation, _pretrained_autoencoder, _quantile_alpha, _response_column, _score_each_iteration, _seed, _stopping_metric, _stopping_rounds, _stopping_tolerance, _train, _tweedie_power, _valid, _weights_column, MAX_SUPPORTED_LEVELS
Constructor and Description 

DeepLearningParameters() 
Modifier and Type  Method and Description 

java.lang.String 
algoName() 
protected double 
defaultStoppingTolerance() 
java.lang.String 
fullName() 
java.lang.String 
javaName() 
double 
missingColumnsType() 
long 
progressUnits() 
checksum_impl, checksum, defaultDropConsCols, getOrMakeRealSeed, hasCheckpoint, read_lock_frames, read_unlock_frames, setTrain, train, valid
public boolean _overwrite_with_best_model
public boolean _autoencoder
public boolean _use_all_factor_levels
public boolean _standardize
public DeepLearningModel.DeepLearningParameters.Activation _activation
public int[] _hidden
public double _epochs
public long _train_samples_per_iteration
public double _target_ratio_comm_to_comp
public boolean _adaptive_rate
public double _rho
public double _epsilon
public double _rate
public double _rate_annealing
public double _rate_decay
public double _momentum_start
public double _momentum_ramp
public double _momentum_stable
public boolean _nesterov_accelerated_gradient
public double _input_dropout_ratio
public double[] _hidden_dropout_ratios
public double _l1
public double _l2
public float _max_w2
public DeepLearningModel.DeepLearningParameters.InitialWeightDistribution _initial_weight_distribution
public double _initial_weight_scale
public water.Key[] _initial_weights
public water.Key[] _initial_biases
public DeepLearningModel.DeepLearningParameters.Loss _loss
public double _score_interval
public long _score_training_samples
public long _score_validation_samples
public double _score_duty_cycle
public double _classification_stop
public double _regression_stop
public boolean _quiet_mode
public DeepLearningModel.DeepLearningParameters.ClassSamplingMethod _score_validation_sampling
public boolean _diagnostics
public boolean _variable_importances
public boolean _fast_mode
public boolean _force_load_balance
public boolean _replicate_training_data
public boolean _single_node_mode
public boolean _shuffle_training_data
public DeepLearningModel.DeepLearningParameters.MissingValuesHandling _missing_values_handling
public boolean _sparse
public boolean _col_major
public double _average_activation
public double _sparsity_beta
public int _max_categorical_features
public boolean _reproducible
public boolean _export_weights_and_biases
public boolean _elastic_averaging
public double _elastic_averaging_moving_rate
public double _elastic_averaging_regularization
public int _mini_batch_size
public java.lang.String algoName()
algoName
in class hex.Model.Parameters
public java.lang.String fullName()
fullName
in class hex.Model.Parameters
public java.lang.String javaName()
javaName
in class hex.Model.Parameters
protected double defaultStoppingTolerance()
defaultStoppingTolerance
in class hex.Model.Parameters
public long progressUnits()
progressUnits
in class hex.Model.Parameters
public double missingColumnsType()
missingColumnsType
in class hex.Model.Parameters