public class DeepWaterParameters
extends hex.Model.Parameters
Modifier and Type | Class and Description |
---|---|
static class |
DeepWaterParameters.Activation
Activation functions
|
static class |
DeepWaterParameters.Backend |
static class |
DeepWaterParameters.MissingValuesHandling |
static class |
DeepWaterParameters.Network |
static class |
DeepWaterParameters.ProblemType |
Modifier and Type | Field and Description |
---|---|
DeepWaterParameters.Activation |
_activation
The activation function (non-linearity) to be used the neurons in the hidden layers.
|
boolean |
_autoencoder |
DeepWaterParameters.Backend |
_backend |
boolean |
_cache_data |
int |
_channels |
double |
_clip_gradient |
int[] |
_device_id |
double |
_epochs
The number of passes over the training dataset to be carried out.
|
java.lang.String |
_export_native_parameters_prefix |
boolean |
_gpu |
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.
|
int[] |
_image_shape |
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 |
_learning_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 |
_learning_rate_annealing
Learning rate annealing reduces the learning rate to "freeze" into
local minima in the optimization landscape.
|
java.lang.String |
_mean_image_file |
int |
_mini_batch_size |
DeepWaterParameters.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.
|
DeepWaterParameters.Network |
_network |
java.lang.String |
_network_definition_file |
java.lang.String |
_network_parameters_file |
boolean |
_overwrite_with_best_model
If enabled, store the best model under the destination key of this model at the end of training.
|
DeepWaterParameters.ProblemType |
_problem_type |
boolean |
_quiet_mode
Enable quiet mode for less output to standard output.
|
boolean |
_replicate_training_data
Replicate the entire training dataset onto every node for faster training on small datasets.
|
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.
|
boolean |
_shuffle_training_data
Enable shuffling of training data (on each node).
|
boolean |
_single_node_mode
Run on a single node for fine-tuning of model parameters.
|
boolean |
_sparse |
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 |
_auto_rebalance, _balance_classes, _categorical_encoding, _checkpoint, _class_sampling_factors, _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 |
---|
DeepWaterParameters() |
Modifier and Type | Method and Description |
---|---|
java.lang.String |
algoName() |
protected double |
defaultStoppingTolerance() |
java.lang.String |
fullName() |
java.lang.String |
javaName() |
float |
learningRate(double n) |
float |
momentum(double n) |
long |
progressUnits() |
checksum_impl, checksum, defaultDropConsCols, getOrMakeRealSeed, hasCheckpoint, missingColumnsType, read_lock_frames, read_unlock_frames, setTrain, train, valid
public double _clip_gradient
public boolean _gpu
public int[] _device_id
public DeepWaterParameters.Network _network
public DeepWaterParameters.Backend _backend
public java.lang.String _network_definition_file
public java.lang.String _network_parameters_file
public java.lang.String _export_native_parameters_prefix
public DeepWaterParameters.ProblemType _problem_type
public int[] _image_shape
public int _channels
public java.lang.String _mean_image_file
public boolean _overwrite_with_best_model
public boolean _autoencoder
public boolean _sparse
public boolean _use_all_factor_levels
public DeepWaterParameters.MissingValuesHandling _missing_values_handling
public boolean _standardize
public double _epochs
public DeepWaterParameters.Activation _activation
public int[] _hidden
public double _input_dropout_ratio
public double[] _hidden_dropout_ratios
public long _train_samples_per_iteration
public double _target_ratio_comm_to_comp
public double _learning_rate
public double _learning_rate_annealing
public double _momentum_start
public double _momentum_ramp
public double _momentum_stable
public double _score_interval
public long _score_training_samples
public long _score_validation_samples
public double _score_duty_cycle
public boolean _quiet_mode
public boolean _replicate_training_data
public boolean _single_node_mode
public boolean _shuffle_training_data
public int _mini_batch_size
public boolean _cache_data
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 float learningRate(double n)
public final float momentum(double n)