hex.schemas

Class DeepWaterV3.DeepWaterParametersV3

java.lang.Object

water.Iced

water.api.Schema<I,S>

water.api.schemas3.SchemaV3<P,S>

water.api.schemas3.ModelParametersSchemaV3<DeepWaterParameters,DeepWaterV3.DeepWaterParametersV3>

hex.schemas.DeepWaterV3.DeepWaterParametersV3

All Implemented Interfaces:

java.io.Externalizable, java.io.Serializable, java.lang.Cloneable, water.Freezable

Enclosing class:

DeepWaterV3

public static final class DeepWaterV3.DeepWaterParametersV3
extends water.api.schemas3.ModelParametersSchemaV3<DeepWaterParameters,DeepWaterV3.DeepWaterParametersV3>

See Also:

Serialized Form

Nested Class Summary

Nested classes/interfaces inherited from class water.api.schemas3.SchemaV3

water.api.schemas3.SchemaV3.Meta

Field Summary

Fields

Modifier and Type	Field and Description
DeepWaterParameters.Activation	activation The activation function (non-linearity) to be used by the neurons in the hidden layers.
boolean	autoencoder
DeepWaterParameters.Backend	backend
boolean	balance_classes For imbalanced data, balance training data class counts via over/under-sampling.
boolean	cache_data
int	channels
float[]	class_sampling_factors Desired over/under-sampling ratios per class (lexicographic order).
double	classification_stop The stopping criteria in terms of classification error (1-accuracy) on the training data scoring dataset.
double	clip_gradient
int[]	device_id
boolean	diagnostics Gather diagnostics for hidden layers, such as mean and RMS values of learning rate, momentum, weights and biases.
double	epochs The number of passes over the training dataset to be carried out.
java.lang.String	export_native_parameters_prefix
static java.lang.String[]	fields
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.
float	max_after_balance_size When classes are balanced, limit the resulting dataset size to the specified multiple of the original dataset size.
int	max_confusion_matrix_size For classification models, the maximum size (in terms of classes) of the confusion matrix for it to be printed.
int	max_hit_ratio_k The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)
java.lang.String	mean_image_file
int	mini_batch_size
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 The activation function (non-linearity) to be used by the neurons in the hidden layers.
boolean	quiet_mode Enable quiet mode for less output to standard output.
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.
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.
long	seed The random seed controls sampling and initialization.
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
double	target_ratio_comm_to_comp
long	train_samples_per_iteration The number of training data rows to be processed per iteration.
boolean	variable_importances Whether to compute variable importances for input features.

Fields inherited from class water.api.schemas3.ModelParametersSchemaV3

categorical_encoding, checkpoint, distribution, fold_assignment, fold_column, huber_alpha, ignore_const_cols, ignored_columns, keep_cross_validation_fold_assignment, keep_cross_validation_predictions, max_categorical_levels, max_runtime_secs, model_id, nfolds, offset_column, parallelize_cross_validation, quantile_alpha, response_column, score_each_iteration, stopping_metric, stopping_rounds, stopping_tolerance, training_frame, tweedie_power, validation_frame, weights_column

Fields inherited from class water.api.schemas3.SchemaV3

__meta

Constructor Summary

Constructors

Constructor and Description
DeepWaterV3.DeepWaterParametersV3()

Method Summary

Methods inherited from class water.api.schemas3.ModelParametersSchemaV3

append_field_arrays, fields, fillFromImpl, fillImpl, writeParametersJSON

Methods inherited from class water.api.schemas3.SchemaV3

writeJSON

Methods inherited from class water.api.Schema

createAndFillImpl, createImpl, extractVersionFromSchemaName, fillFromImpl, fillFromImpl, fillFromParms, fillFromParms, fillImpl, getImplClass, getImplClass, getSchemaName, getSchemaType, getSchemaVersion, init_meta, markdown, markdown, newInstance, newInstance, setField, setSchemaType_doNotCall

Methods inherited from class water.Iced

asBytes, clone, copyOver, frozenType, read, readExternal, readJSON, reloadFromBytes, toJsonString, write, writeExternal

Methods inherited from class java.lang.Object

equals, finalize, getClass, hashCode, notify, notifyAll, toString, wait, wait, wait

Field Detail

fields

public static java.lang.String[] fields

problem_type

@API(level=critical,
     direction=INOUT,
     values={"auto","image","dataset"},
     help="Problem type, auto-detected by default. If set to image, the H2OFrame must contain a string column containing the path (URI or URL) to the images in the first column. If set to text, the H2OFrame must contain a string column containing the text in the first column. If set to dataset, Deep Water behaves just like any other H2O Model and builds a model on the provided H2OFrame (non-String columns).")
public DeepWaterParameters.ProblemType problem_type

The activation function (non-linearity) to be used by the neurons in the hidden layers. Rectifier: Rectifier Linear Unit: Chooses the maximum of (0, x) where x is the input value. Tanh: Hyperbolic tangent function (same as scaled and shifted sigmoid).

activation

@API(level=critical,
     direction=INOUT,
     gridable=true,
     values={"Rectifier","Tanh"},
     help="Activation function. Only used if no user-defined network architecture file is provided, and only for problem_type=dataset.")
public DeepWaterParameters.Activation activation

The activation function (non-linearity) to be used by the neurons in the hidden layers. Rectifier: Rectifier Linear Unit: Chooses the maximum of (0, x) where x is the input value. Tanh: Hyperbolic tangent function (same as scaled and shifted sigmoid).

hidden

@API(level=critical,
     direction=INOUT,
     gridable=true,
     help="Hidden layer sizes (e.g. [200, 200]). Only used if no user-defined network architecture file is provided, and only for problem_type=dataset.")
public int[] hidden

The number and size of each hidden layer in the model. For example, if a user specifies "100,200,100" a model with 3 hidden layers will be produced, and the middle hidden layer will have 200 neurons.

input_dropout_ratio

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Input layer dropout ratio (can improve generalization, try 0.1 or 0.2).")
public 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).

hidden_dropout_ratios

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.")
public double[] hidden_dropout_ratios

A fraction of the inputs for each hidden layer to be omitted from training in order to improve generalization. Defaults to 0.5 for each hidden layer if omitted.

max_confusion_matrix_size

@API(level=secondary,
     direction=INOUT,
     gridable=false,
     help="[Deprecated] Maximum size (# classes) for confusion matrices to be printed in the Logs.")
public int max_confusion_matrix_size

For classification models, the maximum size (in terms of classes) of the confusion matrix for it to be printed. This option is meant to avoid printing extremely large confusion matrices.

sparse

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Sparse data handling (more efficient for data with lots of 0 values).")
public boolean sparse

max_hit_ratio_k

@API(level=secondary,
     direction=INOUT,
     gridable=false,
     help="Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable).")
public int max_hit_ratio_k

The maximum number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable)

epochs

@API(level=critical,
     direction=INOUT,
     gridable=true,
     help="How many times the dataset should be iterated (streamed), can be fractional.")
public double epochs

The number of passes over the training dataset to be carried out. It is recommended to start with lower values for initial grid searches. This value can be modified during checkpoint restarts and allows continuation of selected models.

train_samples_per_iteration

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Number of training samples (globally) per MapReduce iteration. Special values are 0: one epoch, -1: all available data (e.g., replicated training data), -2: automatic.")
public long train_samples_per_iteration

The number of training data rows to be processed per iteration. Note that independent of this parameter, each row is used immediately to update the model with (online) stochastic gradient descent. This parameter controls the synchronization period between nodes in a distributed environment and the frequency at which scoring and model cancellation can happen. For example, if it is set to 10,000 on H2O running on 4 nodes, then each node will process 2,500 rows per iteration, sampling randomly from their local data. Then, model averaging between the nodes takes place, and scoring can happen (dependent on scoring interval and duty factor). Special values are 0 for one epoch per iteration, -1 for processing the maximum amount of data per iteration (if **replicate training data** is enabled, N epochs will be trained per iteration on N nodes, otherwise one epoch). Special value of -2 turns on automatic mode (auto-tuning).

target_ratio_comm_to_comp

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning).")
public double target_ratio_comm_to_comp

seed

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Seed for random numbers (affects sampling) - Note: only reproducible when running single threaded.")
public long seed

The random seed controls sampling and initialization. Reproducible results are only expected with single-threaded operation (i.e., when running on one node, turning off load balancing and providing a small dataset that fits in one chunk). In general, the multi-threaded asynchronous updates to the model parameters will result in (intentional) race conditions and non-reproducible results. Note that deterministic sampling and initialization might still lead to some weak sense of determinism in the model.

learning_rate

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Learning rate (higher => less stable, lower => slower convergence).")
public 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. That difference, generally called delta, is only available at the output layer. To correct the output at each hidden layer, back propagation is used. Momentum modifies back propagation by allowing prior iterations to influence the current update. Using the momentum parameter can aid in avoiding local minima and the associated instability. Too much momentum can lead to instabilities, that's why the momentum is best ramped up slowly. This parameter is only active if adaptive learning rate is disabled.

learning_rate_annealing

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Learning rate annealing: rate / (1 + rate_annealing * samples).")
public double learning_rate_annealing

Learning rate annealing reduces the learning rate to "freeze" into local minima in the optimization landscape. The annealing rate is the inverse of the number of training samples it takes to cut the learning rate in half (e.g., 1e-6 means that it takes 1e6 training samples to halve the learning rate). This parameter is only active if adaptive learning rate is disabled.

momentum_start

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Initial momentum at the beginning of training (try 0.5).")
public double momentum_start

The momentum_start parameter controls the amount of momentum at the beginning of training. This parameter is only active if adaptive learning rate is disabled.

momentum_ramp

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Number of training samples for which momentum increases.")
public double momentum_ramp

The momentum_ramp parameter controls the amount of learning for which momentum increases (assuming momentum_stable is larger than momentum_start). The ramp is measured in the number of training samples. This parameter is only active if adaptive learning rate is disabled.

momentum_stable

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Final momentum after the ramp is over (try 0.99).")
public double momentum_stable

The momentum_stable parameter controls the final momentum value reached after momentum_ramp training samples. The momentum used for training will remain the same for training beyond reaching that point. This parameter is only active if adaptive learning rate is disabled.

score_interval

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Shortest time interval (in seconds) between model scoring.")
public double score_interval

The minimum time (in seconds) to elapse between model scoring. The actual interval is determined by the number of training samples per iteration and the scoring duty cycle.

score_training_samples

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Number of training set samples for scoring (0 for all).")
public long score_training_samples

The number of training dataset points to be used for scoring. Will be randomly sampled. Use 0 for selecting the entire training dataset.

score_validation_samples

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Number of validation set samples for scoring (0 for all).")
public long score_validation_samples

The number of validation dataset points to be used for scoring. Can be randomly sampled or stratified (if "balance classes" is set and "score validation sampling" is set to stratify). Use 0 for selecting the entire training dataset.

score_duty_cycle

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring).")
public 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).

classification_stop

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Stopping criterion for classification error fraction on training data (-1 to disable).")
public double classification_stop

The stopping criteria in terms of classification error (1-accuracy) on the training data scoring dataset. When the error is at or below this threshold, training stops.

regression_stop

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Stopping criterion for regression error (MSE) on training data (-1 to disable).")
public double regression_stop

The stopping criteria in terms of regression error (MSE) on the training data scoring dataset. When the error is at or below this threshold, training stops.

quiet_mode

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Enable quiet mode for less output to standard output.")
public boolean quiet_mode

Enable quiet mode for less output to standard output.

overwrite_with_best_model

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="If enabled, override the final model with the best model found during training.")
public boolean overwrite_with_best_model

If enabled, store the best model under the destination key of this model at the end of training. Only applicable if training is not cancelled.

autoencoder

@API(level=secondary,
     direction=INOUT,
     help="Auto-Encoder.")
public boolean autoencoder

diagnostics

@API(level=expert,
     direction=INOUT,
     help="Enable diagnostics for hidden layers.")
public boolean diagnostics

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

variable_importances

@API(level=critical,
     direction=INOUT,
     gridable=true,
     help="Compute variable importances for input features (Gedeon method) - can be slow for large networks.")
public boolean variable_importances

Whether to compute variable importances for input features. The implemented method (by Gedeon) considers the weights connecting the input features to the first two hidden layers.

replicate_training_data

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Replicate the entire training dataset onto every node for faster training on small datasets.")
public boolean replicate_training_data

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

single_node_mode

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Run on a single node for fine-tuning of model parameters.")
public boolean single_node_mode

Run on a single node for fine-tuning of model parameters. Can be useful for checkpoint resumes after training on multiple nodes for fast initial convergence.

shuffle_training_data

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Enable global shuffling of training data.")
public boolean shuffle_training_data

Enable shuffling of training data (on each node). This option is recommended if training data is replicated on N nodes, and the number of training samples per iteration is close to N times the dataset size, where all nodes train will (almost) all the data. It is automatically enabled if the number of training samples per iteration is set to -1 (or to N times the dataset size or larger).

mini_batch_size

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Mini-batch size (smaller leads to better fit, larger can speed up and generalize better).")
public int mini_batch_size

clip_gradient

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Clip gradients once their absolute value is larger than this value.")
public double clip_gradient

network

@API(level=critical,
     direction=INOUT,
     gridable=true,
     values={"auto","user","lenet","alexnet","vgg","googlenet","inception_bn","resnet"},
     help="Network architecture.")
public DeepWaterParameters.Network network

backend

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     values={"mxnet","caffe","tensorflow"},
     help="Deep Learning Backend.")
public DeepWaterParameters.Backend backend

image_shape

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Width and height of image.")
public int[] image_shape

channels

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Number of (color) channels.")
public int channels

gpu

@API(level=expert,
     direction=INOUT,
     help="Whether to use a GPU (if available).")
public boolean gpu

device_id

@API(level=expert,
     direction=INOUT,
     help="Device IDs (which GPUs to use).")
public int[] device_id

cache_data

@API(level=expert,
     direction=INOUT,
     help="Whether to cache the data in memory (automatically disabled if data size is too large).")
public boolean cache_data

network_definition_file

@API(level=secondary,
     direction=INOUT,
     help="Path of file containing network definition (graph, architecture).")
public java.lang.String network_definition_file

network_parameters_file

@API(level=secondary,
     direction=INOUT,
     help="Path of file containing network (initial) parameters (weights, biases).")
public java.lang.String network_parameters_file

mean_image_file

@API(level=secondary,
     direction=INOUT,
     help="Path of file containing the mean image data for data normalization.")
public java.lang.String mean_image_file

export_native_parameters_prefix

@API(level=secondary,
     direction=INOUT,
     help="Path (prefix) where to export the native model parameters after every iteration.")
public java.lang.String export_native_parameters_prefix

standardize

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data.")
public boolean standardize

balance_classes

@API(level=secondary,
     direction=INOUT,
     gridable=true,
     help="Balance training data class counts via over/under-sampling (for imbalanced data).")
public boolean balance_classes

For imbalanced data, balance training data class counts via over/under-sampling. This can result in improved predictive accuracy.

class_sampling_factors

@API(level=expert,
     direction=INOUT,
     gridable=true,
     help="Desired over/under-sampling ratios per class (in lexicographic order). If not specified, sampling factors will be automatically computed to obtain class balance during training. Requires balance_classes.")
public float[] class_sampling_factors

Desired over/under-sampling ratios per class (lexicographic order). Only when balance_classes is enabled. If not specified, they will be automatically computed to obtain class balance during training.

max_after_balance_size

@API(level=expert,
     direction=INOUT,
     gridable=false,
     help="Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes.")
public float max_after_balance_size

When classes are balanced, limit the resulting dataset size to the specified multiple of the original dataset size.

Constructor Detail

DeepWaterV3.DeepWaterParametersV3

public DeepWaterV3.DeepWaterParametersV3()