h2o.deeplearning: Build a Deep Neural Network model using CPUs
In h2o: R Interface for the 'H2O' Scalable Machine Learning Platform

h2o.deeplearning

R Documentation

Build a Deep Neural Network model using CPUs

Description

Builds a feed-forward multilayer artificial neural network on an H2OFrame.

Usage

h2o.deeplearning(
  x,
  y,
  training_frame,
  model_id = NULL,
  validation_frame = NULL,
  nfolds = 0,
  keep_cross_validation_models = TRUE,
  keep_cross_validation_predictions = FALSE,
  keep_cross_validation_fold_assignment = FALSE,
  fold_assignment = c("AUTO", "Random", "Modulo", "Stratified"),
  fold_column = NULL,
  ignore_const_cols = TRUE,
  score_each_iteration = FALSE,
  weights_column = NULL,
  offset_column = NULL,
  balance_classes = FALSE,
  class_sampling_factors = NULL,
  max_after_balance_size = 5,
  checkpoint = NULL,
  pretrained_autoencoder = NULL,
  overwrite_with_best_model = TRUE,
  use_all_factor_levels = TRUE,
  standardize = TRUE,
  activation = c("Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout",
    "Maxout", "MaxoutWithDropout"),
  hidden = c(200, 200),
  epochs = 10,
  train_samples_per_iteration = -2,
  target_ratio_comm_to_comp = 0.05,
  seed = -1,
  adaptive_rate = TRUE,
  rho = 0.99,
  epsilon = 1e-08,
  rate = 0.005,
  rate_annealing = 1e-06,
  rate_decay = 1,
  momentum_start = 0,
  momentum_ramp = 1e+06,
  momentum_stable = 0,
  nesterov_accelerated_gradient = TRUE,
  input_dropout_ratio = 0,
  hidden_dropout_ratios = NULL,
  l1 = 0,
  l2 = 0,
  max_w2 = 3.4028235e+38,
  initial_weight_distribution = c("UniformAdaptive", "Uniform", "Normal"),
  initial_weight_scale = 1,
  initial_weights = NULL,
  initial_biases = NULL,
  loss = c("Automatic", "CrossEntropy", "Quadratic", "Huber", "Absolute", "Quantile"),
  distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma",
    "tweedie", "laplace", "quantile", "huber"),
  quantile_alpha = 0.5,
  tweedie_power = 1.5,
  huber_alpha = 0.9,
  score_interval = 5,
  score_training_samples = 10000,
  score_validation_samples = 0,
  score_duty_cycle = 0.1,
  classification_stop = 0,
  regression_stop = 1e-06,
  stopping_rounds = 5,
  stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE",
    "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error",
    "custom", "custom_increasing"),
  stopping_tolerance = 0,
  max_runtime_secs = 0,
  score_validation_sampling = c("Uniform", "Stratified"),
  diagnostics = TRUE,
  fast_mode = TRUE,
  force_load_balance = TRUE,
  variable_importances = TRUE,
  replicate_training_data = TRUE,
  single_node_mode = FALSE,
  shuffle_training_data = FALSE,
  missing_values_handling = c("MeanImputation", "Skip"),
  quiet_mode = FALSE,
  autoencoder = FALSE,
  sparse = FALSE,
  col_major = FALSE,
  average_activation = 0,
  sparsity_beta = 0,
  max_categorical_features = 2147483647,
  reproducible = FALSE,
  export_weights_and_biases = FALSE,
  mini_batch_size = 1,
  categorical_encoding = c("AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary",
    "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited"),
  elastic_averaging = FALSE,
  elastic_averaging_moving_rate = 0.9,
  elastic_averaging_regularization = 0.001,
  export_checkpoints_dir = NULL,
  auc_type = c("AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO"),
  custom_metric_func = NULL,
  gainslift_bins = -1,
  verbose = FALSE
)

Arguments

`x`	(Optional) A vector containing the names or indices of the predictor variables to use in building the model. If x is missing, then all columns except y are used.
`y`	The name or column index of the response variable in the data. The response must be either a numeric or a categorical/factor variable. If the response is numeric, then a regression model will be trained, otherwise it will train a classification model.
`training_frame`	Id of the training data frame.
`model_id`	Destination id for this model; auto-generated if not specified.
`validation_frame`	Id of the validation data frame.
`nfolds`	Number of folds for K-fold cross-validation (0 to disable or >= 2). Defaults to 0.
`keep_cross_validation_models`	`Logical`. Whether to keep the cross-validation models. Defaults to TRUE.
`keep_cross_validation_predictions`	`Logical`. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
`keep_cross_validation_fold_assignment`	`Logical`. Whether to keep the cross-validation fold assignment. Defaults to FALSE.
`fold_assignment`	Cross-validation fold assignment scheme, if fold_column is not specified. The 'Stratified' option will stratify the folds based on the response variable, for classification problems. Must be one of: "AUTO", "Random", "Modulo", "Stratified". Defaults to AUTO.
`fold_column`	Column with cross-validation fold index assignment per observation.
`ignore_const_cols`	`Logical`. Ignore constant columns. Defaults to TRUE.
`score_each_iteration`	`Logical`. Whether to score during each iteration of model training. Defaults to FALSE.
`weights_column`	Column with observation weights. Giving some observation a weight of zero is equivalent to excluding it from the dataset; giving an observation a relative weight of 2 is equivalent to repeating that row twice. Negative weights are not allowed. Note: Weights are per-row observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but non-integer values are supported as well. During training, rows with higher weights matter more, due to the larger loss function pre-factor. If you set weight = 0 for a row, the returned prediction frame at that row is zero and this is incorrect. To get an accurate prediction, remove all rows with weight == 0.
`offset_column`	Offset column. This will be added to the combination of columns before applying the link function.
`balance_classes`	`Logical`. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to FALSE.
`class_sampling_factors`	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.
`max_after_balance_size`	Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0.
`checkpoint`	Model checkpoint to resume training with.
`pretrained_autoencoder`	Pretrained autoencoder model to initialize this model with.
`overwrite_with_best_model`	`Logical`. If enabled, override the final model with the best model found during training. Defaults to TRUE.
`use_all_factor_levels`	`Logical`. Use all factor levels of categorical variables. Otherwise, the first factor level is omitted (without loss of accuracy). Useful for variable importances and auto-enabled for autoencoder. Defaults to TRUE.
`standardize`	`Logical`. If enabled, automatically standardize the data. If disabled, the user must provide properly scaled input data. Defaults to TRUE.
`activation`	Activation function. Must be one of: "Tanh", "TanhWithDropout", "Rectifier", "RectifierWithDropout", "Maxout", "MaxoutWithDropout". Defaults to Rectifier.
`hidden`	Hidden layer sizes (e.g. [100, 100]). Defaults to c(200, 200).
`epochs`	How many times the dataset should be iterated (streamed), can be fractional. Defaults to 10.
`train_samples_per_iteration`	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. Defaults to -2.
`target_ratio_comm_to_comp`	Target ratio of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning). Defaults to 0.05.
`seed`	Seed for random numbers (affects certain parts of the algo that are stochastic and those might or might not be enabled by default). Note: only reproducible when running single threaded. Defaults to -1 (time-based random number).
`adaptive_rate`	`Logical`. Adaptive learning rate. Defaults to TRUE.
`rho`	Adaptive learning rate time decay factor (similarity to prior updates). Defaults to 0.99.
`epsilon`	Adaptive learning rate smoothing factor (to avoid divisions by zero and allow progress). Defaults to 1e-08.
`rate`	Learning rate (higher => less stable, lower => slower convergence). Defaults to 0.005.
`rate_annealing`	Learning rate annealing: rate / (1 + rate_annealing * samples). Defaults to 1e-06.
`rate_decay`	Learning rate decay factor between layers (N-th layer: rate * rate_decay ^ (n - 1). Defaults to 1.
`momentum_start`	Initial momentum at the beginning of training (try 0.5). Defaults to 0.
`momentum_ramp`	Number of training samples for which momentum increases. Defaults to 1000000.
`momentum_stable`	Final momentum after the ramp is over (try 0.99). Defaults to 0.
`nesterov_accelerated_gradient`	`Logical`. Use Nesterov accelerated gradient (recommended). Defaults to TRUE.
`input_dropout_ratio`	Input layer dropout ratio (can improve generalization, try 0.1 or 0.2). Defaults to 0.
`hidden_dropout_ratios`	Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.
`l1`	L1 regularization (can add stability and improve generalization, causes many weights to become 0). Defaults to 0.
`l2`	L2 regularization (can add stability and improve generalization, causes many weights to be small. Defaults to 0.
`max_w2`	Constraint for squared sum of incoming weights per unit (e.g. for Rectifier). Defaults to 3.4028235e+38.
`initial_weight_distribution`	Initial weight distribution. Must be one of: "UniformAdaptive", "Uniform", "Normal". Defaults to UniformAdaptive.
`initial_weight_scale`	Uniform: -value...value, Normal: stddev. Defaults to 1.
`initial_weights`	A list of H2OFrame ids to initialize the weight matrices of this model with.
`initial_biases`	A list of H2OFrame ids to initialize the bias vectors of this model with.
`loss`	Loss function. Must be one of: "Automatic", "CrossEntropy", "Quadratic", "Huber", "Absolute", "Quantile". Defaults to Automatic.
`distribution`	Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.
`quantile_alpha`	Desired quantile for Quantile regression, must be between 0 and 1. Defaults to 0.5.
`tweedie_power`	Tweedie power for Tweedie regression, must be between 1 and 2. Defaults to 1.5.
`huber_alpha`	Desired quantile for Huber/M-regression (threshold between quadratic and linear loss, must be between 0 and 1). Defaults to 0.9.
`score_interval`	Shortest time interval (in seconds) between model scoring. Defaults to 5.
`score_training_samples`	Number of training set samples for scoring (0 for all). Defaults to 10000.
`score_validation_samples`	Number of validation set samples for scoring (0 for all). Defaults to 0.
`score_duty_cycle`	Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring). Defaults to 0.1.
`classification_stop`	Stopping criterion for classification error fraction on training data (-1 to disable). Defaults to 0.
`regression_stop`	Stopping criterion for regression error (MSE) on training data (-1 to disable). Defaults to 1e-06.
`stopping_rounds`	Early stopping based on convergence of stopping_metric. Stop if simple moving average of length k of the stopping_metric does not improve for k:=stopping_rounds scoring events (0 to disable) Defaults to 5.
`stopping_metric`	Metric to use for early stopping (AUTO: logloss for classification, deviance for regression and anomaly_score for Isolation Forest). Note that custom and custom_increasing can only be used in GBM and DRF with the Python client. Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "AUCPR", "lift_top_group", "misclassification", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO.
`stopping_tolerance`	Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.
`max_runtime_secs`	Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
`score_validation_sampling`	Method used to sample validation dataset for scoring. Must be one of: "Uniform", "Stratified". Defaults to Uniform.
`diagnostics`	`Logical`. Enable diagnostics for hidden layers. Defaults to TRUE.
`fast_mode`	`Logical`. Enable fast mode (minor approximation in back-propagation). Defaults to TRUE.
`force_load_balance`	`Logical`. Force extra load balancing to increase training speed for small datasets (to keep all cores busy). Defaults to TRUE.
`variable_importances`	`Logical`. Compute variable importances for input features (Gedeon method) - can be slow for large networks. Defaults to TRUE.
`replicate_training_data`	`Logical`. Replicate the entire training dataset onto every node for faster training on small datasets. Defaults to TRUE.
`single_node_mode`	`Logical`. Run on a single node for fine-tuning of model parameters. Defaults to FALSE.
`shuffle_training_data`	`Logical`. Enable shuffling of training data (recommended if training data is replicated and train_samples_per_iteration is close to #nodes x #rows, of if using balance_classes). Defaults to FALSE.
`missing_values_handling`	Handling of missing values. Either MeanImputation or Skip. Must be one of: "MeanImputation", "Skip". Defaults to MeanImputation.
`quiet_mode`	`Logical`. Enable quiet mode for less output to standard output. Defaults to FALSE.
`autoencoder`	`Logical`. Auto-Encoder. Defaults to FALSE.
`sparse`	`Logical`. Sparse data handling (more efficient for data with lots of 0 values). Defaults to FALSE.
`col_major`	`Logical`. #DEPRECATED Use a column major weight matrix for input layer. Can speed up forward propagation, but might slow down backpropagation. Defaults to FALSE.
`average_activation`	Average activation for sparse auto-encoder. #Experimental Defaults to 0.
`sparsity_beta`	Sparsity regularization. #Experimental Defaults to 0.
`max_categorical_features`	Max. number of categorical features, enforced via hashing. #Experimental Defaults to 2147483647.
`reproducible`	`Logical`. Force reproducibility on small data (will be slow - only uses 1 thread). Defaults to FALSE.
`export_weights_and_biases`	`Logical`. Whether to export Neural Network weights and biases to H2O Frames. Defaults to FALSE.
`mini_batch_size`	Mini-batch size (smaller leads to better fit, larger can speed up and generalize better). Defaults to 1.
`categorical_encoding`	Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
`elastic_averaging`	`Logical`. Elastic averaging between compute nodes can improve distributed model convergence. #Experimental Defaults to FALSE.
`elastic_averaging_moving_rate`	Elastic averaging moving rate (only if elastic averaging is enabled). Defaults to 0.9.
`elastic_averaging_regularization`	Elastic averaging regularization strength (only if elastic averaging is enabled). Defaults to 0.001.
`export_checkpoints_dir`	Automatically export generated models to this directory.
`auc_type`	Set default multinomial AUC type. Must be one of: "AUTO", "NONE", "MACRO_OVR", "WEIGHTED_OVR", "MACRO_OVO", "WEIGHTED_OVO". Defaults to AUTO.
`custom_metric_func`	Reference to custom evaluation function, format: 'language:keyName=funcName'
`gainslift_bins`	Gains/Lift table number of bins. 0 means disabled.. Default value -1 means automatic binning. Defaults to -1.
`verbose`	`Logical`. Print scoring history to the console (Metrics per epoch). Defaults to FALSE.

Examples

## Not run: 
library(h2o)
h2o.init()
iris_hf <- as.h2o(iris)
iris_dl <- h2o.deeplearning(x = 1:4, y = 5, training_frame = iris_hf, seed=123456)

# now make a prediction
predictions <- h2o.predict(iris_dl, iris_hf)

## End(Not run)

h2o documentation built on May 29, 2024, 4:26 a.m.