Build a Deep Neural Network model using CPUs

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

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,
  max_hit_ratio_k = 0,
  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,
  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.
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.
max_hit_ratio_k	Max. number (top K) of predictions to use for hit ratio computation (for multi-class only, 0 to disable). Defaults to 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 anonomaly_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.
verbose	Logical. Print scoring history to the console (Metrics per epoch). Defaults to FALSE.

See also

predict.H2OModel for prediction

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)
# }