Builds a feedforward 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 = 1e08, rate = 0.005, rate_annealing = 1e06, 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 = 1e06, stopping_rounds = 5, stopping_metric = c("AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "AUCPR", "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)
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; autogenerated if not specified. 
validation_frame  Id of the validation data frame. 
nfolds  Number of folds for Kfold crossvalidation (0 to disable or >= 2). Defaults to 0. 
keep_cross_validation_models 

keep_cross_validation_predictions 

keep_cross_validation_fold_assignment 

fold_assignment  Crossvalidation 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 crossvalidation fold index assignment per observation. 
ignore_const_cols 

score_each_iteration 

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 perrow observation weights and do not increase the size of the data frame. This is typically the number of times a row is repeated, but noninteger values are supported as well. During training, rows with higher weights matter more, due to the larger loss function prefactor. 
offset_column  Offset column. This will be added to the combination of columns before applying the link function. 
balance_classes 

class_sampling_factors  Desired over/undersampling 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 multiclass 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 

use_all_factor_levels 

standardize 

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 multinode operation and train_samples_per_iteration = 2 (autotuning). 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 (timebased random number). 
adaptive_rate 

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 1e08. 
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 1e06. 
rate_decay  Learning rate decay factor between layers (Nth 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 

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/Mregression (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 1e06. 
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", "lift_top_group", "misclassification", "AUCPR", "mean_per_class_error", "custom", "custom_increasing". Defaults to AUTO. 
stopping_tolerance  Relative tolerance for metricbased 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 

fast_mode 

force_load_balance 

variable_importances 

replicate_training_data 

single_node_mode 

shuffle_training_data 

missing_values_handling  Handling of missing values. Either MeanImputation or Skip. Must be one of: "MeanImputation", "Skip". Defaults to MeanImputation. 
quiet_mode 

autoencoder 

sparse 

col_major 

average_activation  Average activation for sparse autoencoder. #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 

export_weights_and_biases 

mini_batch_size  Minibatch 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 

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 

predict.H2OModel
for prediction
# 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) # }