Build a Deep Learning model using multiple native GPU backends Builds a deep neural network on an H2OFrame containing various data sources
h2o.deepwater(x, y, training_frame, model_id = NULL, checkpoint = NULL,
autoencoder = FALSE, validation_frame = NULL, nfolds = 0,
balance_classes = FALSE, max_after_balance_size = 5,
class_sampling_factors = NULL, keep_cross_validation_predictions = FALSE,
keep_cross_validation_fold_assignment = FALSE, fold_assignment = c("AUTO",
"Random", "Modulo", "Stratified"), fold_column = NULL,
offset_column = NULL, weights_column = NULL,
score_each_iteration = FALSE, categorical_encoding = c("AUTO", "Enum",
"OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder",
"SortByResponse", "EnumLimited"), overwrite_with_best_model = TRUE,
epochs = 10, train_samples_per_iteration = -2,
target_ratio_comm_to_comp = 0.05, seed = -1, standardize = TRUE,
learning_rate = 0.001, learning_rate_annealing = 1e-06,
momentum_start = 0.9, momentum_ramp = 10000, momentum_stable = 0.9,
distribution = c("AUTO", "bernoulli", "multinomial", "gaussian", "poisson",
"gamma", "tweedie", "laplace", "quantile", "huber"), score_interval = 5,
score_training_samples = 10000, score_validation_samples = 0,
score_duty_cycle = 0.1, classification_stop = 0, regression_stop = 0,
stopping_rounds = 5, stopping_metric = c("AUTO", "deviance", "logloss",
"MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group", "misclassification",
"mean_per_class_error"), stopping_tolerance = 0, max_runtime_secs = 0,
ignore_const_cols = TRUE, shuffle_training_data = TRUE,
mini_batch_size = 32, clip_gradient = 10, network = c("auto", "user",
"lenet", "alexnet", "vgg", "googlenet", "inception_bn", "resnet"),
backend = c("mxnet", "caffe", "tensorflow"), image_shape = c(0, 0),
channels = 3, sparse = FALSE, gpu = TRUE, device_id = c(0),
cache_data = TRUE, network_definition_file = NULL,
network_parameters_file = NULL, mean_image_file = NULL,
export_native_parameters_prefix = NULL, activation = c("Rectifier",
"Tanh"), hidden = NULL, input_dropout_ratio = 0,
hidden_dropout_ratios = NULL, problem_type = c("auto", "image",
"dataset"))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.
The name of the response variable in the model.If the data does not contain a header, this is the first column index, and increasing from left to right. (The response must be either an integer or a categorical variable).
Id of the training data frame (Not required, to allow initial validation of model parameters).
Destination id for this model; auto-generated if not specified.
Model checkpoint to resume training with.
Logical. Auto-Encoder. Defaults to FALSE.
Id of the validation data frame.
Number of folds for N-fold cross-validation (0 to disable or >= 2). Defaults to 0.
Logical. Balance training data class counts via over/under-sampling (for imbalanced data). Defaults to
FALSE.
Maximum relative size of the training data after balancing class counts (can be less than 1.0). Requires balance_classes. Defaults to 5.0.
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.
Logical. Whether to keep the predictions of the cross-validation models. Defaults to FALSE.
Logical. Whether to keep the cross-validation fold assignment. Defaults to FALSE.
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.
Column with cross-validation fold index assignment per observation.
Offset column. This will be added to the combination of columns before applying the link function.
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.
Logical. Whether to score during each iteration of model training. Defaults to FALSE.
Encoding scheme for categorical features Must be one of: "AUTO", "Enum", "OneHotInternal", "OneHotExplicit", "Binary", "Eigen", "LabelEncoder", "SortByResponse", "EnumLimited". Defaults to AUTO.
Logical. If enabled, override the final model with the best model found during training. Defaults to
TRUE.
How many times the dataset should be iterated (streamed), can be fractional. Defaults to 10.
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 of communication overhead to computation. Only for multi-node operation and train_samples_per_iteration = -2 (auto-tuning). Defaults to 0.05.
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).
Logical. If enabled, automatically standardize the data. If disabled, the user must provide properly
scaled input data. Defaults to TRUE.
Learning rate (higher => less stable, lower => slower convergence). Defaults to 0.001.
Learning rate annealing: rate / (1 + rate_annealing * samples). Defaults to 1e-06.
Initial momentum at the beginning of training (try 0.5). Defaults to 0.9.
Number of training samples for which momentum increases. Defaults to 10000.
Final momentum after the ramp is over (try 0.99). Defaults to 0.9.
Distribution function Must be one of: "AUTO", "bernoulli", "multinomial", "gaussian", "poisson", "gamma", "tweedie", "laplace", "quantile", "huber". Defaults to AUTO.
Shortest time interval (in seconds) between model scoring. Defaults to 5.
Number of training set samples for scoring (0 for all). Defaults to 10000.
Number of validation set samples for scoring (0 for all). Defaults to 0.
Maximum duty cycle fraction for scoring (lower: more training, higher: more scoring). Defaults to 0.1.
Stopping criterion for classification error fraction on training data (-1 to disable). Defaults to 0.
Stopping criterion for regression error (MSE) on training data (-1 to disable). Defaults to 0.
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.
Metric to use for early stopping (AUTO: logloss for classification, deviance for regression) Must be one of: "AUTO", "deviance", "logloss", "MSE", "RMSE", "MAE", "RMSLE", "AUC", "lift_top_group", "misclassification", "mean_per_class_error". Defaults to AUTO.
Relative tolerance for metric-based stopping criterion (stop if relative improvement is not at least this much) Defaults to 0.
Maximum allowed runtime in seconds for model training. Use 0 to disable. Defaults to 0.
Logical. Ignore constant columns. Defaults to TRUE.
Logical. Enable global shuffling of training data. Defaults to TRUE.
Mini-batch size (smaller leads to better fit, larger can speed up and generalize better). Defaults to 32.
Clip gradients once their absolute value is larger than this value. Defaults to 10.
Network architecture. Must be one of: "auto", "user", "lenet", "alexnet", "vgg", "googlenet", "inception_bn", "resnet". Defaults to auto.
Deep Learning Backend. Must be one of: "mxnet", "caffe", "tensorflow". Defaults to mxnet.
Width and height of image. Defaults to [0, 0].
Number of (color) channels. Defaults to 3.
Logical. Sparse data handling (more efficient for data with lots of 0 values). Defaults to FALSE.
Logical. Whether to use a GPU (if available). Defaults to TRUE.
Device IDs (which GPUs to use). Defaults to [0].
Logical. Whether to cache the data in memory (automatically disabled if data size is too large).
Defaults to TRUE.
Path of file containing network definition (graph, architecture).
Path of file containing network (initial) parameters (weights, biases).
Path of file containing the mean image data for data normalization.
Path (prefix) where to export the native model parameters after every iteration.
Activation function. Only used if no user-defined network architecture file is provided, and only for problem_type=dataset. Must be one of: "Rectifier", "Tanh".
Hidden layer sizes (e.g. [200, 200]). Only used if no user-defined network architecture file is provided, and only for problem_type=dataset.
Input layer dropout ratio (can improve generalization, try 0.1 or 0.2). Defaults to 0.
Hidden layer dropout ratios (can improve generalization), specify one value per hidden layer, defaults to 0.5.
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). Must be one of: "auto", "image", "dataset". Defaults to auto.