#' Deep Neural Networks
#'
#' Create a deep neural network (DNN) estimator.
#'
#' @inheritParams estimators
#'
#' @template roxlate-activation-fn
#' @templateVar name activation_fn
#' @templateVar default relu
#'
#' @param hidden_units An integer vector, indicating the number of hidden
#' units in each layer. All layers are fully connected. For example,
#' `c(64, 32)` means the first layer has 64 nodes, and the second layer
#' has 32 nodes.
#' @param optimizer Either the name of the optimizer to be used when training
#' the model, or a TensorFlow optimizer instance. Defaults to the Adagrad
#' optimizer.
#' @param dropout When not `NULL`, the probability we will drop out a given
#' coordinate.
#'
#' @family canned estimators
#' @name dnn_estimators
NULL
#' @inheritParams dnn_estimators
#' @name dnn_estimators
#' @export
dnn_regressor <- function(hidden_units,
feature_columns,
model_dir = NULL,
label_dimension = 1L,
weight_column = NULL,
optimizer = "Adagrad",
activation_fn = "relu",
dropout = NULL,
input_layer_partitioner = NULL,
config = NULL)
{
args <- as.list(environment(), all.names = TRUE)
estimator <- py_suppress_warnings(
tf$estimator$DNNRegressor(
hidden_units = cast_integer_list(hidden_units),
feature_columns = ensure_nullable_list(feature_columns),
model_dir = resolve_model_dir(model_dir),
label_dimension = cast_scalar_integer(label_dimension),
weight_column = cast_nullable_string(weight_column),
optimizer = optimizer,
activation_fn = resolve_activation_fn(activation_fn),
dropout = cast_nullable_scalar_double(dropout),
input_layer_partitioner = input_layer_partitioner,
config = config
)
)
new_tf_regressor(estimator, args = args,
subclass = "tf_estimator_regressor_dnn_regressor")
}
#' @inheritParams dnn_estimators
#' @name dnn_estimators
#' @export
dnn_classifier <- function(hidden_units,
feature_columns,
model_dir = NULL,
n_classes = 2L,
weight_column = NULL,
label_vocabulary = NULL,
optimizer = "Adagrad",
activation_fn = "relu",
dropout = NULL,
input_layer_partitioner = NULL,
config = NULL)
{
args <- as.list(environment(), all.names = TRUE)
estimator <- py_suppress_warnings(
tf$estimator$DNNClassifier(
hidden_units = cast_integer_list(hidden_units),
feature_columns = ensure_nullable_list(feature_columns),
model_dir = resolve_model_dir(model_dir),
n_classes = cast_scalar_integer(n_classes),
weight_column = cast_nullable_string(weight_column),
label_vocabulary = label_vocabulary,
optimizer = optimizer,
activation_fn = resolve_activation_fn(activation_fn),
dropout = cast_nullable_scalar_double(dropout),
input_layer_partitioner = input_layer_partitioner,
config = config
)
)
new_tf_classifier(estimator, args = args,
subclass = "tf_estimator_classifier_dnn_classifier")
}
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