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#' Linear Combined Deep Neural Networks
#'
#' Also known as \code{wide-n-deep} estimators, these are estimators for
#' TensorFlow Linear and DNN joined models for regression.
#'
#' @inheritParams estimators
#'
#' @template roxlate-activation-fn
#' @templateVar name dnn_activation_fn
#' @templateVar default relu
#'
#' @param linear_feature_columns The feature columns used by linear (wide) part
#' of the model.
#' @param linear_optimizer Either the name of the optimizer to be used when
#' training the model, or a TensorFlow optimizer instance. Defaults to the
#' FTRL optimizer.
#' @param dnn_feature_columns The feature columns used by the neural network
#' (deep) part in the model.
#' @param dnn_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 dnn_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 dnn_dropout When not `NULL`, the probability we will drop out a given
#' coordinate.
#'
#' @family canned estimators
#' @name dnn_linear_combined_estimators
NULL
#' @inheritParams dnn_linear_combined_estimators
#' @name dnn_linear_combined_estimators
#' @export
dnn_linear_combined_regressor <- function(model_dir = NULL,
linear_feature_columns = NULL,
linear_optimizer = "Ftrl",
dnn_feature_columns = NULL,
dnn_optimizer = "Adagrad",
dnn_hidden_units = NULL,
dnn_activation_fn = "relu",
dnn_dropout = NULL,
label_dimension = 1L,
weight_column = NULL,
input_layer_partitioner = NULL,
config = NULL)
{
args <- as.list(environment(), all = TRUE)
estimator <- py_suppress_warnings(
tf$estimator$DNNLinearCombinedRegressor(
model_dir = resolve_model_dir(model_dir),
linear_feature_columns = ensure_nullable_list(linear_feature_columns),
linear_optimizer = linear_optimizer,
dnn_feature_columns = ensure_nullable_list(dnn_feature_columns),
dnn_optimizer = dnn_optimizer,
dnn_hidden_units = cast_integer_list(dnn_hidden_units),
dnn_activation_fn = resolve_activation_fn(dnn_activation_fn),
dnn_dropout = cast_nullable_scalar_double(dnn_dropout),
label_dimension = cast_scalar_integer(label_dimension),
weight_column = cast_nullable_string(weight_column),
input_layer_partitioner = input_layer_partitioner,
config = config
)
)
new_tf_regressor(estimator, args = args,
subclass = "tf_estimator_dnn_linear_combined_regressor")
}
#' @inheritParams dnn_linear_combined_estimators
#' @name dnn_linear_combined_estimators
#' @export
dnn_linear_combined_classifier <- function(model_dir = NULL,
linear_feature_columns = NULL,
linear_optimizer = "Ftrl",
dnn_feature_columns = NULL,
dnn_optimizer = "Adagrad",
dnn_hidden_units = NULL,
dnn_activation_fn = "relu",
dnn_dropout = NULL,
n_classes = 2L,
weight_column = NULL,
label_vocabulary = NULL,
input_layer_partitioner = NULL,
config = NULL)
{
args <- as.list(environment(), all = TRUE)
estimator <- py_suppress_warnings(
tf$estimator$DNNLinearCombinedClassifier(
model_dir = resolve_model_dir(model_dir),
linear_feature_columns = ensure_nullable_list(linear_feature_columns),
linear_optimizer = linear_optimizer,
dnn_feature_columns = ensure_nullable_list(dnn_feature_columns),
dnn_optimizer = dnn_optimizer,
dnn_hidden_units = cast_integer_list(dnn_hidden_units),
dnn_activation_fn = resolve_activation_fn(dnn_activation_fn),
dnn_dropout = cast_nullable_scalar_double(dnn_dropout),
n_classes = cast_scalar_integer(n_classes),
weight_column = cast_nullable_string(weight_column),
label_vocabulary = label_vocabulary,
input_layer_partitioner = input_layer_partitioner,
config = config
)
)
new_tf_classifier(estimator, args = args,
subclass = "tf_estimator_classifier_dnn_linear_combined_classifier")
}
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