dnn_linear_combined_estimators: Linear Combined Deep Neural Networks
In tfestimators: Interface to 'TensorFlow' Estimators

Description Usage Arguments See Also

Also known as wide-n-deep estimators, these are estimators for TensorFlow Linear and DNN joined models for regression.

dnn_linear_combined_regressor(
  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
)

dnn_linear_combined_classifier(
  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
)

`model_dir`	Directory to save the model parameters, graph, and so on. This can also be used to load checkpoints from the directory into a estimator to continue training a previously saved model.
`linear_feature_columns`	The feature columns used by linear (wide) part of the model.
`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.
`dnn_feature_columns`	The feature columns used by the neural network (deep) part in the model.
`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.
`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.
`dnn_activation_fn`	The activation function to apply to each layer. This can either be an actual activation function (e.g. `tf$nn$relu`), or the name of an activation function (e.g. `"relu"`). Defaults to the `"relu"` activation function. See https://www.tensorflow.org/versions/r1.15/api_docs/python/tf/nn for documentation related to the set of activation functions available in TensorFlow.
`dnn_dropout`	When not `NULL`, the probability we will drop out a given coordinate.
`label_dimension`	Number of regression targets per example. This is the size of the last dimension of the labels and logits `Tensor` objects (typically, these have shape `[batch_size, label_dimension]`).
`weight_column`	A string, or a numeric column created by `column_numeric()` defining feature column representing weights. It is used to down weight or boost examples during training. It will be multiplied by the loss of the example. If it is a string, it is used as a key to fetch weight tensor from the `features` argument. If it is a numeric column, then the raw tensor is fetched by key `weight_column$key`, then `weight_column$normalizer_fn` is applied on it to get weight tensor.
`input_layer_partitioner`	An optional partitioner for the input layer. Defaults to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
`config`	A run configuration created by `run_config()`, used to configure the runtime settings.
`n_classes`	The number of label classes.
`label_vocabulary`	A list of strings represents possible label values. If given, labels must be string type and have any value in `label_vocabulary`. If it is not given, that means labels are already encoded as integer or float within `[0, 1]` for `n_classes == 2` and encoded as integer values in `{0, 1,..., n_classes -1}` for `n_classes > 2`. Also there will be errors if vocabulary is not provided and labels are string.