class_docstr: Builds a DNN subnetwork for AdaNet.
docstr: Initializes a `_DNNBuilder`.
  
  Args:
    feature_columns: An iterable containing all the feature columns used by
      the model. All items in the set should be instances of classes derived
      from `FeatureColumn`.
    optimizer: An `Optimizer` instance for training both the subnetwork and
      the mixture weights.
    layer_size: The number of nodes to output at each hidden layer.
    num_layers: The number of hidden layers.
    learn_mixture_weights: Whether to solve a learning problem to find the
      best mixture weights, or use their default value according to the
      mixture weight type. When `False`, the subnetworks will return a no_op
      for the mixture weight train op.
    dropout: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
      10% of input units.
    seed: A random seed.
  
  Returns:
    An instance of `_DNNBuilder`.
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code: def __init__(
      self,
      feature_columns,
      optimizer,
      layer_size,
      num_layers,
      learn_mixture_weights,
      dropout,
      seed,
  ):
      """Initializes a `_DNNBuilder`.
  
      Args:
        feature_columns: An iterable containing all the feature columns used by
          the model. All items in the set should be instances of classes derived
          from `FeatureColumn`.
        optimizer: An `Optimizer` instance for training both the subnetwork and
          the mixture weights.
        layer_size: The number of nodes to output at each hidden layer.
        num_layers: The number of hidden layers.
        learn_mixture_weights: Whether to solve a learning problem to find the
          best mixture weights, or use their default value according to the
          mixture weight type. When `False`, the subnetworks will return a no_op
          for the mixture weight train op.
        dropout: The dropout rate, between 0 and 1. E.g. "rate=0.1" would drop out
          10% of input units.
        seed: A random seed.
  
      Returns:
        An instance of `_DNNBuilder`.
      """
  
      self._feature_columns = feature_columns
      self._optimizer = optimizer
      self._layer_size = layer_size
      self._num_layers = num_layers
      self._learn_mixture_weights = learn_mixture_weights
      self._dropout = dropout
      self._seed = seed
  
file_path: adanet/adanet/examples/simple_dnn.py
imports: import functools
  import adanet
  import tensorflow.compat.v2
label_desc: Use image node_id 1 to create a new _SimpleDNNBuilder object from inherited base classes: adanet.subnetwork with example: obj = _SimpleDNNBuilder(feature_columns, optimizer, layer_size, num_layers, learn_mixture_weights, dropout, seed)