parameters: (self,
                 head,
                 hidden_units,
                 feature_columns,
                 model_dir=None,
                 optimizer='Adagrad',
                 activation_fn=nn.relu,
                 dropout=None,
                 input_layer_partitioner=None,
                 config=None)
docstring: Initializes a `DNNClassifier` instance.
  
      Args:
        head: A `_Head` instance constructed with a method such as
          `tf.contrib.estimator.multi_label_head`.
        hidden_units: Iterable of number hidden units per layer. All layers are
          fully connected. Ex. `[64, 32]` means first layer has 64 nodes and
          second one has 32.
        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`.
        model_dir: Directory to save model parameters, graph and etc. This can
          also be used to load checkpoints from the directory into a estimator to
          continue training a previously saved model.
        optimizer: An instance of `tf.Optimizer` used to train the model. Defaults
          to Adagrad optimizer.
        activation_fn: Activation function applied to each layer. If `None`, will
          use `tf.nn.relu`.
        dropout: When not `None`, the probability we will drop out a given
          coordinate.
        input_layer_partitioner: Optional. Partitioner for input layer. Defaults
          to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
        config: `RunConfig` object to configure the runtime settings.
docstring_summary: Initializes a `DNNClassifier` instance.
docstring_tokens: Initializes a DNNClassifier instance .
function: def __init__(self,
                 head,
                 hidden_units,
                 feature_columns,
                 model_dir=None,
                 optimizer='Adagrad',
                 activation_fn=nn.relu,
                 dropout=None,
                 input_layer_partitioner=None,
                 config=None):
      """Initializes a `DNNClassifier` instance.
  
      Args:
        head: A `_Head` instance constructed with a method such as
          `tf.contrib.estimator.multi_label_head`.
        hidden_units: Iterable of number hidden units per layer. All layers are
          fully connected. Ex. `[64, 32]` means first layer has 64 nodes and
          second one has 32.
        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`.
        model_dir: Directory to save model parameters, graph and etc. This can
          also be used to load checkpoints from the directory into a estimator to
          continue training a previously saved model.
        optimizer: An instance of `tf.Optimizer` used to train the model. Defaults
          to Adagrad optimizer.
        activation_fn: Activation function applied to each layer. If `None`, will
          use `tf.nn.relu`.
        dropout: When not `None`, the probability we will drop out a given
          coordinate.
        input_layer_partitioner: Optional. Partitioner for input layer. Defaults
          to `min_max_variable_partitioner` with `min_slice_size` 64 << 20.
        config: `RunConfig` object to configure the runtime settings.
      """
      def _model_fn(features, labels, mode, config):
        return dnn_lib._dnn_model_fn(  # pylint: disable=protected-access
            features=features,
            labels=labels,
            mode=mode,
            head=head,
            hidden_units=hidden_units,
            feature_columns=tuple(feature_columns or []),
            optimizer=optimizer,
            activation_fn=activation_fn,
            dropout=dropout,
            input_layer_partitioner=input_layer_partitioner,
            config=config)
      super(DNNEstimator, self).__init__(
          model_fn=_model_fn, model_dir=model_dir, config=config)
function_tokens: def __init__ ( self , head , hidden_units , feature_columns , model_dir = None , optimizer = 'Adagrad' , activation_fn = nn . relu , dropout = None , input_layer_partitioner = None , config = None ) : def _model_fn ( features , labels , mode , config ) : return dnn_lib . _dnn_model_fn ( # pylint: disable=protected-access features = features , labels = labels , mode = mode , head = head , hidden_units = hidden_units , feature_columns = tuple ( feature_columns or [ ] ) , optimizer = optimizer , activation_fn = activation_fn , dropout = dropout , input_layer_partitioner = input_layer_partitioner , config = config ) super ( DNNEstimator , self ) . __init__ ( model_fn = _model_fn , model_dir = model_dir , config = config )