parameters: (self,
                 feature_columns=None,
                 model_dir=None,
                 n_classes=2,
                 weight_column_name=None,
                 optimizer=None,
                 gradient_clip_norm=None,
                 enable_centered_bias=True,
                 config=None)
docstring: Construct a `LinearClassifier` estimator object.
  
      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`.
        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.
        n_classes: number of target classes. Default is binary classification.
        weight_column_name: A string defining feature column name representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example.
        optimizer: The optimizer used to train the model. If specified, it should
          be either an instance of `tf.Optimizer` or the SDCAOptimizer. If `None`,
          the Ftrl optimizer will be used.
        gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
          to their global norm with this clipping ratio. See
          `tf.clip_by_global_norm` for more details.
        enable_centered_bias: A bool. If True, estimator will learn a centered
          bias variable for each class. Rest of the model structure learns the
          residual after centered bias.
        config: `RunConfig` object to configure the runtime settings.
  
      Returns:
        A `LinearClassifier` estimator.
docstring_summary: Construct a `LinearClassifier` estimator object.
docstring_tokens: Construct a LinearClassifier estimator object .
function: def __init__(self,
                 feature_columns=None,
                 model_dir=None,
                 n_classes=2,
                 weight_column_name=None,
                 optimizer=None,
                 gradient_clip_norm=None,
                 enable_centered_bias=True,
                 config=None):
      """Construct a `LinearClassifier` estimator object.
  
      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`.
        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.
        n_classes: number of target classes. Default is binary classification.
        weight_column_name: A string defining feature column name representing
          weights. It is used to down weight or boost examples during training. It
          will be multiplied by the loss of the example.
        optimizer: The optimizer used to train the model. If specified, it should
          be either an instance of `tf.Optimizer` or the SDCAOptimizer. If `None`,
          the Ftrl optimizer will be used.
        gradient_clip_norm: A `float` > 0. If provided, gradients are clipped
          to their global norm with this clipping ratio. See
          `tf.clip_by_global_norm` for more details.
        enable_centered_bias: A bool. If True, estimator will learn a centered
          bias variable for each class. Rest of the model structure learns the
          residual after centered bias.
        config: `RunConfig` object to configure the runtime settings.
  
      Returns:
        A `LinearClassifier` estimator.
      """
      _changing(feature_columns)
      super(LinearClassifier, self).__init__(
          model_dir=model_dir,
          n_classes=n_classes,
          weight_column_name=weight_column_name,
          linear_feature_columns=feature_columns,
          linear_optimizer=optimizer,
          gradient_clip_norm=gradient_clip_norm,
          enable_centered_bias=enable_centered_bias,
          config=config)
      self._feature_columns_inferred = False
function_tokens: def __init__ ( self , feature_columns = None , model_dir = None , n_classes = 2 , weight_column_name = None , optimizer = None , gradient_clip_norm = None , enable_centered_bias = True , config = None ) : _changing ( feature_columns ) super ( LinearClassifier , self ) . __init__ ( model_dir = model_dir , n_classes = n_classes , weight_column_name = weight_column_name , linear_feature_columns = feature_columns , linear_optimizer = optimizer , gradient_clip_norm = gradient_clip_norm , enable_centered_bias = enable_centered_bias , config = config ) self . _feature_columns_inferred = False