def __init__()

in tensorflow_ranking/python/keras/canned/gam.py [0:0]

• 49 lines of code
• 8 McCabe index (conditional complexity)

  def __init__(self,
               context_feature_columns=None,
               example_feature_columns=None,
               context_hidden_layer_dims=None,
               example_hidden_layer_dims=None,
               activation=None,
               use_batch_norm=True,
               batch_norm_moment=0.999,
               dropout=0.5,
               name='gam_ranking_model',
               **kwargs):
    """Initializes an instance of `GAMRankingNetwork`.

    Args:
      context_feature_columns: A dict containing all the context feature columns
        used by the network. Keys are feature names, and values are instances of
        classes derived from `_FeatureColumn`.
      example_feature_columns: A dict containing all the example feature columns
        used by the network. Keys are feature names, and values are instances of
        classes derived from `_FeatureColumn`.
      context_hidden_layer_dims: Iterable of number hidden units per layer for
        context features. See `example_hidden_units`.
      example_hidden_layer_dims: Iterable of number hidden units per layer for
        example features. All layers are fully connected. Ex. `[64, 32]` means
        first layer has 64 nodes and second one has 32.
      activation: Activation function applied to each layer. If `None`, will use
        an identity activation, which is default behavior in Keras activations.
      use_batch_norm: Whether to use batch normalization after each hidden
        layer.
      batch_norm_moment: Momentum for the moving average in batch normalization.
      dropout: When not `None`, the probability we will drop out a given
        coordinate.
      name: name of the keras network.
      **kwargs: Keyword arguments.

    Raises:
       `ValueError` if `example_feature_columns` is empty or if
       `example_hidden_lyaer_dims` is empty.
    """
    if not example_feature_columns or not example_hidden_layer_dims:
      raise ValueError('example_feature_columns or example_hidden_layer_dims '
                       'must not be empty.')
    super(GAMRankingNetwork, self).__init__(
        context_feature_columns=context_feature_columns,
        example_feature_columns=example_feature_columns,
        name=name,
        **kwargs)
    context_hidden_layer_dims = context_hidden_layer_dims or []
    self._context_hidden_layer_dims = [
        int(d) for d in context_hidden_layer_dims
    ]
    self._example_hidden_layer_dims = [
        int(d) for d in example_hidden_layer_dims
    ]
    self._num_features = len(self.example_feature_columns)

    self._activation = activation
    self._use_batch_norm = use_batch_norm
    self._batch_norm_moment = batch_norm_moment
    self._dropout = dropout

    self._per_context_feature_layers = {}
    for name in self._context_feature_columns:
      self._per_context_feature_layers[name] = _make_tower_layers(
          hidden_layer_dims=self._context_hidden_layer_dims,
          output_units=self._num_features,
          activation=self._activation,
          use_batch_norm=self._use_batch_norm,
          batch_norm_moment=self._batch_norm_moment,
          dropout=self._dropout)

    self._per_example_feature_layers = {}
    for name in self._example_feature_columns:
      self._per_example_feature_layers[name] = _make_tower_layers(
          hidden_layer_dims=self._example_hidden_layer_dims,
          output_units=1,
          activation=self._activation,
          use_batch_norm=self._use_batch_norm,
          batch_norm_moment=self._batch_norm_moment,
          dropout=self._dropout)