def __init__()

in models/src/wavenet_vocoder/tfcompat/hparam.py [0:0]

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

    def __init__(self, hparam_def=None, model_structure=None, **kwargs):
        """Create an instance of `HParams` from keyword arguments.

        The keyword arguments specify name-values pairs for the hyperparameters.
        The parameter types are inferred from the type of the values passed.

        The parameter names are added as attributes of `HParams` object, so they
        can be accessed directly with the dot notation `hparams._name_`.

        Example:

        ```python
        # Define 3 hyperparameters: 'learning_rate' is a float parameter,
        # 'num_hidden_units' an integer parameter, and 'activation' a string
        # parameter.
        hparams = tf.HParams(
            learning_rate=0.1, num_hidden_units=100, activation='relu')

        hparams.activation ==> 'relu'
        ```

        Note that a few names are reserved and cannot be used as hyperparameter
        names.  If you use one of the reserved name the constructor raises a
        `ValueError`.

        Args:
          hparam_def: Serialized hyperparameters, encoded as a hparam_pb2.HParamDef
            protocol buffer. If provided, this object is initialized by
            deserializing hparam_def.  Otherwise **kwargs is used.
          model_structure: An instance of ModelStructure, defining the feature
            crosses to be used in the Trial.
          **kwargs: Key-value pairs where the key is the hyperparameter name and
            the value is the value for the parameter.

        Raises:
          ValueError: If both `hparam_def` and initialization values are provided,
            or if one of the arguments is invalid.

        """
        # Register the hyperparameters and their type in _hparam_types.
        # This simplifies the implementation of parse().
        # _hparam_types maps the parameter name to a tuple (type, bool).
        # The type value is the type of the parameter for scalar hyperparameters,
        # or the type of the list elements for multidimensional hyperparameters.
        # The bool value is True if the value is a list, False otherwise.
        self._hparam_types = {}
        self._model_structure = model_structure
        if hparam_def:
            ##       self._init_from_proto(hparam_def)
            ##       if kwargs:
            ##         raise ValueError('hparam_def and initialization values are '
            ##                          'mutually exclusive')
            raise ValueError("hparam_def has been disabled in this version")
        else:
            for name, value in six.iteritems(kwargs):
                self.add_hparam(name, value)