parameters: (self, learning_rate,
                 learning_rate_power=-0.5,
                 initial_accumulator_value=0.1,
                 l1_regularization_strength=0.0,
                 l2_regularization_strength=0.0,
                 use_locking=False, name="Ftrl")
docstring: Construct a new FTRL optimizer.
  
      Args:
        learning_rate: A float value or a constant float `Tensor`.
        learning_rate_power: A float value, must be less or equal to zero.
        initial_accumulator_value: The starting value for accumulators.
          Only positive values are allowed.
        l1_regularization_strength: A float value, must be greater than or
          equal to zero.
        l2_regularization_strength: A float value, must be greater than or
          equal to zero.
        use_locking: If `True` use locks for update operations.
        name: Optional name prefix for the operations created when applying
          gradients.  Defaults to "Ftrl".
  
      Raises:
        ValueError: If one of the arguments is invalid.
docstring_summary: Construct a new FTRL optimizer.
docstring_tokens: Construct a new FTRL optimizer .
function: def __init__(self, learning_rate,
                 learning_rate_power=-0.5,
                 initial_accumulator_value=0.1,
                 l1_regularization_strength=0.0,
                 l2_regularization_strength=0.0,
                 use_locking=False, name="Ftrl"):
      """Construct a new FTRL optimizer.
  
      Args:
        learning_rate: A float value or a constant float `Tensor`.
        learning_rate_power: A float value, must be less or equal to zero.
        initial_accumulator_value: The starting value for accumulators.
          Only positive values are allowed.
        l1_regularization_strength: A float value, must be greater than or
          equal to zero.
        l2_regularization_strength: A float value, must be greater than or
          equal to zero.
        use_locking: If `True` use locks for update operations.
        name: Optional name prefix for the operations created when applying
          gradients.  Defaults to "Ftrl".
  
      Raises:
        ValueError: If one of the arguments is invalid.
      """
      super(FtrlOptimizer, self).__init__(use_locking, name)
  
      if initial_accumulator_value <= 0.0:
        raise ValueError("initial_accumulator_value %f needs to be positive" %
                         initial_accumulator_value)
      if learning_rate_power > 0.0:
        raise ValueError("learning_rate_power %f needs to be negative or zero" %
                         learning_rate_power)
      if l1_regularization_strength < 0.0:
        raise ValueError(
            "l1_regularization_strength %f needs to be positive or zero" %
            l1_regularization_strength)
      if l2_regularization_strength < 0.0:
        raise ValueError(
            "l2_regularization_strength %f needs to be positive or zero" %
            l2_regularization_strength)
  
      self._learning_rate = learning_rate
      self._learning_rate_power = learning_rate_power
      self._initial_accumulator_value = initial_accumulator_value
      self._l1_regularization_strength = l1_regularization_strength
      self._l2_regularization_strength = l2_regularization_strength
      self._learning_rate_tensor = None
      self._learning_rate_power_tensor = None
      self._l1_regularization_strength_tensor = None
      self._l2_regularization_strength_tensor = None
function_tokens: def __init__ ( self , learning_rate , learning_rate_power = - 0.5 , initial_accumulator_value = 0.1 , l1_regularization_strength = 0.0 , l2_regularization_strength = 0.0 , use_locking = False , name = "Ftrl" ) : super ( FtrlOptimizer , self ) . __init__ ( use_locking , name ) if initial_accumulator_value <= 0.0 : raise ValueError ( "initial_accumulator_value %f needs to be positive" % initial_accumulator_value ) if learning_rate_power > 0.0 : raise ValueError ( "learning_rate_power %f needs to be negative or zero" % learning_rate_power ) if l1_regularization_strength < 0.0 : raise ValueError ( "l1_regularization_strength %f needs to be positive or zero" % l1_regularization_strength ) if l2_regularization_strength < 0.0 : raise ValueError ( "l2_regularization_strength %f needs to be positive or zero" % l2_regularization_strength ) self . _learning_rate = learning_rate self . _learning_rate_power = learning_rate_power self . _initial_accumulator_value = initial_accumulator_value self . _l1_regularization_strength = l1_regularization_strength self . _l2_regularization_strength = l2_regularization_strength self . _learning_rate_tensor = None self . _learning_rate_power_tensor = None self . _l1_regularization_strength_tensor = None self . _l2_regularization_strength_tensor = None