(self,
               num_units,
               forget_bias=1.0,
               input_size=None,
               activation=tf.math.tanh,
               layer_norm=True,
               norm_gain=1.0,
               norm_shift=0.0,
               dropout_keep_prob=1.0,
               dropout_prob_seed=None,
               reuse=None)
Initializes the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      activation: Activation function of the inner states.
      layer_norm: If `True`, layer normalization will be applied.
      norm_gain: float, The layer normalization gain initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      norm_shift: float, The layer normalization shift initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      dropout_keep_prob: unit Tensor or float between 0 and 1 representing the
        recurrent dropout probability value. If float and 1.0, no dropout will
        be applied.
      dropout_prob_seed: (optional) integer, the randomness seed.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
Initializes the basic LSTM cell.
Initializes the basic LSTM cell .
def __init__(self,
               num_units,
               forget_bias=1.0,
               input_size=None,
               activation=tf.math.tanh,
               layer_norm=True,
               norm_gain=1.0,
               norm_shift=0.0,
               dropout_keep_prob=1.0,
               dropout_prob_seed=None,
               reuse=None):
    """Initializes the basic LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell.
      forget_bias: float, The bias added to forget gates (see above).
      input_size: Deprecated and unused.
      activation: Activation function of the inner states.
      layer_norm: If `True`, layer normalization will be applied.
      norm_gain: float, The layer normalization gain initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      norm_shift: float, The layer normalization shift initial value. If
        `layer_norm` has been set to `False`, this argument will be ignored.
      dropout_keep_prob: unit Tensor or float between 0 and 1 representing the
        recurrent dropout probability value. If float and 1.0, no dropout will
        be applied.
      dropout_prob_seed: (optional) integer, the randomness seed.
      reuse: (optional) Python boolean describing whether to reuse variables
        in an existing scope.  If not `True`, and the existing scope already has
        the given variables, an error is raised.
    """
    super(LayerNormBasicLSTMCell, self).__init__(_reuse=reuse)

    if input_size is not None:
      tf.logging.warn("%s: The input_size parameter is deprecated.", self)

    self._num_units = num_units
    self._activation = activation
    self._forget_bias = forget_bias
    self._keep_prob = dropout_keep_prob
    self._seed = dropout_prob_seed
    self._layer_norm = layer_norm
    self._norm_gain = norm_gain
    self._norm_shift = norm_shift
    self._reuse = reuse
def __init__ ( self , num_units , forget_bias = 1.0 , input_size = None , activation = tf . math . tanh , layer_norm = True , norm_gain = 1.0 , norm_shift = 0.0 , dropout_keep_prob = 1.0 , dropout_prob_seed = None , reuse = None ) : super ( LayerNormBasicLSTMCell , self ) . __init__ ( _reuse = reuse ) if input_size is not None : tf . logging . warn ( "%s: The input_size parameter is deprecated." , self ) self . _num_units = num_units self . _activation = activation self . _forget_bias = forget_bias self . _keep_prob = dropout_keep_prob self . _seed = dropout_prob_seed self . _layer_norm = layer_norm self . _norm_gain = norm_gain self . _norm_shift = norm_shift self . _reuse = reuse