Method __init__

tensorflow/python/ops/rnn_cell_impl.py:660–714

Initialize 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). Must set to `0.0` manually when restoring from CudnnLSTM-trained checkpoints. state_is_tuple: If True, accepted a

(self,
               num_units,
               forget_bias=1.0,
               state_is_tuple=True,
               activation=None,
               reuse=None,
               name=None,
               dtype=None,
               **kwargs)

  @deprecated(None, "This class is equivalent as tf.keras.layers.LSTMCell,"
              " and will be replaced by that in Tensorflow 2.0.")
  def __init__(self,
               num_units,
               forget_bias=1.0,
               state_is_tuple=True,
               activation=None,
               reuse=None,
               name=None,
               dtype=None,
               **kwargs):
    """Initialize 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). Must set
        to `0.0` manually when restoring from CudnnLSTM-trained checkpoints.
      state_is_tuple: If True, accepted and returned states are 2-tuples of the
        `c_state` and `m_state`.  If False, they are concatenated along the
        column axis.  The latter behavior will soon be deprecated.
      activation: Activation function of the inner states.  Default: `tanh`. It
        could also be string that is within Keras activation function names.
      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.
      name: String, the name of the layer. Layers with the same name will share
        weights, but to avoid mistakes we require reuse=True in such cases.
      dtype: Default dtype of the layer (default of `None` means use the type of
        the first input). Required when `build` is called before `call`.
      **kwargs: Dict, keyword named properties for common layer attributes, like
        `trainable` etc when constructing the cell from configs of get_config().
        When restoring from CudnnLSTM-trained checkpoints, must use
        `CudnnCompatibleLSTMCell` instead.
    """
    super(BasicLSTMCell, self).__init__(
        _reuse=reuse, name=name, dtype=dtype, **kwargs)
    _check_supported_dtypes(self.dtype)
    if not state_is_tuple:
      logging.warn(
          "%s: Using a concatenated state is slower and will soon be "
          "deprecated.  Use state_is_tuple=True.", self)
    if context.executing_eagerly() and context.num_gpus() > 0:
      logging.warn(
          "%s: Note that this cell is not optimized for performance. "
          "Please use tf.contrib.cudnn_rnn.CudnnLSTM for better "
          "performance on GPU.", self)

    # Inputs must be 2-dimensional.
    self.input_spec = input_spec.InputSpec(ndim=2)

    self._num_units = num_units
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    if activation:
      self._activation = activations.get(activation)
    else:
      self._activation = math_ops.tanh

  @property
  def state_size(self):