(self, num_units, input_size=None,
               use_peepholes=False, cell_clip=None,
               initializer=None, num_proj=None, proj_clip=None,
               num_unit_shards=None, num_proj_shards=None,
               forget_bias=1.0, state_is_tuple=True,
               activation=tanh)
Initialize the parameters for an LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell
      input_size: Deprecated and unused.
      use_peepholes: bool, set True to enable diagonal/peephole connections.
      cell_clip: (optional) A float value, if provided the cell state is clipped
        by this value prior to the cell output activation.
      initializer: (optional) The initializer to use for the weight and
        projection matrices.
      num_proj: (optional) int, The output dimensionality for the projection
        matrices.  If None, no projection is performed.
      proj_clip: (optional) A float value.  If `num_proj > 0` and `proj_clip` is
        provided, then the projected values are clipped elementwise to within
        `[-proj_clip, proj_clip]`.
      num_unit_shards: Deprecated, will be removed by Jan. 2017.
        Use a variable_scope partitioner instead.
      num_proj_shards: Deprecated, will be removed by Jan. 2017.
        Use a variable_scope partitioner instead.
      forget_bias: Biases of the forget gate are initialized by default to 1
        in order to reduce the scale of forgetting at the beginning of
        the training.
      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.  This latter behavior will soon be deprecated.
      activation: Activation function of the inner states.
Initialize the parameters for an LSTM cell.
Initialize the parameters for an LSTM cell .
def __init__(self, num_units, input_size=None,
               use_peepholes=False, cell_clip=None,
               initializer=None, num_proj=None, proj_clip=None,
               num_unit_shards=None, num_proj_shards=None,
               forget_bias=1.0, state_is_tuple=True,
               activation=tanh):
    """Initialize the parameters for an LSTM cell.

    Args:
      num_units: int, The number of units in the LSTM cell
      input_size: Deprecated and unused.
      use_peepholes: bool, set True to enable diagonal/peephole connections.
      cell_clip: (optional) A float value, if provided the cell state is clipped
        by this value prior to the cell output activation.
      initializer: (optional) The initializer to use for the weight and
        projection matrices.
      num_proj: (optional) int, The output dimensionality for the projection
        matrices.  If None, no projection is performed.
      proj_clip: (optional) A float value.  If `num_proj > 0` and `proj_clip` is
        provided, then the projected values are clipped elementwise to within
        `[-proj_clip, proj_clip]`.
      num_unit_shards: Deprecated, will be removed by Jan. 2017.
        Use a variable_scope partitioner instead.
      num_proj_shards: Deprecated, will be removed by Jan. 2017.
        Use a variable_scope partitioner instead.
      forget_bias: Biases of the forget gate are initialized by default to 1
        in order to reduce the scale of forgetting at the beginning of
        the training.
      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.  This latter behavior will soon be deprecated.
      activation: Activation function of the inner states.
    """
    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 input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    if num_unit_shards is not None or num_proj_shards is not None:
      logging.warn(
          "%s: The num_unit_shards and proj_unit_shards parameters are "
          "deprecated and will be removed in Jan 2017.  "
          "Use a variable scope with a partitioner instead.", self)

    self._num_units = num_units
    self._use_peepholes = use_peepholes
    self._cell_clip = cell_clip
    self._initializer = initializer
    self._num_proj = num_proj
    self._proj_clip = proj_clip
    self._num_unit_shards = num_unit_shards
    self._num_proj_shards = num_proj_shards
    self._forget_bias = forget_bias
    self._state_is_tuple = state_is_tuple
    self._activation = activation

    if num_proj:
      self._state_size = (
          LSTMStateTuple(num_units, num_proj)
          if state_is_tuple else num_units + num_proj)
      self._output_size = num_proj
    else:
      self._state_size = (
          LSTMStateTuple(num_units, num_units)
          if state_is_tuple else 2 * num_units)
      self._output_size = num_units
def __init__ ( self , num_units , input_size = None , use_peepholes = False , cell_clip = None , initializer = None , num_proj = None , proj_clip = None , num_unit_shards = None , num_proj_shards = None , forget_bias = 1.0 , state_is_tuple = True , activation = tanh ) : 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 input_size is not None : logging . warn ( "%s: The input_size parameter is deprecated." , self ) if num_unit_shards is not None or num_proj_shards is not None : logging . warn ( "%s: The num_unit_shards and proj_unit_shards parameters are " "deprecated and will be removed in Jan 2017.  " "Use a variable scope with a partitioner instead." , self ) self . _num_units = num_units self . _use_peepholes = use_peepholes self . _cell_clip = cell_clip self . _initializer = initializer self . _num_proj = num_proj self . _proj_clip = proj_clip self . _num_unit_shards = num_unit_shards self . _num_proj_shards = num_proj_shards self . _forget_bias = forget_bias self . _state_is_tuple = state_is_tuple self . _activation = activation if num_proj : self . _state_size = ( LSTMStateTuple ( num_units , num_proj ) if state_is_tuple else num_units + num_proj ) self . _output_size = num_proj else : self . _state_size = ( LSTMStateTuple ( num_units , num_units ) if state_is_tuple else 2 * num_units ) self . _output_size = num_units