Python tensorflow.python.ops.nn_ops.dropout() Examples

def call(self, inputs, state):
    """LSTM cell with layer normalization and recurrent dropout."""
    c, h = state
    args = array_ops.concat([inputs, h], 1)
    concat = self._linear(args)

    i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
    if self._layer_norm:
      i = self._norm(i, "input")
      j = self._norm(j, "transform")
      f = self._norm(f, "forget")
      o = self._norm(o, "output")

    g = self._activation(j)
    if (not isinstance(self._keep_prob, float)) or self._keep_prob < 1:
      g = nn_ops.dropout(g, self._keep_prob, seed=self._seed)

    new_c = (c * math_ops.sigmoid(f + self._forget_bias)
             + math_ops.sigmoid(i) * g)
    if self._layer_norm:
      new_c = self._norm(new_c, "state")
    new_h = self._activation(new_c) * math_ops.sigmoid(o)

    new_state = rnn_cell_impl.LSTMStateTuple(new_c, new_h)
    return new_h, new_state 

def __call__(self, inputs, state, scope=None):
    """Run the cell with the declared dropouts."""
    def _should_dropout(p):
      return (not isinstance(p, float)) or p < 1

    if _should_dropout(self._input_keep_prob):
      inputs = self._dropout(inputs, "input",
                             self._recurrent_input_noise,
                             self._input_keep_prob)
    output, new_state = self._cell(inputs, state, scope)
    if _should_dropout(self._state_keep_prob):
      # Identify which subsets of the state to perform dropout on and
      # which ones to keep.
      shallow_filtered_substructure = nest.get_traverse_shallow_structure(
          self._dropout_state_filter, new_state)
      new_state = self._dropout(new_state, "state",
                                self._recurrent_state_noise,
                                self._state_keep_prob,
                                shallow_filtered_substructure)
    if _should_dropout(self._output_keep_prob):
      output = self._dropout(output, "output",
                             self._recurrent_output_noise,
                             self._output_keep_prob)
    return output, new_state 

def pre(self, inputs, scope=None):
        """Preprocess inputs to be used by the cell. Assumes [N, J, *]
        [x, u]"""
        is_train = self._is_train
        keep_prob = self._keep_prob
        gate_size = self._gate_size
        with tf.variable_scope(scope or "pre"):
            x, u, _, _ = tf.split(2, 4, tf.slice(inputs, [0, 0, gate_size], [-1, -1, -1]))  # [N, J, d]
            a_raw = linear([x * u], gate_size, True, scope='a_raw', var_on_cpu=self._var_on_cpu,
                           wd=self._wd, initializer=self._initializer)
            a = tf.sigmoid(a_raw - self._forget_bias, name='a')
            if keep_prob < 1.0:
                x = tf.cond(is_train, lambda: tf.nn.dropout(x, keep_prob), lambda: x)
                u = tf.cond(is_train, lambda: tf.nn.dropout(u, keep_prob), lambda: u)
            v_t = tf.nn.tanh(linear([x, u], self._num_units, True,
                             var_on_cpu=self._var_on_cpu, wd=self._wd, scope='v_raw'), name='v')
            new_inputs = tf.concat(2, [a, x, u, v_t])  # [N, J, 3*d + 1]
        return new_inputs 

def __call__(self, inputs, state, scope=None):
    """Run the cell with the declared dropouts."""
    def _should_dropout(p):
      return (not isinstance(p, float)) or p < 1

    if _should_dropout(self._input_keep_prob):
      inputs = self._dropout(inputs, "input",
                             self._recurrent_input_noise,
                             self._input_keep_prob)
    output, new_state = self._cell(inputs, state, scope=scope)
    if _should_dropout(self._state_keep_prob):
      # Identify which subsets of the state to perform dropout on and
      # which ones to keep.
      #shallow_filtered_substructure = nest.get_traverse_shallow_structure(
      #    self._dropout_state_filter, new_state)
      shallow_filtered_substructure = self._dropout_state_filter(new_state) # TODO: GK hack
      new_state = self._dropout(new_state, "state",
                                self._recurrent_state_noise,
                                self._state_keep_prob,
                                shallow_filtered_substructure)
    if _should_dropout(self._output_keep_prob):
      output = self._dropout(output, "output",
                             self._recurrent_output_noise,
                             self._output_keep_prob)
    return output, new_state 

def __call__(self, inputs, state, scope=None):
    if isinstance(self.state_size,
                  tuple) != isinstance(self._zoneout_prob, tuple):
      raise TypeError("Subdivided states need subdivided zoneouts.")
    if isinstance(self.state_size,
                  tuple) and len(tuple(self.state_size)
                                ) != len(tuple(self._zoneout_prob)):
      raise ValueError("State and zoneout need equally many parts.")
    output, new_state = self._cell(inputs, state, scope)
    if isinstance(self.state_size, tuple):
      if self._is_training:
        new_state = tuple(
            (1 - state_part_zoneout_prob) * dropout(
                new_state_part - state_part, (1 - state_part_zoneout_prob),
                seed=self._seed
            ) + state_part
            for new_state_part, state_part, state_part_zoneout_prob in
            zip(new_state, state, self._zoneout_prob)
        )
      else:
        new_state = tuple(
            state_part_zoneout_prob * state_part +
            (1 - state_part_zoneout_prob) * new_state_part
            for new_state_part, state_part, state_part_zoneout_prob in
            zip(new_state, state, self._zoneout_prob)
        )
      new_state = rnn_cell_impl.LSTMStateTuple(new_state[0], new_state[1])
    else:
      raise ValueError("Only states that are tuples are supported")
    return output, new_state 

def __call__(self, inputs, state, scope=None):
    """LSTM cell with layer normalization and recurrent dropout."""

    with vs.variable_scope(scope or type(self).__name__) as scope:  # LayerNormBasicLSTMCell  # pylint: disable=unused-variables
      c, h = state
      args = array_ops.concat(1, [inputs, h])
      concat = self._linear(args)

      i, j, f, o = array_ops.split(1, 4, concat)
      if self._layer_norm:
        i = self._norm(i, "input")
        j = self._norm(j, "transform")
        f = self._norm(f, "forget")
        o = self._norm(o, "output")

      g = self._activation(j)
      if (not isinstance(self._keep_prob, float)) or self._keep_prob < 1:
        g = nn_ops.dropout(g, self._keep_prob, seed=self._seed)

      new_c = (c * math_ops.sigmoid(f + self._forget_bias)
               + math_ops.sigmoid(i) * g)
      if self._layer_norm:
        new_c = self._norm(new_c, "state")
      new_h = self._activation(new_c) * math_ops.sigmoid(o)

      new_state = rnn_cell.LSTMStateTuple(new_c, new_h)
      return new_h, new_state 

def __call__(self, inputs, state, scope=None):
    """Run the cell with the declared zoneouts."""

    # compute output and new state as before
    output, new_state = self._cell(inputs, state, scope)

    # if either hidden state or memory cell zoneout is applied, then split state and process
    if self._has_hidden_state_zoneout or self._has_memory_cell_zoneout:
      # split state
      c_old, m_old = state
      c_new, m_new = new_state

      # apply zoneout to memory cell and hidden state
      c_and_m = []
      for s_old, s_new, p, has_zoneout in [(c_old, c_new, self._memory_cell_keep_prob,  self._has_memory_cell_zoneout), 
                                           (m_old, m_new, self._hidden_state_keep_prob, self._has_hidden_state_zoneout)]:
        if has_zoneout:
          if self._is_training:
            mask = nn_ops.dropout(array_ops.ones_like(s_new), p, seed=self._seed) * p # this should just random ops instead. See dropout code for how.
            s = ((1. - mask) * s_old) + (mask * s_new)
          else:
            s = ((1. - p) * s_old) + (p * s_new)
        else:
          s = s_new

        c_and_m.append(s)

      # package final results
      new_state = LSTMStateTuple(*c_and_m)
      output = new_state.h

    return output, new_state 

def __call__(self, inputs, state, scope=None):
        """Run the cell with the declared dropouts."""
        if (not isinstance(self._input_keep_prob, float) or
                self._input_keep_prob < 1):
            inputs = nn_ops.dropout(inputs, self._input_keep_prob, seed=self._seed)
        output, new_state = self._cell(inputs, state, scope)
        if (not isinstance(self._output_keep_prob, float) or
                self._output_keep_prob < 1):
            output = nn_ops.dropout(output, self._output_keep_prob, seed=self._seed)
        return output, new_state 

def _variational_recurrent_dropout_value(
      self, index, value, noise, keep_prob):
    """Performs dropout given the pre-calculated noise tensor."""
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob + noise

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = math_ops.floor(random_tensor)
    ret = math_ops.div(value, keep_prob) * binary_tensor
    ret.set_shape(value.get_shape())
    return ret 

def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob):
    """Decides whether to perform standard dropout or recurrent dropout."""
    if not self._variational_recurrent:
      def dropout(i, v):
        return nn_ops.dropout(
            v, keep_prob=keep_prob, seed=self._gen_seed(salt_prefix, i))
      return _enumerated_map_structure(dropout, values)
    else:
      def dropout(i, v, n):
        return self._variational_recurrent_dropout_value(i, v, n, keep_prob)
      return _enumerated_map_structure(dropout, values, recurrent_noise) 

def call(self, inputs, state):
        """2D Convolutional LSTM cell with (optional) normalization and recurrent dropout."""
        c, h = state
        tile_concat = isinstance(inputs, (list, tuple))
        if tile_concat:
            inputs, inputs_non_spatial = inputs
        args = array_ops.concat([inputs, h], -1)
        concat = self._conv2d(args)
        if tile_concat:
            concat = concat + self._dense(inputs_non_spatial)[:, None, None, :]

        if self._normalizer_fn and not self._separate_norms:
            concat = self._norm(concat, "input_transform_forget_output")
        i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=-1)
        if self._normalizer_fn and self._separate_norms:
            i = self._norm(i, "input")
            j = self._norm(j, "transform")
            f = self._norm(f, "forget")
            o = self._norm(o, "output")

        g = self._activation_fn(j)
        if (not isinstance(self._keep_prob, float)) or self._keep_prob < 1:
            g = nn_ops.dropout(g, self._keep_prob, seed=self._seed)

        new_c = (c * math_ops.sigmoid(f + self._forget_bias)
                 + math_ops.sigmoid(i) * g)
        if self._normalizer_fn:
            new_c = self._norm(new_c, "state")
        new_h = self._activation_fn(new_c) * math_ops.sigmoid(o)

        if self._skip_connection:
            new_h = array_ops.concat([new_h, inputs], axis=-1)

        new_state = rnn_cell_impl.LSTMStateTuple(new_c, new_h)
        return new_h, new_state 

def _default_dropout_state_filter_visitor(substate):
  if isinstance(substate, LSTMStateTuple):
    # Do not perform dropout on the memory state.
    return LSTMStateTuple(c=False, h=True)
  elif isinstance(substate, tensor_array_ops.TensorArray):
    return False
  return True 

def _variational_recurrent_dropout_value(
      self, index, value, noise, keep_prob):
    """Performs dropout given the pre-calculated noise tensor."""
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob + noise

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = math_ops.floor(random_tensor)
    ret = math_ops.div(value, keep_prob) * binary_tensor
    ret.set_shape(value.get_shape())
    return ret 

def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob,
               shallow_filtered_substructure=None):
    """Decides whether to perform standard dropout or recurrent dropout."""

    if shallow_filtered_substructure is None:
      # Put something so we traverse the entire structure; inside the
      # dropout function we check to see if leafs of this are bool or not.
      shallow_filtered_substructure = values

    if not self._variational_recurrent:
      def dropout(i, do_dropout, v):
        if not isinstance(do_dropout, bool) or do_dropout:
          return nn_ops.dropout(
              v, keep_prob=keep_prob, seed=self._gen_seed(salt_prefix, i))
        else:
          return v
      return _enumerated_map_structure_up_to(
          shallow_filtered_substructure, dropout,
          *[shallow_filtered_substructure, values])
    else:
      def dropout(i, do_dropout, v, n):
        if not isinstance(do_dropout, bool) or do_dropout:
          return self._variational_recurrent_dropout_value(i, v, n, keep_prob)
        else:
          return v
      return _enumerated_map_structure_up_to(
          shallow_filtered_substructure, dropout,
          *[shallow_filtered_substructure, values, recurrent_noise]) 

def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
                             seed=None):
        """Create a cell with added input and/or output dropout.

        Dropout is never used on the state.

        Args:
            cell: an RNNCell, a projection to output_size is added to it.
            input_keep_prob: unit Tensor or float between 0 and 1, input keep
                probability; if it is float and 1, no input dropout will be added.
            output_keep_prob: unit Tensor or float between 0 and 1, output keep
                probability; if it is float and 1, no output dropout will be added.
            seed: (optional) integer, the randomness seed.

        Raises:
            TypeError: if cell is not an RNNCell.
            ValueError: if keep_prob is not between 0 and 1.
        """
        if not isinstance(cell, RNNCell):
            raise TypeError("The parameter cell is not a RNNCell.")
        if (isinstance(input_keep_prob, float) and
                not (input_keep_prob >= 0.0 and input_keep_prob <= 1.0)):
            raise ValueError("Parameter input_keep_prob must be between 0 and 1: %d"
                                             % input_keep_prob)
        if (isinstance(output_keep_prob, float) and
                not (output_keep_prob >= 0.0 and output_keep_prob <= 1.0)):
            raise ValueError("Parameter output_keep_prob must be between 0 and 1: %d"
                                             % output_keep_prob)
        self._cell = cell
        self._input_keep_prob = input_keep_prob
        self._output_keep_prob = output_keep_prob
        self._seed = seed 

def _default_dropout_state_filter_visitor(substate):
  if isinstance(substate, LSTMStateTuple):
    # Do not perform dropout on the memory state.
    return LSTMStateTuple(c=False, h=True)
  elif isinstance(substate, tensor_array_ops.TensorArray):
    return False
  return True 

def _variational_recurrent_dropout_value(
      self, index, value, noise, keep_prob):
    """Performs dropout given the pre-calculated noise tensor."""
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob + noise

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = math_ops.floor(random_tensor)
    ret = math_ops.div(value, keep_prob) * binary_tensor
    ret.set_shape(value.get_shape())
    return ret 

def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob,
               shallow_filtered_substructure=None):
    """Decides whether to perform standard dropout or recurrent dropout."""

    if shallow_filtered_substructure is None:
      # Put something so we traverse the entire structure; inside the
      # dropout function we check to see if leafs of this are bool or not.
      shallow_filtered_substructure = values

    if not self._variational_recurrent:
      def dropout(i, do_dropout, v):
        if not isinstance(do_dropout, bool) or do_dropout:
          return nn_ops.dropout(
              v, keep_prob=keep_prob, seed=self._gen_seed(salt_prefix, i))
        else:
          return v
      return _enumerated_map_structure_up_to(
          shallow_filtered_substructure, dropout,
          *[shallow_filtered_substructure, values])
    else:
      def dropout(i, do_dropout, v, n):
        if not isinstance(do_dropout, bool) or do_dropout:
          return self._variational_recurrent_dropout_value(i, v, n, keep_prob)
        else:
          return v
      return _enumerated_map_structure_up_to(
          shallow_filtered_substructure, dropout,
          *[shallow_filtered_substructure, values, recurrent_noise]) 

def __init__(self, num_units, forget_bias=1.0,
               input_size=None, activation=math_ops.tanh,
               layer_norm=True, norm_gain=1.0, norm_shift=0.0,
               dropout_keep_prob=1.0, dropout_prob_seed=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.
    """

    if input_size is not None:
      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._g = norm_gain
    self._b = norm_shift 

def __call__(self, inputs, state, scope=None):
    """LSTM cell with layer normalization and recurrent dropout."""

    with vs.variable_scope(scope or "layer_norm_basic_lstm_cell"):
      c, h = state
      args = array_ops.concat([inputs, h], 1)
      concat = self._linear(args)

      i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
      if self._layer_norm:
        i = self._norm(i, "input")
        j = self._norm(j, "transform")
        f = self._norm(f, "forget")
        o = self._norm(o, "output")

      g = self._activation(j)
      if (not isinstance(self._keep_prob, float)) or self._keep_prob < 1:
        g = nn_ops.dropout(g, self._keep_prob, seed=self._seed)

      new_c = (c * math_ops.sigmoid(f + self._forget_bias)
               + math_ops.sigmoid(i) * g)
      if self._layer_norm:
        new_c = self._norm(new_c, "state")
      new_h = self._activation(new_c) * math_ops.sigmoid(o)

      new_state = core_rnn_cell.LSTMStateTuple(new_c, new_h)
      return new_h, new_state 

def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
               seed=None):
    """Create a cell with added input and/or output dropout.

    Dropout is never used on the state.

    Args:
      cell: an RNNCell, a projection to output_size is added to it.
      input_keep_prob: unit Tensor or float between 0 and 1, input keep
        probability; if it is float and 1, no input dropout will be added.
      output_keep_prob: unit Tensor or float between 0 and 1, output keep
        probability; if it is float and 1, no output dropout will be added.
      seed: (optional) integer, the randomness seed.

    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if keep_prob is not between 0 and 1.
    """
    if not isinstance(cell, RNNCell):
      raise TypeError("The parameter cell is not a RNNCell.")
    if (isinstance(input_keep_prob, float) and
        not (input_keep_prob >= 0.0 and input_keep_prob <= 1.0)):
      raise ValueError("Parameter input_keep_prob must be between 0 and 1: %d"
                       % input_keep_prob)
    if (isinstance(output_keep_prob, float) and
        not (output_keep_prob >= 0.0 and output_keep_prob <= 1.0)):
      raise ValueError("Parameter output_keep_prob must be between 0 and 1: %d"
                       % output_keep_prob)
    self._cell = cell
    self._input_keep_prob = input_keep_prob
    self._output_keep_prob = output_keep_prob
    self._seed = seed 

def __call__(self, inputs, state, scope=None):
    """Run the cell with the declared dropouts."""
    if (not isinstance(self._input_keep_prob, float) or
        self._input_keep_prob < 1):
      inputs = nn_ops.dropout(inputs, self._input_keep_prob, seed=self._seed)
    output, new_state = self._cell(inputs, state, scope)
    if (not isinstance(self._output_keep_prob, float) or
        self._output_keep_prob < 1):
      output = nn_ops.dropout(output, self._output_keep_prob, seed=self._seed)
    return output, new_state 

def __call__(self, inputs, state, scope=None):
    """Run the cell with the declared dropouts."""
    if (not isinstance(self._input_keep_prob, float) or
        self._input_keep_prob < 1):
      inputs = nn_ops.dropout(inputs, self._input_keep_prob, seed=self._seed)
    output, new_state = self._cell(inputs, state, scope)
    if (not isinstance(self._output_keep_prob, float) or
        self._output_keep_prob < 1):
      output = nn_ops.dropout(output, self._output_keep_prob, seed=self._seed)
    return output, new_state 

def _variational_recurrent_dropout_value(
      self, index, value, noise, keep_prob):
    """Performs dropout given the pre-calculated noise tensor."""
    # uniform [keep_prob, 1.0 + keep_prob)
    random_tensor = keep_prob + noise

    # 0. if [keep_prob, 1.0) and 1. if [1.0, 1.0 + keep_prob)
    binary_tensor = math_ops.floor(random_tensor)
    ret = math_ops.div(value, keep_prob) * binary_tensor
    ret.set_shape(value.get_shape())
    return ret 

def _dropout(self, values, salt_prefix, recurrent_noise, keep_prob):
    """Decides whether to perform standard dropout or recurrent dropout."""
    if not self._variational_recurrent:
      def dropout(i, v):
        return nn_ops.dropout(
            v, keep_prob=keep_prob, seed=self._gen_seed(salt_prefix, i))
      return _enumerated_map_structure(dropout, values)
    else:
      def dropout(i, v, n):
        return self._variational_recurrent_dropout_value(i, v, n, keep_prob)
      return _enumerated_map_structure(dropout, values, recurrent_noise) 

def __init__(self, num_units, forget_bias=1.0,
               input_size=None, activation=math_ops.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:
      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._g = norm_gain
    self._b = norm_shift
    self._reuse = reuse 

def __init__(self, num_units, forget_bias=1.0,
               input_size=None, activation=math_ops.tanh,
               layer_norm=True, norm_gain=1.0, norm_shift=0.0,
               dropout_keep_prob=1.0, dropout_prob_seed=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.
    """

    if input_size is not None:
      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._g = norm_gain
    self._b = norm_shift 

def __call__(self, inputs, state, scope=None):
    """LSTM cell with layer normalization and recurrent dropout."""

    with vs.variable_scope(scope or "layer_norm_basic_lstm_cell"):
      c, h = state
      args = array_ops.concat([inputs, h], 1)
      concat = self._linear(args)

      i, j, f, o = array_ops.split(value=concat, num_or_size_splits=4, axis=1)
      if self._layer_norm:
        i = self._norm(i, "input")
        j = self._norm(j, "transform")
        f = self._norm(f, "forget")
        o = self._norm(o, "output")

      g = self._activation(j)
      if (not isinstance(self._keep_prob, float)) or self._keep_prob < 1:
        g = nn_ops.dropout(g, self._keep_prob, seed=self._seed)

      new_c = (c * math_ops.sigmoid(f + self._forget_bias)
               + math_ops.sigmoid(i) * g)
      if self._layer_norm:
        new_c = self._norm(new_c, "state")
      new_h = self._activation(new_c) * math_ops.sigmoid(o)

      new_state = core_rnn_cell.LSTMStateTuple(new_c, new_h)
      return new_h, new_state 

def __init__(self, cell, input_keep_prob=1.0, output_keep_prob=1.0,
               seed=None):
    """Create a cell with added input and/or output dropout.

    Dropout is never used on the state.

    Args:
      cell: an RNNCell, a projection to output_size is added to it.
      input_keep_prob: unit Tensor or float between 0 and 1, input keep
        probability; if it is float and 1, no input dropout will be added.
      output_keep_prob: unit Tensor or float between 0 and 1, output keep
        probability; if it is float and 1, no output dropout will be added.
      seed: (optional) integer, the randomness seed.

    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if keep_prob is not between 0 and 1.
    """
    if not isinstance(cell, RNNCell):
      raise TypeError("The parameter cell is not a RNNCell.")
    if (isinstance(input_keep_prob, float) and
        not (input_keep_prob >= 0.0 and input_keep_prob <= 1.0)):
      raise ValueError("Parameter input_keep_prob must be between 0 and 1: %d"
                       % input_keep_prob)
    if (isinstance(output_keep_prob, float) and
        not (output_keep_prob >= 0.0 and output_keep_prob <= 1.0)):
      raise ValueError("Parameter output_keep_prob must be between 0 and 1: %d"
                       % output_keep_prob)
    self._cell = cell
    self._input_keep_prob = input_keep_prob
    self._output_keep_prob = output_keep_prob
    self._seed = seed 

def __call__(self, inputs, state, scope=None):
    """Run the cell with the declared dropouts."""
    if (not isinstance(self._input_keep_prob, float) or
        self._input_keep_prob < 1):
      inputs = nn_ops.dropout(inputs, self._input_keep_prob, seed=self._seed)
    output, new_state = self._cell(inputs, state, scope)
    if (not isinstance(self._output_keep_prob, float) or
        self._output_keep_prob < 1):
      output = nn_ops.dropout(output, self._output_keep_prob, seed=self._seed)
    return output, new_state