Python rnn_cell_impl._like_rnncell方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, output_size, activation=None, reuse=None):
    """Create a cell with output projection.

    Args:
      cell: an RNNCell, a projection to output_size is added to it.
      output_size: integer, the size of the output after projection.
      activation: (optional) an optional activation function.
      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.

    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if output_size is not positive.
    """
    super(OutputProjectionWrapper, self).__init__(_reuse=reuse)
    if not _like_rnncell(cell):
      raise TypeError("The parameter cell is not RNNCell.")
    if output_size < 1:
      raise ValueError("Parameter output_size must be > 0: %d." % output_size)
    self._cell = cell
    self._output_size = output_size
    self._activation = activation 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, helper, initial_state, output_layer=None):
		"""Initialize CustomDecoder.
		Args:
			cell: An `RNNCell` instance.
			helper: A `Helper` instance.
			initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
				The initial state of the RNNCell.
			output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
				`tf.layers.Dense`. Optional layer to apply to the RNN output prior
				to storing the result or sampling.
		Raises:
			TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
		"""
		if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
			raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
		if not isinstance(helper, helper_py.Helper):
			raise TypeError("helper must be a Helper, received: %s" % type(helper))
		if (output_layer is not None
				and not isinstance(output_layer, layers_base.Layer)):
			raise TypeError(
					"output_layer must be a Layer, received: %s" % type(output_layer))
		self._cell = cell
		self._helper = helper
		self._initial_state = initial_state
		self._output_layer = output_layer 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, output_size, activation=None, reuse=None):
    """Create a cell with output projection.
    Args:
      cell: an RNNCell, a projection to output_size is added to it.
      output_size: integer, the size of the output after projection.
      activation: (optional) an optional activation function.
      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.
    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if output_size is not positive.
    """
    super(OutputProjectionWrapper, self).__init__(_reuse=reuse)
    if not _like_rnncell(cell):
      raise TypeError("The parameter cell is not RNNCell.")
    if output_size < 1:
      raise ValueError("Parameter output_size must be > 0: %d." % output_size)
    self._cell = cell
    self._output_size = output_size
    self._activation = activation
    self._linear = None 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, helper, initial_state, latent_vector, output_layer=None):
        """Initialize BasicDecoder.
        Args:
          cell: An `RNNCell` instance.
          helper: A `Helper` instance.
          initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
            The initial state of the RNNCell.
          output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
            `tf.layers.Dense`.  Optional layer to apply to the RNN output prior
            to storing the result or sampling.
        Raises:
          TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
        """
        if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
            raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
        if not isinstance(helper, helper_py.Helper):
            raise TypeError("helper must be a Helper, received: %s" % type(helper))
        if (output_layer is not None and not isinstance(output_layer, layers_base.Layer)):
            raise TypeError("output_layer must be a Layer, received: %s" % type(output_layer))
        self._cell = cell
        self._helper = helper
        self._initial_state = initial_state
        self._output_layer = output_layer
        self._latent_vector = latent_vector 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, helper, initial_state, output_layer=None):
    """Initialize BasicDecoder.

    Args:
      cell: An `RNNCell` instance.
      helper: A `Helper` instance.
      initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
        The initial state of the RNNCell.
      output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
        `tf.layers.Dense`.  Optional layer to apply to the RNN output prior
        to storing the result or sampling.

    Raises:
      TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
    """
    if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
      raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
    if not isinstance(helper, helper_py.Helper):
      raise TypeError("helper must be a Helper, received: %s" % type(helper))
    if (output_layer is not None
        and not isinstance(output_layer, layers_base.Layer)):
      raise TypeError(
          "output_layer must be a Layer, received: %s" % type(output_layer))
    self._cell = cell
    self._helper = helper
    self._initial_state = initial_state
    self._output_layer = output_layer 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, helper, initial_state, latent_vector, output_layer=None):
        """Initialize BasicDecoder.
        Args:
          cell: An `RNNCell` instance.
          helper: A `Helper` instance.
          initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
            The initial state of the RNNCell.
          output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
            `tf.layers.Dense`.  Optional layer to apply to the RNN output prior
            to storing the result or sampling.
        Raises:
          TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
        """
        if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
            raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
        if not isinstance(helper, helper_py.Helper):
            raise TypeError("helper must be a Helper, received: %s" % type(helper))
        if (output_layer is not None and not isinstance(output_layer, layers_base.Layer)):
            raise TypeError("output_layer must be a Layer, received: %s" % type(output_layer))
        self._cell = cell
        self._helper = helper
        self._initial_state = initial_state
        self._output_layer = output_layer
        self._latent_vector = latent_vector
        self._intermediate_context_kl_loss = tf.zeros(shape=(helper.batch_size,))  # shape of (batch_size,)
        # CHANGE-1: Variable to keep adding the c_kl_losses from each timestep 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, embedding, use_context = True):
        super(WeanWrapper, self).__init__()
        if not _like_rnncell(cell):
            raise TypeError('The parameter cell is not RNNCell.')

        self._cell = cell
        self._embedding = embedding
        self._use_context = use_context
        self._linear = None 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, helper, initial_state, output_layer=None):
        """Initialize BasicVectorDecoder.

        Args:
            cell: An `RNNCell` instance.
            helper: A `Helper` instance.
            initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
                The initial state of the RNNCell.
            output_layer:An instance of `tf.layers.Layer`, i.e., `tf.layers.Dense`.
                If not provided, use 1 fc layer.

        Raises:
            TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
        """
        if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
            raise TypeError("cell must be an RNNCell, received: %s" % type(cell))
        if not isinstance(helper, helper_py.Helper):
            raise TypeError("helper must be a Helper, received: %s" % type(helper))
        if (output_layer is not None and not isinstance(output_layer, layers_base.Layer)):
            raise TypeError(
                "output_layer must be a Layer, received: %s" % type(output_layer))
        self._cell = cell
        self._helper = helper
        self._initial_state = initial_state
        if output_layer is None:
            self._output_layer = layer_core.Dense(2, use_bias=True,
                                                  name="stop_predictor")
        else:
            self._output_layer = output_layer 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self,
                 cell,
                 embedding,
                 start_tokens,
                 end_token,
                 initial_state,
                 beam_width,
                 output_layer=None,
                 length_penalty_weight=0.0):
        """Initialize BeamSearchDecoder.
        Args:
          cell: An `RNNCell` instance.
          embedding: A callable that takes a vector tensor of `ids` (argmax ids),
            or the `params` argument for `embedding_lookup`.
          start_tokens: `int32` vector shaped `[batch_size]`, the start tokens.
          end_token: `int32` scalar, the token that marks end of decoding.
          initial_state: A (possibly nested tuple of...) tensors and TensorArrays.
          output_layer: (Optional) An instance of `tf.layers.Layer`, i.e.,
            `tf.layers.Dense`.  Optional layer to apply to the RNN output prior
            to storing the result or sampling.
          length_penalty_weight: Float weight to penalize length. Disabled with 0.0.
        Raises:
          TypeError: if `cell` is not an instance of `RNNCell`,
            or `output_layer` is not an instance of `tf.layers.Layer`.
          ValueError: If `start_tokens` is not a vector or
            `end_token` is not a scalar.
        """
        if not rnn_cell_impl._like_rnncell(cell):
            raise TypeError(
                "cell must be an RNNCell, received: %s" % type(cell))
        if (output_layer is not None
                and not isinstance(output_layer, layers_base.Layer)):
            raise TypeError(
                "output_layer must be a Layer, received: %s" % type(output_layer))
        self._cell = cell
        self._output_layer = output_layer

        if callable(embedding):
            self._embedding_fn = embedding
        else:
            self._embedding_fn = (
                lambda ids: embedding_ops.embedding_lookup(embedding, ids))

        self._start_tokens = ops.convert_to_tensor(
            start_tokens, dtype=dtypes.int32, name="start_tokens")
        if self._start_tokens.get_shape().ndims != 1:
            raise ValueError("start_tokens must be a vector")
        self._end_token = ops.convert_to_tensor(
            end_token, dtype=dtypes.int32, name="end_token")
        if self._end_token.get_shape().ndims != 0:
            raise ValueError("end_token must be a scalar")

        self._batch_size = tf.size(start_tokens)
        self._beam_width = beam_width
        self._length_penalty_weight = length_penalty_weight
        self._initial_cell_state = nest.map_structure(
            self._maybe_split_batch_beams,
            initial_state, self._cell.state_size)
        self._start_tokens = tf.tile(
            tf.expand_dims(self._start_tokens, 1), [1, self._beam_width])
        self._start_inputs = self._embedding_fn(self._start_tokens) 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, attn_length, attn_size=None, attn_vec_size=None,
               input_size=None, state_is_tuple=True, reuse=None):
    """Create a cell with attention.

    Args:
      cell: an RNNCell, an attention is added to it.
      attn_length: integer, the size of an attention window.
      attn_size: integer, the size of an attention vector. Equal to
          cell.output_size by default.
      attn_vec_size: integer, the number of convolutional features calculated
          on attention state and a size of the hidden layer built from
          base cell state. Equal attn_size to by default.
      input_size: integer, the size of a hidden linear layer,
          built from inputs and attention. Derived from the input tensor
          by default.
      state_is_tuple: If True, accepted and returned states are n-tuples, where
        `n = len(cells)`.  By default (False), the states are all
        concatenated along the column axis.
      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.

    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if cell returns a state tuple but the flag
          `state_is_tuple` is `False` or if attn_length is zero or less.
    """
    super(AttentionCellWrapper, self).__init__(_reuse=reuse)
    if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
      raise TypeError("The parameter cell is not RNNCell.")
    if nest.is_sequence(cell.state_size) and not state_is_tuple:
      raise ValueError("Cell returns tuple of states, but the flag "
                       "state_is_tuple is not set. State size is: %s"
                       % str(cell.state_size))
    if attn_length <= 0:
      raise ValueError("attn_length should be greater than zero, got %s"
                       % str(attn_length))
    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 attn_size is None:
      attn_size = cell.output_size
    if attn_vec_size is None:
      attn_vec_size = attn_size
    self._state_is_tuple = state_is_tuple
    self._cell = cell
    self._attn_vec_size = attn_vec_size
    self._input_size = input_size
    self._attn_size = attn_size
    self._attn_length = attn_length
    self._reuse = reuse 

# 需要導入模塊: from tensorflow.python.ops import rnn_cell_impl [as 別名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import _like_rnncell [as 別名]
def __init__(self, cell, attn_length, attn_size=None, attn_vec_size=None,
               input_size=None, state_is_tuple=True, reuse=None):
    """Create a cell with attention.
    Args:
      cell: an RNNCell, an attention is added to it.
      attn_length: integer, the size of an attention window.
      attn_size: integer, the size of an attention vector. Equal to
          cell.output_size by default.
      attn_vec_size: integer, the number of convolutional features calculated
          on attention state and a size of the hidden layer built from
          base cell state. Equal attn_size to by default.
      input_size: integer, the size of a hidden linear layer,
          built from inputs and attention. Derived from the input tensor
          by default.
      state_is_tuple: If True, accepted and returned states are n-tuples, where
        `n = len(cells)`.  By default (False), the states are all
        concatenated along the column axis.
      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.
    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if cell returns a state tuple but the flag
          `state_is_tuple` is `False` or if attn_length is zero or less.
    """
    super(AttentionCellWrapper, self).__init__(_reuse=reuse)
    if not rnn_cell_impl._like_rnncell(cell):  # pylint: disable=protected-access
      raise TypeError("The parameter cell is not RNNCell.")
    if nest.is_sequence(cell.state_size) and not state_is_tuple:
      raise ValueError("Cell returns tuple of states, but the flag "
                       "state_is_tuple is not set. State size is: %s"
                       % str(cell.state_size))
    if attn_length <= 0:
      raise ValueError("attn_length should be greater than zero, got %s"
                       % str(attn_length))
    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 attn_size is None:
      attn_size = cell.output_size
    if attn_vec_size is None:
      attn_vec_size = attn_size
    self._state_is_tuple = state_is_tuple
    self._cell = cell
    self._attn_vec_size = attn_vec_size
    self._input_size = input_size
    self._attn_size = attn_size
    self._attn_length = attn_length
    self._reuse = reuse
    self._linear1 = None
    self._linear2 = None
    self._linear3 = None