Python tensorflow.python.ops.rnn_cell_impl._RNNCell() Examples

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Example #1
Source File: core_rnn_cell_impl.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def __init__(self, cell, output_size):
    """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.

    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if output_size is not positive.
    """
    if not isinstance(cell, RNNCell):
      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
Example #2
Source File: core_rnn_cell_impl.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def __init__(self, cell, num_proj, input_size=None):
    """Create a cell with input projection.

    Args:
      cell: an RNNCell, a projection of inputs is added before it.
      num_proj: Python integer.  The dimension to project to.
      input_size: Deprecated and unused.

    Raises:
      TypeError: if cell is not an RNNCell.
    """
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    if not isinstance(cell, RNNCell):
      raise TypeError("The parameter cell is not RNNCell.")
    self._cell = cell
    self._num_proj = num_proj
Example #3
Source File: core_rnn_cell_impl.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
def __init__(self, cell, embedding_classes, embedding_size, initializer=None):
    """Create a cell with an added input embedding.

    Args:
      cell: an RNNCell, an embedding will be put before its inputs.
      embedding_classes: integer, how many symbols will be embedded.
      embedding_size: integer, the size of the vectors we embed into.
      initializer: an initializer to use when creating the embedding;
        if None, the initializer from variable scope or a default one is used.

    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if embedding_classes is not positive.
    """
    if not isinstance(cell, RNNCell):
      raise TypeError("The parameter cell is not RNNCell.")
    if embedding_classes <= 0 or embedding_size <= 0:
      raise ValueError("Both embedding_classes and embedding_size must be > 0: "
                       "%d, %d." % (embedding_classes, embedding_size))
    self._cell = cell
    self._embedding_classes = embedding_classes
    self._embedding_size = embedding_size
    self._initializer = initializer
Example #4
Source File: core_rnn_cell_impl.py    From keras-lambda with MIT License 6 votes vote down vote up 
def __init__(self, cell, output_size):
    """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.

    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if output_size is not positive.
    """
    if not isinstance(cell, RNNCell):
      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
Example #5
Source File: core_rnn_cell_impl.py    From keras-lambda with MIT License 6 votes vote down vote up 
def __init__(self, cell, num_proj, input_size=None):
    """Create a cell with input projection.

    Args:
      cell: an RNNCell, a projection of inputs is added before it.
      num_proj: Python integer.  The dimension to project to.
      input_size: Deprecated and unused.

    Raises:
      TypeError: if cell is not an RNNCell.
    """
    if input_size is not None:
      logging.warn("%s: The input_size parameter is deprecated.", self)
    if not isinstance(cell, RNNCell):
      raise TypeError("The parameter cell is not RNNCell.")
    self._cell = cell
    self._num_proj = num_proj
Example #6
Source File: core_rnn_cell_impl.py    From keras-lambda with MIT License 6 votes vote down vote up 
def __init__(self, cell, embedding_classes, embedding_size, initializer=None):
    """Create a cell with an added input embedding.

    Args:
      cell: an RNNCell, an embedding will be put before its inputs.
      embedding_classes: integer, how many symbols will be embedded.
      embedding_size: integer, the size of the vectors we embed into.
      initializer: an initializer to use when creating the embedding;
        if None, the initializer from variable scope or a default one is used.

    Raises:
      TypeError: if cell is not an RNNCell.
      ValueError: if embedding_classes is not positive.
    """
    if not isinstance(cell, RNNCell):
      raise TypeError("The parameter cell is not RNNCell.")
    if embedding_classes <= 0 or embedding_size <= 0:
      raise ValueError("Both embedding_classes and embedding_size must be > 0: "
                       "%d, %d." % (embedding_classes, embedding_size))
    self._cell = cell
    self._embedding_classes = embedding_classes
    self._embedding_size = embedding_size
    self._initializer = initializer
Example #7
Source File: core_rnn_cell_impl.py    From auto-alt-text-lambda-api with MIT License 5 votes vote down vote up 
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
Example #8
Source File: rnn.py    From hart with GNU General Public License v3.0 5 votes vote down vote up 
def __init__(self, cell, zoneout_prob, is_training=True, seed=None):
        if not isinstance(cell, RNNCell):
            raise TypeError("The parameter cell is not an RNNCell.")
        if (isinstance(zoneout_prob, float) and
                not (zoneout_prob >= 0.0 and zoneout_prob <= 1.0)):
            raise ValueError("Parameter zoneout_prob must be between 0 and 1: %d"
                             % zoneout_prob)
        self._cell = cell
        self._zoneout_prob = zoneout_prob
        self._seed = seed
        self.is_training = tf.convert_to_tensor(is_training)
Example #9
Source File: core_rnn_cell_impl.py    From keras-lambda with MIT License 5 votes vote down vote up 
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