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

The following are 5 code examples of tensorflow.python.ops.rnn_cell_impl.assert_like_rnncell(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.rnn_cell_impl , or try the search function .
Example #1
Source File: custom_decoder.py    From linguistic-style-transfer with Apache License 2.0 6 votes vote down vote up 
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.
          latent_vector: A hidden state intended to be concatenated with the
            hidden state at every time-step of decoding
          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.
        """
        rnn_cell_impl.assert_like_rnncell("cell must be an RNNCell, received: %s" % type(cell), 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
Example #2
Source File: custom_decoder.py    From Tacotron-2 with MIT License 6 votes vote down vote up 
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.
		"""
		rnn_cell_impl.assert_like_rnncell(type(cell), 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
Example #3
Source File: custom_decoder.py    From tacotron2-mandarin-griffin-lim with MIT License 6 votes vote down vote up 
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.
		"""
		rnn_cell_impl.assert_like_rnncell(type(cell), 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
Example #4
Source File: custom_decoder.py    From style-token_tacotron2 with MIT License 6 votes vote down vote up 
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.
		"""
		# rnn_cell_impl.assert_like_rnncell(type(cell), 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
Example #5
Source File: tacotron_decoder.py    From OpenSeq2Seq with Apache License 2.0 4 votes vote down vote up 
def __init__(
      self,
      decoder_cell,
      helper,
      initial_decoder_state,
      attention_type,
      spec_layer,
      stop_token_layer,
      prenet=None,
      dtype=dtypes.float32,
      train=True
  ):
    """Initialize TacotronDecoder.

    Args:
      decoder_cell: An `RNNCell` instance.
      helper: A `Helper` instance.
      initial_decoder_state: A (possibly nested tuple of...) tensors and
        TensorArrays. The initial state of the RNNCell.
      attention_type: The type of attention used
      stop_token_layer: An instance of `tf.layers.Layer`, i.e.,
        `tf.layers.Dense`. Stop token layer to apply to the RNN output to
        predict when to stop the decoder
      spec_layer: An instance of `tf.layers.Layer`, i.e.,
        `tf.layers.Dense`. Output layer to apply to the RNN output to map
        the ressult to a spectrogram
      prenet: The prenet to apply to inputs

    Raises:
      TypeError: if `cell`, `helper` or `output_layer` have an incorrect type.
    """
    rnn_cell_impl.assert_like_rnncell("cell", decoder_cell)
    if not isinstance(helper, helper_py.Helper):
      raise TypeError("helper must be a Helper, received: %s" % type(helper))
    if (
        spec_layer is not None and
        not isinstance(spec_layer, layers_base.Layer)
    ):
      raise TypeError(
          "spec_layer must be a Layer, received: %s" % type(spec_layer)
      )
    self._decoder_cell = decoder_cell
    self._helper = helper
    self._decoder_initial_state = initial_decoder_state
    self._spec_layer = spec_layer
    self._stop_token_layer = stop_token_layer
    self._attention_type = attention_type
    self._dtype = dtype
    self._prenet = prenet

    if train:
      self._spec_layer = None
      self._stop_token_layer = None