Python rnn_cell_impl.assert_like_rnncell方法代码示例

# 需要导入模块: from tensorflow.python.ops import rnn_cell_impl [as 别名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import assert_like_rnncell [as 别名]
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.
          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 

# 需要导入模块: from tensorflow.python.ops import rnn_cell_impl [as 别名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import assert_like_rnncell [as 别名]
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.
		"""
		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 

# 需要导入模块: from tensorflow.python.ops import rnn_cell_impl [as 别名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import assert_like_rnncell [as 别名]
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.
		"""
		# 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 

# 需要导入模块: from tensorflow.python.ops import rnn_cell_impl [as 别名]
# 或者: from tensorflow.python.ops.rnn_cell_impl import assert_like_rnncell [as 别名]
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