Python tensorflow.python.ops.rnn_cell.EmbeddingWrapper() Examples

def embedding_rnn_seq2seq(encoder_inputs, decoder_inputs, cell,
                          num_encoder_symbols, num_decoder_symbols,
                          embedding_size, output_projection=None,
                          feed_previous=False, dtype=dtypes.float32,
                          scope=None, beam_search=True, beam_size=10):
  """Embedding RNN sequence-to-sequence model.

  This model first embeds encoder_inputs by a newly created embedding (of shape
  [num_encoder_symbols x input_size]). Then it runs an RNN to encode
  embedded encoder_inputs into a state vector. Next, it embeds decoder_inputs
  by another newly created embedding (of shape [num_decoder_symbols x
  input_size]). Then it runs RNN decoder, initialized with the last
  encoder state, on embedded decoder_inputs.

  Args:
    encoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
    decoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
    cell: rnn_cell.RNNCell defining the cell function and size.
    num_encoder_symbols: Integer; number of symbols on the encoder side.
    num_decoder_symbols: Integer; number of symbols on the decoder side.
    embedding_size: Integer, the length of the embedding vector for each symbol.
    output_projection: None or a pair (W, B) of output projection weights and
      biases; W has shape [output_size x num_decoder_symbols] and B has
      shape [num_decoder_symbols]; if provided and feed_previous=True, each
      fed previous output will first be multiplied by W and added B.
    feed_previous: Boolean or scalar Boolean Tensor; if True, only the first
      of decoder_inputs will be used (the "GO" symbol), and all other decoder
      inputs will be taken from previous outputs (as in embedding_rnn_decoder).
      If False, decoder_inputs are used as given (the standard decoder case).
    dtype: The dtype of the initial state for both the encoder and encoder
      rnn cells (default: tf.float32).
    scope: VariableScope for the created subgraph; defaults to
      "embedding_rnn_seq2seq"

  Returns:
    A tuple of the form (outputs, state), where:
      outputs: A list of the same length as decoder_inputs of 2D Tensors with
        shape [batch_size x num_decoder_symbols] containing the generated
        outputs.
      state: The state of each decoder cell in each time-step. This is a list
        with length len(decoder_inputs) -- one item for each time-step.
        It is a 2D Tensor of shape [batch_size x cell.state_size].
  """
  with variable_scope.variable_scope(scope or "embedding_rnn_seq2seq"):
    # Encoder.
    encoder_cell = rnn_cell.EmbeddingWrapper(
        cell, embedding_classes=num_encoder_symbols,
        embedding_size=embedding_size)
    _, encoder_state = rnn.rnn(encoder_cell, encoder_inputs, dtype=dtype)

    # Decoder.
    if output_projection is None:
      cell = rnn_cell.OutputProjectionWrapper(cell, num_decoder_symbols)


    return embedding_rnn_decoder(
          decoder_inputs, encoder_state, cell, num_decoder_symbols,
          embedding_size, output_projection=output_projection,
          feed_previous=feed_previous, beam_search=beam_search, beam_size=beam_size)