# tf.contrib.legacy_seq2seq.embedding_tied_rnn_seq2seq

tf.contrib.legacy_seq2seq.embedding_tied_rnn_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
num_symbols,
embedding_size,
num_decoder_symbols=None,
output_projection=None,
feed_previous=False,
dtype=None,
scope=None
)


Embedding RNN sequence-to-sequence model with tied (shared) parameters.

This model first embeds encoder_inputs by a newly created embedding (of shape [num_symbols x input_size]). Then it runs an RNN to encode embedded encoder_inputs into a state vector. Next, it embeds decoder_inputs using the same embedding. Then it runs RNN decoder, initialized with the last encoder state, on embedded decoder_inputs. The decoder output is over symbols from 0 to num_decoder_symbols - 1 if num_decoder_symbols is none; otherwise it is over 0 to num_symbols - 1.

#### 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: tf.nn.rnn_cell.RNNCell defining the cell function and size.
• num_symbols: Integer; number of symbols for both encoder and decoder.
• embedding_size: Integer, the length of the embedding vector for each symbol.
• num_decoder_symbols: Integer; number of output symbols for decoder. If provided, the decoder output is over symbols 0 to num_decoder_symbols - 1. Otherwise, decoder output is over symbols 0 to num_symbols - 1. Note that this assumes that the vocabulary is set up such that the first num_decoder_symbols of num_symbols are part of decoding.
• output_projection: None or a pair (W, B) of output projection weights and biases; W has shape [output_size x num_symbols] and B has shape [num_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 to use for the initial RNN states (default: tf.float32).
• scope: VariableScope for the created subgraph; defaults to "embedding_tied_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 output_symbols] containing the generated outputs where output_symbols = num_decoder_symbols if num_decoder_symbols is not None otherwise output_symbols = num_symbols. * state: The state of each decoder cell at the final time-step. It is a 2D Tensor of shape [batch_size x cell.state_size].

#### Raises:

• ValueError: When output_projection has the wrong shape.