# tf.contrib.legacy_seq2seq.embedding_attention_seq2seq

tf.contrib.legacy_seq2seq.embedding_attention_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
num_encoder_symbols,
num_decoder_symbols,
embedding_size,
output_projection=None,
feed_previous=False,
dtype=None,
scope=None,
initial_state_attention=False
)


Embedding sequence-to-sequence model with attention.

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. It keeps the outputs of this RNN at every step to use for attention later. Next, it embeds decoder_inputs by another newly created embedding (of shape [num_decoder_symbols x input_size]). Then it runs attention decoder, initialized with the last encoder state, on embedded decoder_inputs and attending to encoder outputs.

#### 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_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.
• num_heads: Number of attention heads that read from attention_states.
• 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 RNN state (default: tf.float32).
• scope: VariableScope for the created subgraph; defaults to "embedding_attention_seq2seq".
• initial_state_attention: If False (default), initial attentions are zero. If True, initialize the attentions from the initial state and attention states.

#### 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 at the final time-step. It is a 2D Tensor of shape [batch_size x cell.state_size].