tf.contrib.legacy_seq2seq.embedding_attention_seq2seq(
encoder_inputs,
decoder_inputs,
cell,
num_encoder_symbols,
num_decoder_symbols,
embedding_size,
num_heads=1,
output_projection=None,
feed_previous=False,
dtype=None,
scope=None,
initial_state_attention=False
)
Defined in tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py
.
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].