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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.

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.compat.v1.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.

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].