# tf.contrib.legacy_seq2seq.embedding_attention_decoder

tf.contrib.legacy_seq2seq.embedding_attention_decoder(
decoder_inputs,
initial_state,
attention_states,
cell,
num_symbols,
embedding_size,
output_size=None,
output_projection=None,
feed_previous=False,
update_embedding_for_previous=True,
dtype=None,
scope=None,
initial_state_attention=False
)


RNN decoder with embedding and attention and a pure-decoding option.

#### Args:

• decoder_inputs: A list of 1D batch-sized int32 Tensors (decoder inputs).
• initial_state: 2D Tensor [batch_size x cell.state_size].
• attention_states: 3D Tensor [batch_size x attn_length x attn_size].
• cell: tf.nn.rnn_cell.RNNCell defining the cell function.
• num_symbols: Integer, how many symbols come into the embedding.
• embedding_size: Integer, the length of the embedding vector for each symbol.
• num_heads: Number of attention heads that read from attention_states.
• output_size: Size of the output vectors; if None, use output_size.
• 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; if True, only the first of decoder_inputs will be used (the "GO" symbol), and all other decoder inputs will be generated by: next = embedding_lookup(embedding, argmax(previous_output)), In effect, this implements a greedy decoder. It can also be used during training to emulate http://arxiv.org/abs/1506.03099. If False, decoder_inputs are used as given (the standard decoder case).
• update_embedding_for_previous: Boolean; if False and feed_previous=True, only the embedding for the first symbol of decoder_inputs (the "GO" symbol) will be updated by back propagation. Embeddings for the symbols generated from the decoder itself remain unchanged. This parameter has no effect if feed_previous=False.
• dtype: The dtype to use for the RNN initial states (default: tf.float32).
• scope: VariableScope for the created subgraph; defaults to "embedding_attention_decoder".
• initial_state_attention: If False (default), initial attentions are zero. If True, initialize the attentions from the initial state and attention states -- useful when we wish to resume decoding from a previously stored decoder 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 output_size] 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].

#### Raises:

• ValueError: When output_projection has the wrong shape.