embedding_attention_decoder

Summary

• As name implies this is decoder
• This is almost proxy to attention_decoder

Code with comments

def embedding_attention_decoder(decoder_inputs,
                                initial_state,
                                attention_states,
                                cell,
                                num_symbols,
                                embedding_size,
                                num_heads=1,
                                output_size=None,
                                output_projection=None,
                                feed_previous=False,
                                update_embedding_for_previous=True,
                                dtype=None,
                                scope=None,
                                initial_state_attention=False,
                                beam_search=True,
                                beam_size=10):
  """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: core_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.
  """
  if output_size is None:
    output_size = cell.output_size
  if output_projection is not None:
    proj_biases = ops.convert_to_tensor(output_projection[1], dtype=dtype)
    proj_biases.get_shape().assert_is_compatible_with([num_symbols])

  with variable_scope.variable_scope(
      scope or "embedding_attention_decoder", dtype=dtype) as scope:

    embedding = variable_scope.get_variable("embedding",
                                            [num_symbols, embedding_size])

    if beam_search:
        loop_function = _extract_beam_search(
            embedding, beam_size, num_symbols, embedding_size, output_projection,
            update_embedding_for_previous) #if feed_previous else None
    else:
        loop_function = _extract_argmax_and_embed(
            embedding, output_projection,
            update_embedding_for_previous) if feed_previous else None
    emb_inp = [
        embedding_ops.embedding_lookup(embedding, i) for i in decoder_inputs]
    if beam_search:
        return beam_attention_decoder(
            emb_inp, initial_state, attention_states, cell, output_size=output_size,
            num_heads=num_heads, loop_function=loop_function,
            initial_state_attention=initial_state_attention, output_projection=output_projection,
            beam_size=beam_size)
    else:

        return attention_decoder(
            emb_inp, initial_state, attention_states, cell, output_size=output_size,
            num_heads=num_heads, loop_function=loop_function,
            initial_state_attention=initial_state_attention)