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RNN decoder with embedding and a pure-decoding option.



  • decoder_inputs: A list of 1D batch-sized int32 Tensors (decoder inputs).
  • initial_state: 2D Tensor [batch_size x cell.state_size].
  • cell: tf.compat.v1.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.
  • 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 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.
  • scope: VariableScope for the created subgraph; defaults to "embedding_rnn_decoder".


A tuple of the form (outputs, state), where: outputs: A list of the same length as decoder_inputs of 2D Tensors. The output is of shape [batch_size x cell.output_size] when output_projection is not None (and represents the dense representation of predicted tokens). It is of shape [batch_size x num_decoder_symbols] when output_projection is None. state: The state of each decoder cell in each time-step. This is a list with length len(decoder_inputs) -- one item for each time-step. It is a 2D Tensor of shape [batch_size x cell.state_size].


  • ValueError: When output_projection has the wrong shape.