tf.contrib.legacy_seq2seq.one2many_rnn_seq2seq

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One-to-many RNN sequence-to-sequence model (multi-task).

tf.contrib.legacy_seq2seq.one2many_rnn_seq2seq(
    encoder_inputs, decoder_inputs_dict, enc_cell, dec_cells_dict,
    num_encoder_symbols, num_decoder_symbols_dict, embedding_size,
    feed_previous=False, dtype=None, scope=None
)

This is a multi-task sequence-to-sequence model with one encoder and multiple decoders. Reference to multi-task sequence-to-sequence learning can be found here: http://arxiv.org/abs/1511.06114

encoder_inputs A list of 1D int32 Tensors of shape [batch_size].
decoder_inputs_dict A dictionary mapping decoder name (string) to the corresponding decoder_inputs; each decoder_inputs is a list of 1D Tensors of shape [batch_size]; num_decoders is defined as len(decoder_inputs_dict).
enc_cell tf.compat.v1.nn.rnn_cell.RNNCell defining the encoder cell function and size.
dec_cells_dict A dictionary mapping encoder name (string) to an instance of tf.nn.rnn_cell.RNNCell.
num_encoder_symbols Integer; number of symbols on the encoder side.
num_decoder_symbols_dict A dictionary mapping decoder name (string) to an integer specifying number of symbols for the corresponding decoder; len(num_decoder_symbols_dict) must be equal to num_decoders.
embedding_size Integer, the length of the embedding vector for each symbol.
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 state for both the encoder and encoder rnn cells (default: tf.float32).
scope VariableScope for the created subgraph; defaults to "one2many_rnn_seq2seq"

A tuple of the form (outputs_dict, state_dict), where: outputs_dict: A mapping from decoder name (string) to a list of the same length as decoder_inputs_dict[name]; each element in the list is a 2D Tensors with shape [batch_size x num_decoder_symbol_list[name]] containing the generated outputs. state_dict: A mapping from decoder name (string) to the final state of the corresponding decoder RNN; it is a 2D Tensor of shape [batch_size x cell.state_size].

TypeError if enc_cell or any of the dec_cells are not instances of RNNCell.
ValueError if len(dec_cells) != len(decoder_inputs_dict).