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

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

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

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

Args:

• encoder_inputs: A list of 1D int32 Tensors of shape [batch_size].
• decoder_inputs_dict: A dictionany 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).
• cell: core_rnn_cell.RNNCell defining the cell function and size.
• 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"

Returns:

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].

Defined in tensorflow/contrib/legacy_seq2seq/python/ops/seq2seq.py.