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author | A. Unique TensorFlower <gardener@tensorflow.org> | 2016-12-04 21:05:14 -0800 |
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committer | TensorFlower Gardener <gardener@tensorflow.org> | 2016-12-04 21:23:37 -0800 |
commit | 70a517936a16e95b5521ef2458fd35b23658f6bc (patch) | |
tree | 2e94944bbba8d0331f6079bc98f6e0d9fcca9e8d /tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.legacy_seq2seq.one2many_rnn_seq2seq.md | |
parent | c5e21b06986d717f3fde68e3664ebc650d934377 (diff) |
Update generated Python Op docs.
Change: 141011628
Diffstat (limited to 'tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.legacy_seq2seq.one2many_rnn_seq2seq.md')
-rw-r--r-- | tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.legacy_seq2seq.one2many_rnn_seq2seq.md | 43 |
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diff --git a/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.legacy_seq2seq.one2many_rnn_seq2seq.md b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.legacy_seq2seq.one2many_rnn_seq2seq.md new file mode 100644 index 0000000000..fd297eae06 --- /dev/null +++ b/tensorflow/g3doc/api_docs/python/functions_and_classes/shard4/tf.contrib.legacy_seq2seq.one2many_rnn_seq2seq.md @@ -0,0 +1,43 @@ +### `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)` {#one2many_rnn_seq2seq} + +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: + + +* <b>`encoder_inputs`</b>: A list of 1D int32 Tensors of shape [batch_size]. +* <b>`decoder_inputs_dict`</b>: 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). +* <b>`cell`</b>: rnn_cell.RNNCell defining the cell function and size. +* <b>`num_encoder_symbols`</b>: Integer; number of symbols on the encoder side. +* <b>`num_decoder_symbols_dict`</b>: 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. +* <b>`embedding_size`</b>: Integer, the length of the embedding vector for each symbol. +* <b>`feed_previous`</b>: 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). +* <b>`dtype`</b>: The dtype of the initial state for both the encoder and encoder + rnn cells (default: tf.float32). +* <b>`scope`</b>: VariableScope for the created subgraph; defaults to + "one2many_rnn_seq2seq" + +##### Returns: + + A tuple of the form (outputs_dict, state_dict), where: + +* <b>`outputs_dict`</b>: 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. +* <b>`state_dict`</b>: 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]. + |