## Decoder Reference

The following tables list available decoder classes and parameters.

`BasicDecoder`

A Recurrent Neural Network decoder that produces a sequence of output tokens.

Name | Default | Description |
---|---|---|

`max_decode_length` |
`100` |
Stop decoding early if a sequence reaches this length threshold. |

`rnn_cell.cell_class` |
`BasicLSTMCell` |
The class of the rnn cell. Cell classes can be fully defined (e.g. `tensorflow.contrib.rnn.BasicRNNCell` ) or must be in `tf.contrib.rnn` or `seq2seq.contrib.rnn_cell` . |

`rnn_cell.cell_params` |
`{"num_units": 128}` |
A dictionary of parameters to pass to the cell class constructor. |

`rnn_cell.dropout_input_keep_prob` |
`1.0` |
Apply dropout to the (non-recurrent) inputs of each RNN layer using this keep probability. A value of `1.0` disables dropout. |

`rnn_cell.dropout_output_keep_prob` |
`1.0` |
Apply dropout to the (non-recurrent) outputs of each RNN layer using this keep probability. A value of `1.0` disables dropout. |

`rnn_cell.num_layers` |
`1` |
Number of RNN layers. |

`rnn_cell.residual_connections` |
`False` |
If true, add residual connections between all RNN layers in the encoder. |

`AttentionDecoder`

A Recurrent Neural Network decoder that produces a sequence of output tokens using an attention mechanisms over its inputs. Parameters are the same as for `BasicDecoder`

.