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.