Reference: Encoders

Encoder Reference

All encoders inherit from the abstract Encoder defined in seq2seq.encoders.encoder and receive params, mode arguments at instantiation time. Available hyperparameters vary by encoder class.

`UnidirectionalRNNEncoder`

Name	Default	Description
`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 RNN layers in the encoder.

`BidirectionalRNNEncoder`

Same as the UnidirectionalRNNEncoder. The same cell is used for forward and backward RNNs.

`StackBidirectionalRNNEncoder`

Same as the UnidirectionalRNNEncoder. The same cell is used for forward and backward RNNs.

`PoolingEncoder`

An encoder that pools over embeddings, as described in https://arxiv.org/abs/1611.02344. The encoder supports optional positions embeddings and a configurable pooling window.

Name	Default	Description
`pooling_fn`	`tensorflow.layers.average_pooling1d`	The 1-d pooling function to use, e.g. `tensorflow.layers.average_pooling1d`.
`pool_size`	`5`	The pooling window, passed as `pool_size` to the pooling function.
`strides`	`1`	The stride during pooling, passed as `strides` the pooling function.
`position_embeddings.enable`	`True`	If true, add position embeddings to the inputs before pooling.
`position_embeddings.combiner_fn`	`tensorflow.add`	Function used to combine the position embeddings with the inputs. For example, `tensorflow.add`.
`position_embeddings.num_positions`	`100`	Size of the position embedding matrix. This should be set to the maximum sequence length of the inputs.

`InceptionV3Encoder`

This encoder is experimental. This encoder puts the image through an InceptionV3 network and uses the last hidden layer before the logits as the feature representation.

Name	Default	Description
`resize_height`	`299`	Resize the image to this height before feeding it into the convolutional network.
`resize_width`	`299`	Resize the image to this width before feeding it into the convolutional network.