tf.contrib.seq2seq.attention_decoder_fn_inference(output_fn, encoder_state, attention_keys, attention_values, attention_score_fn, attention_construct_fn, embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, num_decoder_symbols, dtype=tf.int32, name=None)

tf.contrib.seq2seq.attention_decoder_fn_inference(output_fn, encoder_state, attention_keys, attention_values, attention_score_fn, attention_construct_fn, embeddings, start_of_sequence_id, end_of_sequence_id, maximum_length, num_decoder_symbols, dtype=tf.int32, name=None)

Attentional decoder function for dynamic_rnn_decoder during inference.

The attention_decoder_fn_inference is a simple inference function for a sequence-to-sequence model. It should be used when dynamic_rnn_decoder is in the inference mode.

The attention_decoder_fn_inference is called with user arguments and returns the decoder_fn, which can be passed to the dynamic_rnn_decoder, such that

dynamic_fn_inference = attention_decoder_fn_inference(...)
outputs_inference, state_inference = dynamic_rnn_decoder(
    decoder_fn=dynamic_fn_inference, ...)

Further usage can be found in the kernel_tests/seq2seq_test.py.

Args:

• output_fn: An output function to project your cell_output onto class logits.

An example of an output function;

tf.variable_scope("decoder") as varscope
  output_fn = lambda x: layers.linear(x, num_decoder_symbols,
                                      scope=varscope)

  outputs_train, state_train = seq2seq.dynamic_rnn_decoder(...)
  logits_train = output_fn(outputs_train)

  varscope.reuse_variables()
  logits_inference, state_inference = seq2seq.dynamic_rnn_decoder(
      output_fn=output_fn, ...)

If None is supplied it will act as an identity function, which might be wanted when using the RNNCell OutputProjectionWrapper.

• encoder_state: The encoded state to initialize the dynamic_rnn_decoder.
• attention_keys: to be compared with target states.
• attention_values: to be used to construct context vectors.
• attention_score_fn: to compute similarity between key and target states.
• attention_construct_fn: to build attention states.
• embeddings: The embeddings matrix used for the decoder sized [num_decoder_symbols, embedding_size].
• start_of_sequence_id: The start of sequence ID in the decoder embeddings.
• end_of_sequence_id: The end of sequence ID in the decoder embeddings.
• maximum_length: The maximum allowed of time steps to decode.
• num_decoder_symbols: The number of classes to decode at each time step.
• dtype: (default: dtypes.int32) The default data type to use when handling integer objects.
• name: (default: None) NameScope for the decoder function; defaults to "attention_decoder_fn_inference"

Returns:

A decoder function with the required interface of dynamic_rnn_decoder intended for inference.

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