org.platanios.tensorflow.api.ops.seq2seq.decoders
RNN cell to use for decoding.
Initial RNN cell state to use for starting the decoding process.
Basic RNN decoder helper to use.
Output layer to use that is applied at the outputs of the provided RNN cell before returning them.
Name prefix used for all created ops.
Scalar INT32
tensor representing the batch size of the input values.
Scalar INT32
tensor representing the batch size of the input values.
RNN cell to use for decoding.
RNN cell to use for decoding.
Performs dynamic decoding using this decoder.
Performs dynamic decoding using this decoder.
This method calls initialize()
once and next()
repeatedly.
Finalizes the output of the decoding process.
Finalizes the output of the decoding process.
Final output after decoding.
Final state after decoding.
Tensor containing the sequence lengths that the decoder cell outputs.
Finalized output and state to return from the decoding process.
Basic RNN decoder helper to use.
Initial RNN cell state to use for starting the decoding process.
This method is called before any decoding iterations.
This method is called before any decoding iterations. It computes the initial input values and the initial state.
Tuple containing: (i) a scalar BOOLEAN
tensor specifying whether initialization has finished,
(ii) the next input, and (iii) the initial decoder state.
Name prefix used for all created ops.
Name prefix used for all created ops.
This method is called once per step of decoding (but only once for dynamic decoding).
This method is called once per step of decoding (but only once for dynamic decoding).
Tuple containing: (i) a scalar BOOLEAN
tensor specifying whether sampling has finished, and
(ii) the next RNN cell tuple.
Output layer to use that is applied at the outputs of the provided RNN cell before returning them.
Describes whether the decoder keeps track of finished states.
Describes whether the decoder keeps track of finished states.
Most decoders will emit a true/false finished
value independently at each time step. In this case, the
dynamicDecode()
function keeps track of which batch entries have already finished, and performs a logical OR to
insert new batches to the finished set.
Some decoders, however, shuffle batches/beams between time steps and dynamicDecode()
will mix up the finished
state across these entries because it does not track the reshuffling across time steps. In this case, it is up to
the decoder to declare that it will keep track of its own finished state by setting this property to true
.
Basic sampling Recurrent Neural Network (RNN) decoder.