if cell, helper or output_layer have an incorrect type.
Attributes
batch_size
The batch size of input values.
output_dtype
A (possibly nested tuple of...) dtype[s].
output_size
A (possibly nested tuple of...) integer[s] or TensorShape object[s].
tracks_own_finished
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 dynamic_decode function keeps track
of which batch entries are 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
dynamic_decode will mix up the finished state across these entries because
it does not track the reshuffle 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.
RNNCell outputs (possibly nested tuple of) tensor[s] for all time
steps.
final_state
RNNCell final state (possibly nested tuple of) tensor[s] for
last time step.
sequence_lengths
1-D int32 tensor containing lengths of each sequence.
Returns
(final_outputs, final_state): final_outputs is an object containing
the final decoder output, final_state is a (structure of) state tensors
and TensorArrays.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2020-10-01 UTC."],[],[]]