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Basic sampling decoder.

Inherits From: Decoder

cell An RNNCell instance.
helper A Helper instance.
initial_state A (possibly nested tuple of...) tensors and TensorArrays. The initial state of the RNNCell.
output_layer (Optional) An instance of tf.compat.v1.layers.Layer, i.e., tf.compat.v1.layers.Dense. Optional layer to apply to the RNN output prior to storing the result or sampling.

TypeError if cell, helper or output_layer have an incorrect type.

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.



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Called after decoding iterations complete.

outputs 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.

(final_outputs, final_state): final_outputs is an object containing the final decoder output, final_state is a (structure of) state tensors and TensorArrays.


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Initialize the decoder.

name Name scope for any created operations.

(finished, first_inputs, initial_state).


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Perform a decoding step.

time scalar int32 tensor.
inputs A (structure of) input tensors.
state A (structure of) state tensors and TensorArrays.
name Name scope for any created operations.

(outputs, next_state, next_inputs, finished).