Natural Language Processing with Machine Learning/

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Decoding Output

Decoding Output

Decode the model's outputs for training and inference.

We'll cover the following...

Chapter Goals:

  • Retrieve the decoder outputs and return the model's logits during training

After creating the decoder object for our model, we can perform the decoding using the decoder object as dynamic_decode function.

Python 3.5
import tensorflow as tf
extended_vocab_size = 500
batch_size = 10
# decoder is a BasicDecoder object used as Dynamic_decoder
outputs = decoder(inputs , initial_state=initial_state , sequence_length=input_seq_lens , training=True )
decoder_output = outputs[0]
logits = decoder_output.rnn_output
decoder_final_state = outputs[1]
decoded_sequence_lengths = outputs[2]

The decoder object takes in input, initial_state, sequence_length, and isTraining as required argument. It returns a tuple containing three elements:

  1. The decoder's output. For a BasicDecoder input, the decoder's output takes the form of a
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