deep_gru_lstm_predict_next.py

def predict_next(self, sentence: str,
                    threshold: float = 0.9) -> str:
    
    # predict the next part of the sentence given as parameter
    
    self.reset_state(1)
    for word in sentence.strip():
        x = words2onehot(self.vocab, [word])
        y_hat = self(x)
    s = ''
    while True:
        word = sample_word(self.vocab,
                            tf.reshape(y_hat, (-1,)).numpy(),
                            threshold)
        if word == EOS:
            break
        s += word
        x = words2onehot(self.vocab, [word])
        y_hat = self(x)
    return s