deep_gru_lstm_sample.py

def sample(self, threshold: float = 0.9) -> str:
    # sample a new sentence from the learned model
    sentence = ''
    self.reset_state(1)
    x = np.zeros((1, self.vocab_size))
    while True:
        y_hat = self(x)
        word = sample_word(self.vocab,
                            tf.reshape(y_hat, (-1,)).numpy(),
                            threshold)
        if word == EOS:
            break
        sentence += word
        x = words2onehot(self.vocab, [word])
    return sentence