用这些权重参数我们可以重新构建模型：
model = build_model(vocab_size, embedding_dim, rnn_units, batch_size=1) model.load_weights(tf.train.latest_checkpoint(checkpoint_dir)) model.build(tf.TensorShape([1, None]))生成文本的步骤：
首先选择一个种子字符串，初始化RNN状态，并设置要生成的字符数。
使用开始字符串和RNN状态获得下一个字符的预测分布。
使用分类分布计算预测字符的索引，并将其作为模型的下一个输入。
模型返回的RNN状态被反馈回自身。
重复步骤2和步骤4，直到生成文本。
def generate_text(model, start_string): # Evaluation step (generating text using the learned model) # Number of characters to generate num_generate = 1000 # Converting our start string to numbers (vectorizing) input_eval = [char2idx[s] for s in start_string] input_eval = tf.expand_dims(input_eval, 0) # Empty string to store our results text_generated = [] # Low temperatures results in more predictable text. # Higher temperatures results in more surprising text. # Experiment to find the best setting. temperature = 1.0 # Here batch size == 1 model.reset_states() for i in range(num_generate): predictions = model(input_eval) # remove the batch dimension predictions = tf.squeeze(predictions, 0) # using a categorical distribution to predict the character returned by the model predictions = predictions / temperature predicted_id = tf.random.categorical(predictions, num_samples=1)[-1,0].numpy() # We pass the predicted character as the next input to the model # along with the previous hidden state input_eval = tf.expand_dims([predicted_id], 0) text_generated.append(idx2char[predicted_id]) return (start_string + \'\'.join(text_generated))最后，给定一个开始字符串，我们可以生成一些有趣的文本。
现在，欣赏一下两个RNN的剧本吧，一个是训练了10个epochs，另一个是30个epochs。
这是训练了10个epochs的 print(generate_text(model, start_string=u"ROMEO: "))ROMEO: how I, away too put That you shall have thieffort, are but love.
JULIET: Go, fight, sir: we say ‘Ay,’ and alack to stand and not to go to; And washt us him to-domm. Ay, my ows young; a man hear from his monsher to thee.
KING RICHARD III: Come, cease. O broteld the costime’s deforment! Thou wilt was quite.
PAULINA: I would you say the hour! Ah, hole for your company: But, good my lord; we have a king, of peace?
BALTHASAR: Cadul and washee could he ha! To curit her I may wench.
GLOUCESTER: Had you here shall such a pierce to temper; Or might his noble offery owe and speed Which seemest thy trims in a weaky amidude By this to the dother, dods citizens.
Third Citizen:
Madam sweet give reward, rebeire them With news gone! Pluck yielding: ’tis sign out things Within risess in strifes all ten times, To dish his finmers for briefily.
JULIET:
Gentlemen, God eveI come approbouting his wife as it, — triumphrous night change you gods, thou goest:
To which will dispersed and France.
哇!仅仅在10个epochs之后，就有了令人印象深刻的理解。这些词的拼写准确性令人怀疑，但其中有明显的情节冲突。写作肯定可以改进。希望30-epoch模型能有更好的表现。
这是训练了30个epochs的欣赏一下完全由RNN一个字一个字地创作出来的作品吧！
BRUTUS:
Could you be atherveshed him, our two,
But much a tale lendly fear;
For which we in thy shade of Naples.
Here’s no increase False to’t, offorit is the war of white give again.
This is the queen, whose vanoar’s head is worthly.
But cere it be a witch, some comfort.
What, nurse, I say!
Go Hamell.
FLORIZEL:
Should she kneel be?
In shall not weep received; unleased me
And unrespective greeting than dwell in, thee,
look’d on me, son in heavenly properly,
That ever you are my father is but straing;
Unless you would repossess him, hath always louded up,
You provokest. Good faith, o’erlar I can repart the heavens like deeds dills
For temper as soon as another maiden here, and he is bann’d upon which springs;
O’er most upon your voysus, I have no thunder; and my good villain!
Alest each other’s sleepings.
A fool; if this business prating duty
Does these traitors other sorrow.
LUCENTIO:
Tell me, they’s honourably.
Shepherd:
I know, my lord, to London, and you my moved join under him,
Great Apollo’s stan to make a book,
Both yet my father away towards Covent. Tut, And thou still’d by the earthmen lord r sensible your mother?