Example of predictions

Let's predict a sentence with the generated model:

sentence = [0]
while sentence[-1] != 1:
    pred = predict_model(sentence)[-1]
    sentence.append(pred)
print(" ".join([ index_[w] for w in sentence[1:-1]]))

Note that we take the most probable next word (argmax), while we must, in order to get some randomness, draw the next word following the predicted probabilities.

At 150 epochs, while the model has still not converged entirely with learning our Shakespeare writings, we can play with the predictions, initiating it with a few words, and see the network generate the end of the sentences:

• First citizen: A word , i know what a word

• How now!

• Do you not this asleep , i say upon this?

• Sicinius: What, art thou my master?

• Well, sir, come.

• I have been myself

• A most hose, you in thy hour, sir

• He shall not this

• Pray you, sir

• Come, come, you

• The crows?

• I'll give you

• What, ho!

• Consider you, sir

• No more!

• Let us be gone, or your UNKNOWN UNKNOWN, i do me to do

• We are not now

From these...