| def generate_text(seed_text, next_words, model, max_sequence_len): | |
| for _ in range(next_words): | |
| token_list = tokenizer.texts_to_sequences([seed_text])[0] | |
| token_list = pad_sequences([token_list], maxlen=max_sequence_len-1, padding=’pre’) | |
| predicted = model.predict_classes(token_list, verbose=0) | |
| output_word = “” | |
| for word,index in tokenizer.word_index.items(): | |
| if index == predicted: | |
| output_word = word | |
| break | |
| seed_text += “ “+output_word | |
| return seed_text.title() |
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