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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