68 lines
2.5 KiB
Python
68 lines
2.5 KiB
Python
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import numpy as np
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from keras.models import Sequential
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from keras.layers import Embedding, LSTM, Dense
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from keras.preprocessing.text import Tokenizer
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from keras.preprocessing.sequence import pad_sequences
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from keras.src.layers import Dropout
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#filename = "rutext.txt"
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filename = "engtext.txt"
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with open(filename, "r", encoding="utf-8") as f:
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text = f.read()
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# Создаем токенизатор и преобразуем текст в последовательности
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tokenizer = Tokenizer()
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tokenizer.fit_on_texts([text])
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total_words = len(tokenizer.word_index) + 1
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input_sequences = []
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for line in text.split('\n'):
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token_list = tokenizer.texts_to_sequences([line])[0]
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for i in range(1, len(token_list)):
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n_gram_sequence = token_list[:i + 1]
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input_sequences.append(n_gram_sequence)
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# Подготавливаем данные для обучения
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max_sequence_length = max([len(x) for x in input_sequences])
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input_sequences = pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')
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X, y = input_sequences[:, :-1], input_sequences[:, -1]
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# Определяем архитектуру нейронной сети
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model = Sequential()
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model.add(Embedding(total_words, 50, input_length=max_sequence_length - 1))
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model.add(LSTM(512))
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model.add(Dropout(0.2))
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model.add(Dense(total_words, activation='softmax'))
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# Компилируем модель
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model.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])
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# Обучаем модель
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model.fit(X, y, epochs=50, verbose=1)
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# Функция для генерации текста
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def generate_text(seed_text, next_words, max_sequence_len, model, tokenizer):
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for _ in range(next_words):
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token_list = tokenizer.texts_to_sequences([seed_text])[0]
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token_list = pad_sequences([token_list], maxlen=max_sequence_len - 1, padding='pre')
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predicted_probs = model.predict(token_list)[0]
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predicted = np.random.choice(len(predicted_probs), p=predicted_probs)
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output_word = ""
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for word, index in tokenizer.word_index.items():
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if index == predicted:
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output_word = word
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break
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seed_text += " " + output_word
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return seed_text
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# Пример использования
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#generated_text = generate_text("Война и", 25, max_sequence_length, model, tokenizer)
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generated_text = generate_text("Shakespeare was", 25, max_sequence_length, model, tokenizer)
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print(generated_text)
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