95 lines
4.2 KiB
Python
95 lines
4.2 KiB
Python
import numpy as np
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import tensorflow as tf
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from tensorflow.keras.models import Sequential
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from tensorflow.keras.layers import Embedding, LSTM, Dense
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from tensorflow.keras.preprocessing.text import Tokenizer
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from tensorflow.keras.preprocessing.sequence import pad_sequences
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# Загрузка русского текста
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with open('text/rus.txt', encoding='utf-8') as file:
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text_rus = file.read()
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# Загрузка английского текста
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with open('text/eng.txt', encoding='utf-8') as file:
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text_eng = file.read()
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# Токенизация русского текста
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tokenizer_rus = Tokenizer()
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tokenizer_rus.fit_on_texts([text_rus])
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total_words_rus = len(tokenizer_rus.word_index) + 1
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# Токенизация английского текста
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tokenizer_eng = Tokenizer()
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tokenizer_eng.fit_on_texts([text_eng])
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total_words_eng = len(tokenizer_eng.word_index) + 1
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# Создание последовательностей для обучения модели русского текста
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input_sequences_rus = []
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for line in text_rus.split('\n'):
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token_list = tokenizer_rus.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_rus.append(n_gram_sequence)
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max_sequence_length_rus = max([len(x) for x in input_sequences_rus])
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input_sequences_rus = pad_sequences(input_sequences_rus, maxlen=max_sequence_length_rus, padding='pre')
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predictors_rus, labels_rus = input_sequences_rus[:, :-1], input_sequences_rus[:, -1]
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# Создание последовательностей для обучения модели английского текста
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input_sequences_eng = []
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for line in text_eng.split('\n'):
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token_list = tokenizer_eng.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_eng.append(n_gram_sequence)
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max_sequence_length_eng = max([len(x) for x in input_sequences_eng])
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input_sequences_eng = pad_sequences(input_sequences_eng, maxlen=max_sequence_length_eng, padding='pre')
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predictors_eng, labels_eng = input_sequences_eng[:, :-1], input_sequences_eng[:, -1]
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# Создание и компиляция модели для русского текста
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model_rus = Sequential([
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Embedding(total_words_rus, 100, input_length=max_sequence_length_rus - 1),
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LSTM(150),
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Dense(total_words_rus, activation='softmax')
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])
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model_rus.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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# Тренировка модели русского текста
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model_rus.fit(predictors_rus, labels_rus, epochs=100, verbose=1)
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# Создание и компиляция модели для английского текста
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model_eng = Sequential([
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Embedding(total_words_eng, 100, input_length=max_sequence_length_eng - 1),
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LSTM(150),
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Dense(total_words_eng, activation='softmax')
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])
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model_eng.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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# Тренировка модели английского текста
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model_eng.fit(predictors_eng, labels_eng, epochs=100, verbose=1)
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# Генерация текста на основе русского текста
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def generate_text(seed_text, next_words, model, max_sequence_length, 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_length - 1, padding='pre')
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predicted_probs = model.predict(token_list)[0]
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predicted_word_index = np.random.choice(len(predicted_probs), p=predicted_probs)
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output_word = tokenizer.index_word.get(predicted_word_index, '')
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seed_text += " " + output_word
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return seed_text
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# Генерация текста на основе русского текста
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generated_text_rus = generate_text("Начало", 50, model_rus, max_sequence_length_rus, tokenizer_rus)
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print("Generated Russian Text:")
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print(generated_text_rus)
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# Генерация текста на основе английского текста
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generated_text_eng = generate_text("Start", 50, model_eng, max_sequence_length_eng, tokenizer_eng)
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print("\nGenerated English Text:")
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print(generated_text_eng) |