65 lines
2.1 KiB
Python
65 lines
2.1 KiB
Python
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import numpy as np
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import tensorflow as tf
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def recurrent_neural_network():
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# Загрузка текстового файла и предварительная обработка данных
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with open('V3001TH2.txt', 'r', encoding='utf-8') as f:
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text = f.read()
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chars = sorted(list(set(text)))
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char_to_index = {char: index for index, char in enumerate(chars)}
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index_to_char = {index: char for index, char in enumerate(chars)}
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num_chars = len(chars)
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text_length = len(text)
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# Генерация тренировочных данных
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seq_length = 100 # Длина входной последовательности
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train_x = []
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train_y = []
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for i in range(0, text_length - seq_length, 1):
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input_seq = text[i:i + seq_length]
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output_seq = text[i + seq_length]
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train_x.append([char_to_index[char] for char in input_seq])
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train_y.append(char_to_index[output_seq])
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train_x = np.reshape(train_x, (len(train_x), seq_length, 1))
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train_x = train_x / float(num_chars)
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train_y = tf.keras.utils.to_categorical(train_y)
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model = tf.keras.Sequential([
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tf.keras.layers.LSTM(128, input_shape=(train_x.shape[1], train_x.shape[2])),
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tf.keras.layers.Dense(num_chars, activation='softmax')
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])
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model.compile(loss='categorical_crossentropy', optimizer='adam')
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# Обучение модели
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model.fit(train_x, train_y, epochs=80, batch_size=128)
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# Генерация текста
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start_index = np.random.randint(0, len(train_x) - 1)
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start_seq = train_x[start_index]
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generated_text = ''
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for _ in range(500):
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x = np.reshape(start_seq, (1, len(start_seq), 1))
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x = x / float(num_chars)
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prediction = model.predict(x, verbose=0)
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index = np.argmax(prediction)
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result = index_to_char[index]
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generated_text += result
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start_seq = np.append(start_seq, index)
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start_seq = start_seq[1:]
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with open('сгенерированный_текст.txt', 'w', encoding='utf-8') as f:
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f.write(generated_text)
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if __name__ == '__main__':
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recurrent_neural_network()
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