98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
import sys
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import numpy as np
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from nltk.tokenize import RegexpTokenizer
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from nltk.corpus import stopwords
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import tensorflow as tf
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from keras.models import Sequential
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from keras.layers import Dense, Dropout, LSTM
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from keras.utils import np_utils
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from keras.callbacks import ModelCheckpoint
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file = open("P:\\ULSTU\\ИИС\\Лабораторные\\Lab7\\texts\\text-en.txt", encoding='utf-8').read()
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def tokenize_words(input):
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# переводим весть текст в строчные буквы
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input = input.lower()
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# инициализируем токенизатор
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tokenizer = RegexpTokenizer(r'\w+')
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tokens = tokenizer.tokenize(input)
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# выбираем и выбрасываем все стоп слова, находящиеся в списке стоп слов русского языка
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filtered = filter(lambda token: token not in stopwords.words('english'), tokens)
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return " ".join(filtered)
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if __name__ == '__main__':
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# предобрабатываем текст, создаём токены
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processed_inputs = tokenize_words(file)
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chars = sorted(list(set(processed_inputs)))
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char_to_num = dict((c, i) for i, c in enumerate(chars))
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input_len = len(processed_inputs)
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vocab_len = len(chars)
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print("Общее кол-во символов:", input_len)
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print("Размер словаря:", vocab_len)
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seq_length = 100
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x_data = []
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y_data = []
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for i in range(0, input_len - seq_length, 1):
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in_seq = processed_inputs[i:i + seq_length]
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out_seq = processed_inputs[i + seq_length]
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x_data.append([char_to_num[char] for char in in_seq])
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y_data.append(char_to_num[out_seq])
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n_patterns = len(x_data)
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print("Кол-во паттернов:", n_patterns)
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X = np.reshape(x_data, (n_patterns, seq_length, 1))
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X = X / float(vocab_len)
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y = np_utils.to_categorical(y_data)
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model = Sequential()
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model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
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model.add(Dropout(0.2))
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model.add(LSTM(256, return_sequences=True))
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model.add(Dropout(0.2))
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model.add(LSTM(256))
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model.add(Dropout(0.2))
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model.add(Dense(y.shape[1], activation='softmax'))
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model.compile(loss='categorical_crossentropy', optimizer='adam')
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filepath = "model_weights_saved.hdf5"
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checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
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desired_callbacks = [checkpoint]
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# Создание распределенной стратегии
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strategy = tf.distribute.MultiWorkerMirroredStrategy()
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# Распределение модели на устройства
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with strategy.scope():
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parallel_model = model
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# Обучение модели на GPU и CPU
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parallel_model.fit(X, y, epochs=100, batch_size=256, callbacks=desired_callbacks)
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model.load_weights(filepath)
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model.compile(loss='categorical_crossentropy', optimizer='adam')
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num_to_char = dict((i, c) for i, c in enumerate(chars))
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start = np.random.randint(0, len(x_data) - 1)
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pattern = x_data[start]
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print("Случайная выборка:")
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print("\"", ''.join([num_to_char[value] for value in pattern]), "\"")
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for i in range(1000):
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x = np.reshape(pattern, (1, len(pattern), 1))
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x = x / float(vocab_len)
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prediction = model.predict(x, verbose=0)
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index = np.argmax(prediction)
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result = num_to_char[index]
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sys.stdout.write(result)
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pattern.append(index)
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pattern = pattern[1:len(pattern)]
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