60 lines
2.3 KiB
Python
60 lines
2.3 KiB
Python
import numpy as np
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from keras.models import Sequential
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from keras.layers import Embedding, LSTM, Dense, Dropout
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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.utils import to_categorical
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#Подготовка текста, получение данных для тренировки модели
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def tokenize(filename):
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with open(filename, encoding='utf-8') as file:
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text = file.read()
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tokenizer = Tokenizer()
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tokenizer.fit_on_texts([text])
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uniq_words_amount = len(tokenizer.word_index) + 1
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sequences = []
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list_token = tokenizer.texts_to_sequences([text])[0]
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for i in range(1, len(list_token)):
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sequences.append(list_token[:i + 1])
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max_seq_length = max([len(x) for x in sequences])
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sequences = pad_sequences(sequences, maxlen=max_seq_length)
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x, y = sequences[:, :-1], sequences[:, -1]
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y = to_categorical(y, num_classes=uniq_words_amount)
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return max_seq_length, uniq_words_amount, tokenizer, x, y
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#Создание и тренировка модели
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def train_model(max_seq_length, uniq_words_amount, x, y, epochs):
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model = Sequential()
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model.add(Embedding(uniq_words_amount, 128, input_length=max_seq_length-1))
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model.add(LSTM(152, return_sequences=False))
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model.add(Dropout(0.2))
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model.add(Dense(uniq_words_amount, activation='softmax'))
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model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
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model.fit(x, y, epochs=epochs, verbose=1)
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return model
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#Генерация текста
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def generate_text(text, tokenizer, model, max_seq_length):
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i = 0
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while(i < 100):
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list_token = tokenizer.texts_to_sequences([text])[0]
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token_list = pad_sequences([list_token], maxlen=max_seq_length-1, padding='pre')
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predict = model.predict(token_list)
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predict_index = np.argmax(predict, axis=-1)
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word = tokenizer.index_word.get(predict_index[0])
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text += " " + word
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i = i+1
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return text
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msl, uwa, tokenizer, x, y = tokenize("text_rus.txt")
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model = train_model(msl, uwa, x, y, 140)
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print("Rus: ", generate_text("Кофе со специями", tokenizer, model, msl))
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msl, uwa, tokenizer, x, y = tokenize("text_eng.txt")
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model = train_model(msl, uwa, x, y, 140)
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print("Eng: ", generate_text("The spiced coffee", tokenizer, model, msl)) |