75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
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from keras import Sequential
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from keras.layers import 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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import numpy as np
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with open('rus_text.txt', 'r', encoding='utf-8') as file:
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text = file.read()
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def create_sequences(text, seq_len):
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sequences = []
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next_chars = []
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for i in range(0, len(text) - seq_len):
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sequences.append(text[i:i + seq_len])
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next_chars.append(text[i + seq_len])
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return sequences, next_chars
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def get_model_data(seq_length):
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tokenizer = Tokenizer(char_level=True)
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tokenizer.fit_on_texts([text])
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token_text = tokenizer.texts_to_sequences([text])[0]
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sequences, next_chars = create_sequences(token_text, seq_length)
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vocab_size = len(tokenizer.word_index) + 1
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x = pad_sequences(sequences, maxlen=seq_length)
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y = np.array(next_chars)
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return x, y, vocab_size, tokenizer
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def model_build(model, vocab_size):
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model.add(LSTM(256, input_shape=(seq_length, 1), return_sequences=True))
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model.add(LSTM(128, input_shape=(seq_length, 1)))
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model.add(Dropout(0.2, input_shape=(60,)))
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model.add(Dense(vocab_size, activation='softmax'))
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model.compile(loss='sparse_categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
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# Функция для генерации текста
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def generate_text(seed_text, gen_length, tokenizer, model):
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generated_text = seed_text
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for _ in range(gen_length):
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sequence = tokenizer.texts_to_sequences([seed_text])[0]
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sequence = pad_sequences([sequence], maxlen=seq_length)
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prediction = model.predict(sequence)[0]
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predicted_index = np.argmax(prediction)
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predicted_char = tokenizer.index_word[predicted_index]
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generated_text += predicted_char
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seed_text += predicted_char
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seed_text = seed_text[1:]
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return generated_text
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seq_length = 10
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seed_text = "господин осматривал свою"
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# Создание экземпляра Tokenizer и обучение на тексте
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X, y, vocab_size, tokenizer = get_model_data(seq_length)
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model = Sequential()
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model_build(model, vocab_size)
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model.fit(X, y, epochs=100, verbose=1)
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generated_text = generate_text(seed_text, 200, tokenizer, model)
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print(generated_text)
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