71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
import torch
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import torch.nn as nn
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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 Embedding, LSTM, Dense
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with open('69209.txt', "r") as file:
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text = file.read()
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# Предварительная обработка текста (в зависимости от вашей задачи)
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# Создание словаря для отображения слов в индексы и обратно
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tokenizer = tf.keras.preprocessing.text.Tokenizer()
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tokenizer.fit_on_texts([text])
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total_words = len(tokenizer.word_index) + 1
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# Подготовка данных для обучения (в зависимости от вашей задачи)
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input_sequences = []
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for line in text.split('\n'):
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token_list = tokenizer.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.append(n_gram_sequence)
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max_sequence_length = max([len(x) for x in input_sequences])
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input_sequences = tf.keras.preprocessing.sequence.pad_sequences(input_sequences, maxlen=max_sequence_length, padding='pre')
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X, y = input_sequences[:,:-1],input_sequences[:,-1]
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y = tf.keras.utils.to_categorical(y, num_classes=total_words)
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# Определение архитектуры модели
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model = Sequential()
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model.add(Embedding(total_words, 50, input_length=max_sequence_length-1))
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model.add(LSTM(100))
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model.add(Dense(total_words, activation='softmax'))
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# Компиляция модели
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model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
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# Обучение модели
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model.fit(X, y, epochs=100, verbose=2)
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# Генерация текста с использованием обученной модели
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def generate_text(seed_text, next_words, model, max_sequence_length):
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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 = tf.keras.preprocessing.sequence.pad_sequences([token_list], maxlen=max_sequence_length - 1,
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padding='pre')
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predicted = model.predict_classes(token_list, verbose=0)
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output_word = ""
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for word, index in tokenizer.word_index.items():
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if index == predicted:
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output_word = word
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break
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seed_text += " " + output_word
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return seed_text
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# Пример генерации текста (замените seed_text и next_words на свои значения)
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seed_text = "your seed text here"
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next_words = 50
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generated_text = generate_text(seed_text, next_words, model, max_sequence_length)
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print(generated_text)
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