85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
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import numpy as np
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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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from flask import Flask, request, jsonify, render_template
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# Загрузка и предобработка данных
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with open('your_text_file.txt', 'r', encoding='utf-8') as file:
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text = file.read()
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# Создание словаря символов
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chars = sorted(list(set(text)))
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char_to_idx = {char: idx for idx, char in enumerate(chars)}
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idx_to_char = {idx: char for idx, char in enumerate(chars)}
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# Подготовка данных для обучения
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seq_length = 100
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step = 3
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sequences = []
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next_chars = []
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for i in range(0, len(text) - seq_length, step):
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seq = text[i:i+seq_length]
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target = text[i+seq_length]
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sequences.append(seq)
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next_chars.append(target)
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# Преобразование данных в числовой формат
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X = np.zeros((len(sequences), seq_length, len(chars)), dtype=bool)
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y = np.zeros((len(sequences), len(chars)), dtype=bool)
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for i, seq in enumerate(sequences):
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for t, char in enumerate(seq):
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X[i, t, char_to_idx[char]] = 1
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y[i, char_to_idx[next_chars[i]]] = 1
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# Построение более сложной модели
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model = Sequential([
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LSTM(256, input_shape=(seq_length, len(chars)), return_sequences=True),
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LSTM(256),
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Dense(len(chars), activation='softmax')
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])
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model.compile(optimizer='adam', loss='categorical_crossentropy')
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# Увеличение количества эпох обучения
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model.fit(X, y, epochs=100, batch_size=128)
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# Функция для генерации текста с параметром температуры
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def generate_text(seed_text, model, length=100, temperature=1.0):
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generated_text = seed_text
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for _ in range(length):
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x = np.zeros((1, seq_length, len(chars)))
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for t, char in enumerate(seed_text):
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x[0, t, char_to_idx[char]] = 1
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preds = model.predict(x, verbose=0)[0]
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preds = np.log(preds) / temperature
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exp_preds = np.exp(preds)
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preds = exp_preds / np.sum(exp_preds)
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next_index = np.random.choice(len(chars), p=preds)
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next_char = idx_to_char[next_index]
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generated_text += next_char
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seed_text = seed_text[1:] + next_char
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return generated_text
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# Создание Flask-приложения
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app = Flask(__name__)
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# Эндпоинт для генерации текста
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@app.route('/')
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def index():
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return render_template('index.html')
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# Эндпоинт для генерации текста
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@app.route('/generate_text', methods=['POST'])
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def generate_text_endpoint():
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data = request.get_json()
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seed_text = data.get('seed_text', '')
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generated_text = generate_text(seed_text, model)
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return jsonify({'generated_text': generated_text})
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# Запуск Flask-сервера
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if __name__ == '__main__':
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app.run(port=5000)
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