61 lines
1.7 KiB
Python
61 lines
1.7 KiB
Python
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import numpy as np
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from keras.models import Sequential
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from keras.layers import LSTM, Dense
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# Загрузка текстового файла
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file_path = "A.txt"
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with open(file_path, "r", encoding="utf-8") as file:
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text = file.read()
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# Предобработка данных
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chars = sorted(list(set(text)))
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char_indices = {char: i for i, char in enumerate(chars)}
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indices_char = {i: char for i, char in enumerate(chars)}
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maxlen = 40
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step = 3
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sentences = []
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next_chars = []
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for i in range(0, len(text) - maxlen, step):
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sentences.append(text[i : i + maxlen])
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next_chars.append(text[i + maxlen])
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x = np.zeros((len(sentences), maxlen, len(chars)), dtype=np.uint8)
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y = np.zeros((len(sentences), len(chars)), dtype=np.uint8)
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for i, sentence in enumerate(sentences):
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for t, char in enumerate(sentence):
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x[i, t, char_indices[char]] = 1
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y[i, char_indices[next_chars[i]]] = 1
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# Определение модели RNN
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model = Sequential()
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model.add(LSTM(128, input_shape=(maxlen, len(chars))))
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model.add(Dense(len(chars), activation="softmax"))
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model.compile(loss="categorical_crossentropy", optimizer="adam")
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# Обучение модели
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model.fit(x, y, batch_size=128, epochs=20)
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# Генерация текста
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start_index = np.random.randint(0, len(text) - maxlen - 1)
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seed_text = text[start_index : start_index + maxlen]
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generated_text = seed_text
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for i in range(400):
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x_pred = np.zeros((1, maxlen, len(chars)))
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for t, char in enumerate(seed_text):
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x_pred[0, t, char_indices[char]] = 1
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preds = model.predict(x_pred, verbose=0)[0]
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next_index = np.argmax(preds)
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next_char = indices_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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print(generated_text)
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