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