Есть код который я полностью скопировал :), так вот при обучении нейросети на первой эпохе получаю: Process finished with exit code -1073740791 (0xC0000409), сам текст написан на русском, но для проверки попробовал взять английский текст и получаю тоже самое
P.s если нужно скину ссылку на сайт где брал код, в диспетчере задач на несколько секунд запускается отчёт об ошибках Windows, Win 11, python 3.10
import numpy
import sys
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from keras.models import Sequential
from keras.layers import Dense, Dropout, LSTM
from keras.utils import np_utils
from keras.callbacks import ModelCheckpoint
data = open("data_on_text_generate.txt", encoding='utf-8').read()
file = data.lower()
def tokenize_words(input):
input = input.lower()
tokenizer = RegexpTokenizer(r'\w+')
tokens = tokenizer.tokenize(input)
filtered = filter(lambda token: token not in stopwords.words('russian'), tokens)
return " ".join(filtered)
processed_inputs = tokenize_words(file)
chars = sorted(list(set(processed_inputs)))
char_to_num = dict((c, i) for i, c in enumerate(chars))
input_len = len(processed_inputs)
vocab_len = len(chars)
# print ("Total number of characters:", input_len)
# print ("Total vocab:", vocab_len)
seq_length = 100
x_data = []
y_data = []
for i in range(0, input_len - seq_length, 1):
in_seq = processed_inputs[i:i + seq_length]
out_seq = processed_inputs[i + seq_length]
x_data.append([char_to_num[char] for char in in_seq])
y_data.append(char_to_num[out_seq])
n_patterns = len(x_data)
# print ("Total Patterns:", n_patterns)
X = numpy.reshape(x_data, (n_patterns, seq_length, 1))
X = X/float(vocab_len)
y = np_utils.to_categorical(y_data)
model = Sequential()
model.add(LSTM(256, input_shape=(X.shape[1], X.shape[2]), return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(256, return_sequences=True))
model.add(Dropout(0.2))
model.add(LSTM(128))
model.add(Dropout(0.2))
model.add(Dense(y.shape[1], activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam')
filepath = "model_weights_saved.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='loss', verbose=1, save_best_only=True, mode='min')
desired_callbacks = [checkpoint]
model.fit(X, y, epochs=4, batch_size=256, callbacks=desired_callbacks)
filename = "model_weights_saved.hdf5"
model.load_weights(filename)
model.compile(loss='categorical_crossentropy', optimizer='adam')
num_to_char = dict((i, c) for i, c in enumerate(chars))
start = numpy.random.randint(0, len(x_data) - 1)
pattern = x_data[start]
print("Random Seed:")
print("\"", ''.join([num_to_char[value] for value in pattern]), "\"")
for i in range(1000):
x = numpy.reshape(pattern, (1, len(pattern), 1))
x = x / float(vocab_len)
prediction = model.predict(x, verbose=0)
index = numpy.argmax(prediction)
result = num_to_char[index]
sys.stdout.write(result)
pattern.append(index)
pattern = pattern[1:len(pattern)]
Простой
