When you create a model using tf.keras.models.Sequential() in TensorFlow, the model information is stored in the memory of the Python runtime until the process is stopped or the model is explicitly deleted.
tf.keras.models.Sequential() 를 사용해 텐서 모델을 생성한 경우 Python, 프로그램이 종료될 때까지, 메모리에 존재한다.This means that if you create a model and then close your Python program without deleting the model, the model information will remain in memory until the Python interpreter is closed. If you later open a new Python program and try to create a new model with the same name, you may run into conflicts or memory errors.
del statement를 사용하거나, pyhton 가비지콜렉터(자동으로 메모리 할당 관리해주는 칭구)를 사용해 메모리에서 모델 데이터를 해제시키지 않는다면, 다음번 프로그램 실행 시 같은 이름으로 model을 생성하거나, 동작을 수행한다면, memory error 가 발생한다.It's important to be aware of memory usage when working with large models or datasets in TensorFlow. You can use tools like tf.keras.backend.clear_session() to free up memory and remove any stored model information from previous sessions. Additionally, it's good practice to delete models when you're done using them, using the del statement or other Python garbage collection mechanisms.
tf.keras.backend.clear_session() 메서드를 사용해 모델 데이터를 메모리로부터 헤제시키는 습관이 중요하다.## Create a Sequential model and add a Dense layer as the first layer.
model = tf.keras.models.Sequential()
model.add(tf.keras.Input(shape=(16,)))
model.add(tf.keras.layers.Dense(32, activation='relu'))
tf.keras.models.Sequential() 함수를 사용하여 Sequential 모델을 만듭니다.