Python
텐서플로 케라스(Tf.keras)에서 L1, L2 정규화 적용하기
Nov. 1, 2022, 4:26 p.m.
이번 포스트에서는 텐서플로 케라스에서 L1, L2 정규화를 적용하는 방법에 대해서 알아보겠습니다.
이미 텐서플로에서 정규화에 관한 함수들이 tf.keras.regularizers 에 정의 되어 있기 때문에 단지 인스턴스를 가져와서 레이어에 적용만 하면 됩니다.
먼저 L1 정규화를 적용해 보겠습니다.
import tensorflow as tf
l1_reg = tf.keras.regularizers.l1(0.01)
conv = tf.keras.layers.Conv2D(16, kernel_size=(2, 2), activation='relu', input_shape=(128, 128),
kernel_regularizer=l1_reg)
정말 간단합니다. Conv2D 레이어에 kernel_regularizer로 넣어주어 l1 규제를 적용하였습니다.
L2 정규화도 마찬가지로,
import tensorflow as tf
l2_reg = tf.keras.regularizers.l2(0.01)
conv = tf.keras.layers.Conv2D(16, kernel_size=(2, 2), activation='relu', input_shape=(128, 128),
kernel_regularizer=l2_reg)
이상입니다.
케라스 인공지능 텐서플로 keras tensorflow
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