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살아 움직이는 머신러닝 파이프라인 설계: 텐서플로와 함께하는 머신러닝 프로젝트 자동화
book

살아 움직이는 머신러닝 파이프라인 설계: 텐서플로와 함께하는 머신러닝 프로젝트 자동화

by 송호연, 하네스 하프케, 캐서린 넬슨
October 2021
Beginner to intermediate
416 pages
9h 3m
Korean
Hanbit Media, Inc.
Content preview from 살아 움직이는 머신러닝 파이프라인 설계: 텐서플로와 함께하는 머신러닝 프로젝트 자동화
365
14
머신러닝을 위한 데이터 개인 정보 보호
그런 다음 차등 개인 옵티마이저를 초기화합니다.
from
tensorflow_privacy.privacy.optimizers.dp_optimizer
\
import
DPGradientDescentGaussianOptimizer
optimizer = DPGradientDescentGaussianOptimizer(
l2_norm_clip=L2_NORM_CLIP,
noise_multiplier=NOISE_MULTIPLIER,
num_microbatches=NUM_MICROBATCHES,
learning_rate=LEARNING_RATE)
# 손실은 전체 미니 배치를 통해 계산하지 말고 데이터 전체로 계산해야 합니다.
loss = tf.keras.losses.BinaryCrossentropy(
from_logits=True, reduction=tf.losses.Reduction.NONE)
개인 모델은 일반적인
tf
.
keras
모델과 똑같이 학습합니다.
model.compile(optimizer=optimizer, loss=loss, metrics=[‘accuracy’])
model.fit(X_train, y_train,
epochs=EPOCHS,
validation_data=(X_test, y_test),
batch_size=BATCH_SIZE) ...
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Publisher Resources

ISBN: 9791162244814