April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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更快速的優化法
|
353
如果你不想要將
η
0
和
s
寫死
，
你可以寫一個函式來回傳設置好的函式
：
def
exponential_decay(lr0, s):
def
exponential_decay_fn(epoch):
return
lr0 * 0.1**(epoch / s)
return
exponential_decay_fn
exponential_decay_fn = exponential_decay(lr0=0.01, s=20)
接著建立
LearningRateScheduler
回呼
，
將排程函數傳給它
，
再將這個回呼傳給
fit()
方法
：
lr_scheduler = keras.callbacks.LearningRateScheduler(exponential_decay_fn)
history = model.fit(X_train_scaled, y_train, [...], callbacks=[lr_scheduler])
LearningRateScheduler
會在各個
epoch
開始時更新
optimizer
的
learning_rate
屬性
。
通常在每個
epoch
更新學習速度一次就夠了
，
但如果你想要更頻繁地更新
，
例如每一步
都更新
，
你也可以自行編寫回呼
（
範例見筆記本的
「Exponential Scheduling」
部分
）。
如
果每個
epoch
都有很多步
，
在每一步更新學習速度是很好的做法
。
你也可以使用
keras.
optimizers.schedules
，
稍後會介紹 ...
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O’Reilly covers everything we've got, with content to help us build a world-class technology community, upgrade the capabilities and competencies of our teams, and improve overall team performance as well as their engagement.Julian F.
I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
I’ve been on the O’Reilly platform for more than eight years. I use a couple of learning platforms, but I'm on O'Reilly more than anybody else. When you're there, you start learning. I'm never disappointed.Amir M.
I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.