April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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自製模型和訓練演算法
|
389
>>>
gradients
[<tf.Tensor: id=828234, shape=(), dtype=float32, numpy=36.0>,
<tf.Tensor: id=828229, shape=(), dtype=float32, numpy=10.0>]
完美
！
算出來的結果不但很準確
（
只有浮點誤差可以限制
precision），
gradient()
方法也
只經歷被記錄下來的計算一次
（
反向順序
），
無論有多少變數
，
所以它的效率極高
，
就像
變魔術一樣
。
為了節省記憶體
，
我只在
tf.GradientTape()
區塊裡面放入最少的程
式
。
你也可以在
tf.GradientTape()
區塊裡面建立一個
with tape.stop_
recording()
區塊來暫停記錄
。
當你呼叫
tape
的
gradient()
方法之後
，
它就立刻被自動清除
，
所以當你試著呼叫
gradient()
兩次時
，
你會得到例外
：
with
tf.GradientTape()
as
tape:
z = f(w1, w2)
dz_dw1 = tape.gradient(z, w1) # => tensor 36.0
dz_dw2 = tape.gradient(z, w2) # RuntimeError!
如果你需要呼叫
gradient()
多次
，
你必須持久保存這個
tape，
並且在每次用完之後都要刪
除它
，
以釋出資源
12
：
with
tf.GradientTape(persistent=True)
as
tape: ...
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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.