April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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預測時間序列
|
491
個數字
，
這個資訊真的不多
，
而且可能不實用
；RNN
會在一個時步到下一個時步之間盡
量使用其他遞迴層的隱藏狀態來取得它需要的所有資訊
，
而且不怎麼使用最後一層的隱
藏狀態
。
此外
，
因為
SimpleRNN
層在預設情況下使用
tanh
觸發函數
，
預測出來的值一定介
於
–1
到
1
之間
。
但如果你想要使用其他的觸發函數呢
？
出於這兩個理由
，
比較好的做法
或許是將輸出層換成
Dense
層
，
它跑得比較快
，
而且準確度大致上相同
，
而且它可讓我們
選擇任何輸出觸發函數
。
如果你要做這種改變
，
也要記得將第二個
（
現在是最後一個
）
遞
迴層的
return_sequences=True
移除
：
model = keras.models.Sequential([
keras.layers.SimpleRNN(20, return_sequences=True, input_shape=[None, 1]),
keras.layers.SimpleRNN(20),
keras.layers.Dense(1)
])
當你訓練這個模型時
，
你會看到它更快收斂
，
並且有一樣好的表現
。
此外
，
你也可以視需
要更換輸出觸發函數
。
提前幾個時步預測未來
我們到目前為止只預測下一個時步的值
，
但我們也可以同樣輕鬆地改變目標來預測幾個時
步之後的值
（
例如
，
要提前預測
10
個時步
，
你只要將目標改成提前
10
個時步
，
而不是提
前
1
個時步
）。
但如果我們想要預測接下來的
10
個值呢
？
第一種做法是使用我們訓練好的模型
，
讓它預測下一個值
，
再將那個值加到輸入
（
假裝這
個預測的值已經存在了 ...
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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.