April 2020
Intermediate to advanced
816 pages
18h 32m
Chinese
Content preview from 精通機器學習
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摺積自動編碼網路
|
557
年來
，
它是唯一有效的深網路訓練方法
，
直到第
11
章介紹的許多技術的出現
，
我們才可
以一次性地直接訓練一個深網路
。
自動編碼網路並非只能使用稠密網路
，
你也可以建立摺積自動編碼網路
，
甚至遞迴自動編
碼網路
。
我們來看一下怎麼做
。
摺積自動編碼網路
如果你想要處理圖像
，
那麼截至目前為止介紹過的自動編碼網路的效果都不太好
（
除非圖
像很小
）：
如同我們在第
14
章看過的
，
摺積神經網路比稠密網路更適合處理圖像
。
所以如
果你想要建立處理圖像的自動編碼網路
（
例如用來做無監督預先訓練或降維
），
你就要建
立一個
摺積自動編碼網路
（convolutional autoencoder）（ https://homl.info/convae）
4
。
這種
編碼網路是一般的
CNN，
裡面有摺積層與池化層
，
它通常可以減少輸入的空間維數
（
也
就是寬度與高度
），
並且增加深度
（
也就是特徵圖的數量
）。
解碼網路必須做反向工作
（
放
大圖像並將它的深度降回原始維數
），
為此
，
你可以使用轉置摺積層
（
或是結合升抽樣層
與摺積層
）。
下面是處理
Fashion MNIST
的簡單摺積自動編碼網路
：
conv_encoder = keras.models.Sequential([
keras.layers.Reshape([28, 28, 1], input_shape=[28, 28]),
keras.layers.Conv2D(16, kernel_size=3, padding="same", activation="selu"),
keras.layers.MaxPool2D(pool_size=2), ...
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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.