January 2019
Beginner to intermediate
576 pages
14h 31m
Chinese
Content preview from 深度學習｜內行人的做法
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層數、參數數量與記憶體
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271
請注意，某些層型態並沒有任何參數（例如子取樣 [subsampling] 層與激活層，以及
DL4J 的
LossLayer
）。如果網路中有卷積層與稠密層（或輸出層）相連的情況，計算上會
稍微複雜一點。針對卷積層 / 子取樣層後面的稠密層（或輸出層），我們必須根據最後一
個卷積層激活陣列的大小，計算出相應的的長度，以做為稠密層 / 輸出層的 n
L – 1
值。舉
例來說，如果最終卷積層輸出 100 個通道的 5 × 5 激活值，我們就應該使用 n
L – 1
= 5 × 5
×
100 = 2,500 做為稠密層 / 輸出層的輸入
5
。
如果我們有一個 DL4J 配置，還有沒有其他方法可以檢查參數的數量呢？這裡有個簡單
的做法：
MultiLayerConfiguration configuration = ...
MultiLayerNetwork network = new MultiLayerNetwork(configuration);
network.init();
System.out.println("Total number of parameters: " + network.numParams());
for( int i=0; i<network.getnLayers(); i++ ){
System.out.println("Layer " + i + " number of parameters: " +
network.getLayer(i).numParams()); ...
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