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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使用多層感知器網路，為 CSV 資料建立模型
|
195
模型的訓練
為了訓練模型，我們建立一個
for
迴圈，它會根據所給定的階段（epoch）數量，針對輸
入資料集對神經網路進行訓練（遍歷整個資料集）。
for ( int n = 0; n < nEpochs; n++) {
model.fit( trainIter );
}
為了訓練整個資料集，我們調用了
MultiLayerNetwork
物件類別實例中的
.fit()
方法。這
個物件類別負責的是，根據所指定的適當超參數（譬如之前在輸入資料集迭代器中所設
置的最小批量處理大小）套用相應的處理機制：
DataSetIterator testIter = new RecordReaderDataSetIterator(rrTest,batchSize,0,2);
在這個例子中，
batchSize
這個變數負責控制從磁碟中取得的樣本數量，這些樣本會
以批量的方式傳遞給模型進行訓練。在訓練模型的過程中，我們應該可以在控制台
（console）看到類似以下的輸出：
o.d.o.l.ScoreIterationListener - Score at iteration 0 is 0.6313823699951172
o.d.o.l.ScoreIterationListener - Score at iteration 10 is 0.4763660430908203
o.d.o.l.ScoreIterationListener - Score at iteration 20 ...
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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.