Backpropagation

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倒傳遞演算法（backpropagation algorithm）旨在解決單層神經網路無法處理的線性可分問題（如：XOR問題），通過梯度下降法和損失函數將資訊反饋回網路。執行步驟包括初始化權重、計算輸出結果、評估誤差並調整權重，直到誤差收斂。雖然此演算法有效，但存在如陷入局部最小值和收斂速度慢的限制。

倒傳遞演算法
Backpropagation algorithm
B0029028 鄭逸軒

1. 目標
2. 簡介
3. 執行步驟
4. 演算法公式
5. 限制
6. 結論
目錄

目標
? 能透過此演算法來尋找一組最適合的輸入輸出
? 解決單層神經網路解不了的問題(Linear separability)
Ex: XOR

簡介
? Backpropagation algorithm (簡稱：BP演算法)
? 結合梯度下降法(Gradient descent)
? 根據loss function將資訊傳回網路
? 提供一組輸入和預期結果--監督式訓練法
? 活化函數必須使用可微分的函數
? S型函數(Sigmoid function)
? EX: Logistic function

執行步驟
? 訓練的流程如下：
1. 初始化權重(random)
2. 提供一組包含輸入資料以及期望結果的學習樣本
3. 利用目前的權重計算輸出結果
4. 計算輸出結果和期望結果的誤差並回傳給隱藏和輸入
層
5. 根據誤差來調整與隱藏層和輸入層間的權重
6. 重複2~5直到誤差收斂

限制
? 梯度下降法可能找到local minimum
? 結果可能非最佳解
? 達到誤差收斂可能會很慢
? 也不保證一定能收斂
? 不須正規化輸入
? 但若正規化能提高效能

結論
? 第一階段--傳遞(Propagation):
? 輸入根據權重透過網路向前激發至輸出層產
生輸出
? 輸出層計算誤差結果delta並倒傳回隱藏層
? 第二階段—更新權重(Weight update):

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