孿生神經網路為一種監督式學習的神經網路，透過共享權重的雙子網路計算輸入對的相似度，廣泛應用於語意相似度判斷、人臉辨識與醫學影像比對等任務。儘管深度神經網路擁有較高的效能，實際應用時往往伴隨高參數量與龐大計算成本，特別是在資源受限的裝置上，若未進行適當優化，易面臨運算效能瓶頸。然而，神經網路雖展現出色的表現能力，其效能卻高度依賴於隱藏層中神經元數量的設定。神經元數量過多可能導致過擬合與運算資源浪費，過少則可能限制模型對特徵的學習能力。為此，本研究提出一種基於主成分分析（Principal Component Analysis, PCA）的方法，評估神經元輸出資訊的冗餘程度，進而輔助選擇適當的神經元數量。實驗結果顯示，此方法能在維持模型效能的同時，有效簡化模型結構，提供具參考價值的神經元配置建議。本研究建構一個基於長短期記憶模型的孿生神經網路架構，針對文本分析語意相似度後進行二分類。同時，為了兼顧模型效能、減少冗餘資訊並提升運行效率，採用主成分分析對 LSTM 各門控進行壓縮，探討不同降維程度對模型準確性與語意保留能力的影響，評估是否能在降低計算負擔的同時維持語意匹配效能，進而提升模型在實務應用中的可行性與推論效率。

Siamese Neural Networks, supervised networks employing shared-weight twins, excel in tasks like semantic similarity and recognition. Despite deep networks' power, their high parameter count and computational cost pose challenges, especially on limited-resource devices. Performance also hinges on hidden layer neuron count; too many cause overfitting, too few limit learning. This study introduces a Principal Component Analysis (PCA)-based method to assess neuron output redundancy, aiding in optimal neuron selection. Experiments show this approach simplifies models while preserving performance, offering neuron configuration guidance. We build an LSTM-based Siamese network for binary semantic similarity classification. To balance performance, reduce redundancy, and enhance efficiency, we apply PCA to compress LSTM gates. In this study, we analyze how varying dimensionality reduction impacts accuracy and semantic retention, evaluating if computational reduction can maintain semantic matching, thus improving the model's practical applicability and inference efficiency.

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