Research Article
An LSTM-Autoencoder Architecture for Anomaly Detection Applied on Compressors Audio Data
Table 2
Model dimensions and operations overview for LSTM-AE architecture (created by authors).
| Operation | | Data | Dimensions | Weights () | Weights (%) |
| Input | ##### | 2 | 259 | | | LSTM | LLLLL | — | — | 62,000 | 66.7% | tanh | ##### | | 50 | | | Dropout | ||| | — | — | 0 | 0.0% | | ##### | | 50 | | | Repeat vector | | — | — | 0 | 0.0% | | ##### | 259 | 50 | | | LSTM | LLLLL | — | — | 20,200 | 21.7% | tanh | ##### | 259 | 50 | | | Dense | ||| | — | — | 0 | 0.0% | | ##### | 259 | 50 | | | Time distributed | | — | — | 102 | 0.1. % | | ##### | 259 | 2 | | | LSTM | LLLLL | — | — | 10,600 | 11.4% | tanh | ##### | | 50 | | | Dense | XXXXX | — | — | 102 | 0.1% | SoftMax | ##### | | 2 | | |
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