|
--- |
|
library_name: keras |
|
tags: |
|
- structured-data-classification |
|
- time-series |
|
- anomaly-detection |
|
--- |
|
|
|
## Model description |
|
|
|
Timeseries anomaly detection using an Autoencoder |
|
|
|
This repo contains the model and the notebook to this [Keras example on Timeseries anomaly detection using an Autoencoder.](https://keras.io/examples/timeseries/timeseries_anomaly_detection/) |
|
|
|
Full credits to: [Pavithra Vijay](https://github.com/pavithrasv) |
|
|
|
|
|
## Intended uses & limitations |
|
|
|
More information needed |
|
|
|
## Training and evaluation data |
|
|
|
More information needed |
|
|
|
## Training procedure |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- optimizer: {'name': 'Adam', 'learning_rate': 0.001, 'decay': 0.0, 'beta_1': 0.9, 'beta_2': 0.999, 'epsilon': 1e-07, 'amsgrad': False} |
|
- training_precision: float32 |
|
|
|
## Training Metrics |
|
|
|
| Epochs | Train Loss | Validation Loss | |
|
|--- |--- |--- | |
|
| 1| 0.011| 0.014| |
|
| 2| 0.011| 0.015| |
|
| 3| 0.01| 0.012| |
|
| 4| 0.01| 0.013| |
|
| 5| 0.01| 0.012| |
|
| 6| 0.009| 0.014| |
|
| 7| 0.009| 0.013| |
|
| 8| 0.009| 0.012| |
|
| 9| 0.009| 0.012| |
|
| 10| 0.009| 0.011| |
|
| 11| 0.008| 0.01| |
|
| 12| 0.008| 0.011| |
|
| 13| 0.008| 0.009| |
|
| 14| 0.008| 0.011| |
|
| 15| 0.008| 0.009| |
|
| 16| 0.008| 0.009| |
|
| 17| 0.008| 0.009| |
|
| 18| 0.007| 0.01| |
|
| 19| 0.007| 0.009| |
|
| 20| 0.007| 0.008| |
|
| 21| 0.007| 0.009| |
|
| 22| 0.007| 0.008| |
|
| 23| 0.007| 0.008| |
|
| 24| 0.007| 0.007| |
|
| 25| 0.007| 0.008| |
|
| 26| 0.006| 0.009| |
|
| 27| 0.006| 0.008| |
|
| 28| 0.006| 0.009| |
|
| 29| 0.006| 0.008| |
|
## Model Plot |
|
|
|
<details> |
|
<summary>View Model Plot</summary> |
|
|
|
 |
|
|
|
</details> |
