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--- |
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library_name: keras |
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tags: |
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- timeseries |
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- time-series-forecasting |
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--- |
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# Timeseries classification from scratch |
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Based on the _Timeseries classification from scratch_ example on [keras.io](https://keras.io/examples/timeseries/timeseries_classification_from_scratch/) created by [hfawaz](https://github.com/hfawaz/). |
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## Model description |
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The model is a Fully Convolutional Neural Network originally proposed in [this paper](https://arxiv.org/abs/1611.06455). |
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The implementation is based on the TF 2 version provided [here](https://github.com/hfawaz/dl-4-tsc/). |
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The hyperparameters (kernel_size, filters, the usage of BatchNorm) were found via random search using [KerasTuner](https://github.com/keras-team/keras-tuner). |
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## Intended uses & limitations |
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Given a time series of 500 samples, the goal is to automatically detect the presence of a specific issue with the engine. |
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The data used to train the model was already _z-normalized_: each timeseries sample has a mean equal to zero and a standard deviation equal to one. |
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## Training and evaluation data |
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The dataset used here is called [FordA](http://www.j-wichard.de/publications/FordPaper.pdf). The data comes from the [UCR archive](https://www.cs.ucr.edu/%7Eeamonn/time_series_data_2018/). The dataset contains: |
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- 3601 training instances |
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- 1320 testing instances |
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Each timeseries corresponds to a measurement of engine noise captured by a motor sensor. |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision | |
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|----|-------------|-----|------|------|-------|-------|------------------| |
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|Adam|9.999999747378752e-05|0.0|0.8999999761581421|0.9990000128746033|1e-07|False|float32| |
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## Model Plot |
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<details> |
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<summary>View Model Plot</summary> |
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</details> |
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<center> |
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Model reproduced by <a href="https://github.com/EdAbati" target="_blank">Edoardo Abati</a> |
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</center> |
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