Timeseries classification from scratch
Based on the Timeseries classification from scratch example on keras.io created by hfawaz.
Model description
The model is a Fully Convolutional Neural Network originally proposed in this paper. The implementation is based on the TF 2 version provided here. The hyperparameters (kernel_size, filters, the usage of BatchNorm) were found via random search using KerasTuner.
Intended uses & limitations
Given a time series of 500 samples, the goal is to automatically detect the presence of a specific issue with the engine.
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.
Training and evaluation data
The dataset used here is called FordA. The data comes from the UCR archive. The dataset contains:
- 3601 training instances
- 1320 testing instances
Each timeseries corresponds to a measurement of engine noise captured by a motor sensor.
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
| name | learning_rate | decay | beta_1 | beta_2 | epsilon | amsgrad | training_precision |
|---|---|---|---|---|---|---|---|
| Adam | 9.999999747378752e-05 | 0.0 | 0.8999999761581421 | 0.9990000128746033 | 1e-07 | False | float32 |
Model Plot
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