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timeseries-classification-from-scratch
keras-io/timeseries-classification-from-scratch
timeseries-classification-from-scratch is a machine learning model from keras-io. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for tf-keras.
Based on the Timeseries classification from scratch example on keras.io created by hfawaz.
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At a glance
- Library
- tf-keras
- Access
- Public
- Created
- Jun 21, 2022
- Updated
- Jul 5, 2024
- SHA
- ad9dc5b8
Model card
From the Hugging Face model README
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
<details> <summary>View Model Plot</summary>
- Library
- tf-keras
- Created
- Jun 21, 2022
- Updated
- Jul 5, 2024