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Time series data, Raw Data, Classification (Keras), Anomaly Detection (K-means)
Time series data, Spectral Analysis, Flatten, Classification (Keras), Anomaly Detection (K-means) Time series data, Raw Data, Classification (Keras), Anomaly Detection (K-means)
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Christopher Mendez / AI Meter Public

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Christopher Mendez / AI Meter Public

Target: Cortex-M7 216MHz Clone this project

Anomaly detection settings

Cluster count

Please provide a valid cluster count (numeric only)

Axes

Select feature axes to include in model training.

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Training output

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Model

By definition there should be as few as possible anomalies in the training dataset, and thus accuracy is not calculated during training. Run Model testing to learn more about the model performance.

Anomaly explorer

X Axis

Y Axis

Test data

No model generated yet.

Retrain this model to reveal additional visualizations

Anomaly score

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Average axis distance

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Target device

Configure your target device for model performance calculations, and to enable target specific optimizations.

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Configure your target device and application budget

Target device

Define your target device requirements to inform model optimizations and performance calculations. No device yet? Use the default settings which you can change at any time.

Target device

Processor type

Processor family

Processor architecture

Clock rate

| MHz

Max

Accelerator

Device ID (optional)

Custom device name (optional)

Application budget

Specify the available RAM and ROM for the model's operation, along with the maximum allowed latency for your specific application. Not sure yet? Start with the defaults and modify them later on.

RAM

| KB

Max

ROM

| KB

Max

Latency

| ms

Max

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