Syntiant / Tutorial: Continuous motion recognition - RASynBoard Public

Neural Network settings

Training settings

Number of training cycles

Please provide a valid number of training cycles (numeric only)

Use learned optimizer

Learning rate

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Training processor

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Advanced training settings

Validation set size

Please provide a valid number for the train/validate split (between 0 and 1)

Split train/validation set on metadata key

Batch size

Auto-weight classes

Profile int8 model

Neural network architecture

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import tensorflow as tf
from tensorflow.keras.models import Sequential
 
 
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Input layer (1,200 features)

Dense layer (256 neurons)

Dropout (rate 0.2)

Dense layer (256 neurons)

Dropout (rate 0.2)

Dense layer (256 neurons)

Dropout (rate 0.2)

Output layer (4 classes)

Model

Model version:

	snake	updown	wave	z_openset
snake	90.6%	0.5%	0.2%	8.7%
updown	0%	98.6%	0%	1.4%
wave	0%	0%	100%	0%
z_openset	10.0%	3.8%	0.8%	85.4%
f1 score	0.91	0.96	0.99	0.87

Last training performance (validation set)

Accuracy

92.9%

Loss

0.19

Confusion matrix (validation set)

	snake	updown	wave	z_openset
snake	90.3%	0.5%	0.2%	9.0%
updown	0%	98.6%	0%	1.4%
wave	0%	0%	100%	0%
z_openset	9.7%	3.3%	0.8%	86.2%
f1 score	0.91	0.97	0.99	0.88

Metrics (validation set)

Metric	Value
Area under ROC Curve	0.99
Weighted average Precision	0.93
Weighted average Recall	0.93
Weighted average F1 score	0.93

Data explorer (full training set)

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Did you know? You have access to the full set of Keras layers through the Expert view (click on to switch),
or can even bring your own model (in PyTorch, Keras or scikit-learn).

Layer type
Dense Fully connected layer, the simplest form of a neural network layer. Use this for processed data, such as the output of a spectral analysis DSP block.
1D Convolution / pooling Learn features that take spatial information into account along a single dimension. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
2D Convolution / pooling Learn features that take spatial information into account along two dimensions. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.
Reshape Turn one-dimensional data from a DSP block into multi-dimensional data. Use this as an input to a convolutional layer. Use this for deep learning on raw data, or to process MFCC output.
Flatten Flatten multi-dimensional data into a single dimension. You need to flatten data from a convolutional layer before returning.
Dropout Reduce the risk of a model overfitting your dataset by randomly cutting a fraction of network connections during training. Can be helpful if your model's training performance is better than its validation performance.

Fully connected layer, the simplest form of a neural network layer. Use this for processed data, such as the output of a spectral analysis DSP block.

1D Convolution / pooling

Learn features that take spatial information into account along a single dimension. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.

2D Convolution / pooling

Learn features that take spatial information into account along two dimensions. Use this for raw data, or for DSP blocks that output spatial data, such as the MFCC block.

Turn one-dimensional data from a DSP block into multi-dimensional data. Use this as an input to a convolutional layer. Use this for deep learning on raw data, or to process MFCC output.

Flatten multi-dimensional data into a single dimension. You need to flatten data from a convolutional layer before returning.

Reduce the risk of a model overfitting your dataset by randomly cutting a fraction of network connections during training. Can be helpful if your model's training performance is better than its validation performance.

Complete architectures

Model	Author
LGBM Random Forest Classifier Professional Enterprise The LightGBM Random Forest Classifier is an efficient, gradient-boosting framework that builds multiple decision trees in parallel for robust and scalable classification tasks.	Edge Impulse Inc.
XGBoost Random Forest Classifier Professional Enterprise The XGBoost Random Forest Classifier leverages the strengths of both gradient boosting and random forest methodologies to provide a powerful, efficient, and scalable solution for classification tasks.	Edge Impulse Inc.
SVM Classifier Professional Enterprise The scikit-learn SVM classifier is a powerful algorithm that constructs a hyperplane or set of hyperplanes in a high-dimensional space for classification, effectively handling both linear and non-linear data.	Edge Impulse Inc.
Random Forest Classifier Professional Enterprise The scikit-learn Random Forest Classifier is a versatile machine learning algorithm that builds multiple decision trees and merges their predictions for robust and accurate classification.	Edge Impulse Inc.
Logistic Regression Professional Enterprise The scikit-learn Logistic Regressor is a linear model for classification that estimates probabilities using a logistic function, effectively handling binary and multiclass classification tasks.	Edge Impulse Inc.
Ridge Classifier Professional Enterprise The scikit-learn Ridge classifier employs L2 regularization to optimize a linear classification model, mitigating overfitting by penalizing large coefficients, thereby enhancing model generalizability across diverse datasets.	Edge Impulse Inc.
RidgeCV Classifier Professional Enterprise The scikit-learn RidgeCV classifier is a linear regression model with built-in cross-validation that applies ridge regularization to prevent overfitting and improve prediction accuracy.	Edge Impulse Inc.

LGBM Random Forest Classifier
Professional

Enterprise

The LightGBM Random Forest Classifier is an efficient, gradient-boosting framework that builds multiple decision trees in parallel for robust and scalable classification tasks.

XGBoost Random Forest Classifier
Professional

Enterprise

The XGBoost Random Forest Classifier leverages the strengths of both gradient boosting and random forest methodologies to provide a powerful, efficient, and scalable solution for classification tasks.

SVM Classifier
Professional

Enterprise

The scikit-learn SVM classifier is a powerful algorithm that constructs a hyperplane or set of hyperplanes in a high-dimensional space for classification, effectively handling both linear and non-linear data.

Random Forest Classifier
Professional

Enterprise

The scikit-learn Random Forest Classifier is a versatile machine learning algorithm that builds multiple decision trees and merges their predictions for robust and accurate classification.

Logistic Regression
Professional

Enterprise

The scikit-learn Logistic Regressor is a linear model for classification that estimates probabilities using a logistic function, effectively handling binary and multiclass classification tasks.

Ridge Classifier
Professional

Enterprise

The scikit-learn Ridge classifier employs L2 regularization to optimize a linear classification model, mitigating overfitting by penalizing large coefficients, thereby enhancing model generalizability across diverse datasets.

RidgeCV Classifier
Professional

Enterprise

The scikit-learn RidgeCV classifier is a linear regression model with built-in cross-validation that applies ridge regularization to prevent overfitting and improve prediction accuracy.

The backbone is the foundational element of a neural network model, responsible for extracting meaningful features from input data, enabling subsequent parts to perform anomaly scoring or classification.

Description	Author
EfficientNet V2B0 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.35 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.50 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.75 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 1.0 Supports both RGB and grayscale.	Edge Impulse
MobileNetV2 0.1 Supports both RGB and grayscale.	Edge Impulse

Supports both RGB and grayscale.

Some backbones have been disabled and hidden because they are not supported by the selected scoring function. Show all backbones anyway

The scoring function operates on the features extracted by the backbone, and returns anomaly scores for each segment of the image.

Description	Author	Recommended
LGBM Random Forest Classifier The LightGBM Random Forest Classifier is an efficient, gradient-boosting framework that builds multiple decision trees in parallel for robust and scalable classification tasks.	Edge Impulse
XGBoost Random Forest Classifier The XGBoost Random Forest Classifier leverages the strengths of both gradient boosting and random forest methodologies to provide a powerful, efficient, and scalable solution for classification tasks.	Edge Impulse
SVM Classifier The scikit-learn SVM classifier is a powerful algorithm that constructs a hyperplane or set of hyperplanes in a high-dimensional space for classification, effectively handling both linear and non-linear data.	Edge Impulse
Random Forest Classifier The scikit-learn Random Forest Classifier is a versatile machine learning algorithm that builds multiple decision trees and merges their predictions for robust and accurate classification.	Edge Impulse
Logistic Regression The scikit-learn Logistic Regressor is a linear model for classification that estimates probabilities using a logistic function, effectively handling binary and multiclass classification tasks.	Edge Impulse
Ridge Classifier The scikit-learn Ridge classifier employs L2 regularization to optimize a linear classification model, mitigating overfitting by penalizing large coefficients, thereby enhancing model generalizability across diverse datasets.	Edge Impulse
RidgeCV Classifier The scikit-learn RidgeCV classifier is a linear regression model with built-in cross-validation that applies ridge regularization to prevent overfitting and improve prediction accuracy.	Edge Impulse

LGBM Random Forest Classifier

The LightGBM Random Forest Classifier is an efficient, gradient-boosting framework that builds multiple decision trees in parallel for robust and scalable classification tasks.

XGBoost Random Forest Classifier

Random Forest Classifier

The scikit-learn Random Forest Classifier is a versatile machine learning algorithm that builds multiple decision trees and merges their predictions for robust and accurate classification.

The scikit-learn Logistic Regressor is a linear model for classification that estimates probabilities using a logistic function, effectively handling binary and multiclass classification tasks.

The scikit-learn RidgeCV classifier is a linear regression model with built-in cross-validation that applies ridge regularization to prevent overfitting and improve prediction accuracy.

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	Name
	Francesco Edge Impulse staff
	David Tischler Edge Impulse staff
	Joshua Buck Edge Impulse staff
	MH Monica Houston
	BW Brian Willess
	Brandon Shibley Edge Impulse staff

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

Every learning block has a threshold. This can be the minimum confidence that a neural network needs to have, or the maximum anomaly score before a sample is tagged as an anomaly. You can configure these thresholds to tweak the sensitivity of these learning blocks. This affects both live classification and model testing.

Classifier

Minimum confidence rating

Should be numeric

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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 family

Clock rate

| MHz

Max

Custom device name

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

YEARLY

$400 /month

billed annually

SAVE 15%

MONTHLY

$475 /month

billed monthly

Neural Network settings

Training settings

Augmentation settings

Advanced training settings

Neural network architecture

Model

Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Metrics (validation set)

Data explorer (full training set)

Settings

Last training performance (validation set)

Accuracy

Loss

Confusion matrix (validation set)

Metrics (validation set)

Data explorer (full training set)

Settings