For many projects, you will need to constrain the EON Tuner to use steps that are defined by your hardware, your customers, or your expertise.
For example:
Your project requires to use a grayscale camera because you already purchased the hardware.
Your engineers have already spent hours working on a dedicated digital signal processing method that has been proven to work with your sensor data.
You have the feeling that a particular neural network architecture will be more suited for your project.
This is why we developed an extension of the EON Tuner: the EON Tuner Search Space.
Please read first the EON Tuner documentation to configure your Target, Task category and desired Time per inference.
Understanding Search Space Configuration
The EON Tuner Search Space allows you to define the structure and constraints of your machine learning projects through the use of templates.
Templates
The Search Space works with templates. The templates can be considered as a config file where you define your constraints. Although templates may seem hard to use in the first place, once you understand the core concept, this tool is extremely powerful!
To understand the core concepts, we recommend having a look at the available templates. We provide templates for different task categories as well as one for your current impulse if it has already been trained.
Load a template
Search parameters
Elements inside an array are considered as parameters. This means, you can stack several combinations of inputBlocks|dspBlocks|learnBlocks in your templates and each block can contain several elements:
window: Details about the windowing approach for time-series data.
Type: array of object with fields:
windowSizeMs: The size of the window in milliseconds.
Type: number
windowIncreaseMs: The step size to increase the window in milliseconds.
Type: number
windowSizeMs: Size of the window in milliseconds if not specified in the window field.
Type: array of number
windowIncreasePct: Percentage to increase the window size each step.
Type: array of number
frequencyHz: Sampling frequency in Hertz.
Type: array of number
padZeros: Whether to pad the time-series data with zeros.
Type: array of boolean
DSP Blocks (dspBlocks)
Common Fields for All DSP Blocks
id: Unique identifier for the DSP block.
Type: number
type: The type of Digital Signal Processing to apply.
Type: string
Valid Options: raw, spectral-analysis, mfe, mfcc, spectrogram, image, flatten, organization (the last one available only if full enterprise search space is enabled)
axes: Name of the data axes in the project.
Type: array of string
implementationVersion: Version of the DSP method used.
Type: number
title: Optional title for the DSP block.
Type: string
Conditional Fields Based on DSP Type
For image Type
channels: Color channels used in the image.
Type: array of string
Valid Options: RGB, Grayscale
For spectral-analysis Type
fft-length: Length of the Fast Fourier Transform applied.
Type: array of number
Enterprise-specific Valid Options: [16, 64]
scale-axes: Scale factor for the axes.
Type: array of number
Enterprise-specific Valid Options: [1]
filter-type: Type of filter applied.
Type: array of string
Valid Options: low, high, none
filter-cutoff: Cutoff frequency for the filter.
Type: array of number
filter-order: Order of the filter.
Type: array of number
do-log: Whether to apply logarithmic scaling.
Type: array of boolean
do-fft-overlap: Whether to overlap FFT windows.
Type: array of boolean
spectral-peaks-count: Number of spectral peaks to identify.
Type: array of number
spectral-peaks-threshold: Threshold for identifying spectral peaks.
Type: array of number
spectral-power-edges: Defines the spectral edges for power calculation.
Type: array of string
autotune: Whether to enable automatic tuning of parameters.
Type: array of boolean
analysis-type: Type of spectral analysis.
Type: array of string
Valid Options: FFT, Wavelet
wavelet-level: Level of wavelet transformation.
Type: array of number
wavelet: Type of wavelet used.
Type: array of string
extra-low-freq: Whether to include extra low frequencies in analysis.
Type: array of boolean
input-decimation-ratio: Ratio for input decimation.
Type: array
For mfcc, mfe Types
num_filters: Number of filters used in MFCC or MFE.
Type: array of number
num_cepstral: Number of cepstral coefficients in MFCC.
Type: array of number
win_size: Window size for the analysis.
Type: array of number
low_frequency: Lower bound of the frequency range.
Type: array of number
high_frequency: Upper bound of the frequency range.
Type: array of number
pre_cof: Pre-emphasis coefficient.
Type: array of number
pre_shift: Shift applied before analysis.
Type: array of number
For raw Type
scale-axes: Scale factor for the axes.
Type: array of number
average, minimum, maximum, rms, stddev, skewness, kurtosis: Statistical measures applied to raw data.
Type: array of boolean
For custom or organization Type
organizationId: Identifier for the organization.
Type: number
organizationDSPId: Specific DSP ID within the organization.
dsp: Links to DSP blocks by their IDs indicating which DSP outputs are used as inputs for this learning model.
Type: array of array of number
title: Optional title for the learning block.
Type: string
implementationVersion: Version of the learning algorithm used.
Type: number
Specific Fields Based on Learning Block Type
Dimension and Architecture
dimension: Specifies the type of neural network architecture.
Type: array of string
Valid Options: dense, conv1d, conv2d
dropout: Specifies the dropout rate to prevent overfitting.
Type: array of number
denseBaseNeurons, denseNeurons: Specifies the number of neurons in dense layers.
Type: array of number
denseLayers: Specifies the number of dense layers.
Type: array of number
convBaseFilters: Base number of filters in convolutional layers.
Type: array of number
convLayers: Number of convolutional layers.
Type: array of number
Training Configuration
trainingCycles: Number of training cycles.
Type: array of number
trainTestSplit: The ratio of training to test data.
Type: array of number
autoClassWeights: Whether to automatically adjust class weights.
Type: array of boolean
minimumConfidenceRating: The minimum confidence threshold for class predictions.
Type: array of number
learningRate: The learning rate for the optimizer.
Type: array of number
batchSize: Number of samples per batch during training.
Type: array of number
Augmentation and Model Policies
augmentationPolicySpectrogram: Defines the data augmentation strategies for spectrogram data.
Type: object
Fields within the object:
enabled: Whether to apply augmentation.
Type: array of boolean
gaussianNoise: Level of Gaussian noise to add.
Type: array of string
Valid Options: none, low, high
timeMasking: Extent of time masking to apply.
Type: array of string
Valid Options: none, low, high
freqMasking: Extent of frequency masking to apply.
Type: array of string
Valid Options: none, low, high
warping: Whether to apply time warping.
Type: array of boolean
augmentationPolicyImage: Defines the data augmentation strategies for image data.
Type: array of string
Valid Options:
all: Apply all available image augmentations.
none: Do not apply any image augmentations.
Advanced Configurations
layers: Specifies the configuration of each layer within the learning model.
Type: array of object
Fields within each layer object:
type: The type of layer (e.g., conv2d, dense).
Type: string
neurons: Specifies the number of neurons for dense layers or number of filters for convolutional layers.
Type: array of number
Valid Options: [8, 16, 32, 64, 10, 20, 40], can vary depending on the full Eon Tuner search space availability.
kernelSize: Size of the kernel in convolutional layers.
Type: array of number
Valid Options: [1, 3, 5], specific to the project’s tuner space.
dropoutRate: Dropout rate for the layer to prevent overfitting.
Type: array of number
Valid Options: [0.1, 0.25, 0.5], determined by the project settings.
columns: Optional field typically used in tabular data or custom setups.
Type: array of number
stack: Defines how many times the layer configuration should be repeated.
Type: array of number
enabled: Flag to enable or disable the layer.
Type: array of boolean
organizationModelId: If using a custom model from an organization, this is the identifier.
Type: number
model: Specifies the base model for transfer learning scenarios.
Type: array of string
Valid Options:
transfer_mobilenetv2_a35
transfer_mobilenetv2_a1
transfer_mobilenetv2_a05
transfer_mobilenetv1_a2_d100
transfer_mobilenetv1_a1_d100
transfer_mobilenetv1_a25_d100
transfer_mobilenetv2_160_a1
transfer_mobilenetv2_160_a75
transfer_mobilenetv2_160_a5
transfer_mobilenetv2_160_a35
fomo_mobilenet_v2_a01
fomo_mobilenet_v2_a35
object_ssd_mobilenet_v2_fpnlite_320x320
transfer_kws_mobilenetv1_a1_d100
transfer_kws_mobilenetv2_a35_d100
transfer_akidanet_imagenet_160_a50
transfer_akidanet_imagenet_224_a50
fomo_akidanet_a50
customValidationMetadataKey: Key for custom metadata used in validation.
Type: array of string
profileInt8: Specifies whether to use INT8 quantization.
Type: array of boolean
skipEmbeddingsAndMemory: Whether to skip certain processing steps to optimize memory usage.
Type: array of boolean
useLearnedOptimizer: Whether to use a learned optimizer during training.
Type: array of boolean
anomalyCapacity: Specifies the model's capacity to handle anomalies.
Type: array of string
Valid Options: low, medium, high
customParameters: Allows for additional custom parameters if full Eon Tuner search space is enabled.
Type: array of object
Additional Notes
The actual availability of certain dimensions or options can depend on whether your project has full enterprise capabilities (projectHasFullEonTunerSearchSpace). This might unlock additional valid values or remove restrictions on certain fields.
Fields within array of array structures (like dimension or window) allow for multi-dimensional setups where each sub-array represents a different configuration that the EON Tuner can evaluate.
Examples
Image classification
Example of a template where we constrained the search space to use 96x96 grayscale images to compare a neural network architecture with a transfer learning architecture using MobileNetv1 and v2:
Object detection models can use either bounding boxes (object location and size) or centroids (object location only). See the object detection documentation to learn more about the differences between these two task categories.
Example of a template where we search for object detection models using bounding boxes (e.g. MobileNet V2 SSD FPN-Lite):
Should you wish to compare models using bounding boxes with models using centroids, you can customize the search space to include impulses for both model types.
Audio
Example of a template where we want to compare, on the one side, MFCC vs MFE pre-processing with a custom NN architecture and on the other side, keyword spotting transfer learning architecture:
