Flood Monitor +1Hr - Impulses

Experiments

Name	Tags	Input	Win_Incr	DSP Blocks	Learn Blocks	f32_T_Acc	Tr_Proc	f32_Latency	f32_RAM	f32_Flash	i8_Latency	i8_RAM	i8_Flash	i8_V_Acc	i8_T_Acc
Impulse #1		21,600,000ms.	10,800,000ms.	Raw data	Regression	-	CPU	1 ms.	1.6K	19.8K	1 ms.	1.3K	13.3K	-	-	View impulse
Classical ML		21,600,000ms.	10,800,000ms.	Raw data	XGBoost Random Forest Regressor	-	CPU	2 ms.	1.2K	15.8K	-	-	-	-	-	View impulse
raw-dense-4ce		5,400,000ms.	900,000ms.	Raw Data	Regression	100.0%	CPU	9 ms.	1.3K	12.8K	1 ms.	1.2K	11.5K		100.0%	View impulse
5400000 #2		5,400,000ms.	2,700,000ms.	Raw Data	Regression	-	CPU	1 ms.	1.3K	12.8K	1 ms.	1.2K	11.5K	-	-	View impulse
raw-dense-484		5,400,000ms.	900,000ms.	Raw Data	Regression	100.0%	CPU	37 ms.	2.3K	112.6K	2 ms.	1.5K	37.3K	-	-	View impulse
raw-dense-cd9	Local Version	5,400,000ms.	900,000ms.	Raw Data	Regression	99.9%	CPU	2 ms.	1.6K	27.3K	1 ms.	1.5K	15.5K	-	-	View impulse

Impulse information

Tags - Impulse tags

Source - Impulse source (Manual vs EON Tuner)

Created by - The user who originally created this impulse

Key metrics

f32_V_Acc - Accuracy (float32, validation set)

f32_T_Acc - Accuracy (float32, test set)

Tr_Proc - Training processor

f32_Latency - float32 Latency for the complete impulse.

f32_RAM - float32 RAM for the complete impulse.

f32_Flash - float32 Flash for the complete impulse.

i8_Latency - int8 Latency for the complete impulse.

i8_RAM - int8 RAM for the complete impulse.

i8_Flash - int8 Flash for the complete impulse.

i8_V_Acc - Accuracy (int8, validation set)

i8_T_Acc - Accuracy (int8, test set)

Input block config

Win_Size - Window size

Win_Incr - Window increase

Freq - Frequency

Zero_Pad - Zero pad data

Processing block config

Norm - Normalization

Sca_axe - Scaling > Scale axes (Processing Block #1)

Learn block config

Model - ML Model architecture (Learn Block #1)

Tc - Training cycles (Learn Block #1)

Lr - Learning rate (Learn Block #1)

Lo - Use learned optimizer? (Learn Block #1)

Acw - Auto-weight classes (Learn Block #1)

Data_Aug - Data augmentation (Learn Block #1)

Val_Size - Validation set size (Learn Block #1)

Val_Key - Split train/validation set on metadata key (Learn Block #1)

Mode - Visual / expert mode (Learn Block #1)

Thr_Mae - Maximum absolute error (Learn Block #1)

Lea_rat - Learning rate (Learn Block #1)

Max_dep - Max. depth (Learn Block #1)

Nbr - Num. boost round (Learn Block #1)

L2 - L2 (Learn Block #1)

L1 - L1 (Learn Block #1)

Max_lea - Max. leaves (Learn Block #1)

Mcw - Min. child weight (Learn Block #1)

Sub - Subsample (Learn Block #1)

Accuracy metrics

f32_V_MSE - float32 Mean squared error (validation set)

f32_V_MAE - float32 Mean absolute error (validation set)

f32_V_EVS - float32 Explained variance score (validation set)

f32_T_MSE - float32 Mean squared error (test set)

f32_T_MAE - float32 Mean absolute error (test set)

f32_T_EVS - float32 Explained variance score (test set)

i8_V_MSE - int8 Mean squared error (validation set)

i8_V_MAE - int8 Mean absolute error (validation set)

i8_V_EVS - int8 Explained variance score (validation set)

i8_T_MSE - int8 Mean squared error (test set)

i8_T_MAE - int8 Mean absolute error (test set)

i8_T_EVS - int8 Explained variance score (test set)

Post-processing block config

No columns

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

Experiments

Impulse #1

Classical ML

raw-dense-4ce

5400000 #2

raw-dense-484

raw-dense-cd9