class.nnn.6: Triaxial neural network for original Sojourn method
In Sojourn.Data: Supporting Objects for Sojourn Accelerometer Methods
Description
Triaxial neural network for original Sojourn method
Usage
1
Format
From print(class.nnn.6):
a 22-25-4 network with 767 weights inputs: X50. X75. X90. acf X10.2 X25.2
X50.2 X75.2 X90.2 acf.2 X25.3 X50.3 X75.3 X90.3 acf.3 X10.vm X25.vm X50.vm
X75.vm X90.vm acf.vm inact.durations output(s): train.6$act.type options were
- skip-layer connections softmax modelling decay=0.03
Sojourn.Data documentation built on May 3, 2021, 5:06 p.m.
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Description
Triaxial neural network for original Sojourn method
Usage
1 |
Format
From print(class.nnn.6):
a 22-25-4 network with 767 weights inputs: X50. X75. X90. acf X10.2 X25.2 X50.2 X75.2 X90.2 acf.2 X25.3 X50.3 X75.3 X90.3 acf.3 X10.vm X25.vm X50.vm X75.vm X90.vm acf.vm inact.durations output(s): train.6$act.type options were - skip-layer connections softmax modelling decay=0.03
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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