Difference between revisions of "Dist net.m"
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| − | {{DISPLAYTITLE: | + | {{DISPLAYTITLE:dist_net.m}} __NOTOC__ |
Returns an untrained distance distribution DEERNet for processing fully sampled data. | Returns an untrained distance distribution DEERNet for processing fully sampled data. | ||
==Syntax== | ==Syntax== | ||
| − | layers=dist_net( | + | layers=dist_net(np_in,np_out) |
| − | == | + | ==Parameters== |
| − | + | np_in - dimension of the input vector | |
| − | + | ||
| + | np_out - dimension of the output vector | ||
==Outputs== | ==Outputs== | ||
| Line 16: | Line 17: | ||
==See also== | ==See also== | ||
| − | [[ | + | [[deernet.m]], [[dist_vet.m]], [[logsLayer.m]], [[renormLayer.m]], [[Neural network module]] |
| − | + | ''Version 2.8, authors: [[Ilya Kuprov]], [[Jake Keeley]], [[Tajwar Choudhury]]'' | |
| − | ''Version 2. | ||
Latest revision as of 19:36, 6 June 2026
Returns an untrained distance distribution DEERNet for processing fully sampled data.
Syntax
layers=dist_net(np_in,np_out)
Parameters
np_in - dimension of the input vector
np_out - dimension of the output vector
Outputs
layers - an untrained network layout
See also
deernet.m, dist_vet.m, logsLayer.m, renormLayer.m, Neural network module
Version 2.8, authors: Ilya Kuprov, Jake Keeley, Tajwar Choudhury
