Comments (10)
Ok, even if the learn flag was off, the network will still learn something though very minor. Can't really see from dwt but the test results are different. But the learning when only learn flag is off is much smaller than when both prjn and learn flag are off.
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I just experimented with ra25 and hip examples, and both obey the Off flag, as in the code below. And the IsOff() method checks both the prjn Off flag and both the send and recv layer Off flags.
However, it is also definitely the case that when you turn it off, any residual DWt values will still show up in the display.
So something strange is happening. we probably need to sit down and debug together.
// DWt computes the weight change (learning) -- calls DWt method on sending projections
func (ly *Layer) DWt() {
for _, p := range ly.SndPrjns {
if p.IsOff() {
continue
}
p.(LeabraPrjn).DWt()
}
}
// WtFmDWt updates the weights from delta-weight changes -- on the sending projections
func (ly *Layer) WtFmDWt() {
for _, p := range ly.SndPrjns {
if p.IsOff() {
continue
}
p.(LeabraPrjn).WtFmDWt()
}
}
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from emergent.
from emergent.
can you add print statements in the DWt method after the IsOff() continue statement to verify that it is going into that code? something is very strange if so as those are really basic functions!
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ps I will be avail after the eeg meeting to go over
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print with name of prjn: fmt.Printf("dwt on: %v\n", pj.Nm)
yep I'll come back to cns
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Closing this one as well.
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