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übung 3

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Florian Babel 2026-10-19 12:28:46 +02:00
parent 21bdd9376a
commit 3aaba244d5
2 changed files with 8304 additions and 57 deletions
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8
Übung 3/SLN.py
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@ -37,19 +37,23 @@ class SLN:
for it in range(maxIter): for it in range(maxIter):
Y = self.neuron(X) # nur ein Aufruf Y = self.neuron(X) # nur ein Aufruf
err = ErrorRate(Y, T) err = ErrorRate(Y, T)
if (it % 20) == 0: # if (it % 20) == 0:
print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b)) print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b))
nnwplot.plotTwoFeatures(X, T, self.neuron) nnwplot.plotTwoFeatures(X, T, self.neuron)
plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht) plt.pause(0.05) # warte auf GUI event loop (je nach python Version notwendig oder nicht)
if err <= maxErrorRate: if err <= maxErrorRate:
break break
# update _W # update _W
total_to_learn = 0
for n in range(N): for n in range(N):
error = T[n] - Y[n] error = T[n] - Y[n]
for j in range(self.dIn): for j in range(self.dIn):
weighted_error = error * X[j, n] weighted_error = error * X[j, n]
to_learn = weighted_error * eta to_learn = weighted_error * eta
self._W[j, 0] += to_learn print("{:.2f} = {} * {} * {}".format(to_learn, error, X[j, n], eta))
total_to_learn += to_learn
print(total_to_learn)
self._W[j, 0] += total_to_learn
# update _b # update _b
... ...
print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b)) print('#{} err:{}\n{}\n{}'.format(it, err, self._W, self._b))

8347
Übung 3/training.ipynb
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