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übung 3 fertig

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FloBab73 2026-10-20 09:12:03 +02:00
parent d9efd7c0ff
commit cd7ec4b7a6
2 changed files with 44 additions and 65 deletions
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9
Übung 3/SLN.py
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@ -3,7 +3,7 @@ import nnwplot
import matplotlib.pyplot as plt import matplotlib.pyplot as plt
def ErrorRate(Y, T): def error_rate(Y, T):
if Y.ndim == 1 or Y.shape[0] == 1: if Y.ndim == 1 or Y.shape[0] == 1:
errors = Y - T errors = Y - T
return errors.sum() / Y.size return errors.sum() / Y.size
@ -29,15 +29,15 @@ class SLN:
for i in range(self.dIn): for i in range(self.dIn):
value += X[i, n] * self._W[i][0] value += X[i, n] * self._W[i][0]
net[n] += value + self._b[0][0] net[n] += value + self._b[0][0]
return net >= 0 return net
def delta_train(self, X, T, eta, maxIter, maxErrorRate): def delta_train(self, X, T, eta, maxIter, maxErrorRate):
N = X.shape[1] # Anzahl Trainingsdaten N = X.shape[1] # Anzahl Trainingsdaten
plt.ion() # interactive mode on plt.ion() # interactive mode on
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 = error_rate(Y, T)
if (it % 20) == 0: if (it % 10) == 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)
@ -45,7 +45,6 @@ class SLN:
break break
# update _W # update _W
for n in range(N): for n in range(N):
error = T[n] - Y[n]
for j in range(self.dIn): for j in range(self.dIn):
weighted_error = err * X[j, n] weighted_error = err * X[j, n]
to_learn = weighted_error * eta to_learn = weighted_error * eta

100
Übung 3/training.ipynb
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