blusson/PT21-22-Reseau-Neurones
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plus d'erreurs dans les maths, mais ca ne converge toujours pas

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eynard
2021-12-16 23:05:27 +01:00
parent 8189a03abf
commit 619f4762ef
2 changed files with 39 additions and 17 deletions
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28
sobek/network.py
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@@ -47,7 +47,7 @@ class network:
if (__storeValues):
self.activations.append(_input.copy())
#reLu application
#activation function application
for neuron in range(len(_input)):
_input[neuron] = network.__sigmoid(_input[neuron])
@@ -67,8 +67,12 @@ class network:
self.__errors = [[0]*(len(layer)) for layer in self.__weights]
for _input, desiredOutput in zip(inputs, desiredOutputs):
#rempli self.activations et self.outputs
self.__output = self.process(_input, True)
self.__desiredOutput = desiredOutput
for layerNumber in range(len(errorSums)-1, -1, -1):
for neuronNumber in range(len(errorSums[layerNumber])):
for weightNumber in range(len(errorSums[layerNumber][neuronNumber])):
@@ -77,12 +81,17 @@ class network:
total = 0
for i in range(len(errorSums)):
for j in range(len(errorSums[i])):
for k in range(len(errorSums[i][j])):
errorSums[i][j][k] = errorSums[i][j][k] / len(inputs)
total += errorSums[i][j][k]
self.__weights[i][j][k] -= learningRate * errorSums[i][j][k]
for layerNumber in range(len(errorSums)):
for neuronNumber in range(len(errorSums[layerNumber])):
for weightNumber in range(len(errorSums[layerNumber][neuronNumber])):
#Probablement faisable avec une multiplication de matrices
errorSums[layerNumber][neuronNumber][weightNumber] = errorSums[layerNumber][neuronNumber][weightNumber] / len(inputs)
total += errorSums[layerNumber][neuronNumber][weightNumber]
#Probablement faisable avec une somme de matrices
self.__weights[layerNumber][neuronNumber][weightNumber] -= learningRate * errorSums[layerNumber][neuronNumber][weightNumber]
print("Error : " + str(total))
@@ -96,9 +105,10 @@ class network:
def __ErrorHiddenLayer(self, layer, neuron):
upperLayerLinksSum = 0
for upperLayerNeuron in range(len(self.__weights[layer+1]-1)):
#Probablement faisable avec une multiplication de matrices
for upperLayerNeuron in range(len(self.__weights[layer+1])):
#A comparer avec un acces direct au erreurs precalcules
upperLayerLinksSum += self.__weights[layer+1][upperLayerNeuron][neuron] * self.__Error(layer+1, neuron)
upperLayerLinksSum += self.__weights[layer+1][upperLayerNeuron][neuron] * self.__Error(layer+1, upperLayerNeuron)
return network.__sigmoid(self.activations[layer][neuron], True) * upperLayerLinksSum
def __partialDerivative(self, layer, neuron, weight):