blusson/PT21-22-Reseau-Neurones
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reglage de bugs

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This commit is contained in:
eynard 2021-12-15 22:18:03 +01:00
parent 2e5e1e0d82
commit 4942feae8b
1 changed files with 13 additions and 8 deletions
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21
sobek/network.py
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@ -35,16 +35,19 @@ class network:
# raise TypeError("The input vector must contain floats!")
if (__storeValues):
self.activations = []
self.outputs = []
self.activations = np.array([])
self.outputs = np.array([])
for layerWeights, bias in zip(self.__weights, self.__biases):
_input = np.matmul(_input, layerWeights)
_input = np.add(_input, bias)
if (__storeValues):
self.activations.append(_input)
np.insert(self.activations, 0, bias)
print("-------------------")
print(bias)
print("-------------------")
self.activations = np.append(self.activations, _input)
self.activations[len(self.activations)-1] = np.insert(self.activations[len(self.activations)-1], 0, bias)
#reLu application
with np.nditer(_input, op_flags=['readwrite'], flags=['refs_ok']) as layer:
@ -53,8 +56,8 @@ class network:
#On peut comparer la performance si on recalcul plus tard
if (__storeValues):
self.outputs.append(_input)
np.insert(self.outputs, 0, 1)
self.outputs = np.append(self.outputs, _input)
self.outputs[len(self.outputs)-1] = np.insert(self.outputs[len(self.outputs)-1], 0, 1)
return _input
@ -63,9 +66,10 @@ class network:
for _input, desiredOutput in zip(inputs, desiredOutputs):
self.__output = self.process(_input, True)
self.__desiredOutput = desiredOutput
for layerNumber in range(len(ErrorSums)):
for layerNumber in range(len(ErrorSums)-1, -1, -1):
ErrorSums[layerNumber][0] += self.__partialDerivative(layerNumber, 0)
for neuronNumber in range(1, len(ErrorSums[layerNumber])):
print("layer : " + str(layerNumber) + " neuron : " + str(neuronNumber))
ErrorSums[layerNumber][neuronNumber] += self.__partialDerivative(layerNumber, neuronNumber)
for i in range(len(ErrorSums)):
for j in range(len(ErrorSums[i])):
@ -74,9 +78,10 @@ class network:
def __Error(self, layer, neuron):
return self.__ErrorFinalLayer(neuron) if (layer == len(self.__weights)) else self.__ErrorHiddenLayer(layer, neuron)
return self.__ErrorFinalLayer(neuron) if (layer == len(self.__weights)-1) else self.__ErrorHiddenLayer(layer, neuron)
def __ErrorFinalLayer(self, neuron):
print(self.activations)
return network.__reLu(self.activations[len(self.activations)-1][neuron], True) * (self.__output[neuron] - self.__desiredOutput[neuron])
def __ErrorHiddenLayer(self, layer, neuron):