blusson/PT21-22-Reseau-Neurones
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cleanup

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eynard
2021-12-22 22:08:20 +01:00
parent 7733de01d2
commit 151343b7bd
11 changed files with 22 additions and 61 deletions
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35
sobek/network.py
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@@ -51,11 +51,8 @@ class network:
self.activations.append(_input)
#activation function application
#for i in range(len(_input)):
# _input[i] = network.__sigmoid(_input)
_input = network.__sigmoid(_input)
#On peut comparer la performance si on recalcul plus tard
if (__storeValues):
self.outputs.append(_input)
@@ -110,18 +107,14 @@ class network:
errorSumsBiases = [np.zeros(layer.shape) for layer in self.biases]
self.__errors = [np.zeros(len(layer)) for layer in self.weights]
#rempli self.activations et self.outputs
#Rempli self.activations et self.outputs
self.process(_input, True)
self.__desiredOutput = desiredOutput
#A optimiser
for layerNumber in range(len(errorSumsWeights)-1, -1, -1):
for neuronNumber in range(len(errorSumsWeights[layerNumber])):
errorSumsBiases[layerNumber][neuronNumber] += self.__Error(layerNumber, neuronNumber)
#for weightNumber in range(len(errorSumsWeights[layerNumber][neuronNumber])):
#print("layer : " + str(layerNumber) + " neuron : " + str(neuronNumber) + " weight : " + str(weightNumber))
#errorSumsWeights[layerNumber][neuronNumber][weightNumber] += self.__PartialDerivative(layerNumber, neuronNumber, weightNumber)
#errorSumsWeights[layerNumber][neuronNumber][weightNumber] = errorSumsBiases[layerNumber][neuronNumber] * self.outputs[layerNumber][weightNumber]
errorSumsWeights[layerNumber][neuronNumber] = np.dot(errorSumsBiases[layerNumber][neuronNumber],self.outputs[layerNumber])
total = 0
@@ -133,27 +126,6 @@ class network:
errorSumsBiases[layerNumber] = np.multiply(errorSumsBiases[layerNumber], -(learningRate/len(inputBatch)))
self.biases[layerNumber] = np.add(self.biases[layerNumber], errorSumsBiases[layerNumber])
#print(self.__biases)
"""
for layerNumber in range(len(errorSumsWeights)):
for neuronNumber in range(len(errorSumsWeights[layerNumber])):
errorSumsBiases[layerNumber][neuronNumber] = errorSumsBiases[layerNumber][neuronNumber] / len(inputBatch)
total += errorSumsBiases[layerNumber][neuronNumber]
self.biases[layerNumber][neuronNumber] -= learningRate * errorSumsBiases[layerNumber][neuronNumber]
for weightNumber in range(len(errorSumsWeights[layerNumber][neuronNumber])):
#Probablement faisable avec une multiplication de matrices
errorSumsWeights[layerNumber][neuronNumber][weightNumber] = errorSumsWeights[layerNumber][neuronNumber][weightNumber] / len(inputBatch)
#total += errorSumsWeights[layerNumber][neuronNumber][weightNumber]
#Probablement faisable avec une somme de matrices
self.weights[layerNumber][neuronNumber][weightNumber] -= learningRate * errorSumsWeights[layerNumber][neuronNumber][weightNumber]
#print("Error : " + str(total))"""
if (visualize):
ani = animation.ArtistAnimation(fig, vizualisationData, interval=100)
plt.show()
@@ -172,9 +144,6 @@ class network:
upperLayerLinksSum += self.weights[layer+1][upperLayerNeuron][neuron] * self.__errors[layer+1][upperLayerNeuron]
return network.__sigmoid(self.activations[layer][neuron], derivative=True) * upperLayerLinksSum
#def __PartialDerivative(self, layer, neuron, weight):
# return self.__Error(layer, neuron) * self.outputs[layer][weight]
def accuracy(self, inputs, desiredOutputs):
if (type(inputs) != list or type(desiredOutputs) != list):
raise TypeError("The inputs and desired outputs must be lists of numpy arrays !")