From c0a705ffb957daf29951e77e88a444127c5bda69 Mon Sep 17 00:00:00 2001 From: eynard Date: Fri, 17 Dec 2021 15:08:53 +0100 Subject: [PATCH] =?UTF-8?q?changements=20de=20boucles=20en=20op=C3=A9ratio?= =?UTF-8?q?ns=20de=20matrices?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- sobek/network.py | 43 +++++++++++++++++++++++++++---------------- testLearning.py | 12 ++++++------ 2 files changed, 33 insertions(+), 22 deletions(-) diff --git a/sobek/network.py b/sobek/network.py index 04aa73b..5a7dd88 100755 --- a/sobek/network.py +++ b/sobek/network.py @@ -63,8 +63,9 @@ class network: def train(self, inputs, desiredOutputs, learningRate): - errorSums = [[[0]*(len(neuron)) for neuron in layer] for layer in self.__weights] - self.__errors = [[0]*(len(layer)) for layer in self.__weights] + errorSumsWeights = [[[0]*len(neuron) for neuron in layer] for layer in self.__weights] + errorSumsBiases = [[0]*len(layer) for layer in self.__biases] + self.__errors = [[0]*len(layer) for layer in self.__weights] for _input, desiredOutput in zip(inputs, desiredOutputs): @@ -73,27 +74,38 @@ class network: 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])): + #Somme de matrice ? + 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)) - errorSums[layerNumber][neuronNumber][weightNumber] += self.__partialDerivative(layerNumber, neuronNumber, weightNumber) + errorSumsWeights[layerNumber][neuronNumber][weightNumber] += self.__PartialDerivative(layerNumber, neuronNumber, weightNumber) total = 0 - for layerNumber in range(len(errorSums)): - for neuronNumber in range(len(errorSums[layerNumber])): - for weightNumber in range(len(errorSums[layerNumber][neuronNumber])): + errorSumsWeights = np.multiply(errorSumsWeights, -(learningRate/len(inputs))) + self.__weights = np.add(self.__weights, errorSumsWeights) + + errorSumsBiases = np.multiply(errorSumsBiases, -(learningRate/len(inputs))) + self.__biases = np.add(self.__biases, errorSumsBiases) + + print(self.__biases) + + """ + for layerNumber in range(len(errorSumsWeights)): + for neuronNumber in range(len(errorSumsWeights[layerNumber])): + for weightNumber in range(len(errorSumsWeights[layerNumber][neuronNumber])): #Probablement faisable avec une multiplication de matrices - errorSums[layerNumber][neuronNumber][weightNumber] = errorSums[layerNumber][neuronNumber][weightNumber] / len(inputs) + errorSumsWeights[layerNumber][neuronNumber][weightNumber] = errorSumsWeights[layerNumber][neuronNumber][weightNumber] / len(inputs) - total += errorSums[layerNumber][neuronNumber][weightNumber] + total += errorSumsWeights[layerNumber][neuronNumber][weightNumber] #Probablement faisable avec une somme de matrices - self.__weights[layerNumber][neuronNumber][weightNumber] -= learningRate * errorSums[layerNumber][neuronNumber][weightNumber] + self.__weights[layerNumber][neuronNumber][weightNumber] -= learningRate * errorSumsWeights[layerNumber][neuronNumber][weightNumber] - print("Error : " + str(total)) + print("Error : " + str(total))""" def __Error(self, layer, neuron): if (self.__errors[layer][neuron] == 0 ): @@ -107,9 +119,8 @@ class network: upperLayerLinksSum = 0 #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, upperLayerNeuron) + upperLayerLinksSum += self.__weights[layer+1][upperLayerNeuron][neuron] * self.__errors[layer+1][upperLayerNeuron] return network.__sigmoid(self.activations[layer][neuron], True) * upperLayerLinksSum - def __partialDerivative(self, layer, neuron, weight): + def __PartialDerivative(self, layer, neuron, weight): return self.__Error(layer, neuron) * self.outputs[layer-1][weight] \ No newline at end of file diff --git a/testLearning.py b/testLearning.py index 55ede76..9a827d9 100644 --- a/testLearning.py +++ b/testLearning.py @@ -4,11 +4,11 @@ from sobek.network import network random.seed() -myNetwork = network(10, 10) +myNetwork = network(10, 10, 10) learningRate = 1 -for j in range(10000): +for j in range(100): inputs = [] inputs2 = [] desiredOutputs = [] @@ -25,15 +25,15 @@ for j in range(10000): desiredOutputs[i][9 - int(inputs[i][0]*10)] = 1.0 desiredOutputs = np.array(desiredOutputs, dtype=object) - for i in range(1000): - inputs2.append([0]*10) - inputs2[i][int(inputs[i][0]*10)] = 1.0 + #for i in range(1000): + # inputs2.append([0]*10) + # inputs2[i][int(inputs[i][0]*10)] = 1.0 inputs2 = np.array(inputs2, dtype=object) if (j%10000 == 0): learningRate*= 0.1 - myNetwork.train(inputs2, desiredOutputs, learningRate) + myNetwork.train(desiredOutputs, desiredOutputs, learningRate) test = [] test.append([0]*10)