Reseau neuronal basique fonctionnel
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eynard
parent
c0a705ffb9
commit
dd47f73356
@ -1,5 +1,4 @@
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import numpy as np
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import math
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class network:
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@ -7,71 +6,71 @@ class network:
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if type(inputLayerSize) != int:
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raise TypeError("The input layer size must be an int!")
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self.__weights = []
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self.weights = []
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self.__inputLayerSize = inputLayerSize
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oldLayerSize = inputLayerSize
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for layerSize in layerSizes:
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self.__weights.append( np.random.random((layerSize, oldLayerSize)) )
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self.weights.append( np.random.randn(layerSize, oldLayerSize) )
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oldLayerSize = layerSize
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self.__biases = [[0]*layerSize for layerSize in layerSizes]
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self.__weights = np.array(self.__weights, dtype=object)
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self.__biases = np.array(self.__biases, dtype=object)
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self.biases = [np.random.randn(layerSize) for layerSize in layerSizes]
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def __reLu(value, derivative=False):
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if (derivative):
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return 0 if (value == 0) else 1
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return 0 if (value < 0) else 1
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return max(0, value)
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def __sigmoid(value, derivative=False):
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if (derivative):
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return network.__sigmoid(value) * (1 - network.__sigmoid(value))
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return 1/(1+math.exp(-value))
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return 1.0/(1.0+np.exp(-value))
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def process(self, _input, __storeValues=False):
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if type(_input) != np.ndarray:
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raise TypeError("The input must be a vector!")
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if _input.size != self.__inputLayerSize:
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raise ValueError("The input vector has the wrong size!")
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#if _input.dtype != np.float64:
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# raise TypeError("The input vector must contain floats!")
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if _input.dtype != np.float64:
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print(_input.dtype)
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raise TypeError("The input vector must contain floats!")
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if (__storeValues):
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self.activations = []
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self.outputs = []
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self.outputs.append(_input)
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for layerWeights, bias in zip(self.__weights, self.__biases):
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for layerWeights, layerBias in zip(self.weights, self.biases):
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_input = np.matmul(layerWeights, _input)
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_input = np.add(_input, bias)
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_input = np.dot(layerWeights, _input)
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_input = np.add(_input, layerBias)
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if (__storeValues):
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self.activations.append(_input.copy())
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if (__storeValues):
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self.activations.append(_input)
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#activation function application
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for neuron in range(len(_input)):
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_input[neuron] = network.__sigmoid(_input[neuron])
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#for i in range(len(_input)):
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# _input[i] = network.__sigmoid(_input)
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_input = network.__sigmoid(_input)
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#On peut comparer la performance si on recalcul plus tard
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if (__storeValues):
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self.outputs.append(_input.copy())
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if (__storeValues):
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self.outputs.append(_input)
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self.activations = np.array(self.activations, dtype=object)
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self.outputs = np.array(self.outputs, dtype=object)
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return _input
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def train(self, inputs, desiredOutputs, learningRate):
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errorSumsWeights = [[[0]*len(neuron) for neuron in layer] for layer in self.__weights]
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errorSumsBiases = [[0]*len(layer) for layer in self.__biases]
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self.__errors = [[0]*len(layer) for layer in self.__weights]
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if (len(inputs) != len(desiredOutputs)):
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raise ValueError("The inputs and desired outputs vectors must have the same amount of data !")
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for _input, desiredOutput in zip(inputs, desiredOutputs):
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#rempli self.activations et self.outputs
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self.__output = self.process(_input, True)
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errorSumsWeights = [np.zeros(layer.shape) for layer in self.weights]
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errorSumsBiases = [np.zeros(layer.shape) for layer in self.biases]
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self.__errors = [np.zeros(len(layer)) for layer in self.weights]
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#rempli self.activations et self.outputs
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self.process(_input, True)
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self.__desiredOutput = desiredOutput
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#Somme de matrice ?
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@ -83,18 +82,24 @@ class network:
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errorSumsWeights[layerNumber][neuronNumber][weightNumber] += self.__PartialDerivative(layerNumber, neuronNumber, weightNumber)
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total = 0
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errorSumsWeights = np.multiply(errorSumsWeights, -(learningRate/len(inputs)))
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self.__weights = np.add(self.__weights, errorSumsWeights)
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self.weights = np.add(self.weights, errorSumsWeights)
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errorSumsBiases = np.multiply(errorSumsBiases, -(learningRate/len(inputs)))
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self.__biases = np.add(self.__biases, errorSumsBiases)
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self.biases = np.add(self.biases, errorSumsBiases)
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print(self.__biases)
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#print(self.__biases)
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"""
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for layerNumber in range(len(errorSumsWeights)):
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for neuronNumber in range(len(errorSumsWeights[layerNumber])):
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errorSumsBiases[layerNumber][neuronNumber] = errorSumsBiases[layerNumber][neuronNumber] / len(inputs)
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total += errorSumsBiases[layerNumber][neuronNumber]
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self.biases[layerNumber][neuronNumber] -= learningRate * errorSumsBiases[layerNumber][neuronNumber]
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for weightNumber in range(len(errorSumsWeights[layerNumber][neuronNumber])):
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#Probablement faisable avec une multiplication de matrices
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@ -103,24 +108,24 @@ class network:
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total += errorSumsWeights[layerNumber][neuronNumber][weightNumber]
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#Probablement faisable avec une somme de matrices
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self.__weights[layerNumber][neuronNumber][weightNumber] -= learningRate * errorSumsWeights[layerNumber][neuronNumber][weightNumber]
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self.weights[layerNumber][neuronNumber][weightNumber] -= learningRate * errorSumsWeights[layerNumber][neuronNumber][weightNumber]
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print("Error : " + str(total))"""
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#print("Error : " + str(total))"""
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def __Error(self, layer, neuron):
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if (self.__errors[layer][neuron] == 0 ):
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self.__errors[layer][neuron] = self.__ErrorFinalLayer(neuron) if (layer == len(self.__weights)-1) else self.__ErrorHiddenLayer(layer, neuron)
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self.__errors[layer][neuron] = self.__ErrorFinalLayer(neuron) if (layer == len(self.weights)-1) else self.__ErrorHiddenLayer(layer, neuron)
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return self.__errors[layer][neuron]
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def __ErrorFinalLayer(self, neuron):
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return network.__sigmoid(self.activations[len(self.activations)-1][neuron], True) * (self.__output[neuron] - self.__desiredOutput[neuron])
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return network.__sigmoid(self.activations[-1][neuron], derivative=True) * (self.outputs[-1][neuron] - self.__desiredOutput[neuron])
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def __ErrorHiddenLayer(self, layer, neuron):
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upperLayerLinksSum = 0
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#Probablement faisable avec une multiplication de matrices
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for upperLayerNeuron in range(len(self.__weights[layer+1])):
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upperLayerLinksSum += self.__weights[layer+1][upperLayerNeuron][neuron] * self.__errors[layer+1][upperLayerNeuron]
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return network.__sigmoid(self.activations[layer][neuron], True) * upperLayerLinksSum
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for upperLayerNeuron in range(len(self.weights[layer+1])):
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upperLayerLinksSum += self.weights[layer+1][upperLayerNeuron][neuron] * self.__errors[layer+1][upperLayerNeuron]
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return network.__sigmoid(self.activations[layer][neuron], derivative=True) * upperLayerLinksSum
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def __PartialDerivative(self, layer, neuron, weight):
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return self.__Error(layer, neuron) * self.outputs[layer-1][weight]
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return self.__Error(layer, neuron) * self.outputs[layer][weight]
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@ -4,42 +4,36 @@ from sobek.network import network
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random.seed()
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myNetwork = network(10, 10, 10)
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myNetwork = network(10, 10)
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learningRate = 1
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learningRate = 3
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for j in range(100):
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for j in range(10000):
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rand = []
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inputs = []
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inputs2 = []
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desiredOutputs = []
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if (j%50 == 0):
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print(j)
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for i in range(1000):
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inputs.append([(random.randrange(10)/10)])
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inputs = np.array(inputs, dtype=object)
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for i in range(10):
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rand.append( random.randrange(10)/10)
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for i in range(1000):
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desiredOutputs.append([0]*10)
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desiredOutputs[i][9 - int(inputs[i][0]*10)] = 1.0
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desiredOutputs = np.array(desiredOutputs, dtype=object)
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for i in range(10):
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desiredOutputs.append(np.zeros(10))
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desiredOutputs[i][9 - int(rand[i]*10)] = 1.0
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#for i in range(1000):
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# inputs2.append([0]*10)
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# inputs2[i][int(inputs[i][0]*10)] = 1.0
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inputs2 = np.array(inputs2, dtype=object)
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for i in range(10):
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inputs.append(np.zeros(10))
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inputs[i][int(rand[i]*10)] = 1.0
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if (j%10000 == 0):
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learningRate*= 0.1
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myNetwork.train(desiredOutputs, desiredOutputs, learningRate)
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myNetwork.train(inputs, desiredOutputs, learningRate)
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test = []
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test.append([0]*10)
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test.append([0]*10)
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test.append(np.zeros(10))
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test.append(np.zeros(10))
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test[0][1] = 1.0
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test[1][8] = 1.0
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test = np.array(test, dtype=object)
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test[1][5] = 1.0
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print(test[0])
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print(myNetwork.process(test[0]))
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print(myNetwork.process(test[1]))
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63
testLearningNAND.py
Normal file
63
testLearningNAND.py
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import numpy as np
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import random
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from sobek.network import network
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random.seed()
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myNetwork = network(2, 1)
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learningRate = 3
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test = []
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result = []
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test[1][1] = 1.0
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test[2][0] = 1.0
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test[3][0] = 1.0
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test[3][1] = 1.0
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result.append(np.ones(1))
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result.append(np.ones(1))
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result.append(np.ones(1))
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result.append(np.zeros(1))
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for j in range(10000):
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inputs = []
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desiredOutputs = []
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if (j%1000 == 0):
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print(j)
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random.shuffle(test)
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for i in range(4):
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if (test[i][0] == 1.0) and (test[i][1] == 1.0):
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result[i][0] = 0.0
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else:
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result[i][0] = 1.0
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myNetwork.train(test, result, learningRate)
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test = []
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result = []
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test[1][1] = 1.0
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test[2][0] = 1.0
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test[3][0] = 1.0
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test[3][1] = 1.0
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result.append(np.ones(1))
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result.append(np.ones(1))
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result.append(np.ones(1))
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result.append(np.zeros(1))
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print(myNetwork.weights)
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print(myNetwork.biases)
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print("0 0 : " + str(myNetwork.process(test[0])) + " == 1 ?")
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print("0 1 : " + str(myNetwork.process(test[1])) + " == 1 ?")
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print("1 0 : " + str(myNetwork.process(test[2])) + " == 1 ?")
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print("1 1 : " + str(myNetwork.process(test[3])) + " == 0 ?")
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25
testNAND.py
Normal file
25
testNAND.py
Normal file
@ -0,0 +1,25 @@
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import numpy as np
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import random
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from sobek.network import network
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myNetwork = network(2, 1)
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test = []
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test.append(np.zeros(2))
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test[1][1] = 1.0
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test[2][0] = 1.0
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test[3][0] = 1.0
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test[3][1] = 1.0
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myNetwork.weights = [np.array([[-10.0, -10.0]])]
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myNetwork.biases = [np.array([15.0])]
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print(myNetwork.weights)
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print(myNetwork.biases)
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print("0 0 : " + str(myNetwork.process(test[0])) + " == 1 ?")
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print("0 1 : " + str(myNetwork.process(test[1])) + " == 1 ?")
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print("1 0 : " + str(myNetwork.process(test[2])) + " == 1 ?")
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print("1 1 : " + str(myNetwork.process(test[3])) + " == 0 ?")
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