105 lines
4.5 KiB
Python
Executable File
105 lines
4.5 KiB
Python
Executable File
import numpy as np
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import math
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class network:
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def __init__(self, inputLayerSize, *layerSizes):
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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.__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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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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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 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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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 (__storeValues):
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self.activations = []
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self.outputs = []
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for layerWeights, bias 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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if (__storeValues):
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self.activations.append(_input.copy())
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#reLu 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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#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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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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errorSums = [[[0]*(len(neuron)) for neuron in layer] for layer in self.__weights]
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self.__errors = [[0]*(len(layer)) for layer in self.__weights]
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for _input, desiredOutput in zip(inputs, desiredOutputs):
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self.__output = self.process(_input, True)
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self.__desiredOutput = desiredOutput
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for layerNumber in range(len(errorSums)-1, -1, -1):
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for neuronNumber in range(len(errorSums[layerNumber])):
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for weightNumber in range(len(errorSums[layerNumber][neuronNumber])):
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#print("layer : " + str(layerNumber) + " neuron : " + str(neuronNumber) + " weight : " + str(weightNumber))
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errorSums[layerNumber][neuronNumber][weightNumber] += self.__partialDerivative(layerNumber, neuronNumber, weightNumber)
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total = 0
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for i in range(len(errorSums)):
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for j in range(len(errorSums[i])):
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for k in range(len(errorSums[i][j])):
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errorSums[i][j][k] = errorSums[i][j][k] / len(inputs)
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total += errorSums[i][j][k]
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self.__weights[i][j][k] -= learningRate * errorSums[i][j][k]
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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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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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def __ErrorHiddenLayer(self, layer, neuron):
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upperLayerLinksSum = 0
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for upperLayerNeuron in range(len(self.__weights[layer+1]-1)):
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#A comparer avec un acces direct au erreurs precalcules
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upperLayerLinksSum += self.__weights[layer+1][upperLayerNeuron][neuron] * self.__Error(layer+1, neuron)
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return network.__sigmoid(self.activations[layer][neuron], 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] |