131 lines
5.9 KiB
Python
Executable File
131 lines
5.9 KiB
Python
Executable File
import numpy as np
|
|
|
|
class network:
|
|
|
|
def __init__(self, inputLayerSize, *layerSizes):
|
|
if type(inputLayerSize) != int:
|
|
raise TypeError("The input layer size must be an int!")
|
|
|
|
self.weights = []
|
|
self.__inputLayerSize = inputLayerSize
|
|
oldLayerSize = inputLayerSize
|
|
for layerSize in layerSizes:
|
|
self.weights.append( np.random.randn(layerSize, oldLayerSize) )
|
|
oldLayerSize = layerSize
|
|
self.biases = [np.random.randn(layerSize) for layerSize in layerSizes]
|
|
|
|
def __reLu(value, derivative=False):
|
|
if (derivative):
|
|
return 0 if (value < 0) else 1
|
|
return max(0, value)
|
|
|
|
def __sigmoid(value, derivative=False):
|
|
if (derivative):
|
|
return network.__sigmoid(value) * (1 - network.__sigmoid(value))
|
|
return 1.0/(1.0+np.exp(-value))
|
|
|
|
def process(self, _input, __storeValues=False):
|
|
if type(_input) != np.ndarray:
|
|
raise TypeError("The input must be a vector!")
|
|
if _input.size != self.__inputLayerSize:
|
|
raise ValueError("The input vector has the wrong size!")
|
|
if _input.dtype != np.float64:
|
|
print(_input.dtype)
|
|
raise TypeError("The input vector must contain floats!")
|
|
|
|
if (__storeValues):
|
|
self.activations = []
|
|
self.outputs = []
|
|
self.outputs.append(_input)
|
|
|
|
for layerWeights, layerBias in zip(self.weights, self.biases):
|
|
|
|
_input = np.dot(layerWeights, _input)
|
|
_input = np.add(_input, layerBias)
|
|
|
|
if (__storeValues):
|
|
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)
|
|
|
|
return _input
|
|
|
|
|
|
|
|
def train(self, inputs, desiredOutputs, learningRate):
|
|
if (len(inputs) != len(desiredOutputs)):
|
|
raise ValueError("The inputs and desired outputs vectors must have the same amount of data !")
|
|
|
|
for _input, desiredOutput in zip(inputs, desiredOutputs):
|
|
|
|
errorSumsWeights = [np.zeros(layer.shape) for layer in self.weights]
|
|
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
|
|
self.process(_input, True)
|
|
self.__desiredOutput = desiredOutput
|
|
|
|
#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))
|
|
errorSumsWeights[layerNumber][neuronNumber][weightNumber] += self.__PartialDerivative(layerNumber, neuronNumber, weightNumber)
|
|
|
|
total = 0
|
|
|
|
|
|
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])):
|
|
|
|
errorSumsBiases[layerNumber][neuronNumber] = errorSumsBiases[layerNumber][neuronNumber] / len(inputs)
|
|
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(inputs)
|
|
|
|
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))"""
|
|
|
|
def __Error(self, layer, neuron):
|
|
if (self.__errors[layer][neuron] == 0 ):
|
|
self.__errors[layer][neuron] = self.__ErrorFinalLayer(neuron) if (layer == len(self.weights)-1) else self.__ErrorHiddenLayer(layer, neuron)
|
|
return self.__errors[layer][neuron]
|
|
|
|
def __ErrorFinalLayer(self, neuron):
|
|
return network.__sigmoid(self.activations[-1][neuron], derivative=True) * (self.outputs[-1][neuron] - self.__desiredOutput[neuron])
|
|
|
|
def __ErrorHiddenLayer(self, layer, neuron):
|
|
upperLayerLinksSum = 0
|
|
#Probablement faisable avec une multiplication de matrices
|
|
for upperLayerNeuron in range(len(self.weights[layer+1])):
|
|
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] |