blusson/PT21-22-Reseau-Neurones
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theoriquement le learning pourrait marcher

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This commit is contained in:
eynard 2021-12-15 16:15:16 +01:00
parent 7e9a933548
commit 2e5e1e0d82
2 changed files with 55 additions and 19 deletions
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49
sobek/network.py
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@ -26,44 +26,55 @@ class network:
return network.__sigmoid(value) * (1 - network.__sigmoid(value))
return 1/(1+np.exp(-value))
def process(self, input, storeValues=False):
if type(input) != np.ndarray:
def process(self, _input, __storeValues=False):
if type(_input) != np.ndarray:
raise TypeError("The input must be a vector!")
if input.size != self.__inputLayerSize:
if _input.size != self.__inputLayerSize:
raise ValueError("The input vector has the wrong size!")
if input.dtype != np.float64:
raise TypeError("The input vector must contain floats!")
#if _input.dtype != np.float64:
# raise TypeError("The input vector must contain floats!")
if (storeValues):
if (__storeValues):
self.activations = []
self.outputs = []
for layerWeights, bias in zip(self.__weights, self.__biases):
input = np.matmul(input, layerWeights)
input = np.add(input, bias)
_input = np.matmul(_input, layerWeights)
_input = np.add(_input, bias)
if (storeValues):
self.activations.append(input)
if (__storeValues):
self.activations.append(_input)
np.insert(self.activations, 0, bias)
#reLu application
with np.nditer(input, op_flags=['readwrite']) as layer:
with np.nditer(_input, op_flags=['readwrite'], flags=['refs_ok']) as layer:
for neuron in layer:
neuron = network.__reLu(neuron)
#On peut comparer la performance si on recalcul plus tard
if (storeValues):
self.outputs.append(input)
if (__storeValues):
self.outputs.append(_input)
np.insert(self.outputs, 0, 1)
return input
return _input
def train(self, inputs, desiredOutputs):
for input, desiredOutput in zip(inputs, desiredOutputs):
self.__output = self.process(input, True)
def train(self, inputs, desiredOutputs, learningRate):
ErrorSums = [[0]*(len(layer)+1) for layer in self.__biases]
for _input, desiredOutput in zip(inputs, desiredOutputs):
self.__output = self.process(_input, True)
self.__desiredOutput = desiredOutput
#partialDerivatives
for layerNumber in range(len(ErrorSums)):
ErrorSums[layerNumber][0] += self.__partialDerivative(layerNumber, 0)
for neuronNumber in range(1, len(ErrorSums[layerNumber])):
ErrorSums[layerNumber][neuronNumber] += self.__partialDerivative(layerNumber, neuronNumber)
for i in range(len(ErrorSums)):
for j in range(len(ErrorSums[i])):
ErrorSums[i][j] = 1 / ErrorSums[i][j]
self.__biases[i, j] -= learningRate * ErrorSums[i][j]
def __Error(self, layer, neuron):
return self.__ErrorFinalLayer(neuron) if (layer == 1) else self.__ErrorHiddenLayer(layer, neuron)
return self.__ErrorFinalLayer(neuron) if (layer == len(self.__weights)) else self.__ErrorHiddenLayer(layer, neuron)
def __ErrorFinalLayer(self, neuron):
return network.__reLu(self.activations[len(self.activations)-1][neuron], True) * (self.__output[neuron] - self.__desiredOutput[neuron])

25
testLearning.py Normal file
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@ -0,0 +1,25 @@
import numpy as np
import random
from sobek.network import network
random.seed()
myNetwork = network(1, 8, 8, 10)
for j in range(5):
inputs = []
desiredOutputs = []
for i in range(1000):
inputs.append([random.randrange(10)])
inputs = np.array(inputs, dtype=object)
for i in range(1000):
desiredOutputs.append([0]*10)
desiredOutputs[i][9 - inputs[i][0]] = 1
desiredOutputs = np.array(desiredOutputs, dtype=object)
myNetwork.train(inputs, desiredOutputs, 0.1)
print(myNetwork.process(np.array([8.0], dtype=object)))
print(myNetwork.process(np.array([7.0], dtype=object)))