文件列表:

DataExtraction (0, 2016-11-15)
DataExtraction\Extraction.py (1595, 2016-11-15)
License (1074, 2016-11-15)
MNIST_Analysis.py (2087, 2016-11-15)
NeuralNetwork (0, 2016-11-15)
NeuralNetwork\Activation.py (435, 2016-11-15)
NeuralNetwork\Adaboost.py (1835, 2016-11-15)
NeuralNetwork\Cost.py (152, 2016-11-15)
NeuralNetwork\Network.py (4166, 2016-11-15)

# AdaBoost_Neural_Network Test the adaBoost algorithm on multiple neural networks. ## Theory In order to improve the backpropagation, it is more accurate to associate a stronger weight to inputs which add the most information. In other words, an input which has a wrong computed output. In order to do so, we developp an algorithm inspired by AdaBoost. ## Execution ``` python3.5 MNIST_Analysis.py ``` ## Results The current example uses three neural networks with 25 hidden nodes with 10 iterations for backpropagation for each : Training set : 54 831 / 60 000 Testing set : 9 091 / 10 000 The result is a little less impressive than a unique neural network with 30 iterations, however, the proof shows that for a huge number of weak classifier the result should exponentially decrease. The second test is with 5 identic neural networks : Training set : 55 031 / 60 000 Testing set : 9 176 / 10 000 ## Libraries Needs struct, urllib.request, io, gzip, numpy and os. Executed with python3.5

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