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Absobel | cc69233487 |
9
RUN.py
9
RUN.py
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@ -3,12 +3,15 @@
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import mnist_loader
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import mnist_loader
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training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
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training_data, validation_data, test_data = mnist_loader.load_data_wrapper()
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print(list(training_data)[0][1])
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import network
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import network
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#net = network.Network([784, 30, 10]) #Testé : 94,56%
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#net = network.Network([784, 30, 10]) #Testé : 94,56%
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# net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
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#net.SGD(training_data, 10, 10, 3.0, test_data=None)
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#print("Results : {} / 10000".format(net.evaluate(test_data)))
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# net = network.Network([784, 100, 10]) #Marche mieux apparemment
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# net = network.Network([784, 100, 10]) #Marche mieux apparemment
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# net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
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# net.SGD(training_data, 30, 10, 3.0, test_data=test_data)
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# net = network.Network([784, 100, 10]) #Marche pas bien apparemment
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# net = network.Network([784, 100, 10]) #Marche pas bien apparemment
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@ -16,5 +19,3 @@ import network
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# net = network.Network([784, 30, 10]) #Marche pas du tout apparemment
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# net = network.Network([784, 30, 10]) #Marche pas du tout apparemment
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# net.SGD(training_data, 30, 10, 100.0, test_data=test_data)
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# net.SGD(training_data, 30, 10, 100.0, test_data=test_data)
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print()
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@ -147,6 +147,3 @@ def sigmoid(z):
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def sigmoid_prime(z):
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def sigmoid_prime(z):
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"""Derivative of the sigmoid function."""
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"""Derivative of the sigmoid function."""
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return sigmoid(z)*(1-sigmoid(z))
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return sigmoid(z)*(1-sigmoid(z))
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