One Neuron

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import numpy

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def sigmoid(x): return 1./(1+numpy.exp(-x))

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def sigmoid_derivation(x): return x*(1.-x)

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X=numpy.array([[.10,.05,.95],[.09,.03,.08],[.01,.09,.91],

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               [.04,.92,.07],[.05,.02,.04],[.07,.97,.05],

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               [.06,.02,.98],[.02,.06,.03],[.01,.09,.03],

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               [.02,.94,.01]])

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Y=numpy.array([[1,0,1,2,0,2,1,0,1,2]]).T

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synapse0=numpy.random.randn(3,1)

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for iter in range(100):

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    layer0=X; layer1=sigmoid(numpy.dot(layer0,synapse0))

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    layer1_error=layer1-Y

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    layer1_delta=layer1_error*sigmoid_derivation(layer1)

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    synapse0_derivative=numpy.dot(layer0.T,layer1_delta)/Y.shape[0]

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    synapse0-=synapse0_derivative

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print(numpy.hstack((numpy.hstack((X,Y)),(layer1.round(3)))))

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print('the 9th row wasn`t labeled rightly')

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print('the neuron has corrected this mistake')

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