Neural Network Training System

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A Neural Network Training System is a model-based training system that implements a neural network training algorithm (to solve a neural network training task which requires a trained neural network).



References

2016

2015

 X = np.array([ [0,0,1],[0,1,1],[1,0,1],[1,1,1] ])
 y = np.array(0,1,1,0).T
 syn0 = 2*np.random.random((3,4)) - 1
 syn1 = 2*np.random.random((4,1)) - 1
 for j in xrange(60000):
   l1 = 1/(1+np.exp(-(np.dot(X,syn0))))
   l2 = 1/(1+np.exp(-(np.dot(l1,syn1))))
   l2_delta = (y - l2)*(l2*(1-l2))
   l1_delta = l2_delta.dot(syn1.T) * (l1 * (1-l1))
   syn1 += l1.T.dot(l2_delta)
   syn0 += X.T.dot(l1_delta)
This neural network attempts to use the input for predicting the output. Here programmer tries to predict the output column of the three input columns. Well, this was all I had to tell you about the neural network in 11 lines of python. This problem of simple backpropagation could be used to make a more advanced 2 layer neural network.