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a guest Aug 11th, 2018 62 Never
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  1. from numpy import exp, array, random, dot
  2.  
  3.  
  4. class NeuralNetwork():
  5.     def __init__(self):
  6.  
  7.         random.seed(2)
  8.  
  9.  
  10.         self.synaptic_weights = 2 * random.random((1, 5)) - 1
  11.  
  12.     def __sigmoid(self, x):
  13.         return 1 / (1 + exp(-x))
  14.  
  15.     def __sigmoid_derivative(self, x):
  16.         return x * (1 - x)
  17.  
  18.     def train(self, training_set_inputs, training_set_outputs, number_of_training_iterations):
  19.         for iteration in range(number_of_training_iterations):
  20.             output = self.think(training_set_inputs)
  21.  
  22.             error = training_set_outputs - output
  23.  
  24.             adjustment = dot(training_set_inputs.T, error * self.__sigmoid_derivative(output))
  25.  
  26.             self.synaptic_weights += adjustment
  27.  
  28.    def think(self, inputs):
  29.     # Pass inputs through our neural network (our single neuron).
  30.     return self.__sigmoid(dot(inputs, self.synaptic_weights))
  31.  
  32.  
  33. if __name__ == "__main__":
  34.  
  35.     neural_network = NeuralNetwork()
  36.  
  37.     print ("Random starting synaptic weights: ")
  38.     print (neural_network.synaptic_weights)
  39.  
  40.     training_set_inputs = array([[0], [30], [45], [60], [90]])
  41.     training_set_outputs = array([[0], [0.5], [0.707], [0.866], [1]]).T
  42.  
  43.     neural_network.train(training_set_inputs, training_set_outputs, 10000)
  44.  
  45.     print ("New synaptic weights after training: ")
  46.     print (neural_network.synaptic_weights)
  47.  
  48.     print ("Considering new situation 56 -> ?: ")
  49.     print (neural_network.think(array([56])))
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