python - Numpy Neural Network error:'NeuralNetwork' object has no attribute 'think' -


i interested in learning machine learnging, started clicking around. started following instructions , created code.

from numpy import exp, array, random, dot  class neuralnetwork():     def __init__(self):         # seed genarator         random.seed(1)         self.synaptic_weights = 2 * random.random((3,1)) - 1      def __sigmoid(self, x):         return 1 /(1 + exp(-x))      def predict(self, inputs):         return self.__sigmoid(dot(inputs, self.synaptic_weights))      def __sigmoid_derivative(self, x):         return x * (x - 1)      def train(self, trainingsetinputs, trainingsetoutputs, numberofiterations):         iteration in range(numberofiterations):             output = self.predict(trainingsetinputs)             error = trainingsetoutputs - output             adjustment = dot(trainingsetinputs.t, error *  self.__sigmoid_derivative(output))             self.synaptic_weights += adjustment      if __name__ == '__main__':     # make 1 network     neuralnetwork = neuralnetwork()      print('random starting synaptic weights')     print(neuralnetwork.synaptic_weights)      # training data     trainingsetinputs = array([[0,0,1], [1,1,1], [1,0,1], [0,1,1]])     trainingsetoutputs = array([[0,1,1,0]]).t      #train network 10000 times     neuralnetwork.train(trainingsetinputs, trainingsetoutputs, 10000)      print('new wheights')     print(neuralnetwork.synaptic_weights)      # test network     print("testing")     print(neuralnetwork.think(array([1,0,1])))

i followed instuctions letter, maybe missed something? tutorial here.

edit: error got was: 'neuralnetwork' object has no attribute 'think'

you need create think() method. looking @ code should be:

def think(self, inputs):     #pass inputs through our single neuron(our single neuron)     return self.___sigmoid(dot(inputs, self.synaptic_weights))

do , you'll fine!


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