ann2.py

from black import out
import numpy as np

class NeuralNetwork():
    def __init__(self):
        np.random.seed(1)

        self.synaptic_weights=2*np.random.random((3,1))-1
    def sigmoid(self,x):
        return 1/(1+np.exp(-x))
    def sigmoid_derivatives(self,x):
        return x*(1-x)
    def train(self,training_inputs,training_outputs,training_iterations):
        for iteration in range(training_iterations):
            output=self.think(training_inputs)
            error=training_outputs-output
            adjustments=np.dot(training_inputs.T,error*self.sigmoid_derivatives(output))
            self.synaptic_weights+=adjustments
    def think(self,inputs):
        inputs=inputs.astype(float)
        output=self.sigmoid(np.dot(inputs,self.synaptic_weights))

        return output


if __name__== "__main__":
    neural_network=NeuralNetwork()
    print('Random Synaptic Weights: ')
    print(neural_network.synaptic_weights)
    
    
    training_inputs=np.array([[0,0,1],[1,1,1],[1,0,1],[0,1,1]])
    training_outputs=np.array([[0,1,1,0]]).T

    neural_network.train(training_inputs,training_outputs,1000)

    print('Synaptic weight after training')
    print(neural_network.synaptic_weights)

    A=str(input("input 1: "))
    B=str(input("input 2: "))
    C=str(input("input 3: "))
    
    print("New Situation input data= ",A,B,C)
    print("Output data: ")
    print(neural_network.think(np.array([A,B,C])))