@IsaacB1/

# Neural Net 2

Files
• main.py
main.py
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```
```import numpy as np #adds math

class NeuralNetwork(): #neural net class

#init
def __init__(self):
np.random.seed(1) #seeding the random

self.synaptic_weights = 2 * np.random.random((3,1)) -1 # initializing synaptic weights as a 3 by 1 matrix

def sigmoid(self, x): #defining sigmoid
return 1/ (1 + np.exp(-x))

def sigmoid_derivative(self, x): #defining sigmoid'
return x * (1-x)

def train(self, training_inputs, training_outputs, training_iterations): # defining train function

for iteration in range(training_iterations): #. main loop

output = self.think(training_inputs) #def outputs
error = training_outputs - output #defining error margin

def think(self, inputs): #def think

inputs = inputs.astype(float) # changing input type to float
output = self.sigmoid(np.dot(inputs, self.synaptic_weights)) #defining output

return output # returning output

if __name__ == "__main__":

#initialize new Neural Network
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([[0,1,1,1],]).T

neural_network.train(training_inputs, training_outputs, 900000)

print("Synaptic weights 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])))

```