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@TheEggoCat/

IDKEKAM

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  • main.py
main.py
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import numpy as np

def sigmoid(x):
  return 1/(1 * np.exp(-x))

def sigmoid_derivative(x):
  return x*(1-x)

training_inputs= np.array([[1,1,0],[0,0,0],[0,1,0],[1,0,1]])

training_outputs= np.array([[1,0,0,1]]).T

np.random.seed(1)

synaptic_weights=2 * np.random.random((3,1)) -1

print('Random starting synaptic weights: ')
print(synaptic_weights)

for iteration in range(20000):
  
  input_layer = training_inputs

  outputs = sigmoid(np.dot(input_layer, synaptic_weights))

  error = training_outputs - outputs

  adjustments = error * sigmoid_derivative(outputs)

  synaptic_weights += np.dot(input_layer.T, adjustments)

print('Synaptic weights after training: ')
print(synaptic_weights)

print('Outputs after training: ')
print(outputs)


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