@benbenn/

Neural net 2

Python

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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))

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

np.random.seed(1)

synaptic_weights = 2 * np.random.random((3, 1)) - 1
print("random synaptic weights:")
print(synaptic_weights)

for iteration in range(1):

    input_layer = training_inputs

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

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