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import numpy as np


    

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def linear(x, derivative=False):
    return np.ones_like(x) if derivative else x

def relu(x, derivative=False):
    if derivative:
        x = np.where(x <= 0, 0, 1)
    return np.maximum(0, x)


    

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x = np.array([[0.1, 0.2, 0.7]])
y = np.array([[1, 0, 0]])
w1 = np.array([[0.1, 0.2, 0.3], [0.3, 0.2, 0.7], [0.4, 0.3, 0.9]])
b1 = np.ones((1,3))
w2 = np.array([[0.2, 0.3, 0.5], [0.3, 0.5, 0.7], [0.6, 0.4, 0.8]])
b2 = np.ones((1,3))
w3 = np.array([[0.1, 0.4, 0.8], [0.3, 0.7, 0.2], [0.5, 0.2, 0.9]])
b3 = np.ones((1,3))

learning_rate = 0.01

for i in range(301):
    # feedforward
    # 1a camada

    
    # 2a camada

    
    # 3a camada

    
    # backpropagation
    # insira seu código aqui!
    

for w in [w1, w2, w3]:
    print(w)