train_slp_sigmoid.py

for i in range(NUM_ITER):
    y_pred = np.dot(x, W) + b
        
    #activation sigmoid
    y_pred = 1.0 / (1.0 + np.exp(-y_pred))
            
    err = y - y_pred

        
    delta_W = learning_rate * np.dot(np.transpose(x) , err)
    delta_b = learning_rate * np.sum(err)
    W = W + delta_W
    b = b + delta_b
    print ("Iterasi ke-" + str(i), err, W, b, "Error :" + str(np.abs(np.sum(err) / np.sum(y))))
    if np.abs(np.sum(err) / np.sum(y)) <= 0.1:
        break