def main():
import random
from pylab import plot, show
def func(x):
return math.pow(x, 2.0) - 10.0 * x + 21
# Variando do x' até x'' (3 -> 7), dividido em 100 partes
train_set = tuple(
([i], [func(i)]) for i in util.divide_arange(1.0, 9.0, 40))
mlp = MLP(1, [10, 30, 1], ACTIVATIONS_FUNCTIONS['sigmoid'],
ACTIVATIONS_FUNCTIONS['custom'])
mlp.randomise_weights(lambda: random.uniform(-0.05, 0.05))
sup = Supervisor(mlp, 0.001)
sup.train_set(train_set, 0.0005, 10000)
validation = tuple(
([x], [func(x)]) for x in util.divide_arange(-1.0, 11.0, 200))
plot([i[0][0] for i in validation], [i[1][0] for i in validation], 'b',
[i[0][0] for i in validation],
[mlp.predict(i[0]) for i in validation], 'r')
show()
def main():
# https://www.mathworks.com/help/deeplearning/ug/improve-neural-network-generalization-and-avoid-overfitting.html;jsessionid=d7ccdb5dad86ecd28c93a845c8c8
def func(x):
return 2*math.pow(x, 3) - math.pow(x, 2) + 10*x - 4
train_set = tuple(
([i], [func(i)])
for i in util.divide_arange(-3.0, 3.0, 40)
)
import random
from pylab import plot, show
mlp = MLP(1, [10, 30, 1],
ACTIVATIONS_FUNCTIONS['sigmoid'],
ACTIVATIONS_FUNCTIONS['linear'])
mlp.randomise_weights(lambda: random.uniform(-1.0, 1.0))
sup = Supervisor(mlp, 0.01)
sup.train_set(train_set, 0.005, 3000)
validation = tuple(
([x], [func(x)])
for x in util.divide_arange(-4.0, 4.0, 200)
)
plot(
[i[0][0] for i in validation], [i[1][0] for i in validation], 'b',
[i[0][0] for i in validation], [mlp.predict(i[0]) for i in validation], 'r'
)
show()