def test_grad(self):
"""Compare analytical gradients with its numerical approximation."""
def _loss_wrapper(scores, loss, true_labels):
return loss.loss(true_labels, scores)
def _dloss_wrapper(scores, loss, true_labels):
return loss.dloss(true_labels, scores)
for loss_id in ('hinge', 'hinge-squared', 'square', 'log'):
self.logger.info("Creating loss: {:}".format(loss_id))
loss_class = CLoss.create(loss_id)
n_elemes = 1
y_true = CArray.randint(0, 2, n_elemes).todense()
score = CArray.randn((n_elemes, ))
check_grad_val = CFunction(
_loss_wrapper, _dloss_wrapper).check_grad(score,
1e-8,
loss=loss_class,
true_labels=y_true)
self.logger.info(
"Gradient difference between analytical svm "
"gradient and numerical gradient: %s", str(check_grad_val))
self.assertLess(
check_grad_val, 1e-4,
"the gradient is wrong {:} for {:} loss".format(
check_grad_val, loss_id))
def test_randn(self):
"""Test for CArray.randn() classmethod."""
self.logger.info("Test for CArray.randn() classmethod.")
for shape in [(1, ), (2, ), (1, 2), (2, 1), (2, 2)]:
res = CArray.randn(shape=shape)
self.logger.info("CArray.randn(shape={:}):\n{:}".format(
shape, res))
self.assertIsInstance(res, CArray)
self.assertEqual(res.shape, shape)
self.assertIsSubDtype(res.dtype, float)
def test_grad(self):
"""Compare analytical gradients with its numerical approximation."""
def _loss_wrapper(scores, loss, true_labels):
return loss.loss(true_labels, scores)
loss_class = CLossCrossEntropy()
y_true = CArray.randint(0, 2, 1)
score = CArray.randn((1, 3))
self.logger.info("Y_TRUE: {:} SCORES: {:}".format(y_true, score))
for pos_label in (None, 0, 1, 2):
self.logger.info("POS_LABEL: {:}".format(pos_label))
# real value of the gradient on x
grad = loss_class.dloss(y_true, score, pos_label)
self.logger.info("GRAD: {:}".format(grad))
approx = CFunction(_loss_wrapper).approx_fprime(
score, eps, loss_class, y_true)
self.logger.info("APPROX (FULL): {:}".format(approx))
pos_label = pos_label if pos_label is not None else y_true.item()
approx = approx[pos_label]
self.logger.info("APPROX (POS_LABEL): {:}".format(approx))
check_grad_val = (grad - approx).norm()
self.logger.info("Gradient difference between analytical svm "
"gradient and numerical gradient: %s",
str(check_grad_val))
self.assertLess(check_grad_val, 1e-4,
"the gradient is wrong {:}".format(check_grad_val))
from secml.array import CArray
from secml.figure import CFigure
fig = CFigure(fontsize=14)
# example data
mu = 100 # mean of distribution
sigma = 15 # standard deviation of distribution
x = mu + sigma * CArray.randn((10000, ))
num_bins = 50
# the histogram of the data
n, bins, patches = fig.sp.hist(x,
num_bins,
density=1,
facecolor='green',
alpha=0.5)
# add a 'best fit' line
y = bins.normpdf(mu, sigma)
fig.sp.plot(bins, y, 'r--')
fig.sp.xlabel('Smarts')
fig.sp.ylabel('Probability')
fig.title(r'Histogram of IQ: $\mu=100$, $\sigma=15$')
# Tweak spacing to prevent clipping of ylabel
fig.subplots_adjust(left=0.15)
fig.sp.grid()
fig.show()
