def test_binary_crossentropy_reshape():
# Reported as https://github.com/Theano/Theano/issues/4086
a = tensor4("a")
for c in (
binary_crossentropy(sigmoid(a.reshape((-1, 1))), 1).sum(),
binary_crossentropy(sigmoid(a).reshape((-1, 1)), 1).sum(),
):
ga = aesara.grad(c, a)
# This only works when "specialize" options are included
mode = aesara.compile.get_default_mode().including("fast_run")
fga = aesara.function([a], ga, mode=mode)
utt.assert_allclose(
fga(np.array([[[[30.0]]]], dtype=config.floatX)),
np.zeros((1, 1, 1, 1), dtype=config.floatX),
)
def test_matches_binary_crossentropy(self):
# Test sigmoid_binary_crossentropy(p, t) ==
# binary_crossentropy(sigmoid(p), t).
pred, target = inputs = vectors("pt")
reference_val = binary_crossentropy(sigmoid(pred), target)
f_reference = aesara.function(inputs, reference_val)
test_val = sigmoid_binary_crossentropy(pred, target)
f_test = aesara.function(inputs, test_val)
test_inputs = self._get_test_inputs()
utt.assert_allclose(f_reference(*test_inputs), f_test(*test_inputs))
def test_matches_binary_crossentropy(self):
# Test sigmoid_binary_crossentropy(p, t) ==
# binary_crossentropy(sigmoid(p), t).
pred, target = inputs = vectors("pt")
reference_val = binary_crossentropy(sigmoid(pred), target)
f_reference = aesara.function(inputs, reference_val)
test_val = sigmoid_binary_crossentropy(pred, target)
f_test = aesara.function(inputs, test_val)
rng = np.random.default_rng(utt.fetch_seed())
pred, target = rng.standard_normal((2, 50)).astype(config.floatX)
test_inputs = [pred, 1 / (1 + np.exp(-target))]
utt.assert_allclose(f_reference(*test_inputs), f_test(*test_inputs))