def test_categorical_hinge(self):
targets = asfloat(np.array([
[0, 0, 1],
[1, 0, 0],
]))
predictions = asfloat(np.array([
[0.1, 0.2, 0.7],
[0.0, 0.9, 0.1],
]))
expected = np.array([0.5, 1.9]).mean()
actual = errors.categorical_hinge(targets, predictions)
self.assertAlmostEqual(expected, self.eval(actual), places=3)
def test_categorical_hinge_without_one_hot_encoding(self):
targets = asfloat(np.array([2, 0]))
predictions = asfloat(np.array([
[0.1, 0.2, 0.7],
[0.0, 0.9, 0.1],
]))
expected = asfloat(np.array([0.5, 1.9]).mean())
prediction_var = T.matrix()
target_var = T.vector()
error_output = errors.categorical_hinge(target_var, prediction_var)
actual = error_output.eval({prediction_var: predictions,
target_var: targets})
self.assertAlmostEqual(expected, actual)
def test_categorical_hinge_without_one_hot_encoding(self):
targets = asfloat(np.array([2, 0]))
predictions = asfloat(np.array([
[0.1, 0.2, 0.7],
[0.0, 0.9, 0.1],
]))
expected = asfloat(np.array([0.5, 1.9]).mean())
prediction_var = T.matrix()
target_var = T.vector()
error_output = errors.categorical_hinge(target_var, prediction_var)
actual = error_output.eval({
prediction_var: predictions,
target_var: targets
})
self.assertAlmostEqual(expected, actual, places=3)
def test_categorical_hinge_invalid_dimension(self):
with self.assertRaises(TypeError):
errors.categorical_hinge(T.tensor3(), T.matrix())
def test_categorical_hinge_invalid_dimension(self):
with self.assertRaises(TypeError):
errors.categorical_hinge(T.tensor3(), T.matrix())
