def test_reset_states(self): tn_obj = metrics.TrueNegatives() model = _get_simple_sequential_model([tn_obj]) x = np.zeros((100, 4)) y = np.zeros((100, 1)) model.evaluate(x, y) self.assertEqual(self.evaluate(tn_obj.accumulator), 100.) model.evaluate(x, y) self.assertEqual(self.evaluate(tn_obj.accumulator), 100.)
def test_weighted(self):
tn_obj = metrics.TrueNegatives()
self.evaluate(variables.variables_initializer(tn_obj.variables))
y_true = constant_op.constant(((0, 1, 0, 1, 0), (0, 0, 1, 1, 1),
(1, 1, 1, 1, 0), (0, 0, 0, 0, 1)))
y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
(0, 1, 0, 1, 0), (1, 1, 1, 1, 1)))
sample_weight = constant_op.constant((1., 1.5, 2., 2.5))
result = tn_obj(y_true, y_pred, sample_weight=sample_weight)
self.assertAllClose([4.], self.evaluate(result))
def test_config(self):
tn_obj = metrics.TrueNegatives(name='my_tn', thresholds=[0.4, 0.9])
self.assertEqual(tn_obj.name, 'my_tn')
self.assertEqual(len(tn_obj.variables), 1)
self.assertEqual(tn_obj.thresholds, [0.4, 0.9])
# Check save and restore config
tn_obj2 = metrics.TrueNegatives.from_config(tn_obj.get_config())
self.assertEqual(tn_obj2.name, 'my_tn')
self.assertEqual(len(tn_obj2.variables), 1)
self.assertEqual(tn_obj2.thresholds, [0.4, 0.9])
def test_weighted_with_thresholds(self):
tn_obj = metrics.TrueNegatives(thresholds=[0.15, 0.5, 0.85])
self.evaluate(variables.variables_initializer(tn_obj.variables))
y_pred = constant_op.constant(((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6),
(0.1, 0.2, 0.4, 0.3), (0, 1, 0.7, 0.3)))
y_true = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0),
(1, 1, 1, 1)))
sample_weight = ((0.0, 2.0, 3.0, 5.0),)
result = tn_obj(y_true, y_pred, sample_weight=sample_weight)
self.assertAllClose([5., 15., 23.], self.evaluate(result))
def test_unweighted_with_thresholds(self):
tn_obj = metrics.TrueNegatives(thresholds=[0.15, 0.5, 0.85])
self.evaluate(variables.variables_initializer(tn_obj.variables))
y_pred = constant_op.constant(((0.9, 0.2, 0.8, 0.1), (0.2, 0.9, 0.7, 0.6),
(0.1, 0.2, 0.4, 0.3), (0, 1, 0.7, 0.3)))
y_true = constant_op.constant(((0, 1, 1, 0), (1, 0, 0, 0), (0, 0, 0, 0),
(1, 1, 1, 1)))
update_op = tn_obj.update_state(y_true, y_pred)
self.evaluate(update_op)
result = tn_obj.result()
self.assertAllClose([2., 5., 7.], result)
def test_unweighted(self):
tn_obj = metrics.TrueNegatives()
self.evaluate(variables.variables_initializer(tn_obj.variables))
y_true = constant_op.constant(((0, 1, 0, 1, 0), (0, 0, 1, 1, 1),
(1, 1, 1, 1, 0), (0, 0, 0, 0, 1)))
y_pred = constant_op.constant(((0, 0, 1, 1, 0), (1, 1, 1, 1, 1),
(0, 1, 0, 1, 0), (1, 1, 1, 1, 1)))
update_op = tn_obj.update_state(y_true, y_pred)
self.evaluate(update_op)
result = tn_obj.result()
self.assertAllClose([3.], result)
def test_config(self): tn_obj = metrics.TrueNegatives(name='my_tn', thresholds=[0.4, 0.9]) self.assertEqual(tn_obj.name, 'my_tn') self.assertEqual(len(tn_obj.variables), 1) self.assertEqual(tn_obj.thresholds, [0.4, 0.9])
