def test_config_gmean():
def _check_config(obj, name):
assert obj.name == name
assert obj.dtype == tf.float32
assert obj.stateful
assert len(obj.variables) == 2
name = "my_gmean"
obj1 = GeometricMean(name=name)
_check_config(obj1, name)
obj2 = GeometricMean.from_config(obj1.get_config())
_check_config(obj2, name)
def test_reset_states():
obj = GeometricMean()
obj.update_state([1, 2, 3, 4, 5])
obj.reset_states()
assert obj.total.numpy() == 0.0
assert obj.count.numpy() == 0.0
def test_call_gmean(values, expected):
obj = GeometricMean()
result = obj(tf.constant(values, tf.float32))
count = obj.count.numpy()
assert_result(expected, result)
np.testing.assert_equal(len(values), count)
def test_vector_update_state_gmean(values, expected):
obj = GeometricMean()
values = tf.constant(values, tf.float32)
obj.update_state(values)
check_result(obj, expected, len(values))
def test_scalar_update_state_gmean(values, expected):
obj = GeometricMean()
values = tf.constant(values, tf.float32)
for v in values:
obj.update_state(v)
check_result(obj, expected, len(values))
def test_init_states_gmean():
obj = GeometricMean()
assert obj.total.numpy() == 0.0
assert obj.count.numpy() == 0.0
assert obj.total.dtype == tf.float32
assert obj.count.dtype == tf.float32
def test_sample_weight_gmean(values, sample_weight, expected):
obj = GeometricMean()
obj.update_state(values, sample_weight=sample_weight)
assert_result(expected, obj.result().numpy())