def test_invalid_value_shape(self):
m = metrics.MeanTensor(dtype=tf.float64)
m([1])
with self.assertRaisesRegex(
ValueError,
'MeanTensor input values must always have the same shape'):
m([1, 5])
def __init__(self):
super().__init__()
self.dense1 = layers.Dense(3,
activation="relu",
kernel_initializer="ones")
self.dense2 = layers.Dense(1,
activation="sigmoid",
kernel_initializer="ones")
self.mean_tensor = metrics.MeanTensor()
def __init__(self):
super(ModelWithMetric, self).__init__()
self.dense1 = layers.Dense(3,
activation='relu',
kernel_initializer='ones')
self.dense2 = layers.Dense(1,
activation='sigmoid',
kernel_initializer='ones')
self.mean_tensor = metrics.MeanTensor()
def test_build_in_tf_function(self):
"""Ensure that variables are created correctly in a tf function."""
m = metrics.MeanTensor(dtype=tf.float64)
@tf.function
def call_metric(x):
return m(x)
with self.test_session():
self.assertAllClose(self.evaluate(call_metric([100, 40])),
[100, 40])
self.assertAllClose(self.evaluate(m.total), [100, 40])
self.assertAllClose(self.evaluate(m.count), [1, 1])
self.assertAllClose(self.evaluate(call_metric([20, 2])), [60, 21])
def test_weighted(self):
with self.test_session():
m = metrics.MeanTensor(dtype=tf.float64)
self.assertEqual(m.dtype, tf.float64)
# check scalar weight
result_t = m([100, 30], sample_weight=0.5)
self.assertAllClose(self.evaluate(result_t), [100, 30])
self.assertAllClose(self.evaluate(m.total), [50, 15])
self.assertAllClose(self.evaluate(m.count), [0.5, 0.5])
# check weights not scalar and weights rank matches values rank
result_t = m([1, 5], sample_weight=[1, 0.2])
result = self.evaluate(result_t)
self.assertAllClose(result, [51 / 1.5, 16 / 0.7], 2)
self.assertAllClose(self.evaluate(m.total), [51, 16])
self.assertAllClose(self.evaluate(m.count), [1.5, 0.7])
# check weights broadcast
result_t = m([1, 2], sample_weight=0.5)
self.assertAllClose(self.evaluate(result_t), [51.5 / 2, 17 / 1.2])
self.assertAllClose(self.evaluate(m.total), [51.5, 17])
self.assertAllClose(self.evaluate(m.count), [2, 1.2])
# check weights squeeze
result_t = m([1, 5], sample_weight=[[1], [0.2]])
self.assertAllClose(self.evaluate(result_t), [52.5 / 3, 18 / 1.4])
self.assertAllClose(self.evaluate(m.total), [52.5, 18])
self.assertAllClose(self.evaluate(m.count), [3, 1.4])
# check weights expand
m = metrics.MeanTensor(dtype=tf.float64)
self.evaluate(tf.compat.v1.variables_initializer(m.variables))
result_t = m([[1], [5]], sample_weight=[1, 0.2])
self.assertAllClose(self.evaluate(result_t), [[1], [5]])
self.assertAllClose(self.evaluate(m.total), [[1], [1]])
self.assertAllClose(self.evaluate(m.count), [[1], [0.2]])
def test_config(self):
with self.test_session():
m = metrics.MeanTensor(name="mean_by_element")
# check config
self.assertEqual(m.name, "mean_by_element")
self.assertTrue(m.stateful)
self.assertEqual(m.dtype, tf.float32)
self.assertEmpty(m.variables)
with self.assertRaisesRegex(ValueError,
"does not have any value yet"):
m.result()
self.evaluate(m([[3], [5], [3]]))
self.assertAllEqual(m._shape, [3, 1])
m2 = metrics.MeanTensor.from_config(m.get_config())
self.assertEqual(m2.name, "mean_by_element")
self.assertTrue(m2.stateful)
self.assertEqual(m2.dtype, tf.float32)
self.assertEmpty(m2.variables)
def test_unweighted(self):
with self.test_session():
m = metrics.MeanTensor(dtype=tf.float64)
# check __call__()
self.assertAllClose(self.evaluate(m([100, 40])), [100, 40])
self.assertAllClose(self.evaluate(m.total), [100, 40])
self.assertAllClose(self.evaluate(m.count), [1, 1])
# check update_state() and result() + state accumulation + tensor input
update_op = m.update_state(
[tf.convert_to_tensor(1),
tf.convert_to_tensor(5)])
self.evaluate(update_op)
self.assertAllClose(self.evaluate(m.result()), [50.5, 22.5])
self.assertAllClose(self.evaluate(m.total), [101, 45])
self.assertAllClose(self.evaluate(m.count), [2, 2])
# check reset_state()
m.reset_state()
self.assertAllClose(self.evaluate(m.total), [0, 0])
self.assertAllClose(self.evaluate(m.count), [0, 0])
