def test_raises_exception_on_invalid_params(self):
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Argument `high` must be greater than `low`'):
b = tfb.SoftClip(5., 3., validate_args=True)
with self.assertRaisesOpError('Argument `high` must be greater than `low`'):
low = tf.Variable(0.)
self.evaluate(low.initializer)
b = tfb.SoftClip(low, 3., validate_args=True)
with tf.control_dependencies([low.assign(5.)]):
self.evaluate(b.forward(4.))
def test_is_nearly_identity_within_range(self):
xs = tf.convert_to_tensor(np.linspace(-3., 3., 20), dtype=self.dtype)
b = tfb.SoftClip(tf.convert_to_tensor(-5., self.dtype),
tf.convert_to_tensor(5., self.dtype),
hinge_softness=0.01)
ys = self.evaluate(b.forward(xs))
self.assertAllClose(ys, xs)
xs_inverted = self.evaluate(b.inverse(ys))
self.assertAllClose(ys, xs_inverted)
def test_raises_exception_on_invalid_input(self):
b = tfb.SoftClip(3., 5., validate_args=True)
with self.assertRaisesRegex(tf.errors.InvalidArgumentError,
'Input must be greater than `low`'):
b.inverse(2.)
with self.assertRaisesOpError('Input must be less than `high`'):
dynamic_y = tf1.placeholder_with_default(input=7., shape=[])
x = b.inverse(dynamic_y)
self.evaluate(x)
def test_is_nearly_identity_within_range(self, low, high):
xs = tf.convert_to_tensor(np.linspace(-3., 3., 20), dtype=self.dtype)
if low is not None:
low = tf.convert_to_tensor(low, self.dtype)
if high is not None:
high = tf.convert_to_tensor(high, self.dtype)
b = tfb.SoftClip(low, high, hinge_softness=0.01)
ys = self.evaluate(b.forward(xs))
self.assertAllClose(ys, xs)
xs_inverted = self.evaluate(b.inverse(ys))
self.assertAllClose(ys, xs_inverted)
def test_constraints_are_satisfied(self, low, high, hinge_softness):
xs = tf.convert_to_tensor(
[-1e6, -100., -3., 0., 1.3, 4., 123., 1e6], self.dtype)
low_tensor = (tf.convert_to_tensor(low, self.dtype)
if low is not None else low)
high_tensor = (tf.convert_to_tensor(high, self.dtype)
if high is not None else high)
hinge_softness = tf.convert_to_tensor(hinge_softness, self.dtype)
b = tfb.SoftClip(low_tensor, high_tensor, hinge_softness)
ys = self.evaluate(b.forward(xs))
if low is not None:
self.assertAllGreaterEqual(ys, low)
self.assertAllClose(ys[0], low)
if high is not None:
self.assertAllLessEqual(ys, high)
self.assertAllClose(ys[-1], high)
def test_variable_gradients(self):
b = tfb.SoftClip(low=tf.Variable(2.), high=tf.Variable(6.))
with tf.GradientTape() as tape:
y = b.forward(.1)
self.assertAllNotNone(tape.gradient(y, b.trainable_variables))
