def test_softshrink(self, dtype):
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=dtype)
expected_result = tf.constant([-1.5, -0.5, 0.0, 0.5, 1.5], dtype=dtype)
self.assertAllCloseAccordingToType(softshrink(x), expected_result)
expected_result = tf.constant([-1.0, 0.0, 0.0, 0.0, 1.0], dtype=dtype)
self.assertAllCloseAccordingToType(
softshrink(x, lower=-1.0, upper=1.0), expected_result)
def test_softshrink(dtype):
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=dtype)
expected_result = tf.constant([-1.5, -0.5, 0.0, 0.5, 1.5], dtype=dtype)
test_utils.assert_allclose_according_to_type(softshrink(x),
expected_result)
expected_result = tf.constant([-1.0, 0.0, 0.0, 0.0, 1.0], dtype=dtype)
test_utils.assert_allclose_according_to_type(
softshrink(x, lower=-1.0, upper=1.0), expected_result)
def test_unknown_shape(self):
fn = softshrink.get_concrete_function(
tf.TensorSpec(shape=None, dtype=tf.float32))
for shape in [(1, ), (1, 2), (1, 2, 3), (1, 2, 3, 4)]:
x = tf.ones(shape=shape, dtype=tf.float32)
self.assertAllClose(fn(x), softshrink(x))
def verify_funcs_are_equivalent(dtype):
x_np = np.random.uniform(-10, 10, size=(4, 4)).astype(dtype)
x = tf.convert_to_tensor(x_np)
lower = np.random.uniform(-10, 10)
upper = lower + np.random.uniform(0, 10)
with tf.GradientTape(persistent=True) as t:
t.watch(x)
y_native = softshrink(x, lower, upper)
y_py = _softshrink_py(x, lower, upper)
test_utils.assert_allclose_according_to_type(y_native, y_py)
grad_native = t.gradient(y_native, x)
grad_py = t.gradient(y_py, x)
test_utils.assert_allclose_according_to_type(grad_native, grad_py)
def verify_funcs_are_equivalent(self, dtype):
x_np = np.random.uniform(-10, 10, size=(4, 4)).astype(dtype)
x = tf.convert_to_tensor(x_np)
lower = np.random.uniform(-10, 10)
upper = lower + np.random.uniform(0, 10)
with tf.GradientTape(persistent=True) as t:
t.watch(x)
y_native = softshrink(x, lower, upper)
y_py = _softshrink_py(x, lower, upper)
self.assertAllCloseAccordingToType(y_native, y_py, atol=1e-4)
grad_native = t.gradient(y_native, x)
grad_py = t.gradient(y_py, x)
self.assertAllCloseAccordingToType(grad_native, grad_py, atol=1e-4)
def test_invalid(self):
with self.assertRaisesOpError(
"lower must be less than or equal to upper."): # pylint: disable=bad-continuation
y = softshrink(tf.ones(shape=(1, 2, 3)), lower=2.0, upper=-2.0)
self.evaluate(y)