def test_clipping_large_min_max_identity(self):
stage = clipping.ClipByValueEncodingStage(-1000.0, 1000.0)
test_data = self.run_one_to_many_encode_decode(stage,
self.default_input)
self.common_asserts_for_test_data(test_data)
# The encoding should act as an identity, if input has smaller values.
self.assertAllEqual(test_data.x, test_data.decoded_x)
def test_different_shapes(self, shape):
stage = clipping.ClipByValueEncodingStage(-1.0, 1.0)
test_data = self.run_one_to_many_encode_decode(
stage, lambda: tf.random.normal(shape))
self.common_asserts_for_test_data(test_data)
self.assertGreaterEqual(1.0, np.amax(test_data.decoded_x))
self.assertLessEqual(-1.0, np.amin(test_data.decoded_x))
def test_clipping_effective(self):
stage = clipping.ClipByValueEncodingStage(-1.0, 1.0)
test_data = self.run_one_to_many_encode_decode(
stage, lambda: tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0]))
self.common_asserts_for_test_data(test_data)
self.assertAllEqual([-2.0, -1.0, 0.0, 1.0, 2.0], test_data.x)
self.assertAllClose([-1.0, -1.0, 0.0, 1.0, 1.0], test_data.decoded_x)
def test_input_types(self, x_dtype, clip_value_min_dtype,
clip_value_max_dtype):
# Tests combinations of input dtypes.
stage = clipping.ClipByValueEncodingStage(
tf.constant(-1.0, clip_value_min_dtype),
tf.constant(1.0, clip_value_max_dtype))
x = tf.constant([-2.0, -1.0, 0.0, 1.0, 2.0], dtype=x_dtype)
encode_params, decode_params = stage.get_params()
encoded_x, decoded_x = self.encode_decode_x(stage, x, encode_params,
decode_params)
test_data = test_utils.TestData(x, encoded_x, decoded_x)
test_data = self.evaluate_test_data(test_data)
self.common_asserts_for_test_data(test_data)
self.assertAllEqual([-2.0, -1.0, 0.0, 1.0, 2.0], test_data.x)
self.assertAllClose([-1.0, -1.0, 0.0, 1.0, 1.0], test_data.decoded_x)
def default_encoding_stage(self):
"""See base class."""
return clipping.ClipByValueEncodingStage(-1.0, 1.0)
