def testApplyBucketsWithInterpolationRaises(self):
# We should raise an exception if you try to scale a non-numeric tensor.
with self.test_session():
x = tf.constant(['a', 'b', 'c'], dtype=tf.string)
boundaries = tf.constant([.2, .4], dtype=tf.float32)
with self.assertRaises(ValueError):
mappers.apply_buckets_with_interpolation(x, boundaries)
def testBucketsWithInterpolationUnsortedBoundaries(self):
with self.test_session() as sess:
x = tf.constant([1, 2, 3, 5], dtype=tf.float32)
boundaries = tf.constant([[4, 2]], dtype=tf.float32)
output = mappers.apply_buckets_with_interpolation(x, boundaries)
# Boundaries must be sorted, so an exception is raised.
with self.assertRaises(tf.errors.InvalidArgumentError):
sess.run(output)
def testApplyBucketsWithInterpolationRaggedTensor(self):
inputs = tf.RaggedTensor.from_row_splits(
values=[15, 10, 20, 17, -1111, 21], row_splits=[0, 1, 1, 2, 4, 5, 6])
boundaries = [[10, 20]]
expected_bucketized = tf.RaggedTensor.from_row_splits(
values=[.5, 0, 1, .7, 0, 1], row_splits=[0, 1, 1, 2, 4, 5, 6])
bucketized = mappers.apply_buckets_with_interpolation(inputs, boundaries)
self.assertAllEqual(expected_bucketized, bucketized)
def testApplyBucketsWithInterpolationAllNanBoundariesRaises(self):
with tf.compat.v1.Graph().as_default():
with self.test_session() as sess:
x = tf.constant([float('-inf'), float('nan'), 0.0, 1.0])
boundaries = tf.constant([[float('nan'), float('nan'), float('nan')]])
with self.assertRaisesRegexp(tf.errors.InvalidArgumentError,
'num_boundaries'):
sess.run(mappers.apply_buckets_with_interpolation(x, boundaries))
def testBucketsWithInterpolationUnknownShapeBoundary(self):
with self.test_session() as sess:
x = tf.constant([0, 1, 5, 12], dtype=tf.float32)
# The shape used to generate the boundaries is random, and therefore
# the size of the boundaries tensor is not known.
num_boundaries = tf.random.uniform([1], 1, 2, dtype=tf.int64)[0]
boundaries = tf.random.uniform([1, num_boundaries], 0, 10)
# We don't assert anything about the outcome because we are intentionally
# using randomized boundaries, but we ensure the operations succeed.
_ = sess.run(mappers.apply_buckets_with_interpolation(x, boundaries))
def testApplyBucketsWithInterpolation(self,
x,
boundaries,
expected_results,
input_dtype=tf.float32,
boundaries_dtype=tf.float32):
with self.test_session() as sess:
x = tf.constant(x, dtype=input_dtype)
boundaries = tf.constant([boundaries], dtype=boundaries_dtype)
output = mappers.apply_buckets_with_interpolation(x, boundaries)
self.assertAllClose(sess.run(output), expected_results, 1e-6)
def testApplyBucketsWithInterpolationSparseTensor(self):
with self.test_session() as sess:
x = tf.SparseTensor(
indices=[[0, 0], [1, 2], [3, 4], [1, 4], [6, 1], [3, 2]],
values=[15, 10, 20, 17, -1111, 21],
dense_shape=[7, 5])
boundaries = tf.constant([[10, 20]], dtype=tf.int64)
output = mappers.apply_buckets_with_interpolation(x, boundaries)
expected_results = tf.SparseTensor(
indices=[[0, 0], [1, 2], [3, 4], [1, 4], [6, 1], [3, 2]],
values=[.5, 0, 1, .7, 0, 1],
dense_shape=[7, 5])
actual_results = sess.run(output)
self.assertAllClose(actual_results.values, expected_results.values, 1e-6)
self.assertAllClose(
actual_results.indices, expected_results.indices, 1e-6)
def testApplyBucketsWithInterpolationSparseTensor(self):
with tf.compat.v1.Graph().as_default():
with self.test_session() as sess:
x = tf.SparseTensor(indices=[[0, 0, 0], [1, 1, 2], [3, 1, 4],
[1, 1, 4], [6, 1, 1], [3, 1, 2]],
values=[15, 10, 20, 17, -1111, 21],
dense_shape=[7, 3, 5])
boundaries = [[10, 20]]
output = mappers.apply_buckets_with_interpolation(
x, boundaries)
expected_results = tf.SparseTensor(indices=x.indices,
values=[.5, 0, 1, .7, 0, 1],
dense_shape=x.dense_shape)
actual_results = sess.run(output)
self.assertAllClose(actual_results.values,
expected_results.values, 1e-6)
self.assertAllEqual(actual_results.indices,
expected_results.indices)
self.assertAllEqual(actual_results.dense_shape,
expected_results.dense_shape)
