tensorflow/python/kernel_tests/math_ops/reduction_ops_test.py
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Functional tests for reduction ops."""
import itertools
import numbers
from absl.testing import parameterized
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import errors
from tensorflow.python.framework import ops
from tensorflow.python.framework import tensor_shape
from tensorflow.python.framework import test_util
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.ops import variables
from tensorflow.python.platform import test
# The maximum input rank to test.
_MAX_RANK = 5
def _powerset(iterable):
"""Helper for generating all possible reduction_axes arguments.
Example:
powerset([0,1,2]): () (0,) (1,) (2,) (0,1) (0,2) (1,2) (0,1,2)
Args:
iterable: An iterable of items to generate the powerset of.
Returns:
The powerset of all items in iterable.
"""
s = list(iterable)
return itertools.chain.from_iterable(
itertools.combinations(s, r) for r in range(len(s) + 1))
class ReducedShapeTest(test.TestCase):
def _check(self, shape, axes, result):
output = math_ops.reduced_shape(shape, axes=axes)
self.assertAllEqual(output, result)
@test_util.run_deprecated_v1
def testSimple(self):
with self.cached_session():
self._check([3], [], [3])
self._check([3], [0], [1])
self._check([5, 3], [], [5, 3])
self._check([5, 3], [0], [1, 3])
self._check([5, 3], [1], [5, 1])
self._check([5, 3], [0, 1], [1, 1])
@test_util.run_deprecated_v1
def testZeros(self):
"""Check that reduced_shape does the right thing with zero dimensions."""
with self.cached_session():
self._check([0], [], [0])
self._check([0], [0], [1])
self._check([0, 3], [], [0, 3])
self._check([0, 3], [0], [1, 3])
self._check([0, 3], [1], [0, 1])
self._check([0, 3], [0, 1], [1, 1])
self._check([3, 0], [], [3, 0])
self._check([3, 0], [0], [1, 0])
self._check([3, 0], [1], [3, 1])
self._check([3, 0], [0, 1], [1, 1])
@test_util.run_deprecated_v1
def testNegAxes(self):
with self.cached_session():
self._check([10, 10, 10], [-1], [10, 10, 1])
self._check([10, 10, 10], [-1, 2], [10, 10, 1])
self._check([10, 10, 10], [-1, -1], [10, 10, 1])
self._check([10, 10, 10], [-1, 0], [1, 10, 1])
self._check([10, 10, 10], [-3], [1, 10, 10])
class ReductionUnknownShape(test.TestCase):
@test_util.run_deprecated_v1
def testBasic(self):
with self.cached_session():
for dtype, reductions in [(dtypes.float32,
(math_ops.reduce_sum, math_ops.reduce_mean,
math_ops.reduce_prod, math_ops.reduce_max,
math_ops.reduce_min,
math_ops.reduce_euclidean_norm)),
(dtypes.bool, (math_ops.reduce_all,
math_ops.reduce_any))]:
for reduction in reductions:
x = array_ops.placeholder(
dtype=dtype, shape=None) # Some tensor w/ unknown shape.
y = reduction(x)
self.assertEqual(y.shape, ())
class ReductionInvalidKeepdims(test.TestCase):
def testBasic(self):
# Test case for GitHub issue 46700.
for dtype, reductions in [
(dtypes.float32, (math_ops.reduce_sum, math_ops.reduce_mean,
math_ops.reduce_prod, math_ops.reduce_max,
math_ops.reduce_min, math_ops.reduce_euclidean_norm)),
(dtypes.bool, (math_ops.reduce_all, math_ops.reduce_any))
]:
for reduction in reductions:
with self.assertRaisesRegex(ValueError, "The truth value"):
x = True if dtype == dtypes.bool else 1
y = reduction(
input_tensor=x, keepdims=np.array([63600, 1], dtype=np.float16))
self.evaluate(y)
class BaseReductionTest(test.TestCase):
def _tf_reduce(self, x, reduction_axes, keepdims):
raise NotImplementedError()
def _np_reduce(self, x, reduction_axes, keepdims):
raise NotImplementedError()
def _makeIncremental(self, shape, dtype):
data = np.arange(np.prod(shape)).reshape(shape).astype(dtype.as_numpy_dtype)
if dtype.is_complex:
data -= 2j * data
return data
def _makeRandom(self, shape, dtype):
data = np.random.rand(*shape).astype(dtype.as_numpy_dtype)
if dtype.is_complex:
data -= 2j * data
return data
def _compare(self,
x,
reduction_axes,
keepdims,
feed_dict=None,
rtol=1e-6,
atol=1e-6):
np_ans = self._np_reduce(x, reduction_axes, keepdims)
with self.cached_session() as sess:
tf_ans = self._tf_reduce(x, reduction_axes, keepdims)
out = sess.run(tf_ans, feed_dict)
self.assertAllCloseAccordingToType(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)
def _compareAll(self,
x,
reduction_axes,
feed_dict=None,
rtol=1e-6,
atol=1e-6):
if reduction_axes is not None and np.shape(reduction_axes) == (1,):
# Test scalar reduction_axes argument
self._compareAll(x, reduction_axes[0], rtol=rtol, atol=atol)
self._compare(
x,
reduction_axes,
keepdims=False,
feed_dict=feed_dict,
rtol=rtol,
atol=atol)
self._compare(
x,
reduction_axes,
keepdims=True,
feed_dict=feed_dict,
rtol=rtol,
atol=atol)
def _compareAllAxes(self, x, feed_dict=None, rtol=1e-6, atol=1e-6):
self._compareAll(x, None, rtol=rtol, atol=atol)
for axes in _powerset(range(x.ndim)):
self._compareAll(x, axes, feed_dict, rtol=rtol, atol=atol)
def _compareGradient(self, x, reduction_axes, rtol=1e-8, atol=1e-8):
if reduction_axes is not None and np.shape(reduction_axes) == (1,):
# Test scalar reduction_axes argument
self._compareGradient(x, reduction_axes[0], rtol=rtol, atol=atol)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = self._tf_reduce(t, reduction_axes, False)
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, x.shape, su, su.get_shape().as_list(), x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=rtol, atol=atol)
def _compareGradientAxes(self, x, rtol=1e-8, atol=1e-8):
self._compareGradient(x, None, rtol=rtol, atol=atol)
self._compareGradient(x, [], rtol=rtol, atol=atol)
self._compareGradient(x, 0, rtol=rtol, atol=atol)
self._compareGradient(x, [1], rtol=rtol, atol=atol)
self._compareGradient(x, [2], rtol=rtol, atol=atol)
self._compareGradient(x, [1, 2], rtol=rtol, atol=atol)
self._compareGradient(x, [0, 1, 2, 3], rtol=rtol, atol=atol)
class SumReductionTest(BaseReductionTest):
def _tf_reduce(self, x, reduction_axes, keepdims):
return math_ops.reduce_sum(x, reduction_axes, keepdims)
def _np_reduce(self, x, reduction_axes, keepdims):
if isinstance(reduction_axes, list) or isinstance(reduction_axes,
np.ndarray):
reduction_axes = tuple(reduction_axes)
return np.sum(x, axis=reduction_axes, keepdims=keepdims)
def testAxesType(self):
for dtype in [dtypes.int64, dtypes.int32]:
with self.cached_session():
v = math_ops.reduce_sum([0, 0], constant_op.constant(0, dtype=dtype))
tf_v = self.evaluate(v)
self.assertAllEqual(tf_v, 0)
@test_util.run_deprecated_v1
def testInfinity(self):
for dtype in [np.float32, np.float64]:
for special_value_x in [-np.inf, np.inf]:
for special_value_y in [-np.inf, np.inf]:
np_arr = np.array([special_value_x, special_value_y]).astype(dtype)
self._compareAll(np_arr, None)
@test_util.run_deprecated_v1
def testInt32(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.int32)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testFloat16(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.float16)
self._compareAllAxes(np_arr)
# test that mean doesn't overflow
# only on GPU, since it has the more accurate implementation
if not test.is_gpu_available():
return
arr = np.ones([68000], dtype=np.float16)
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_arr = variables.Variable(arr)
self.evaluate(variables.global_variables_initializer())
tf_mean = math_ops.reduce_mean(tf_arr, 0, False)
tf_out_mean = self.evaluate(tf_mean)
self.assertAllClose(tf_out_mean, 1.)
@test_util.run_deprecated_v1
def testBfloat16(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.bfloat16)
self._compareAllAxes(np_arr, rtol=1e-3, atol=5.)
@test_util.run_deprecated_v1
def testFloat32(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.float32)
self._compareAllAxes(np_arr)
for _ in range(10):
size_x = int(2**np.random.uniform(0, 15))
size_y = int(2**np.random.uniform(0, 15))
if size_x * size_y > 1e7:
size_y = int(1e7 / size_x)
arr = np.ones([size_x, size_y], dtype=np.float32)
col_sum = np.sum(arr, axis=0)
row_sum = np.sum(arr, axis=1)
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_row_sum = self._tf_reduce(arr, 1, False)
tf_col_sum = self._tf_reduce(arr, 0, False)
tf_out_row, tf_out_col = self.evaluate([tf_row_sum, tf_col_sum])
self.assertAllClose(col_sum, tf_out_col)
self.assertAllClose(row_sum, tf_out_row)
for size_x in [1, 3, 16, 33]:
for size_y in [1, 3, 16, 33]:
for size_z in [1, 3, 16, 33]:
arr = np.ones([size_x, size_y, size_z], dtype=np.float32)
sum_y = np.sum(arr, axis=1)
sum_xz = np.sum(arr, axis=(0, 2))
with self.session(graph=ops.Graph(), use_gpu=True) as sess:
tf_sum_xz = self._tf_reduce(arr, [0, 2], False)
tf_sum_y = self._tf_reduce(arr, 1, False)
tf_out_sum_xz, tf_out_sum_y = self.evaluate([tf_sum_xz, tf_sum_y])
self.assertAllClose(sum_y, tf_out_sum_y)
self.assertAllClose(sum_xz, tf_out_sum_xz)
@test_util.run_deprecated_v1
def testFloat32BFloat16(self):
for dtype in [dtypes.float32, dtypes.bfloat16]:
dtype_np = np.float32 if dtype == dtypes.float32 else dtype.as_numpy_dtype
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtype)
self._compareAllAxes(np_arr)
for _ in range(10):
size_x = int(2 ** np.random.uniform(0, 7))
size_y = int(2 ** np.random.uniform(0, 7))
if size_x * size_y > 1e7:
size_y = int(1e7 / size_x)
arr = np.ones([size_x, size_y], dtype=dtype_np)
col_sum = np.sum(arr, axis=0)
row_sum = np.sum(arr, axis=1)
tf_row_sum = self._tf_reduce(arr, 1, False)
tf_col_sum = self._tf_reduce(arr, 0, False)
tf_out_row, tf_out_col = self.evaluate([tf_row_sum, tf_col_sum])
self.assertAllCloseAccordingToType(col_sum, tf_out_col)
self.assertAllCloseAccordingToType(row_sum, tf_out_row)
for size_x in [1, 3, 16]:
for size_y in [1, 3, 16]:
for size_z in [1, 3, 16]:
arr = np.ones([size_x, size_y, size_z], dtype=dtype_np)
sum_y = np.sum(arr, axis=1)
sum_xz = np.sum(arr, axis=(0, 2))
tf_sum_xz = self._tf_reduce(arr, [0, 2], False)
tf_sum_y = self._tf_reduce(arr, 1, False)
tf_out_sum_xz, tf_out_sum_y = self.evaluate([tf_sum_xz, tf_sum_y])
self.assertAllCloseAccordingToType(sum_y, tf_out_sum_y)
self.assertAllCloseAccordingToType(sum_xz, tf_out_sum_xz)
@test_util.run_deprecated_v1
def testFloat64(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.float64)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testComplex64(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.complex64)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testComplex128(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.complex128)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testInvalidIndex(self):
np_arr = np.arange(0, 10).reshape([2, 5]).astype(np.float32)
input_tensor = ops.convert_to_tensor(np_arr)
with self.assertRaisesWithPredicateMatch(
ValueError, lambda e: "Invalid reduction dimension" in str(e)):
math_ops.reduce_sum(input_tensor, [-3])
with self.assertRaisesWithPredicateMatch(
ValueError, lambda e: "Invalid reduction dimension" in str(e)):
math_ops.reduce_sum(input_tensor, [2])
with self.assertRaisesWithPredicateMatch(
ValueError, lambda e: "Invalid reduction dimension" in str(e)):
math_ops.reduce_sum(input_tensor, [0, 2])
@test_util.run_deprecated_v1
def testPartialShapes(self):
np.random.seed(1618)
# Input shape is unknown.
reduction_axes = [1, 2]
c_unknown = array_ops.placeholder(dtypes.float32)
s_unknown = math_ops.reduce_sum(c_unknown, reduction_axes)
self.assertEqual(tensor_shape.unknown_shape(), s_unknown.get_shape())
np_input = np.random.randn(3, 3, 3)
self._compareAll(np_input, reduction_axes, {c_unknown: np_input})
# Input shape only has known rank.
c_known_rank = array_ops.placeholder(dtypes.float32)
c_known_rank.set_shape(tensor_shape.unknown_shape(rank=3))
s_known_rank = math_ops.reduce_sum(
c_known_rank, reduction_axes, keepdims=True)
self.assertEqual(3, s_known_rank.get_shape().rank)
np_input = np.random.randn(3, 3, 3)
self._compareAll(np_input, reduction_axes, {c_known_rank: np_input})
# Reduction indices are unknown.
unknown_indices = array_ops.placeholder(dtypes.int32)
c_unknown_indices = constant_op.constant([[10.0], [20.0]])
s_unknown_indices = math_ops.reduce_sum(
c_unknown_indices, unknown_indices, keepdims=False)
self.assertEqual(tensor_shape.unknown_shape(),
s_unknown_indices.get_shape())
s_unknown_indices_keep = math_ops.reduce_sum(
c_unknown_indices, unknown_indices, keepdims=True)
self.assertEqual(2, s_unknown_indices_keep.get_shape().rank)
@test_util.run_deprecated_v1
def testWrongShapeForReductionIndices(self):
reduction_axes = [[1], [2]]
c_unknown = array_ops.placeholder(dtypes.float32)
with self.assertRaisesWithPredicateMatch(ValueError,
".*must be at most rank 1.*"):
math_ops.reduce_sum(c_unknown, reduction_axes)
def testInvalidRepeatedReductionIndices(self):
reduction_axes = constant_op.constant([0, 0])
c = constant_op.constant([1.0, 2.0])
with self.assertRaisesWithPredicateMatch(
errors.InvalidArgumentError,
".*Axes contains duplicate dimension: 0.*"):
self.evaluate(math_ops.reduce_sum(c, reduction_axes))
# Int64??
@test_util.run_deprecated_v1
def testGradient(self):
for dtype in [
dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128
]:
x = self._makeIncremental([2, 3, 4, 2], dtype)
self._compareGradientAxes(x)
@test_util.run_deprecated_v1
def testHighRank(self):
# Do a bunch of random high dimensional reductions
np.random.seed(42)
for _ in range(20):
rank = np.random.randint(4, 10 + 1)
axes, = np.nonzero(np.random.randint(2, size=rank))
shape = tuple(np.random.randint(1, 3 + 1, size=rank))
data = np.random.randint(1024, size=shape)
self._compareAll(data, axes)
# Check some particular axis patterns
for rank in 4, 7, 10:
shape = tuple(np.random.randint(1, 3 + 1, size=rank))
data = np.random.randint(1024, size=shape)
for axes in ([], np.arange(rank), np.arange(0, rank, 2),
np.arange(1, rank, 2)):
self._compareAll(data, axes)
@test_util.run_deprecated_v1
def testExpand(self):
# Reduce an empty tensor to a nonempty tensor
x = np.zeros((5, 0))
self._compareAll(x, [1])
@test_util.run_deprecated_v1
def testEmptyGradients(self):
with self.session():
x = array_ops.zeros([0, 3])
y = math_ops.reduce_sum(x, [1])
error = gradient_checker.compute_gradient_error(x, [0, 3], y, [0])
self.assertEqual(error, 0)
@test_util.run_deprecated_v1
def testDegenerate(self):
with self.session():
for dtype in (dtypes.bfloat16, dtypes.float16, dtypes.float32,
dtypes.float64, dtypes.complex64, dtypes.complex128):
# A large number is needed to get Eigen to die
x = array_ops.zeros((0, 9938), dtype=dtype)
y = math_ops.reduce_sum(x, [0])
self.assertAllEqual(y, np.zeros(9938))
class MeanReductionTest(BaseReductionTest):
def _tf_reduce(self, x, reduction_axes, keepdims):
return math_ops.reduce_mean(x, reduction_axes, keepdims)
def _np_reduce(self, x, reduction_axes, keepdims):
if isinstance(reduction_axes, list) or isinstance(reduction_axes,
np.ndarray):
reduction_axes = tuple(reduction_axes)
elif isinstance(reduction_axes, numbers.Integral):
reduction_axes = (reduction_axes,)
if reduction_axes is None:
count = np.prod(x.shape)
else:
count = np.prod([x.shape[ax] for ax in reduction_axes])
# np.mean automatically converts integer inputs to float, while TensorFlow's
# reduce_mean does not. For integer inputs, we emulate TensorFlow's behavior
# using np.sum and truncating division.
np_sum = np.sum(x, axis=reduction_axes, keepdims=keepdims)
if np.issubdtype(x.dtype, np.integer):
return np_sum // count
return np_sum / count
def testAxesType(self):
for dtype in [dtypes.int64, dtypes.int32]:
with self.cached_session():
v = math_ops.reduce_mean([0, 0], constant_op.constant(0, dtype=dtype))
tf_v = self.evaluate(v)
self.assertAllEqual(tf_v, 0)
@test_util.run_deprecated_v1
def testInfinity(self):
for dtype in [np.float32, np.float64]:
for special_value_x in [-np.inf, np.inf]:
for special_value_y in [-np.inf, np.inf]:
np_arr = np.array([special_value_x, special_value_y]).astype(dtype)
self._compareAll(np_arr, None)
@test_util.run_deprecated_v1
def testInt32(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.int32)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testUint8(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeRandom((2,) * rank, dtypes.uint8)
self._compareAllAxes(np_arr)
# This tests the issue reported in b/145030710.
@test_util.run_deprecated_v1
def testSizeOverflowUint8(self):
np_arr = self._makeRandom((2**8,), dtypes.uint8)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testSizeOverflowInt8(self):
np_arr = self._makeRandom((2**7,), dtypes.int8)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testSizeOverflowUint16(self):
np_arr = self._makeRandom((2**16,), dtypes.uint16)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testSizeOverflowInt16(self):
np_arr = self._makeRandom((2**15,), dtypes.int16)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testFloat32(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.float32)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testBFloat16(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.bfloat16)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testFloat64(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.float64)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testComplex64(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.complex64)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testComplex128(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.complex128)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testGradient(self):
s = [2, 3, 4, 2]
for dtype in [dtypes.float32, dtypes.float64]:
x = self._makeIncremental(s, dtype)
self._compareGradientAxes(x, rtol=1e-3, atol=1e-3)
@test_util.run_deprecated_v1
def testEmptyGradients(self):
with self.session():
x = array_ops.zeros([0, 3])
y = math_ops.reduce_mean(x, [1])
error = gradient_checker.compute_gradient_error(x, [0, 3], y, [0])
self.assertEqual(error, 0)
@test_util.run_deprecated_v1
def testDegenerate(self):
with self.session():
for dtype in (dtypes.bfloat16, dtypes.float16, dtypes.float32,
dtypes.float64):
# A large number is needed to get Eigen to die
x = array_ops.zeros((0, 9938), dtype=dtype)
y = math_ops.reduce_mean(x, [0]).eval()
self.assertEqual(y.shape, (9938,))
self.assertTrue(np.all(np.isnan(y)))
class EuclideanNormReductionTest(BaseReductionTest, parameterized.TestCase):
def _tf_reduce(self, x, reduction_axes, keepdims):
return math_ops.reduce_euclidean_norm(x, reduction_axes, keepdims)
def _np_reduce(self, x, reduction_axes, keepdims):
if isinstance(reduction_axes, list) or isinstance(reduction_axes,
np.ndarray):
reduction_axes = tuple(reduction_axes)
np_fro = np.sqrt(
np.sum(x * np.conj(x), axis=reduction_axes, keepdims=keepdims))
if np.issubdtype(x.dtype, np.integer):
np_fro = np.floor(np_fro)
return np_fro
@test_util.run_deprecated_v1
def testAxesType(self):
for dtype in [dtypes.int64, dtypes.int32]:
with self.cached_session():
v = math_ops.reduce_mean([0, 0], constant_op.constant(0, dtype=dtype))
tf_v = self.evaluate(v)
self.assertAllEqual(tf_v, 0)
@test_util.run_deprecated_v1
def testInfinity(self):
for dtype in [np.float32, np.float64]:
for special_value_x in [-np.inf, np.inf]:
for special_value_y in [-np.inf, np.inf]:
np_arr = np.array([special_value_x, special_value_y]).astype(dtype)
self._compareAll(np_arr, None)
@test_util.run_deprecated_v1
def testSingleton(self):
for dtype in [np.float32, np.float64]:
np_arr = np.array([-1.]).astype(dtype)
self._compareAll(np_arr, None)
@parameterized.parameters(
dtypes.bfloat16,
dtypes.float32,
dtypes.float64,
dtypes.int32,
dtypes.complex64,
dtypes.complex128,
)
@test_util.run_deprecated_v1
def testTypes(self, dtype):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtype)
rtol, atol = (1e-2, 5e-1) if dtype == dtypes.bfloat16 else (1e-6, 1e-6)
self._compareAllAxes(np_arr, rtol=rtol, atol=atol)
@test_util.run_deprecated_v1
def testDegenerate(self):
with self.session():
for dtype in (dtypes.bfloat16, dtypes.float16, dtypes.float32,
dtypes.float64):
# A large number is needed to get Eigen to die
x = array_ops.zeros((0, 9938), dtype=dtype)
y = math_ops.reduce_euclidean_norm(x, [0]).eval()
self.assertEqual(y.shape, (9938,))
self.assertAllEqual(y, np.zeros(9938))
@test_util.run_deprecated_v1
def testGradient(self):
shape = [2, 3, 4, 2]
for dtype in [dtypes.float32, dtypes.float64]:
# zero value entry will result NaN gradient if reduction doesn't happen.
# e.g., `tf.math.reduce_sum([0, 1], axis=[])` so add one to avoid it.
x = self._makeIncremental(shape, dtype) + 1.0
self._compareGradientAxes(x, rtol=1e-2, atol=1e-2)
class ProdReductionTest(BaseReductionTest):
def _tf_reduce(self, x, reduction_axes, keepdims):
return math_ops.reduce_prod(x, reduction_axes, keepdims)
def _np_reduce(self, x, reduction_axes, keepdims):
if isinstance(reduction_axes, list) or isinstance(reduction_axes,
np.ndarray):
reduction_axes = tuple(reduction_axes)
return np.prod(x, axis=reduction_axes, keepdims=keepdims)
def testAxesType(self):
for dtype in [dtypes.int64, dtypes.int32]:
with self.cached_session():
v = math_ops.reduce_prod([0, 0], constant_op.constant(0, dtype=dtype))
tf_v = self.evaluate(v)
self.assertAllEqual(tf_v, 0)
@test_util.run_deprecated_v1
def testInfinity(self):
for dtype in [np.float32, np.float64]:
for special_value_x in [-np.inf, np.inf]:
for special_value_y in [-np.inf, np.inf]:
np_arr = np.array([special_value_x, special_value_y]).astype(dtype)
self._compareAll(np_arr, None)
@test_util.run_deprecated_v1
def testInt32(self):
# Numpy automatically upgrades the type of np.prod from int32 to int64, so
# Numpy does not overflow an int32 np.prod while TensorFlow does. To avoid
# overflow, limit array values.
for rank in range(1, _MAX_RANK):
np_arr = self._makeIncremental((2,) * rank, dtypes.int32) % 5 + 1
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testInt64(self):
for rank in range(1, _MAX_RANK):
# Avoid overflow by limiting array values.
np_arr = self._makeIncremental((2,) * rank, dtypes.int64) % 11 + 1
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testFloat32(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.float32)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testBfloat16(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.bfloat16) * \
np.array([0.01]).astype(dtypes.bfloat16.as_numpy_dtype)
self._compareAllAxes(np_arr, rtol=1e-2, atol=1e-2)
@test_util.run_deprecated_v1
def testFloat64(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.float64)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testComplex64(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.complex64)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testComplex128(self):
for rank in range(1, _MAX_RANK + 1):
np_arr = self._makeIncremental((2,) * rank, dtypes.complex128)
self._compareAllAxes(np_arr)
@test_util.run_deprecated_v1
def testGradientWithZeros(self):
s = [2, 3, 4, 2]
x = self._makeIncremental(s, dtypes.float32) / 20.
# No zeros in input
self._compareGradientAxes(x, rtol=1e-3, atol=1e-3)
# Zero at beginning
x1 = x.copy()
x1[:, :, 0, :] = 0
self._compareGradientAxes(x1, rtol=1e-3, atol=1e-3)
# Zero at end
x2 = x.copy()
x2[:, :, -1, :] = 0
self._compareGradientAxes(x2, rtol=1e-3, atol=1e-3)
# Zero in middle
x3 = x.copy()
x3[:, :, 2, :] = 0
self._compareGradientAxes(x3, rtol=1e-3, atol=1e-3)
# All zeros
x4 = x.copy()
x4[:, :, :, :] = 0
self._compareGradientAxes(x4, rtol=1e-3, atol=1e-3)
@test_util.run_deprecated_v1
def testEmptyGradients(self):
with self.session():
x = array_ops.zeros([0, 3])
y = math_ops.reduce_prod(x, [1])
error = gradient_checker.compute_gradient_error(x, [0, 3], y, [0])
self.assertEqual(error, 0)
@test_util.run_deprecated_v1
def testDegenerate(self):
with self.session():
for dtype in (dtypes.bfloat16, dtypes.float16, dtypes.float32,
dtypes.float64):
# A large number is needed to get Eigen to die
x = array_ops.zeros((0, 9938), dtype=dtype)
y = math_ops.reduce_prod(x, [0])
self.assertAllEqual(y, np.ones(9938))
class MinReductionTest(test.TestCase):
def _compare(self, x, reduction_axes, keepdims, use_gpu=False):
np_ans = x
if reduction_axes is None:
np_ans = np.amin(np_ans, keepdims=keepdims)
else:
for ra in reduction_axes[::-1]:
np_ans = np.amin(np_ans, axis=ra, keepdims=keepdims)
with self.cached_session(use_gpu=use_gpu):
if reduction_axes is not None:
reduction_axes = np.array(reduction_axes).astype(np.int32)
tf_ans = math_ops.reduce_min(x, reduction_axes, keepdims)
out = self.evaluate(tf_ans)
self.assertAllClose(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)
def _compareAll(self, x, reduction_axes):
self._compare(x, reduction_axes, False, use_gpu=True)
self._compare(x, reduction_axes, True, use_gpu=True)
def testAxesType(self):
for dtype in [dtypes.int64, dtypes.int32]:
with self.cached_session():
v = math_ops.reduce_min([0, 0], constant_op.constant(0, dtype=dtype))
tf_v = self.evaluate(v)
self.assertAllEqual(tf_v, 0)
@test_util.disable_xla("b/168718272") # XLA handling of NaN is inconsistent
def testSpecialValues(self):
for dtype in [np.float32, np.float64]:
for size in range(1, 4):
for arr in itertools.product([-np.inf, 1., np.nan, np.inf],
repeat=size):
self._compareAll(np.array(arr, dtype=dtype), None)
def testFloatReduce3D(self):
# Create a 3D array of floats and reduce across all possible
# dimensions
np_arr = np.arange(1, 31).reshape([2, 3, 5]).astype(np.float32)
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
def testDoubleReduce3D(self):
# Create a 3D array of doubles and reduce across all possible
# dimensions
np_arr = np.arange(1, 31).reshape([2, 3, 5]).astype(np.float64)
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
@test_util.run_deprecated_v1
def testGradient(self):
s = [2, 3, 4, 2]
x = np.arange(1.0, 49.0).reshape(s).astype(np.float64)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = math_ops.reduce_min(t, [1, 2])
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, s, su, [2, 2], x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
@test_util.run_deprecated_v1
def testGradient2(self):
s = [2, 3, 4, 2]
x = np.arange(1.0, 49.0).reshape(s).astype(np.float64)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = math_ops.reduce_min(t, [1])
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, s, su, [2, 4, 2], x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
@test_util.run_deprecated_v1
def testGradient3(self):
s = [2, 3, 4, 2]
x = np.arange(1.0, 49.0).reshape(s).astype(np.float64)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = math_ops.reduce_min(t, [2])
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, s, su, [2, 3, 2], x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
@test_util.run_deprecated_v1
def testGradient4(self):
s = [2, 3, 4, 2]
x = np.arange(1.0, 49.0).reshape(s).astype(np.float64)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = math_ops.reduce_min(t)
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, s, su, [1], x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
@test_util.run_deprecated_v1
def testEmptyGradients(self):
with self.cached_session():
x = array_ops.zeros([0, 3])
y = math_ops.reduce_min(x, [1])
error = gradient_checker.compute_gradient_error(x, [0, 3], y, [0])
self.assertEqual(error, 0)
class MaxReductionTest(test.TestCase):
def _compare(self, x, reduction_axes, keepdims, use_gpu=False):
np_ans = x
if reduction_axes is None:
np_ans = np.amax(np_ans, keepdims=keepdims)
else:
for ra in reduction_axes[::-1]:
np_ans = np.amax(np_ans, axis=ra, keepdims=keepdims)
with self.cached_session(use_gpu=use_gpu):
if reduction_axes is not None:
reduction_axes = np.array(reduction_axes).astype(np.int32)
tf_ans = math_ops.reduce_max(x, reduction_axes, keepdims)
out = self.evaluate(tf_ans)
self.assertAllClose(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)
def _compareAll(self, x, reduction_axes):
self._compare(x, reduction_axes, False, use_gpu=True)
self._compare(x, reduction_axes, True, use_gpu=True)
def testAxesType(self):
for dtype in [dtypes.int64, dtypes.int32]:
with self.cached_session():
v = math_ops.reduce_max([0, 0], constant_op.constant(0, dtype=dtype))
tf_v = self.evaluate(v)
self.assertAllEqual(tf_v, 0)
@test_util.disable_xla("b/168718272") # XLA handling of NaN is inconsistent
def testSpecialValues(self):
for dtype in [np.float32, np.float64]:
for size in range(1, 4):
for arr in itertools.product([-np.inf, 1., np.nan, np.inf],
repeat=size):
self._compareAll(np.array(arr, dtype=dtype), None)
def testInt64Reduce3D(self):
# Create a 3D array of int64s and reduce across all possible
# dimensions
np_arr = np.arange(-31, -1).reshape([2, 3, 5]).astype(np.int64)
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
def testFloatReduce3D(self):
# Create a 3D array of floats and reduce across all possible
# dimensions
np_arr = np.arange(-31, -1).reshape([2, 3, 5]).astype(np.float32)
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
def testDoubleReduce3D(self):
# Create a 3D array of doubles and reduce across all possible
# dimensions
np_arr = np.arange(-31, -1).reshape([2, 3, 5]).astype(np.float64)
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
def testBfloat16Reduce3D(self):
# Create a 3D array of floats and reduce across all possible
# dimensions
np_arr = np.arange(-31,
-1).reshape([2, 3,
5]).astype(dtypes.bfloat16.as_numpy_dtype)
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
@test_util.run_deprecated_v1
def testGradient(self):
s = [2, 3, 4, 2]
x = np.arange(-49.0, -1.0).reshape(s).astype(np.float64)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = math_ops.reduce_max(t, [1, 2])
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, s, su, [2, 2], x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
@test_util.run_deprecated_v1
def testGradient2(self):
s = [2, 3, 4, 2]
x = np.arange(-49.0, -1.0).reshape(s).astype(np.float64)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = math_ops.reduce_max(t, [1])
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, s, su, [2, 4, 2], x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
@test_util.run_deprecated_v1
def testGradient3(self):
s = [2, 3, 4, 2]
x = np.arange(-49.0, -1.0).reshape(s).astype(np.float64)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = math_ops.reduce_max(t, [2])
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, s, su, [2, 3, 2], x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
@test_util.run_deprecated_v1
def testGradient4(self):
s = [2, 3, 4, 2]
x = np.arange(-49.0, -1.0).reshape(s).astype(np.float64)
with self.cached_session():
t = ops.convert_to_tensor(x)
su = math_ops.reduce_max(t)
jacob_t, jacob_n = gradient_checker.compute_gradient(
t, s, su, [1], x_init_value=x, delta=1)
self.assertAllClose(jacob_t, jacob_n, rtol=1e-8, atol=1e-8)
@test_util.run_deprecated_v1
def testEmptyGradients(self):
with self.cached_session():
x = array_ops.zeros([0, 3])
y = math_ops.reduce_max(x, [1])
error = gradient_checker.compute_gradient_error(x, [0, 3], y, [0])
self.assertEqual(error, 0)
class AllReductionTest(test.TestCase):
def _compare(self, x, reduction_axes, keepdims, use_gpu=False):
np_ans = x
if reduction_axes is None:
np_ans = np.all(np_ans, keepdims=keepdims)
else:
for ra in reduction_axes[::-1]:
np_ans = np.all(np_ans, axis=ra, keepdims=keepdims)
with self.cached_session(use_gpu=use_gpu):
if reduction_axes is not None:
reduction_axes = np.array(reduction_axes).astype(np.int32)
tf_ans = math_ops.reduce_all(x, reduction_axes, keepdims)
out = self.evaluate(tf_ans)
self.assertAllEqual(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)
def _compareAll(self, x, reduction_axes):
self._compare(x, reduction_axes, False, use_gpu=True)
self._compare(x, reduction_axes, False, use_gpu=False)
self._compare(x, reduction_axes, True, use_gpu=True)
self._compare(x, reduction_axes, True, use_gpu=False)
def testAxesType(self):
for dtype in [dtypes.int64, dtypes.int32]:
with self.session():
v = math_ops.reduce_all([True, True],
constant_op.constant(0, dtype=dtype))
tf_v = self.evaluate(v)
self.assertAllEqual(tf_v, True)
def testAll3D(self):
# Create a 3D array of bools and reduce across all possible
# dimensions
np_arr = (np.random.uniform(0, 1, 30) > 0.1).reshape([2, 3, 5])
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
def testEmpty(self):
self._compareAll([], [0])
class AnyReductionTest(test.TestCase):
def _compare(self, x, reduction_axes, keepdims, use_gpu=False):
np_ans = x
if reduction_axes is None:
np_ans = np.any(np_ans, keepdims=keepdims)
else:
for ra in reduction_axes[::-1]:
np_ans = np.any(np_ans, axis=ra, keepdims=keepdims)
with self.cached_session(use_gpu=use_gpu):
if reduction_axes is not None:
reduction_axes = np.array(reduction_axes).astype(np.int32)
tf_ans = math_ops.reduce_any(x, reduction_axes, keepdims)
out = self.evaluate(tf_ans)
self.assertAllEqual(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)
def _compareAll(self, x, reduction_axes):
self._compare(x, reduction_axes, False, use_gpu=True)
self._compare(x, reduction_axes, False, use_gpu=False)
self._compare(x, reduction_axes, True, use_gpu=True)
self._compare(x, reduction_axes, True, use_gpu=False)
def testAxesType(self):
for dtype in [dtypes.int64, dtypes.int32]:
with self.session():
v = math_ops.reduce_any([True, True],
constant_op.constant(0, dtype=dtype))
tf_v = self.evaluate(v)
self.assertAllEqual(tf_v, True)
def testAll3D(self):
# Create a 3D array of bools and reduce across all possible
# dimensions
np_arr = (np.random.uniform(0, 1, 30) > 0.9).reshape([2, 3, 5])
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
def testEmpty(self):
self._compareAll([], [0])
class CountNonzeroReductionTest(test.TestCase):
def _compare(self, x, reduction_axes, keepdims, use_gpu=False, zero=0,
feed_dict=None):
np_ans = (x != zero).astype(np.int32)
if reduction_axes is None:
np_ans = np.sum(np_ans, keepdims=keepdims)
else:
reduction_axes = np.array(reduction_axes).astype(np.int32)
for ra in reduction_axes.ravel()[::-1]:
np_ans = np.sum(np_ans, axis=ra, keepdims=keepdims)
with self.cached_session(use_gpu=use_gpu) as sess:
tf_ans = math_ops.count_nonzero(x, reduction_axes, keepdims)
out = sess.run(tf_ans, feed_dict)
self.assertAllClose(np_ans, out)
self.assertShapeEqual(np_ans, tf_ans)
def _compareAll(self, x, reduction_axes, feed_dict=None):
if reduction_axes is not None and np.shape(reduction_axes) == (1,):
# Test scalar reduction_axes argument
self._compareAll(x, reduction_axes[0])
self._compare(x, reduction_axes, False, use_gpu=True, feed_dict=feed_dict)
self._compare(x, reduction_axes, False, use_gpu=False, feed_dict=feed_dict)
self._compare(x, reduction_axes, True, use_gpu=True, feed_dict=feed_dict)
self._compare(x, reduction_axes, True, use_gpu=False, feed_dict=feed_dict)
@test_util.run_deprecated_v1
def testBoolReduce1D(self):
# Create a 1D array of floats
np_arr = np.asarray([False, False, True, False, False, True])
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
@test_util.run_deprecated_v1
def testFloatReduce1D(self):
# Create a 1D array of floats
np_arr = np.asarray([0.0, 1.0, -1.0, 0.0, 0.0, 3.0]).astype(np.float32)
self._compareAll(np_arr, [0])
@test_util.run_deprecated_v1
def testFloatReduce4D(self):
# Create a 4D array of floats and reduce across some
# dimensions
np_arr = np.floor(np.arange(0.0, 210.0) / 100.0).reshape([2, 3, 5,
7]).astype(
np.float32)
self._compareAll(np_arr, None)
self._compareAll(np_arr, [])
self._compareAll(np_arr, [0])
self._compareAll(np_arr, [1])
self._compareAll(np_arr, [2])
self._compareAll(np_arr, [0, 1])
self._compareAll(np_arr, [1, 2])
# Need specialization for reduce(4D, [0, 2])
# self._compareAll(np_arr, [0, 2])
self._compareAll(np_arr, [0, 1, 2])
self._compareAll(np_arr, [1, 2, 3])
self._compareAll(np_arr, [0, 1, 2, 3])
@test_util.run_deprecated_v1
def testExpand(self):
# Reduce an empty tensor to a nonempty tensor
x = np.zeros((5, 0))
self._compareAll(x, [1])
@test_util.run_deprecated_v1
def testDegenerate(self):
for use_gpu in False, True:
with self.cached_session(use_gpu=use_gpu):
for dtype in (dtypes.bool,):
# A large number is needed to get Eigen to die
x = array_ops.zeros((0, 9938), dtype=dtype)
y = math_ops.count_nonzero(x, [0])
self.assertAllEqual(y, np.zeros(9938))
def testStringReduce(self):
# Test case for GitHub issue 18712
with self.cached_session() as sess:
v = math_ops.count_nonzero(constant_op.constant(["test"]))
self.assertAllClose(self.evaluate(v), 1)
@test_util.run_deprecated_v1
def testStringReduce1D(self):
# Create a 1D array of strings
x = np.asarray(["", "", "a", "", "", "b"])
self._compare(x, None, keepdims=False, zero=np.str_(""))
self._compare(x, [], keepdims=False, zero=np.str_(""))
self._compare(x, [0], keepdims=False, zero=np.str_(""))
self._compare(x, None, keepdims=True, zero=np.str_(""))
self._compare(x, [], keepdims=True, zero=np.str_(""))
self._compare(x, [0], keepdims=True, zero=np.str_(""))
@test_util.run_deprecated_v1
def testStringReduce2D(self):
# Create a 2D array of strings
x = np.asarray([["", "", "a", "", "", "b"],
["", "c", "", "d", "", ""],
["e", "", "f", "", "", ""]])
self._compare(x, None, keepdims=False, zero=np.str_(""))
self._compare(x, [], keepdims=False, zero=np.str_(""))
self._compare(x, [0], keepdims=False, zero=np.str_(""))
self._compare(x, [1], keepdims=False, zero=np.str_(""))
self._compare(x, [0, 1], keepdims=False, zero=np.str_(""))
self._compare(x, None, keepdims=True, zero=np.str_(""))
self._compare(x, [], keepdims=True, zero=np.str_(""))
self._compare(x, [0], keepdims=True, zero=np.str_(""))
self._compare(x, [0, 1], keepdims=True, zero=np.str_(""))
if __name__ == "__main__":
test.main()