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# Copyright 2016 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.
# ==============================================================================
"""Tests for Python ops defined in math_grad.py."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.framework import ops
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
class SquaredDifferenceOpTest(test.TestCase):
def _testGrad(self, left_shape, right_shape):
if len(left_shape) > len(right_shape):
output_shape = left_shape
else:
output_shape = right_shape
l = np.random.randn(*left_shape)
r = np.random.randn(*right_shape)
with self.test_session(use_gpu=True):
left_tensor = constant_op.constant(l, shape=left_shape)
right_tensor = constant_op.constant(r, shape=right_shape)
output = math_ops.squared_difference(left_tensor, right_tensor)
left_err = gradient_checker.compute_gradient_error(
left_tensor, left_shape, output, output_shape, x_init_value=l)
right_err = gradient_checker.compute_gradient_error(
right_tensor, right_shape, output, output_shape, x_init_value=r)
self.assertLess(left_err, 1e-10)
self.assertLess(right_err, 1e-10)
def testGrad(self):
self._testGrad([1, 2, 3, 2], [3, 2])
self._testGrad([2, 4], [3, 2, 4])
class AbsOpTest(test.TestCase):
def _biasedRandN(self, shape, bias=0.1, sigma=1.0):
"""Returns samples from a normal distribution shifted `bias` away from 0."""
value = np.random.randn(*shape) * sigma
return value + np.sign(value) * bias
def _testGrad(self, shape, dtype=None, max_error=None, bias=None, sigma=None):
np.random.seed(7)
if dtype in (dtypes.complex64, dtypes.complex128):
value = math_ops.complex(
self._biasedRandN(
shape, bias=bias, sigma=sigma),
self._biasedRandN(
shape, bias=bias, sigma=sigma))
else:
value = ops.convert_to_tensor(
self._biasedRandN(
shape, bias=bias), dtype=dtype)
with self.test_session(use_gpu=True):
output = math_ops.abs(value)
error = gradient_checker.compute_gradient_error(
value, shape, output, output.get_shape().as_list())
self.assertLess(error, max_error)
def testComplexAbs(self):
# Bias random test values away from zero to avoid numeric instabilities.
self._testGrad(
[3, 3], dtype=dtypes.float32, max_error=2e-5, bias=0.1, sigma=1.0)
self._testGrad(
[3, 3], dtype=dtypes.complex64, max_error=2e-5, bias=0.1, sigma=1.0)
# Ensure stability near the pole at zero.
self._testGrad(
[3, 3], dtype=dtypes.float32, max_error=100.0, bias=0.0, sigma=0.1)
self._testGrad(
[3, 3], dtype=dtypes.complex64, max_error=100.0, bias=0.0, sigma=0.1)
class MinOrMaxGradientTest(test.TestCase):
def testMinGradient(self):
inputs = constant_op.constant([1.0], dtype=dtypes.float32)
outputs = math_ops.reduce_min(array_ops.concat([inputs, inputs], 0))
with self.test_session():
error = gradient_checker.compute_gradient_error(inputs, [1], outputs, [])
self.assertLess(error, 1e-4)
def testMaxGradient(self):
inputs = constant_op.constant([1.0], dtype=dtypes.float32)
outputs = math_ops.reduce_max(array_ops.concat([inputs, inputs], 0))
with self.test_session():
error = gradient_checker.compute_gradient_error(inputs, [1], outputs, [])
self.assertLess(error, 1e-4)
class MaximumOrMinimumGradientTest(test.TestCase):
def testMaximumGradient(self):
inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32)
outputs = math_ops.maximum(inputs, 3.0)
with self.test_session():
error = gradient_checker.compute_gradient_error(inputs, [4], outputs, [4])
self.assertLess(error, 1e-4)
def testMinimumGradient(self):
inputs = constant_op.constant([1.0, 2.0, 3.0, 4.0], dtype=dtypes.float32)
outputs = math_ops.minimum(inputs, 2.0)
with self.test_session():
error = gradient_checker.compute_gradient_error(inputs, [4], outputs, [4])
self.assertLess(error, 1e-4)
class ProdGradientTest(test.TestCase):
def testProdGradient(self):
inputs = constant_op.constant([[1., 2.], [3., 4.]],
dtype=dtypes.float32)
outputs = math_ops.reduce_prod(inputs)
with self.test_session():
error = gradient_checker.compute_gradient_error(
inputs, inputs.get_shape().as_list(),
outputs, outputs.get_shape().as_list())
self.assertLess(error, 1e-4)
def testProdGradientForNegativeAxis(self):
inputs = constant_op.constant([[1., 2.], [3., 4.]],
dtype=dtypes.float32)
outputs = math_ops.reduce_prod(inputs, -1)
with self.test_session():
error = gradient_checker.compute_gradient_error(
inputs, inputs.get_shape().as_list(),
outputs, outputs.get_shape().as_list())
self.assertLess(error, 1e-4)
class SegmentMinOrMaxGradientTest(test.TestCase):
def testSegmentMinGradient(self):
data = constant_op.constant([1.0, 2.0, 3.0], dtype=dtypes.float32)
segment_ids = constant_op.constant([0, 0, 1], dtype=dtypes.int64)
segment_min = math_ops.segment_min(data, segment_ids)
with self.test_session():
error = gradient_checker.compute_gradient_error(data, [3], segment_min,
[2])
self.assertLess(error, 1e-4)
def testSegmentMaxGradient(self):
data = constant_op.constant([1.0, 2.0, 3.0], dtype=dtypes.float32)
segment_ids = constant_op.constant([0, 0, 1], dtype=dtypes.int64)
segment_max = math_ops.segment_max(data, segment_ids)
with self.test_session():
error = gradient_checker.compute_gradient_error(data, [3], segment_max,
[2])
self.assertLess(error, 1e-4)
def testSegmentMinGradientWithTies(self):
inputs = constant_op.constant([1.0], dtype=dtypes.float32)
data = array_ops.concat([inputs, inputs], 0)
segment_ids = constant_op.constant([0, 0], dtype=dtypes.int64)
segment_min = math_ops.segment_min(data, segment_ids)
with self.test_session():
error = gradient_checker.compute_gradient_error(inputs, [1], segment_min,
[1])
self.assertLess(error, 1e-4)
def testSegmentMaxGradientWithTies(self):
inputs = constant_op.constant([1.0], dtype=dtypes.float32)
data = array_ops.concat([inputs, inputs], 0)
segment_ids = constant_op.constant([0, 0], dtype=dtypes.int64)
segment_max = math_ops.segment_max(data, segment_ids)
with self.test_session():
error = gradient_checker.compute_gradient_error(inputs, [1], segment_max,
[1])
self.assertLess(error, 1e-4)
class FloorModGradientTest(test.TestCase):
def testFloorModGradient(self):
# Making sure the input is not near the discontinuity point where
# x/y == floor(x/y)
ns = constant_op.constant([17.], dtype=dtypes.float32)
inputs = constant_op.constant([131.], dtype=dtypes.float32)
floor_mod = math_ops.floormod(inputs, ns)
with self.test_session():
error = gradient_checker.compute_gradient_error(inputs, [1],
floor_mod, [1])
self.assertLess(error, 1e-4)
if __name__ == "__main__":
test.main()
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