669 lines
26 KiB
Python
669 lines
26 KiB
Python
# 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.debug.lib import check_numerics_callback
|
|
from tensorflow.python.eager import backprop
|
|
from tensorflow.python.eager import context
|
|
from tensorflow.python.framework import constant_op
|
|
from tensorflow.python.framework import dtypes
|
|
from tensorflow.python.framework import ops
|
|
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 gradient_checker_v2
|
|
from tensorflow.python.ops import gradients
|
|
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.cached_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)
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_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)
|
|
|
|
@test_util.run_deprecated_v1
|
|
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):
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(inputs, [1], outputs, [])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(inputs, [1], outputs, [])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
|
|
class MaximumOrMinimumGradientTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(inputs, [4], outputs, [4])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(inputs, [4], outputs, [4])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
|
|
class ProdGradientTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testProdGradient(self):
|
|
inputs = constant_op.constant([[1., 2.], [3., 4.]],
|
|
dtype=dtypes.float32)
|
|
outputs = math_ops.reduce_prod(inputs)
|
|
with self.cached_session():
|
|
error = gradient_checker.compute_gradient_error(
|
|
inputs, inputs.get_shape().as_list(),
|
|
outputs, outputs.get_shape().as_list())
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testProdGradientForNegativeAxis(self):
|
|
inputs = constant_op.constant([[1., 2.], [3., 4.]],
|
|
dtype=dtypes.float32)
|
|
outputs = math_ops.reduce_prod(inputs, -1)
|
|
with self.cached_session():
|
|
error = gradient_checker.compute_gradient_error(
|
|
inputs, inputs.get_shape().as_list(),
|
|
outputs, outputs.get_shape().as_list())
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testProdGradientComplex(self):
|
|
for dtype in dtypes.complex64, dtypes.complex128:
|
|
inputs = constant_op.constant([[1 + 3j, 2 - 1j], [3j, 4]],
|
|
dtype=dtype)
|
|
outputs = math_ops.reduce_prod(inputs)
|
|
with self.cached_session():
|
|
error = gradient_checker.compute_gradient_error(
|
|
inputs, inputs.get_shape().as_list(),
|
|
outputs, outputs.get_shape().as_list())
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testProdGradientForNegativeAxisComplex(self):
|
|
for dtype in dtypes.complex64, dtypes.complex128:
|
|
inputs = constant_op.constant([[1 + 3j, 2 - 1j], [3j, 4]],
|
|
dtype=dtype)
|
|
outputs = math_ops.reduce_prod(inputs, -1)
|
|
with self.cached_session():
|
|
error = gradient_checker.compute_gradient_error(
|
|
inputs, inputs.get_shape().as_list(),
|
|
outputs, outputs.get_shape().as_list())
|
|
self.assertLess(error, 1e-4)
|
|
|
|
|
|
@test_util.run_all_in_graph_and_eager_modes
|
|
class EuclideanNormGradientTest(test.TestCase):
|
|
|
|
def testBasic(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([3], dtype=dtype)
|
|
grad = gradient_checker_v2.compute_gradient(
|
|
math_ops.reduce_euclidean_norm, [x])
|
|
err = gradient_checker_v2.max_error(*grad)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def testNegative(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([-3], dtype=dtype)
|
|
grad = gradient_checker_v2.compute_gradient(
|
|
math_ops.reduce_euclidean_norm, [x])
|
|
err = gradient_checker_v2.max_error(*grad)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def testKeepdims(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([3], dtype=dtype)
|
|
grad = gradient_checker_v2.compute_gradient(
|
|
math_ops.reduce_euclidean_norm, [x])
|
|
err = gradient_checker_v2.max_error(*grad)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def testGradientChain(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([3], dtype=dtype)
|
|
grad = gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.reduce_euclidean_norm(x) * 5, [x])
|
|
err = gradient_checker_v2.max_error(*grad)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def testTwoElements(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([3, -4], dtype=dtype)
|
|
grad = gradient_checker_v2.compute_gradient(
|
|
math_ops.reduce_euclidean_norm, [x])
|
|
err = gradient_checker_v2.max_error(*grad)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def testNegativeZero(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([1.0, -0.0], dtype=dtype)
|
|
|
|
with backprop.GradientTape() as tape:
|
|
tape.watch(x)
|
|
y = math_ops.reduce_euclidean_norm(x)
|
|
|
|
dx = tape.gradient(y, x)
|
|
dx_answer = constant_op.constant([1.0, -0.0], dtype=dtype)
|
|
self.assertAllClose(dx, dx_answer)
|
|
self.assertAllClose(1.0 / dx, 1.0 / dx_answer)
|
|
|
|
def testZeros(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([0.0, -0.0], dtype=dtype)
|
|
|
|
with backprop.GradientTape() as tape:
|
|
tape.watch(x)
|
|
y = math_ops.reduce_euclidean_norm(x)
|
|
|
|
dx = tape.gradient(y, x)
|
|
dx_answer = constant_op.constant(
|
|
[float("NaN"), float("NaN")], dtype=dtype)
|
|
self.assertAllClose(dx, dx_answer)
|
|
|
|
def test2D_1(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([[-3, 5], [7, 11]], dtype=dtype)
|
|
grads = gradient_checker_v2.compute_gradient(
|
|
math_ops.reduce_euclidean_norm, [x])
|
|
err = gradient_checker_v2.max_error(*grads)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def test2D_2(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([[-3, 5], [7, 11]], dtype=dtype)
|
|
grads = gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.reduce_euclidean_norm(x, 0), [x])
|
|
err = gradient_checker_v2.max_error(*grads)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def test2D_3(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([[-3, 5], [7, 11]], dtype=dtype)
|
|
grads = gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.reduce_euclidean_norm(x, 1), [x])
|
|
err = gradient_checker_v2.max_error(*grads)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def test2D_4(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([[3], [4]], dtype=dtype)
|
|
grads = gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.reduce_euclidean_norm(x, 1), [x])
|
|
err = gradient_checker_v2.max_error(*grads)
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def test3D_1(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([[[-3, 5], [7, 11]], [[13, 17], [19, 23]]],
|
|
dtype=dtype)
|
|
grads = gradient_checker_v2.compute_gradient(
|
|
math_ops.reduce_euclidean_norm, [x])
|
|
err = gradient_checker_v2.max_error(*grads)
|
|
self.assertLess(err, 2e-3)
|
|
|
|
def test3D_2(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([[[-3, 5], [7, 11]], [[13, 17], [19, 23]]],
|
|
dtype=dtype)
|
|
grads = gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.reduce_euclidean_norm(x, 0), [x])
|
|
err = gradient_checker_v2.max_error(*grads)
|
|
self.assertLess(err, 2e-3)
|
|
|
|
def test3D_3(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([[[-3, 5], [7, 11]], [[13, 17], [19, 23]]],
|
|
dtype=dtype)
|
|
grads = gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.reduce_euclidean_norm(x, 1), [x])
|
|
err = gradient_checker_v2.max_error(*grads)
|
|
self.assertLess(err, 3e-3)
|
|
|
|
def test3D_4(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant([[[-3, 5], [7, 11]], [[13, 17], [19, 23]]],
|
|
dtype=dtype)
|
|
grads = gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.reduce_euclidean_norm(x, 2), [x])
|
|
err = gradient_checker_v2.max_error(*grads)
|
|
self.assertLess(err, 2e-3)
|
|
|
|
|
|
class SegmentMinOrMaxGradientTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(data, [3], segment_min,
|
|
[2])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(data, [3], segment_max,
|
|
[2])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(inputs, [1], segment_min,
|
|
[1])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(inputs, [1], segment_max,
|
|
[1])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
|
|
class FloorModGradientTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
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.cached_session():
|
|
error = gradient_checker.compute_gradient_error(inputs, [1],
|
|
floor_mod, [1])
|
|
self.assertLess(error, 1e-4)
|
|
|
|
|
|
class DivNoNanGradientTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testBasicGradient(self):
|
|
inputs = constant_op.constant(np.arange(-3, 3),
|
|
dtype=dtypes.float32)
|
|
outputs = math_ops.div_no_nan(inputs, 1 + math_ops.abs(inputs))
|
|
with self.cached_session():
|
|
error = gradient_checker.compute_gradient_error(
|
|
inputs,
|
|
inputs.get_shape().as_list(), outputs,
|
|
outputs.get_shape().as_list())
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testGradientWithDenominatorIsZero(self):
|
|
x = constant_op.constant(np.arange(-3, 3),
|
|
dtype=dtypes.float32)
|
|
y = array_ops.zeros_like(x,
|
|
dtype=dtypes.float32)
|
|
outputs = math_ops.div_no_nan(x, y)
|
|
with self.cached_session():
|
|
dx, dy = gradients.gradients(outputs, [x, y])
|
|
self.assertAllClose(dx.eval(), np.zeros(x.shape.as_list()))
|
|
self.assertAllClose(dy.eval(), np.zeros(y.shape.as_list()))
|
|
|
|
|
|
class MulNoNanGradientTest(test.TestCase):
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testBasicGradient(self):
|
|
inputs = constant_op.constant(np.arange(-3, 3), dtype=dtypes.float32)
|
|
outputs = math_ops.mul_no_nan(inputs, 1 + math_ops.abs(inputs))
|
|
with self.cached_session():
|
|
error = gradient_checker.compute_gradient_error(
|
|
inputs,
|
|
inputs.get_shape().as_list(), outputs,
|
|
outputs.get_shape().as_list())
|
|
self.assertLess(error, 1e-4)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testGradientWithRhsIsZero(self):
|
|
x_vals = [0, 1.0, np.nan, np.inf, np.NINF]
|
|
x = constant_op.constant(x_vals, dtype=dtypes.float32)
|
|
y = array_ops.zeros_like(x, dtype=dtypes.float32)
|
|
outputs = math_ops.mul_no_nan(x, y)
|
|
with self.cached_session():
|
|
dx, dy = gradients.gradients(outputs, [x, y])
|
|
self.assertAllClose(dx.eval(), np.zeros(x.shape.as_list()))
|
|
self.assertAllClose(dy.eval(), x_vals)
|
|
|
|
|
|
class XlogyTest(test.TestCase):
|
|
|
|
def _xlogy_gradients(self, x, y):
|
|
xlogy_xgrad = self.evaluate(gradients.gradients(math_ops.xlogy(x, y), x)[0])
|
|
xlogy_ygrad = self.evaluate(gradients.gradients(math_ops.xlogy(x, y), y)[0])
|
|
return xlogy_xgrad, xlogy_ygrad
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testNonZeroValuesGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0.1, dtype=dtype)
|
|
y = constant_op.constant(3.1, dtype=dtype)
|
|
xlogy_xgrad, xlogy_ygrad = self._xlogy_gradients(x, y)
|
|
xlogy_expected_xgrad = self.evaluate(math_ops.log(y))
|
|
xlogy_expected_ygrad = self.evaluate(x / y)
|
|
self.assertAllClose(xlogy_expected_xgrad, xlogy_xgrad)
|
|
self.assertAllClose(xlogy_expected_ygrad, xlogy_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testZeroXGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0., dtype=dtype)
|
|
y = constant_op.constant(3.1, dtype=dtype)
|
|
xlogy_xgrad, xlogy_ygrad = self._xlogy_gradients(x, y)
|
|
zero = self.evaluate(x)
|
|
self.assertAllClose(zero, xlogy_xgrad)
|
|
self.assertAllClose(zero, xlogy_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testZeroYGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0.1, dtype=dtype)
|
|
y = constant_op.constant(0., dtype=dtype)
|
|
xlogy_xgrad, xlogy_ygrad = self._xlogy_gradients(x, y)
|
|
self.assertAllClose(-np.inf, xlogy_xgrad)
|
|
self.assertAllClose(np.inf, xlogy_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testZeroXYGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0., dtype=dtype)
|
|
y = constant_op.constant(0., dtype=dtype)
|
|
xlogy_xgrad, xlogy_ygrad = self._xlogy_gradients(x, y)
|
|
zero = self.evaluate(x)
|
|
self.assertAllClose(zero, xlogy_xgrad)
|
|
self.assertAllClose(zero, xlogy_ygrad)
|
|
|
|
|
|
class Xlog1pyTest(test.TestCase):
|
|
|
|
def _xlog1py_gradients(self, x, y):
|
|
xlog1py_xgrad = self.evaluate(
|
|
gradients.gradients(math_ops.xlog1py(x, y), x)[0])
|
|
xlog1py_ygrad = self.evaluate(
|
|
gradients.gradients(math_ops.xlog1py(x, y), y)[0])
|
|
return xlog1py_xgrad, xlog1py_ygrad
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testNonZeroValuesGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0.1, dtype=dtype)
|
|
y = constant_op.constant(3.1, dtype=dtype)
|
|
xlog1py_xgrad, xlog1py_ygrad = self._xlog1py_gradients(x, y)
|
|
xlog1py_expected_xgrad = self.evaluate(math_ops.log1p(y))
|
|
xlog1py_expected_ygrad = self.evaluate(x / (1. + y))
|
|
self.assertAllClose(xlog1py_expected_xgrad, xlog1py_xgrad)
|
|
self.assertAllClose(xlog1py_expected_ygrad, xlog1py_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testZeroXGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0., dtype=dtype)
|
|
y = constant_op.constant(3.1, dtype=dtype)
|
|
xlog1py_xgrad, xlog1py_ygrad = self._xlog1py_gradients(x, y)
|
|
zero = self.evaluate(x)
|
|
self.assertAllClose(zero, xlog1py_xgrad)
|
|
self.assertAllClose(zero, xlog1py_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testNegOneYGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0.1, dtype=dtype)
|
|
y = constant_op.constant(-1., dtype=dtype)
|
|
xlog1py_xgrad, xlog1py_ygrad = self._xlog1py_gradients(x, y)
|
|
self.assertAllClose(-np.inf, xlog1py_xgrad)
|
|
self.assertAllClose(np.inf, xlog1py_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testZeroXNegOneYGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0., dtype=dtype)
|
|
y = constant_op.constant(-1., dtype=dtype)
|
|
xlog1py_xgrad, xlog1py_ygrad = self._xlog1py_gradients(x, y)
|
|
zero = self.evaluate(x)
|
|
self.assertAllClose(zero, xlog1py_xgrad)
|
|
self.assertAllClose(zero, xlog1py_ygrad)
|
|
|
|
|
|
class XdivyTest(test.TestCase):
|
|
|
|
def _xdivy_gradients(self, x, y):
|
|
xdivy_xgrad = self.evaluate(gradients.gradients(math_ops.xdivy(x, y), x)[0])
|
|
xdivy_ygrad = self.evaluate(gradients.gradients(math_ops.xdivy(x, y), y)[0])
|
|
return xdivy_xgrad, xdivy_ygrad
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testNonZeroValuesGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0.1, dtype=dtype)
|
|
y = constant_op.constant(3.1, dtype=dtype)
|
|
xdivy_xgrad, xdivy_ygrad = self._xdivy_gradients(x, y)
|
|
xdivy_expected_xgrad = self.evaluate(1 / y)
|
|
xdivy_expected_ygrad = self.evaluate(-x / y**2)
|
|
self.assertAllClose(xdivy_expected_xgrad, xdivy_xgrad)
|
|
self.assertAllClose(xdivy_expected_ygrad, xdivy_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testZeroXGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0., dtype=dtype)
|
|
y = constant_op.constant(3.1, dtype=dtype)
|
|
xdivy_xgrad, xdivy_ygrad = self._xdivy_gradients(x, y)
|
|
zero = self.evaluate(x)
|
|
self.assertAllClose(zero, xdivy_xgrad)
|
|
self.assertAllClose(zero, xdivy_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testZeroYGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0.1, dtype=dtype)
|
|
y = constant_op.constant(0., dtype=dtype)
|
|
xdivy_xgrad, xdivy_ygrad = self._xdivy_gradients(x, y)
|
|
self.assertAllClose(np.inf, xdivy_xgrad)
|
|
self.assertAllClose(-np.inf, xdivy_ygrad)
|
|
|
|
@test_util.run_deprecated_v1
|
|
def testZeroXYGrad(self):
|
|
for dtype in [dtypes.float16, dtypes.float32, dtypes.float64]:
|
|
x = constant_op.constant(0., dtype=dtype)
|
|
y = constant_op.constant(0., dtype=dtype)
|
|
xdivy_xgrad, xdivy_ygrad = self._xdivy_gradients(x, y)
|
|
zero = self.evaluate(x)
|
|
self.assertAllClose(zero, xdivy_xgrad)
|
|
self.assertAllClose(zero, xdivy_ygrad)
|
|
|
|
|
|
@test_util.run_all_in_graph_and_eager_modes
|
|
class PowGradTest(test.TestCase):
|
|
|
|
def test_zero_grad_tf_gradients(self):
|
|
if context.executing_eagerly():
|
|
self.skipTest("tf.gradients not supported in eager.")
|
|
|
|
x = constant_op.constant([-1., 0., 1.])
|
|
g = self.evaluate(gradients.gradients(math_ops.pow(x, 2), x)[0])
|
|
self.assertAllClose([-2., 0., 2.], g)
|
|
|
|
def test_zero_grad_tape(self):
|
|
try:
|
|
check_numerics_callback.enable_check_numerics()
|
|
x = constant_op.constant([-1, 0., 1.])
|
|
with backprop.GradientTape() as tape:
|
|
tape.watch(x)
|
|
g = tape.gradient(math_ops.pow(x, 2), x)
|
|
g = self.evaluate(g)
|
|
self.assertAllClose([-2., 0., 2.], g)
|
|
finally:
|
|
check_numerics_callback.disable_check_numerics()
|
|
|
|
|
|
@test_util.run_all_in_graph_and_eager_modes
|
|
class NextAfterTest(test.TestCase):
|
|
|
|
def _nextafter_gradient(self, x1, x2):
|
|
with backprop.GradientTape() as tape:
|
|
tape.watch(x1)
|
|
tape.watch(x2)
|
|
y = math_ops.nextafter(x1, x2)
|
|
return tape.gradient(y, [x1, x2])
|
|
|
|
def testBasic(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
x1 = constant_op.constant(0.1, dtype=dtype)
|
|
x2 = constant_op.constant(3.1, dtype=dtype)
|
|
dx1, dx2 = self._nextafter_gradient(x1, x2)
|
|
expected_dx1 = constant_op.constant(1, dtype=dtype)
|
|
expected_dx2 = constant_op.constant(0, dtype=dtype)
|
|
self.assertAllClose(expected_dx1, dx1)
|
|
self.assertAllClose(expected_dx2, dx2)
|
|
|
|
def testDynamicShapes(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
default_x1 = constant_op.constant(0.1, dtype=dtype)
|
|
default_x2 = constant_op.constant(3.1, dtype=dtype)
|
|
x1 = array_ops.placeholder_with_default(default_x1, shape=None)
|
|
x2 = array_ops.placeholder_with_default(default_x2, shape=None)
|
|
dx1, dx2 = self._nextafter_gradient(x1, x2)
|
|
expected_dx1 = constant_op.constant(1, dtype=dtype)
|
|
expected_dx2 = constant_op.constant(0, dtype=dtype)
|
|
self.assertAllClose(expected_dx1, dx1)
|
|
self.assertAllClose(expected_dx2, dx2)
|
|
|
|
def testWithGradientChecker(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
with self.cached_session():
|
|
x1 = np.array([-1, 0, 1, 2, 3], dtype=dtype.as_numpy_dtype)
|
|
x2 = np.array([2, 2, 2, 2, 2], dtype=dtype.as_numpy_dtype)
|
|
err = gradient_checker_v2.max_error(
|
|
*gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.nextafter(x, x2), [x1])) # pylint: disable=cell-var-from-loop
|
|
self.assertLess(err, 1e-3)
|
|
|
|
def testBroadcastingWithGradientChecker(self):
|
|
for dtype in [dtypes.float32, dtypes.float64]:
|
|
with self.cached_session():
|
|
x1 = np.array([-1, 0, 1, 2, 3], dtype=dtype.as_numpy_dtype)
|
|
x2 = np.array([2], dtype=dtype.as_numpy_dtype)
|
|
err = gradient_checker_v2.max_error(
|
|
*gradient_checker_v2.compute_gradient(
|
|
lambda x: math_ops.nextafter(x, x2), [x1])) # pylint: disable=cell-var-from-loop
|
|
self.assertLess(err, 1e-3)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
test.main()
|