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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.
# ==============================================================================
"""Functional tests for 3d convolutional operations."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import itertools
import numpy as np
from tensorflow.python.framework import constant_op
from tensorflow.python.framework import dtypes
from tensorflow.python.ops import array_ops
from tensorflow.python.ops import gradient_checker
from tensorflow.python.ops import gradients_impl
from tensorflow.python.ops import math_ops
from tensorflow.python.platform import test
from tensorflow.python.platform import tf_logging
class BetaincTest(test.TestCase):
def _testBetaInc(self, a_s, b_s, x_s, dtype):
try:
from scipy import special # pylint: disable=g-import-not-at-top
np_dt = dtype.as_numpy_dtype
# Test random values
a_s = a_s.astype(np_dt) # in (0, infty)
b_s = b_s.astype(np_dt) # in (0, infty)
x_s = x_s.astype(np_dt) # in (0, 1)
tf_a_s = constant_op.constant(a_s, dtype=dtype)
tf_b_s = constant_op.constant(b_s, dtype=dtype)
tf_x_s = constant_op.constant(x_s, dtype=dtype)
tf_out_t = math_ops.betainc(tf_a_s, tf_b_s, tf_x_s)
with self.cached_session():
tf_out = tf_out_t.eval()
scipy_out = special.betainc(a_s, b_s, x_s).astype(np_dt)
# the scipy version of betainc uses a double-only implementation.
# TODO(ebrevdo): identify reasons for (sometime) precision loss
# with doubles
tol = 1e-4 if dtype == dtypes.float32 else 5e-5
self.assertAllCloseAccordingToType(scipy_out, tf_out, rtol=tol, atol=0)
# Test out-of-range values (most should return nan output)
combinations = list(itertools.product([-1, 0, 0.5, 1.0, 1.5], repeat=3))
a_comb, b_comb, x_comb = np.asarray(list(zip(*combinations)), dtype=np_dt)
with self.cached_session():
tf_comb = math_ops.betainc(a_comb, b_comb, x_comb).eval()
scipy_comb = special.betainc(a_comb, b_comb, x_comb).astype(np_dt)
self.assertAllCloseAccordingToType(scipy_comb, tf_comb)
# Test broadcasting between scalars and other shapes
with self.cached_session():
self.assertAllCloseAccordingToType(
special.betainc(0.1, b_s, x_s).astype(np_dt),
math_ops.betainc(0.1, b_s, x_s).eval(),
rtol=tol,
atol=0)
self.assertAllCloseAccordingToType(
special.betainc(a_s, 0.1, x_s).astype(np_dt),
math_ops.betainc(a_s, 0.1, x_s).eval(),
rtol=tol,
atol=0)
self.assertAllCloseAccordingToType(
special.betainc(a_s, b_s, 0.1).astype(np_dt),
math_ops.betainc(a_s, b_s, 0.1).eval(),
rtol=tol,
atol=0)
self.assertAllCloseAccordingToType(
special.betainc(0.1, b_s, 0.1).astype(np_dt),
math_ops.betainc(0.1, b_s, 0.1).eval(),
rtol=tol,
atol=0)
self.assertAllCloseAccordingToType(
special.betainc(0.1, 0.1, 0.1).astype(np_dt),
math_ops.betainc(0.1, 0.1, 0.1).eval(),
rtol=tol,
atol=0)
with self.assertRaisesRegexp(ValueError, "must be equal"):
math_ops.betainc(0.5, [0.5], [[0.5]])
with self.cached_session():
with self.assertRaisesOpError("Shapes of .* are inconsistent"):
a_p = array_ops.placeholder(dtype)
b_p = array_ops.placeholder(dtype)
x_p = array_ops.placeholder(dtype)
math_ops.betainc(a_p, b_p, x_p).eval(
feed_dict={a_p: 0.5,
b_p: [0.5],
x_p: [[0.5]]})
except ImportError as e:
tf_logging.warn("Cannot test special functions: %s" % str(e))
def testBetaIncFloat(self):
a_s = np.abs(np.random.randn(10, 10) * 30) # in (0, infty)
b_s = np.abs(np.random.randn(10, 10) * 30) # in (0, infty)
x_s = np.random.rand(10, 10) # in (0, 1)
self._testBetaInc(a_s, b_s, x_s, dtypes.float32)
def testBetaIncDouble(self):
a_s = np.abs(np.random.randn(10, 10) * 30) # in (0, infty)
b_s = np.abs(np.random.randn(10, 10) * 30) # in (0, infty)
x_s = np.random.rand(10, 10) # in (0, 1)
self._testBetaInc(a_s, b_s, x_s, dtypes.float64)
def testBetaIncDoubleVeryLargeValues(self):
a_s = np.abs(np.random.randn(10, 10) * 1e15) # in (0, infty)
b_s = np.abs(np.random.randn(10, 10) * 1e15) # in (0, infty)
x_s = np.random.rand(10, 10) # in (0, 1)
self._testBetaInc(a_s, b_s, x_s, dtypes.float64)
def testBetaIncDoubleVerySmallValues(self):
a_s = np.abs(np.random.randn(10, 10) * 1e-16) # in (0, infty)
b_s = np.abs(np.random.randn(10, 10) * 1e-16) # in (0, infty)
x_s = np.random.rand(10, 10) # in (0, 1)
self._testBetaInc(a_s, b_s, x_s, dtypes.float64)
def testBetaIncFloatVerySmallValues(self):
a_s = np.abs(np.random.randn(10, 10) * 1e-8) # in (0, infty)
b_s = np.abs(np.random.randn(10, 10) * 1e-8) # in (0, infty)
x_s = np.random.rand(10, 10) # in (0, 1)
self._testBetaInc(a_s, b_s, x_s, dtypes.float32)
def testBetaIncFpropAndBpropAreNeverNAN(self):
with self.cached_session() as sess:
space = np.logspace(-8, 5).tolist()
space_x = np.linspace(1e-16, 1 - 1e-16).tolist()
ga_s, gb_s, gx_s = zip(*list(itertools.product(space, space, space_x)))
# Test grads are never nan
ga_s_t = constant_op.constant(ga_s, dtype=dtypes.float32)
gb_s_t = constant_op.constant(gb_s, dtype=dtypes.float32)
gx_s_t = constant_op.constant(gx_s, dtype=dtypes.float32)
tf_gout_t = math_ops.betainc(ga_s_t, gb_s_t, gx_s_t)
tf_gout, grads_x = sess.run(
[tf_gout_t,
gradients_impl.gradients(tf_gout_t, [ga_s_t, gb_s_t, gx_s_t])[2]])
# Equivalent to `assertAllFalse` (if it existed).
self.assertAllEqual(np.zeros_like(grads_x).astype(np.bool),
np.isnan(tf_gout))
self.assertAllEqual(np.zeros_like(grads_x).astype(np.bool),
np.isnan(grads_x))
def testBetaIncGrads(self):
err_tolerance = 1e-3
with self.cached_session():
# Test gradient
ga_s = np.abs(np.random.randn(2, 2) * 30) # in (0, infty)
gb_s = np.abs(np.random.randn(2, 2) * 30) # in (0, infty)
gx_s = np.random.rand(2, 2) # in (0, 1)
tf_ga_s = constant_op.constant(ga_s, dtype=dtypes.float64)
tf_gb_s = constant_op.constant(gb_s, dtype=dtypes.float64)
tf_gx_s = constant_op.constant(gx_s, dtype=dtypes.float64)
tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s)
err = gradient_checker.compute_gradient_error(
[tf_gx_s], [gx_s.shape], tf_gout_t, gx_s.shape)
tf_logging.info("betainc gradient err = %g " % err)
self.assertLess(err, err_tolerance)
# Test broadcast gradient
gx_s = np.random.rand() # in (0, 1)
tf_gx_s = constant_op.constant(gx_s, dtype=dtypes.float64)
tf_gout_t = math_ops.betainc(tf_ga_s, tf_gb_s, tf_gx_s)
err = gradient_checker.compute_gradient_error(
[tf_gx_s], [()], tf_gout_t, ga_s.shape)
tf_logging.info("betainc gradient err = %g " % err)
self.assertLess(err, err_tolerance)
if __name__ == "__main__":
test.main()
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