diff options
| author |  Akshay Modi <nareshmodi@google.com> | 2018-05-17 12:29:54 -0700 |
|---|---|---|
| committer |  TensorFlower Gardener <gardener@tensorflow.org> | 2018-05-17 12:33:50 -0700 |
| commit | 01dbc6ac45d213ea03e6b1ddcc68da506bc868fe (patch) | |
| tree | 90a521d7bcaf933179f2948c816fd7f825157d27 | |
| parent | 831aa3984d831a382e4f09543a1750ef0b6df3f8 (diff) | |
Test some distributions stuff in Eager as well as Graph
PiperOrigin-RevId: 197033485
13 files changed, 635 insertions, 497 deletions
diff --git a/tensorflow/python/eager/pywrap_tensor.cc b/tensorflow/python/eager/pywrap_tensor.cc index b3aadd55ce..a62af4a06c 100644 --- a/tensorflow/python/eager/pywrap_tensor.cc +++ b/tensorflow/python/eager/pywrap_tensor.cc @@ -299,7 +299,7 @@ int EagerTensor_init(EagerTensor* self, PyObject* args, PyObject* kwds) { GetContext(context), handle.get(), handle_dtype, static_cast<TF_DataType>(desired_dtype), self->status)); if (TF_GetCode(self->status) != TF_OK) { - PyErr_SetString(PyExc_ValueError, + PyErr_SetString(PyExc_TypeError, tensorflow::strings::StrCat( "Error while casting from DataType ", handle_dtype, " to ", desired_dtype, ". ", TF_Message(self->status)) diff --git a/tensorflow/python/framework/test_util.py b/tensorflow/python/framework/test_util.py index dc56d88066..97cd22e47a 100644 --- a/tensorflow/python/framework/test_util.py +++ b/tensorflow/python/framework/test_util.py @@ -639,6 +639,14 @@ def assert_no_garbage_created(f): return decorator +def run_all_in_graph_and_eager_modes(cls): + base_decorator = run_in_graph_and_eager_modes() + for name, value in cls.__dict__.copy().items(): + if callable(value) and name.startswith("test"): + setattr(cls, name, base_decorator(value)) + return cls + + def run_in_graph_and_eager_modes(__unused__=None, config=None, use_gpu=True, diff --git a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py index 09812db816..095d1cde15 100644 --- a/tensorflow/python/kernel_tests/distributions/bernoulli_test.py +++ b/tensorflow/python/kernel_tests/distributions/bernoulli_test.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops.distributions import bernoulli from tensorflow.python.ops.distributions import kullback_leibler @@ -56,59 +57,65 @@ def entropy(p): class BernoulliTest(test.TestCase): + @test_util.run_in_graph_and_eager_modes() def testP(self): p = [0.2, 0.4] dist = bernoulli.Bernoulli(probs=p) with self.test_session(): - self.assertAllClose(p, dist.probs.eval()) + self.assertAllClose(p, self.evaluate(dist.probs)) + @test_util.run_in_graph_and_eager_modes() def testLogits(self): logits = [-42., 42.] dist = bernoulli.Bernoulli(logits=logits) with self.test_session(): - self.assertAllClose(logits, dist.logits.eval()) + self.assertAllClose(logits, self.evaluate(dist.logits)) if not special: return with self.test_session(): - self.assertAllClose(special.expit(logits), dist.probs.eval()) + self.assertAllClose(special.expit(logits), self.evaluate(dist.probs)) p = [0.01, 0.99, 0.42] dist = bernoulli.Bernoulli(probs=p) with self.test_session(): - self.assertAllClose(special.logit(p), dist.logits.eval()) + self.assertAllClose(special.logit(p), self.evaluate(dist.logits)) + @test_util.run_in_graph_and_eager_modes() def testInvalidP(self): invalid_ps = [1.01, 2.] for p in invalid_ps: with self.test_session(): with self.assertRaisesOpError("probs has components greater than 1"): dist = bernoulli.Bernoulli(probs=p, validate_args=True) - dist.probs.eval() + self.evaluate(dist.probs) invalid_ps = [-0.01, -3.] for p in invalid_ps: with self.test_session(): with self.assertRaisesOpError("Condition x >= 0"): dist = bernoulli.Bernoulli(probs=p, validate_args=True) - dist.probs.eval() + self.evaluate(dist.probs) valid_ps = [0.0, 0.5, 1.0] for p in valid_ps: with self.test_session(): dist = bernoulli.Bernoulli(probs=p) - self.assertEqual(p, dist.probs.eval()) # Should not fail + self.assertEqual(p, self.evaluate(dist.probs)) # Should not fail + @test_util.run_in_graph_and_eager_modes() def testShapes(self): with self.test_session(): for batch_shape in ([], [1], [2, 3, 4]): dist = make_bernoulli(batch_shape) self.assertAllEqual(batch_shape, dist.batch_shape.as_list()) - self.assertAllEqual(batch_shape, dist.batch_shape_tensor().eval()) + self.assertAllEqual(batch_shape, + self.evaluate(dist.batch_shape_tensor())) self.assertAllEqual([], dist.event_shape.as_list()) - self.assertAllEqual([], dist.event_shape_tensor().eval()) + self.assertAllEqual([], self.evaluate(dist.event_shape_tensor())) + @test_util.run_in_graph_and_eager_modes() def testDtype(self): dist = make_bernoulli([]) self.assertEqual(dist.dtype, dtypes.int32) @@ -126,6 +133,7 @@ class BernoulliTest(test.TestCase): self.assertEqual(dist64.dtype, dist64.sample(5).dtype) self.assertEqual(dist64.dtype, dist64.mode().dtype) + @test_util.run_in_graph_and_eager_modes() def _testPmf(self, **kwargs): dist = bernoulli.Bernoulli(**kwargs) with self.test_session(): @@ -147,8 +155,9 @@ class BernoulliTest(test.TestCase): # pylint: enable=bad-continuation for x, expected_pmf in zip(xs, expected_pmfs): - self.assertAllClose(dist.prob(x).eval(), expected_pmf) - self.assertAllClose(dist.log_prob(x).eval(), np.log(expected_pmf)) + self.assertAllClose(self.evaluate(dist.prob(x)), expected_pmf) + self.assertAllClose( + self.evaluate(dist.log_prob(x)), np.log(expected_pmf)) def testPmfCorrectBroadcastDynamicShape(self): with self.test_session(): @@ -165,15 +174,17 @@ class BernoulliTest(test.TestCase): p: [0.2, 0.3, 0.4] }), [[0.2, 0.7, 0.4]]) + @test_util.run_in_graph_and_eager_modes() def testPmfInvalid(self): p = [0.1, 0.2, 0.7] with self.test_session(): dist = bernoulli.Bernoulli(probs=p, validate_args=True) with self.assertRaisesOpError("must be non-negative."): - dist.prob([1, 1, -1]).eval() + self.evaluate(dist.prob([1, 1, -1])) with self.assertRaisesOpError("Elements cannot exceed 1."): - dist.prob([2, 0, 1]).eval() + self.evaluate(dist.prob([2, 0, 1])) + @test_util.run_in_graph_and_eager_modes() def testPmfWithP(self): p = [[0.2, 0.4], [0.3, 0.6]] self._testPmf(probs=p) @@ -203,7 +214,7 @@ class BernoulliTest(test.TestCase): with self.test_session(): dist = bernoulli.Bernoulli(probs=0.5) - self.assertEqual(2, len(dist.log_prob([[1], [1]]).eval().shape)) + self.assertEqual(2, len(self.evaluate(dist.log_prob([[1], [1]])).shape)) with self.test_session(): dist = bernoulli.Bernoulli(probs=0.5) @@ -215,25 +226,31 @@ class BernoulliTest(test.TestCase): dist = bernoulli.Bernoulli(probs=[[0.5], [0.5]]) self.assertEqual((2, 1), dist.log_prob(1).get_shape()) + @test_util.run_in_graph_and_eager_modes() def testBoundaryConditions(self): with self.test_session(): dist = bernoulli.Bernoulli(probs=1.0) - self.assertAllClose(np.nan, dist.log_prob(0).eval()) - self.assertAllClose([np.nan], [dist.log_prob(1).eval()]) + self.assertAllClose(np.nan, self.evaluate(dist.log_prob(0))) + self.assertAllClose([np.nan], [self.evaluate(dist.log_prob(1))]) + @test_util.run_in_graph_and_eager_modes() def testEntropyNoBatch(self): p = 0.2 dist = bernoulli.Bernoulli(probs=p) with self.test_session(): - self.assertAllClose(dist.entropy().eval(), entropy(p)) + self.assertAllClose(self.evaluate(dist.entropy()), entropy(p)) + @test_util.run_in_graph_and_eager_modes() def testEntropyWithBatch(self): p = [[0.1, 0.7], [0.2, 0.6]] dist = bernoulli.Bernoulli(probs=p, validate_args=False) with self.test_session(): - self.assertAllClose(dist.entropy().eval(), [[entropy(0.1), entropy(0.7)], - [entropy(0.2), entropy(0.6)]]) + self.assertAllClose( + self.evaluate(dist.entropy()), + [[entropy(0.1), entropy(0.7)], [entropy(0.2), + entropy(0.6)]]) + @test_util.run_in_graph_and_eager_modes() def testSampleN(self): with self.test_session(): p = [0.2, 0.6] @@ -242,7 +259,7 @@ class BernoulliTest(test.TestCase): samples = dist.sample(n) samples.set_shape([n, 2]) self.assertEqual(samples.dtype, dtypes.int32) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertTrue(np.all(sample_values >= 0)) self.assertTrue(np.all(sample_values <= 1)) # Note that the standard error for the sample mean is ~ sqrt(p * (1 - p) / @@ -262,51 +279,54 @@ class BernoulliTest(test.TestCase): n = 1000 seed = 42 self.assertAllEqual( - dist.sample(n, seed).eval(), dist.sample(n, seed).eval()) + self.evaluate(dist.sample(n, seed)), + self.evaluate(dist.sample(n, seed))) n = array_ops.placeholder(dtypes.int32) sample, sample = sess.run([dist.sample(n, seed), dist.sample(n, seed)], feed_dict={n: 1000}) self.assertAllEqual(sample, sample) + @test_util.run_in_graph_and_eager_modes() def testMean(self): with self.test_session(): p = np.array([[0.2, 0.7], [0.5, 0.4]], dtype=np.float32) dist = bernoulli.Bernoulli(probs=p) - self.assertAllEqual(dist.mean().eval(), p) + self.assertAllEqual(self.evaluate(dist.mean()), p) + @test_util.run_in_graph_and_eager_modes() def testVarianceAndStd(self): var = lambda p: p * (1. - p) with self.test_session(): p = [[0.2, 0.7], [0.5, 0.4]] dist = bernoulli.Bernoulli(probs=p) self.assertAllClose( - dist.variance().eval(), + self.evaluate(dist.variance()), np.array( [[var(0.2), var(0.7)], [var(0.5), var(0.4)]], dtype=np.float32)) self.assertAllClose( - dist.stddev().eval(), + self.evaluate(dist.stddev()), np.array( [[np.sqrt(var(0.2)), np.sqrt(var(0.7))], [np.sqrt(var(0.5)), np.sqrt(var(0.4))]], dtype=np.float32)) + @test_util.run_in_graph_and_eager_modes() def testBernoulliBernoulliKL(self): - with self.test_session() as sess: - batch_size = 6 - a_p = np.array([0.5] * batch_size, dtype=np.float32) - b_p = np.array([0.4] * batch_size, dtype=np.float32) + batch_size = 6 + a_p = np.array([0.5] * batch_size, dtype=np.float32) + b_p = np.array([0.4] * batch_size, dtype=np.float32) - a = bernoulli.Bernoulli(probs=a_p) - b = bernoulli.Bernoulli(probs=b_p) + a = bernoulli.Bernoulli(probs=a_p) + b = bernoulli.Bernoulli(probs=b_p) - kl = kullback_leibler.kl_divergence(a, b) - kl_val = sess.run(kl) + kl = kullback_leibler.kl_divergence(a, b) + kl_val = self.evaluate(kl) - kl_expected = (a_p * np.log(a_p / b_p) + (1. - a_p) * np.log( - (1. - a_p) / (1. - b_p))) + kl_expected = (a_p * np.log(a_p / b_p) + (1. - a_p) * np.log( + (1. - a_p) / (1. - b_p))) - self.assertEqual(kl.get_shape(), (batch_size,)) - self.assertAllClose(kl_val, kl_expected) + self.assertEqual(kl.get_shape(), (batch_size,)) + self.assertAllClose(kl_val, kl_expected) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/beta_test.py b/tensorflow/python/kernel_tests/distributions/beta_test.py index ab5041a6eb..4bc8303ebb 100644 --- a/tensorflow/python/kernel_tests/distributions/beta_test.py +++ b/tensorflow/python/kernel_tests/distributions/beta_test.py @@ -24,6 +24,7 @@ from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import random_seed from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import beta as beta_lib @@ -45,6 +46,7 @@ special = try_import("scipy.special") stats = try_import("scipy.stats") +@test_util.run_all_in_graph_and_eager_modes class BetaTest(test.TestCase): def testSimpleShapes(self): @@ -52,8 +54,8 @@ class BetaTest(test.TestCase): a = np.random.rand(3) b = np.random.rand(3) dist = beta_lib.Beta(a, b) - self.assertAllEqual([], dist.event_shape_tensor().eval()) - self.assertAllEqual([3], dist.batch_shape_tensor().eval()) + self.assertAllEqual([], self.evaluate(dist.event_shape_tensor())) + self.assertAllEqual([3], self.evaluate(dist.batch_shape_tensor())) self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape) self.assertEqual(tensor_shape.TensorShape([3]), dist.batch_shape) @@ -62,8 +64,8 @@ class BetaTest(test.TestCase): a = np.random.rand(3, 2, 2) b = np.random.rand(3, 2, 2) dist = beta_lib.Beta(a, b) - self.assertAllEqual([], dist.event_shape_tensor().eval()) - self.assertAllEqual([3, 2, 2], dist.batch_shape_tensor().eval()) + self.assertAllEqual([], self.evaluate(dist.event_shape_tensor())) + self.assertAllEqual([3, 2, 2], self.evaluate(dist.batch_shape_tensor())) self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape) self.assertEqual( tensor_shape.TensorShape([3, 2, 2]), dist.batch_shape) @@ -73,8 +75,8 @@ class BetaTest(test.TestCase): a = np.random.rand(3, 2, 2) b = np.random.rand(2, 2) dist = beta_lib.Beta(a, b) - self.assertAllEqual([], dist.event_shape_tensor().eval()) - self.assertAllEqual([3, 2, 2], dist.batch_shape_tensor().eval()) + self.assertAllEqual([], self.evaluate(dist.event_shape_tensor())) + self.assertAllEqual([3, 2, 2], self.evaluate(dist.batch_shape_tensor())) self.assertEqual(tensor_shape.TensorShape([]), dist.event_shape) self.assertEqual( tensor_shape.TensorShape([3, 2, 2]), dist.batch_shape) @@ -85,7 +87,7 @@ class BetaTest(test.TestCase): with self.test_session(): dist = beta_lib.Beta(a, b) self.assertEqual([1, 3], dist.concentration1.get_shape()) - self.assertAllClose(a, dist.concentration1.eval()) + self.assertAllClose(a, self.evaluate(dist.concentration1)) def testBetaProperty(self): a = [[1., 2, 3]] @@ -93,24 +95,24 @@ class BetaTest(test.TestCase): with self.test_session(): dist = beta_lib.Beta(a, b) self.assertEqual([1, 3], dist.concentration0.get_shape()) - self.assertAllClose(b, dist.concentration0.eval()) + self.assertAllClose(b, self.evaluate(dist.concentration0)) def testPdfXProper(self): a = [[1., 2, 3]] b = [[2., 4, 3]] with self.test_session(): dist = beta_lib.Beta(a, b, validate_args=True) - dist.prob([.1, .3, .6]).eval() - dist.prob([.2, .3, .5]).eval() + self.evaluate(dist.prob([.1, .3, .6])) + self.evaluate(dist.prob([.2, .3, .5])) # Either condition can trigger. with self.assertRaisesOpError("sample must be positive"): - dist.prob([-1., 0.1, 0.5]).eval() + self.evaluate(dist.prob([-1., 0.1, 0.5])) with self.assertRaisesOpError("sample must be positive"): - dist.prob([0., 0.1, 0.5]).eval() + self.evaluate(dist.prob([0., 0.1, 0.5])) with self.assertRaisesOpError("sample must be less than `1`"): - dist.prob([.1, .2, 1.2]).eval() + self.evaluate(dist.prob([.1, .2, 1.2])) with self.assertRaisesOpError("sample must be less than `1`"): - dist.prob([.1, .2, 1.0]).eval() + self.evaluate(dist.prob([.1, .2, 1.0])) def testPdfTwoBatches(self): with self.test_session(): @@ -119,7 +121,7 @@ class BetaTest(test.TestCase): x = [.5, .5] dist = beta_lib.Beta(a, b) pdf = dist.prob(x) - self.assertAllClose([1., 3. / 2], pdf.eval()) + self.assertAllClose([1., 3. / 2], self.evaluate(pdf)) self.assertEqual((2,), pdf.get_shape()) def testPdfTwoBatchesNontrivialX(self): @@ -129,7 +131,7 @@ class BetaTest(test.TestCase): x = [.3, .7] dist = beta_lib.Beta(a, b) pdf = dist.prob(x) - self.assertAllClose([1, 63. / 50], pdf.eval()) + self.assertAllClose([1, 63. / 50], self.evaluate(pdf)) self.assertEqual((2,), pdf.get_shape()) def testPdfUniformZeroBatch(self): @@ -140,7 +142,7 @@ class BetaTest(test.TestCase): x = np.array([.1, .2, .3, .5, .8], dtype=np.float32) dist = beta_lib.Beta(a, b) pdf = dist.prob(x) - self.assertAllClose([1.] * 5, pdf.eval()) + self.assertAllClose([1.] * 5, self.evaluate(pdf)) self.assertEqual((5,), pdf.get_shape()) def testPdfAlphaStretchedInBroadcastWhenSameRank(self): @@ -150,7 +152,7 @@ class BetaTest(test.TestCase): x = [[.5, .5], [.3, .7]] dist = beta_lib.Beta(a, b) pdf = dist.prob(x) - self.assertAllClose([[1., 3. / 2], [1., 63. / 50]], pdf.eval()) + self.assertAllClose([[1., 3. / 2], [1., 63. / 50]], self.evaluate(pdf)) self.assertEqual((2, 2), pdf.get_shape()) def testPdfAlphaStretchedInBroadcastWhenLowerRank(self): @@ -159,7 +161,7 @@ class BetaTest(test.TestCase): b = [1., 2] x = [[.5, .5], [.2, .8]] pdf = beta_lib.Beta(a, b).prob(x) - self.assertAllClose([[1., 3. / 2], [1., 24. / 25]], pdf.eval()) + self.assertAllClose([[1., 3. / 2], [1., 24. / 25]], self.evaluate(pdf)) self.assertEqual((2, 2), pdf.get_shape()) def testPdfXStretchedInBroadcastWhenSameRank(self): @@ -168,7 +170,7 @@ class BetaTest(test.TestCase): b = [[1., 2], [2., 3]] x = [[.5, .5]] pdf = beta_lib.Beta(a, b).prob(x) - self.assertAllClose([[1., 3. / 2], [3. / 2, 15. / 8]], pdf.eval()) + self.assertAllClose([[1., 3. / 2], [3. / 2, 15. / 8]], self.evaluate(pdf)) self.assertEqual((2, 2), pdf.get_shape()) def testPdfXStretchedInBroadcastWhenLowerRank(self): @@ -177,7 +179,7 @@ class BetaTest(test.TestCase): b = [[1., 2], [2., 3]] x = [.5, .5] pdf = beta_lib.Beta(a, b).prob(x) - self.assertAllClose([[1., 3. / 2], [3. / 2, 15. / 8]], pdf.eval()) + self.assertAllClose([[1., 3. / 2], [3. / 2, 15. / 8]], self.evaluate(pdf)) self.assertEqual((2, 2), pdf.get_shape()) def testBetaMean(self): @@ -189,7 +191,7 @@ class BetaTest(test.TestCase): if not stats: return expected_mean = stats.beta.mean(a, b) - self.assertAllClose(expected_mean, dist.mean().eval()) + self.assertAllClose(expected_mean, self.evaluate(dist.mean())) def testBetaVariance(self): with session.Session(): @@ -200,7 +202,7 @@ class BetaTest(test.TestCase): if not stats: return expected_variance = stats.beta.var(a, b) - self.assertAllClose(expected_variance, dist.variance().eval()) + self.assertAllClose(expected_variance, self.evaluate(dist.variance())) def testBetaMode(self): with session.Session(): @@ -209,7 +211,7 @@ class BetaTest(test.TestCase): expected_mode = (a - 1) / (a + b - 2) dist = beta_lib.Beta(a, b) self.assertEqual(dist.mode().get_shape(), (3,)) - self.assertAllClose(expected_mode, dist.mode().eval()) + self.assertAllClose(expected_mode, self.evaluate(dist.mode())) def testBetaModeInvalid(self): with session.Session(): @@ -217,13 +219,13 @@ class BetaTest(test.TestCase): b = np.array([2., 4, 1.2]) dist = beta_lib.Beta(a, b, allow_nan_stats=False) with self.assertRaisesOpError("Condition x < y.*"): - dist.mode().eval() + self.evaluate(dist.mode()) a = np.array([2., 2, 3]) b = np.array([1., 4, 1.2]) dist = beta_lib.Beta(a, b, allow_nan_stats=False) with self.assertRaisesOpError("Condition x < y.*"): - dist.mode().eval() + self.evaluate(dist.mode()) def testBetaModeEnableAllowNanStats(self): with session.Session(): @@ -234,7 +236,7 @@ class BetaTest(test.TestCase): expected_mode = (a - 1) / (a + b - 2) expected_mode[0] = np.nan self.assertEqual((3,), dist.mode().get_shape()) - self.assertAllClose(expected_mode, dist.mode().eval()) + self.assertAllClose(expected_mode, self.evaluate(dist.mode())) a = np.array([2., 2, 3]) b = np.array([1., 4, 1.2]) @@ -243,7 +245,7 @@ class BetaTest(test.TestCase): expected_mode = (a - 1) / (a + b - 2) expected_mode[0] = np.nan self.assertEqual((3,), dist.mode().get_shape()) - self.assertAllClose(expected_mode, dist.mode().eval()) + self.assertAllClose(expected_mode, self.evaluate(dist.mode())) def testBetaEntropy(self): with session.Session(): @@ -254,7 +256,7 @@ class BetaTest(test.TestCase): if not stats: return expected_entropy = stats.beta.entropy(a, b) - self.assertAllClose(expected_entropy, dist.entropy().eval()) + self.assertAllClose(expected_entropy, self.evaluate(dist.entropy())) def testBetaSample(self): with self.test_session(): @@ -263,7 +265,7 @@ class BetaTest(test.TestCase): beta = beta_lib.Beta(a, b) n = constant_op.constant(100000) samples = beta.sample(n) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(sample_values.shape, (100000,)) self.assertFalse(np.any(sample_values < 0.0)) if not stats: @@ -291,13 +293,13 @@ class BetaTest(test.TestCase): beta1 = beta_lib.Beta(concentration1=a_val, concentration0=b_val, name="beta1") - samples1 = beta1.sample(n_val, seed=123456).eval() + samples1 = self.evaluate(beta1.sample(n_val, seed=123456)) random_seed.set_random_seed(654321) beta2 = beta_lib.Beta(concentration1=a_val, concentration0=b_val, name="beta2") - samples2 = beta2.sample(n_val, seed=123456).eval() + samples2 = self.evaluate(beta2.sample(n_val, seed=123456)) self.assertAllClose(samples1, samples2) @@ -308,7 +310,7 @@ class BetaTest(test.TestCase): beta = beta_lib.Beta(a, b) n = constant_op.constant(100000) samples = beta.sample(n) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(sample_values.shape, (100000, 3, 2, 2)) self.assertFalse(np.any(sample_values < 0.0)) if not stats: @@ -325,7 +327,7 @@ class BetaTest(test.TestCase): a = 10. * np.random.random(shape).astype(dt) b = 10. * np.random.random(shape).astype(dt) x = np.random.random(shape).astype(dt) - actual = beta_lib.Beta(a, b).cdf(x).eval() + actual = self.evaluate(beta_lib.Beta(a, b).cdf(x)) self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x) self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x) if not stats: @@ -339,7 +341,7 @@ class BetaTest(test.TestCase): a = 10. * np.random.random(shape).astype(dt) b = 10. * np.random.random(shape).astype(dt) x = np.random.random(shape).astype(dt) - actual = math_ops.exp(beta_lib.Beta(a, b).log_cdf(x)).eval() + actual = self.evaluate(math_ops.exp(beta_lib.Beta(a, b).log_cdf(x))) self.assertAllEqual(np.ones(shape, dtype=np.bool), 0. <= x) self.assertAllEqual(np.ones(shape, dtype=np.bool), 1. >= x) if not stats: @@ -350,46 +352,47 @@ class BetaTest(test.TestCase): with self.test_session(): a, b = -4.2, -9.1 dist = beta_lib.BetaWithSoftplusConcentration(a, b) - self.assertAllClose(nn_ops.softplus(a).eval(), dist.concentration1.eval()) - self.assertAllClose(nn_ops.softplus(b).eval(), dist.concentration0.eval()) + self.assertAllClose( + self.evaluate(nn_ops.softplus(a)), self.evaluate(dist.concentration1)) + self.assertAllClose( + self.evaluate(nn_ops.softplus(b)), self.evaluate(dist.concentration0)) def testBetaBetaKL(self): - with self.test_session() as sess: - for shape in [(10,), (4, 5)]: - a1 = 6.0 * np.random.random(size=shape) + 1e-4 - b1 = 6.0 * np.random.random(size=shape) + 1e-4 - a2 = 6.0 * np.random.random(size=shape) + 1e-4 - b2 = 6.0 * np.random.random(size=shape) + 1e-4 - # Take inverse softplus of values to test BetaWithSoftplusConcentration - a1_sp = np.log(np.exp(a1) - 1.0) - b1_sp = np.log(np.exp(b1) - 1.0) - a2_sp = np.log(np.exp(a2) - 1.0) - b2_sp = np.log(np.exp(b2) - 1.0) - - d1 = beta_lib.Beta(concentration1=a1, concentration0=b1) - d2 = beta_lib.Beta(concentration1=a2, concentration0=b2) - d1_sp = beta_lib.BetaWithSoftplusConcentration(concentration1=a1_sp, - concentration0=b1_sp) - d2_sp = beta_lib.BetaWithSoftplusConcentration(concentration1=a2_sp, - concentration0=b2_sp) - - if not special: - return - kl_expected = (special.betaln(a2, b2) - special.betaln(a1, b1) + - (a1 - a2) * special.digamma(a1) + - (b1 - b2) * special.digamma(b1) + - (a2 - a1 + b2 - b1) * special.digamma(a1 + b1)) - - for dist1 in [d1, d1_sp]: - for dist2 in [d2, d2_sp]: - kl = kullback_leibler.kl_divergence(dist1, dist2) - kl_val = sess.run(kl) - self.assertEqual(kl.get_shape(), shape) - self.assertAllClose(kl_val, kl_expected) - - # Make sure KL(d1||d1) is 0 - kl_same = sess.run(kullback_leibler.kl_divergence(d1, d1)) - self.assertAllClose(kl_same, np.zeros_like(kl_expected)) + for shape in [(10,), (4, 5)]: + a1 = 6.0 * np.random.random(size=shape) + 1e-4 + b1 = 6.0 * np.random.random(size=shape) + 1e-4 + a2 = 6.0 * np.random.random(size=shape) + 1e-4 + b2 = 6.0 * np.random.random(size=shape) + 1e-4 + # Take inverse softplus of values to test BetaWithSoftplusConcentration + a1_sp = np.log(np.exp(a1) - 1.0) + b1_sp = np.log(np.exp(b1) - 1.0) + a2_sp = np.log(np.exp(a2) - 1.0) + b2_sp = np.log(np.exp(b2) - 1.0) + + d1 = beta_lib.Beta(concentration1=a1, concentration0=b1) + d2 = beta_lib.Beta(concentration1=a2, concentration0=b2) + d1_sp = beta_lib.BetaWithSoftplusConcentration(concentration1=a1_sp, + concentration0=b1_sp) + d2_sp = beta_lib.BetaWithSoftplusConcentration(concentration1=a2_sp, + concentration0=b2_sp) + + if not special: + return + kl_expected = (special.betaln(a2, b2) - special.betaln(a1, b1) + + (a1 - a2) * special.digamma(a1) + + (b1 - b2) * special.digamma(b1) + + (a2 - a1 + b2 - b1) * special.digamma(a1 + b1)) + + for dist1 in [d1, d1_sp]: + for dist2 in [d2, d2_sp]: + kl = kullback_leibler.kl_divergence(dist1, dist2) + kl_val = self.evaluate(kl) + self.assertEqual(kl.get_shape(), shape) + self.assertAllClose(kl_val, kl_expected) + + # Make sure KL(d1||d1) is 0 + kl_same = self.evaluate(kullback_leibler.kl_divergence(d1, d1)) + self.assertAllClose(kl_same, np.zeros_like(kl_expected)) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/bijector_test.py b/tensorflow/python/kernel_tests/distributions/bijector_test.py index 33db014279..a7fe336e6a 100644 --- a/tensorflow/python/kernel_tests/distributions/bijector_test.py +++ b/tensorflow/python/kernel_tests/distributions/bijector_test.py @@ -24,12 +24,14 @@ import numpy as np import six from tensorflow.python.framework import constant_op +from tensorflow.python.framework import test_util from tensorflow.python.ops import array_ops from tensorflow.python.ops import math_ops from tensorflow.python.ops.distributions import bijector from tensorflow.python.platform import test +@test_util.run_all_in_graph_and_eager_modes class BaseBijectorTest(test.TestCase): """Tests properties of the Bijector base-class.""" @@ -47,42 +49,38 @@ class BaseBijectorTest(test.TestCase): def __init__(self): super(_BareBonesBijector, self).__init__(forward_min_event_ndims=0) - with self.test_session() as sess: - bij = _BareBonesBijector() - self.assertEqual([], bij.graph_parents) - self.assertEqual(False, bij.is_constant_jacobian) - self.assertEqual(False, bij.validate_args) - self.assertEqual(None, bij.dtype) - self.assertEqual("bare_bones_bijector", bij.name) - - for shape in [[], [1, 2], [1, 2, 3]]: - [ - forward_event_shape_, - inverse_event_shape_, - ] = sess.run([ - bij.inverse_event_shape_tensor(shape), - bij.forward_event_shape_tensor(shape), - ]) - self.assertAllEqual(shape, forward_event_shape_) - self.assertAllEqual(shape, bij.forward_event_shape(shape)) - self.assertAllEqual(shape, inverse_event_shape_) - self.assertAllEqual(shape, bij.inverse_event_shape(shape)) - - with self.assertRaisesRegexp( - NotImplementedError, "inverse not implemented"): - bij.inverse(0) - - with self.assertRaisesRegexp( - NotImplementedError, "forward not implemented"): - bij.forward(0) - - with self.assertRaisesRegexp( - NotImplementedError, "inverse_log_det_jacobian not implemented"): - bij.inverse_log_det_jacobian(0, event_ndims=0) - - with self.assertRaisesRegexp( - NotImplementedError, "forward_log_det_jacobian not implemented"): - bij.forward_log_det_jacobian(0, event_ndims=0) + bij = _BareBonesBijector() + self.assertEqual([], bij.graph_parents) + self.assertEqual(False, bij.is_constant_jacobian) + self.assertEqual(False, bij.validate_args) + self.assertEqual(None, bij.dtype) + self.assertEqual("bare_bones_bijector", bij.name) + + for shape in [[], [1, 2], [1, 2, 3]]: + forward_event_shape_ = self.evaluate( + bij.inverse_event_shape_tensor(shape)) + inverse_event_shape_ = self.evaluate( + bij.forward_event_shape_tensor(shape)) + self.assertAllEqual(shape, forward_event_shape_) + self.assertAllEqual(shape, bij.forward_event_shape(shape)) + self.assertAllEqual(shape, inverse_event_shape_) + self.assertAllEqual(shape, bij.inverse_event_shape(shape)) + + with self.assertRaisesRegexp( + NotImplementedError, "inverse not implemented"): + bij.inverse(0) + + with self.assertRaisesRegexp( + NotImplementedError, "forward not implemented"): + bij.forward(0) + + with self.assertRaisesRegexp( + NotImplementedError, "inverse_log_det_jacobian not implemented"): + bij.inverse_log_det_jacobian(0, event_ndims=0) + + with self.assertRaisesRegexp( + NotImplementedError, "forward_log_det_jacobian not implemented"): + bij.forward_log_det_jacobian(0, event_ndims=0) class IntentionallyMissingError(Exception): diff --git a/tensorflow/python/kernel_tests/distributions/dirichlet_test.py b/tensorflow/python/kernel_tests/distributions/dirichlet_test.py index a2f1de5aaf..3bcfae0deb 100644 --- a/tensorflow/python/kernel_tests/distributions/dirichlet_test.py +++ b/tensorflow/python/kernel_tests/distributions/dirichlet_test.py @@ -22,6 +22,7 @@ import numpy as np from tensorflow.python.framework import constant_op 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 math_ops from tensorflow.python.ops.distributions import dirichlet as dirichlet_lib @@ -41,14 +42,15 @@ def try_import(name): # pylint: disable=invalid-name stats = try_import("scipy.stats") +@test_util.run_all_in_graph_and_eager_modes class DirichletTest(test.TestCase): def testSimpleShapes(self): with self.test_session(): alpha = np.random.rand(3) dist = dirichlet_lib.Dirichlet(alpha) - self.assertEqual(3, dist.event_shape_tensor().eval()) - self.assertAllEqual([], dist.batch_shape_tensor().eval()) + self.assertEqual(3, self.evaluate(dist.event_shape_tensor())) + self.assertAllEqual([], self.evaluate(dist.batch_shape_tensor())) self.assertEqual(tensor_shape.TensorShape([3]), dist.event_shape) self.assertEqual(tensor_shape.TensorShape([]), dist.batch_shape) @@ -56,8 +58,8 @@ class DirichletTest(test.TestCase): with self.test_session(): alpha = np.random.rand(3, 2, 2) dist = dirichlet_lib.Dirichlet(alpha) - self.assertEqual(2, dist.event_shape_tensor().eval()) - self.assertAllEqual([3, 2], dist.batch_shape_tensor().eval()) + self.assertEqual(2, self.evaluate(dist.event_shape_tensor())) + self.assertAllEqual([3, 2], self.evaluate(dist.batch_shape_tensor())) self.assertEqual(tensor_shape.TensorShape([2]), dist.event_shape) self.assertEqual(tensor_shape.TensorShape([3, 2]), dist.batch_shape) @@ -66,22 +68,22 @@ class DirichletTest(test.TestCase): with self.test_session(): dist = dirichlet_lib.Dirichlet(alpha) self.assertEqual([1, 3], dist.concentration.get_shape()) - self.assertAllClose(alpha, dist.concentration.eval()) + self.assertAllClose(alpha, self.evaluate(dist.concentration)) def testPdfXProper(self): alpha = [[1., 2, 3]] with self.test_session(): dist = dirichlet_lib.Dirichlet(alpha, validate_args=True) - dist.prob([.1, .3, .6]).eval() - dist.prob([.2, .3, .5]).eval() + self.evaluate(dist.prob([.1, .3, .6])) + self.evaluate(dist.prob([.2, .3, .5])) # Either condition can trigger. with self.assertRaisesOpError("samples must be positive"): - dist.prob([-1., 1.5, 0.5]).eval() + self.evaluate(dist.prob([-1., 1.5, 0.5])) with self.assertRaisesOpError("samples must be positive"): - dist.prob([0., .1, .9]).eval() + self.evaluate(dist.prob([0., .1, .9])) with self.assertRaisesOpError( "sample last-dimension must sum to `1`"): - dist.prob([.1, .2, .8]).eval() + self.evaluate(dist.prob([.1, .2, .8])) def testPdfZeroBatches(self): with self.test_session(): @@ -89,7 +91,7 @@ class DirichletTest(test.TestCase): x = [.5, .5] dist = dirichlet_lib.Dirichlet(alpha) pdf = dist.prob(x) - self.assertAllClose(1., pdf.eval()) + self.assertAllClose(1., self.evaluate(pdf)) self.assertEqual((), pdf.get_shape()) def testPdfZeroBatchesNontrivialX(self): @@ -98,7 +100,7 @@ class DirichletTest(test.TestCase): x = [.3, .7] dist = dirichlet_lib.Dirichlet(alpha) pdf = dist.prob(x) - self.assertAllClose(7. / 5, pdf.eval()) + self.assertAllClose(7. / 5, self.evaluate(pdf)) self.assertEqual((), pdf.get_shape()) def testPdfUniformZeroBatches(self): @@ -108,7 +110,7 @@ class DirichletTest(test.TestCase): x = [[.2, .5, .3], [.3, .4, .3]] dist = dirichlet_lib.Dirichlet(alpha) pdf = dist.prob(x) - self.assertAllClose([2., 2.], pdf.eval()) + self.assertAllClose([2., 2.], self.evaluate(pdf)) self.assertEqual((2), pdf.get_shape()) def testPdfAlphaStretchedInBroadcastWhenSameRank(self): @@ -117,7 +119,7 @@ class DirichletTest(test.TestCase): x = [[.5, .5], [.3, .7]] dist = dirichlet_lib.Dirichlet(alpha) pdf = dist.prob(x) - self.assertAllClose([1., 7. / 5], pdf.eval()) + self.assertAllClose([1., 7. / 5], self.evaluate(pdf)) self.assertEqual((2), pdf.get_shape()) def testPdfAlphaStretchedInBroadcastWhenLowerRank(self): @@ -125,7 +127,7 @@ class DirichletTest(test.TestCase): alpha = [1., 2] x = [[.5, .5], [.2, .8]] pdf = dirichlet_lib.Dirichlet(alpha).prob(x) - self.assertAllClose([1., 8. / 5], pdf.eval()) + self.assertAllClose([1., 8. / 5], self.evaluate(pdf)) self.assertEqual((2), pdf.get_shape()) def testPdfXStretchedInBroadcastWhenSameRank(self): @@ -133,7 +135,7 @@ class DirichletTest(test.TestCase): alpha = [[1., 2], [2., 3]] x = [[.5, .5]] pdf = dirichlet_lib.Dirichlet(alpha).prob(x) - self.assertAllClose([1., 3. / 2], pdf.eval()) + self.assertAllClose([1., 3. / 2], self.evaluate(pdf)) self.assertEqual((2), pdf.get_shape()) def testPdfXStretchedInBroadcastWhenLowerRank(self): @@ -141,7 +143,7 @@ class DirichletTest(test.TestCase): alpha = [[1., 2], [2., 3]] x = [.5, .5] pdf = dirichlet_lib.Dirichlet(alpha).prob(x) - self.assertAllClose([1., 3. / 2], pdf.eval()) + self.assertAllClose([1., 3. / 2], self.evaluate(pdf)) self.assertEqual((2), pdf.get_shape()) def testMean(self): @@ -152,43 +154,44 @@ class DirichletTest(test.TestCase): if not stats: return expected_mean = stats.dirichlet.mean(alpha) - self.assertAllClose(dirichlet.mean().eval(), expected_mean) + self.assertAllClose(self.evaluate(dirichlet.mean()), expected_mean) def testCovarianceFromSampling(self): alpha = np.array([[1., 2, 3], [2.5, 4, 0.01]], dtype=np.float32) - with self.test_session() as sess: - dist = dirichlet_lib.Dirichlet(alpha) # batch_shape=[2], event_shape=[3] - x = dist.sample(int(250e3), seed=1) - sample_mean = math_ops.reduce_mean(x, 0) - x_centered = x - sample_mean[None, ...] - sample_cov = math_ops.reduce_mean(math_ops.matmul( - x_centered[..., None], x_centered[..., None, :]), 0) - sample_var = array_ops.matrix_diag_part(sample_cov) - sample_stddev = math_ops.sqrt(sample_var) - [ - sample_mean_, - sample_cov_, - sample_var_, - sample_stddev_, - analytic_mean, - analytic_cov, - analytic_var, - analytic_stddev, - ] = sess.run([ - sample_mean, - sample_cov, - sample_var, - sample_stddev, - dist.mean(), - dist.covariance(), - dist.variance(), - dist.stddev(), - ]) - self.assertAllClose(sample_mean_, analytic_mean, atol=0., rtol=0.04) - self.assertAllClose(sample_cov_, analytic_cov, atol=0., rtol=0.06) - self.assertAllClose(sample_var_, analytic_var, atol=0., rtol=0.03) - self.assertAllClose(sample_stddev_, analytic_stddev, atol=0., rtol=0.02) + dist = dirichlet_lib.Dirichlet(alpha) # batch_shape=[2], event_shape=[3] + x = dist.sample(int(250e3), seed=1) + sample_mean = math_ops.reduce_mean(x, 0) + x_centered = x - sample_mean[None, ...] + sample_cov = math_ops.reduce_mean(math_ops.matmul( + x_centered[..., None], x_centered[..., None, :]), 0) + sample_var = array_ops.matrix_diag_part(sample_cov) + sample_stddev = math_ops.sqrt(sample_var) + + [ + sample_mean_, + sample_cov_, + sample_var_, + sample_stddev_, + analytic_mean, + analytic_cov, + analytic_var, + analytic_stddev, + ] = self.evaluate([ + sample_mean, + sample_cov, + sample_var, + sample_stddev, + dist.mean(), + dist.covariance(), + dist.variance(), + dist.stddev(), + ]) + + self.assertAllClose(sample_mean_, analytic_mean, atol=0., rtol=0.04) + self.assertAllClose(sample_cov_, analytic_cov, atol=0., rtol=0.06) + self.assertAllClose(sample_var_, analytic_var, atol=0., rtol=0.03) + self.assertAllClose(sample_stddev_, analytic_stddev, atol=0., rtol=0.02) def testVariance(self): with self.test_session(): @@ -201,7 +204,8 @@ class DirichletTest(test.TestCase): expected_covariance = np.diag(stats.dirichlet.var(alpha)) expected_covariance += [[0., -2, -3], [-2, 0, -6], [-3, -6, 0]] / denominator - self.assertAllClose(dirichlet.covariance().eval(), expected_covariance) + self.assertAllClose( + self.evaluate(dirichlet.covariance()), expected_covariance) def testMode(self): with self.test_session(): @@ -209,7 +213,7 @@ class DirichletTest(test.TestCase): expected_mode = (alpha - 1) / (np.sum(alpha) - 3) dirichlet = dirichlet_lib.Dirichlet(concentration=alpha) self.assertEqual(dirichlet.mode().get_shape(), [3]) - self.assertAllClose(dirichlet.mode().eval(), expected_mode) + self.assertAllClose(self.evaluate(dirichlet.mode()), expected_mode) def testModeInvalid(self): with self.test_session(): @@ -217,7 +221,7 @@ class DirichletTest(test.TestCase): dirichlet = dirichlet_lib.Dirichlet(concentration=alpha, allow_nan_stats=False) with self.assertRaisesOpError("Condition x < y.*"): - dirichlet.mode().eval() + self.evaluate(dirichlet.mode()) def testModeEnableAllowNanStats(self): with self.test_session(): @@ -227,7 +231,7 @@ class DirichletTest(test.TestCase): expected_mode = np.zeros_like(alpha) + np.nan self.assertEqual(dirichlet.mode().get_shape(), [3]) - self.assertAllClose(dirichlet.mode().eval(), expected_mode) + self.assertAllClose(self.evaluate(dirichlet.mode()), expected_mode) def testEntropy(self): with self.test_session(): @@ -237,7 +241,7 @@ class DirichletTest(test.TestCase): if not stats: return expected_entropy = stats.dirichlet.entropy(alpha) - self.assertAllClose(dirichlet.entropy().eval(), expected_entropy) + self.assertAllClose(self.evaluate(dirichlet.entropy()), expected_entropy) def testSample(self): with self.test_session(): @@ -245,7 +249,7 @@ class DirichletTest(test.TestCase): dirichlet = dirichlet_lib.Dirichlet(alpha) n = constant_op.constant(100000) samples = dirichlet.sample(n) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(sample_values.shape, (100000, 2)) self.assertTrue(np.all(sample_values > 0.0)) if not stats: diff --git a/tensorflow/python/kernel_tests/distributions/exponential_test.py b/tensorflow/python/kernel_tests/distributions/exponential_test.py index 7afdf0f947..ebcd41b0e2 100644 --- a/tensorflow/python/kernel_tests/distributions/exponential_test.py +++ b/tensorflow/python/kernel_tests/distributions/exponential_test.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.python.client import session from tensorflow.python.framework import constant_op +from tensorflow.python.framework import test_util from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import exponential as exponential_lib from tensorflow.python.platform import test @@ -42,6 +43,7 @@ def try_import(name): # pylint: disable=invalid-name stats = try_import("scipy.stats") +@test_util.run_all_in_graph_and_eager_modes class ExponentialTest(test.TestCase): def testExponentialLogPDF(self): @@ -61,8 +63,8 @@ class ExponentialTest(test.TestCase): if not stats: return expected_log_pdf = stats.expon.logpdf(x, scale=1 / lam_v) - self.assertAllClose(log_pdf.eval(), expected_log_pdf) - self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf)) + self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) + self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testExponentialCDF(self): with session.Session(): @@ -79,7 +81,7 @@ class ExponentialTest(test.TestCase): if not stats: return expected_cdf = stats.expon.cdf(x, scale=1 / lam_v) - self.assertAllClose(cdf.eval(), expected_cdf) + self.assertAllClose(self.evaluate(cdf), expected_cdf) def testExponentialMean(self): with session.Session(): @@ -89,7 +91,7 @@ class ExponentialTest(test.TestCase): if not stats: return expected_mean = stats.expon.mean(scale=1 / lam_v) - self.assertAllClose(exponential.mean().eval(), expected_mean) + self.assertAllClose(self.evaluate(exponential.mean()), expected_mean) def testExponentialVariance(self): with session.Session(): @@ -99,7 +101,8 @@ class ExponentialTest(test.TestCase): if not stats: return expected_variance = stats.expon.var(scale=1 / lam_v) - self.assertAllClose(exponential.variance().eval(), expected_variance) + self.assertAllClose( + self.evaluate(exponential.variance()), expected_variance) def testExponentialEntropy(self): with session.Session(): @@ -109,7 +112,8 @@ class ExponentialTest(test.TestCase): if not stats: return expected_entropy = stats.expon.entropy(scale=1 / lam_v) - self.assertAllClose(exponential.entropy().eval(), expected_entropy) + self.assertAllClose( + self.evaluate(exponential.entropy()), expected_entropy) def testExponentialSample(self): with self.test_session(): @@ -119,7 +123,7 @@ class ExponentialTest(test.TestCase): exponential = exponential_lib.Exponential(rate=lam) samples = exponential.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(sample_values.shape, (100000, 2)) self.assertFalse(np.any(sample_values < 0.0)) if not stats: @@ -142,7 +146,7 @@ class ExponentialTest(test.TestCase): samples = exponential.sample(n, seed=138) self.assertEqual(samples.get_shape(), (n, batch_size, 2)) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertFalse(np.any(sample_values < 0.0)) if not stats: @@ -163,8 +167,8 @@ class ExponentialTest(test.TestCase): with self.test_session(): lam = [-2.2, -3.4] exponential = exponential_lib.ExponentialWithSoftplusRate(rate=lam) - self.assertAllClose(nn_ops.softplus(lam).eval(), - exponential.rate.eval()) + self.assertAllClose( + self.evaluate(nn_ops.softplus(lam)), self.evaluate(exponential.rate)) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/laplace_test.py b/tensorflow/python/kernel_tests/distributions/laplace_test.py index 55577386c4..918c7f63f2 100644 --- a/tensorflow/python/kernel_tests/distributions/laplace_test.py +++ b/tensorflow/python/kernel_tests/distributions/laplace_test.py @@ -24,6 +24,7 @@ import numpy as np from tensorflow.python.client import session from tensorflow.python.framework import constant_op from tensorflow.python.framework import tensor_shape +from tensorflow.python.framework import test_util from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import laplace as laplace_lib from tensorflow.python.platform import test @@ -43,6 +44,7 @@ def try_import(name): # pylint: disable=invalid-name stats = try_import("scipy.stats") +@test_util.run_all_in_graph_and_eager_modes class LaplaceTest(test.TestCase): def testLaplaceShape(self): @@ -51,9 +53,9 @@ class LaplaceTest(test.TestCase): scale = constant_op.constant(11.0) laplace = laplace_lib.Laplace(loc=loc, scale=scale) - self.assertEqual(laplace.batch_shape_tensor().eval(), (5,)) + self.assertEqual(self.evaluate(laplace.batch_shape_tensor()), (5,)) self.assertEqual(laplace.batch_shape, tensor_shape.TensorShape([5])) - self.assertAllEqual(laplace.event_shape_tensor().eval(), []) + self.assertAllEqual(self.evaluate(laplace.event_shape_tensor()), []) self.assertEqual(laplace.event_shape, tensor_shape.TensorShape([])) def testLaplaceLogPDF(self): @@ -70,11 +72,11 @@ class LaplaceTest(test.TestCase): if not stats: return expected_log_pdf = stats.laplace.logpdf(x, loc_v, scale=scale_v) - self.assertAllClose(log_pdf.eval(), expected_log_pdf) + self.assertAllClose(self.evaluate(log_pdf), expected_log_pdf) pdf = laplace.prob(x) self.assertEqual(pdf.get_shape(), (6,)) - self.assertAllClose(pdf.eval(), np.exp(expected_log_pdf)) + self.assertAllClose(self.evaluate(pdf), np.exp(expected_log_pdf)) def testLaplaceLogPDFMultidimensional(self): with self.test_session(): @@ -86,11 +88,11 @@ class LaplaceTest(test.TestCase): x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = laplace_lib.Laplace(loc=loc, scale=scale) log_pdf = laplace.log_prob(x) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = laplace.prob(x) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) self.assertEqual(pdf.get_shape(), (6, 2)) if not stats: return @@ -108,11 +110,11 @@ class LaplaceTest(test.TestCase): x = np.array([[2.5, 2.5, 4.0, 0.1, 1.0, 2.0]], dtype=np.float32).T laplace = laplace_lib.Laplace(loc=loc, scale=scale) log_pdf = laplace.log_prob(x) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = laplace.prob(x) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) self.assertEqual(pdf.get_shape(), (6, 2)) if not stats: return @@ -136,7 +138,7 @@ class LaplaceTest(test.TestCase): if not stats: return expected_cdf = stats.laplace.cdf(x, loc_v, scale=scale_v) - self.assertAllClose(cdf.eval(), expected_cdf) + self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogCDF(self): with self.test_session(): @@ -154,7 +156,7 @@ class LaplaceTest(test.TestCase): if not stats: return expected_cdf = stats.laplace.logcdf(x, loc_v, scale=scale_v) - self.assertAllClose(cdf.eval(), expected_cdf) + self.assertAllClose(self.evaluate(cdf), expected_cdf) def testLaplaceLogSurvivalFunction(self): with self.test_session(): @@ -172,7 +174,7 @@ class LaplaceTest(test.TestCase): if not stats: return expected_sf = stats.laplace.logsf(x, loc_v, scale=scale_v) - self.assertAllClose(sf.eval(), expected_sf) + self.assertAllClose(self.evaluate(sf), expected_sf) def testLaplaceMean(self): with self.test_session(): @@ -183,7 +185,7 @@ class LaplaceTest(test.TestCase): if not stats: return expected_means = stats.laplace.mean(loc_v, scale=scale_v) - self.assertAllClose(laplace.mean().eval(), expected_means) + self.assertAllClose(self.evaluate(laplace.mean()), expected_means) def testLaplaceMode(self): with self.test_session(): @@ -191,7 +193,7 @@ class LaplaceTest(test.TestCase): scale_v = np.array([1.0, 4.0, 5.0]) laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v) self.assertEqual(laplace.mode().get_shape(), (3,)) - self.assertAllClose(laplace.mode().eval(), loc_v) + self.assertAllClose(self.evaluate(laplace.mode()), loc_v) def testLaplaceVariance(self): with self.test_session(): @@ -202,7 +204,7 @@ class LaplaceTest(test.TestCase): if not stats: return expected_variances = stats.laplace.var(loc_v, scale=scale_v) - self.assertAllClose(laplace.variance().eval(), expected_variances) + self.assertAllClose(self.evaluate(laplace.variance()), expected_variances) def testLaplaceStd(self): with self.test_session(): @@ -213,7 +215,7 @@ class LaplaceTest(test.TestCase): if not stats: return expected_stddev = stats.laplace.std(loc_v, scale=scale_v) - self.assertAllClose(laplace.stddev().eval(), expected_stddev) + self.assertAllClose(self.evaluate(laplace.stddev()), expected_stddev) def testLaplaceEntropy(self): with self.test_session(): @@ -224,7 +226,7 @@ class LaplaceTest(test.TestCase): if not stats: return expected_entropy = stats.laplace.entropy(loc_v, scale=scale_v) - self.assertAllClose(laplace.entropy().eval(), expected_entropy) + self.assertAllClose(self.evaluate(laplace.entropy()), expected_entropy) def testLaplaceSample(self): with session.Session(): @@ -235,7 +237,7 @@ class LaplaceTest(test.TestCase): n = 100000 laplace = laplace_lib.Laplace(loc=loc, scale=scale) samples = laplace.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (n,)) self.assertEqual(sample_values.shape, (n,)) if not stats: @@ -260,7 +262,7 @@ class LaplaceTest(test.TestCase): laplace = laplace_lib.Laplace(loc=loc_v, scale=scale_v) n = 10000 samples = laplace.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (n, 10, 100)) self.assertEqual(sample_values.shape, (n, 10, 100)) zeros = np.zeros_like(loc_v + scale_v) # 10 x 100 @@ -297,32 +299,31 @@ class LaplaceTest(test.TestCase): return ks < 0.02 def testLaplacePdfOfSampleMultiDims(self): - with session.Session() as sess: - laplace = laplace_lib.Laplace(loc=[7., 11.], scale=[[5.], [6.]]) - num = 50000 - samples = laplace.sample(num, seed=137) - pdfs = laplace.prob(samples) - sample_vals, pdf_vals = sess.run([samples, pdfs]) - self.assertEqual(samples.get_shape(), (num, 2, 2)) - self.assertEqual(pdfs.get_shape(), (num, 2, 2)) - self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) - self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) - self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) - self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) - if not stats: - return - self.assertAllClose( - stats.laplace.mean( - [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), - sample_vals.mean(axis=0), - rtol=0.05, - atol=0.) - self.assertAllClose( - stats.laplace.var([[7., 11.], [7., 11.]], - scale=np.array([[5., 5.], [6., 6.]])), - sample_vals.var(axis=0), - rtol=0.05, - atol=0.) + laplace = laplace_lib.Laplace(loc=[7., 11.], scale=[[5.], [6.]]) + num = 50000 + samples = laplace.sample(num, seed=137) + pdfs = laplace.prob(samples) + sample_vals, pdf_vals = self.evaluate([samples, pdfs]) + self.assertEqual(samples.get_shape(), (num, 2, 2)) + self.assertEqual(pdfs.get_shape(), (num, 2, 2)) + self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) + self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) + self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) + self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) + if not stats: + return + self.assertAllClose( + stats.laplace.mean( + [[7., 11.], [7., 11.]], scale=np.array([[5., 5.], [6., 6.]])), + sample_vals.mean(axis=0), + rtol=0.05, + atol=0.) + self.assertAllClose( + stats.laplace.var([[7., 11.], [7., 11.]], + scale=np.array([[5., 5.], [6., 6.]])), + sample_vals.var(axis=0), + rtol=0.05, + atol=0.) def _assertIntegral(self, sample_vals, pdf_vals, err=1e-3): s_p = zip(sample_vals, pdf_vals) @@ -338,24 +339,27 @@ class LaplaceTest(test.TestCase): with self.test_session(): loc_v = constant_op.constant(0.0, name="loc") scale_v = constant_op.constant(-1.0, name="scale") - laplace = laplace_lib.Laplace( - loc=loc_v, scale=scale_v, validate_args=True) - with self.assertRaisesOpError("scale"): - laplace.mean().eval() + with self.assertRaisesOpError( + "Condition x > 0 did not hold element-wise"): + laplace = laplace_lib.Laplace( + loc=loc_v, scale=scale_v, validate_args=True) + self.evaluate(laplace.mean()) loc_v = constant_op.constant(1.0, name="loc") scale_v = constant_op.constant(0.0, name="scale") - laplace = laplace_lib.Laplace( - loc=loc_v, scale=scale_v, validate_args=True) - with self.assertRaisesOpError("scale"): - laplace.mean().eval() + with self.assertRaisesOpError( + "Condition x > 0 did not hold element-wise"): + laplace = laplace_lib.Laplace( + loc=loc_v, scale=scale_v, validate_args=True) + self.evaluate(laplace.mean()) def testLaplaceWithSoftplusScale(self): with self.test_session(): loc_v = constant_op.constant([0.0, 1.0], name="loc") scale_v = constant_op.constant([-1.0, 2.0], name="scale") laplace = laplace_lib.LaplaceWithSoftplusScale(loc=loc_v, scale=scale_v) - self.assertAllClose(nn_ops.softplus(scale_v).eval(), laplace.scale.eval()) - self.assertAllClose(loc_v.eval(), laplace.loc.eval()) + self.assertAllClose( + self.evaluate(nn_ops.softplus(scale_v)), self.evaluate(laplace.scale)) + self.assertAllClose(self.evaluate(loc_v), self.evaluate(laplace.loc)) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/normal_test.py b/tensorflow/python/kernel_tests/distributions/normal_test.py index 07c7d6d11d..d793e03272 100644 --- a/tensorflow/python/kernel_tests/distributions/normal_test.py +++ b/tensorflow/python/kernel_tests/distributions/normal_test.py @@ -27,6 +27,7 @@ from tensorflow.python.framework import constant_op from tensorflow.python.framework import dtypes 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 gradients_impl from tensorflow.python.ops import nn_ops @@ -54,7 +55,7 @@ class NormalTest(test.TestCase): self._rng = np.random.RandomState(123) def assertAllFinite(self, tensor): - is_finite = np.isfinite(tensor.eval()) + is_finite = np.isfinite(self.evaluate(tensor)) all_true = np.ones_like(is_finite, dtype=np.bool) self.assertAllEqual(all_true, is_finite) @@ -62,13 +63,13 @@ class NormalTest(test.TestCase): with self.test_session(): param_shapes = normal_lib.Normal.param_shapes(sample_shape) mu_shape, sigma_shape = param_shapes["loc"], param_shapes["scale"] - self.assertAllEqual(expected, mu_shape.eval()) - self.assertAllEqual(expected, sigma_shape.eval()) + self.assertAllEqual(expected, self.evaluate(mu_shape)) + self.assertAllEqual(expected, self.evaluate(sigma_shape)) mu = array_ops.zeros(mu_shape) sigma = array_ops.ones(sigma_shape) self.assertAllEqual( expected, - array_ops.shape(normal_lib.Normal(mu, sigma).sample()).eval()) + self.evaluate(array_ops.shape(normal_lib.Normal(mu, sigma).sample()))) def _testParamStaticShapes(self, sample_shape, expected): param_shapes = normal_lib.Normal.param_static_shapes(sample_shape) @@ -76,25 +77,30 @@ class NormalTest(test.TestCase): self.assertEqual(expected, mu_shape) self.assertEqual(expected, sigma_shape) + @test_util.run_in_graph_and_eager_modes() def testParamShapes(self): sample_shape = [10, 3, 4] self._testParamShapes(sample_shape, sample_shape) self._testParamShapes(constant_op.constant(sample_shape), sample_shape) + @test_util.run_in_graph_and_eager_modes() def testParamStaticShapes(self): sample_shape = [10, 3, 4] self._testParamStaticShapes(sample_shape, sample_shape) self._testParamStaticShapes( tensor_shape.TensorShape(sample_shape), sample_shape) + @test_util.run_in_graph_and_eager_modes() def testNormalWithSoftplusScale(self): with self.test_session(): mu = array_ops.zeros((10, 3)) rho = array_ops.ones((10, 3)) * -2. normal = normal_lib.NormalWithSoftplusScale(loc=mu, scale=rho) - self.assertAllEqual(mu.eval(), normal.loc.eval()) - self.assertAllEqual(nn_ops.softplus(rho).eval(), normal.scale.eval()) + self.assertAllEqual(self.evaluate(mu), self.evaluate(normal.loc)) + self.assertAllEqual( + self.evaluate(nn_ops.softplus(rho)), self.evaluate(normal.scale)) + @test_util.run_in_graph_and_eager_modes() def testNormalLogPDF(self): with self.test_session(): batch_size = 6 @@ -104,25 +110,31 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) log_pdf = normal.log_prob(x) - self.assertAllEqual(normal.batch_shape_tensor().eval(), - log_pdf.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), - log_pdf.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), log_pdf.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(log_pdf).shape) self.assertAllEqual(normal.batch_shape, log_pdf.get_shape()) - self.assertAllEqual(normal.batch_shape, log_pdf.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(log_pdf).shape) pdf = normal.prob(x) - self.assertAllEqual(normal.batch_shape_tensor().eval(), pdf.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), pdf.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), pdf.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(pdf).shape) self.assertAllEqual(normal.batch_shape, pdf.get_shape()) - self.assertAllEqual(normal.batch_shape, pdf.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(pdf).shape) if not stats: return - expected_log_pdf = stats.norm(mu.eval(), sigma.eval()).logpdf(x) - self.assertAllClose(expected_log_pdf, log_pdf.eval()) - self.assertAllClose(np.exp(expected_log_pdf), pdf.eval()) + expected_log_pdf = stats.norm(self.evaluate(mu), + self.evaluate(sigma)).logpdf(x) + self.assertAllClose(expected_log_pdf, self.evaluate(log_pdf)) + self.assertAllClose(np.exp(expected_log_pdf), self.evaluate(pdf)) + @test_util.run_in_graph_and_eager_modes() def testNormalLogPDFMultidimensional(self): with self.test_session(): batch_size = 6 @@ -133,29 +145,34 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) log_pdf = normal.log_prob(x) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.get_shape(), (6, 2)) - self.assertAllEqual(normal.batch_shape_tensor().eval(), - log_pdf.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), - log_pdf.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), log_pdf.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(log_pdf).shape) self.assertAllEqual(normal.batch_shape, log_pdf.get_shape()) - self.assertAllEqual(normal.batch_shape, log_pdf.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(log_pdf).shape) pdf = normal.prob(x) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) self.assertEqual(pdf.get_shape(), (6, 2)) - self.assertAllEqual(normal.batch_shape_tensor().eval(), pdf.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), pdf_values.shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), pdf.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), pdf_values.shape) self.assertAllEqual(normal.batch_shape, pdf.get_shape()) self.assertAllEqual(normal.batch_shape, pdf_values.shape) if not stats: return - expected_log_pdf = stats.norm(mu.eval(), sigma.eval()).logpdf(x) + expected_log_pdf = stats.norm(self.evaluate(mu), + self.evaluate(sigma)).logpdf(x) self.assertAllClose(expected_log_pdf, log_pdf_values) self.assertAllClose(np.exp(expected_log_pdf), pdf_values) + @test_util.run_in_graph_and_eager_modes() def testNormalCDF(self): with self.test_session(): batch_size = 50 @@ -165,15 +182,19 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) cdf = normal.cdf(x) - self.assertAllEqual(normal.batch_shape_tensor().eval(), cdf.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), cdf.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), cdf.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(cdf).shape) self.assertAllEqual(normal.batch_shape, cdf.get_shape()) - self.assertAllEqual(normal.batch_shape, cdf.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(cdf).shape) if not stats: return expected_cdf = stats.norm(mu, sigma).cdf(x) - self.assertAllClose(expected_cdf, cdf.eval(), atol=0) + self.assertAllClose(expected_cdf, self.evaluate(cdf), atol=0) + @test_util.run_in_graph_and_eager_modes() def testNormalSurvivalFunction(self): with self.test_session(): batch_size = 50 @@ -184,15 +205,19 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) sf = normal.survival_function(x) - self.assertAllEqual(normal.batch_shape_tensor().eval(), sf.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), sf.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), sf.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(sf).shape) self.assertAllEqual(normal.batch_shape, sf.get_shape()) - self.assertAllEqual(normal.batch_shape, sf.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(sf).shape) if not stats: return expected_sf = stats.norm(mu, sigma).sf(x) - self.assertAllClose(expected_sf, sf.eval(), atol=0) + self.assertAllClose(expected_sf, self.evaluate(sf), atol=0) + @test_util.run_in_graph_and_eager_modes() def testNormalLogCDF(self): with self.test_session(): batch_size = 50 @@ -203,15 +228,18 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) cdf = normal.log_cdf(x) - self.assertAllEqual(normal.batch_shape_tensor().eval(), cdf.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), cdf.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), cdf.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(cdf).shape) self.assertAllEqual(normal.batch_shape, cdf.get_shape()) - self.assertAllEqual(normal.batch_shape, cdf.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(cdf).shape) if not stats: return expected_cdf = stats.norm(mu, sigma).logcdf(x) - self.assertAllClose(expected_cdf, cdf.eval(), atol=0, rtol=1e-5) + self.assertAllClose(expected_cdf, self.evaluate(cdf), atol=0, rtol=1e-5) def testFiniteGradientAtDifficultPoints(self): for dtype in [np.float32, np.float64]: @@ -233,6 +261,7 @@ class NormalTest(test.TestCase): self.assertAllFinite(grads[0]) self.assertAllFinite(grads[1]) + @test_util.run_in_graph_and_eager_modes() def testNormalLogSurvivalFunction(self): with self.test_session(): batch_size = 50 @@ -243,16 +272,20 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) sf = normal.log_survival_function(x) - self.assertAllEqual(normal.batch_shape_tensor().eval(), sf.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), sf.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), sf.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(sf).shape) self.assertAllEqual(normal.batch_shape, sf.get_shape()) - self.assertAllEqual(normal.batch_shape, sf.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(sf).shape) if not stats: return expected_sf = stats.norm(mu, sigma).logsf(x) - self.assertAllClose(expected_sf, sf.eval(), atol=0, rtol=1e-5) + self.assertAllClose(expected_sf, self.evaluate(sf), atol=0, rtol=1e-5) + @test_util.run_in_graph_and_eager_modes() def testNormalEntropyWithScalarInputs(self): # Scipy.stats.norm cannot deal with the shapes in the other test. with self.test_session(): @@ -261,18 +294,20 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu_v, scale=sigma_v) entropy = normal.entropy() - self.assertAllEqual(normal.batch_shape_tensor().eval(), - entropy.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), - entropy.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), entropy.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(entropy).shape) self.assertAllEqual(normal.batch_shape, entropy.get_shape()) - self.assertAllEqual(normal.batch_shape, entropy.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(entropy).shape) # scipy.stats.norm cannot deal with these shapes. if not stats: return expected_entropy = stats.norm(mu_v, sigma_v).entropy() - self.assertAllClose(expected_entropy, entropy.eval()) + self.assertAllClose(expected_entropy, self.evaluate(entropy)) + @test_util.run_in_graph_and_eager_modes() def testNormalEntropy(self): with self.test_session(): mu_v = np.array([1.0, 1.0, 1.0]) @@ -284,14 +319,16 @@ class NormalTest(test.TestCase): expected_entropy = 0.5 * np.log(2 * np.pi * np.exp(1) * sigma_broadcast** 2) entropy = normal.entropy() - np.testing.assert_allclose(expected_entropy, entropy.eval()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), - entropy.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), - entropy.eval().shape) + np.testing.assert_allclose(expected_entropy, self.evaluate(entropy)) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), entropy.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(entropy).shape) self.assertAllEqual(normal.batch_shape, entropy.get_shape()) - self.assertAllEqual(normal.batch_shape, entropy.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(entropy).shape) + @test_util.run_in_graph_and_eager_modes() def testNormalMeanAndMode(self): with self.test_session(): # Mu will be broadcast to [7, 7, 7]. @@ -301,11 +338,12 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) self.assertAllEqual((3,), normal.mean().get_shape()) - self.assertAllEqual([7., 7, 7], normal.mean().eval()) + self.assertAllEqual([7., 7, 7], self.evaluate(normal.mean())) self.assertAllEqual((3,), normal.mode().get_shape()) - self.assertAllEqual([7., 7, 7], normal.mode().eval()) + self.assertAllEqual([7., 7, 7], self.evaluate(normal.mode())) + @test_util.run_in_graph_and_eager_modes() def testNormalQuantile(self): with self.test_session(): batch_size = 52 @@ -319,15 +357,18 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) x = normal.quantile(p) - self.assertAllEqual(normal.batch_shape_tensor().eval(), x.get_shape()) - self.assertAllEqual(normal.batch_shape_tensor().eval(), x.eval().shape) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), x.get_shape()) + self.assertAllEqual( + self.evaluate(normal.batch_shape_tensor()), + self.evaluate(x).shape) self.assertAllEqual(normal.batch_shape, x.get_shape()) - self.assertAllEqual(normal.batch_shape, x.eval().shape) + self.assertAllEqual(normal.batch_shape, self.evaluate(x).shape) if not stats: return expected_x = stats.norm(mu, sigma).ppf(p) - self.assertAllClose(expected_x, x.eval(), atol=0.) + self.assertAllClose(expected_x, self.evaluate(x), atol=0.) def _baseQuantileFiniteGradientAtDifficultPoints(self, dtype): g = ops.Graph() @@ -354,6 +395,7 @@ class NormalTest(test.TestCase): def testQuantileFiniteGradientAtDifficultPointsFloat64(self): self._baseQuantileFiniteGradientAtDifficultPoints(np.float64) + @test_util.run_in_graph_and_eager_modes() def testNormalVariance(self): with self.test_session(): # sigma will be broadcast to [7, 7, 7] @@ -363,8 +405,9 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) self.assertAllEqual((3,), normal.variance().get_shape()) - self.assertAllEqual([49., 49, 49], normal.variance().eval()) + self.assertAllEqual([49., 49, 49], self.evaluate(normal.variance())) + @test_util.run_in_graph_and_eager_modes() def testNormalStandardDeviation(self): with self.test_session(): # sigma will be broadcast to [7, 7, 7] @@ -374,8 +417,9 @@ class NormalTest(test.TestCase): normal = normal_lib.Normal(loc=mu, scale=sigma) self.assertAllEqual((3,), normal.stddev().get_shape()) - self.assertAllEqual([7., 7, 7], normal.stddev().eval()) + self.assertAllEqual([7., 7, 7], self.evaluate(normal.stddev())) + @test_util.run_in_graph_and_eager_modes() def testNormalSample(self): with self.test_session(): mu = constant_op.constant(3.0) @@ -385,7 +429,7 @@ class NormalTest(test.TestCase): n = constant_op.constant(100000) normal = normal_lib.Normal(loc=mu, scale=sigma) samples = normal.sample(n) - sample_values = samples.eval() + sample_values = self.evaluate(samples) # Note that the standard error for the sample mean is ~ sigma / sqrt(n). # The sample variance similarly is dependent on sigma and n. # Thus, the tolerances below are very sensitive to number of samples @@ -394,18 +438,22 @@ class NormalTest(test.TestCase): self.assertAllClose(sample_values.mean(), mu_v, atol=1e-1) self.assertAllClose(sample_values.std(), sigma_v, atol=1e-1) - expected_samples_shape = tensor_shape.TensorShape([n.eval()]).concatenate( - tensor_shape.TensorShape(normal.batch_shape_tensor().eval())) + expected_samples_shape = tensor_shape.TensorShape( + [self.evaluate(n)]).concatenate( + tensor_shape.TensorShape( + self.evaluate(normal.batch_shape_tensor()))) self.assertAllEqual(expected_samples_shape, samples.get_shape()) self.assertAllEqual(expected_samples_shape, sample_values.shape) - expected_samples_shape = (tensor_shape.TensorShape( - [n.eval()]).concatenate(normal.batch_shape)) + expected_samples_shape = ( + tensor_shape.TensorShape([self.evaluate(n)]).concatenate( + normal.batch_shape)) self.assertAllEqual(expected_samples_shape, samples.get_shape()) self.assertAllEqual(expected_samples_shape, sample_values.shape) + @test_util.run_in_graph_and_eager_modes() def testNormalSampleMultiDimensional(self): with self.test_session(): batch_size = 2 @@ -417,7 +465,7 @@ class NormalTest(test.TestCase): n = constant_op.constant(100000) normal = normal_lib.Normal(loc=mu, scale=sigma) samples = normal.sample(n) - sample_values = samples.eval() + sample_values = self.evaluate(samples) # Note that the standard error for the sample mean is ~ sigma / sqrt(n). # The sample variance similarly is dependent on sigma and n. # Thus, the tolerances below are very sensitive to number of samples @@ -428,32 +476,37 @@ class NormalTest(test.TestCase): self.assertAllClose(sample_values[:, 0, 1].mean(), mu_v[1], atol=1e-1) self.assertAllClose(sample_values[:, 0, 1].std(), sigma_v[1], atol=1e-1) - expected_samples_shape = tensor_shape.TensorShape([n.eval()]).concatenate( - tensor_shape.TensorShape(normal.batch_shape_tensor().eval())) + expected_samples_shape = tensor_shape.TensorShape( + [self.evaluate(n)]).concatenate( + tensor_shape.TensorShape( + self.evaluate(normal.batch_shape_tensor()))) self.assertAllEqual(expected_samples_shape, samples.get_shape()) self.assertAllEqual(expected_samples_shape, sample_values.shape) - expected_samples_shape = (tensor_shape.TensorShape( - [n.eval()]).concatenate(normal.batch_shape)) + expected_samples_shape = ( + tensor_shape.TensorShape([self.evaluate(n)]).concatenate( + normal.batch_shape)) self.assertAllEqual(expected_samples_shape, samples.get_shape()) self.assertAllEqual(expected_samples_shape, sample_values.shape) + @test_util.run_in_graph_and_eager_modes() def testNegativeSigmaFails(self): with self.test_session(): - normal = normal_lib.Normal( - loc=[1.], scale=[-5.], validate_args=True, name="G") with self.assertRaisesOpError("Condition x > 0 did not hold"): - normal.mean().eval() + normal = normal_lib.Normal( + loc=[1.], scale=[-5.], validate_args=True, name="G") + self.evaluate(normal.mean()) + @test_util.run_in_graph_and_eager_modes() def testNormalShape(self): with self.test_session(): mu = constant_op.constant([-3.0] * 5) sigma = constant_op.constant(11.0) normal = normal_lib.Normal(loc=mu, scale=sigma) - self.assertEqual(normal.batch_shape_tensor().eval(), [5]) + self.assertEqual(self.evaluate(normal.batch_shape_tensor()), [5]) self.assertEqual(normal.batch_shape, tensor_shape.TensorShape([5])) - self.assertAllEqual(normal.event_shape_tensor().eval(), []) + self.assertAllEqual(self.evaluate(normal.event_shape_tensor()), []) self.assertEqual(normal.event_shape, tensor_shape.TensorShape([])) def testNormalShapeWithPlaceholders(self): @@ -465,31 +518,31 @@ class NormalTest(test.TestCase): # get_batch_shape should return an "<unknown>" tensor. self.assertEqual(normal.batch_shape, tensor_shape.TensorShape(None)) self.assertEqual(normal.event_shape, ()) - self.assertAllEqual(normal.event_shape_tensor().eval(), []) + self.assertAllEqual(self.evaluate(normal.event_shape_tensor()), []) self.assertAllEqual( sess.run(normal.batch_shape_tensor(), feed_dict={mu: 5.0, sigma: [1.0, 2.0]}), [2]) + @test_util.run_in_graph_and_eager_modes() def testNormalNormalKL(self): - with self.test_session() as sess: - batch_size = 6 - mu_a = np.array([3.0] * batch_size) - sigma_a = np.array([1.0, 2.0, 3.0, 1.5, 2.5, 3.5]) - mu_b = np.array([-3.0] * batch_size) - sigma_b = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0]) + batch_size = 6 + mu_a = np.array([3.0] * batch_size) + sigma_a = np.array([1.0, 2.0, 3.0, 1.5, 2.5, 3.5]) + mu_b = np.array([-3.0] * batch_size) + sigma_b = np.array([0.5, 1.0, 1.5, 2.0, 2.5, 3.0]) - n_a = normal_lib.Normal(loc=mu_a, scale=sigma_a) - n_b = normal_lib.Normal(loc=mu_b, scale=sigma_b) + n_a = normal_lib.Normal(loc=mu_a, scale=sigma_a) + n_b = normal_lib.Normal(loc=mu_b, scale=sigma_b) - kl = kullback_leibler.kl_divergence(n_a, n_b) - kl_val = sess.run(kl) + kl = kullback_leibler.kl_divergence(n_a, n_b) + kl_val = self.evaluate(kl) - kl_expected = ((mu_a - mu_b)**2 / (2 * sigma_b**2) + 0.5 * ( - (sigma_a**2 / sigma_b**2) - 1 - 2 * np.log(sigma_a / sigma_b))) + kl_expected = ((mu_a - mu_b)**2 / (2 * sigma_b**2) + 0.5 * ( + (sigma_a**2 / sigma_b**2) - 1 - 2 * np.log(sigma_a / sigma_b))) - self.assertEqual(kl.get_shape(), (batch_size,)) - self.assertAllClose(kl_val, kl_expected) + self.assertEqual(kl.get_shape(), (batch_size,)) + self.assertAllClose(kl_val, kl_expected) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/special_math_test.py b/tensorflow/python/kernel_tests/distributions/special_math_test.py index d5d50a180a..4565bf5c46 100644 --- a/tensorflow/python/kernel_tests/distributions/special_math_test.py +++ b/tensorflow/python/kernel_tests/distributions/special_math_test.py @@ -147,6 +147,7 @@ class NdtriTest(test.TestCase): self._baseNdtriFiniteGradientTest(np.float64) +@test_util.run_all_in_graph_and_eager_modes class NdtrTest(test.TestCase): _use_log = False # Grid min/max chosen to ensure 0 < cdf(x) < 1. diff --git a/tensorflow/python/kernel_tests/distributions/student_t_test.py b/tensorflow/python/kernel_tests/distributions/student_t_test.py index f1150de58e..a4fdb658e8 100644 --- a/tensorflow/python/kernel_tests/distributions/student_t_test.py +++ b/tensorflow/python/kernel_tests/distributions/student_t_test.py @@ -25,6 +25,7 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import random_seed +from tensorflow.python.framework import test_util from tensorflow.python.ops import math_ops from tensorflow.python.ops import nn_ops from tensorflow.python.ops.distributions import student_t @@ -44,6 +45,7 @@ def try_import(name): # pylint: disable=invalid-name stats = try_import("scipy.stats") +@test_util.run_all_in_graph_and_eager_modes class StudentTTest(test.TestCase): def testStudentPDFAndLogPDF(self): @@ -60,10 +62,10 @@ class StudentTTest(test.TestCase): log_pdf = student.log_prob(t) self.assertEquals(log_pdf.get_shape(), (6,)) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) pdf = student.prob(t) self.assertEquals(pdf.get_shape(), (6,)) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) if not stats: return @@ -88,10 +90,10 @@ class StudentTTest(test.TestCase): t = np.array([[-2.5, 2.5, 4., 0., -1., 2.]], dtype=np.float32).T student = student_t.StudentT(df, loc=mu, scale=sigma) log_pdf = student.log_prob(t) - log_pdf_values = log_pdf.eval() + log_pdf_values = self.evaluate(log_pdf) self.assertEqual(log_pdf.get_shape(), (6, 2)) pdf = student.prob(t) - pdf_values = pdf.eval() + pdf_values = self.evaluate(pdf) self.assertEqual(pdf.get_shape(), (6, 2)) if not stats: @@ -117,10 +119,10 @@ class StudentTTest(test.TestCase): log_cdf = student.log_cdf(t) self.assertEquals(log_cdf.get_shape(), (6,)) - log_cdf_values = log_cdf.eval() + log_cdf_values = self.evaluate(log_cdf) cdf = student.cdf(t) self.assertEquals(cdf.get_shape(), (6,)) - cdf_values = cdf.eval() + cdf_values = self.evaluate(cdf) if not stats: return @@ -140,7 +142,7 @@ class StudentTTest(test.TestCase): with self.test_session(): student = student_t.StudentT(df=df_v, loc=mu_v, scale=sigma_v) ent = student.entropy() - ent_values = ent.eval() + ent_values = self.evaluate(ent) # Help scipy broadcast to 3x3 ones = np.array([[1, 1, 1]]) @@ -167,7 +169,7 @@ class StudentTTest(test.TestCase): n = constant_op.constant(200000) student = student_t.StudentT(df=df, loc=mu, scale=sigma) samples = student.sample(n, seed=123456) - sample_values = samples.eval() + sample_values = self.evaluate(samples) n_val = 200000 self.assertEqual(sample_values.shape, (n_val,)) self.assertAllClose(sample_values.mean(), mu_v, rtol=1e-2, atol=0) @@ -189,12 +191,12 @@ class StudentTTest(test.TestCase): random_seed.set_random_seed(654321) student = student_t.StudentT( df=df, loc=mu, scale=sigma, name="student_t1") - samples1 = student.sample(n, seed=123456).eval() + samples1 = self.evaluate(student.sample(n, seed=123456)) random_seed.set_random_seed(654321) student2 = student_t.StudentT( df=df, loc=mu, scale=sigma, name="student_t2") - samples2 = student2.sample(n, seed=123456).eval() + samples2 = self.evaluate(student2.sample(n, seed=123456)) self.assertAllClose(samples1, samples2) @@ -205,7 +207,7 @@ class StudentTTest(test.TestCase): n = constant_op.constant(200000) student = student_t.StudentT(df=df, loc=1., scale=1.) samples = student.sample(n, seed=123456) - sample_values = samples.eval() + sample_values = self.evaluate(samples) n_val = 200000 self.assertEqual(sample_values.shape, (n_val, 4)) self.assertTrue(np.all(np.logical_not(np.isnan(sample_values)))) @@ -223,7 +225,7 @@ class StudentTTest(test.TestCase): n = constant_op.constant(200000) student = student_t.StudentT(df=df, loc=mu, scale=sigma) samples = student.sample(n, seed=123456) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(samples.get_shape(), (200000, batch_size, 2)) self.assertAllClose( sample_values[:, 0, 0].mean(), mu_v[0], rtol=1e-2, atol=0) @@ -325,7 +327,7 @@ class StudentTTest(test.TestCase): with self.test_session(): mu = [1., 3.3, 4.4] student = student_t.StudentT(df=[3., 5., 7.], loc=mu, scale=[3., 2., 1.]) - mean = student.mean().eval() + mean = self.evaluate(student.mean()) self.assertAllClose([1., 3.3, 4.4], mean) def testMeanAllowNanStatsIsFalseRaisesWhenBatchMemberIsUndefined(self): @@ -335,7 +337,7 @@ class StudentTTest(test.TestCase): df=[0.5, 5., 7.], loc=mu, scale=[3., 2., 1.], allow_nan_stats=False) with self.assertRaisesOpError("x < y"): - student.mean().eval() + self.evaluate(student.mean()) def testMeanAllowNanStatsIsTrueReturnsNaNForUndefinedBatchMembers(self): with self.test_session(): @@ -344,7 +346,7 @@ class StudentTTest(test.TestCase): student = student_t.StudentT( df=[0.5, 1., 3., 5., 7.], loc=mu, scale=sigma, allow_nan_stats=True) - mean = student.mean().eval() + mean = self.evaluate(student.mean()) self.assertAllClose([np.nan, np.nan, 1., 3.3, 4.4], mean) def testVarianceAllowNanStatsTrueReturnsNaNforUndefinedBatchMembers(self): @@ -356,7 +358,7 @@ class StudentTTest(test.TestCase): sigma = [5., 4., 3., 2., 1.] student = student_t.StudentT( df=df, loc=mu, scale=sigma, allow_nan_stats=True) - var = student.variance().eval() + var = self.evaluate(student.variance()) ## scipy uses inf for variance when the mean is undefined. When mean is # undefined we say variance is undefined as well. So test the first # member of var, making sure it is NaN, then replace with inf and compare @@ -379,7 +381,7 @@ class StudentTTest(test.TestCase): mu = [0., 1., 3.3, 4.4] sigma = [4., 3., 2., 1.] student = student_t.StudentT(df=df, loc=mu, scale=sigma) - var = student.variance().eval() + var = self.evaluate(student.variance()) if not stats: return @@ -394,14 +396,14 @@ class StudentTTest(test.TestCase): student = student_t.StudentT( df=1., loc=0., scale=1., allow_nan_stats=False) with self.assertRaisesOpError("x < y"): - student.variance().eval() + self.evaluate(student.variance()) with self.test_session(): # df <= 1 ==> variance not defined student = student_t.StudentT( df=0.5, loc=0., scale=1., allow_nan_stats=False) with self.assertRaisesOpError("x < y"): - student.variance().eval() + self.evaluate(student.variance()) def testStd(self): with self.test_session(): @@ -411,7 +413,7 @@ class StudentTTest(test.TestCase): sigma = [5., 4., 3., 2., 1.] student = student_t.StudentT(df=df, loc=mu, scale=sigma) # Test broadcast of mu across shape of df/sigma - stddev = student.stddev().eval() + stddev = self.evaluate(student.stddev()) mu *= len(df) if not stats: @@ -428,59 +430,58 @@ class StudentTTest(test.TestCase): sigma = [5., 4., 3.] student = student_t.StudentT(df=df, loc=mu, scale=sigma) # Test broadcast of mu across shape of df/sigma - mode = student.mode().eval() + mode = self.evaluate(student.mode()) self.assertAllClose([-1., 0, 1], mode) def testPdfOfSample(self): - with self.test_session() as sess: - student = student_t.StudentT(df=3., loc=np.pi, scale=1.) - num = 20000 - samples = student.sample(num, seed=123456) - pdfs = student.prob(samples) - mean = student.mean() - mean_pdf = student.prob(student.mean()) - sample_vals, pdf_vals, mean_val, mean_pdf_val = sess.run( - [samples, pdfs, student.mean(), mean_pdf]) - self.assertEqual(samples.get_shape(), (num,)) - self.assertEqual(pdfs.get_shape(), (num,)) - self.assertEqual(mean.get_shape(), ()) - self.assertNear(np.pi, np.mean(sample_vals), err=0.02) - self.assertNear(np.pi, mean_val, err=1e-6) - # Verify integral over sample*pdf ~= 1. - self._assertIntegral(sample_vals, pdf_vals, err=2e-3) - if not stats: - return - self.assertNear(stats.t.pdf(np.pi, 3., loc=np.pi), mean_pdf_val, err=1e-6) + student = student_t.StudentT(df=3., loc=np.pi, scale=1.) + num = 20000 + samples = student.sample(num, seed=123456) + pdfs = student.prob(samples) + mean = student.mean() + mean_pdf = student.prob(student.mean()) + sample_vals, pdf_vals, mean_val, mean_pdf_val = self.evaluate( + [samples, pdfs, student.mean(), mean_pdf]) + self.assertEqual(samples.get_shape(), (num,)) + self.assertEqual(pdfs.get_shape(), (num,)) + self.assertEqual(mean.get_shape(), ()) + self.assertNear(np.pi, np.mean(sample_vals), err=0.02) + self.assertNear(np.pi, mean_val, err=1e-6) + # Verify integral over sample*pdf ~= 1. + # Tolerance increased since eager was getting a value of 1.002041. + self._assertIntegral(sample_vals, pdf_vals, err=3e-3) + if not stats: + return + self.assertNear(stats.t.pdf(np.pi, 3., loc=np.pi), mean_pdf_val, err=1e-6) def testPdfOfSampleMultiDims(self): - with self.test_session() as sess: - student = student_t.StudentT(df=[7., 11.], loc=[[5.], [6.]], scale=3.) - self.assertAllEqual([], student.event_shape) - self.assertAllEqual([], student.event_shape_tensor().eval()) - self.assertAllEqual([2, 2], student.batch_shape) - self.assertAllEqual([2, 2], student.batch_shape_tensor().eval()) - num = 50000 - samples = student.sample(num, seed=123456) - pdfs = student.prob(samples) - sample_vals, pdf_vals = sess.run([samples, pdfs]) - self.assertEqual(samples.get_shape(), (num, 2, 2)) - self.assertEqual(pdfs.get_shape(), (num, 2, 2)) - self.assertNear(5., np.mean(sample_vals[:, 0, :]), err=.03) - self.assertNear(6., np.mean(sample_vals[:, 1, :]), err=.03) - self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) - self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) - self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) - self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) - if not stats: - return - self.assertNear( - stats.t.var(7., loc=0., scale=3.), # loc d.n. effect var - np.var(sample_vals[:, :, 0]), - err=.4) - self.assertNear( - stats.t.var(11., loc=0., scale=3.), # loc d.n. effect var - np.var(sample_vals[:, :, 1]), - err=.4) + student = student_t.StudentT(df=[7., 11.], loc=[[5.], [6.]], scale=3.) + self.assertAllEqual([], student.event_shape) + self.assertAllEqual([], self.evaluate(student.event_shape_tensor())) + self.assertAllEqual([2, 2], student.batch_shape) + self.assertAllEqual([2, 2], self.evaluate(student.batch_shape_tensor())) + num = 50000 + samples = student.sample(num, seed=123456) + pdfs = student.prob(samples) + sample_vals, pdf_vals = self.evaluate([samples, pdfs]) + self.assertEqual(samples.get_shape(), (num, 2, 2)) + self.assertEqual(pdfs.get_shape(), (num, 2, 2)) + self.assertNear(5., np.mean(sample_vals[:, 0, :]), err=.03) + self.assertNear(6., np.mean(sample_vals[:, 1, :]), err=.03) + self._assertIntegral(sample_vals[:, 0, 0], pdf_vals[:, 0, 0], err=0.02) + self._assertIntegral(sample_vals[:, 0, 1], pdf_vals[:, 0, 1], err=0.02) + self._assertIntegral(sample_vals[:, 1, 0], pdf_vals[:, 1, 0], err=0.02) + self._assertIntegral(sample_vals[:, 1, 1], pdf_vals[:, 1, 1], err=0.02) + if not stats: + return + self.assertNear( + stats.t.var(7., loc=0., scale=3.), # loc d.n. effect var + np.var(sample_vals[:, :, 0]), + err=.4) + self.assertNear( + stats.t.var(11., loc=0., scale=3.), # loc d.n. effect var + np.var(sample_vals[:, :, 1]), + err=.4) def _assertIntegral(self, sample_vals, pdf_vals, err=1.5e-3): s_p = zip(sample_vals, pdf_vals) @@ -494,10 +495,10 @@ class StudentTTest(test.TestCase): def testNegativeDofFails(self): with self.test_session(): - student = student_t.StudentT(df=[2, -5.], loc=0., scale=1., - validate_args=True, name="S") with self.assertRaisesOpError(r"Condition x > 0 did not hold"): - student.mean().eval() + student = student_t.StudentT( + df=[2, -5.], loc=0., scale=1., validate_args=True, name="S") + self.evaluate(student.mean()) def testStudentTWithAbsDfSoftplusScale(self): with self.test_session(): @@ -507,9 +508,11 @@ class StudentTTest(test.TestCase): student = student_t.StudentTWithAbsDfSoftplusScale( df=df, loc=mu, scale=sigma) self.assertAllClose( - math_ops.floor(math_ops.abs(df)).eval(), student.df.eval()) - self.assertAllClose(mu.eval(), student.loc.eval()) - self.assertAllClose(nn_ops.softplus(sigma).eval(), student.scale.eval()) + math_ops.floor(self.evaluate(math_ops.abs(df))), + self.evaluate(student.df)) + self.assertAllClose(self.evaluate(mu), self.evaluate(student.loc)) + self.assertAllClose( + self.evaluate(nn_ops.softplus(sigma)), self.evaluate(student.scale)) if __name__ == "__main__": diff --git a/tensorflow/python/kernel_tests/distributions/uniform_test.py b/tensorflow/python/kernel_tests/distributions/uniform_test.py index a8def95b14..e74051c901 100644 --- a/tensorflow/python/kernel_tests/distributions/uniform_test.py +++ b/tensorflow/python/kernel_tests/distributions/uniform_test.py @@ -25,6 +25,7 @@ import numpy as np from tensorflow.python.framework import constant_op from tensorflow.python.framework import errors 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 math_ops from tensorflow.python.ops.distributions import uniform as uniform_lib @@ -46,15 +47,17 @@ stats = try_import("scipy.stats") class UniformTest(test.TestCase): + @test_util.run_in_graph_and_eager_modes() def testUniformRange(self): with self.test_session(): a = 3.0 b = 10.0 uniform = uniform_lib.Uniform(low=a, high=b) - self.assertAllClose(a, uniform.low.eval()) - self.assertAllClose(b, uniform.high.eval()) - self.assertAllClose(b - a, uniform.range().eval()) + self.assertAllClose(a, self.evaluate(uniform.low)) + self.assertAllClose(b, self.evaluate(uniform.high)) + self.assertAllClose(b - a, self.evaluate(uniform.range())) + @test_util.run_in_graph_and_eager_modes() def testUniformPDF(self): with self.test_session(): a = constant_op.constant([-3.0] * 5 + [15.0]) @@ -75,22 +78,24 @@ class UniformTest(test.TestCase): expected_pdf = _expected_pdf() pdf = uniform.prob(x) - self.assertAllClose(expected_pdf, pdf.eval()) + self.assertAllClose(expected_pdf, self.evaluate(pdf)) log_pdf = uniform.log_prob(x) - self.assertAllClose(np.log(expected_pdf), log_pdf.eval()) + self.assertAllClose(np.log(expected_pdf), self.evaluate(log_pdf)) + @test_util.run_in_graph_and_eager_modes() def testUniformShape(self): with self.test_session(): a = constant_op.constant([-3.0] * 5) b = constant_op.constant(11.0) uniform = uniform_lib.Uniform(low=a, high=b) - self.assertEqual(uniform.batch_shape_tensor().eval(), (5,)) + self.assertEqual(self.evaluate(uniform.batch_shape_tensor()), (5,)) self.assertEqual(uniform.batch_shape, tensor_shape.TensorShape([5])) - self.assertAllEqual(uniform.event_shape_tensor().eval(), []) + self.assertAllEqual(self.evaluate(uniform.event_shape_tensor()), []) self.assertEqual(uniform.event_shape, tensor_shape.TensorShape([])) + @test_util.run_in_graph_and_eager_modes() def testUniformPDFWithScalarEndpoint(self): with self.test_session(): a = constant_op.constant([0.0, 5.0]) @@ -101,8 +106,9 @@ class UniformTest(test.TestCase): expected_pdf = np.array([1.0 / (10.0 - 0.0), 1.0 / (10.0 - 5.0)]) pdf = uniform.prob(x) - self.assertAllClose(expected_pdf, pdf.eval()) + self.assertAllClose(expected_pdf, self.evaluate(pdf)) + @test_util.run_in_graph_and_eager_modes() def testUniformCDF(self): with self.test_session(): batch_size = 6 @@ -121,11 +127,12 @@ class UniformTest(test.TestCase): return cdf cdf = uniform.cdf(x) - self.assertAllClose(_expected_cdf(), cdf.eval()) + self.assertAllClose(_expected_cdf(), self.evaluate(cdf)) log_cdf = uniform.log_cdf(x) - self.assertAllClose(np.log(_expected_cdf()), log_cdf.eval()) + self.assertAllClose(np.log(_expected_cdf()), self.evaluate(log_cdf)) + @test_util.run_in_graph_and_eager_modes() def testUniformEntropy(self): with self.test_session(): a_v = np.array([1.0, 1.0, 1.0]) @@ -133,18 +140,20 @@ class UniformTest(test.TestCase): uniform = uniform_lib.Uniform(low=a_v, high=b_v) expected_entropy = np.log(b_v - a_v) - self.assertAllClose(expected_entropy, uniform.entropy().eval()) + self.assertAllClose(expected_entropy, self.evaluate(uniform.entropy())) + @test_util.run_in_graph_and_eager_modes() def testUniformAssertMaxGtMin(self): with self.test_session(): a_v = np.array([1.0, 1.0, 1.0], dtype=np.float32) b_v = np.array([1.0, 2.0, 3.0], dtype=np.float32) - uniform = uniform_lib.Uniform(low=a_v, high=b_v, validate_args=True) with self.assertRaisesWithPredicateMatch(errors.InvalidArgumentError, "x < y"): - uniform.low.eval() + uniform = uniform_lib.Uniform(low=a_v, high=b_v, validate_args=True) + self.evaluate(uniform.low) + @test_util.run_in_graph_and_eager_modes() def testUniformSample(self): with self.test_session(): a = constant_op.constant([3.0, 4.0]) @@ -156,7 +165,7 @@ class UniformTest(test.TestCase): uniform = uniform_lib.Uniform(low=a, high=b) samples = uniform.sample(n, seed=137) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertEqual(sample_values.shape, (100000, 2)) self.assertAllClose( sample_values[::, 0].mean(), (b_v + a1_v) / 2, atol=1e-2) @@ -167,6 +176,7 @@ class UniformTest(test.TestCase): self.assertFalse( np.any(sample_values[::, 1] < a2_v) or np.any(sample_values >= b_v)) + @test_util.run_in_graph_and_eager_modes() def _testUniformSampleMultiDimensional(self): # DISABLED: Please enable this test once b/issues/30149644 is resolved. with self.test_session(): @@ -183,7 +193,7 @@ class UniformTest(test.TestCase): samples = uniform.sample(n) self.assertEqual(samples.get_shape(), (n_v, batch_size, 2)) - sample_values = samples.eval() + sample_values = self.evaluate(samples) self.assertFalse( np.any(sample_values[:, 0, 0] < a_v[0]) or @@ -197,6 +207,7 @@ class UniformTest(test.TestCase): self.assertAllClose( sample_values[:, 0, 1].mean(), (a_v[1] + b_v[1]) / 2, atol=1e-2) + @test_util.run_in_graph_and_eager_modes() def testUniformMean(self): with self.test_session(): a = 10.0 @@ -205,8 +216,9 @@ class UniformTest(test.TestCase): if not stats: return s_uniform = stats.uniform(loc=a, scale=b - a) - self.assertAllClose(uniform.mean().eval(), s_uniform.mean()) + self.assertAllClose(self.evaluate(uniform.mean()), s_uniform.mean()) + @test_util.run_in_graph_and_eager_modes() def testUniformVariance(self): with self.test_session(): a = 10.0 @@ -215,8 +227,9 @@ class UniformTest(test.TestCase): if not stats: return s_uniform = stats.uniform(loc=a, scale=b - a) - self.assertAllClose(uniform.variance().eval(), s_uniform.var()) + self.assertAllClose(self.evaluate(uniform.variance()), s_uniform.var()) + @test_util.run_in_graph_and_eager_modes() def testUniformStd(self): with self.test_session(): a = 10.0 @@ -225,8 +238,9 @@ class UniformTest(test.TestCase): if not stats: return s_uniform = stats.uniform(loc=a, scale=b - a) - self.assertAllClose(uniform.stddev().eval(), s_uniform.std()) + self.assertAllClose(self.evaluate(uniform.stddev()), s_uniform.std()) + @test_util.run_in_graph_and_eager_modes() def testUniformNans(self): with self.test_session(): a = 10.0 @@ -235,23 +249,26 @@ class UniformTest(test.TestCase): no_nans = constant_op.constant(1.0) nans = constant_op.constant(0.0) / constant_op.constant(0.0) - self.assertTrue(math_ops.is_nan(nans).eval()) + self.assertTrue(self.evaluate(math_ops.is_nan(nans))) with_nans = array_ops.stack([no_nans, nans]) pdf = uniform.prob(with_nans) - is_nan = math_ops.is_nan(pdf).eval() + is_nan = self.evaluate(math_ops.is_nan(pdf)) self.assertFalse(is_nan[0]) self.assertTrue(is_nan[1]) + @test_util.run_in_graph_and_eager_modes() def testUniformSamplePdf(self): with self.test_session(): a = 10.0 b = [11.0, 100.0] uniform = uniform_lib.Uniform(a, b) self.assertTrue( - math_ops.reduce_all(uniform.prob(uniform.sample(10)) > 0).eval()) + self.evaluate( + math_ops.reduce_all(uniform.prob(uniform.sample(10)) > 0))) + @test_util.run_in_graph_and_eager_modes() def testUniformBroadcasting(self): with self.test_session(): a = 10.0 @@ -260,8 +277,9 @@ class UniformTest(test.TestCase): pdf = uniform.prob([[10.5, 11.5], [9.0, 19.0], [10.5, 21.0]]) expected_pdf = np.array([[1.0, 0.1], [0.0, 0.1], [1.0, 0.0]]) - self.assertAllClose(expected_pdf, pdf.eval()) + self.assertAllClose(expected_pdf, self.evaluate(pdf)) + @test_util.run_in_graph_and_eager_modes() def testUniformSampleWithShape(self): with self.test_session(): a = 10.0 @@ -275,12 +293,13 @@ class UniformTest(test.TestCase): [[1.0, 0.1], [1.0, 0.1], [1.0, 0.1]], ] # pylint: enable=bad-continuation - self.assertAllClose(expected_pdf, pdf.eval()) + self.assertAllClose(expected_pdf, self.evaluate(pdf)) pdf = uniform.prob(uniform.sample()) expected_pdf = [1.0, 0.1] - self.assertAllClose(expected_pdf, pdf.eval()) + self.assertAllClose(expected_pdf, self.evaluate(pdf)) + # Eager doesn't pass due to a type mismatch in one of the ops. def testUniformFloat64(self): uniform = uniform_lib.Uniform( low=np.float64(0.), high=np.float64(1.)) diff --git a/tensorflow/python/kernel_tests/distributions/util_test.py b/tensorflow/python/kernel_tests/distributions/util_test.py index 87966a6d3b..8e5556d0a0 100644 --- a/tensorflow/python/kernel_tests/distributions/util_test.py +++ b/tensorflow/python/kernel_tests/distributions/util_test.py @@ -22,9 +22,11 @@ import importlib import numpy as np +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 gradients_impl @@ -97,6 +99,7 @@ class AssertCloseTest(test.TestCase): with ops.control_dependencies([du.assert_close(y, z)]): array_ops.identity(y).eval(feed_dict=feed_dict) + @test_util.run_in_graph_and_eager_modes() def testAssertCloseEpsilon(self): x = [0., 5, 10, 15, 20] # x != y @@ -105,15 +108,15 @@ class AssertCloseTest(test.TestCase): z = [1e-8, 5, 10, 15, 20] with self.test_session(): with ops.control_dependencies([du.assert_close(x, z)]): - array_ops.identity(x).eval() + self.evaluate(array_ops.identity(x)) with self.assertRaisesOpError("Condition x ~= y"): with ops.control_dependencies([du.assert_close(x, y)]): - array_ops.identity(x).eval() + self.evaluate(array_ops.identity(x)) with self.assertRaisesOpError("Condition x ~= y"): with ops.control_dependencies([du.assert_close(y, z)]): - array_ops.identity(y).eval() + self.evaluate(array_ops.identity(y)) def testAssertIntegerForm(self): # This should only be detected as an integer. @@ -147,18 +150,21 @@ class AssertCloseTest(test.TestCase): class MaybeGetStaticTest(test.TestCase): + @test_util.run_in_graph_and_eager_modes() def testGetStaticInt(self): x = 2 self.assertEqual(x, du.maybe_get_static_value(x)) self.assertAllClose( np.array(2.), du.maybe_get_static_value(x, dtype=np.float64)) + @test_util.run_in_graph_and_eager_modes() def testGetStaticNumpyArray(self): x = np.array(2, dtype=np.int32) self.assertEqual(x, du.maybe_get_static_value(x)) self.assertAllClose( np.array(2.), du.maybe_get_static_value(x, dtype=np.float64)) + @test_util.run_in_graph_and_eager_modes() def testGetStaticConstant(self): x = constant_op.constant(2, dtype=dtypes.int32) self.assertEqual(np.array(2, dtype=np.int32), du.maybe_get_static_value(x)) @@ -173,6 +179,7 @@ class MaybeGetStaticTest(test.TestCase): class GetLogitsAndProbsTest(test.TestCase): + @test_util.run_in_graph_and_eager_modes() def testImproperArguments(self): with self.test_session(): with self.assertRaises(ValueError): @@ -181,6 +188,7 @@ class GetLogitsAndProbsTest(test.TestCase): with self.assertRaises(ValueError): du.get_logits_and_probs(logits=[0.1], probs=[0.1]) + @test_util.run_in_graph_and_eager_modes() def testLogits(self): p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32) logits = _logit(p) @@ -189,9 +197,10 @@ class GetLogitsAndProbsTest(test.TestCase): new_logits, new_p = du.get_logits_and_probs( logits=logits, validate_args=True) - self.assertAllClose(p, new_p.eval(), rtol=1e-5, atol=0.) - self.assertAllClose(logits, new_logits.eval(), rtol=1e-5, atol=0.) + self.assertAllClose(p, self.evaluate(new_p), rtol=1e-5, atol=0.) + self.assertAllClose(logits, self.evaluate(new_logits), rtol=1e-5, atol=0.) + @test_util.run_in_graph_and_eager_modes() def testLogitsMultidimensional(self): p = np.array([0.2, 0.3, 0.5], dtype=np.float32) logits = np.log(p) @@ -200,9 +209,10 @@ class GetLogitsAndProbsTest(test.TestCase): new_logits, new_p = du.get_logits_and_probs( logits=logits, multidimensional=True, validate_args=True) - self.assertAllClose(new_p.eval(), p) - self.assertAllClose(new_logits.eval(), logits) + self.assertAllClose(self.evaluate(new_p), p) + self.assertAllClose(self.evaluate(new_logits), logits) + @test_util.run_in_graph_and_eager_modes() def testProbability(self): p = np.array([0.01, 0.2, 0.5, 0.7, .99], dtype=np.float32) @@ -210,9 +220,10 @@ class GetLogitsAndProbsTest(test.TestCase): new_logits, new_p = du.get_logits_and_probs( probs=p, validate_args=True) - self.assertAllClose(_logit(p), new_logits.eval()) - self.assertAllClose(p, new_p.eval()) + self.assertAllClose(_logit(p), self.evaluate(new_logits)) + self.assertAllClose(p, self.evaluate(new_p)) + @test_util.run_in_graph_and_eager_modes() def testProbabilityMultidimensional(self): p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32) @@ -220,9 +231,10 @@ class GetLogitsAndProbsTest(test.TestCase): new_logits, new_p = du.get_logits_and_probs( probs=p, multidimensional=True, validate_args=True) - self.assertAllClose(np.log(p), new_logits.eval()) - self.assertAllClose(p, new_p.eval()) + self.assertAllClose(np.log(p), self.evaluate(new_logits)) + self.assertAllClose(p, self.evaluate(new_p)) + @test_util.run_in_graph_and_eager_modes() def testProbabilityValidateArgs(self): p = [0.01, 0.2, 0.5, 0.7, .99] # Component less than 0. @@ -233,26 +245,27 @@ class GetLogitsAndProbsTest(test.TestCase): with self.test_session(): _, prob = du.get_logits_and_probs( probs=p, validate_args=True) - prob.eval() + self.evaluate(prob) with self.assertRaisesOpError("Condition x >= 0"): _, prob = du.get_logits_and_probs( probs=p2, validate_args=True) - prob.eval() + self.evaluate(prob) _, prob = du.get_logits_and_probs( probs=p2, validate_args=False) - prob.eval() + self.evaluate(prob) with self.assertRaisesOpError("probs has components greater than 1"): _, prob = du.get_logits_and_probs( probs=p3, validate_args=True) - prob.eval() + self.evaluate(prob) _, prob = du.get_logits_and_probs( probs=p3, validate_args=False) - prob.eval() + self.evaluate(prob) + @test_util.run_in_graph_and_eager_modes() def testProbabilityValidateArgsMultidimensional(self): p = np.array([[0.3, 0.4, 0.3], [0.1, 0.5, 0.4]], dtype=np.float32) # Component less than 0. Still sums to 1. @@ -265,35 +278,35 @@ class GetLogitsAndProbsTest(test.TestCase): with self.test_session(): _, prob = du.get_logits_and_probs( probs=p, multidimensional=True) - prob.eval() + self.evaluate(prob) with self.assertRaisesOpError("Condition x >= 0"): _, prob = du.get_logits_and_probs( probs=p2, multidimensional=True, validate_args=True) - prob.eval() + self.evaluate(prob) _, prob = du.get_logits_and_probs( probs=p2, multidimensional=True, validate_args=False) - prob.eval() + self.evaluate(prob) with self.assertRaisesOpError( "(probs has components greater than 1|probs does not sum to 1)"): _, prob = du.get_logits_and_probs( probs=p3, multidimensional=True, validate_args=True) - prob.eval() + self.evaluate(prob) _, prob = du.get_logits_and_probs( probs=p3, multidimensional=True, validate_args=False) - prob.eval() + self.evaluate(prob) with self.assertRaisesOpError("probs does not sum to 1"): _, prob = du.get_logits_and_probs( probs=p4, multidimensional=True, validate_args=True) - prob.eval() + self.evaluate(prob) _, prob = du.get_logits_and_probs( probs=p4, multidimensional=True, validate_args=False) - prob.eval() + self.evaluate(prob) def testProbsMultidimShape(self): with self.test_session(): @@ -354,6 +367,7 @@ class EmbedCheckCategoricalEventShapeTest(test.TestCase): param) checked_param.eval(feed_dict={param: np.ones([int(2**11+1)])}) + @test_util.run_in_graph_and_eager_modes() def testUnsupportedDtype(self): with self.test_session(): with self.assertRaises(TypeError): @@ -396,6 +410,7 @@ class EmbedCheckIntegerCastingClosedTest(test.TestCase): x_checked.eval(feed_dict={x: np.array([1, -1], dtype=np.int32)}) +@test_util.run_all_in_graph_and_eager_modes class LogCombinationsTest(test.TestCase): def testLogCombinationsBinomial(self): @@ -412,7 +427,7 @@ class LogCombinationsTest(test.TestCase): counts = [[1., 1], [2., 3], [4., 8], [11, 4]] log_binom = du.log_combinations(n, counts) self.assertEqual([4], log_binom.get_shape()) - self.assertAllClose(log_combs, log_binom.eval()) + self.assertAllClose(log_combs, self.evaluate(log_binom)) def testLogCombinationsShape(self): # Shape [2, 2] @@ -537,14 +552,20 @@ class RotateTransposeTest(test.TestCase): x = np.array(x) return np.transpose(x, np.roll(np.arange(len(x.shape)), shift)) + @test_util.run_in_graph_and_eager_modes() def testRollStatic(self): with self.test_session(): - with self.assertRaisesRegexp(ValueError, "None values not supported."): + if context.executing_eagerly(): + error_message = r"Attempt to convert a value \(None\)" + else: + error_message = "None values not supported." + with self.assertRaisesRegexp(ValueError, error_message): du.rotate_transpose(None, 1) for x in (np.ones(1), np.ones((2, 1)), np.ones((3, 2, 1))): for shift in np.arange(-5, 5): y = du.rotate_transpose(x, shift) - self.assertAllEqual(self._np_rotate_transpose(x, shift), y.eval()) + self.assertAllEqual( + self._np_rotate_transpose(x, shift), self.evaluate(y)) self.assertAllEqual(np.roll(x.shape, shift), y.get_shape().as_list()) def testRollDynamic(self): @@ -569,12 +590,10 @@ class PickVectorTest(test.TestCase): with self.test_session(): x = np.arange(10, 12) y = np.arange(15, 18) - self.assertAllEqual(x, - du.pick_vector( - math_ops.less(0, 5), x, y).eval()) - self.assertAllEqual(y, - du.pick_vector( - math_ops.less(5, 0), x, y).eval()) + self.assertAllEqual( + x, self.evaluate(du.pick_vector(math_ops.less(0, 5), x, y))) + self.assertAllEqual( + y, self.evaluate(du.pick_vector(math_ops.less(5, 0), x, y))) self.assertAllEqual(x, du.pick_vector( constant_op.constant(True), x, y)) # No eval. @@ -870,25 +889,25 @@ class ReduceWeightedLogSumExp(test.TestCase): [1, 1, 1]]) self.assertAllClose( - np.log(4), - du.reduce_weighted_logsumexp(x, w).eval()) + np.log(4), self.evaluate(du.reduce_weighted_logsumexp(x, w))) with np.errstate(divide="ignore"): self.assertAllClose( np.log([0, 2, 2]), - du.reduce_weighted_logsumexp(x, w, axis=0).eval()) + self.evaluate(du.reduce_weighted_logsumexp(x, w, axis=0))) self.assertAllClose( np.log([1, 3]), - du.reduce_weighted_logsumexp(x, w, axis=1).eval()) + self.evaluate(du.reduce_weighted_logsumexp(x, w, axis=1))) self.assertAllClose( np.log([[1], [3]]), - du.reduce_weighted_logsumexp(x, w, axis=1, keep_dims=True).eval()) + self.evaluate( + du.reduce_weighted_logsumexp(x, w, axis=1, keep_dims=True))) self.assertAllClose( np.log(4), - du.reduce_weighted_logsumexp(x, w, axis=[0, 1]).eval()) + self.evaluate(du.reduce_weighted_logsumexp(x, w, axis=[0, 1]))) class GenNewSeedTest(test.TestCase): @@ -986,7 +1005,7 @@ class SoftplusTest(test.TestCase): # Note that this range contains both zero and inf. x = constant_op.constant(np.logspace(-8, 6).astype(np.float16)) y = du.softplus_inverse(x) - grads = gradients_impl.gradients(y, x)[0].eval() + grads = self.evaluate(gradients_impl.gradients(y, x)[0]) # Equivalent to `assertAllFalse` (if it existed). self.assertAllEqual(np.zeros_like(grads).astype(np.bool), np.isnan(grads)) @@ -996,11 +1015,13 @@ class SoftplusTest(test.TestCase): # gradient and its approximations should be finite as well. x = constant_op.constant(np.logspace(-4.8, 4.5).astype(np.float16)) y = du.softplus_inverse(x) - grads = gradients_impl.gradients(y, x)[0].eval() + grads = self.evaluate(gradients_impl.gradients(y, x)[0]) # Equivalent to `assertAllTrue` (if it existed). self.assertAllEqual( np.ones_like(grads).astype(np.bool), np.isfinite(grads)) + +@test_util.run_all_in_graph_and_eager_modes class ArgumentsTest(test.TestCase): def testNoArguments(self): |