Apply tf1-tf2 renames to tensorflow/python/kernel_tests docstrings and comments.
No code changes, only doc-strings and comments. PiperOrigin-RevId: 244372113
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@ -1058,8 +1058,8 @@ class StridedSliceAssignChecker(object):
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var = variables.Variable(self.x)
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var = variables.Variable(self.x)
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sess.run(variables.variables_initializer([var]))
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sess.run(variables.variables_initializer([var]))
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val = sess.run(var[index].assign(value))
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val = sess.run(var[index].assign(value))
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# val_copy is used to check that tf.assign works equivalently to the
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# val_copy is used to check that tf.compat.v1.assign works equivalently
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# assign method above.
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# to the assign method above.
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val_copy = sess.run(state_ops.assign(var[index], value))
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val_copy = sess.run(state_ops.assign(var[index], value))
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valnp = np.copy(self.x_np)
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valnp = np.copy(self.x_np)
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valnp[index] = np.array(value)
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valnp[index] = np.array(value)
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@ -486,7 +486,7 @@ class ClipTest(test.TestCase):
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def testClipByAverageNormReplacedWithClipByNorm(self):
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def testClipByAverageNormReplacedWithClipByNorm(self):
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# Check clip_by_average_norm(t) is the same as
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# Check clip_by_average_norm(t) is the same as
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# clip_by_norm(t, clip_norm * tf.to_float(tf.size(t)))
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# clip_by_norm(t, clip_norm * tf.compat.v1.to_float(tf.size(t)))
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with self.session(use_gpu=True):
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with self.session(use_gpu=True):
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x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
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x = constant_op.constant([-3.0, 0.0, 0.0, 4.0, 0.0, 0.0], shape=[2, 3])
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# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
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# Average norm of x = sqrt(3^2 + 4^2) / 6 = 0.83333333
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@ -865,7 +865,7 @@ class PlaceholderTest(test.TestCase):
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# Load graph generated from earlier version of TF where
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# Load graph generated from earlier version of TF where
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# placeholder shape was not set.
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# placeholder shape was not set.
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#
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#
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# a = tf.placeholder(tf.float32)
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# a = tf.compat.v1.placeholder(tf.float32)
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# b = a + 1.0
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# b = a + 1.0
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#
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#
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# Older graph's default shape is 'shape {}', not 'shape {
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# Older graph's default shape is 'shape {}', not 'shape {
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@ -295,8 +295,9 @@ class NdtrGradientTest(test.TestCase):
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# grad_eval.shape = (N, N), with grad_eval[i, j] the partial derivative of
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# grad_eval.shape = (N, N), with grad_eval[i, j] the partial derivative of
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# the ith output point w.r.t. the jth grid point. We only expect the
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# the ith output point w.r.t. the jth grid point. We only expect the
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# diagonal to be nonzero.
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# diagonal to be nonzero.
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# TODO(b/31131137): Replace tf.test.compute_gradient with our own custom
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# TODO(b/31131137): Replace tf.compat.v1.test.compute_gradient with our
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# gradient evaluation to ensure we correctly handle small function delta.
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# own custom gradient evaluation to ensure we correctly handle small
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# function delta.
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grad_eval, _ = gradient_checker.compute_gradient(grid, grid_spec.shape,
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grad_eval, _ = gradient_checker.compute_gradient(grid, grid_spec.shape,
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fn(grid),
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fn(grid),
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grid_spec.shape)
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grid_spec.shape)
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@ -118,7 +118,8 @@ class ExtractImagePatchesGradTest(test.TestCase):
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rates=[1, 1, 1, 1],
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rates=[1, 1, 1, 1],
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padding='SAME')
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padding='SAME')
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# Github issue: #20146
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# Github issue: #20146
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# tf.extract_image_patches() gradient very slow at graph construction time
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# tf.image.extract_image_patches() gradient very slow at graph construction
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# time
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gradients = gradients_impl.gradients(patches, images)
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gradients = gradients_impl.gradients(patches, images)
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# Won't time out.
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# Won't time out.
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self.assertIsNotNone(gradients)
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self.assertIsNotNone(gradients)
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@ -272,7 +272,8 @@ class GatherTest(test.TestCase, parameterized.TestCase):
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expected=[[[[8, 9], [9, 8]], [[8, 8], [9, 9]]],
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expected=[[[[8, 9], [9, 8]], [[8, 8], [9, 9]]],
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[[[9, 9], [8, 8]], [[8, 9], [9, 8]]]]),
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[[[9, 9], [8, 8]], [[8, 9], [9, 8]]]]),
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# batch_dims=indices.shape.ndims - 1 (equivalent to tf.batch_gather)
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# batch_dims=indices.shape.ndims - 1
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# (equivalent to tf.compat.v1.batch_gather)
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dict( # 2D indices (1 batch dim)
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dict( # 2D indices (1 batch dim)
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batch_dims=1,
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batch_dims=1,
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params=[[10, 11, 12, 13], [20, 21, 22, 23]],
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params=[[10, 11, 12, 13], [20, 21, 22, 23]],
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@ -39,7 +39,7 @@ class LinearOperatorIdentityTest(
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@property
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@property
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def _dtypes_to_test(self):
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def _dtypes_to_test(self):
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# TODO(langmore) Test tf.float16 once tf.matrix_solve works in
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# TODO(langmore) Test tf.float16 once tf.linalg.solve works in
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# 16bit.
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# 16bit.
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return [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128]
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return [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128]
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@ -80,7 +80,7 @@ class LinearOperatorIdentityTest(
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operator.assert_self_adjoint().run() # Should not fail
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operator.assert_self_adjoint().run() # Should not fail
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def test_float16_matmul(self):
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def test_float16_matmul(self):
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# float16 cannot be tested by base test class because tf.matrix_solve does
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# float16 cannot be tested by base test class because tf.linalg.solve does
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# not work with float16.
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# not work with float16.
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with self.cached_session():
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with self.cached_session():
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operator = linalg_lib.LinearOperatorIdentity(
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operator = linalg_lib.LinearOperatorIdentity(
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@ -287,7 +287,7 @@ class LinearOperatorScaledIdentityTest(
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@property
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@property
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def _dtypes_to_test(self):
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def _dtypes_to_test(self):
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# TODO(langmore) Test tf.float16 once tf.matrix_solve works in
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# TODO(langmore) Test tf.float16 once tf.linalg.solve works in
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# 16bit.
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# 16bit.
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return [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128]
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return [dtypes.float32, dtypes.float64, dtypes.complex64, dtypes.complex128]
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@ -374,7 +374,7 @@ class LinearOperatorScaledIdentityTest(
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operator.assert_self_adjoint().run()
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operator.assert_self_adjoint().run()
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def test_float16_matmul(self):
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def test_float16_matmul(self):
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# float16 cannot be tested by base test class because tf.matrix_solve does
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# float16 cannot be tested by base test class because tf.linalg.solve does
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# not work with float16.
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# not work with float16.
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with self.cached_session():
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with self.cached_session():
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multiplier = rng.rand(3).astype(np.float16)
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multiplier = rng.rand(3).astype(np.float16)
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@ -212,7 +212,7 @@ class ParseExampleTest(test.TestCase):
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"a": parsing_ops.FixedLenFeature((1, 3), dtypes.float32)
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"a": parsing_ops.FixedLenFeature((1, 3), dtypes.float32)
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}
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}
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},
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},
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# TODO(mrry): Consider matching the `tf.parse_example()` error message.
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# TODO(mrry): Consider matching the `io.parse_example()` error message.
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expected_err=(errors_impl.OpError, "Key: a."))
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expected_err=(errors_impl.OpError, "Key: a."))
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def testDenseDefaultNoShapeShouldFail(self):
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def testDenseDefaultNoShapeShouldFail(self):
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@ -774,7 +774,7 @@ class ParseExampleTest(test.TestCase):
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(2, 1, 1), dtype=dtypes.string, allow_missing=True),
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(2, 1, 1), dtype=dtypes.string, allow_missing=True),
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}
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}
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},
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},
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# TODO(mrry): Consider matching the `tf.parse_example()` error message.
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# TODO(mrry): Consider matching the `io.parse_example()` error message.
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expected_err=(errors_impl.OpError, "Key: b."))
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expected_err=(errors_impl.OpError, "Key: b."))
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self._test(
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self._test(
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@ -1131,7 +1131,7 @@ class ParseSequenceExampleTest(test.TestCase):
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expected_context_values=None,
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expected_context_values=None,
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expected_feat_list_values=None,
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expected_feat_list_values=None,
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expected_err=None):
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expected_err=None):
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# Test using tf.parse_single_sequence_example
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# Test using tf.io.parse_single_sequence_example
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self._test(
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self._test(
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kwargs,
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kwargs,
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expected_context_values=expected_context_values,
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expected_context_values=expected_context_values,
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@ -335,8 +335,8 @@ class PyFuncTest(test.TestCase):
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@test_util.run_v1_only("b/120545219")
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@test_util.run_v1_only("b/120545219")
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def testGradientFunction(self):
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def testGradientFunction(self):
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# Input to tf.py_func is necessary, otherwise get_gradient_function()
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# Input to tf.compat.v1.py_func is necessary,
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# returns None per default.
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# otherwise get_gradient_function() returns None per default.
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a = constant_op.constant(0)
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a = constant_op.constant(0)
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x, = script_ops.py_func(lambda a: 0, [a], [dtypes.int64])
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x, = script_ops.py_func(lambda a: 0, [a], [dtypes.int64])
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y, = script_ops.py_func(lambda a: 0, [a], [dtypes.int64], stateful=False)
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y, = script_ops.py_func(lambda a: 0, [a], [dtypes.int64], stateful=False)
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@ -353,7 +353,8 @@ class PyFuncTest(test.TestCase):
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@test_util.run_v1_only("b/120545219")
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@test_util.run_v1_only("b/120545219")
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def testParallel(self):
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def testParallel(self):
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# Tests that tf.py_func's can run in parallel if they release the GIL.
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# Tests that tf.compat.v1.py_func's can run in parallel if they release
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# the GIL.
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with self.cached_session() as session:
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with self.cached_session() as session:
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q = queue.Queue(1)
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q = queue.Queue(1)
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@ -1144,7 +1144,8 @@ class ResourceVariableOpsTest(test_util.TensorFlowTestCase,
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expected=[[[[8, 9], [9, 8]], [[8, 8], [9, 9]]],
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expected=[[[[8, 9], [9, 8]], [[8, 8], [9, 9]]],
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[[[9, 9], [8, 8]], [[8, 9], [9, 8]]]]),
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[[[9, 9], [8, 8]], [[8, 9], [9, 8]]]]),
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# batch_dims=indices.shape.ndims - 1 (equivalent to tf.batch_gather)
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# batch_dims=indices.shape.ndims - 1 (equivalent to
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# tf.compat.v1.batch_gather)
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dict( # 2D indices (1 batch dim)
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dict( # 2D indices (1 batch dim)
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batch_dims=1,
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batch_dims=1,
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params=[[10, 11, 12, 13], [20, 21, 22, 23]],
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params=[[10, 11, 12, 13], [20, 21, 22, 23]],
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@ -250,8 +250,8 @@ class StatefulScatterNdTest(test.TestCase):
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# def testBooleanScatterUpdate(self):
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# def testBooleanScatterUpdate(self):
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# with self.session(use_gpu=False) as session:
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# with self.session(use_gpu=False) as session:
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# var = tf.Variable([True, False])
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# var = tf.Variable([True, False])
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# update0 = tf.scatter_nd_update(var, [[1]], [True])
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# update0 = tf.compat.v1.scatter_nd_update(var, [[1]], [True])
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# update1 = tf.scatter_nd_update(
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# update1 = tf.compat.v1.scatter_nd_update(
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# var, tf.constant(
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# var, tf.constant(
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# [[0]], dtype=tf.int64), [False])
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# [[0]], dtype=tf.int64), [False])
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# var.initializer.run()
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# var.initializer.run()
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@ -31,11 +31,11 @@ def grappler_optimize(graph, fetches=None, config_proto=None):
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fetches: An optional list of `Tensor`s to fetch (i.e. not optimize away).
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fetches: An optional list of `Tensor`s to fetch (i.e. not optimize away).
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Grappler uses the 'train_op' collection to look for fetches, so if not
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Grappler uses the 'train_op' collection to look for fetches, so if not
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provided this collection should be non-empty.
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provided this collection should be non-empty.
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config_proto: An optional `tf.ConfigProto` to use when rewriting the
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config_proto: An optional `tf.compat.v1.ConfigProto` to use when rewriting
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graph.
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the graph.
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Returns:
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Returns:
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A `tf.GraphDef` containing the rewritten graph.
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A `tf.compat.v1.GraphDef` containing the rewritten graph.
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"""
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"""
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if config_proto is None:
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if config_proto is None:
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config_proto = config_pb2.ConfigProto()
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config_proto = config_pb2.ConfigProto()
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@ -12,7 +12,7 @@
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# See the License for the specific language governing permissions and
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# limitations under the License.
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# ==============================================================================
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# ==============================================================================
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"""Tests for the gradient of `tf.sparse_tensor_dense_matmul()`."""
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"""Tests for the gradient of `tf.sparse.sparse_dense_matmul()`."""
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from __future__ import absolute_import
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import division
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@ -737,7 +737,7 @@ class SummaryWriterTest(test_util.TensorFlowTestCase):
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with summary_ops.create_file_writer_v2(
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with summary_ops.create_file_writer_v2(
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logdir, max_queue=1, flush_millis=999999).as_default():
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logdir, max_queue=1, flush_millis=999999).as_default():
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get_total = lambda: len(events_from_logdir(logdir))
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get_total = lambda: len(events_from_logdir(logdir))
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# Note: First tf.Event is always file_version.
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# Note: First tf.compat.v1.Event is always file_version.
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self.assertEqual(1, get_total())
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self.assertEqual(1, get_total())
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summary_ops.write('tag', 1, step=0)
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summary_ops.write('tag', 1, step=0)
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self.assertEqual(1, get_total())
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self.assertEqual(1, get_total())
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@ -769,7 +769,7 @@ class SummaryWriterTest(test_util.TensorFlowTestCase):
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logdir, max_queue=999999, flush_millis=999999)
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logdir, max_queue=999999, flush_millis=999999)
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with writer.as_default():
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with writer.as_default():
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get_total = lambda: len(events_from_logdir(logdir))
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get_total = lambda: len(events_from_logdir(logdir))
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# Note: First tf.Event is always file_version.
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# Note: First tf.compat.v1.Event is always file_version.
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self.assertEqual(1, get_total())
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self.assertEqual(1, get_total())
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summary_ops.write('tag', 1, step=0)
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summary_ops.write('tag', 1, step=0)
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summary_ops.write('tag', 1, step=0)
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summary_ops.write('tag', 1, step=0)
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