135 lines
5.1 KiB
Python
135 lines
5.1 KiB
Python
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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# ==============================================================================
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"""Tests for tensorflow.ops.numerics."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.framework import constant_op
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import control_flow_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import numerics
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from tensorflow.python.platform import test
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class VerifyTensorAllFiniteTest(test.TestCase):
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def testVerifyTensorAllFiniteSucceeds(self):
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x_shape = [5, 4]
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x = np.random.random_sample(x_shape).astype(np.float32)
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with test_util.use_gpu():
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t = constant_op.constant(x, shape=x_shape, dtype=dtypes.float32)
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t_verified = numerics.verify_tensor_all_finite(t,
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"Input is not a number.")
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self.assertAllClose(x, self.evaluate(t_verified))
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def testVerifyTensorAllFiniteFails(self):
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x_shape = [5, 4]
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x = np.random.random_sample(x_shape).astype(np.float32)
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my_msg = "Input is not a number."
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# Test NaN.
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x[0] = np.nan
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with test_util.use_gpu():
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with self.assertRaisesOpError(my_msg):
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t = constant_op.constant(x, shape=x_shape, dtype=dtypes.float32)
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t_verified = numerics.verify_tensor_all_finite(t, my_msg)
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self.evaluate(t_verified)
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# Test Inf.
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x[0] = np.inf
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with test_util.use_gpu():
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with self.assertRaisesOpError(my_msg):
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t = constant_op.constant(x, shape=x_shape, dtype=dtypes.float32)
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t_verified = numerics.verify_tensor_all_finite(t, my_msg)
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self.evaluate(t_verified)
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@test_util.run_v1_only("add_check_numerics_op() is meant to be a v1-only API")
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class NumericsTest(test.TestCase):
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def testInf(self):
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with self.session(graph=ops.Graph()):
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t1 = constant_op.constant(1.0)
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t2 = constant_op.constant(0.0)
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a = math_ops.div(t1, t2)
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check = numerics.add_check_numerics_ops()
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a = control_flow_ops.with_dependencies([check], a)
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with self.assertRaisesOpError("Inf"):
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self.evaluate(a)
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def testNaN(self):
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with self.session(graph=ops.Graph()):
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t1 = constant_op.constant(0.0)
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t2 = constant_op.constant(0.0)
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a = math_ops.div(t1, t2)
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check = numerics.add_check_numerics_ops()
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a = control_flow_ops.with_dependencies([check], a)
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with self.assertRaisesOpError("NaN"):
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self.evaluate(a)
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def testBoth(self):
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with self.session(graph=ops.Graph()):
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t1 = constant_op.constant([1.0, 0.0])
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t2 = constant_op.constant([0.0, 0.0])
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a = math_ops.div(t1, t2)
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check = numerics.add_check_numerics_ops()
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a = control_flow_ops.with_dependencies([check], a)
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with self.assertRaisesOpError("Inf and NaN"):
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self.evaluate(a)
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def testPassThrough(self):
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with self.session(graph=ops.Graph()):
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t1 = constant_op.constant([1.0, 2.0, 3.0, 4.0, 5.0, 6.0], shape=[2, 3])
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checked = array_ops.check_numerics(t1, message="pass through test")
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value = self.evaluate(checked)
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self.assertAllEqual(np.array([[1.0, 2.0, 3.0], [4.0, 5.0, 6.0]]), value)
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self.assertEqual([2, 3], checked.get_shape())
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def testControlFlowCond(self):
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predicate = array_ops.placeholder(dtypes.bool, shape=[])
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_ = control_flow_ops.cond(predicate,
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lambda: constant_op.constant([37.]),
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lambda: constant_op.constant([42.]))
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with self.assertRaisesRegexp(
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ValueError,
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r"`tf\.add_check_numerics_ops\(\) is not compatible with "
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r"TensorFlow control flow operations such as `tf\.cond\(\)` "
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r"or `tf.while_loop\(\)`\."):
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numerics.add_check_numerics_ops()
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def testControlFlowWhile(self):
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predicate = array_ops.placeholder(dtypes.bool, shape=[])
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_ = control_flow_ops.while_loop(lambda _: predicate,
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lambda _: constant_op.constant([37.]),
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[constant_op.constant([42.])])
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with self.assertRaisesRegexp(
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ValueError,
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r"`tf\.add_check_numerics_ops\(\) is not compatible with "
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r"TensorFlow control flow operations such as `tf\.cond\(\)` "
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r"or `tf.while_loop\(\)`\."):
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numerics.add_check_numerics_ops()
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if __name__ == "__main__":
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test.main()
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