e4ec26f50c
The step argument has been removed in TF2. PiperOrigin-RevId: 332477799 Change-Id: Ifcfa8bba7228baad73ba3993a9024031bee70fe0
131 lines
5.0 KiB
Python
131 lines
5.0 KiB
Python
# Copyright 2018 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 Keras metrics."""
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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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from absl.testing import parameterized
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from tensorflow.python.data.ops import dataset_ops
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from tensorflow.python.distribute import combinations as ds_combinations
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from tensorflow.python.distribute import strategy_combinations
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_combinations as combinations
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from tensorflow.python.keras import metrics
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from tensorflow.python.ops import math_ops
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from tensorflow.python.platform import test
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def _labeled_dataset_fn():
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# First four batches of x: labels, predictions -> (labels == predictions)
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# 0: 0, 0 -> True; 1: 1, 1 -> True; 2: 2, 2 -> True; 3: 3, 0 -> False
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# 4: 4, 1 -> False; 5: 0, 2 -> False; 6: 1, 0 -> False; 7: 2, 1 -> False
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# 8: 3, 2 -> False; 9: 4, 0 -> False; 10: 0, 1 -> False; 11: 1, 2 -> False
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# 12: 2, 0 -> False; 13: 3, 1 -> False; 14: 4, 2 -> False; 15: 0, 0 -> True
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return dataset_ops.Dataset.range(1000).map(
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lambda x: {"labels": x % 5, "predictions": x % 3}).batch(
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4, drop_remainder=True)
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def _boolean_dataset_fn():
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# First four batches of labels, predictions: {TP, FP, TN, FN}
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# with a threshold of 0.5:
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# T, T -> TP; F, T -> FP; T, F -> FN
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# F, F -> TN; T, T -> TP; F, T -> FP
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# T, F -> FN; F, F -> TN; T, T -> TP
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# F, T -> FP; T, F -> FN; F, F -> TN
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return dataset_ops.Dataset.from_tensor_slices({
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"labels": [True, False, True, False],
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"predictions": [True, True, False, False]}).repeat().batch(
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3, drop_remainder=True)
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def _threshold_dataset_fn():
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# First four batches of labels, predictions: {TP, FP, TN, FN}
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# with a threshold of 0.5:
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# True, 1.0 -> TP; False, .75 -> FP; True, .25 -> FN
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# False, 0.0 -> TN; True, 1.0 -> TP; False, .75 -> FP
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# True, .25 -> FN; False, 0.0 -> TN; True, 1.0 -> TP
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# False, .75 -> FP; True, .25 -> FN; False, 0.0 -> TN
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return dataset_ops.Dataset.from_tensor_slices({
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"labels": [True, False, True, False],
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"predictions": [1.0, 0.75, 0.25, 0.]}).repeat().batch(
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3, drop_remainder=True)
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def _regression_dataset_fn():
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return dataset_ops.Dataset.from_tensor_slices({
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"labels": [1., .5, 1., 0.],
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"predictions": [1., .75, .25, 0.]}).repeat()
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def all_combinations():
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return combinations.combine(
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distribution=[
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strategy_combinations.default_strategy,
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strategy_combinations.one_device_strategy,
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strategy_combinations.mirrored_strategy_with_gpu_and_cpu,
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strategy_combinations.mirrored_strategy_with_two_gpus,
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],
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mode=["graph"])
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def tpu_combinations():
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return combinations.combine(
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distribution=[strategy_combinations.tpu_strategy,],
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mode=["graph"])
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class KerasMetricsTest(test.TestCase, parameterized.TestCase):
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def _test_metric(self, distribution, dataset_fn, metric_init_fn, expected_fn):
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with ops.Graph().as_default(), distribution.scope():
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metric = metric_init_fn()
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iterator = distribution.make_input_fn_iterator(lambda _: dataset_fn())
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updates = distribution.experimental_local_results(
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distribution.run(metric, args=(iterator.get_next(),)))
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batches_per_update = distribution.num_replicas_in_sync
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self.evaluate(iterator.initializer)
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self.evaluate([v.initializer for v in metric.variables])
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batches_consumed = 0
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for i in range(4):
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batches_consumed += batches_per_update
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self.evaluate(updates)
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self.assertAllClose(expected_fn(batches_consumed),
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self.evaluate(metric.result()),
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0.001,
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msg="After update #" + str(i+1))
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if batches_consumed >= 4: # Consume 4 input batches in total.
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break
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@ds_combinations.generate(all_combinations() + tpu_combinations())
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def testMean(self, distribution):
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def _dataset_fn():
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return dataset_ops.Dataset.range(1000).map(math_ops.to_float).batch(
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4, drop_remainder=True)
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def _expected_fn(num_batches):
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# Mean(0..3) = 1.5, Mean(0..7) = 3.5, Mean(0..11) = 5.5, etc.
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return num_batches * 2 - 0.5
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self._test_metric(distribution, _dataset_fn, metrics.Mean, _expected_fn)
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if __name__ == "__main__":
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test.main()
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