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keras_metrics_test.py
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# Copyright 2019, Google LLC.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import tensorflow as tf
from utils import keras_metrics
class NumTokensCounterTest(tf.test.TestCase):
def test_constructor_no_masked_token(self):
metric_name = 'my_test_metric'
metric = keras_metrics.NumTokensCounter(name=metric_name)
self.assertIsInstance(metric, tf.keras.metrics.Metric)
self.assertEqual(metric.name, metric_name)
self.assertEqual(self.evaluate(metric.result()), 0)
def test_counts_total_examples_without_zero_mask_no_sample_weight(self):
metric = keras_metrics.NumTokensCounter()
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[
0
# y_pred is thrown away
])
self.assertEqual(self.evaluate(metric.result()), 8)
def test_counts_total_examples_with_zero_mask_no_sample_weight(self):
metric = keras_metrics.NumTokensCounter(masked_tokens=[0])
metric.update_state(y_true=[[1, 2, 3, 4], [0, 0, 0, 0]], y_pred=[0])
self.assertEqual(self.evaluate(metric.result()), 4)
def test_counts_total_examples_without_zero_mask_with_sample_weight(self):
metric = keras_metrics.NumTokensCounter()
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[0],
sample_weight=[[1, 2, 3, 4], [1, 1, 1, 1]])
self.assertEqual(self.evaluate(metric.result()), 14)
def test_counts_total_examples_with_zero_mask_with_sample_weight(self):
metric = keras_metrics.NumTokensCounter(masked_tokens=[0])
metric.update_state(
y_true=[[1, 2, 3, 0], [1, 0, 0, 0]],
y_pred=[0],
sample_weight=[[1, 2, 3, 4], [1, 1, 1, 1]])
self.assertEqual(self.evaluate(metric.result()), 7)
class MaskedCategoricalAccuracyTest(tf.test.TestCase):
def test_constructor_no_masked_token(self):
metric_name = 'my_test_metric'
metric = keras_metrics.MaskedCategoricalAccuracy(name=metric_name)
self.assertIsInstance(metric, tf.keras.metrics.Metric)
self.assertEqual(metric.name, metric_name)
self.assertAllEqual(metric.get_config()['masked_tokens'], [])
self.assertEqual(self.evaluate(metric.result()), 0.0)
def test_constructor_with_masked_token(self):
metric_name = 'my_test_metric'
metric = keras_metrics.MaskedCategoricalAccuracy(
name=metric_name, masked_tokens=[100])
self.assertIsInstance(metric, tf.keras.metrics.Metric)
self.assertEqual(metric.name, metric_name)
self.assertAllEqual(metric.get_config()['masked_tokens'], [100])
self.assertEqual(self.evaluate(metric.result()), 0.0)
def test_update_state_with_special_character(self):
metric = keras_metrics.MaskedCategoricalAccuracy(masked_tokens=[4])
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[
# A batch with 100% accruacy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.9, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
# A batch with 50% accruacy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.0],
],
])
self.assertAllClose(self.evaluate(metric.result()), 5 / 7.0)
metric.update_state(
y_true=[[0, 4, 1, 2]],
y_pred=[
# A batch with 33% accruacy.
[
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
])
self.assertAllClose(self.evaluate(metric.result()), 6 / 10.0)
def test_update_state_with_no_special_character(self):
metric = keras_metrics.MaskedCategoricalAccuracy()
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[
# A batch with 100% accruacy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.9, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
# A batch with 50% accruacy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.0],
],
])
self.assertEqual(self.evaluate(metric.result()), 6 / 8.0)
metric.update_state(
y_true=[[0, 4, 1, 2]],
y_pred=[
# A batch with 25% accruacy.
[
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
])
self.assertAllClose(self.evaluate(metric.result()), 8 / 12.0)
def test_weighted_update_state_with_masked_token(self):
metric = keras_metrics.MaskedCategoricalAccuracy(masked_tokens=[4])
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[
# A batch with 100% accuracy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.9, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
# A batch with 50% accuracy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.0],
],
],
# A weight for each `y_true` scalar.
sample_weight=[[1.0, 2.0, 1.0, 2.0], [1.0, 2.0, 1.0, 2.0]])
self.assertAllClose(self.evaluate(metric.result()), (4 + 4) / 10.0)
metric.update_state(
y_true=[[0, 4, 1, 2]],
y_pred=[
# A batch with 25% accruacy.
[
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
],
sample_weight=[1.0, 1.0, 2.0, 2.0])
self.assertAllClose(self.evaluate(metric.result()), (4 + 4 + 1) / 15.0)
def test_weighted_update_state_no_special_character(self):
metric = keras_metrics.MaskedCategoricalAccuracy()
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[
# A batch with 100% accuracy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.9, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
# A batch with 50% accuracy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.0],
],
],
# A weight for each `y_true` scalar.
sample_weight=[1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0])
self.assertAllClose(self.evaluate(metric.result()), (6 + 4) / 12.0)
metric.update_state(
y_true=[[0, 4, 1, 2]],
y_pred=[
# A batch with 25% accruacy.
[
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
],
sample_weight=[1.0, 1.0, 2.0, 2.0])
self.assertAllClose(self.evaluate(metric.result()), (6 + 4 + 2) / 18.0)
def test_weighted_update_state_no_special_character_rank_2_sample_weight(
self):
metric = keras_metrics.MaskedCategoricalAccuracy()
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[
# A batch with 100% accuracy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.9, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
# A batch with 50% accuracy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.0],
],
],
# A weight for each `y_true` scalar.
sample_weight=[[1.0, 2.0, 1.0, 2.0], [1.0, 2.0, 1.0, 2.0]])
self.assertAllClose(self.evaluate(metric.result()), (6 + 4) / 12.0)
def test_weighted_update_state_with_scalar_weight(self):
metric = keras_metrics.MaskedCategoricalAccuracy()
metric.update_state(
y_true=[[1, 2, 3, 4]],
y_pred=[
# A batch with 50% accuracy.
[
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
],
sample_weight=1.0)
self.assertAllClose(self.evaluate(metric.result()), .5)
def test_weighted_update_state_special_character_rank_2_sample_weight(self):
metric = keras_metrics.MaskedCategoricalAccuracy(masked_tokens=[4])
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[
# A batch with 100% accuracy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.9, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
# A batch with 50% accuracy.
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.0],
],
],
# A weight for each `y_true` scalar.
sample_weight=[[1.0, 2.0, 1.0, 2.0], [1.0, 2.0, 1.0, 2.0]])
self.assertAllClose(self.evaluate(metric.result()), (6 + 2) / 10.0)
def test_update_state_with_multiple_tokens_masked(self):
metric = keras_metrics.MaskedCategoricalAccuracy(masked_tokens=[1, 2, 3, 4])
metric.update_state(
y_true=[[1, 2, 3, 4], [0, 0, 0, 0]],
y_pred=[
[
# This batch should be masked.
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.9, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
[
# Batch with 50% accuracy
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.0],
],
])
self.assertAllClose(self.evaluate(metric.result()), 0.5)
def test_update_state_with_all_tokens_masked(self):
metric = keras_metrics.MaskedCategoricalAccuracy(masked_tokens=[1, 2, 3, 4])
metric.update_state(
# All batches should be masked.
y_true=[[1, 2, 3, 4], [4, 3, 2, 1]],
y_pred=[
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.9, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.1, 0.1, 0.1, 0.1, 0.9],
],
[
[0.1, 0.9, 0.1, 0.1, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.1],
[0.1, 0.1, 0.1, 0.9, 0.1],
[0.9, 0.1, 0.1, 0.1, 0.0],
],
])
self.assertAllClose(self.evaluate(metric.result()), 0.0)
if __name__ == '__main__':
tf.test.main()