| # Copyright 2020 The TensorFlow Authors. All Rights Reserved. |
| # |
| # 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. |
| # ============================================================================== |
| """Tests for keras.layers.preprocessing.normalization.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import numpy as np |
| |
| from tensorflow.python import keras |
| from tensorflow.python.data.ops import dataset_ops |
| from tensorflow.python.distribute import combinations |
| from tensorflow.python.distribute import strategy_combinations |
| from tensorflow.python.framework import config |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.keras import keras_parameterized |
| from tensorflow.python.keras.layers.preprocessing import hashing |
| from tensorflow.python.keras.layers.preprocessing import preprocessing_test_utils |
| from tensorflow.python.platform import test |
| |
| |
| @combinations.generate( |
| combinations.combine( |
| distribution=strategy_combinations.all_strategies, |
| mode=["eager", "graph"])) |
| class HashingDistributionTest(keras_parameterized.TestCase, |
| preprocessing_test_utils.PreprocessingLayerTest): |
| |
| def test_distribution(self, distribution): |
| input_data = np.asarray([["omar"], ["stringer"], ["marlo"], ["wire"]]) |
| input_dataset = dataset_ops.Dataset.from_tensor_slices(input_data).batch( |
| 2, drop_remainder=True) |
| expected_output = [[0], [0], [1], [0]] |
| |
| config.set_soft_device_placement(True) |
| |
| with distribution.scope(): |
| input_data = keras.Input(shape=(None,), dtype=dtypes.string) |
| layer = hashing.Hashing(num_bins=2) |
| int_data = layer(input_data) |
| model = keras.Model(inputs=input_data, outputs=int_data) |
| output_dataset = model.predict(input_dataset) |
| self.assertAllEqual(expected_output, output_dataset) |
| |
| |
| if __name__ == "__main__": |
| test.main() |