| # Copyright 2019 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. |
| # ============================================================================== |
| """Distribution tests for keras.layers.preprocessing.discretization.""" |
| |
| import numpy as np |
| |
| from tensorflow.python import keras |
| from tensorflow.python.compat import v2_compat |
| from tensorflow.python.distribute import combinations as ds_combinations |
| from tensorflow.python.distribute import multi_process_runner |
| from tensorflow.python.framework import config |
| from tensorflow.python.framework import test_combinations as combinations |
| from tensorflow.python.keras import keras_parameterized |
| from tensorflow.python.keras.distribute import strategy_combinations |
| from tensorflow.python.keras.layers.preprocessing import discretization |
| from tensorflow.python.keras.layers.preprocessing import preprocessing_test_utils |
| |
| |
| @ds_combinations.generate( |
| combinations.combine( |
| strategy=strategy_combinations.all_strategies + |
| strategy_combinations.multi_worker_mirrored_strategies, |
| mode=["eager", "graph"])) |
| class DiscretizationDistributionTest( |
| keras_parameterized.TestCase, |
| preprocessing_test_utils.PreprocessingLayerTest): |
| |
| def test_distribution(self, strategy): |
| input_array = np.array([[-1.5, 1.0, 3.4, .5], [0.0, 3.0, 1.3, 0.0]]) |
| |
| expected_output = [[0, 2, 3, 1], [1, 3, 2, 1]] |
| expected_output_shape = [None, 4] |
| |
| config.set_soft_device_placement(True) |
| |
| with strategy.scope(): |
| input_data = keras.Input(shape=(4,)) |
| layer = discretization.Discretization(bin_boundaries=[0., 1., 2.]) |
| bucket_data = layer(input_data) |
| self.assertAllEqual(expected_output_shape, bucket_data.shape.as_list()) |
| |
| model = keras.Model(inputs=input_data, outputs=bucket_data) |
| output_dataset = model.predict(input_array) |
| self.assertAllEqual(expected_output, output_dataset) |
| |
| |
| if __name__ == "__main__": |
| v2_compat.enable_v2_behavior() |
| multi_process_runner.test_main() |