209 lines
7.6 KiB
Python
209 lines
7.6 KiB
Python
# Copyright 2016 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 regularizers."""
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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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import numpy as np
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from tensorflow.python import keras
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from tensorflow.python.eager import context
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras import regularizers
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.keras.utils import np_utils
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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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DATA_DIM = 5
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NUM_CLASSES = 2
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class KerasRegularizersTest(keras_parameterized.TestCase,
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parameterized.TestCase):
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def create_model(self, kernel_regularizer=None, activity_regularizer=None):
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model = keras.models.Sequential()
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model.add(keras.layers.Dense(NUM_CLASSES,
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kernel_regularizer=kernel_regularizer,
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activity_regularizer=activity_regularizer,
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input_shape=(DATA_DIM,)))
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return model
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def get_data(self):
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(x_train, y_train), (x_test, y_test) = testing_utils.get_test_data(
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train_samples=10,
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test_samples=10,
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input_shape=(DATA_DIM,),
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num_classes=NUM_CLASSES)
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y_train = np_utils.to_categorical(y_train, NUM_CLASSES)
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y_test = np_utils.to_categorical(y_test, NUM_CLASSES)
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return (x_train, y_train), (x_test, y_test)
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def create_multi_input_model_from(self, layer1, layer2):
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input_1 = keras.layers.Input(shape=(DATA_DIM,))
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input_2 = keras.layers.Input(shape=(DATA_DIM,))
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out1 = layer1(input_1)
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out2 = layer2(input_2)
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out = keras.layers.Average()([out1, out2])
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model = keras.models.Model([input_1, input_2], out)
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model.add_loss(keras.backend.mean(out2))
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model.add_loss(math_ops.reduce_sum(input_1))
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return model
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@keras_parameterized.run_all_keras_modes
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@parameterized.named_parameters([
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('l1', regularizers.l1()),
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('l2', regularizers.l2()),
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('l1_l2', regularizers.l1_l2()),
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])
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def test_kernel_regularization(self, regularizer):
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(x_train, y_train), _ = self.get_data()
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model = self.create_model(kernel_regularizer=regularizer)
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model.compile(
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loss='categorical_crossentropy',
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optimizer='sgd',
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run_eagerly=testing_utils.should_run_eagerly())
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self.assertEqual(len(model.losses), 1)
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model.fit(x_train, y_train, batch_size=10, epochs=1, verbose=0)
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@keras_parameterized.run_all_keras_modes
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@parameterized.named_parameters([
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('l1', regularizers.l1()),
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('l2', regularizers.l2()),
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('l1_l2', regularizers.l1_l2()),
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('l2_zero', keras.regularizers.l2(0.)),
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])
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def test_activity_regularization(self, regularizer):
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(x_train, y_train), _ = self.get_data()
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model = self.create_model(activity_regularizer=regularizer)
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model.compile(
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loss='categorical_crossentropy',
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optimizer='sgd',
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run_eagerly=testing_utils.should_run_eagerly())
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self.assertEqual(len(model.losses), 1 if context.executing_eagerly() else 1)
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model.fit(x_train, y_train, batch_size=10, epochs=1, verbose=0)
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@keras_parameterized.run_all_keras_modes
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@keras_parameterized.run_with_all_model_types
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def test_zero_regularization(self):
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# Verifies that training with zero regularization works.
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x, y = np.ones((10, 10)), np.ones((10, 3))
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model = testing_utils.get_model_from_layers(
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[keras.layers.Dense(3, kernel_regularizer=keras.regularizers.l2(0))],
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input_shape=(10,))
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model.compile(
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'sgd',
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'mse',
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run_eagerly=testing_utils.should_run_eagerly())
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model.fit(x, y, batch_size=5, epochs=1)
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def test_custom_regularizer_saving(self):
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def my_regularizer(weights):
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return math_ops.reduce_sum(math_ops.abs(weights))
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inputs = keras.Input((10,))
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outputs = keras.layers.Dense(1, kernel_regularizer=my_regularizer)(inputs)
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model = keras.Model(inputs, outputs)
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model2 = model.from_config(
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model.get_config(), custom_objects={'my_regularizer': my_regularizer})
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self.assertEqual(model2.layers[1].kernel_regularizer, my_regularizer)
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@keras_parameterized.run_all_keras_modes
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@parameterized.named_parameters([
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('l1', regularizers.l1()),
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('l2', regularizers.l2()),
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('l1_l2', regularizers.l1_l2()),
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])
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def test_regularization_shared_layer(self, regularizer):
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dense_layer = keras.layers.Dense(
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NUM_CLASSES,
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kernel_regularizer=regularizer,
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activity_regularizer=regularizer)
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model = self.create_multi_input_model_from(dense_layer, dense_layer)
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model.compile(
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loss='categorical_crossentropy',
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optimizer='sgd',
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run_eagerly=testing_utils.should_run_eagerly())
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self.assertLen(model.losses, 5)
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@keras_parameterized.run_all_keras_modes
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@parameterized.named_parameters([
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('l1', regularizers.l1()),
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('l2', regularizers.l2()),
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('l1_l2', regularizers.l1_l2()),
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])
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def test_regularization_shared_model(self, regularizer):
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dense_layer = keras.layers.Dense(
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NUM_CLASSES,
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kernel_regularizer=regularizer,
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activity_regularizer=regularizer)
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input_tensor = keras.layers.Input(shape=(DATA_DIM,))
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dummy_model = keras.models.Model(input_tensor, dense_layer(input_tensor))
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model = self.create_multi_input_model_from(dummy_model, dummy_model)
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model.compile(
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loss='categorical_crossentropy',
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optimizer='sgd',
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run_eagerly=testing_utils.should_run_eagerly())
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self.assertLen(model.losses, 6)
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@keras_parameterized.run_all_keras_modes
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@parameterized.named_parameters([
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('l1', regularizers.l1()),
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('l2', regularizers.l2()),
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('l1_l2', regularizers.l1_l2()),
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])
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def test_regularization_shared_layer_in_different_models(self, regularizer):
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shared_dense = keras.layers.Dense(
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NUM_CLASSES,
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kernel_regularizer=regularizer,
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activity_regularizer=regularizer)
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models = []
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for _ in range(2):
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input_tensor = keras.layers.Input(shape=(DATA_DIM,))
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unshared_dense = keras.layers.Dense(
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NUM_CLASSES, kernel_regularizer=regularizer)
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out = unshared_dense(shared_dense(input_tensor))
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models.append(keras.models.Model(input_tensor, out))
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model = self.create_multi_input_model_from(
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layer1=models[0], layer2=models[1])
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model.compile(
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loss='categorical_crossentropy',
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optimizer='sgd',
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run_eagerly=testing_utils.should_run_eagerly())
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# We expect to see 9 losses on the model:
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# - 2 from the 2 add_loss calls on the outer model.
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# - 3 from the weight regularizers on the shared_dense layer, unshared_dense
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# in inner model 1, unshared_dense in inner model 2.
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# - 4 from activity regularizers on the shared_dense layer.
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self.assertLen(model.losses, 9)
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def test_deserialization_error(self):
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with self.assertRaisesRegex(ValueError, 'Could not interpret regularizer'):
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keras.regularizers.get(0)
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if __name__ == '__main__':
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test.main()
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