389 lines
13 KiB
Python
389 lines
13 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 generic Python utils."""
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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 functools import partial
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import numpy as np
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from tensorflow.python import keras
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from tensorflow.python.platform import test
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class HasArgTest(test.TestCase):
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def test_has_arg(self):
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def f_x(x):
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return x
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def f_x_args(x, *args):
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_ = args
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return x
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def f_x_kwargs(x, **kwargs):
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_ = kwargs
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return x
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def f(a, b, c):
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return a + b + c
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partial_f = partial(f, b=1)
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self.assertTrue(keras.utils.generic_utils.has_arg(
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f_x, 'x', accept_all=False))
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self.assertFalse(keras.utils.generic_utils.has_arg(
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f_x, 'y', accept_all=False))
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self.assertTrue(keras.utils.generic_utils.has_arg(
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f_x_args, 'x', accept_all=False))
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self.assertFalse(keras.utils.generic_utils.has_arg(
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f_x_args, 'y', accept_all=False))
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self.assertTrue(keras.utils.generic_utils.has_arg(
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f_x_kwargs, 'x', accept_all=False))
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self.assertFalse(keras.utils.generic_utils.has_arg(
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f_x_kwargs, 'y', accept_all=False))
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self.assertTrue(keras.utils.generic_utils.has_arg(
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f_x_kwargs, 'y', accept_all=True))
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self.assertTrue(
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keras.utils.generic_utils.has_arg(partial_f, 'c', accept_all=True))
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class TestCustomObjectScope(test.TestCase):
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def test_custom_object_scope(self):
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def custom_fn():
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pass
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class CustomClass(object):
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pass
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with keras.utils.generic_utils.custom_object_scope(
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{'CustomClass': CustomClass, 'custom_fn': custom_fn}):
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act = keras.activations.get('custom_fn')
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self.assertEqual(act, custom_fn)
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cl = keras.regularizers.get('CustomClass')
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self.assertEqual(cl.__class__, CustomClass)
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class SerializeKerasObjectTest(test.TestCase):
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def test_serialize_none(self):
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serialized = keras.utils.generic_utils.serialize_keras_object(None)
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self.assertEqual(serialized, None)
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deserialized = keras.utils.generic_utils.deserialize_keras_object(
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serialized)
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self.assertEqual(deserialized, None)
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def test_serialize_custom_class_with_default_name(self):
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@keras.utils.generic_utils.register_keras_serializable()
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class TestClass(object):
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def __init__(self, value):
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self._value = value
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def get_config(self):
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return {'value': self._value}
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serialized_name = 'Custom>TestClass'
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inst = TestClass(value=10)
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class_name = keras.utils.generic_utils._GLOBAL_CUSTOM_NAMES[TestClass]
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self.assertEqual(serialized_name, class_name)
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config = keras.utils.generic_utils.serialize_keras_object(inst)
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self.assertEqual(class_name, config['class_name'])
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new_inst = keras.utils.generic_utils.deserialize_keras_object(config)
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self.assertIsNot(inst, new_inst)
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self.assertIsInstance(new_inst, TestClass)
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self.assertEqual(10, new_inst._value)
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# Make sure registering a new class with same name will fail.
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with self.assertRaisesRegex(ValueError, '.*has already been registered.*'):
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@keras.utils.generic_utils.register_keras_serializable() # pylint: disable=function-redefined
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class TestClass(object):
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def __init__(self, value):
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self._value = value
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def get_config(self):
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return {'value': self._value}
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def test_serialize_custom_class_with_custom_name(self):
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@keras.utils.generic_utils.register_keras_serializable(
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'TestPackage', 'CustomName')
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class OtherTestClass(object):
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def __init__(self, val):
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self._val = val
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def get_config(self):
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return {'val': self._val}
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serialized_name = 'TestPackage>CustomName'
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inst = OtherTestClass(val=5)
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class_name = keras.utils.generic_utils._GLOBAL_CUSTOM_NAMES[OtherTestClass]
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self.assertEqual(serialized_name, class_name)
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fn_class_name = keras.utils.generic_utils.get_registered_name(
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OtherTestClass)
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self.assertEqual(fn_class_name, class_name)
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cls = keras.utils.generic_utils.get_registered_object(fn_class_name)
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self.assertEqual(OtherTestClass, cls)
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config = keras.utils.generic_utils.serialize_keras_object(inst)
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self.assertEqual(class_name, config['class_name'])
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new_inst = keras.utils.generic_utils.deserialize_keras_object(config)
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self.assertIsNot(inst, new_inst)
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self.assertIsInstance(new_inst, OtherTestClass)
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self.assertEqual(5, new_inst._val)
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def test_serialize_custom_function(self):
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@keras.utils.generic_utils.register_keras_serializable()
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def my_fn():
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return 42
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serialized_name = 'Custom>my_fn'
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class_name = keras.utils.generic_utils._GLOBAL_CUSTOM_NAMES[my_fn]
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self.assertEqual(serialized_name, class_name)
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fn_class_name = keras.utils.generic_utils.get_registered_name(my_fn)
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self.assertEqual(fn_class_name, class_name)
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config = keras.utils.generic_utils.serialize_keras_object(my_fn)
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self.assertEqual(class_name, config)
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fn = keras.utils.generic_utils.deserialize_keras_object(config)
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self.assertEqual(42, fn())
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fn_2 = keras.utils.generic_utils.get_registered_object(fn_class_name)
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self.assertEqual(42, fn_2())
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def test_serialize_custom_class_without_get_config_fails(self):
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with self.assertRaisesRegex(
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ValueError, 'Cannot register a class that does '
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'not have a get_config.*'):
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@keras.utils.generic_utils.register_keras_serializable( # pylint: disable=unused-variable
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'TestPackage', 'TestClass')
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class TestClass(object):
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def __init__(self, value):
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self._value = value
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def test_serializable_object(self):
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class SerializableInt(int):
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"""A serializable object to pass out of a test layer's config."""
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def __new__(cls, value):
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return int.__new__(cls, value)
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def get_config(self):
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return {'value': int(self)}
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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layer = keras.layers.Dense(
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SerializableInt(3),
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activation='relu',
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kernel_initializer='ones',
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bias_regularizer='l2')
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config = keras.layers.serialize(layer)
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new_layer = keras.layers.deserialize(
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config, custom_objects={'SerializableInt': SerializableInt})
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self.assertEqual(new_layer.activation, keras.activations.relu)
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self.assertEqual(new_layer.bias_regularizer.__class__,
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keras.regularizers.L2)
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self.assertEqual(new_layer.units.__class__, SerializableInt)
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self.assertEqual(new_layer.units, 3)
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def test_nested_serializable_object(self):
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class SerializableInt(int):
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"""A serializable object to pass out of a test layer's config."""
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def __new__(cls, value):
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return int.__new__(cls, value)
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def get_config(self):
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return {'value': int(self)}
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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class SerializableNestedInt(int):
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"""A serializable object containing another serializable object."""
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def __new__(cls, value, int_obj):
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obj = int.__new__(cls, value)
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obj.int_obj = int_obj
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return obj
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def get_config(self):
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return {'value': int(self), 'int_obj': self.int_obj}
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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nested_int = SerializableInt(4)
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layer = keras.layers.Dense(
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SerializableNestedInt(3, nested_int),
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name='SerializableNestedInt',
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activation='relu',
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kernel_initializer='ones',
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bias_regularizer='l2')
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config = keras.layers.serialize(layer)
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new_layer = keras.layers.deserialize(
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config,
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custom_objects={
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'SerializableInt': SerializableInt,
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'SerializableNestedInt': SerializableNestedInt
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})
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# Make sure the string field doesn't get convert to custom object, even
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# they have same value.
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self.assertEqual(new_layer.name, 'SerializableNestedInt')
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self.assertEqual(new_layer.activation, keras.activations.relu)
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self.assertEqual(new_layer.bias_regularizer.__class__,
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keras.regularizers.L2)
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self.assertEqual(new_layer.units.__class__, SerializableNestedInt)
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self.assertEqual(new_layer.units, 3)
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self.assertEqual(new_layer.units.int_obj.__class__, SerializableInt)
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self.assertEqual(new_layer.units.int_obj, 4)
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def test_nested_serializable_fn(self):
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def serializable_fn(x):
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"""A serializable function to pass out of a test layer's config."""
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return x
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class SerializableNestedInt(int):
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"""A serializable object containing a serializable function."""
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def __new__(cls, value, fn):
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obj = int.__new__(cls, value)
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obj.fn = fn
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return obj
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def get_config(self):
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return {'value': int(self), 'fn': self.fn}
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@classmethod
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def from_config(cls, config):
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return cls(**config)
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layer = keras.layers.Dense(
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SerializableNestedInt(3, serializable_fn),
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activation='relu',
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kernel_initializer='ones',
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bias_regularizer='l2')
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config = keras.layers.serialize(layer)
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new_layer = keras.layers.deserialize(
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config,
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custom_objects={
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'serializable_fn': serializable_fn,
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'SerializableNestedInt': SerializableNestedInt
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})
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self.assertEqual(new_layer.activation, keras.activations.relu)
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self.assertIsInstance(new_layer.bias_regularizer, keras.regularizers.L2)
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self.assertIsInstance(new_layer.units, SerializableNestedInt)
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self.assertEqual(new_layer.units, 3)
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self.assertIs(new_layer.units.fn, serializable_fn)
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def test_serializable_with_old_config(self):
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# model config generated by tf-1.2.1
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old_model_config = {
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'class_name':
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'Sequential',
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'config': [{
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'class_name': 'Dense',
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'config': {
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'name': 'dense_1',
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'trainable': True,
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'batch_input_shape': [None, 784],
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'dtype': 'float32',
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'units': 32,
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'activation': 'linear',
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'use_bias': True,
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'kernel_initializer': {
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'class_name': 'Ones',
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'config': {
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'dtype': 'float32'
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}
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},
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'bias_initializer': {
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'class_name': 'Zeros',
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'config': {
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'dtype': 'float32'
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}
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},
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'kernel_regularizer': None,
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'bias_regularizer': None,
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'activity_regularizer': None,
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'kernel_constraint': None,
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'bias_constraint': None
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}
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}]
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}
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old_model = keras.utils.generic_utils.deserialize_keras_object(
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old_model_config, module_objects={'Sequential': keras.Sequential})
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new_model = keras.Sequential([
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keras.layers.Dense(32, input_dim=784, kernel_initializer='Ones'),
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])
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input_data = np.random.normal(2, 1, (5, 784))
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output = old_model.predict(input_data)
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expected_output = new_model.predict(input_data)
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self.assertAllEqual(output, expected_output)
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def test_deserialize_unknown_object(self):
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class CustomLayer(keras.layers.Layer):
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pass
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layer = CustomLayer()
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config = keras.utils.generic_utils.serialize_keras_object(layer)
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with self.assertRaisesRegexp(ValueError,
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'passed to the `custom_objects` arg'):
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keras.utils.generic_utils.deserialize_keras_object(config)
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restored = keras.utils.generic_utils.deserialize_keras_object(
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config, custom_objects={'CustomLayer': CustomLayer})
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self.assertIsInstance(restored, CustomLayer)
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class SliceArraysTest(test.TestCase):
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def test_slice_arrays(self):
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input_a = list([1, 2, 3])
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self.assertEqual(
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keras.utils.generic_utils.slice_arrays(input_a, start=0),
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[None, None, None])
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self.assertEqual(
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keras.utils.generic_utils.slice_arrays(input_a, stop=3),
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[None, None, None])
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self.assertEqual(
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keras.utils.generic_utils.slice_arrays(input_a, start=0, stop=1),
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[None, None, None])
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if __name__ == '__main__':
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test.main()
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