83 lines
2.9 KiB
Python
83 lines
2.9 KiB
Python
# Copyright 2019 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 subclassed layers utilizing desired user syntax."""
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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 tensorflow.python import keras
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from tensorflow.python.eager import context
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from tensorflow.python.framework import ops
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.keras.utils import tf_utils
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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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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class SubclassedLayersTest(keras_parameterized.TestCase):
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def test_simple_build_with_constant(self):
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class BuildConstantLayer(keras.layers.Layer):
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def build(self, input_shape):
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self.b = ops.convert_to_tensor_v2(2.0)
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def call(self, inputs):
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return self.b * inputs
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layer = BuildConstantLayer()
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model = testing_utils.get_model_from_layers(
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[layer, keras.layers.Dense(1)], input_shape=(1,))
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x = ops.convert_to_tensor_v2([[3.0]])
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self.assertEqual(
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tf_utils.is_symbolic_tensor(model(x)), not context.executing_eagerly())
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self.assertEqual(
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tf_utils.is_symbolic_tensor(layer(x)), not context.executing_eagerly())
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self.assertAllClose(keras.backend.get_value(layer(x)), [[6.0]])
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def test_build_with_derived_constant(self):
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class BuildDerivedConstantLayer(keras.layers.Layer):
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def build(self, input_shape):
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a = ops.convert_to_tensor_v2(1.0)
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b = 2.0 * a
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self.variable = variables.Variable(b)
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self.constant = ops.convert_to_tensor_v2(self.variable)
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def call(self, inputs):
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return self.variable * self.constant * inputs
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layer = BuildDerivedConstantLayer()
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model = testing_utils.get_model_from_layers(
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[layer, keras.layers.Dense(1)], input_shape=(1,))
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x = ops.convert_to_tensor_v2([[3.0]])
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self.assertEqual(
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tf_utils.is_symbolic_tensor(model(x)), not context.executing_eagerly())
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self.assertEqual(
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tf_utils.is_symbolic_tensor(layer(x)), not context.executing_eagerly())
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self.assertAllClose(keras.backend.get_value(layer(x)), [[12.0]])
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if __name__ == '__main__':
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test.main()
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