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100 lines
4.0 KiB
100 lines
4.0 KiB
# Copyright (c) 2018 PaddlePaddle 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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import unittest
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import paddle.fluid.framework as framework
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import paddle.fluid.optimizer as optimizer
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import paddle.fluid.regularizer as regularizer
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from paddle.fluid.backward import append_backward
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class TestL2DecayRegularizer(unittest.TestCase):
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def test_l2decay_regularizer(self):
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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regularizer=regularizer.L2DecayRegularizer(0.5))
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self.assertTrue(mul_x.regularizer is not None)
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self.assertTrue(
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isinstance(mul_x.regularizer, regularizer.L2DecayRegularizer))
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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params_grads = append_backward(mean_out)
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self.assertEqual(len(params_grads), 1)
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count_ops = len(block.ops)
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params_grads = optimizer.append_regularization_ops(params_grads)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(block.ops), count_ops + 2)
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self.assertEqual(block.ops[-1].type, 'elementwise_add')
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self.assertEqual(block.ops[-2].type, 'scale')
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class TestL1DecayRegularizer(unittest.TestCase):
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def test_l2decay_regularizer(self):
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program = framework.Program()
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block = program.global_block()
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mul_x = block.create_parameter(
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dtype="float32",
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shape=[5, 10],
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lod_level=0,
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name="mul.x",
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regularizer=regularizer.L1DecayRegularizer(0.5))
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self.assertTrue(mul_x.regularizer is not None)
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self.assertTrue(
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isinstance(mul_x.regularizer, regularizer.L1DecayRegularizer))
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mul_y = block.create_var(
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dtype="float32", shape=[10, 8], lod_level=0, name="mul.y")
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mul_out = block.create_var(
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dtype="float32", shape=[5, 8], lod_level=0, name="mul.out")
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block.append_op(
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type="mul",
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inputs={"X": mul_x,
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"Y": mul_y},
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outputs={"Out": mul_out},
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attrs={"x_num_col_dims": 1})
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mean_out = block.create_var(
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dtype="float32", shape=[1], lod_level=0, name="mean.out")
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block.append_op(
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type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out})
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params_grads = append_backward(mean_out)
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self.assertEqual(len(params_grads), 1)
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count_ops = len(block.ops)
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params_grads = optimizer.append_regularization_ops(params_grads)
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self.assertEqual(len(params_grads), 1)
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self.assertEqual(len(block.ops), count_ops + 3)
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self.assertEqual(block.ops[-1].type, 'elementwise_add')
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self.assertEqual(block.ops[-2].type, 'scale')
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self.assertEqual(block.ops[-3].type, 'sign')
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if __name__ == '__main__':
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unittest.main()
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