wehub-resource-sync
86 lines
2.6 KiB
Python
86 lines
2.6 KiB
Python
# Copyright (c) 2020 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 numpy as np
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import paddle
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class TestAdamaxAPI(unittest.TestCase):
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def test_adamax_api_dygraph(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear = paddle.nn.Linear(13, 5)
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adam = paddle.optimizer.Adamax(
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learning_rate=0.01,
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parameters=linear.parameters(),
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weight_decay=0.01,
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)
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out = linear(a)
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out.backward()
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adam.step()
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adam.clear_gradients()
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class TestAdamaxAPIWeightDecay(unittest.TestCase):
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def test_weight_decay_int(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear = paddle.nn.Linear(13, 5)
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adam = paddle.optimizer.Adamax(
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learning_rate=0.01,
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parameters=linear.parameters(),
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weight_decay=1,
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)
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out = linear(a)
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out.backward()
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adam.step()
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adam.clear_gradients()
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class TestAdamaxAPIGroup(TestAdamaxAPI):
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def test_adamax_api_dygraph(self):
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paddle.disable_static()
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value = np.arange(26).reshape(2, 13).astype("float32")
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a = paddle.to_tensor(value)
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linear_1 = paddle.nn.Linear(13, 5)
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linear_2 = paddle.nn.Linear(5, 3)
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# This can be any optimizer supported by dygraph.
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adam = paddle.optimizer.Adamax(
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learning_rate=0.01,
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parameters=[
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{'params': linear_1.parameters()},
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{
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'params': linear_2.parameters(),
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'weight_decay': 0.001,
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'beta1': 0.1,
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'beta2': 0.99,
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},
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],
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weight_decay=0.1,
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)
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out = linear_1(a)
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out = linear_2(out)
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out.backward()
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adam.step()
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adam.clear_gradients()
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if __name__ == "__main__":
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unittest.main()
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