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68 lines
2.2 KiB
68 lines
2.2 KiB
# 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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from __future__ import print_function
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import unittest
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import numpy as np
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from op_test import OpTest
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import paddle
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import paddle.fluid as fluid
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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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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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def test_adamax_api(self):
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place = fluid.CPUPlace()
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shape = [2, 3, 8, 8]
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exe = fluid.Executor(place)
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train_prog = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(train_prog, startup):
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with fluid.unique_name.guard():
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data = fluid.data(name="data", shape=shape)
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conv = fluid.layers.conv2d(data, 8, 3)
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loss = paddle.mean(conv)
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beta1 = 0.85
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beta2 = 0.95
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opt = paddle.optimizer.Adamax(
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learning_rate=1e-5,
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beta1=beta1,
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beta2=beta2,
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weight_decay=0.01,
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epsilon=1e-8)
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opt.minimize(loss)
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exe.run(startup)
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data_np = np.random.random(shape).astype('float32')
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rets = exe.run(train_prog, feed={"data": data_np}, fetch_list=[loss])
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assert rets[0] is not None
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if __name__ == "__main__":
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unittest.main()
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