wehub-resource-sync
109 lines
4.0 KiB
Python
109 lines
4.0 KiB
Python
# Copyright (c) 2023 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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paddle.enable_static()
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class TestBuildModule(unittest.TestCase):
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def test_basic_network(self):
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = paddle.static.data('x', [4, 4], dtype='float32')
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y = paddle.static.data('y', [4, 4], dtype='float32')
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divide_out = paddle.divide(x, y)
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sum_out = paddle.sum(divide_out)
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exe = paddle.static.Executor()
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x_feed = np.ones([4, 4], dtype=np.float32) * 10
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y_feed = np.ones([4, 4], dtype=np.float32) * 2
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(sum_value,) = exe.run(
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main_program,
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feed={'x': x_feed, 'y': y_feed},
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fetch_list=[sum_out],
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)
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self.assertEqual(sum_value, 5 * 4 * 4)
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main_program = paddle.static.Program()
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with paddle.static.program_guard(main_program):
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x = paddle.static.data('x', [4, 4], dtype='float32')
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out = paddle.mean(x)
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exe = paddle.static.Executor()
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x_feed = np.ones([4, 4], dtype=np.float32) * 10
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(sum_value,) = exe.run(feed={'x': x_feed}, fetch_list=[out])
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self.assertEqual(sum_value, 10)
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def test_basic_network_without_guard(self):
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x = paddle.static.data('x', [4, 4], dtype='float32')
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y = paddle.static.data('y', [4, 4], dtype='float32')
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divide_out = paddle.divide(x, y)
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sum_out = paddle.sum(divide_out)
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exe = paddle.static.Executor()
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x_feed = np.ones([4, 4], dtype=np.float32) * 10
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y_feed = np.ones([4, 4], dtype=np.float32) * 2
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(sum_value,) = exe.run(
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feed={'x': x_feed, 'y': y_feed},
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fetch_list=[sum_out],
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)
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self.assertEqual(sum_value, 5 * 4 * 4)
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out = paddle.mean(x)
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exe = paddle.static.Executor()
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x_feed = np.ones([4, 4], dtype=np.float32) * 10
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(sum_value,) = exe.run(
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feed={'x': x_feed, 'y': y_feed}, fetch_list=[out]
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)
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self.assertEqual(sum_value, 10)
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def test_train_network(self):
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x_data = np.array(
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[[1.0], [3.0], [5.0], [9.0], [10.0], [20.0]], dtype="float32"
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)
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y_data = np.array(
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[[12.0], [16.0], [20.0], [28.0], [30.0], [50.0]], dtype="float32"
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)
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main_program = paddle.static.Program()
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startup_program = paddle.static.Program()
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with paddle.static.program_guard(main_program, startup_program):
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x = paddle.static.data(name="x", shape=[6, 1], dtype="float32")
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y = paddle.static.data(name="y", shape=[6, 1], dtype="float32")
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linear = paddle.nn.Linear(in_features=1, out_features=1)
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mse_loss = paddle.nn.MSELoss()
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sgd_optimizer = paddle.optimizer.SGD(
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learning_rate=0.001, parameters=linear.parameters()
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)
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exe = paddle.static.Executor()
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y_predict = linear(x)
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loss = mse_loss(y_predict, y)
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sgd_optimizer.minimize(loss)
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exe.run(startup_program)
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total_epoch = 5000
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for i in range(total_epoch):
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(loss_value,) = exe.run(
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feed={'x': x_data, 'y': y_data}, fetch_list=[loss]
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)
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print(f"loss is {loss_value} after {total_epoch} iteration")
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self.assertLess(loss_value, 0.1)
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if __name__ == "__main__":
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unittest.main()
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