wehub-resource-sync
63 lines
1.9 KiB
Python
63 lines
1.9 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 shutil
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import tempfile
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import unittest
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import paddle
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import paddle.vision.transforms as T
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from paddle.static import InputSpec
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from paddle.vision.datasets import MNIST
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class MnistDataset(MNIST):
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def __len__(self):
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return 512
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class TestCallbacks(unittest.TestCase):
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def setUp(self):
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self.save_dir = tempfile.mkdtemp()
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def tearDown(self):
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shutil.rmtree(self.save_dir)
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def test_visualdl_callback(self):
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inputs = [InputSpec([-1, 1, 28, 28], 'float32', 'image')]
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labels = [InputSpec([None, 1], 'int64', 'label')]
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transform = T.Compose([T.Transpose(), T.Normalize([127.5], [127.5])])
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train_dataset = MnistDataset(mode='train', transform=transform)
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eval_dataset = MnistDataset(mode='test', transform=transform)
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net = paddle.vision.models.LeNet()
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model = paddle.Model(net, inputs, labels)
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optim = paddle.optimizer.Adam(0.001, parameters=net.parameters())
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model.prepare(
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optimizer=optim,
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loss=paddle.nn.CrossEntropyLoss(),
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metrics=paddle.metric.Accuracy(),
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)
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callback = paddle.callbacks.VisualDL(log_dir='visualdl_log_dir')
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model.fit(
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train_dataset, eval_dataset, batch_size=64, callbacks=callback
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)
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if __name__ == '__main__':
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unittest.main()
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