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87 lines
2.8 KiB
87 lines
2.8 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 paddle.fluid as fluid
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import paddle
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import unittest
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import tarfile
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import os
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import shutil
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proto_str = ('name: "MultiSlotDataFeed"\n'
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'batch_size: 2\n'
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'multi_slot_desc {\n'
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' slots {\n'
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' name: "words"\n'
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' type: "uint64"\n'
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' is_dense: false\n'
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' is_used: true\n'
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' }\n'
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' slots {\n'
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' name: "label"\n'
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' type: "uint64"\n'
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' is_dense: false\n'
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' is_used: true\n'
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' }\n'
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'}')
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URL = 'http://paddle-unittest-data.gz.bcebos.com/python_paddle_fluid_tests_demo_async-executor/train_data.tar.gz'
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MD5 = '2a405a31508969b3ab823f42c0f522ca'
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def bow_net(data,
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label,
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dict_dim=89528,
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emb_dim=128,
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hid_dim=128,
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hid_dim2=96,
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class_dim=2):
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"""
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BOW net
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This model is from https://github.com/PaddlePaddle/models:
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models/fluid/PaddleNLP/text_classification/nets.py
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"""
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# embedding
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emb = fluid.layers.embedding(
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input=data, size=[dict_dim, emb_dim], is_sparse=True)
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bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
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bowh = fluid.layers.tanh(bow)
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# fc layer after conv
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fc_1 = fluid.layers.fc(input=bowh, size=hid_dim, act="tanh")
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fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
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# probability of each class
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prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
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# cross entropy loss
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cost = fluid.layers.cross_entropy(input=prediction, label=label)
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# mean loss
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avg_cost = fluid.layers.mean(x=cost)
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acc = fluid.layers.accuracy(input=prediction, label=label)
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return avg_cost, acc, prediction
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class TestAsyncExecutor(unittest.TestCase):
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def setUp(self):
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with open('./data.prototxt', 'w+') as f:
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f.write(proto_str)
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f.close()
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with tarfile.open(paddle.dataset.common.download(URL, "imdb",
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MD5)) as tarf:
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tarf.extractall(path='./')
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tarf.close()
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if __name__ == '__main__':
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unittest.main()
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