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85 lines
3.7 KiB
85 lines
3.7 KiB
# Copyright PaddlePaddle contributors. 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 paddle.v2.layer as layer
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import paddle.v2.topology as topology
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import paddle.v2.data_type as data_type
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import paddle.trainer_config_helpers as conf_helps
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import paddle.trainer.PyDataProvider2 as pydp2
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class TestTopology(unittest.TestCase):
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def test_data_type(self):
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pixel = layer.data(name='pixel', type=data_type.dense_vector(784))
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label = layer.data(name='label', type=data_type.integer_value(10))
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hidden = layer.fc(input=pixel,
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size=100,
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act=conf_helps.SigmoidActivation())
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inference = layer.fc(input=hidden,
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size=10,
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act=conf_helps.SoftmaxActivation())
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cost = layer.classification_cost(input=inference, label=label)
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topo = topology.Topology(cost)
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data_types = topo.data_type()
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self.assertEqual(len(data_types), 2)
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pixel_data_type = filter(lambda type: type[0] == "pixel", data_types)
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self.assertEqual(len(pixel_data_type), 1)
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pixel_data_type = pixel_data_type[0]
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self.assertEqual(pixel_data_type[1].type, pydp2.DataType.Dense)
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self.assertEqual(pixel_data_type[1].dim, 784)
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label_data_type = filter(lambda type: type[0] == "label", data_types)
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self.assertEqual(len(label_data_type), 1)
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label_data_type = label_data_type[0]
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self.assertEqual(label_data_type[1].type, pydp2.DataType.Index)
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self.assertEqual(label_data_type[1].dim, 10)
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def test_get_layer(self):
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pixel = layer.data(name='pixel2', type=data_type.dense_vector(784))
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label = layer.data(name='label2', type=data_type.integer_value(10))
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hidden = layer.fc(input=pixel,
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size=100,
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act=conf_helps.SigmoidActivation())
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inference = layer.fc(input=hidden,
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size=10,
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act=conf_helps.SoftmaxActivation())
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cost = layer.classification_cost(input=inference, label=label)
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topo = topology.Topology(cost)
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pixel_layer = topo.get_layer("pixel2")
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label_layer = topo.get_layer("label2")
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self.assertEqual(pixel_layer, pixel)
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self.assertEqual(label_layer, label)
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def test_parse(self):
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pixel = layer.data(name='pixel3', type=data_type.dense_vector(784))
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label = layer.data(name='label3', type=data_type.integer_value(10))
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hidden = layer.fc(input=pixel,
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size=100,
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act=conf_helps.SigmoidActivation())
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inference = layer.fc(input=hidden,
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size=10,
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act=conf_helps.SoftmaxActivation())
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maxid = layer.max_id(input=inference)
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cost1 = layer.classification_cost(input=inference, label=label)
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cost2 = layer.cross_entropy_cost(input=inference, label=label)
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topology.Topology(cost2).proto()
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topology.Topology([cost1]).proto()
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topology.Topology([cost1, cost2]).proto()
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topology.Topology([inference, maxid]).proto()
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if __name__ == '__main__':
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unittest.main()
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