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58 lines
2.4 KiB
58 lines
2.4 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.activation as activation
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import paddle.v2.attr as attr
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import paddle.v2.data_type as data_type
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import paddle.v2.layer as layer
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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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weight = layer.data(name='weight', type=data_type.dense_vector(10))
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score = layer.data(name='score', type=data_type.dense_vector(1))
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hidden = layer.fc(input=pixel,
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size=100,
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act=activation.Sigmoid(),
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param_attr=attr.Param(name='hidden'))
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inference = layer.fc(input=hidden, size=10, act=activation.Softmax())
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class CostLayerTest(unittest.TestCase):
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def test_cost_layer(self):
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cost1 = layer.classification_cost(input=inference, label=label)
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cost2 = layer.classification_cost(
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input=inference, label=label, weight=weight)
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cost3 = layer.cross_entropy_cost(input=inference, label=label)
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cost4 = layer.cross_entropy_with_selfnorm_cost(
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input=inference, label=label)
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cost5 = layer.regression_cost(input=inference, label=label)
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cost6 = layer.regression_cost(
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input=inference, label=label, weight=weight)
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cost7 = layer.multi_binary_label_cross_entropy_cost(
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input=inference, label=label)
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cost8 = layer.rank_cost(left=score, right=score, label=score)
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cost9 = layer.lambda_cost(input=inference, score=score)
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cost10 = layer.sum_cost(input=inference)
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cost11 = layer.huber_cost(input=score, label=label)
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print layer.parse_network(cost1, cost2)
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print layer.parse_network(cost3, cost4)
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print layer.parse_network(cost5, cost6)
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print layer.parse_network(cost7, cost8, cost9, cost10, cost11)
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if __name__ == '__main__':
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unittest.main()
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