55 lines
1.8 KiB
55 lines
1.8 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 unittest
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import paddle.fluid.layers as layers
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import paddle.fluid.core as core
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from paddle.fluid.framework import default_startup_program, default_main_program
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from paddle.fluid.executor import Executor
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from paddle.fluid.backward import append_backward
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from paddle.fluid.layers.control_flow import ConditionalBlock
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import numpy
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class ConditionalBlockTest(unittest.TestCase):
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def test_forward(self):
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data = layers.data(name='X', shape=[1], dtype='float32')
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data.stop_gradient = False
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cond = ConditionalBlock(inputs=[data])
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out = layers.create_tensor(dtype='float32')
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with cond.block():
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hidden = layers.fc(input=data, size=10)
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layers.assign(hidden, out)
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cpu = core.CPUPlace()
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exe = Executor(cpu)
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exe.run(default_startup_program())
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x = numpy.random.random(size=(10, 1)).astype('float32')
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outs = exe.run(feed={'X': x}, fetch_list=[out])[0]
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print(outs)
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loss = layers.mean(out)
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append_backward(loss=loss)
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outs = exe.run(
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feed={'X': x},
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fetch_list=[
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default_main_program().block(0).var(data.name + "@GRAD")
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])[0]
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print(outs)
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if __name__ == '__main__':
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unittest.main()
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