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89 lines
3.1 KiB
89 lines
3.1 KiB
# Copyright (c) 2019 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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from __future__ import print_function
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import unittest
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import paddle.fluid as fluid
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from simple_nets import init_data
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def case1_fill_grad_vars():
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x = fluid.layers.data(name='image', shape=[784], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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feature = fluid.layers.fc(input=x, size=20, act=None)
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part1, part2 = fluid.layers.split(feature, num_or_sections=[10, 10], dim=1)
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# Note that: part2 is not used.
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loss = fluid.layers.cross_entropy(input=part1, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def case2_prune_no_grad_branch():
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x = fluid.layers.data(name='image', shape=[784], dtype='float32')
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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feature = fluid.layers.fc(input=x, size=10, act=None)
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label = fluid.layers.cast(label, dtype="float32")
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label = fluid.layers.cast(label, dtype='int64')
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# Note that the label is not persistable in fluid.layers.cross_entropy.
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loss = fluid.layers.cross_entropy(input=feature, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def case3_prune_no_grad_branch2():
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label = fluid.layers.data(name='label', shape=[1], dtype='int64')
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label = fluid.layers.cast(label, dtype="float32")
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label = fluid.layers.cast(label, dtype='int64')
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out = fluid.layers.one_hot(input=label, depth=100)
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loss = fluid.layers.mean(out)
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return loss
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def case4_with_no_grad_op_maker():
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out = fluid.layers.gaussian_random(shape=[20, 30])
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loss = fluid.layers.mean(out)
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return loss
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class TestBackward(unittest.TestCase):
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def check_backward(self, model, feed_dict):
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place = fluid.CPUPlace()
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exe = fluid.Executor(place)
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main = fluid.Program()
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startup = fluid.Program()
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with fluid.program_guard(main, startup):
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loss = model()
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optimizer = fluid.optimizer.SGD(learning_rate=0.1)
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optimizer.minimize(loss)
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exe.run(fluid.default_startup_program())
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exe.run(feed=feed_dict)
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def test_backward(self):
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batch_size = 2
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img, label = init_data(batch_size, img_shape=[784], label_range=9)
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feed_dict = {'image': img, 'label': label}
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self.check_backward(case1_fill_grad_vars, feed_dict)
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self.check_backward(case2_prune_no_grad_branch, feed_dict)
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self.check_backward(case3_prune_no_grad_branch2, {'label': label})
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self.check_backward(case4_with_no_grad_op_maker, {})
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if __name__ == '__main__':
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unittest.main()
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