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109 lines
3.7 KiB
109 lines
3.7 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 as fluid
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import paddle.v2 as paddle
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import paddle.v2.dataset.mnist as mnist
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import numpy
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def simple_fc_net():
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reader = fluid.layers.open_recordio_file(
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filename='./mnist.recordio',
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shapes=[[-1, 784], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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img, label = fluid.layers.read_file(reader)
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hidden = img
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for _ in xrange(4):
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hidden = fluid.layers.fc(
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hidden,
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size=200,
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act='tanh',
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=1.0)))
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prediction = fluid.layers.fc(hidden, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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def fc_with_batchnorm():
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reader = fluid.layers.open_recordio_file(
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filename='./mnist.recordio',
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shapes=[[-1, 784], [-1, 1]],
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lod_levels=[0, 0],
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dtypes=['float32', 'int64'])
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img, label = fluid.layers.read_file(reader)
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hidden = img
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for _ in xrange(4):
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hidden = fluid.layers.fc(
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hidden,
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size=200,
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act='tanh',
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bias_attr=fluid.ParamAttr(
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initializer=fluid.initializer.Constant(value=1.0)))
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hidden = fluid.layers.batch_norm(input=hidden)
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prediction = fluid.layers.fc(hidden, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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loss = fluid.layers.mean(loss)
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return loss
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class ParallelExecutor(unittest.TestCase):
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@classmethod
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def setUpClass(cls):
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# Convert mnist to recordio file
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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reader = paddle.batch(mnist.train(), batch_size=32)
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feeder = fluid.DataFeeder(
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feed_list=[ # order is image and label
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fluid.layers.data(
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name='image', shape=[784]),
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fluid.layers.data(
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name='label', shape=[1], dtype='int64'),
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],
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place=fluid.CPUPlace())
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fluid.recordio_writer.convert_reader_to_recordio_file(
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'./mnist.recordio', reader, feeder)
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def test_simple_fc(self):
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self.check_network_convergence(simple_fc_net)
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def test_batchnorm_fc(self):
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self.check_network_convergence(fc_with_batchnorm)
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def check_network_convergence(self, method):
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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 = method()
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adam = fluid.optimizer.Adam()
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adam.minimize(loss)
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exe = fluid.ParallelExecutor(loss_name=loss.name, use_cuda=True)
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first_loss, = exe.run([loss.name])
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first_loss = numpy.array(first_loss)
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for i in xrange(10):
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exe.run([])
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last_loss, = exe.run([loss.name])
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last_loss = numpy.array(last_loss)
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print first_loss, last_loss
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self.assertGreater(first_loss[0], last_loss[0])
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