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118 lines
3.8 KiB
118 lines
3.8 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 paddle.fluid as fluid
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import paddle.dataset.mnist as mnist
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import paddle
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import threading
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import numpy
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def network(is_train):
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reader, queue = fluid.layers.py_reader(
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capacity=10,
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shapes=((-1, 784), (-1, 1)),
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dtypes=('float32', 'int64'),
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name="train_reader" if is_train else "test_reader")
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img, label = fluid.layers.read_file(fluid.layers.double_buffer(reader))
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hidden = img
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for i in xrange(2):
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hidden = fluid.layers.fc(input=hidden, size=100, act='tanh')
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hidden = fluid.layers.dropout(
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hidden, dropout_prob=0.5, is_test=not is_train)
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prediction = fluid.layers.fc(input=hidden, size=10, act='softmax')
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loss = fluid.layers.cross_entropy(input=prediction, label=label)
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return fluid.layers.mean(loss), queue, reader
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def pipe_reader_to_queue(reader_creator, queue):
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with fluid.program_guard(fluid.Program(), fluid.Program()):
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feeder = fluid.DataFeeder(
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feed_list=[
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fluid.layers.data(
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name='img', dtype='float32', shape=[784]),
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fluid.layers.data(
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name='label', dtype='int64', shape=[1])
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],
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place=fluid.CPUPlace())
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def __thread_main__():
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for data in feeder.decorate_reader(
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reader_creator, multi_devices=False)():
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tmp = fluid.core.LoDTensorArray()
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tmp.append(data['img'])
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tmp.append(data['label'])
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queue.push(tmp)
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queue.close()
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th = threading.Thread(target=__thread_main__)
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th.start()
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return th
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def main():
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train_prog = fluid.Program()
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startup_prog = fluid.Program()
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with fluid.program_guard(train_prog, startup_prog):
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with fluid.unique_name.guard():
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loss, train_queue, train_reader = network(True)
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adam = fluid.optimizer.Adam(learning_rate=0.01)
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adam.minimize(loss)
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test_prog = fluid.Program()
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with fluid.program_guard(test_prog, fluid.Program()):
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with fluid.unique_name.guard():
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test_loss, test_queue, test_reader = network(False)
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fluid.Executor(fluid.CUDAPlace(0)).run(startup_prog)
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trainer = fluid.ParallelExecutor(
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use_cuda=True, loss_name=loss.name, main_program=train_prog)
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tester = fluid.ParallelExecutor(
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use_cuda=True, share_vars_from=trainer, main_program=test_prog)
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for epoch_id in xrange(10):
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train_data_thread = pipe_reader_to_queue(
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paddle.batch(mnist.train(), 32), train_queue)
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try:
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while True:
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print 'train_loss', numpy.array(
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trainer.run(fetch_list=[loss.name]))
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except fluid.core.EOFException:
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print 'End of epoch', epoch_id
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train_reader.reset()
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train_data_thread.join()
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test_data_thread = pipe_reader_to_queue(
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paddle.batch(mnist.test(), 32), test_queue)
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try:
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while True:
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print 'test loss', numpy.array(
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tester.run(fetch_list=[test_loss.name]))
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except fluid.core.EOFException:
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print 'End of testing'
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test_reader.reset()
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test_data_thread.join()
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break
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if __name__ == '__main__':
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main()
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