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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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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.v2 as paddle
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import paddle.v2.fluid as fluid
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x = fluid.layers.data(name='x', shape=[13], dtype='float32')
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y = fluid.layers.data(name='y', shape=[1], dtype='float32')
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places = fluid.layers.get_places()
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pd = fluid.layers.ParallelDo(places=places)
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with pd.do():
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x_ = pd.read_input(x)
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y_ = pd.read_input(y)
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y_predict = fluid.layers.fc(input=x_, size=1, act=None)
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cost = fluid.layers.square_error_cost(input=y_predict, label=y_)
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pd.write_output(fluid.layers.mean(x=cost))
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avg_cost = fluid.layers.mean(x=pd())
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sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
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sgd_optimizer.minimize(avg_cost)
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BATCH_SIZE = 20
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train_reader = paddle.batch(
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paddle.reader.shuffle(
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paddle.dataset.uci_housing.train(), buf_size=500),
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batch_size=BATCH_SIZE)
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place = fluid.CPUPlace()
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feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
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exe = fluid.Executor(place)
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exe.run(fluid.default_startup_program())
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PASS_NUM = 100
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for pass_id in range(PASS_NUM):
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for data in train_reader():
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avg_loss_value, = exe.run(fluid.default_main_program(),
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feed=feeder.feed(data),
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fetch_list=[avg_cost])
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print(avg_loss_value)
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if avg_loss_value[0] < 10.0:
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exit(0) # if avg cost less than 10.0, we think our code is good.
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exit(1)
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