PaddlePaddle学习之旅：00_安装

@TOC

1：运行PaddlePaddle的机器环境配置

1: 本套学习之旅使用的阿里云的Centos7.3服务器(Alibaba Cloud Elastic Compute Service)
2: python环境为3.7.2
3: PaddlePaddle1.3

1.1 Centos yum源使用阿里云

cd /etc/yum.repos.d 
mv CentOS-Base.repo CentOS-Base.repo.bak
wget -O CentOS-Base.repo http://mirrors.aliyun.com/repo/Centos-7.repo
yum clean all 
yum makecache

1.2 Python环境准备

1.2.1 安装依赖python依赖环境

yum -y install zlib-devel bzip2-devel openssl-devel ncurses-devel sqlite-devel readline-devel tk-devel gdbm-devel db4-devel libpcap-devel xz-devel libffi-devel

1.2.2 安装python3.7.2，源码安装

wget https://www.python.org/ftp/python/3.7.2/Python-3.7.2.tgz
tar xf Python-3.7.2.tgz
cd Python-3.7.2
./configure --prefix=/usr/local/python3
echo $?
make && make install
echo $?
ln -s /usr/local/python3/bin/python3.7 /usr/local/bin/python3.7
ln -s /usr/local/bin/python3.7 /usr/local/bin/python3

1.2.3 检查安装

可以python2与python3共存，python2.7和3.7版本同时安装在系统中

python -V
Python 2.7.5
python3.7 -V
Python 3.7.2

1.2.4 设置pip3.7

ln -s /usr/local/python3/bin/pip3.7 /usr/local/bin/pip3.7
ln -s /usr/local/bin/pip3.7 /usr/local/bin/pip3

2：PaddlePaddle安装

学习中暂时只安装CPU版本的

pip3 install paddlepaddle

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3：测试PaddlePaddle

测试安装是否成功，进入到Python3环境，并输入以下代码，没有出现任何错误就表示成功。

3.1 在terminal下测试

import paddle.fluid

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3.2 编写代码测试

创建一个Python程序文件，并命名为test_paddle.py，编写并执行以下测试代码，现在看不懂没有关系，跟着这个系列教程来学，我们会熟悉使用PaddlePaddle的：

3.2.1 创建一个python文件，如test.py

# Include libraries.
import paddle
import paddle.fluid as fluid
import numpy
import six

# Configure the neural network.
def net(x, y):
    y_predict = fluid.layers.fc(input=x, size=1, act=None)
    cost = fluid.layers.square_error_cost(input=y_predict, label=y)
    avg_cost = fluid.layers.mean(cost)
    return y_predict, avg_cost

                                
# Define train function.
def train(save_dirname):
    x = fluid.layers.data(name='x', shape=[13], dtype='float32')
    y = fluid.layers.data(name='y', shape=[1], dtype='float32')
    y_predict, avg_cost = net(x, y)
    sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
    sgd_optimizer.minimize(avg_cost)
    train_reader = paddle.batch(
        paddle.reader.shuffle(paddle.dataset.uci_housing.train(), buf_size=500),
        batch_size=20)
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)
    def train_loop(main_program):
        feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
        exe.run(fluid.default_startup_program())

        PASS_NUM = 1000
        for pass_id in range(PASS_NUM):
            total_loss_pass = 0
            for data in train_reader():
                avg_loss_value, = exe.run(
                    main_program, feed=feeder.feed(data), fetch_list=[avg_cost])
                total_loss_pass += avg_loss_value
                if avg_loss_value < 5.0:
                    if save_dirname is not None:
                        fluid.io.save_inference_model(
                            save_dirname, ['x'], [y_predict], exe)
                    return
            print("Pass %d, total avg cost = %f" % (pass_id, total_loss_pass))

    train_loop(fluid.default_main_program())

# Infer by using provided test data.
def infer(save_dirname=None):
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)
    inference_scope = fluid.core.Scope()
    with fluid.scope_guard(inference_scope):
        [inference_program, feed_target_names, fetch_targets] = (
            fluid.io.load_inference_model(save_dirname, exe))
        test_reader = paddle.batch(paddle.dataset.uci_housing.test(), batch_size=20)

        test_data = six.next(test_reader())
        test_feat = numpy.array(list(map(lambda x: x[0], test_data))).astype("float32")
        test_label = numpy.array(list(map(lambda x: x[1], test_data))).astype("float32")

        results = exe.run(inference_program,
                          feed={feed_target_names[0]: numpy.array(test_feat)},
                          fetch_list=fetch_targets)
        print("infer results: ", results[0])
        print("ground truth: ", test_label)

                                
# Run train and infer.
if __name__ == "__main__":
    save_dirname = "fit_a_line.inference.model"
    train(save_dirname)
    infer(save_dirname)

3.2.2 运行test.py，结果如下

• 运行结果：

  
  
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• 生成两个目录

  
  
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以上就是本次的全部内容。