PaddlePaddle一步一步预测波士顿房价

最新推荐文章于 2024-04-01 21:39:11 发布

艺菲

最新推荐文章于 2024-04-01 21:39:11 发布

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分类专栏： PaddlePaddle 文章标签： PaddlePaddle 波士顿房价线性回归网络

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PaddlePaddle一步一步预测波士顿房价

如何开始

针对PaddlePaddle新手入门的阅读顺序，本人建议先阅读Fluid编程指南，然后再看快速入门，在针对上一篇搭建简单网络之后，本次为大家解读一下快速入门-线性回归，个人感觉目录的名有点问题，线性回归应该改为房价预测，才能与数字识别对应上，哈哈。

现实问题

房价是大家都关心的事情，但是房价由哪些因素决定，在已知一些因素之后房价大概是多少？我们需要使用PaddlePaddle解决线性回归的问题。

转换为PaddlePaddle的问题

加载房价数据，使用PaddlePaddle定义全连接网络（线性回归模型），加载数据到网络中进行学习，通过前向计算和反向传播后（PaddlePaddle已封装好），得到优化后参数，给定一些房子相关信息，加载优化后的参数，得到预测的房价。

使用PyCharm创建工程、显示数据

在这里插入图片描述
创建InferBostonHousingPrice工程，使用之前已有的环境，新建train.py文件测试paddlepaddle是否可用。

import paddle.fluid

在这里插入图片描述

显示波士顿郊区房价数据

train_reader = paddle.dataset.uci_housing.train()
test_reader = paddle.dataset.uci_housing.test()
print(paddle.dataset.uci_housing.feature_names)

print("-----------train---------------")
for i, data in enumerate(train_reader()):
    print(i, data)

print("-----------test---------------")
for i, data in enumerate(test_reader()):
    print(i, data)

一共506个数据，404个训练数据，102个测试数据。

原版的说明文件大家可以参考一下：

Title: Boston Housing Data
Sources:
(a) Origin: This dataset was taken from the StatLib library which is
maintained at Carnegie Mellon University.
(b) Creator: Harrison, D. and Rubinfeld, D.L. ‘Hedonic prices and the
demand for clean air’, J. Environ. Economics & Management,
vol.5, 81-102, 1978.
© Date: July 7, 1993
Past Usage:
- Used in Belsley, Kuh & Welsch, ‘Regression diagnostics …’, Wiley,
  1980. N.B. Various transformations are used in the table on
  pages 244-261.
- Quinlan,R. (1993). Combining Instance-Based and Model-Based Learning.
  In Proceedings on the Tenth International Conference of Machine
  Learning, 236-243, University of Massachusetts, Amherst. Morgan
  Kaufmann.
Relevant Information:

Concerns housing values in suburbs of Boston.
Number of Instances: 506
Number of Attributes: 13 continuous attributes (including “class”
attribute “MEDV”), 1 binary-valued attribute.
Attribute Information:
1. CRIM per capita crime rate by town
2. ZN proportion of residential land zoned for lots over
  25,000 sq.ft.
3. INDUS proportion of non-retail business acres per town
4. CHAS Charles River dummy variable (= 1 if tract bounds
  river; 0 otherwise)
5. NOX nitric oxides concentration (parts per 10 million)
6. RM average number of rooms per dwelling
7. AGE proportion of owner-occupied units built prior to 1940
8. DIS weighted distances to five Boston employment centres
9. RAD index of accessibility to radial highways
10. TAX full-value property-tax rate per $10,000
11. PTRATIO pupil-teacher ratio by town
12. B 1000(Bk - 0.63)^2 where Bk is the proportion of blacks
  by town
13. LSTAT % lower status of the population
14. MEDV Median value of owner-occupied homes in $1000’s
Missing Attribute Values: None.

定义网络

# 将数据转换为feed所需的数据类型
list = []
for data in train_reader():
    list.append(data)
    
# 定义网络
x = fluid.layers.data(name="x", shape=[13], dtype='float32')
y = fluid.layers.data(name="y", shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
# 定义损失函数
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)

加载数据并进行训练

feeder = fluid.DataFeeder(place=cpu, feed_list=[x, y])

for i in range(1000):
    avg_loss_value = exe.run(
        None,
        feed=feeder.feed(list),
        fetch_list=[avg_cost])

参数保存到文件

model_path = "./infer_bhp_model"
fluid.io.save_params(executor=exe, dirname=model_path, main_program=None)

选择预测所需的数据

在原数据集中任意选择几个样本，作为模拟房价的13个变量的值


# 定义数据

new_x = numpy.array([[-0.0405441 ,  0.06636364, -0.32356227, -0.06916996, -0.03435197,
        0.05563625, -0.03475696,  0.02682186, -0.37171335, -0.21419304,
       -0.33569506,  0.10143217, -0.21172912]]).astype('float32')
#24
new_x1 = numpy.array([[-0.03894423, -0.11363636, -0.0944567 ,
-0.06916996, -0.07138901,0.08476061,
 0.11663336, -0.09250268, -0.19780031,
 -0.04625411,0.26004962,  0.09603603, -0.08921256]]).astype('float32')

#27.5
new_x2 = numpy.array([[-0.01460746, -0.11363636,  0.30950225,
         -0.06916996,  0.10350811,-0.07753102,
         0.29583006, -0.12788538, -0.19780031,
         -0.00999457,-0.39952485, -0.02154428, -0.01719269]]).astype('float32')
#19.1

定义网络进行预测

# 定义网络
x = fluid.layers.data(name="x", shape=[13], dtype='float32')
y = fluid.layers.data(name="y", shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)

# 参数初始化
cpu = fluid.core.CPUPlace()
exe = fluid.Executor(cpu)

param_path = "./infer_bhp_model"
prog = fluid.default_main_program()
fluid.io.load_params(executor=exe, dirname=param_path,
                     main_program=prog)

outs = exe.run(
    feed={'x': new_x2},
    fetch_list=[y_predict.name])

print(outs)

完整代码

文件train.py

import paddle
import paddle.fluid as fluid
import numpy

# 显示数据

train_reader = paddle.dataset.uci_housing.train()
test_reader = paddle.dataset.uci_housing.test()

print(paddle.dataset.uci_housing.feature_names)

print("-----------train---------------")
for i, data in enumerate(train_reader()):
    print(i, data)

print("-----------test---------------")
for i, data in enumerate(test_reader()):
    print(i, data)


#
list = []
for data in train_reader():
    list.append(data)

# 定义网络
x = fluid.layers.data(name="x", shape=[13], dtype='float32')
y = fluid.layers.data(name="y", shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
# 定义损失函数
cost = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_cost = fluid.layers.mean(cost)
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
# 参数初始化
cpu = fluid.core.CPUPlace()
exe = fluid.Executor(cpu)
exe.run(fluid.default_startup_program())

feeder = fluid.DataFeeder(place=cpu, feed_list=[x, y])

for i in range(1000):
    avg_loss_value = exe.run(
        None,
        feed=feeder.feed(list),
        fetch_list=[avg_cost])
print(avg_loss_value)
model_path = "./infer_bhp_model"
fluid.io.save_params(executor=exe, dirname=model_path, main_program=None)

文件infer.py


import paddle.fluid as fluid
import numpy

# 定义数据

new_x = numpy.array([[-0.0405441 ,  0.06636364, -0.32356227, -0.06916996, -0.03435197,
        0.05563625, -0.03475696,  0.02682186, -0.37171335, -0.21419304,
       -0.33569506,  0.10143217, -0.21172912]]).astype('float32')
#24
new_x1 = numpy.array([[-0.03894423, -0.11363636, -0.0944567 ,
-0.06916996, -0.07138901,0.08476061,
 0.11663336, -0.09250268, -0.19780031,
 -0.04625411,0.26004962,  0.09603603, -0.08921256]]).astype('float32')

#27.5
new_x2 = numpy.array([[-0.01460746, -0.11363636,  0.30950225,
         -0.06916996,  0.10350811,-0.07753102,
         0.29583006, -0.12788538, -0.19780031,
         -0.00999457,-0.39952485, -0.02154428, -0.01719269]]).astype('float32')
#19.1

# 定义网络
x = fluid.layers.data(name="x", shape=[13], dtype='float32')
y = fluid.layers.data(name="y", shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)

# 参数初始化
cpu = fluid.core.CPUPlace()
exe = fluid.Executor(cpu)

param_path = "./infer_bhp_model"
prog = fluid.default_main_program()
fluid.io.load_params(executor=exe, dirname=param_path,
                     main_program=prog)

outs = exe.run(
    feed={'x': new_x2},
    fetch_list=[y_predict.name])

print(outs)

结果：
在这里插入图片描述