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用PaddlePaddle预测土壤含氮量

2020-03-17 01:25 162 查看

3 月，跳不动了？>>>

PaddlePaddle实现线性回归

在本次实验中我们将使用PaddlePaddle来搭建一个简单的线性回归模型，并利用这一模型实现已知某地给定有机质含量对土壤的含氮量进行预测。并且在学习模型搭建的过程中，了解到机器学习的若干重要概念，掌握一个机器学习预测的基本流程。

** 线性回归的基本概念 **

线性回归是机器学习中最简单也是最重要的模型之一，其模型建立遵循此流程：获取数据、数据预处理、训练模型、应用模型。

回归模型可以理解为：存在一个点集，用一条曲线去拟合它分布的过程。如果拟合曲线是一条直线，则称为线性回归。如果是一条二次曲线，则被称为二次回归。线性回归是回归模型中最简单的一种。

在线性回归中有几个基本的概念需要掌握：

  • 假设函数（Hypothesis Function）
  • 损失函数（Loss Function）
  • 优化算法（Optimization Algorithm）

假设函数：

假设函数是指，用数学的方法描述自变量和因变量之间的关系，它们之间可以是一个线性函数或非线性函数。 在本次线性回顾模型中，我们的假设函数为 $ \hat{Y}= aX_1+b $ ，其中，Y^\hat{Y}​Y​^​​表示模型的预测结果（预测房价），用来和真实的Y区分。模型要学习的参数即：a,b。

损失函数：

损失函数是指，用数学的方法衡量假设函数预测结果与真实值之间的误差。这个差距越小预测越准确，而算法的任务就是使这个差距越来越小。

建立模型后，我们需要给模型一个优化目标，使得学到的参数能够让预测值Y^\hat{Y}​Y​^​​尽可能地接近真实值Y。输入任意一个数据样本的目标值yiy_iy​i​​和模型给出的预测值Yi^\hat{Y_i}​Y​i​​​^​​，损失函数输出一个非负的实值。这个实值通常用来反映模型误差的大小。

对于线性模型来讲，最常用的损失函数就是均方误差（Mean Squared Error， MSE）。

MSE=1n∑i=1n(Yi^−Yi)2MSE=\frac{1}{n}\sum_{i=1}^{n}(\hat{Y_i}-Y_i)^2MSE=​n​​1​​​i=1​∑​n​​(​Y​i​​​^​​−Y​i​​)​2​​

即对于一个大小为n的测试集，MSE是n个数据预测结果误差平方的均值。

优化算法：

在模型训练中优化算法也是至关重要的，它决定了一个模型的精度和运算速度。本章的线性回归实例中主要使用了梯度下降法进行优化。

现在，让我们正式进入实验吧！

1 - 引用库

首先载入需要用到的库，它们分别是：

  • numpy：一个python的基本库，用于科学计算
  • matplotlib.pyplot：用于生成图，在验证模型准确率和展示成本变化趋势时会使用到
  • paddle.fluid：PaddlePaddle其中一种深度学习框架
  • pandas:一种基于NumPy的工具，高效处理数据

第一步，先运行!ls /home/aistudio/data/代码看自己的数据集在哪，如我的为data2054，在data2054里面有data_soil.txt

In[2]

!ls /home/aistudio/data/
data2054

In[3]

!ls /home/aistudio/data/data2054
data_soil.txt

安装pandas(一般情况已经存在)

In[4]

!pip install pandas
DEPRECATION: Python 2.7 will reach the end of its life on January 1st, 2020. Please upgrade your Python as Python 2.7 won't be maintained after that date. A future version of pip will drop support for Python 2.7.
Looking in indexes: https://pypi.mirrors.ustc.edu.cn/simple/
Requirement already satisfied: pandas in /opt/conda/envs/python27-paddle120-env/lib/python2.7/site-packages (0.24.2)
Requirement already satisfied: pytz>=2011k in /opt/conda/envs/python27-paddle120-env/lib/python2.7/site-packages (from pandas) (2018.9)
Requirement already satisfied: numpy>=1.12.0 in /opt/conda/envs/python27-paddle120-env/lib/python2.7/site-packages (from pandas) (1.16.2)
Requirement already satisfied: python-dateutil>=2.5.0 in /opt/conda/envs/python27-paddle120-env/lib/python2.7/site-packages (from pandas) (2.8.0)
Requirement already satisfied: six>=1.5 in /opt/conda/envs/python27-paddle120-env/lib/python2.7/site-packages (from python-dateutil>=2.5.0->pandas) (1.12.0)

添加需要的库

In[5]

import sys
import paddle
import paddle.fluid as fluid
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
from paddle.utils.plot import Ploter
from __future__ import print_function
from paddle.fluid.contrib.trainer import *
from paddle.fluid.contrib.inferencer import *

查看当前环境中的Python版本和Paddle版本

In[6]

print(sys.version)
print(paddle.__version__)
2.7.15 | packaged by conda-forge | (default, Feb 28 2019, 04:00:11)
[GCC 7.3.0]
1.5.0

2 - 数据预处理 本次数据集使用的是某地区的土壤有机质含量和氮含量的数据。氮含量和有机质含量近似成线性关系，数据集只有两列，以TXT的形式储存。本次预测要得到的他们之间的线性关系。 当真实数据被收集到后，它们往往不能直接使用，需要进行预处理。我们首先以表格的形式输出数据的前五行看一下

In[7]

colnames = ['有机质含量']+['氮含量']
print_data = pd.read_csv('/home/aistudio/data/data2054/data_soil.txt',names = colnames)
print_data.head()
  有机质含量 氮含量
0 3.75 0.40
1 7.95 0.41
2 5.44 0.42
3 7.57 0.43
4 9.83 0.44

小技巧

如果不确定某个组件下有哪些方法或者属性, 可以尝试使用.之后按下tab键. 这个tab键也可以提示方法或函数需要的参数

归一化 观察一下数据的分布特征，一般而言，如果样本有多个属性，那么各维属性的取值范围差异会很大，这就要用到一个常见的操作-归一化（normalization）了。归一化的目标是把各维属性的取值范围放缩到差不多的区间，例如[-0.5, 0.5]。这里我们使用一种很常见的操作方法：减掉均值，然后除以原取值范围。

In[8]

# coding = utf-8 #
global x_raw,train_data,test_data
#下载原始数据赋值给data,将data分为train_data与test_data
data = np.loadtxt('/home/aistudio/data/data2054/data_soil.txt',delimiter = ',')
x_raw = data.T[0].copy()

maximums, minimums, avgs = data.max(axis=0), data.min(axis=0), data.sum(axis=0)/data.shape[0]
print("the raw area :",data[:,0].max(axis = 0))

#进行归一化操作
feature_num = 2
for i in range(feature_num-1):
#输入归一化的代码
#需要自行编程实现
data[:,i]=(data[:,i]-avgs[i])/(maximums[i] - minimums[i])
print('normalization:',data[:,0].max(axis = 0))
the raw area : 85.67
normalization: 0.5386874418397725

数据集分割 将原始数据处理为可用数据后，为了评估模型的好坏，我们将数据分成两份：训练集和测试集。 训练集数据用于调整模型的参数，即进行模型的训练，模型在这份数据集上的误差被称为训练误差； 测试集数据被用来测试，模型在这份数据集上的误差被称为测试误差。 我们训练模型的目的是为了通过从训练数据中找到规律来预测未知的新数据，所以测试误差是更能反映模型表现的指标。分割数据的比例要考虑到两个因素：更多的训练数据会降低参数估计的方差，从而得到更可信的模型；而更多的测试数据会降低测试误差的方差，从而得到更可信的测试误差。我们这个例子中设置的分割比例为8:2。 定义reader 构造read_data()函数，来读取训练数据集train_set或者测试数据集test_set。它的具体实现是在read_data()函数内部构造一个reader()，使用yield关键字来让reader()成为一个Generator（生成器），注意，yield关键字的作用和使用方法类似return关键字，不同之处在于yield关键字可以构造生成器（Generator）。虽然我们可以直接创建一个包含所有数据的列表，但是由于内存限制，我们不可能创建一个无限大的或者巨大的列表，并且很多时候在创建了一个百万数量级别的列表之后，我们却只需要用到开头的几个或几十个数据，这样造成了极大的浪费，而生成器的工作方式是在每次循环时计算下一个值，不断推算出后续的元素，不会创建完整的数据集列表，从而节约了内存使用。

In[9]

ratio = 0.8
offset = int(data.shape[0]*ratio)

train_data = data[:offset].copy()
test_data = data[offset:].copy()

#可以把分割结果打印出来进行观察
print(len(data))
print(len(train_data))

def read_data(data_set):
def reader():
for data in data_set:
yield data[:-1],data[-1:]      #这里是执行迭代，从后往前数的话，最后一个位置为-1
return reader

"""获取训练数据集和测试数据集

定义一个reader来获取训练数据集及其标签
Args：
Return：
read_data -- 用于获取训练数据集及其标签的reader"""

def train():

global train_data
return read_data(train_data)

def test():
global test_data
return read_data(test_data)
524
419

设置训练参数 同学们可以试着调整一下参数值，看会有什么变化。 关于参数的解释如下： paddle.reader.shuffle(train(), buf_size=400)表示trainer从train()这个reader中读取了buf_size=400大小的数据并打乱顺序 paddle.batch(reader(), batch_size=BATCH_SIZE)表示从打乱的数据中再取出BATCH_SIZE=20大小的数据进行一次迭代训练

In[10]

BATCH_SIZE = 30

train_reader = paddle.batch(
paddle.reader.shuffle(
train(),
buf_size=400),
batch_size=BATCH_SIZE)

test_reader = paddle.batch(
paddle.reader.shuffle(
test(),
buf_size=400),
batch_size=BATCH_SIZE)

将设计完成的网络参数写入 train_program() 函数，便于训练时调用 相关函数定义可以查找paddlepaddle的使用文档http://www.paddlepaddle.org/documentation/api/zh/0.14.0/layers.html#permalink-47-fc

In[11]

def train_program():
y = fluid.layers.data(name='y', shape=[1], dtype='float32')

# feature vector of length 13
x = fluid.layers.data(name='x', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)

loss = fluid.layers.square_error_cost(input=y_predict, label=y)
avg_loss = fluid.layers.mean(loss)

return avg_loss

损失函数定义确定后，需要定义参数优化方法。

In[12]

def optimizer_program():
return fluid.optimizer.SGD(learning_rate=0.01)

定义运算场所 首先进行最基本的运算场所定义，在 fluid 中使用 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 来进行初始化： place 表示fluid program的执行设备，常见的有 fluid.CUDAPlace(0) 和 fluid.CPUPlace() use_cuda = False 表示不使用 GPU 进行加速训练

In[13]

use_cuda = False
place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace()

创建训练器 创建训练器时需要提供3个主要信息： 一个配置好的网络拓扑结构 训练的硬件场所 具体的优化方法

In[14]

trainer = Trainer(
train_func=train_program,
place=place,
optimizer_func=optimizer_program)

其它配置 feed_order=['x', 'y'] 是数据层名称和数组索引的映射，用于定义数据的读取顺序。 params_dirname用于定义模型保存路径。 最后定义事件处理器 event_handler_plot(event) 用于打印训练过程

In[15]

feed_order=['x', 'y']
# Specify the directory to save the parameters
import os
params_dirname = "/home/aistudio/inference_model"
# 如果保存路径不存在就创建
if not os.path.exists(params_dirname):
os.makedirs(params_dirname)
import shutil
shutil.rmtree(params_dirname)    #递归删除文件夹

train_title = "Train cost"
test_title = "Test cost"
plot_cost = Ploter(train_title, test_title)

step = 0

# event_handler prints training and testing info
def event_handler(event):
global step
if isinstance(event, EndStepEvent):#每步触发事件
if step % 10 == 0:   # record a train cost every 10 batches
print("%s, Step %d, Cost %f" %(train_title, step, event.metrics[0]))

if step % 100 == 0:  # record a test cost every 100 batches
test_metrics = trainer.test(
reader=test_reader, feed_order=feed_order)
print("%s, Step %d, Cost %f" %(test_title, step, test_metrics[0]))

if test_metrics[0] < 0.01:
# If the accuracy is good enough, we can stop the training.
print('loss is less than 0.001, stop')
trainer.stop()
step += 1

if isinstance(event, EndEpochEvent):#每次迭代触发事件
if event.epoch % 10 == 0:
# We can save the trained parameters for the inferences later
if params_dirname is not None:
trainer.save_params(params_dirname)

开始训练 我们现在可以通过调用trainer.train()来开始训练 关于参数的解释如下： 参数feed_order用到了之前定义的feed_order索引，将数据层x和y按顺序输入trainer，也就是数据的来源。 参数event_handler是事件管理机制，读者可以自定义event_handler，根据事件信息作相应的操作。 参数num_epochs=100表示迭代训练100次后停止训练。

In[16]

%matplotlib inline
# The training could take up to a few minutes.
trainer.train(
reader=train_reader,
num_epochs=300,
event_handler=event_handler,
feed_order=feed_order)
Train cost, Step 0, Cost 4.768987
Test cost, Step 0, Cost 8.029277
Train cost, Step 10, Cost 3.512895
Train cost, Step 20, Cost 4.517720
Train cost, Step 30, Cost 2.638228
Train cost, Step 40, Cost 2.876105
Train cost, Step 50, Cost 2.152664
Train cost, Step 60, Cost 1.750877
Train cost, Step 70, Cost 2.495707
Train cost, Step 80, Cost 1.044865
Train cost, Step 90, Cost 1.266695
Train cost, Step 100, Cost 1.617073
Test cost, Step 100, Cost 1.710509
Train cost, Step 110, Cost 0.870742
Train cost, Step 120, Cost 1.326341
Train cost, Step 130, Cost 1.310452
Train cost, Step 140, Cost 1.335390
Train cost, Step 150, Cost 1.182201
Train cost, Step 160, Cost 0.992906
Train cost, Step 170, Cost 0.919412
Train cost, Step 180, Cost 1.154889
Train cost, Step 190, Cost 0.921385
Train cost, Step 200, Cost 1.428739
Test cost, Step 200, Cost 1.025548
Train cost, Step 210, Cost 0.892617
Train cost, Step 220, Cost 0.624673
Train cost, Step 230, Cost 1.201186
Train cost, Step 240, Cost 0.744943
Train cost, Step 250, Cost 0.881837
Train cost, Step 260, Cost 0.671617
Train cost, Step 270, Cost 0.846477
Train cost, Step 280, Cost 1.087123
Train cost, Step 290, Cost 0.979044
Train cost, Step 300, Cost 0.848427
Test cost, Step 300, Cost 0.790917
Train cost, Step 310, Cost 0.929944
Train cost, Step 320, Cost 0.647987
Train cost, Step 330, Cost 0.558589
Train cost, Step 340, Cost 0.673265
Train cost, Step 350, Cost 0.748646
Train cost, Step 360, Cost 0.664201
Train cost, Step 370, Cost 0.738837
Train cost, Step 380, Cost 0.680803
Train cost, Step 390, Cost 0.592835
Train cost, Step 400, Cost 0.461612
Test cost, Step 400, Cost 0.606999
Train cost, Step 410, Cost 0.588587
Train cost, Step 420, Cost 0.708379
Train cost, Step 430, Cost 0.652785
Train cost, Step 440, Cost 0.739320
Train cost, Step 450, Cost 0.703653
Train cost, Step 460, Cost 0.772891
Train cost, Step 470, Cost 0.569921
Train cost, Step 480, Cost 0.605819
Train cost, Step 490, Cost 0.629869
Train cost, Step 500, Cost 0.564279
Test cost, Step 500, Cost 0.483584
Train cost, Step 510, Cost 0.529205
Train cost, Step 520, Cost 0.453852
Train cost, Step 530, Cost 0.500234
Train cost, Step 540, Cost 0.387460
Train cost, Step 550, Cost 0.469682
Train cost, Step 560, Cost 0.549738
Train cost, Step 570, Cost 0.449353
Train cost, Step 580, Cost 0.432828
Train cost, Step 590, Cost 0.504905
Train cost, Step 600, Cost 0.466302
Test cost, Step 600, Cost 0.378664
Train cost, Step 610, Cost 0.461219
Train cost, Step 620, Cost 0.319102
Train cost, Step 630, Cost 0.476844
Train cost, Step 640, Cost 0.337013
Train cost, Step 650, Cost 0.290477
Train cost, Step 660, Cost 0.396287
Train cost, Step 670, Cost 0.379541
Train cost, Step 680, Cost 0.396960
Train cost, Step 690, Cost 0.426767
Train cost, Step 700, Cost 0.321290
Test cost, Step 700, Cost 0.317931
Train cost, Step 710, Cost 0.467874
Train cost, Step 720, Cost 0.346082
Train cost, Step 730, Cost 0.307868
Train cost, Step 740, Cost 0.394967
Train cost, Step 750, Cost 0.305963
Train cost, Step 760, Cost 0.292577
Train cost, Step 770, Cost 0.321144
Train cost, Step 780, Cost 0.328115
Train cost, Step 790, Cost 0.336515
Train cost, Step 800, Cost 0.263018
Test cost, Step 800, Cost 0.242078
Train cost, Step 810, Cost 0.240048
Train cost, Step 820, Cost 0.246798
Train cost, Step 830, Cost 0.286270
Train cost, Step 840, Cost 0.339424
Train cost, Step 850, Cost 0.252880
Train cost, Step 860, Cost 0.295079
Train cost, Step 870, Cost 0.241332
Train cost, Step 880, Cost 0.212187
Train cost, Step 890, Cost 0.218729
Train cost, Step 900, Cost 0.242188
Test cost, Step 900, Cost 0.198788
Train cost, Step 910, Cost 0.241578
Train cost, Step 920, Cost 0.222606
Train cost, Step 930, Cost 0.189436
Train cost, Step 940, Cost 0.191209
Train cost, Step 950, Cost 0.224156
Train cost, Step 960, Cost 0.209254
Train cost, Step 970, Cost 0.197532
Train cost, Step 980, Cost 0.198264
Train cost, Step 990, Cost 0.206929
Train cost, Step 1000, Cost 0.198488
Test cost, Step 1000, Cost 0.153360
Train cost, Step 1010, Cost 0.135618
Train cost, Step 1020, Cost 0.137078
Train cost, Step 1030, Cost 0.159243
Train cost, Step 1040, Cost 0.175211
Train cost, Step 1050, Cost 0.144821
Train cost, Step 1060, Cost 0.162203
Train cost, Step 1070, Cost 0.197415
Train cost, Step 1080, Cost 0.143427
Train cost, Step 1090, Cost 0.147314
Train cost, Step 1100, Cost 0.142196
Test cost, Step 1100, Cost 0.123154
Train cost, Step 1110, Cost 0.174198
Train cost, Step 1120, Cost 0.201148
Train cost, Step 1130, Cost 0.100205
Train cost, Step 1140, Cost 0.111531
Train cost, Step 1150, Cost 0.096112
Train cost, Step 1160, Cost 0.121104
Train cost, Step 1170, Cost 0.111347
Train cost, Step 1180, Cost 0.148391
Train cost, Step 1190, Cost 0.131636
Train cost, Step 1200, Cost 0.152686
Test cost, Step 1200, Cost 0.098063
Train cost, Step 1210, Cost 0.097262
Train cost, Step 1220, Cost 0.111449
Train cost, Step 1230, Cost 0.154582
Train cost, Step 1240, Cost 0.111647
Train cost, Step 1250, Cost 0.122552
Train cost, Step 1260, Cost 0.113828
Train cost, Step 1270, Cost 0.094590
Train cost, Step 1280, Cost 0.085252
Train cost, Step 1290, Cost 0.095512
Train cost, Step 1300, Cost 0.100617
Test cost, Step 1300, Cost 0.072462
Train cost, Step 1310, Cost 0.106737
Train cost, Step 1320, Cost 0.102805
Train cost, Step 1330, Cost 0.098214
Train cost, Step 1340, Cost 0.078186
Train cost, Step 1350, Cost 0.096731
Train cost, Step 1360, Cost 0.092324
Train cost, Step 1370, Cost 0.096081
Train cost, Step 1380, Cost 0.080706
Train cost, Step 1390, Cost 0.085458
Train cost, Step 1400, Cost 0.066053
Test cost, Step 1400, Cost 0.062014
Train cost, Step 1410, Cost 0.158391
Train cost, Step 1420, Cost 0.096896
Train cost, Step 1430, Cost 0.076681
Train cost, Step 1440, Cost 0.095945
Train cost, Step 1450, Cost 0.061191
Train cost, Step 1460, Cost 0.089816
Train cost, Step 1470, Cost 0.060841
Train cost, Step 1480, Cost 0.075491
Train cost, Step 1490, Cost 0.093773
Train cost, Step 1500, Cost 0.082257
Test cost, Step 1500, Cost 0.054648
Train cost, Step 1510, Cost 0.062478
Train cost, Step 1520, Cost 0.088672
Train cost, Step 1530, Cost 0.098577
Train cost, Step 1540, Cost 0.068808
Train cost, Step 1550, Cost 0.063292
Train cost, Step 1560, Cost 0.046482
Train cost, Step 1570, Cost 0.072079
Train cost, Step 1580, Cost 0.066417
Train cost, Step 1590, Cost 0.056396
Train cost, Step 1600, Cost 0.066187
Test cost, Step 1600, Cost 0.043654
Train cost, Step 1610, Cost 0.064506
Train cost, Step 1620, Cost 0.057062
Train cost, Step 1630, Cost 0.058497
Train cost, Step 1640, Cost 0.049261
Train cost, Step 1650, Cost 0.045824
Train cost, Step 1660, Cost 0.070338
Train cost, Step 1670, Cost 0.068806
Train cost, Step 1680, Cost 0.053915
Train cost, Step 1690, Cost 0.051097
Train cost, Step 1700, Cost 0.068100
Test cost, Step 1700, Cost 0.039543
Train cost, Step 1710, Cost 0.049441
Train cost, Step 1720, Cost 0.051990
Train cost, Step 1730, Cost 0.050742
Train cost, Step 1740, Cost 0.069195
Train cost, Step 1750, Cost 0.058446
Train cost, Step 1760, Cost 0.057564
Train cost, Step 1770, Cost 0.071641
Train cost, Step 1780, Cost 0.052876
Train cost, Step 1790, Cost 0.052155
Train cost, Step 1800, Cost 0.082488
Test cost, Step 1800, Cost 0.034690
Train cost, Step 1810, Cost 0.043171
Train cost, Step 1820, Cost 0.051249
Train cost, Step 1830, Cost 0.042519
Train cost, Step 1840, Cost 0.054430
Train cost, Step 1850, Cost 0.060285
Train cost, Step 1860, Cost 0.054826
Train cost, Step 1870, Cost 0.057194
Train cost, Step 1880, Cost 0.055687
Train cost, Step 1890, Cost 0.071762
Train cost, Step 1900, Cost 0.058110
Test cost, Step 1900, Cost 0.031468
Train cost, Step 1910, Cost 0.044074
Train cost, Step 1920, Cost 0.040255
Train cost, Step 1930, Cost 0.046137
Train cost, Step 1940, Cost 0.050012
Train cost, Step 1950, Cost 0.033987
Train cost, Step 1960, Cost 0.045765
Train cost, Step 1970, Cost 0.048595
Train cost, Step 1980, Cost 0.038905
Train cost, Step 1990, Cost 0.047263
Train cost, Step 2000, Cost 0.033901
Test cost, Step 2000, Cost 0.029656
Train cost, Step 2010, Cost 0.030197
Train cost, Step 2020, Cost 0.036784
Train cost, Step 2030, Cost 0.041115
Train cost, Step 2040, Cost 0.053284
Train cost, Step 2050, Cost 0.040142
Train cost, Step 2060, Cost 0.037007
Train cost, Step 2070, Cost 0.045154
Train cost, Step 2080, Cost 0.027582
Train cost, Step 2090, Cost 0.035088
Train cost, Step 2100, Cost 0.034761
Test cost, Step 2100, Cost 0.027043
Train cost, Step 2110, Cost 0.030808
Train cost, Step 2120, Cost 0.027776
Train cost, Step 2130, Cost 0.037169
Train cost, Step 2140, Cost 0.066838
Train cost, Step 2150, Cost 0.032508
Train cost, Step 2160, Cost 0.033583
Train cost, Step 2170, Cost 0.042142
Train cost, Step 2180, Cost 0.038644
Train cost, Step 2190, Cost 0.037633
Train cost, Step 2200, Cost 0.033084
Test cost, Step 2200, Cost 0.025096
Train cost, Step 2210, Cost 0.039980
Train cost, Step 2220, Cost 0.029732
Train cost, Step 2230, Cost 0.050501
Train cost, Step 2240, Cost 0.041677
Train cost, Step 2250, Cost 0.023526
Train cost, Step 2260, Cost 0.024553
Train cost, Step 2270, Cost 0.036005
Train cost, Step 2280, Cost 0.032678
Train cost, Step 2290, Cost 0.032366
Train cost, Step 2300, Cost 0.023447
Test cost, Step 2300, Cost 0.023911
Train cost, Step 2310, Cost 0.030922
Train cost, Step 2320, Cost 0.028721
Train cost, Step 2330, Cost 0.034513
Train cost, Step 2340, Cost 0.026559
Train cost, Step 2350, Cost 0.043622
Train cost, Step 2360, Cost 0.040104
Train cost, Step 2370, Cost 0.050138
Train cost, Step 2380, Cost 0.029433
Train cost, Step 2390, Cost 0.023240
Train cost, Step 2400, Cost 0.038487
Test cost, Step 2400, Cost 0.025467
Train cost, Step 2410, Cost 0.015854
Train cost, Step 2420, Cost 0.039864
Train cost, Step 2430, Cost 0.033333
Train cost, Step 2440, Cost 0.039830
Train cost, Step 2450, Cost 0.025698
Train cost, Step 2460, Cost 0.023239
Train cost, Step 2470, Cost 0.017733
Train cost, Step 2480, Cost 0.029231
Train cost, Step 2490, Cost 0.036119
Train cost, Step 2500, Cost 0.024184
Test cost, Step 2500, Cost 0.023387
Train cost, Step 2510, Cost 0.033727
Train cost, Step 2520, Cost 0.036388
Train cost, Step 2530, Cost 0.025946
Train cost, Step 2540, Cost 0.039988
Train cost, Step 2550, Cost 0.039029
Train cost, Step 2560, Cost 0.034118
Train cost, Step 2570, Cost 0.037878
Train cost, Step 2580, Cost 0.019043
Train cost, Step 2590, Cost 0.014606
Train cost, Step 2600, Cost 0.017237
Test cost, Step 2600, Cost 0.024894
Train cost, Step 2610, Cost 0.032909
Train cost, Step 2620, Cost 0.046552
Train cost, Step 2630, Cost 0.019126
Train cost, Step 2640, Cost 0.027269
Train cost, Step 2650, Cost 0.023830
Train cost, Step 2660, Cost 0.038029
Train cost, Step 2670, Cost 0.029304
Train cost, Step 2680, Cost 0.023559
Train cost, Step 2690, Cost 0.037625
Train cost, Step 2700, Cost 0.028565
Test cost, Step 2700, Cost 0.024941
Train cost, Step 2710, Cost 0.031061
Train cost, Step 2720, Cost 0.025516
Train cost, Step 2730, Cost 0.034017
Train cost, Step 2740, Cost 0.045984
Train cost, Step 2750, Cost 0.034554
Train cost, Step 2760, Cost 0.023234
Train cost, Step 2770, Cost 0.021406
Train cost, Step 2780, Cost 0.064184
Train cost, Step 2790, Cost 0.043807
Train cost, Step 2800, Cost 0.037964
Test cost, Step 2800, Cost 0.024270
Train cost, Step 2810, Cost 0.025262
Train cost, Step 2820, Cost 0.020918
Train cost, Step 2830, Cost 0.025071
Train cost, Step 2840, Cost 0.032951
Train cost, Step 2850, Cost 0.034100
Train cost, Step 2860, Cost 0.035140
Train cost, Step 2870, Cost 0.026192
Train cost, Step 2880, Cost 0.014990
Train cost, Step 2890, Cost 0.049053
Train cost, Step 2900, Cost 0.026420
Test cost, Step 2900, Cost 0.026627
Train cost, Step 2910, Cost 0.034137
Train cost, Step 2920, Cost 0.032673
Train cost, Step 2930, Cost 0.024193
Train cost, Step 2940, Cost 0.031714
Train cost, Step 2950, Cost 0.024828
Train cost, Step 2960, Cost 0.025747
Train cost, Step 2970, Cost 0.021495
Train cost, Step 2980, Cost 0.017658
Train cost, Step 2990, Cost 0.025132
Train cost, Step 3000, Cost 0.017609
Test cost, Step 3000, Cost 0.024128
Train cost, Step 3010, Cost 0.028900
Train cost, Step 3020, Cost 0.016700
Train cost, Step 3030, Cost 0.026613
Train cost, Step 3040, Cost 0.019744
Train cost, Step 3050, Cost 0.045507
Train cost, Step 3060, Cost 0.026242
Train cost, Step 3070, Cost 0.022545
Train cost, Step 3080, Cost 0.035223
Train cost, Step 3090, Cost 0.022581
Train cost, Step 3100, Cost 0.024111
Test cost, Step 3100, Cost 0.025594
Train cost, Step 3110, Cost 0.034491
Train cost, Step 3120, Cost 0.014520
Train cost, Step 3130, Cost 0.015593
Train cost, Step 3140, Cost 0.023285
Train cost, Step 3150, Cost 0.012638
Train cost, Step 3160, Cost 0.022198
Train cost, Step 3170, Cost 0.019563
Train cost, Step 3180, Cost 0.041903
Train cost, Step 3190, Cost 0.023916
Train cost, Step 3200, Cost 0.021095
Test cost, Step 3200, Cost 0.028093
Train cost, Step 3210, Cost 0.040792
Train cost, Step 3220, Cost 0.029057
Train cost, Step 3230, Cost 0.020520
Train cost, Step 3240, Cost 0.022582
Train cost, Step 3250, Cost 0.017649
Train cost, Step 3260, Cost 0.061936
Train cost, Step 3270, Cost 0.020003
Train cost, Step 3280, Cost 0.020403
Train cost, Step 3290, Cost 0.024036
Train cost, Step 3300, Cost 0.037860
Test cost, Step 3300, Cost 0.026783
Train cost, Step 3310, Cost 0.029722
Train cost, Step 3320, Cost 0.022559
Train cost, Step 3330, Cost 0.032497
Train cost, Step 3340, Cost 0.027590
Train cost, Step 3350, Cost 0.030427
Train cost, Step 3360, Cost 0.037586
Train cost, Step 3370, Cost 0.027731
Train cost, Step 3380, Cost 0.018294
Train cost, Step 3390, Cost 0.032997
Train cost, Step 3400, Cost 0.025673
Test cost, Step 3400, Cost 0.026371
Train cost, Step 3410, Cost 0.020853
Train cost, Step 3420, Cost 0.015484
Train cost, Step 3430, Cost 0.025746
Train cost, Step 3440, Cost 0.030819
Train cost, Step 3450, Cost 0.027891
Train cost, Step 3460, Cost 0.031681
Train cost, Step 3470, Cost 0.023304
Train cost, Step 3480, Cost 0.021340
Train cost, Step 3490, Cost 0.015192
Train cost, Step 3500, Cost 0.030663
Test cost, Step 3500, Cost 0.025029
Train cost, Step 3510, Cost 0.032962
Train cost, Step 3520, Cost 0.020400
Train cost, Step 3530, Cost 0.029337
Train cost, Step 3540, Cost 0.014134
Train cost, Step 3550, Cost 0.040396
Train cost, Step 3560, Cost 0.021215
Train cost, Step 3570, Cost 0.017495
Train cost, Step 3580, Cost 0.024108
Train cost, Step 3590, Cost 0.010949
Train cost, Step 3600, Cost 0.012924
Test cost, Step 3600, Cost 0.028458
Train cost, Step 3610, Cost 0.026737
Train cost, Step 3620, Cost 0.019719
Train cost, Step 3630, Cost 0.015369
Train cost, Step 3640, Cost 0.028902
Train cost, Step 3650, Cost 0.039268
Train cost, Step 3660, Cost 0.017939
Train cost, Step 3670, Cost 0.036227
Train cost, Step 3680, Cost 0.012675
Train cost, Step 3690, Cost 0.018152
Train cost, Step 3700, Cost 0.024145
Test cost, Step 3700, Cost 0.026889
Train cost, Step 3710, Cost 0.025453
Train cost, Step 3720, Cost 0.032178
Train cost, Step 3730, Cost 0.023277
Train cost, Step 3740, Cost 0.038356
Train cost, Step 3750, Cost 0.018018
Train cost, Step 3760, Cost 0.011010
Train cost, Step 3770, Cost 0.023033
Train cost, Step 3780, Cost 0.045767
Train cost, Step 3790, Cost 0.012840
Train cost, Step 3800, Cost 0.019810
Test cost, Step 3800, Cost 0.025651
Train cost, Step 3810, Cost 0.034081
Train cost, Step 3820, Cost 0.041910
Train cost, Step 3830, Cost 0.037905
Train cost, Step 3840, Cost 0.025698
Train cost, Step 3850, Cost 0.039071
Train cost, Step 3860, Cost 0.019030
Train cost, Step 3870, Cost 0.034477
Train cost, Step 3880, Cost 0.024240
Train cost, Step 3890, Cost 0.015157
Train cost, Step 3900, Cost 0.040621
Test cost, Step 3900, Cost 0.025927
Train cost, Step 3910, Cost 0.021204
Train cost, Step 3920, Cost 0.015538
Train cost, Step 3930, Cost 0.023660
Train cost, Step 3940, Cost 0.014193
Train cost, Step 3950, Cost 0.013150
Train cost, Step 3960, Cost 0.017309
Train cost, Step 3970, Cost 0.016231
Train cost, Step 3980, Cost 0.037976
Train cost, Step 3990, Cost 0.035511
Train cost, Step 4000, Cost 0.043626
Test cost, Step 4000, Cost 0.027540
Train cost, Step 4010, Cost 0.028400
Train cost, Step 4020, Cost 0.031141
Train cost, Step 4030, Cost 0.044042
Train cost, Step 4040, Cost 0.045470
Train cost, Step 4050, Cost 0.035228
Train cost, Step 4060, Cost 0.015033
Train cost, Step 4070, Cost 0.014766
Train cost, Step 4080, Cost 0.042850
Train cost, Step 4090, Cost 0.034066
Train cost, Step 4100, Cost 0.016928
Test cost, Step 4100, Cost 0.026744
Train cost, Step 4110, Cost 0.023494
Train cost, Step 4120, Cost 0.045882
Train cost, Step 4130, Cost 0.026959
Train cost, Step 4140, Cost 0.015221
Train cost, Step 4150, Cost 0.030598
Train cost, Step 4160, Cost 0.028655
Train cost, Step 4170, Cost 0.016936
Train cost, Step 4180, Cost 0.026485
Train cost, Step 4190, Cost 0.017895

查看inference_model中的权值与偏执值、学习率

In[17]

!ls /home/aistudio/inference_model
fc_0.b_0  fc_0.w_0  learning_rate_0

设定预测程序 类似于 trainer.train，预测器需要一个预测程序来做预测。我们可以稍加修改我们的训练程序来把预测值包含进来。

In[18]

def inference_program():
x = fluid.layers.data(name='x', shape=[1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=1, act=None)
return y_predict

预测 预测器会从params_dirname中读取已经训练好的模型，来对从未遇见过的数据进行预测。 tensor_x:生成batch_size个[0,1]区间的随机数，以 tensor 的格式储存 results：预测对应 tensor_x 有机质含量的氮含量 raw_x:由于数据处理时我们做了归一化操作，为了更直观的判断预测是否准确，将数据进行反归一化，得到随机数对应的原始数据。

In[19]

inferencer = Inferencer(
infer_func=inference_program, param_path=params_dirname, place=place)

batch_size = 2
tensor_x = np.random.uniform(0, 1, [batch_size, 1]).astype("float32")

results = inferencer.infer({'x': tensor_x})
raw_x = tensor_x*(maximums[i]-minimums[i])+avgs[i]
print("有机质含量:",raw_x)
print("含氮量: ", results[0])
有机质含量: [[79.4205  ]
[47.303215]]
含氮量:  [[3.8053818]
[2.2940538]]

根据线性模型的原理，计算a和b的值

  • 数学原理：已知两点求直线方程

In[20]

a = (results[0][0][0] - results[0][1][0]) / (raw_x[0][0]-raw_x[1][0])
b = (results[0][0][0] - a * raw_x[0][0])
print(a,b)
0.047056526 0.068128824

绘制拟合图像 通过训练，本次线性回归模型输出了一条拟合的直线，想要直观的判断模型好坏可将拟合直线与数据的图像绘制出来。

In[21]

import numpy as np
import matplotlib.pyplot as plt
import sys          #python2  如果要运行在python3需要将这三句话注释
reload(sys)         #python2
sys.setdefaultencoding('utf-8')  #python2

from matplotlib import rc
rc('font',**{'family':'sans-serif','sans-serif':['AR PL KaitiM GB']})

data = np.loadtxt('/home/aistudio/data/data2054/data_soil.txt',delimiter = ',')

def plot_data(data):
x = data[:,0]
y = data[:,1]
y_predict = x*a + b
plt.scatter(x,y,marker='.',c='r',label='True')
plt.title('Organic matter nitrogen content')#有机质氮含量
plt.xlabel('Organic matter content')#有机质含量
plt.ylabel('Nitrogen content')#氮含量
plt.xlim(0,90)
plt.ylim(0,4)
predict = plt.plot(x,y_predict,label='Predict')
plt.legend(loc='upper left')
plt.savefig('result1.png')
plt.show()

plot_data(data)

总结 通过这个练习我们应该记住： 机器学习的典型过程： 获取数据 数据预处理 -训练模型 -应用模型 fluid训练模型的基本步骤: 配置网络结构： 定义成本函数avg_cost 定义优化器optimizer 获取训练数据 定义运算场所(place)和执行器(exe) 提供数据(feeder) 执行训练(exe.run) 预测infer()并输出拟合图像 练习中的许多参数可以作调整，例如修改学习率会对模型结果产生很大影响，大家可以在本练习或者后面的练习中多做些尝试。 至此线性回归模型的训练工作完成，希望通过本次课程的学习，读者可以利用提供的代码完成一个简单的房价预测模型。通过这一过程，初步了解PaddlePaddle这一易学易用的分布式平台。 本节课作为PaddlePaddle的快速入门章节，希望可以开启您的下一步深度学习之门。

>> 访问 PaddlePaddle 官网，了解更多相关内容。

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