用PaddlePaddle預測土壤含氮量

PaddlePaddle實現線性迴歸

在本次實驗中咱們將使用PaddlePaddle來搭建一個簡單的線性迴歸模型，並利用這一模型實現已知某地給定有機質含量對土壤的含氮量進行預測。而且在學習模型搭建的過程當中，瞭解到機器學習的若干重要概念，掌握一個機器學習預測的基本流程。

** 線性迴歸的基本概念 **

線性迴歸是機器學習中最簡單也是最重要的模型之一，其模型創建遵循此流程：獲取數據、數據預處理、訓練模型、應用模型。

迴歸模型能夠理解爲：存在一個點集，用一條曲線去擬合它分佈的過程。若是擬合曲線是一條直線，則稱爲線性迴歸。若是是一條二次曲線，則被稱爲二次迴歸。線性迴歸是迴歸模型中最簡單的一種。

在線性迴歸中有幾個基本的概念須要掌握：

• 假設函數（Hypothesis Function）
• 損失函數（Loss Function）
• 優化算法（Optimization Algorithm）

假設函數：

假設函數是指，用數學的方法描述自變量和因變量之間的關係，它們之間能夠是一個線性函數或非線性函數。 在本次線性回顧模型中，咱們的假設函數爲 $ \hat{Y}= aX_1+b $ ，其中，Y^\hat{Y}​Y​^​​表示模型的預測結果（預測房價），用來和真實的Y區分。模型要學習的參數即：a,b。

損失函數：

損失函數是指，用數學的方法衡量假設函數預測結果與真實值之間的偏差。這個差距越小預測越準確，而算法的任務就是使這個差距愈來愈小。

創建模型後，咱們須要給模型一個優化目標，使得學到的參數可以讓預測值Y^\hat{Y}​Y​^​​儘量地接近真實值Y。輸入任意一個數據樣本的目標值yiy_iy​i​​和模型給出的預測值Yi^\hat{Y_i}​Y​i​​​^​​，損失函數輸出一個非負的實值。這個實值一般用來反映模型偏差的大小。

對於線性模型來說，最經常使用的損失函數就是均方偏差（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​​

即對於一個大小爲n的測試集，MSE是n個數據預測結果偏差平方的均值。

優化算法：

在模型訓練中優化算法也是相當重要的，它決定了一個模型的精度和運算速度。本章的線性迴歸實例中主要使用了梯度降低法進行優化。

如今，讓咱們正式進入實驗吧！

1 - 引用庫

首先載入須要用到的庫，它們分別是：

• numpy：一個python的基本庫，用於科學計算
• matplotlib.pyplot：用於生成圖，在驗證模型準確率和展現成本變化趨勢時會使用到
• paddle.fluid：PaddlePaddle其中一種深度學習框架
• pandas:一種基於NumPy的工具，高效處理數據

第一步，先運行!ls /home/aistudio/data/代碼看本身的數據集在哪，如個人爲data2054，在data2054裏面有data_soil.txt

In[2]

!ls /home/aistudio/data/

data2054

In[3]

!ls /home/aistudio/data/data2054

data_soil.txt

安裝pandas(通常狀況已經存在)

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)

添加須要的庫

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 *

查看當前環境中的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 - 數據預處理 本次數據集使用的是某地區的土壤有機質含量和氮含量的數據。氮含量和有機質含量近似成線性關係，數據集只有兩列，以TXT的形式儲存。本次預測要獲得的他們之間的線性關係。 當真實數據被收集到後，它們每每不能直接使用，須要進行預處理。咱們首先以表格的形式輸出數據的前五行看一下

In[7]

colnames = ['有機質含量']+['氮含量']
print_data = pd.read_csv('/home/aistudio/data/data2054/data_soil.txt',names = colnames)
print_data.head()

	有機質含量	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

若是不肯定某個組件下有哪些方法或者屬性, 能夠嘗試使用.以後按下tab鍵. 這個tab鍵也能夠提示方法或函數須要的參數

歸一化 觀察一下數據的分佈特徵，通常而言，若是樣本有多個屬性，那麼各維屬性的取值範圍差別會很大，這就要用到一個常見的操做-歸一化（normalization）了。歸一化的目標是把各維屬性的取值範圍放縮到差很少的區間，例如[-0.5, 0.5]。這裏咱們使用一種很常見的操做方法：減掉均值，而後除以原取值範圍。

In[8]

# coding = utf-8 #
global x_raw,train_data,test_data
#下載原始數據賦值給data,將data分爲train_data與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))
 
#進行歸一化操做
feature_num = 2
for i in range(feature_num-1):   
    #輸入歸一化的代碼
    #須要自行編程實現
    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

數據集分割 將原始數據處理爲可用數據後，爲了評估模型的好壞，咱們將數據分紅兩份：訓練集和測試集。 訓練集數據用於調整模型的參數，即進行模型的訓練，模型在這份數據集上的偏差被稱爲訓練偏差； 測試集數據被用來測試，模型在這份數據集上的偏差被稱爲測試偏差。 咱們訓練模型的目的是爲了經過從訓練數據中找到規律來預測未知的新數據，因此測試偏差是更能反映模型表現的指標。分割數據的比例要考慮到兩個因素：更多的訓練數據會下降參數估計的方差，從而獲得更可信的模型；而更多的測試數據會下降測試偏差的方差，從而獲得更可信的測試偏差。咱們這個例子中設置的分割比例爲8:2。 定義reader 構造read_data()函數，來讀取訓練數據集train_set或者測試數據集test_set。它的具體實現是在read_data()函數內部構造一個reader()，使用yield關鍵字來讓reader()成爲一個Generator（生成器），注意，yield關鍵字的做用和使用方法相似return關鍵字，不一樣之處在於yield關鍵字能夠構造生成器（Generator）。雖然咱們能夠直接建立一個包含全部數據的列表，可是因爲內存限制，咱們不可能建立一個無限大的或者巨大的列表，而且不少時候在建立了一個百萬數量級別的列表以後，咱們卻只須要用到開頭的幾個或幾十個數據，這樣形成了極大的浪費，而生成器的工做方式是在每次循環時計算下一個值，不斷推算出後續的元素，不會建立完整的數據集列表，從而節約了內存使用。

In[9]

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

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

#能夠把分割結果打印出來進行觀察
print(len(data))
print(len(train_data))


def read_data(data_set):
    def reader():
        for data in data_set:
            yield data[:-1],data[-1:]      #這裏是執行迭代，從後往前數的話，最後一個位置爲-1                  
    return reader

"""獲取訓練數據集和測試數據集

    定義一個reader來獲取訓練數據集及其標籤
    Args：
    Return：
        read_data -- 用於獲取訓練數據集及其標籤的reader"""

def train():

    global train_data
    return read_data(train_data)

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

524
419

設置訓練參數 同窗們能夠試着調整一下參數值，看會有什麼變化。 關於參數的解釋以下： paddle.reader.shuffle(train(), buf_size=400)表示trainer從train()這個reader中讀取了buf_size=400大小的數據並打亂順序 paddle.batch(reader(), batch_size=BATCH_SIZE)表示從打亂的數據中再取出BATCH_SIZE=20大小的數據進行一次迭代訓練

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)

將設計完成的網絡參數寫入 train_program() 函數，便於訓練時調用 相關函數定義能夠查找paddlepaddle的使用文檔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

損失函數定義肯定後，須要定義參數優化方法。

In[12]

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

定義運算場所 首先進行最基本的運算場所定義，在 fluid 中使用 place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 來進行初始化： place 表示fluid program的執行設備，常見的有 fluid.CUDAPlace(0) 和 fluid.CPUPlace() use_cuda = False 表示不使用 GPU 進行加速訓練

In[13]

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

建立訓練器 建立訓練器時須要提供3個主要信息： 一個配置好的網絡拓撲結構 訓練的硬件場所 具體的優化方法

In[14]

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

其它配置 feed_order=['x', 'y'] 是數據層名稱和數組索引的映射，用於定義數據的讀取順序。 params_dirname用於定義模型保存路徑。 最後定義事件處理器 event_handler_plot(event) 用於打印訓練過程

In[15]

feed_order=['x', 'y']
# Specify the directory to save the parameters
import os
params_dirname = "/home/aistudio/inference_model"
 # 若是保存路徑不存在就建立
if not os.path.exists(params_dirname):
    os.makedirs(params_dirname)
import shutil
shutil.rmtree(params_dirname)    #遞歸刪除文件夾

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):#每步觸發事件
        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):#每次迭代觸發事件
        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)

開始訓練 咱們如今能夠經過調用trainer.train()來開始訓練 關於參數的解釋以下： 參數feed_order用到了以前定義的feed_order索引，將數據層x和y按順序輸入trainer，也就是數據的來源。 參數event_handler是事件管理機制，讀者能夠自定義event_handler，根據事件信息做相應的操做。 參數num_epochs=100表示迭代訓練100次後中止訓練。

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中的權值與偏執值、學習率

In[17]

!ls /home/aistudio/inference_model

fc_0.b_0  fc_0.w_0  learning_rate_0

設定預測程序 相似於 trainer.train，預測器須要一個預測程序來作預測。咱們能夠稍加修改咱們的訓練程序來把預測值包含進來。

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

預測 預測器會從params_dirname中讀取已經訓練好的模型，來對從未碰見過的數據進行預測。 tensor_x:生成batch_size個[0,1]區間的隨機數，以 tensor 的格式儲存 results：預測對應 tensor_x 有機質含量的氮含量 raw_x:因爲數據處理時咱們作了歸一化操做，爲了更直觀的判斷預測是否準確，將數據進行反歸一化，獲得隨機數對應的原始數據。

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("有機質含量:",raw_x)
print("含氮量: ", results[0])

有機質含量: [[79.4205  ]
 [47.303215]]
含氮量:  [[3.8053818]
 [2.2940538]]

根據線性模型的原理，計算a和b的值

• 數學原理：已知兩點求直線方程

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

繪製擬合圖像 經過訓練，本次線性迴歸模型輸出了一條擬合的直線，想要直觀的判斷模型好壞可將擬合直線與數據的圖像繪製出來。

In[21]

import numpy as np
import matplotlib.pyplot as plt
import sys          #python2  若是要運行在python3須要將這三句話註釋
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')#有機質氮含量
    plt.xlabel('Organic matter content')#有機質含量
    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)

總結 經過這個練習咱們應該記住： 機器學習的典型過程： 獲取數據 數據預處理 -訓練模型 -應用模型 fluid訓練模型的基本步驟: 配置網絡結構： 定義成本函數avg_cost 定義優化器optimizer 獲取訓練數據 定義運算場所(place)和執行器(exe) 提供數據(feeder) 執行訓練(exe.run) 預測infer()並輸出擬合圖像 練習中的許多參數能夠做調整，例如修改學習率會對模型結果產生很大影響，你們能夠在本練習或者後面的練習中多作些嘗試。 至此線性迴歸模型的訓練工做完成，但願經過本次課程的學習，讀者能夠利用提供的代碼完成一個簡單的房價預測模型。經過這一過程，初步瞭解PaddlePaddle這一易學易用的分佈式平臺。 本節課做爲PaddlePaddle的快速入門章節，但願能夠開啓您的下一步深度學習之門。

>> 訪問 PaddlePaddle 官網，瞭解更多相關內容。