模型构建

1.示例代码linear_regression_model.py

#!/usr/bin/python

# -*- coding: utf-8 -*

import tensorflow as tf

import numpy as np

class linearRegressionModel:

  def __init__(self,x_dimen):

    self.x_dimen = x_dimen

    self._index_in_epoch = 0

    self.constructModel()

    self.sess = tf.Session()

    self.sess.run(tf.global_variables_initializer())

  #权重初始化

  def weight_variable(self,shape):

    initial = tf.truncated_normal(shape,stddev = 0.1)

    return tf.Variable(initial)

  #偏置项初始化

  def bias_variable(self,shape):

    initial = tf.constant(0.1,shape = shape)

    return tf.Variable(initial)

  #每次选取100个样本，如果选完，重新打乱

  def next_batch(self,batch_size):

    start = self._index_in_epoch

    self._index_in_epoch += batch_size

    if self._index_in_epoch > self._num_datas:

        perm = np.arange(self._num_datas)

        np.random.shuffle(perm)

        self._datas = self._datas[perm]

        self._labels = self._labels[perm]

        start = 0

        self._index_in_epoch = batch_size

        assert batch_size <= self._num_datas

    end = self._index_in_epoch

    return self._datas[start:end],self._labels[start:end]

  def constructModel(self):

    self.x = tf.placeholder(tf.float32, [None,self.x_dimen])

    self.y = tf.placeholder(tf.float32,[None,1])

    self.w = self.weight_variable([self.x_dimen,1])

    self.b = self.bias_variable([1])

    self.y_prec = tf.nn.bias_add(tf.matmul(self.x, self.w), self.b)

    mse = tf.reduce_mean(tf.squared_difference(self.y_prec, self.y))

    l2 = tf.reduce_mean(tf.square(self.w))

    self.loss = mse + 0.15*l2

    self.train_step = tf.train.AdamOptimizer(0.1).minimize(self.loss)

  def train(self,x_train,y_train,x_test,y_test):

    self._datas = x_train

    self._labels = y_train

    self._num_datas = x_train.shape[0]

    for i in range(5000):

        batch = self.next_batch(100)

        self.sess.run(self.train_step,feed_dict={self.x:batch[0],self.y:batch[1]})

        if i%10 == 0:

            train_loss = self.sess.run(self.loss,feed_dict={self.x:batch[0],self.y:batch[1]})

            print('step %d,test_loss %f' % (i,train_loss))

  def predict_batch(self,arr,batch_size):

    for i in range(0,len(arr),batch_size):

        yield arr[i:i + batch_size]

  def predict(self, x_predict):

    pred_list = []

    for x_test_batch in self.predict_batch(x_predict,100):

      pred = self.sess.run(self.y_prec, {self.x:x_test_batch})

      pred_list.append(pred)

    return np.vstack(pred_list)

2.创建run.py

#!/usr/bin/python

# -*- coding: utf-8 -*

from sklearn.model_selection import train_test_split

from sklearn.metrics import r2_score

from sklearn.datasets import make_regression

from sklearn.linear_model import LinearRegression

from linear_regression_model import linearRegressionModel as lrm

if __name__ == '__main__':

    x, y = make_regression(7000)

    x_train,x_test,y_train, y_test = train_test_split(x, y, test_size=0.5)

    y_lrm_train = y_train.reshape(-1, 1)

    y_lrm_test = y_test.reshape(-1, 1)

    linear = lrm(x.shape[1])

    linear.train(x_train, y_lrm_train,x_test,y_lrm_test)

    y_predict = linear.predict(x_test)

    print("Tensorflow R2: ", r2_score(y_predict.ravel(), y_lrm_test.ravel()))

    lr = LinearRegression()

    y_predict = lr.fit(x_train, y_train).predict(x_test)

    print("Sklearn R2: ", r2_score(y_predict, y_test)) #采用r2_score评分函数

运行结果：

step 0,test_loss 27078.781250

step 10,test_loss 29246.253906

step 20,test_loss 21168.052734

step 30,test_loss 22109.154297

step 40,test_loss 28030.435547

step 50,test_loss 24265.765625

step 60,test_loss 28433.816406

step 70,test_loss 24395.164062

step 80,test_loss 19135.515625

step 90,test_loss 20932.734375

step 100,test_loss 17176.033203

step 110,test_loss 19729.275391

step 120,test_loss 18076.587891

step 130,test_loss 24546.722656

step 140,test_loss 22370.619141

step 150,test_loss 17227.343750

step 160,test_loss 21498.363281

step 170,test_loss 17482.292969

step 180,test_loss 16188.901367

step 190,test_loss 17961.816406

step 200,test_loss 15168.850586

step 210,test_loss 14205.447266

step 220,test_loss 15992.610352

step 230,test_loss 12878.104492

step 240,test_loss 15663.670898

step 250,test_loss 11105.211914

step 260,test_loss 11135.759766

step 270,test_loss 12083.872070

step 280,test_loss 9544.156250

step 290,test_loss 12040.689453

step 300,test_loss 8685.537109

step 310,test_loss 11533.030273

step 320,test_loss 11031.776367

step 330,test_loss 11258.272461

step 340,test_loss 9219.499023

step 350,test_loss 7839.248047

step 360,test_loss 9757.743164

step 370,test_loss 7579.228027

step 380,test_loss 8326.705078

step 390,test_loss 8823.761719

step 400,test_loss 8431.373047

step 410,test_loss 8025.544922

step 420,test_loss 7954.462891

step 430,test_loss 9809.444336

step 440,test_loss 5645.476074

step 450,test_loss 7813.232422

step 460,test_loss 6410.347656

step 470,test_loss 6623.901367

step 480,test_loss 7697.770508

step 490,test_loss 5924.088867

step 500,test_loss 5174.365234

step 510,test_loss 5223.140625

step 520,test_loss 5655.796387

step 530,test_loss 4949.434570

step 540,test_loss 4330.499023

step 550,test_loss 5321.663086

step 560,test_loss 4629.940918

step 570,test_loss 3220.557373

step 580,test_loss 4162.278320

step 590,test_loss 4546.246582

step 600,test_loss 4487.117188

step 610,test_loss 5037.617676

step 620,test_loss 3526.248047

step 630,test_loss 3432.793457

step 640,test_loss 3385.915527

step 650,test_loss 3272.809814

step 660,test_loss 2710.681396

step 670,test_loss 3326.879883

step 680,test_loss 3275.361084

step 690,test_loss 2347.117432

step 700,test_loss 2957.036621

step 710,test_loss 1699.123535

step 720,test_loss 2293.731445

step 730,test_loss 2275.772705

step 740,test_loss 2176.456055

step 750,test_loss 2457.974121

step 760,test_loss 2203.473877

step 770,test_loss 1920.002686

step 780,test_loss 2047.632446

step 790,test_loss 1736.505615

step 800,test_loss 2039.262451

step 810,test_loss 2055.947510

step 820,test_loss 1908.234375

step 830,test_loss 1280.326904

step 840,test_loss 1412.927856

step 850,test_loss 1737.114258

step 860,test_loss 1251.464111

step 870,test_loss 1589.670532

step 880,test_loss 1396.735474

step 890,test_loss 1706.040527

step 900,test_loss 1558.866333

step 910,test_loss 1334.543213

step 920,test_loss 1306.657471

step 930,test_loss 942.939819

step 940,test_loss 1200.833008

step 950,test_loss 932.249695

step 960,test_loss 1328.827271

step 970,test_loss 1191.408081

step 980,test_loss 832.388062

step 990,test_loss 1052.487427

step 1000,test_loss 896.287964

step 1010,test_loss 707.095093

step 1020,test_loss 622.292297

step 1030,test_loss 798.665649

step 1040,test_loss 789.424316

step 1050,test_loss 606.861450

step 1060,test_loss 573.976074

step 1070,test_loss 465.951965

step 1080,test_loss 631.956543

step 1090,test_loss 679.685913

step 1100,test_loss 440.278046

step 1110,test_loss 476.793945

step 1120,test_loss 450.453278

step 1130,test_loss 541.740479

step 1140,test_loss 502.860077

step 1150,test_loss 363.825653

step 1160,test_loss 378.313232

step 1170,test_loss 364.206024

step 1180,test_loss 359.042999

step 1190,test_loss 304.770569

step 1200,test_loss 354.092407

step 1210,test_loss 296.288147

step 1220,test_loss 313.082031

step 1230,test_loss 321.331512

step 1240,test_loss 327.985718

step 1250,test_loss 257.409210

step 1260,test_loss 250.276291

step 1270,test_loss 191.458878

step 1280,test_loss 216.972244

step 1290,test_loss 229.754684

step 1300,test_loss 219.731140

step 1310,test_loss 197.320190

step 1320,test_loss 185.500366

step 1330,test_loss 180.765671

step 1340,test_loss 223.783081

step 1350,test_loss 166.295975

step 1360,test_loss 146.334641

step 1370,test_loss 191.004700

step 1380,test_loss 137.425964

step 1390,test_loss 155.957443

step 1400,test_loss 137.031784

step 1410,test_loss 144.765793

step 1420,test_loss 123.946625

step 1430,test_loss 133.717957

step 1440,test_loss 136.200287

step 1450,test_loss 109.962036

step 1460,test_loss 107.478485

step 1470,test_loss 111.343063

step 1480,test_loss 113.355667

step 1490,test_loss 110.620399

step 1500,test_loss 116.955994

step 1510,test_loss 102.297958

step 1520,test_loss 107.474968

step 1530,test_loss 88.769562

step 1540,test_loss 88.092247

step 1550,test_loss 93.228027

step 1560,test_loss 78.206909

step 1570,test_loss 99.623810

step 1580,test_loss 67.202003

step 1590,test_loss 77.569229

step 1600,test_loss 78.516144

step 1610,test_loss 76.165176

step 1620,test_loss 64.493408

step 1630,test_loss 70.672768

step 1640,test_loss 68.577499

step 1650,test_loss 72.143890

step 1660,test_loss 63.308643

step 1670,test_loss 64.004288

step 1680,test_loss 64.626549

step 1690,test_loss 59.137959

step 1700,test_loss 63.122589

step 1710,test_loss 56.314068

step 1720,test_loss 51.382557

step 1730,test_loss 58.105713

step 1740,test_loss 57.619289

step 1750,test_loss 54.326633

step 1760,test_loss 51.271332

step 1770,test_loss 56.553986

step 1780,test_loss 51.459373

step 1790,test_loss 49.371822

step 1800,test_loss 52.714359

step 1810,test_loss 50.442295

step 1820,test_loss 49.796776

step 1830,test_loss 48.404625

step 1840,test_loss 47.714275

step 1850,test_loss 49.141331

step 1860,test_loss 46.075230

step 1870,test_loss 47.250427

step 1880,test_loss 47.220695

step 1890,test_loss 47.975838

step 1900,test_loss 47.080906

step 1910,test_loss 45.991798

step 1920,test_loss 45.940758

step 1930,test_loss 45.241516

step 1940,test_loss 45.457054

step 1950,test_loss 44.415176

step 1960,test_loss 44.690414

step 1970,test_loss 44.910900

step 1980,test_loss 43.690544

step 1990,test_loss 42.880653

step 2000,test_loss 42.956898

step 2010,test_loss 43.080429

step 2020,test_loss 43.176693

step 2030,test_loss 43.030117

step 2040,test_loss 43.170925

step 2050,test_loss 42.681801

step 2060,test_loss 42.610954

step 2070,test_loss 42.576504

step 2080,test_loss 42.255066

step 2090,test_loss 42.081310

step 2100,test_loss 42.341095

step 2110,test_loss 42.025223

step 2120,test_loss 42.204201

step 2130,test_loss 42.335026

step 2140,test_loss 41.973049

step 2150,test_loss 42.003143

step 2160,test_loss 41.904259

step 2170,test_loss 41.881233

step 2180,test_loss 41.608265

step 2190,test_loss 41.525867

step 2200,test_loss 41.472271

step 2210,test_loss 41.472610

step 2220,test_loss 41.598587

step 2230,test_loss 41.459789

step 2240,test_loss 41.376347

step 2250,test_loss 41.300011

step 2260,test_loss 41.316811

step 2270,test_loss 41.432549

step 2280,test_loss 41.290428

step 2290,test_loss 41.279583

step 2300,test_loss 41.197216

step 2310,test_loss 41.269833

step 2320,test_loss 41.240284

step 2330,test_loss 41.202190

step 2340,test_loss 41.211605

step 2350,test_loss 41.224072

step 2360,test_loss 41.169403

step 2370,test_loss 41.151337

step 2380,test_loss 41.162971

step 2390,test_loss 41.127731

step 2400,test_loss 41.094795

step 2410,test_loss 41.089066

step 2420,test_loss 41.137642

step 2430,test_loss 41.085999

step 2440,test_loss 41.096901

step 2450,test_loss 41.096237

step 2460,test_loss 41.072151

step 2470,test_loss 41.094440

step 2480,test_loss 41.049301

step 2490,test_loss 41.062485

step 2500,test_loss 41.053036

step 2510,test_loss 41.042328

step 2520,test_loss 41.049831

step 2530,test_loss 41.078171

step 2540,test_loss 41.013088

step 2550,test_loss 41.039490

step 2560,test_loss 41.040127

step 2570,test_loss 41.047153

step 2580,test_loss 41.059521

step 2590,test_loss 41.067646

step 2600,test_loss 41.027416

step 2610,test_loss 41.019939

step 2620,test_loss 41.030586

step 2630,test_loss 41.028877

step 2640,test_loss 41.027557

step 2650,test_loss 41.026352

step 2660,test_loss 41.023903

step 2670,test_loss 41.006763

step 2680,test_loss 41.024330

step 2690,test_loss 41.046272

step 2700,test_loss 41.018227

step 2710,test_loss 41.016628

step 2720,test_loss 41.025139

step 2730,test_loss 41.019703

step 2740,test_loss 41.016834

step 2750,test_loss 41.033138

step 2760,test_loss 41.031982

step 2770,test_loss 41.027203

step 2780,test_loss 41.036865

step 2790,test_loss 41.039066

step 2800,test_loss 41.015831

step 2810,test_loss 41.021862

step 2820,test_loss 41.037052

step 2830,test_loss 41.030590

step 2840,test_loss 41.026188

step 2850,test_loss 41.019707

step 2860,test_loss 41.021141

step 2870,test_loss 41.019894

step 2880,test_loss 41.020607

step 2890,test_loss 41.024086

step 2900,test_loss 41.037041

step 2910,test_loss 41.023495

step 2920,test_loss 41.011646

step 2930,test_loss 41.022732

step 2940,test_loss 41.017460

step 2950,test_loss 41.042557

step 2960,test_loss 41.025982

step 2970,test_loss 41.023857

step 2980,test_loss 41.029766

step 2990,test_loss 41.021320

step 3000,test_loss 41.036278

step 3010,test_loss 41.026100

step 3020,test_loss 41.029068

step 3030,test_loss 41.007935

step 3040,test_loss 41.024139

step 3050,test_loss 41.023842

step 3060,test_loss 41.023033

step 3070,test_loss 41.041313

step 3080,test_loss 41.013794

step 3090,test_loss 41.021595

step 3100,test_loss 41.023506

step 3110,test_loss 41.027863

step 3120,test_loss 41.049881

step 3130,test_loss 41.037209

step 3140,test_loss 41.013416

step 3150,test_loss 41.044666

step 3160,test_loss 41.022858

step 3170,test_loss 41.026386

step 3180,test_loss 41.025173

step 3190,test_loss 41.025276

step 3200,test_loss 41.031715

step 3210,test_loss 41.019821

step 3220,test_loss 41.023750

step 3230,test_loss 41.026768

step 3240,test_loss 41.025543

step 3250,test_loss 41.030800

step 3260,test_loss 41.032837

step 3270,test_loss 41.020596

step 3280,test_loss 41.024185

step 3290,test_loss 41.014019

step 3300,test_loss 41.017628

step 3310,test_loss 41.039688

step 3320,test_loss 41.036552

step 3330,test_loss 41.041679

step 3340,test_loss 41.010323

step 3350,test_loss 41.019321

step 3360,test_loss 41.003582

step 3370,test_loss 41.039524

step 3380,test_loss 41.041386

step 3390,test_loss 41.014439

step 3400,test_loss 41.031914

step 3410,test_loss 41.047981

step 3420,test_loss 41.020836

step 3430,test_loss 41.035324

step 3440,test_loss 41.021690

step 3450,test_loss 41.026123

step 3460,test_loss 41.029877

step 3470,test_loss 41.027092

step 3480,test_loss 41.027649

step 3490,test_loss 41.023071

step 3500,test_loss 41.027126

step 3510,test_loss 41.018978

step 3520,test_loss 41.030590

step 3530,test_loss 41.026154

step 3540,test_loss 41.021610

step 3550,test_loss 41.014198

step 3560,test_loss 41.032345

step 3570,test_loss 41.030876

step 3580,test_loss 41.013630

step 3590,test_loss 41.025135

step 3600,test_loss 41.035576

step 3610,test_loss 41.018707

step 3620,test_loss 41.019424

step 3630,test_loss 41.028542

step 3640,test_loss 41.039867

step 3650,test_loss 41.014717

step 3660,test_loss 41.035339

step 3670,test_loss 41.031448

step 3680,test_loss 41.016773

step 3690,test_loss 41.025093

step 3700,test_loss 41.030968

step 3710,test_loss 41.027367

step 3720,test_loss 41.039196

step 3730,test_loss 41.024532

step 3740,test_loss 41.039036

step 3750,test_loss 41.003342

step 3760,test_loss 41.035763

step 3770,test_loss 41.035271

step 3780,test_loss 41.009220

step 3790,test_loss 41.030884

step 3800,test_loss 41.029705

step 3810,test_loss 41.029217

step 3820,test_loss 41.028343

step 3830,test_loss 41.020901

step 3840,test_loss 41.039314

step 3850,test_loss 41.045189

step 3860,test_loss 41.028725

step 3870,test_loss 41.026402

step 3880,test_loss 41.014465

step 3890,test_loss 41.027691

step 3900,test_loss 41.027061

step 3910,test_loss 41.023037

step 3920,test_loss 41.028137

step 3930,test_loss 41.035686

step 3940,test_loss 41.021793

step 3950,test_loss 41.014446

step 3960,test_loss 41.018074

step 3970,test_loss 41.037655

step 3980,test_loss 41.019314

step 3990,test_loss 41.022900

step 4000,test_loss 41.026077

step 4010,test_loss 41.035042

step 4020,test_loss 41.022713

step 4030,test_loss 41.029526

step 4040,test_loss 41.026649

step 4050,test_loss 41.033508

step 4060,test_loss 41.028713

step 4070,test_loss 41.031872

step 4080,test_loss 41.017612

step 4090,test_loss 41.031342

step 4100,test_loss 41.024128

step 4110,test_loss 41.021511

step 4120,test_loss 41.028091

step 4130,test_loss 41.025402

step 4140,test_loss 41.028831

step 4150,test_loss 41.025154

step 4160,test_loss 41.028797

step 4170,test_loss 41.023502

step 4180,test_loss 41.023289

step 4190,test_loss 41.026257

step 4200,test_loss 41.023941

step 4210,test_loss 41.017677

step 4220,test_loss 41.018219

step 4230,test_loss 41.021465

step 4240,test_loss 41.022671

step 4250,test_loss 41.035088

step 4260,test_loss 41.028889

step 4270,test_loss 41.015503

step 4280,test_loss 41.011471

step 4290,test_loss 41.034992

step 4300,test_loss 41.024700

step 4310,test_loss 41.021152

step 4320,test_loss 41.033760

step 4330,test_loss 41.022285

step 4340,test_loss 41.023975

step 4350,test_loss 41.047928

step 4360,test_loss 41.040417

step 4370,test_loss 41.015713

step 4380,test_loss 41.021191

step 4390,test_loss 41.028423

step 4400,test_loss 41.046730

step 4410,test_loss 41.019470

step 4420,test_loss 41.023933

step 4430,test_loss 41.023426

step 4440,test_loss 41.044052

step 4450,test_loss 41.023289

step 4460,test_loss 41.037994

step 4470,test_loss 41.027950

step 4480,test_loss 41.018356

step 4490,test_loss 41.026508

step 4500,test_loss 41.024136

step 4510,test_loss 41.032318

step 4520,test_loss 41.028934

step 4530,test_loss 41.027802

step 4540,test_loss 41.034740

step 4550,test_loss 41.018875

step 4560,test_loss 41.009151

step 4570,test_loss 41.028728

step 4580,test_loss 41.013172

step 4590,test_loss 41.023643

step 4600,test_loss 41.036564

step 4610,test_loss 41.023758

step 4620,test_loss 41.010895

step 4630,test_loss 41.016830

step 4640,test_loss 41.025158

step 4650,test_loss 41.031147

step 4660,test_loss 41.030773

step 4670,test_loss 41.014057

step 4680,test_loss 41.012878

step 4690,test_loss 41.020706

step 4700,test_loss 41.024204

step 4710,test_loss 41.030964

step 4720,test_loss 41.042183

step 4730,test_loss 41.004620

step 4740,test_loss 41.043163

step 4750,test_loss 41.026157

step 4760,test_loss 41.016129

step 4770,test_loss 41.028667

step 4780,test_loss 41.033478

step 4790,test_loss 41.032280

step 4800,test_loss 41.029270

step 4810,test_loss 41.032330

step 4820,test_loss 41.026970

step 4830,test_loss 41.034531

step 4840,test_loss 41.038826

step 4850,test_loss 41.033676

step 4860,test_loss 41.037766

step 4870,test_loss 41.026272

step 4880,test_loss 41.024136

step 4890,test_loss 41.020840

step 4900,test_loss 41.028576

step 4910,test_loss 41.013222

step 4920,test_loss 41.042625

step 4930,test_loss 41.035049

step 4940,test_loss 41.023026

step 4950,test_loss 41.023335

step 4960,test_loss 41.028851

step 4970,test_loss 41.024628

step 4980,test_loss 41.019810

step 4990,test_loss 41.026733

Tensorflow R2:  0.999997486127

Sklearn R2:  1.0

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