继续

0x00.前言

0x01.引用

1.0 TensorFlow实现线性回归模型代码

1.1 前期准备

TensorFlow相关API可以到在实验TensorFlow - 相关 API中学习。

1.2 模型构建

现在您可以在/home/ubuntu目录下创建源文件linear_regression_model.py,内容可参考:

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#!/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)

1.3 训练模型并和sklearn库线性回归模型对比

现在您可以在/home/ubuntu目录下创建源文件run.py,内容可参考:

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#!/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

然后执行:
cd /home/ubuntu
python run.py
执行结果:

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step 2410,test_loss 26.531937
step 2420,test_loss 26.542793
step 2430,test_loss 26.533974
step 2440,test_loss 26.530540
step 2450,test_loss 26.551474
step 2460,test_loss 26.541542
step 2470,test_loss 26.560783
step 2480,test_loss 26.538080
step 2490,test_loss 26.535666
('Tensorflow R2: ', 0.99999612588302389)
('Sklearn R2: ', 1.0)

0x02.后记

emmm……真的好快


未完待续……