TensorFlow笔记二：线性回归预测(Linear Regression)

代码：

import tensorflow as tf
import numpy as np
import xlrd
import matplotlib.pyplot as plt

DATA_FILE = 'fire_theft.xls'

# 1.read from data file
book=xlrd.open_workbook(DATA_FILE,encoding_override="utf-8")
sheet=book.sheet_by_index(0)
data=np.asarray([sheet.row_values(i) for i in range(1,sheet.nrows)])
n_samples=sheet.nrows-1

# 2.creat placeholders for input x(number of file) and label Y(number of theft)
X=tf.placeholder(tf.float32,name='X')
Y=tf.placeholder(tf.float32,name='Y')

# 3.creat weight and bias ,init to 0
w=tf.Variable(0.0,name='weights')
b=tf.Variable(0.0,name='bias')

# 4.build model to predict Y
Y_predicted = X* w +b

# 5.use square error as the lose function
loss=tf.square(Y-Y_predicted,name='loss')

# 6.using gradient descent with learning rate 0.01 to minimize loss
optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.001).minimize(loss)

with tf.Session() as sess:
    # 7.init necessary variables (w and b)
    sess.run(tf.global_variables_initializer())
    
    writer=tf.summary.FileWriter('./my_graph/linear_reg',sess.graph)
    
    # 8.train the model 100 times
    for i in range(100):
        total_loss =0
        for x,y in data:
            #session runs train_op and fetch values of loss
            _,l=sess.run([optimizer,loss],feed_dict={X:x,Y:y})
            total_loss +=l
        print('Epoch {0}:{1}'.format(i,total_loss/n_samples))
    
    # close the writer
    writer.close()
            
    # 9.output the value of w and b
    w_value,b_value=sess.run([w,b])
    
# plot the result
X,Y=data.T[0],data.T[1]
plt.plot(X,Y,'bo',label='Real data')
plt.plot(X,X*w_value+b_value,'r',label='Predected data')
plt.legend()
plt.show()

fire_theft.xls

                                                         

 

图例：

TFboard：                                  tensorboard --logdir="./my_graph/linear_reg" --port 6006