基于tensorflow的逻辑分类

#!/usr/local/bin/python3

##ljj [2]
##logic classify model

 
import tensorflow as tf
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

data_set = pd.read_csv('LogiReg_data.txt',sep=',')

#data_set.describe()

w = tf.Variable(tf.random_normal([2,1]),dtype="float32")
b = tf.Variable(tf.random_normal([1]),dtype="float32")

y = tf.placeholder(tf.float32)
x = tf.placeholder(tf.float32,shape=(1,2))

loss_list = []

with tf.Session() as sess:
#定义逻辑回归模型
        logits = tf.add(tf.matmul(x,w),b)
        y_predict = tf.nn.sigmoid(logits)
        
        loss = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(logits=logits,labels=y))
           
        train = tf.train.AdamOptimizer(0.001).minimize(loss)
        sess.run(tf.global_variables_initializer())
        
        for j in range(1500):
                #shuffle data_set
                #index = np.random.permutation(100)
                #data_set = data_set.reindex(index)
                
                for i in range(100):
                        w_,b_,loss_,_ = sess.run([w,b,loss,train],feed_dict={x:data_set[i:i+1][['math','english']],y:data_set[i:i+1][['result']]})
                if j%100==0:
                        print('epoch={}, w={},b={},loss={}'.format(j,w_,b_,loss_))
                loss_list.append(loss_)
        print('final result : ')
        print('w={},b={},loss={}'.format(w_,b_,loss_))
      
        train_data = data_set.values
        x1 = train_data[:,0]
        x2 = train_data[:,1]
        y = train_data[:,-1:]  
        
        for x1p, x2p, yp in zip(x1, x2, y):
                if yp == 0:
                        plt.scatter(x1p, x2p, marker='x', c='r')
                else:
                        plt.scatter(x1p, x2p, marker='o', c='g')
                
        # 根据参数得到直线
        x = np.linspace(20, 100, 10)
        y = []
        for i in x:
            y.append((i * -w_[1] - b_) / w_[0])
            
        plt.plot(x, y)
        plt.show()

 运行输出：

ljjdeMBP:logic_classify lingjiajun$ ./logic_regression.py 

/usr/local/Cellar/python3/3.6.2/Frameworks/Python.framework/Versions/3.6/lib/python3.6/importlib/_bootstrap.py:205: RuntimeWarning: compiletime version 3.5 of module 'tensorflow.python.framework.fast_tensor_util' does not match runtime version 3.6

  return f(*args, **kwds)

2018-05-06 21:48:14.420588: I tensorflow/core/platform/cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: SSE4.1 SSE4.2 AVX AVX2 FMA

epoch=0, w=[[-0.87034857]

 [ 0.13095166]],b=[ 1.48850453],loss=52.04541015625

epoch=100, w=[[ 0.01144427]

 [ 0.0005749 ]],b=[ 0.58342618],loss=0.21249079704284668

epoch=200, w=[[ 0.02113499]

 [ 0.01194377]],b=[-0.85776216],loss=0.16103345155715942

epoch=300, w=[[ 0.03020949]

 [ 0.02251359]],b=[-2.18144464],loss=0.12141948938369751

epoch=400, w=[[ 0.03859403]

 [ 0.03212684]],b=[-3.3784802],loss=0.092116579413414

epoch=500, w=[[ 0.04626059]

 [ 0.04076466]],b=[-4.4525094],loss=0.07090871036052704

epoch=600, w=[[ 0.05323409]

 [ 0.04850558]],b=[-5.41535854],loss=0.05559533089399338

epoch=700, w=[[ 0.059574  ]

 [ 0.05546409]],b=[-6.28165531],loss=0.04442552104592323

epoch=800, w=[[ 0.06535295]

 [ 0.06175429]],b=[-7.06552744],loss=0.03614450991153717

epoch=900, w=[[ 0.07064275]

 [ 0.067476  ]],b=[-7.77926588],loss=0.029891693964600563

epoch=1000, w=[[ 0.07550841]

 [ 0.07271299]],b=[-8.43318176],loss=0.02508264034986496

epoch=1100, w=[[ 0.08000626]

 [ 0.07753391]],b=[-9.0357523],loss=0.021319210529327393

epoch=1200, w=[[ 0.08418395]

 [ 0.0819957 ]],b=[-9.59397316],loss=0.01832636632025242

epoch=1300, w=[[ 0.08808059]

 [ 0.08614379]],b=[-10.11344337],loss=0.015911955386400223

epoch=1400, w=[[ 0.09172987]

 [ 0.09001698]],b=[-10.59893036],loss=0.01393833290785551

final result : 

w=[[ 0.09512767]

 [ 0.09361333]],b=[-11.05011368],loss=0.012320424430072308