二分类问题

# -*- coding: utf-8

-*-

"""

Spyder Editor


This is a temporary

script file.

"""



import tensorflow as

tf


from numpy.random

import RandomState


batch_size = 8


w1 =

tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))

w2 =

tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))



x=

tf.placeholder(tf.float32,shape=[None,2],name='x-input')


y_=tf.placeholder(tf.float32,shape=[None,1],name='y-input')


a=tf.matmul(x,w1)

y=tf.matmul(a,w2)


y=tf.sigmoid(y)



#定义损失函数来刻画预测值与真实值的差距

cross_entropy =

-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0))+(1-y)*tf.log(tf.clip_by_value(1-y,1e-10,1.0)))


train_step =

tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

#softmax封住后的交叉熵

#cross_entropy =

tf.nn.softmax_cross_entropy_with_logits(labels=y_,logits=y)


rdm = RandomState(1)


dataset_size=128

X =

rdm.rand(dataset_size,2)

print(X)

Y = [[int

(x1+x2<1)] for (x1,x2) in X]

print(Y)

with tf.Session() as

sess:


    init_op = tf.global_variables_initializer()


    sess.run(init_op)



    print(sess.run(w1))

    print(sess.run(w2))


    STEPS = 5000

    for i in range(STEPS):

        start = (i*batch_size) % dataset_size

        end = min(start+batch_size,dataset_size)


        sess.run(train_step,

                feed_dict={x:X[start:end],y_:Y[start:end]})

        if i % 1000 == 0 :

            total_cross_entropy =sess.run(cross_entropy,feed_dict={x:X,y_:Y})


            print("after %d trainingstep(s),cross entropy on all data is %g"%(i,total_cross_entropy))


    print(sess.run(w1))

    print(sess.run(w2))