利用tensorflow实现一个简单的二分类

直接上代码，在实践中学习

#coding:utf-8

import tensorflow as tf
from numpy.random import RandomState

batch_size = 8

w1 = tf.Variable(tf.random_normal([2,3],stddev=1.0,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1.0,seed=1))

x = tf.placeholder(tf.float32,shape=(None,2),name="x-input")
y_ = tf.placeholder(tf.float32,shape=(None,1),name="y-input")

#定义神经网络前向传播
a = tf.matmul(x,w1)
y = tf.matmul(a,w2)

#定义损失函数，这里使用交叉熵
#cross_entropy = -1*tf.reduce_mean(tf.reduce_sum(y_*tf.log(y),axis=[1]))
cross_entropy = -1*tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))

#定义求解算法
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

#生成数据集与标签
rdm = RandomState(1)
dataset_size = 128
X = rdm.rand(dataset_size,2)

Y = [[int(x1+x2<1)] for (x1,x2) in X]#这里是LIST，和X不同，X是array

#创建一个Session()来运行程序
with tf.Session() as sess:
init_op = tf.global_variables_initializer()
sess.run(init_op)
print sess.run(w1)
print sess.run(w2)

#设定训练的轮数
STEPS = 5000
for i in range(STEPS):
start = (i*batch_size) % dataset_size
end = min(start+batch_size,dataset_size)

feed_dict1 = {x:X[start:end],y_:Y[start:end]}
sess.run(train_step,feed_dict=feed_dict1)
feed_dict2 = {x:X,y_:Y}
if i % 100 ==0:
total_cross_entropy = sess.run(cross_entropy,feed_dict=feed_dict2)
print "step ",i,"   ","loss is ",total_cross_entropy