最近需要练习一下tensorflow的使用，看了一下实战Google深度学习框架这本书，感觉不错，照着书上的例子敲一下。纯用来记忆一些语法。

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#coding:utf-8
import tensorflow as tf

from numpy.random import RandomState

# 一个完整的神经网络程序

batch_size=8

#tf,Variable的作用就是保存和更新神经网络的参数5
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)

#定义损失函数
cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))

#设置优化器，进行权重参数(w)的自动更新，这是tensorflow实现的
train_step=tf.train.AdamOptimizer(0.001).minimize(cross_entropy)

# generate a random dataset.
rdm=RandomState(1)
dataset_size=128

X=rdm.rand(dataset_size,2)

Y=[[int(x1+x2<1)] for (x1, x2) in X]


with tf.Session() as sess:
    init_op=tf.initialize_all_variables()
    sess.run(init_op)
    print sess.run(w1)
    print sess.run(w2)

    STEPS=10000
    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 training step(s), cross entropy on all data is %g" % (i,total_cross_entropy))
    print  sess.run(w1)
    print  sess.run(w2)