完整的神经网络样例程序（一）

import tensorflow as tf
from numpy.random import RandomState

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

batch_size=8
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)
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)
rdm=RandomState(1)
X=rdm.rand(128,2)
Y=[[int(x1+x2<1)]for(x1,x2)in X]

with tf.Session() as sess:
    init_op=tf.global_variables_initializer()
    sess.run(init_op)
    print(sess.run(w1))
    print(sess.run(w2))
    print("\n")

    STEPS=5000
    for i in range(STEPS):
        start=(i*batch_size)%128
        end=(i*batch_size)%128+batch_size
        sess.run([train_step,y,y_],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("\n")
    print(sess.run(w1))
    print(sess.run(w2))