TensorFlow之神经网络练习

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TensorFlow实现神经网络：

import tensorflow as tffrom numpy.random import RandomStatebatch_size=8

#定义权值、x、yw1=tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))w2=tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))biases1=tf.Variable(tf.constant(0.1,shape=[3]))biases2=tf.Variable(tf.constant(0.1,shape=[1]))x=tf.placeholder(tf.float32,shape=(None,2),name="x-input")y_=tf.placeholder(tf.float32,shape=(None,1),name="y-input")

#输入、中间层、输出计算a=tf.nn.relu(tf.matmul(x,w1)+biases1)y=tf.nn.relu(tf.matmul(a,w2)+biases2)

#定义损失函数、训练优化cross_entropy=-tf.reduce_mean(y_*tf.log(tf.clip_by_value(y,1e-10,1.0)))train_step = tf.train.AdamOptimizer(0.0001).minimize(cross_entropy)

#随机定义输入值rdm = RandomState(1)X = rdm.rand(128,2)Y = [[int(x1+x2<1)] for (x1,x2) in X]

#生成会话执行

with tf.Session() as sess:    init_op = tf.global_variables_initializer()    sess.run(init_op)        print "w1", sess.run(w1)    print "w2", sess.run(w2)    print "\n"    #迭代    STEPS = 10001    for i in range(STEPS):        start = (i*batch_size)%128        end = start+batch_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)#输出损失值                print "\n"    print "w1:",sess.run(w1)    print "w2:",sess.run(w2)

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