将下面的代码保存成test.py

import os
os.environ["TF_CPP_MIN_LOG_LEVEL"]="3"

import tensorflow as tf
#导入TensorFlow工具包并简称为tf

from numpy.random import RandomState
#导入numpy工具包，生成模拟数据集

batch_size = 8
#定义训练数据batch的大小

w1 = tf.Variable(tf.random_normal([2,3],stddev=1,seed=1))
w2 = tf.Variable(tf.random_normal([3,1],stddev=1,seed=1))
#分别定义一二层和二三层之间的网络参数，标准差为1，随机产生的数保持一致

x = tf.placeholder(tf.float32,shape=(None,2),name='x-input')
y_ = tf.placeholder(tf.float32,shape=(None,1),name='y-input')
#输入为两个维度，即两个特征，输出为一个标签,声明数据类型float32，None即一个batch大小
#y_是真实的标签

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

cross_entropy = -tf.reduce_mean(y_ * tf.log(tf.clip_by_value(y,1e-10,1.0)))
train_step = tf.train.AdamOptimizer(0.001).minimize(cross_entropy)
#定义损失函数和反向传播算法

rdm = RandomState(1)
dataset_size = 128
#产生128组数据
X = rdm.rand(dataset_size,2)
Y = [[int(x1+x2 < 1)] for (x1,x2) in X]
#将所有x1+x2<1的样本视为正样本，表示为1；其余为0

#创建会话来运行TensorFlow程序
#为session分配显存
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.333)

with tf.Session(config=tf.ConfigProto(gpu_options=gpu_options)) as sess:
    init_op = tf.global_variables_initializer()
    #初始化变量
    sess.run(init_op)

    print(sess.run(w1))
    print(sess.run(w2))
    #打印出训练网络之前网络参数的值

    STEPS = 5000
    #设置训练的轮数
    for i in range(STEPS):
        start = (i * batch_size) % dataset_size
        end = min(start+batch_size,dataset_size)
    #每次选取batch_size个样本进行训练
    
        sess.run(train_step,feed_dict={x:X[start:end],y_:Y[start:end]})
    #通过选取的样本训练神经网络并更新参数
    
        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))
    #每隔一段时间计算在所有数据上的交叉熵并输出，随着训练的进行，交叉熵逐渐变小

    print(sess.run(w1))
    print(sess.run(w2))
    #打印出训练之后神经网络参数的值

然后打开cmd并切换至该文件的目录,然后运行python test.py,如果你的TensorFlow安装配置成功,则运行结果如下

[[-0.8113182   1.4845988   0.06532937]
 [-2.4427042   0.0992484   0.5912243 ]]
[[-0.8113182 ]
 [ 1.4845988 ]
 [ 0.06532937]]
After 0 training step(s),cross entropy on all data is 0.0674925
After 1000 training step(s),cross entropy on all data is 0.0163385
After 2000 training step(s),cross entropy on all data is 0.00907547
After 3000 training step(s),cross entropy on all data is 0.00714436
After 4000 training step(s),cross entropy on all data is 0.00578471
[[-1.9618274  2.582354   1.6820378]
 [-3.4681718  1.0698233  2.11789  ]]
[[-1.8247149]
 [ 2.6854665]
 [ 1.4181951]]