【文件属性】：
文件名称：5-3tensorboard 网络运行
文件大小：10KB
文件格式：IPYNB
更新时间：2022-05-11 12:38:37
代码
载入数据集
mnist=input_data.read_data_sets("MNIST_data",one_hot=True)
#设定训练批次的大小
batch_size=50
#计算多少个批次
n_batch=mnist.train.num_examples//batch_size
def variable_summaries(var):
with tf.name_scope('summaries'):
mean=tf.reduce_mean(var)
tf.summary.scalar('mean',mean)#平均值
with tf.name_scope('stddev'):
stddev=tf.sqrt(tf.reduce_mean(tf.square(var-mean)))
tf.summary.scalar('stddev',stddev)#标准差
tf.summary.scalar('max',tf.reduce_max(var))#最大值
tf.summary.scalar('min',tf.reduce_max(var))#最小值
tf.summary.histogram('histogram',var)#直方图
#命名空间
with tf.name_scope('input'):
#定义两个placeholder
x=tf.placeholder(tf.float32,[None,784],name='x-input')
y=tf.placeholder(tf.float32,[None,10],name='y-input')
with tf.name_scope('layer'):
#建立神经网络
with tf.name_scope('wights'):
W=tf.Variable(tf.zeros([784,10]),name='W')
variable_summaries(W)
with tf.name_scope('biases'):
b=tf.Variable(tf.zeros([10]),name='b')
variable_summaries(b)
with tf.name_scope('wx_plus_b'):
wx_plus_b=tf.matmul(x,W)+b
with tf.name_scope('softmax'):
predicton=tf.nn.softmax(wx_plus_b)
#定义二次代价函数
# loss=tf.reduce_mean(tf.square(y-predicton))
with tf.name_scope('loss'):
loss=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y,logits=predicton))
tf.summary.scalar('loss',loss)
with tf.name_scope('train'):
#使用梯度下降法
train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)
#初始化变量
init=tf.global_variables_initializer()
with tf.name_scope('accuracy'):
with tf.name_scope('predicton_correct'):
#预测结果用布尔型列表存放
predicton_correct=tf.equal(tf.argmax(y,1),tf.argmax(predicton,1))#argmax返回一维张量中最大值所在位置
with tf.name_scope('accuracy'):
#计算准确率
accuracy=tf.reduce_mean(tf.cast(predicton_correct,tf.float32))
tf.summary.scalar('accuracy',accuracy)
#h合并所有summary
merged=tf.summary.merge_all()
#建立会话
with tf.Session() as sess:
sess.run(init)
writer=tf.summary.FileWriter('logs/',sess.graph)
#设置循环次数
for epoch in range(51):
for batch in range(n_batch):
batch_x,batch_y=mnist.train.next_batch(batch_size)
summary,_=sess.run([merged,train_step],feed_dict={x:batch_x,y:batch_y})
writer.add_summary(summary,epoch)
#导入测试集计算准确率
acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
#打印正确率
print("Iter "+str(epoch)+",Testing Accuray "+str(acc))