TensorFlow(七)：tensorboard网络执行

# MNIST数据集 手写数字
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 参数概要
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_min(var)) # 最小值
        tf.summary.histogram(‘histogram‘,var) # 直方图



# 载入数据集
mnist=input_data.read_data_sets(‘MNIST_data‘,one_hot=True)



# 每个批次的大小
batch_size=100
# 计算一共有多少个批次
n_batch=mnist.train.num_examples//batch_size

# 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‘):
    # 创建一个简单的神经网络
    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‘):
        prediction=tf.nn.softmax(wx_plus_b)

with tf.name_scope(‘loss‘):
    # 二次代价函数
    loss=tf.reduce_mean(tf.square(y-prediction))
    tf.summary.scalar(‘loss‘,loss) # 一个值就不用调用函数了
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(‘correct_prediction‘):
        # 求最大值在哪个位置，结果存放在一个布尔值列表中
        correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))# argmax返回一维张量中最大值所在的位置
    with tf.name_scope(‘accuracy‘):
        # 求准确率
        accuracy=tf.reduce_mean(tf.cast(correct_prediction,tf.float32)) # cast作用是将布尔值转换为浮点型。
        tf.summary.scalar(‘accuracy‘,accuracy) # 一个值就不用调用函数了
        
# 合并所有的summary
merged=tf.summary.merge_all()


with tf.Session() as sess:
    sess.run(init)
    writer=tf.summary.FileWriter(‘logs/‘,sess.graph) # 写入文件
    
    
    for epoch in range(10):
        for batch in range(n_batch):
            batch_xs,batch_ys=mnist.train.next_batch(batch_size)
            summary,_=sess.run([merged,train_step],feed_dict={x:batch_xs,y:batch_ys})
        
        # 添加样本点
        writer.add_summary(summary,epoch)
        #求准确率
        acc=sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print(‘Iter:‘+str(epoch)+‘,Testing Accuracy:‘+str(acc))