深度学习框架tensorflow学习与应用8（tensorboard网络结构）

#载入数据集
mnist = input_data.read_data_sets("MNIST_data",one_hot=True) #如果没有就下载，然后以独热码的形式载入，有的话就不下载


#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

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')


#创建一个简单的神经网络
with tf.name_scope('layer'):
    with tf.name_scope('weights'):
          W = tf.Variable(tf.zeros([784,10]),name='W')
    with tf.name_scope('biases'):
          b = tf.Variable(tf.zeros([10]),name="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('lose'):
     #二次代价函数
    loss = tf.reduce_mean(tf.square(y - prediction))
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_prefdiction'):
     #结果存放在一个布尔型列表中
        correct_prediction =tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一维张量中最大的的值所在的位置
    with tf.name_scope('accuracy'):    
        #求准确率
        accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))

        
with tf.Session() as sess:
    sess.run(init)
    wirter = tf.summary.FileWriter('logs/',sess.graph)
    for epoch in range(1):
        for batch in range(n_batch):
            batch_xs,batch_ys = mnist.train.next_batch(batch_size)
            sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
            
        acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        #acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})
        print("Tter"+str(epoch)+",Testing Accuracy"+str(acc))
        

实线：数据传输

粗细：表示的是两个节点之间传输的标量维度。

使用以下代码可以看多更多的点：

for epoch in range(51):
    for batch in range(n_batch):
        batch_xs, batch_ys = mnist.train.next_batch(batch_size)
        # sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys})
        summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys})

    wirter.add_summary(summary, epoch)
    acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
    print("Tter" + str(epoch) + ",Testing Accuracy" + str(acc))