#載入資料集
mnist = input_data.read_data_sets("MNIST_data",one_hot=True) #如果沒有就下載，然後以獨熱碼的形式載入，有的話就不下載


#每個批次的大小
batch_size =100
#計算一共有多少個批次
n_batch = mnist.train.num_examples// batch_size

with tf.name_scope('input'):
        #定義兩個placeholder
     x = tf.placeholder(tf.float32,[None,784],name='x-input')
     y = tf.placeholder(tf.float32,[None,10],name='y-input')


#建立一個簡單的神經網路
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'):
     #二次代價函式
    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)

#初始化變數
init = tf.global_variables_initializer()

with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prefdiction'):
     #結果存放在一個布林型列表中
        correct_prediction =tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一維張量中最大的的值所在的位置
    with tf.name_scope('accuracy'):    
        #求準確率
        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))
        
 

實線：資料傳輸

粗細：表示的是兩個節點之間傳輸的標量維度。

使用以下程式碼可以看多更多的點：

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))