import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

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"):
    #定义输入输出
    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('weights'):
        W = tf.Variable(tf.zeros([784, 10]))
    with tf.name_scope('bias'):
        b = tf.Variable(tf.zeros([1, 10]))
    with tf.name_scope('predictions'):
        predictions_1 = tf.matmul(x, W) + b
#定义二次代价函数
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=predictions_1))
#使用梯度下降
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
#初始化变量
init = tf.global_variables_initializer()

#结果存放在一个bool型列表中
with tf.name_scope('acc'):
    with tf.name_scope('correction'):
        correction = tf.equal(tf.argmax(y, 1), tf.argmax(predictions_1, 1))#argmax返回一维张量中最大的值所在的位置
    #求准确率
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(correction, tf.float32))

with tf.Session() as sess:
    sess.run(init)
    writer = 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})
        print("epoch " + str(epoch) + ", accuracy " + str(acc))

命令：
tensorboard --logdir=E:\correct_dove\MLSFromSegV2\2\MLSFromSegV2\logs

graph