首先書寫如下的程式

#coding=utf-8
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# number 1 to 10 data
mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

def add_layer(inputs, in_size, out_size, activation_function=None ,):
    # add one more layer and return the output of this layer
    Weights = tf.Variable(tf.random_normal([in_size, out_size]))
    biases = tf.Variable(tf.zeros([1, out_size]) + 0.1 ,)
    Wx_plus_b = tf.matmul(inputs, Weights) + biases
    if activation_function is None:
        outputs = Wx_plus_b
    else:
        outputs = activation_function(Wx_plus_b ,)
    return outputs

def compute_accuracy(v_ys, y_pre):
    #global prediction
    #判斷為1的位置是否相同
    correct_prediction = tf.equal(tf.argmax(y_pre ,1), tf.argmax(v_ys ,1))#
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    return accuracy

# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 784]) # 28x28
ys = tf.placeholder(tf.float32, [None, 10])

# add output layer
prediction = add_layer(xs,784, 10,  activation_function=tf.nn.softmax)

# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                              reduction_indices=[1]))       # loss
tf.summary.scalar('/loss', cross_entropy)
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(cross_entropy)

accuracy=compute_accuracy(ys, prediction)
tf.summary.scalar('/accuracy', accuracy)
sess = tf.Session()
# 初始化及進行總結summary的整合
sess.run(tf.initialize_all_variables())
merged = tf.summary.merge_all()
summary_writer = tf.summary.FileWriter('D:/PycharmProjects/mybookpractice/forshow/logs', sess.graph)
for i in range(10000):
    batch_xs, batch_ys = mnist.train.next_batch(100)
    sess.run(train_step, feed_dict={xs: batch_xs, ys: batch_ys})
    if i % 50 == 0:
        #loss=sess.run(cross_entropy, feed_dict={xs: batch_xs, ys: batch_ys})
        print(sess.run(accuracy, feed_dict={xs: batch_xs, ys: batch_ys}))
        train_result = sess.run(merged,feed_dict={xs: batch_xs, ys: batch_ys})
        summary_writer.add_summary(train_result, i)
summary_writer.close()
 

程式執行中，可以看到如下的輸出結果：

進一步的我們開啟TensorBoard，觀察視覺化的損失曲線情況。

在瀏覽器中輸入shell中提示的埠，視覺化計算圖如下：

損失和正確率變化曲線如下：