本程序是基于mnist手写数据集，利用softmax函数来预测准确率，程序进行了详细注释。

tensorboard是一个强大的可视化工具，可以看出构建的网络的结构。它支持GRAPHS,SCALARS, DISTRIBUTIONS, HISTOGRAMS等可视化。

程序如下：

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

# 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

# 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

# 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)

# 二次代价函数

# loss = tf.reduce_mean(tf.square(y-prediction))

with tf.name_scope('loss'):

loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))

tf.summary.scalar('loss', loss)

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'):

# 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))

tf.summary.scalar('accuracy', accuracy)

# 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)

for epoch in range(51):

for batch in range(n_batch):

batch_xs, batch_ys = mnist.train.next_batch(batch_size)

# merged返回值存入summary中

summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys})

# 记录summary和epoch到文件中

writer.add_summary(summary, epoch)

acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})

print("Iter " + str(epoch) + ",Testing Accuracy " + str(acc))

tensorboard可视化结果如下：

GRAPHS（模型流图）：

DISTRIBUTION：

HISTOGRAMS(直方图):

自己可以运行一下代码，视觉可视化让人兴奋不已，tensorflow太厉害了。