利用Tensorboard绘制网络识别准确率和loss曲线实例

利用利用Tensorboard绘制网络识别准确率和绘制网络识别准确率和loss曲线实例曲线实例

今天小编就为大家分享一篇利用Tensorboard绘制网络识别准确率和loss曲线实例，具有很好的参考价值，希望

对大家有所帮助。一起跟随小编过来看看吧

废话不多说，直接上代码看吧！

import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data

#载入数据集

#定义函数

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))

tf.summary.scalar('min', tf.reduce_min(var))

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]), name='W')

variable_summaries(W)

with tf.name_scope('biases'):

b = tf.Variable(tf.zeros([10]), name='b')

variable_summaries(b)

#交叉熵代价函数

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()

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("log/", sess.graph) #写入到的位置

for epoch in range(51):