利用Tensorboard绘制网络识别准确率和loss曲线实例

Hester ·

更新时间:2024-11-15

· 692 次阅读

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 
mnist = input_data.read_data_sets("MNIST_data",one_hot=True) 
#每个批次的大小和总共有多少个批次 
batch_size = 100 
n_batch = mnist.train.num_examples // batch_size 
#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))
    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('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)
  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('loss'):
  #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() 
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("log/", sess.graph) #写入到的位置
  for epoch in range(51): 
    for batch in range(n_batch): 
      batch_xs,batch_ys = mnist.train.next_batch(batch_size) 
      summary,_ = sess.run([merged,train_step],feed_dict={x:batch_xs, y:batch_ys}) 
    writer.add_summary(summary,epoch) 
    acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels}) 
    print("epoch " + str(epoch)+ "  acc " +str(acc))

运行程序，打开命令行界面，切换到 log 所在目录，输入