利用Tensorboard繪制網(wǎng)絡識別準確率和loss曲線實例
廢話不多說,直接上代碼看吧!
import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data #載入數(shù)據(jù)集 mnist = input_data.read_data_sets("MNIST_data",one_hot=True) #每個批次的大小和總共有多少個批次 batch_size = 100 n_batch = mnist.train.num_examples // batch_size #定義函數(shù) 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) #標準差 tf.summary.scalar('max', tf.reduce_max(var)) tf.summary.scalar('min', tf.reduce_min(var)) tf.summary.histogram('histogram', var) #直方圖 #命名空間 with tf.name_scope("input"): #定義兩個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"): #創(chuàng)建一個簡單的神經(jīng)網(wǎng)絡 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'): #交叉熵代價函數(shù) 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) #初始化變量 init = tf.global_variables_initializer() with tf.name_scope('accuracy'): with tf.name_scope('correct_prediction'): #結果存放在一個布爾型列表中 correct_prediction = tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))#argmax返回一維張量中最大的值所在的位置 with tf.name_scope('accuracy'): #求準確率 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) #寫入到的位置 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))
運行程序,打開命令行界面,切換到 log 所在目錄,輸入
tensorboard --logdir= --logdir=C:\Users\Administrator\Desktop\Python\log
接著會返回一個鏈接,類似 http://PC-20160926YCLU:6006
打開谷歌瀏覽器或者火狐,輸入網(wǎng)址即可查看搭建的網(wǎng)絡結構以及識別準確率和損失函數(shù)的曲線圖。
注意:如果對網(wǎng)絡進行更改之后,在運行之前應該先刪除log下的文件,在Jupyter中應該選擇Kernel----->Restar & Run All, 否則新網(wǎng)絡會和之前的混疊到一起。因為每次的網(wǎng)址都是一樣的,在瀏覽器刷新頁面即可。
以上這篇利用Tensorboard繪制網(wǎng)絡識別準確率和loss曲線實例就是小編分享給大家的全部內(nèi)容了,希望能給大家一個參考,也希望大家多多支持腳本之家。

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