## 利用Tensorboard绘制网络识别准确率和loss曲线实例

2020年02月18日 编程语言 ⁄ 共 2589字 ⁄ 字号 评论关闭

import tensorflow as tffrom 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 = 100n_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summarymerged = 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))