Tensorflow可视化MNIST手写数字训练

【简述】

　　我们在学习编程语言时，往往第一个程序就是打印“Hello World”，那么对于人工智能学习系统平台来说，他的“Hello World”小程序就是MNIST手写数字训练了。MNIST是一个手写数字的数据集，官网是Yann LeCun's website。数据集总共包含了60000行的训练数据集（mnist.train）和10000行的测试数据集（mnist.test），每一个数字的大小为28*28像素。通过利用Tensorflow人工智能平台，我们可以学习到人工智能学习平台是如何通过数据进行学习的。

【数据准备】

　　下载mnist数据集，和mnist_10k_sprite.png图片，分别放在MNIST_data文件夹和projector/data文件夹下。

【代码】

 import tensorflow as tf from tensorflow.examples.tutorials.mnist import input_data from tensorflow.contrib.tensorboard.plugins import projector  #载入数据集 mnist = input_data.read_data_sets("MNIST_data/",one_hot=True) #运行次数 max_steps = 1001 #图片数量 image_num = 3000 #文件路径 DIR = "E:/Github/TensorFlow/trunk/Test/"  #定义会话 sess = tf.Session()  #载入图片 embedding = tf.Variable(tf.stack(mnist.test.images[:image_num]), trainable=False, name='embedding')  #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'):     #这里的none表示第一个维度可以是任意的长度     x = tf.placeholder(tf.float32,[None,784],name='x-input')     #正确的标签     y = tf.placeholder(tf.float32,[None,10],name='y-input')  #显示图片 with tf.name_scope('input_reshape'):     image_shaped_input = tf.reshape(x, [-1, 28, 28, 1])     tf.summary.image('input', image_shaped_input, 10)  with tf.name_scope('layer'):     #创建一个简单神经网络     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(0.5).minimize(loss)  #初始化变量 sess.run(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))#把correct_prediction变为float32类型         tf.summary.scalar('accuracy',accuracy)  #产生metadata文件 if tf.gfile.Exists(DIR + 'projector/projector/metadata.tsv'):     tf.gfile.DeleteRecursively(DIR + 'projector/projector/metadata.tsv') with open(DIR + 'projector/projector/metadata.tsv', 'w') as f:     labels = sess.run(tf.argmax(mnist.test.labels[:],1))     for i in range(image_num):            f.write(str(labels[i]) + '\n')                  #合并所有的summary merged = tf.summary.merge_all()      projector_writer = tf.summary.FileWriter(DIR + 'projector/projector',sess.graph) saver = tf.train.Saver() config = projector.ProjectorConfig() embed = config.embeddings.add() embed.tensor_name = embedding.name embed.metadata_path = DIR + 'projector/projector/metadata.tsv' embed.sprite.image_path = DIR + 'projector/data/mnist_10k_sprite.png' embed.sprite.single_image_dim.extend([28,28]) projector.visualize_embeddings(projector_writer,config)  for i in range(max_steps):     #每个批次100个样本     batch_xs,batch_ys = mnist.train.next_batch(100)     run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)     run_metadata = tf.RunMetadata()     sess.run(train_step,feed_dict={x:batch_xs,y:batch_ys},options=run_options,run_metadata=run_metadata)     summary = sess.run(merged,feed_dict={x:batch_xs,y:batch_ys},options=run_options,run_metadata=run_metadata)     projector_writer.add_run_metadata(run_metadata, 'step%03d' % i)     projector_writer.add_summary(summary, i)          if i%100 == 0:         acc = sess.run(accuracy,feed_dict={x:mnist.test.images,y:mnist.test.labels})         print ("Iter " + str(i) + ", Testing Accuracy= " + str(acc))  saver.save(sess, DIR + 'projector/projector/a_model.ckpt', global_step=max_steps) projector_writer.close() sess.close()

　　直接运行代码