【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]) + ' ') #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()
【运行】
直接运行代码
【可视化界面】
1、在cmd命令行输入tensorboard --logdir=progector文件夹路径;
2、在浏览器打开http://localhost:6006路径即可查看可视化效果。
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