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 # 最多10000，因为测试集为10000 #文件路径 DIR = "C:/Users/FELIX/Desktop/tensor学习/" #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]) # -1表示不确定的值 tf.summary.image('input', image_shaped_input, 10) # 一共放10张图片 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_v2(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'):# 检测是否已存在 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() # 用来保存网络模型 config = projector.ProjectorConfig() # 定义了配置文件 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) # 可视化的一个工具 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() summary,_ = sess.run([merged,train_step],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) # 每训练100次打印准确率 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)) # 训练完保存模型 saver.save(sess, DIR + 'projector/projector/a_model.ckpt', global_step=max_steps) projector_writer.close() sess.close()
执行之前先在当前目录下建立projector文件夹，然后在projector文件夹下建立data和projector文件夹。
在data文件夹下放入数据图片--》数据图片下载地址 提取码：vhkl
然后运行后打开cmd，进入当前文件夹，执行：tensorboard --logdir=C:\Users\FELIX\Desktop\tensor学习\projector\projector
然后就可以看到全部的可视化。
迭代500多次后，由原来较混乱的逐渐的分类，因为模型的准确率只有90%左右，所有有一些会分错类的情况
