TensorBoard可视化代码

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=2000
#文件路径
DIR="C:/Users/thisi/PycharmProjects/20181127/"

#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)
        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_shape_input=tf.reshape(x,[-1,28,28,1])
    tf.summary.image('input',image_shape_input,10)

#创建一个简单神经网络
with tf.name_scope('layer'):
    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))
            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()
    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)

    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()