tensorboard可视化

1.可视化神经网络图：

   with tf.name_scope("input"):
        with tf.name_scope("image"):
            X = tf.placeholder(tf.float32, [None, IMAGE_HEIGHT*IMAGE_WIDTH])
        with tf.name_scope("label"):
            Y = tf.placeholder(tf.float32, [None, MAX_CAPTCHA*CHAR_SET_LEN])
        with tf.name_scope("dropout"):
            keep_prob = tf.placeholder(tf.float32) # dropout

with tf.name_scope("input"):  进行命名空间限制

2.可视化loss,accuracy曲线：

tf.summary.scalar("loss",loss)

tf.summary.scalar("accyracy",accuracy)

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)  
        tf.summary.scalar('max', tf.reduce_max(var))  
        tf.summary.scalar('min', tf.reduce_min(var))  
        tf.summary.histogram('histogram', var)  

3.汇总所有绘制信息，图、曲线等等

tf.summary.merge_all(key='summaries')  

4.将汇总的protobuf 写入到event文件中

writer = tf.summary.FileWriter(r"C:\Users\Administrator\log")

[_, s] = sess.run([optimizer, summ], feed_dict={X: batch_x, Y: batch_y, keep_prob: 0.75})

writer.add_summary(s, step)

对于网络图的保存 必须把graph单独声明要不然不会保存图

writer = tf.summary.FileWriter(r"F:\env\log2",sess.graph)

5.在cmd运行中执行（写入event的上一层列入上面在Administrator下执行）：

tensorboard --logdir=./log

参考地址：https://blog.csdn.net/smf0504/article/details/56369758