tf.summary.scalar使用方法

import tensorflow as tf


a = tf.placeholder(tf.float32, shape=[])
b = tf.constant(1, dtype=tf.int32)

tf.summary.scalar("a", a)
tf.summary.scalar("b", b)

sess = tf.Session()

init_op = tf.global_variables_initializer()
merged_summaries = tf.summary.merge_all()
writer = tf.summary.FileWriter("train", sess.graph)

sess.run(init_op)

for step in range(6):
    feed_dict = {a: step}
    summary = sess.run(merged_summaries, feed_dict=feed_dict)
    writer.add_summary(summary=summary, global_step=step)

import tensorflow as tf
g1 = tf.Graph()

with g1.as_default():
    sess = tf.Session()
    a = tf.constant(5, name="a")
    b = tf.constant(5, name="b")
    c = tf.add(a, b, "add_in_g1")
    tf.summary.scalar("c", c)
    merged_summary = tf.summary.merge_all()
    writer = tf.summary.FileWriter("./train", sess.graph)
    summary = sess.run(merged_summary)
    writer.add_summary(summary=summary, global_step=1)
    writer.close()
    print(sess.run(c))
    sess.close()