tensorboard同时显示训练数据和测试数据的曲线

学着用tensorboard在一个模型里面有训练模型和测试模型的损失函数曲线对比，上网找了好多，但是还都是一个曲线，即自己画的是这样的

但是想要的是这样的：

到底应该怎么样呢？

简单操作：

tensorboard --logdir=run1:"/home/.../summary",run2:"/home/.../summary" 

其实只要在终端同时运行几个events即可，events就是summary生成的那个东西，

然后注意了，还有个骚操作：

注意,左上角的ignore outliers in chart scaling 没点时是上图，如果点了，就是下面的图了

最后附上我的代码，可以自己耍一下。

# -*- coding: utf-8 -*-
"""
Created on Tue May 29 09:17:55 2018

@author: 618
"""

import tensorflow as tf
import numpy as np

trX = np.linspace(-1, 1, 100)
trY = trX + np.random.randn(*trX.shape)*0.33

teX = np.linspace(2, 3, 100)
teY = teX

X = tf.placeholder(tf.float32, [100,], name="input_x")
Y = tf.placeholder(tf.float32, [100,], name="output_y")
X1 = tf.placeholder(tf.float32, [100,], name="input_x1")
Y1 = tf.placeholder(tf.float32, [100,], name="output_y1")

def model(X, w):
    return tf.multiply(X, w)

w = tf.Variable(0.1, name='weights')

with tf.name_scope("cost"):
    y_model = model(X, w)
    cost = tf.reduce_mean(tf.square(Y - y_model))
tf.summary.scalar('loss', cost)

with tf.name_scope("train"):
    train_op = tf.train.AdamOptimizer(0.01).minimize(cost)

with tf.name_scope("test_cost"):
    y_model = model(X, w)
    test_cost = tf.reduce_mean(tf.square(Y - y_model))
tf.summary.scalar('test_loss', test_cost)

merged = tf.summary.merge_all()
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    summary_writer = tf.summary.FileWriter('./log/train', sess.graph)
    summary_writer1 = tf.summary.FileWriter('./log/test')
    for i in range(1000):
        feed_dict = {}
        if i % 100 == 0:
            print(sess.run(cost, feed_dict={X: trX, Y: trY}))
            print(sess.run(test_cost, feed_dict={X: teX, Y: teY}))
            summary, _ = sess.run([merged, test_cost], feed_dict={X: teX, Y: teY})
            summary_writer1.add_summary(summary, i)
        else:
            summary, _ =sess.run([merged, train_op], feed_dict={X: trX, Y: trY})
            summary_writer.add_summary(summary,i)
    print(sess.run(w))
    summary_writer.close()
    summary_writer1.close()



'''
import tensorflow as tf
import numpy as np

trX = np.linspace(-1, 1, 100)
trY = trX + np.random.randn(*trX.shape)*0.33

teX = np.linspace(2, 3, 100)
teY = teX + 1

X = tf.placeholder(tf.float32, [100,], name="input_x")
Y = tf.placeholder(tf.float32, [100,], name="output_y")
X1 = tf.placeholder(tf.float32, [100,], name="input_x1")
Y1 = tf.placeholder(tf.float32, [100,], name="output_y1")

def model(X, w):
    return tf.multiply(X, w)

w = tf.Variable(0.1, name='weights')

y_model = model(X, w)
cost = tf.reduce_mean(tf.square(Y - y_model))
tf.summary.scalar('loss', cost)
train_op = tf.train.AdamOptimizer(0.01).minimize(cost)

y_model = model(X1, w)
test_cost = tf.reduce_mean(tf.square(Y1 - y_model))
tf.summary.scalar('test_loss', test_cost)

merged = tf.summary.merge_all()
with tf.Session() as sess:
    tf.global_variables_initializer().run()
    summary_writer = tf.summary.FileWriter('./log', sess.graph)
    for i in range(1000):
        sess.run([train_op], feed_dict={X: trX, Y: trY, X1: teX, Y1: teY})
        if i % 100 == 0:
            print(sess.run(cost, feed_dict={X: trX, Y: trY, X1: teX, Y1: teY}))
            print(sess.run(test_cost, feed_dict={X: trX, Y: trY, X1: teX, Y1: teY}))
        summary = sess.run(merged, feed_dict={X: trX, Y: trY, X1: teX, Y1: teY})
        summary_writer.add_summary(summary,i)
    print(sess.run(w))
    summary_writer.close()
'''