tensorboard一直顯示inactive解決方案

最近使用tensorboard顯示神經網絡，但是一連找了1個多小時一直都是inactive，沒有任何顯示。
  
  我的環境：
  win10，anaconda3搭建的python3.7虛拟環境
  
  指令行操作：
  E:\PycharmProjects\untitled>conda activate python37
  (python37) E:\PycharmProjects\untitled>tensorboard --logdir ./log
  TensorBoard 1.15.0a20190727 at http://DESKTOP-0M5OH3G:6006/ (Press CTRL+C to quit)
  
  現象：
  一直都是inactive，啥都沒有
  
  解決方案：
  由于我是在虛拟環境進入到檔案目錄的，好像直接用./log會有問題，後來我就換成了絕對路徑就ok了。正常後發現還是inactive但是有圖了。
  (python37) E:\PycharmProjects\untitled>tensorboard --logdir E:\PycharmProjects\untitled\logs
  TensorBoard 1.15.0a20190727 at http://DESKTOP-0M5OH3G:6006/ (Press CTRL+C to quit)

  其它說法：
  路徑不能有中文，路徑中不能有空格，使用http://localhost:6006等等。
  
  我的代碼如下：
           

import tensorflow as tf
import numpy as np


def add_layer(inputs, in_size, out_size, activation_function=None):
    with tf.name_scope('layer'):
        with tf.name_scope('weights'):
            Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
        with tf.name_scope('biases'):
            biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
        with tf.name_scope('Wx_plus_b'):
            Wx_plus_b = tf.add(tf.matmul(inputs, Weights), biases)
        if activation_function is None:
            outputs = Wx_plus_b
        else:
            outputs = activation_function(Wx_plus_b)
        return outputs


x_data = np.linspace(-1, 1, 300)[:, np.newaxis]
noise = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise

with tf.name_scope('inputs'):
    xs = tf.placeholder(tf.float32, [None, 1], name='x_input')
    ys = tf.placeholder(tf.float32, [None, 1], name='y_input')

l1 = add_layer(xs, 1, 10, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, activation_function=None)

with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                     reduction_indices=[1]))
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()
writer = tf.summary.FileWriter('./logs/', tf.get_default_graph())
sess.run(tf.global_variables_initializer())
writer.close()
sess.close()