title: Always says "No dashboards are active for the current data set" when activating Tensorboard
body: <p>I am using Python 3.7.3 in macOS system in Anaconda environment. Tensorflow (1.14.0), Matplotlib (3.1.0) and other modules were installed there and everything worked fine. I wrote the following codes and run it. </p>
  
  <pre class="lang-py prettyprint-override"><code>
  import tensorflow as tf
  import numpy as np
  import matplotlib.pyplot as plt
  def add_layer(inputs, inputs_size, outputs_size,activation_function = None):
      with tf.name_scope('layer'):
          with tf.name_scope('weight'):
              Weights = tf.Variable(tf.random.normal([inputs_size, outputs_size]))        
          with tf.name_scope('biase'):
              biases = tf.Variable(tf.zeros([1,outputs_size])+0.1)
          with tf.name_scope('wx_plus_b'):
              Wx_plus_b = tf.matmul(inputs, Weights) + biases
          if activation_function == None:outputs = Wx_plus_b
          else: outputs = activation_function(Wx_plus_b)
          return outputs
  
  '''
  multiple lines omitted here
  '''
  
  writer = tf.compat.v1.summary.FileWriter("logs/",sess.graph)
  </code></pre>
  
  <p>I can see a local file with name </p>
  
  <pre class="lang-py prettyprint-override"><code>"events.out.tfevents.1561289962.Botaos-MacBook-Pro.local"
  </code></pre>
  
  <p>generated in "logs/" folder. I opened terminal and cd to that folder with Anaconda environment activated. Then I typed </p>
  
  <pre class="lang-py prettyprint-override"><code>"python -m tensorboard.main --logdir=‘logs/‘ --host localhost --port 6006" 
  </code></pre>
  
  <p>and got response </p>
  
  <pre class="lang-py prettyprint-override"><code>TensorBoard 1.14.0 at http://localhost:6006/ (Press CTRL+C to quit)
  </code></pre>
  
  <p>Then no matter I use safari or chrome to open "<a href="http://localhost:6006/" rel="nofollow noreferrer">http://localhost:6006/</a>", there's always nothing shown except "No dashboards are active for the current data set."
  <a href="https://i.stack.imgur.com/Q3FQa.png" rel="nofollow noreferrer">enter image description here</a>
  Actually I also tried other commends such as </p>
  
  <pre class="lang-py prettyprint-override"><code>python -m tensorboard.main --logd logs --host localhost --port 6006
  
  </code></pre>
  
  <pre class="lang-py prettyprint-override"><code>python -m tensorboard.main --logd logs --host localhost --port 6006
  </code></pre>
  
  <p>But there's no difference.</p>
  
  <p>The original codes as follows:</p>
  
  <pre class="lang-py prettyprint-override"><code>import tensorflow as tf
  import numpy as np
  import matplotlib.pyplot as plt
  def add_layer(inputs, inputs_size, outputs_size,activation_function = None):
      with tf.name_scope('layer'):
          with tf.name_scope('weight'):
              Weights = tf.Variable(tf.random.normal([inputs_size, outputs_size]))        
          with tf.name_scope('biase'):
              biases = tf.Variable(tf.zeros([1,outputs_size])+0.1)
          with tf.name_scope('wx_plus_b'):
              Wx_plus_b = tf.matmul(inputs, Weights) + biases
          if activation_function == None:outputs = Wx_plus_b
          else: outputs = activation_function(Wx_plus_b)
          return outputs
  x_data = np.linspace(-1,1,300,dtype = np.float32)[:,np.newaxis] 
  noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)    
  y_data = np.square(x_data) - 0.5 + noise
  
  with tf.name_scope('inputs'):
      xs = tf.compat.v1.placeholder(tf.float32,[None,1],name='x_in')
      ys = tf.compat.v1.placeholder(tf.float32,[None,1],name='y_in')
  
  l1 = add_layer(xs, 1, 10, tf.nn.relu) 
  
  prediction = add_layer(l1, 10, 1, None)
  
  with tf.name_scope('loss'):
      loss = tf.reduce_mean(tf.reduce_sum(tf.square(prediction - ys),reduction_indices=[1])) #no need to do tf.sum() as in link.                  #tf.reduce_mean()
  with tf.name_scope('train'):
      train_step = tf.compat.v1.train.GradientDescentOptimizer(0.1).minimize(loss)
  
  
  sess = tf.compat.v1.Session()
  
  writer = tf.compat.v1.summary.FileWriter("logs/",sess.graph)
  sess.run(tf.compat.v1.global_variables_initializer())
  </code></pre>