text: from __future__ import print_function
  import tensorflow as tf
  import numpy as np
  
  
  def add_layer(inputs, in_size, out_size, n_layer, activation_function=None):
      # add one more layer and return the output of this layer
      layer_name = 'layer%s' % n_layer
      with tf.name_scope(layer_name):
          with tf.name_scope('weights'):
              Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')
              tf.summary.histogram(layer_name + '/weights', Weights)
          with tf.name_scope('biases'):
              biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
              tf.summary.histogram(layer_name + '/biases', biases)
          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, )
          tf.summary.histogram(layer_name + '/outputs', outputs)
      return outputs
  
  
  # Make up some real data
  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
  
  # define placeholder for inputs to network
  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')
  
  # add hidden layer
  l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
  # add output layer
  prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)
  
  # the error between prediciton and real data
  with tf.name_scope('loss'):
      loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
                                          reduction_indices=[1]))
      tf.summary.scalar('loss', loss)
  
  with tf.name_scope('train'):
      train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
  
  sess = tf.Session()
  merged = tf.summary.merge_all()
  writer = tf.summary.FileWriter("C:\\Users\Sean\Desktop\logs3/", sess.graph)
  init = tf.global_variables_initializer()
  sess.run(init)
  
  for i in range(1000):
      sess.run(train_step, feed_dict={xs: x_data, ys: y_data})
      if i % 50 == 0:
          result = sess.run(merged,
                            feed_dict={xs: x_data, ys: y_data})
          writer.add_summary(result, i)
  
  # direct to the local dir and run this in terminal:
  # $ tensorboard --logdir logs
  