import tensorflow as tf

def add_layer(inputs, in_size, out_size, activation_function=None):
  # add one more layer and return the output of this layer
  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


# 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, activation_function=tf.nn.relu)
# add output layer
prediction = add_layer(l1, 10, 1, 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]))

with tf.name_scope('train'):
  train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

sess = tf.Session()

# tf.train.SummaryWriter soon be deprecated, use following
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:  # tensorflow version < 0.12
  writer = tf.train.SummaryWriter('logs/', sess.graph)
else: # tensorflow version >= 0.12
  writer = tf.summary.FileWriter("logs/", sess.graph)

# tf.initialize_all_variables() no long valid from
# 2017-03-02 if using tensorflow >= 0.12
if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:
  init = tf.initialize_all_variables()
else:
  init = tf.global_variables_initializer()
sess.run(init)

# direct to the local dir and run this in terminal:
# $ tensorboard --logdir=logs

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("logs/", 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