How to interpret tensorboard graph in tensorflow?
<p>I am trying to understand how tensorboard visualizes the graph. I am using a simple linear regression for this purpose. Here is my code:</p>

<pre><code># LINEAR REGRESSION IN TENSORFLOW

# generate points
import numpy as np 
import os
import time
import tensorflow as tf

num_points = 1000
vectors_set = []
for i in xrange(num_points):
    x1 = np.random.normal(0.0, 0.55)
    y1 = x1 * 0.1 + 0.3 + np.random.normal(0.0, 0.03)
    vectors_set.append([x1, y1])

with tf.name_scope('data') as scope:
    x_data = [v[0] for v in vectors_set]
    y_data = [v[1] for v in vectors_set]

# Cost function and gradient descent algorithm
with tf.name_scope('model') as scope:
    W = tf.Variable(tf.random_uniform([1], -1, 1), name = "W")
    b = tf.Variable(tf.zeros([1]), name = "b")
    z = tf.add(W * x_data, b, name = "z")

with tf.name_scope('loss') as scope:
    loss = tf.reduce_mean(tf.square(z - y_data))

optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# Running the algorithm
init = tf.initialize_all_variables()

sess = tf.Session()
sess.run(init)

timestamp = str(int(time.time()))
print timestamp
train_summary_writer = tf.train.SummaryWriter(
      os.path.join(
          "./", "summaries", timestamp), sess.graph)
train_summary_writer.add_graph(sess.graph)
</code></pre>

<p>Here is tensorboard visualization:
<a href="https://i.stack.imgur.com/hXoW9.png" rel="nofollow noreferrer"><img src="https://i.stack.imgur.com/hXoW9.png" alt="enter image description here"></a></p>

<p><strong>My questions are:</strong></p>

<ol>
<li>I did not define gradients in my graph. Does it come with tensorboard by default?</li>
<li>Why are there 8 tensors going from loss to gradients? And why 5 tensors from model to gradients?</li>
<li>I did not define a variable y. Does tensorboard automatically assign y to constants? How can I change it?</li>
<li>Why does not my graph show arrows between ops?</li>
</ol>

<p>Thank you very much!</p>