How to create a Tensorflow Tensorboard Empty Graph

Question

launch tensorboard with tensorboard --logdir=/home/vagrant/notebook

at tensorboard:6006 > graph, it says No graph definition files were found.

To store a graph, create a tf.python.training.summary_io.SummaryWriter and pass the graph either via the constructor, or by calling its add_graph() method.

import tensorflow as tf

sess = tf.Session()
writer = tf.python.training.summary_io.SummaryWriter("/home/vagrant/notebook", sess.graph_def)

However the page is still empty, how can I start playing with tensorboard?

current tensorboard

result wanted

An empty graph that can add nodes, editable.

update

Seems like tensorboard is unable to create a graph to add nodes, drag and edit etc ( I am confused by the official video ).

running https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/tutorials/mnist/fully_connected_feed.py and then tensorboard --logdir=/home/vagrant/notebook/data is able to view the graph

However seems like tensorflow only provide ability to view summary, nothing much different to make it standout

Answer 1

TensorBoard is a tool for visualizing the TensorFlow graph and analyzing recorded metrics during training and inference. The graph is created using the Python API, then written out using the tf.train.SummaryWriter.add_graph() method. When you load the file written by the SummaryWriter into TensorBoard, you can see the graph that was saved, and interactively explore it.

However, TensorBoard is not a tool for building the graph itself. It does not have any support for adding nodes to the graph.

Answer 2

Starting from the following Code Example, I can add one line as shown below:

import tensorflow as tf
import numpy as np
sess = tf.InteractiveSession()  #define a session
# Create 100 phony x, y data points in NumPy, y = x * 0.1 + 0.3
x_data = np.random.rand(100).astype("float32")
y_data = x_data * 0.1 + 0.3

# Try to find values for W and b that compute y_data = W * x_data + b
# (We know that W should be 0.1 and b 0.3, but Tensorflow will
# figure that out for us.)
W = tf.Variable(tf.random_uniform([1], -1.0, 1.0))
b = tf.Variable(tf.zeros([1]))
y = W * x_data + b

# Minimize the mean squared errors.
loss = tf.reduce_mean(tf.square(y - y_data))
optimizer = tf.train.GradientDescentOptimizer(0.5)
train = optimizer.minimize(loss)

# Before starting, initialize the variables.  We will 'run' this first.
init = tf.initialize_all_variables()

# Launch the graph.
sess = tf.Session()
sess.run(init)

#### ----> ADD THIS LINE <---- ####
writer = tf.train.SummaryWriter("/tmp/test", sess.graph)

# Fit the line.
for step in xrange(201):
    sess.run(train)
    if step % 20 == 0:
        print(step, sess.run(W), sess.run(b))

# Learns best fit is W: [0.1], b: [0.3]

And then run tensorboard from the command line, pointing to the appropriate directory. This shows a complete call for the SummaryWriter. It is important to note the following things:

1. SummaryWriter is passed a Session, and so must happen after the Session (or InteractiveSession) is created
2. That Session may be created early in the program, but when the Session is passed to the SummaryWriter, the graph as it exists at that point is written to the file that the TensorBoard will use.