Display image of graph in TensorFlow?

问题:

I wrote a simple script to calculate the golden ratio from 1,2,5. Is there a way to actually produce a visual through tensorflow (possibly with the aid of matplotlib or networkx) of the actual graph structure? The doc of tensorflow is pretty similar to a factor graph so I was wondering:

How can an image of the graph structure be generated through tensorflow?

In this example below, it would be C_1, C_2, C_3 as individual nodes, and then C_1 would have the tf.sqrt operation followed by the operation that brings them together. Maybe the graph structure (nodes,edges) can be imported into networkx? I see that the tensor objects have a graph attribute but I haven't found out how to actually use this for imaging purposes.

#!/usr/bin/python

import tensorflow as tf
C_1 = tf.constant(5.0)
C_2 = tf.constant(1.0)
C_3 = tf.constant(2.0)

golden_ratio = (tf.sqrt(C_1) + C_2)/C_3

sess = tf.Session()
print sess.run(golden_ratio) #1.61803
sess.close()

回答1:

You can get an image of the graph using Tensorboard. You need to edit your code to output the graph, and then you can launch tensorboard and see it. See, in particular, TensorBoard: Graph Visualization. You create a SummaryWriter and include the sess.graph_def in it. The graph def will be output to the log directory.

回答2:

This is exactly what tensorboard was created for. You need to slightly modify your code to store the information about your graph.

import tensorflow as tf
C_1 = tf.constant(5.0)
C_2 = tf.constant(1.0)
C_3 = tf.constant(2.0)

golden_ratio = (tf.sqrt(C_1) + C_2)/C_3

with tf.Session() as sess:
    writer = tf.summary.FileWriter('logs', sess.graph)
    print sess.run(golden_ratio)
    writer.close()

This will create a logs folder with event files in your working directory. After this you should run tensorboard from your command line tensorboard --logdir="logs" and navigate to the url it gives you (http://127.0.0.1:6006). In your browser go to GRAPHS tab and enjoy your graph.

You will use TB a lot if you are going to do anything with TF. So it makes sense to learn about it more from official tutorials and from this video.