# Visualization of Computational Graph¶

As neural networks get larger and complicated, it gets much harder to confirm if their architectures are constructed properly. Chainer supports visualization of computational graphs. Users can generate computational graphs by invoking build_computational_graph(). Generated computational graphs are dumped to specified format (Currently Dot Language is supported).

Basic usage is as follows:

import chainer.computational_graph as c
...
g = c.build_computational_graph(vs)
with open('path/to/output/file', 'w') as o:
o.write(g.dump())


where vs is list of Variable instances and g is an instance of ComputationalGraph. This code generates the computational graph that are backward-reachable (i.e. reachable by repetition of steps backward) from at least one of vs.

Here is an example of (a part of) the generated graph (inception(3a) in GoogLeNet). This example is from example/imagenet.

 chainer.computational_graph.build_computational_graph Builds a graph of functions and variables backward-reachable from outputs. chainer.computational_graph.ComputationalGraph Class that represents computational graph.