# Visualization of Computational GraphΒΆ

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. |