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 backwardreachable (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
.
Builds a graph of functions and variables backwardreachable from outputs. 

Class that represents computational graph. 