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())
vs is list of
Variable instances and
g is an instance of
This code generates the computational graph that are backward-reachable (i.e. reachable by repetition of steps backward) from at least one of
Here is an example of (a part of) the generated graph (inception(3a) in GoogLeNet). This example is from
build_computational_graph(outputs, remove_split=True, variable_style=None, function_style=None, rankdir='TB')¶
Builds a graph of functions and variables backward-reachable from outputs.
- outputs (list) – nodes from which the graph is constructed.
Each element of outputs must be either
- remove_split (bool) – It must be
True. This argument is left for backward compatibility.
- variable_style (dict) – Dot node style for variable. Possible keys are ‘shape’, ‘color’, ‘fillcolor’, ‘style’, and etc.
- function_style (dict) – Dot node style for function.
- rankdir (str) – Direction of the graph that must be TB (top to bottom), BT (bottom to top), LR (left to right) or RL (right to left).
A graph consisting of nodes and edges that are backward-reachable from at least one of
unchain_backwardwas called in some variable in the computational graph before this function, backward step is stopped at this variable.
For example, suppose that computational graph is as follows:
|--> f ---> y x --+ |--> g ---> z
outputs = [y, z]. Then the full graph is emitted.
outputs = [y]. Note that
gare not backward-reachable from
y. The resulting graph would be following:
x ---> f ---> y
- outputs (list) – nodes from which the graph is constructed. Each element of outputs must be either
ComputationalGraph(nodes, edges, variable_style=None, function_style=None, rankdir='TB')¶
Class that represents computational graph.
We assume that the computational graph is directed and acyclic.