Initial Release.

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Deepmind Team
2019-08-20 09:43:37 +01:00
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# How to Contribute
# Pull Requests
Please send in fixes or feature additions through Pull Requests.
## Contributor License Agreement
Contributions to this project must be accompanied by a Contributor License
Agreement. You (or your employer) retain the copyright to your contribution,
this simply gives us permission to use and redistribute your contributions as
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You generally only need to submit a CLA once, so if you've already submitted one
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## Code reviews
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# DeepMind Research
This repository contains implementations and illustrative code to accompany DeepMind publications. Along with publishing papers to accompany research conducted at DeepMind, we release open source [environments](https://deepmind.com/research/open-source/open-source-environments/), [data sets](https://deepmind.com/research/open-source/open-source-datasets/), and [code](https://deepmind.com/research/open-source/open-source-code/) to enable the broader research community to engage with our work and build upon it, with the ultimate goal of accelerating scientific progress to benefit society. For example, you can build on our implementations of the [Deep Q-Network](https://github.com/deepmind/dqn) or [Differential Neural Computer](https://github.com/deepmind/dnc), or experiment in the same environments we use for our research, such as [DeepMind Lab](https://github.com/deepmind/lab) or [StarCraft II](https://github.com/deepmind/pysc2).
This repository contains implementations and illustrative code to accompany
DeepMind publications. Along with publishing papers to accompany research
conducted at DeepMind, we release open-source
[environments](https://deepmind.com/research/open-source/open-source-environments/),
[data sets](https://deepmind.com/research/open-source/open-source-datasets/),
and [code](https://deepmind.com/research/open-source/open-source-code/) to
enable the broader research community to engage with our work and build upon it,
with the ultimate goal of accelerating scientific progress to benefit society.
For example, you can build on our implementations of the
[Deep Q-Network](https://github.com/deepmind/dqn) or
[Differential Neural Computer](https://github.com/deepmind/dnc), or experiment
in the same environments we use for our research, such as
[DeepMind Lab](https://github.com/deepmind/lab) or
[StarCraft II](https://github.com/deepmind/pysc2).
If you enjoy building tools, environments, software libraries, and other infrastructure of the kind listed below, you can view open positions to work in related areas on our [careers page](https://deepmind.com/careers/).
If you enjoy building tools, environments, software libraries, and other
infrastructure of the kind listed below, you can view open positions to work in
related areas on our [careers page](https://deepmind.com/careers/).
For a full list of our publications, please see https://deepmind.com/research/publications/
For a full list of our publications, please see
https://deepmind.com/research/publications/
## Projects
* [Graph Matching Networks for Learning the Similarity of Graph Structured
Objects](graph_matching_networks), ICML 2019
* [REGAL: Transfer Learning for Fast Optimization of Computation Graphs](regal)
## Disclaimer
*This is not an official Google product.*
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# coding=utf-8
# Copyright 2019 Deepmind Technologies Limited.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
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# Graph Matching Networks for Learning the Similarity of Graph Structured Objects
This is the example code for the following ICML 2019 paper. If you use the code
here please cite this paper.
> Yujia Li, Chenjie Gu, Thomas Dullien, Oriol Vinyals, Pushmeet Kohli. *Graph Matching Networks for Learning the Similarity of Graph Structured Objects*. ICML 2019. [\[arXiv\]](https://arxiv.org/abs/1904.12787).
## Running the code
The code is in the format of a colab notebook, which includes:
* an example implementation of the model,
* an example graph similarity learning task,
* an example training loop, and
* some attention visualization tools.
To launch the notebook in Google colab, [click here](https://colab.research.google.com/github/deepmind/deepmind_research/blob/master/graph_matching_networks/graph_matching_networks.ipynb).
You can also download the notebook and run it locally with jupyter. The
notebook assumes you are on python 3 and have the latest (as of July 24,
2019) tensorflow, sonnet, numpy etc. installed. You can install the dependencies by running `pip3 install --user -r requirements.txt`.
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matplotlib==3.1.1
networkx==2.3
dm-sonnet==1.34
numpy==1.16.4
tensorflow==1.14
six==1.12
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# REGAL
This dataset contains dataflow computational graphs generated procedurally,
intended for training and evaluating algorithms that optimize execution (e.g.
placement and scheduling), in
[TensorFlow's CostGraphDef](https://github.com/tensorflow/tensorflow/blob/59ee7f9138482d85cd93c004aca961bea35820c7/tensorflow/core/framework/cost_graph.proto#L12)
[protocol buffer](https://en.wikipedia.org/wiki/Protocol_Buffers) format and
encoded as
[text](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.text_format).
Original paper
[REGAL: Transfer Learning For Fast Optimization of Computation Graphs](https://arxiv.org/abs/1905.02494)
(Paliwal, Gimeno, Nair, Li, Lubin, Kohli, Vinyals)
## Folder structure
There are 10000 training graphs, 1000 validation graphs and 1000 test graphs.
The file names follow the format of "graph_" plus a hash of the graph topology
plus ".pbtxt".
## Filtering
For each set (train, valid, test) there are not two graphs with the same
topology. We used the Biased Random Key Genetic Algorithm (BRKGA) to filter out
graphs that did not have "room for improvement"
"Room for improvement" was defined as the union of two conditions:
* if BRKGA with a low fitness evaluation limit (number of calls to fitness
function) did not fit the hardware constraints and BRKGA with a high number
did.
* if BRKGA with a high fitness evaluation limit was 20% faster in running time
that BRKGA with a low number.
## Example Graph
```protobuf
node {
name: "_SOURCE"
}
node {
name: "node_0"
id: 1
control_input: 0
}
node {
name: "node_1"
id: 2
output_info {
size: 70
alias_input_port: -1
}
control_input: 0
compute_cost: 58
}
node {
name: "node_2"
id: 3
output_info {
size: 52
alias_input_port: -1
}
control_input: 0
compute_cost: 47
}
node {
name: "node_3"
id: 4
input_info {
preceding_node: 2
}
output_info {
size: 55
alias_input_port: -1
}
control_input: 0
compute_cost: 58
}
```
## Dataset Location
The dataset is available in the following
[link](https://storage.googleapis.com/synthetic-graphs-dataset/synthetic-graphs.tar.gz)
## Dataset Metadata
The following table is necessary for this dataset to be indexed by search
engines such as <a href="https://g.co/datasetsearch">Google Dataset Search</a>.
<div itemscope itemtype="http://schema.org/Dataset">
<table>
<tr>
<th>property</th>
<th>value</th>
</tr>
<tr>
<td>name</td>
<td><code itemprop="name">REGAL CostGraphDef Synthetic Dataset</code></td>
</tr>
<tr>
<td>url</td>
<td><code itemprop="url">https://github.com/deepmind/deepmind_research/regal</code></td>
</tr>
<tr>
<td>sameAs</td>
<td><code itemprop="sameAs">https://github.com/deepmind/deepmind_research/regal</code></td>
</tr>
<tr>
<td>description</td>
<td><code itemprop="description">
This dataset contains dataflow computational graphs generated
procedurally, intended for training and evaluating algorithms that
optimize execution (e.g. placement and scheduling), in
[TensorFlow's CostGraphDef](https://github.com/tensorflow/tensorflow/blob/59ee7f9138482d85cd93c004aca961bea35820c7/tensorflow/core/framework/cost_graph.proto#L12)
[protocol buffer](https://en.wikipedia.org/wiki/Protocol_Buffers)
format and encoded as
[text](https://developers.google.com/protocol-buffers/docs/reference/cpp/google.protobuf.text_format).
</code></td>
</tr>
<tr>
<td>provider</td>
<td>
<div itemscope itemtype="http://schema.org/Organization" itemprop="provider">
<table>
<tr>
<th>property</th>
<th>value</th>
</tr>
<tr>
<td>name</td>
<td><code itemprop="name">DeepMind</code></td>
</tr>
<tr>
<td>sameAs</td>
<td><code itemprop="sameAs">https://en.wikipedia.org/wiki/DeepMind</code></td>
</tr>
</table>
</div>
</td>
</tr>
<tr>
<td>citation</td>
<td><code itemprop="citation">https://identifiers.org/arxiv:1905.02494</code></td>
</tr>
</table>
</div>
## Disclaimer
This is not an officially supported Google product.