wehub-resource-sync
82 lines
3.5 KiB
Markdown
82 lines
3.5 KiB
Markdown
Spatio-Temporal Graph Convolutional Networks
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============
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- Paper link: [arXiv](https://arxiv.org/pdf/1709.04875v4.pdf)
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- Author's code repo: https://github.com/VeritasYin/STGCN_IJCAI-18.
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- See [this blog](https://towardsdatascience.com/build-your-first-graph-neural-network-model-to-predict-traffic-speed-in-20-minutes-b593f8f838e5) for more details about running the code.
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- Dependencies
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- PyTorch 1.1.0+
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- scikit-learn
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- dgl
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- tables
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How to run
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----------
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please get METR_LA dataset from [this Google drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX).
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and [this Github repo](https://github.com/chnsh/DCRNN_PyTorch)
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An experiment in default settings can be run with
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```bash
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python main.py
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```
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An experiment on the METR_LA dataset in customized settings can be run with
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```bash
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python main.py --lr --seed --disable-cuda --batch_size <batch-size> --epochs <number-of-epochs>
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```
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If one wishes to adjust the model structure, you can change the arguments `control_str` and `channels`
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```bash
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python main.py --control_str <control-string> --channels <n-input-channel> <n-hidden-channels-1> <n-hidden-channels-2> ... <n-output-channels>
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```
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`<control-string>` is a string of the following characters representing a sequence of neural network modules:
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* `T`: representing a dilated temporal convolution layer, working on the temporal dimension. The dilation factor is always twice as much as the previous temporal convolution layer.
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* `S`: representing a graph convolution layer, working on the spatial dimension. The input channels and output channels are the same.
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* `N`: a Layer Normalization.
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The argument list following `--channels` represents the output channels on each temporal convolution layer. The list should have `N + 1` elements, where `N` is the number of `T`'s in `<control-string>`.
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The activation function between two layers are always ReLU.
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For example, the following command
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```bash
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python main.py --control_str TNTSTNTST --channels 1 16 32 32 64 128
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```
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specifies the following architecture:
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```
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+------------------------------------------------------------+
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| Input |
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+------------------------------------------------------------+
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| 1D Conv, in_channel = 1, out_channel = 16, dilation = 1 |
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+------------------------------------------------------------+
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| Layer Normalization |
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+------------------------------------------------------------+
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| 1D Conv, in_channel = 16, out_channel = 32, dilation = 2 |
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+------------------------------------------------------------+
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| Graph Conv, in_channel = 32, out_channel = 32 |
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+------------------------------------------------------------+
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| 1D Conv, in_channel = 32, out_channel = 32, dilation = 4 |
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+------------------------------------------------------------+
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| Layer Normalization |
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+------------------------------------------------------------+
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| 1D Conv, in_channel = 32, out_channel = 64, dilation = 8 |
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+------------------------------------------------------------+
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| Graph Conv, in_channel = 64, out_channel = 64 |
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+------------------------------------------------------------+
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| 1D Conv, in_channel = 64, out_channel = 128, dilation = 16 |
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+------------------------------------------------------------+
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```
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Results
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-------
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```bash
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python main.py
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```
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METR_LA MAE: ~5.76
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