[GNNFlux] Translate Traffic prediction Pluto notebook to Literate
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| # ## Dataset: METR-LA | ||
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| # We use the `METR-LA` dataset from the paper [Diffusion Convolutional Recurrent Neural Network: Data-driven Traffic Forecasting](https://arxiv.org/pdf/1707.01926.pdf), which contains traffic data from loop detectors in the highway of Los Angeles County. The dataset contains traffic speed data from March 1, 2012 to June 30, 2012. The data is collected every 5 minutes, resulting in 12 observations per hour, from 207 sensors. Each sensor is a node in the graph, and the edges represent the distances between the sensors. |
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What does it mean the edges represent the distances between the sensors? should be clarified
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| train_loader = zip(features[1:200], targets[1:200]); | ||
| test_loader = zip(features[2001:2288], targets[2001:2288]); |
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motivate this choice of ranges
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| for epoch in 1:100 | ||
| for (x, y) in train_loader | ||
| x, y = (x, y) | ||
| grads = Flux.gradient(model) do model | ||
| ŷ = model(graph, x) | ||
| Flux.mae(ŷ, y) | ||
| end | ||
| Flux.update!(opt, model, grads[1]) | ||
| end | ||
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| if epoch % 10 == 0 | ||
| loss = mean([Flux.mae(model(graph,x), y) for (x, y) in train_loader]) | ||
| @show epoch, loss | ||
| end | ||
| end |
| model = GNNChain(TGCN(2 => 100; add_self_loops = false), Dense(100, 1)) | ||
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| #  | ||
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here it would be useful to show the output of the model and how it is interpreted as a prediction
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#545