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DCRNN
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Add PEMS-BAY configuration.

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liyaguang 2019-01-08 11:21:22 -08:00
parent 1481b24b1b
commit c8a676604b
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README.md
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@ -20,28 +20,38 @@ pip install -r requirements.txt
```
## Data Preparation
The traffic data file for Los Angeles, i.e., `df_highway_2012_4mon_sample.h5`, is available at [Google Drive](https://drive.google.com/open?id=1tjf5aXCgUoimvADyxKqb-YUlxP8O46pb) or [Baidu Yun](https://pan.baidu.com/s/1rsCq38a9SRyFO1F68tUscA), and should be
put into the `data/METR-LA` folder.
Besides, the locations of sensors are available at [data/sensor_graph/graph_sensor_locations.csv](https://github.com/liyaguang/DCRNN/blob/master/data/sensor_graph/graph_sensor_locations.csv).
The traffic data files for Los Angeles and the Bay Area, i.e., `metr-la.h5` and `pems-bay.h5`, are available at [Google Drive](https://drive.google.com/open?id=10FOTa6HXPqX8Pf5WRoRwcFnW9BrNZEIX) or [Baidu Yun](hbttps://pan.baidu.com/s/14Yy9isAIZYdU__OYEQGa_g), and should be
put into the `data/` folder.
Besides, the locations of sensors Los Angeles are available at [data/sensor_graph/graph_sensor_locations.csv](https://github.com/liyaguang/DCRNN/blob/master/data/sensor_graph/graph_sensor_locations.csv).
```bash
python -m scripts.generate_training_data --output_dir=data/METR-LA
mkdir -p data/{METR-LA,PEMS-BAY}
# METR-LA
python -m scripts.generate_training_data --output_dir=data/METR-LA --traffic_df_filename=data/metr-la.h5
# PEMS-BAY
python -m scripts.generate_training_data --output_dir=data/PEMS-BAY --traffic_df_filename=data/pems-bay.h5
```
The generated train/val/test dataset will be saved at `data/METR-LA/{train,val,test}.npz`.
The generated train/val/test dataset will be saved at `data/{METR-LA,PEMS-BAY}/{train,val,test}.npz`.
## Run the Pre-trained Model
## Run the Pre-trained Model on METR-LA
```bash
python run_demo.py
```
The generated prediction of DCRNN is in `data/results/dcrnn_predictions_[1-12].h5`.
The generated prediction of DCRNN of METR-LA is in `data/results/dcrnn_predictions_[1-12].h5`.
## Model Training
```bash
python dcrnn_train.py --config_filename=data/model/dcrnn_config.yaml
# METR-LA
python dcrnn_train.py --config_filename=data/model/dcrnn_la.yaml
# PEMS-BAY
python dcrnn_train.py --config_filename=data/model/dcrnn_bay.yaml
```
Each epoch takes about 5min with a single GTX 1080 Ti.
Each epoch takes about 5min or 10 min on a single GTX 1080 Ti for METR-LA or PEMS-BAY respectively.
## Graph Construction
As the currently implementation is based on pre-calculated road network distances between sensors, it currently only

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data/model/dcrnn_bay.yaml Normal file
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@ -0,0 +1,39 @@
---
base_dir: data/model
log_level: INFO
data:
batch_size: 64
dataset_dir: data/PEMS-BAY
test_batch_size: 64
val_batch_size: 64
graph_pkl_filename: data/sensor_graph/adj_mx_bay.pkl
model:
cl_decay_steps: 2000
filter_type: dual_random_walk
horizon: 12
input_dim: 2
l1_decay: 0
max_diffusion_step: 2
num_nodes: 325
num_rnn_layers: 2
output_dim: 1
rnn_units: 64
seq_len: 12
use_curriculum_learning: true
train:
base_lr: 0.01
dropout: 0
epoch: 0
epochs: 100
epsilon: 1.0e-3
global_step: 0
lr_decay_ratio: 0.1
max_grad_norm: 5
max_to_keep: 100
min_learning_rate: 2.0e-06
optimizer: adam
patience: 50
steps: [20, 30, 40, 50]
test_every_n_epochs: 10

0
data/model/dcrnn_config.yaml → data/model/dcrnn_la.yaml
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