Skip to content

Latest commit

 

History

History
78 lines (59 loc) · 2.84 KB

README.md

File metadata and controls

78 lines (59 loc) · 2.84 KB
Raw

How to reproduce the experiments

This model has been trained to predict the delay. In this directory, you will find the needed files to train/validate and predict the metrics.

Dependencies

Recommended: Python 3.7

Please, ensure you use Python 3.7. Otherwise, we do not guarantee the correct installation of dependencies.

You can install all the dependencies by running the following commands.

pip install -r requirements.txt

Download the data

You can download the datasets for this particular experiment here:

Otherwise, you can download the datasets using the following command:

wget -O real_traces.zip https://bnn.upc.edu/download/dataset-v6-real-traces/

Note that this dataset is a zip file, so you need to decompress it first. Also, these experiments suppose that the data is in a directory file called data located at the root of the repository. If you want to change this, you can change the path sent as input to the input_fn function.

Training and Validation

If you want to train the model, you can use the following command:

python main.py

If everything has been done correctly, you should see the following output:

Starting training from scratch...
Epoch 1/200
36/4000 [..............................] - ETA: 3:06 - loss: 156.5287

The model will train during 50 epochs. You can change this number by changing the epochs parameter in the fit function.

Finally, the models will be saved in a ckp_dir directory. You can change this path by changing the ckp_dir variable.

Prediction

We have already provided the trained models for each one of the experiments. If you want, you can skip the Training and Validation part and just use the following command to predict the metrics:

python predict.py

This script will, first, load the best model located in the ckp_dir directory. Then, it will evaluate the metrics for each one of the sample datasets and finally, store the results in a predictions.npy file.

Again, if you configured everything correctly, you should see something like this:

BEST CHECKOINT FOUND: 44-4.62
    114/Unknown - 6s 33ms/step - loss: 3.5782

License

See LICENSE for full of the license text.

Copyright Copyright 2023 Universitat Politècnica de Catalunya

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.