Implementation of SANAS (see paper on arXiv ), a model able to dynamically adapt the architecture of a Deep Neural Network at test time for efficient sequence classification.

• Create an environment with Python 3.6
• pip install -r requirements.txt

• Download the Speech command v0.01 archive.
• Extract the dataset and give the extracted folder path as root_path argument (defaults to ./data/speech_commands_v0.01).
• Implementation of the Speech Commands data processing is based on honk, credits goes to the authors!
• Speech Commands dataset paper on arXiv.

• python main.py with adam speech_commands gru kwscnn static=True use_mongo=False ex_path=<path_to_save_location>/runs
• If no ex_path is specified, logs and models will be saved under ./runs

• Create json file containing the required connection informations:

{
 "user": "Me",
 "passwd": "MySecurePassword",
 "host": "localhost",
 "port": "27017",
 "db": "sanas",
 "collection": "runs"
}

• python main.py with adam speech_commands gru kwscnn static=True use_mongo=False mongo_config_path=<path_to_config>/mongo_config.json
• mongo_config_path defaults to ./resources/mongo_credentials.json

• python main.py with adam speech_commands gru kwscnn static=True use_visdom=False

• Visdom will connect to localhost:8097 by default. To specify the server, create a config file:

{
 "server": "http://localhost",
 "port": 8097
}

• python main.py with adam speech_commands gru kwscnn static=True visdom_config_path=<path_to_config>/vis_config.json

The __get_item__(self, idx) of a dataset should return a tuple (x,y) with:

• x of size seq_len x feature_dims. For example, feature_dims for traditional images is (C,H,W)
• y of size seq_len.

It is possible to use the PadCollate class in the dataloader to pad each sequence to the length of the longest one in the sampled batch.