Pynet | pynet is meant to be a flexible and modular deep learning | Machine Learning library

Pynet is a Python library typically used in Artificial Intelligence, Machine Learning, Deep Learning, Pytorch, Tensorflow applications. Pynet has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can download it from GitHub.

pynet is used to train an autoencoder to extract low dimensional features for speech synthesis. the pynet is used to implement the results from the paper [deep denoising auto-encoder for statistical speech synthesis] in pynet, there are three environment variables to be set. in order to build and run an autoencoder, we need to put together the various components (model, layer, dataset, learning_rule, log, cost function) into a train_object and run the training. the example model below is saved to the script [ae_example.py] ../example/ae_example.py). in order to do hyperparams search, run the script in [launch.py] ../hps/launch.py) in [hps dir] ../hps). to do that, first log into helios. inside model_config.py, if the values is placed in a tuple for a variable, it means that during the sampling of values for a variable, the value are sampled uniformly from the values in the tuple. for example for. to sample one set of hyperparams and run it locally, issue. to submit 5 jobs to the gpu cluster, issue. after finished running, you can checkout the