  Tensor Decomposition Networks(TDNs) prevail for their inherent compact
architectures. For providing convenience, we present a toolkit named TedNet
that is based on the Pytorch framework, to give more researchers a flexible way
to exploit TDNs. TedNet implements 5 kinds of tensor decomposition(i.e.,
CANDECOMP/PARAFAC(CP), Block-Term Tucker(BT), Tucker-2, Tensor Train(TT) and
Tensor Ring(TR)) on traditional deep neural layers, the convolutional layer and
the fully-connected layer. By utilizing these basic layers, it is simple to
construct a variety of TDNs like TR-ResNet, TT-LSTM, etc. TedNet is available
at https://github.com/tnbar/tednet.
