Metadata-Version: 2.1
Name: tednet
Version: 0.1.2
Summary: tednet: a framework of tensor decomposition network.
Home-page: https://github.com/perryuu/tednet
Author: Perry
Maintainer: Perry
License: MIT
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        # tednet
        `tednet` is a toolkit for tensor decomposition networks. Tensor decomposition networks are neural networks whose layers are decomposed by tensor decomposition, including CANDECOMP/PARAFAC, Tucker2, Tensor Train, Tensor Ring and so on. For a convenience to do research on it, ``tednet`` provides excellent tools to deal with tensorial networks.
        
        
        Now, **tednet** is easy to be installed by `pip`:
        
        ```shell script
        pip install tednet
        ```
        
        More information could be found in [Document](https://tednet.readthedocs.io/en/latest/index.html).
        
        
        ---
        
        ### Quick Start
        
        ##### Operation
        There are some operations supported in `tednet`, and it is convinient to use them. First, import it:
        
        ```python
        import tednet as tdt
        ```
        
        Create matrix whose diagonal elements are ones:
        ```python
        diag_matrix = tdt.eye(5, 5)
        ```
        
        A way to transfer the Pytorch tensor into numpy array:
        
        ```python
        diag_matrix = tdt.to_numpy(diag_matrix)
        ```
        
        Similarly, the numpy array can be taken into Pytorch tensor by:
        
        ```python
        diag_matrix = tdt.to_tensor(diag_matrix)
        ```
        
        ##### Tensor Decomposition Networks (Tensor Ring for Sample)
        To use tensor ring decomposition models, simply calling the tensor ring module is enough.
        
        ```python
        import tednet.tnn.tensor_ring as tr
        
        # Define a TR-LeNet5
        model = tr.TRLeNet5(10, [6, 6, 6, 6])
        ```
        
Platform: any
Description-Content-Type: text/markdown
