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https://github.com/Bihaqo/TensorNet


https://github.com/Bihaqo/TensorNet

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README 

        
# TensorNet



This is a MATLAB and Theano+Lasagne implementation of the _Tensor Train layer_ (_TT-layer_) of a neural network. For a [TensorFlow implementation](https://github.com/timgaripov/TensorNet-TF) see a separate repository.



In short, the TT-layer acts as a fully-connected layer but is much more compact and allows to use lots of hidden units without slowing down the learning and inference.   

For the additional information see the following paper:



Tensorizing Neural Networks  

Alexander Novikov, Dmitry Podoprikhin, Anton Osokin, Dmitry Vetrov; In _Advances in Neural Information Processing Systems 28_ (NIPS-2015) [[arXiv](http://arxiv.org/abs/1509.06569)].



Please cite it if you write a scientific paper using this code.  

In BiBTeX format:

```latex

@incollection{novikov15tensornet,

  author    = {Novikov, Alexander and Podoprikhin, Dmitry and Osokin, Anton and Vetrov, Dmitry},

  title     = {Tensorizing Neural Networks},

  booktitle = {Advances in Neural Information Processing Systems 28 (NIPS)},

  year      = {2015},

}

```



# Installation



### MATLAB version



Install the [TT-Toolbox](https://github.com/oseledets/TT-Toolbox) (just download it and run `setup.m` to add everything important into the MATLAB path).



Install the [MatConvNet framework](http://www.vlfeat.org/matconvnet/) (preferably with the GPU support). TensorNet works with MatConvNet 1.0-beta11 (April 2015) and higher (the latest tested version is 1.0-beta14).  

Add the `mataconvnet_path/examples` folder to the MATLAB path to be able to use the `cnn_train` function.



Copy this repository and add the `src/matlab` folder into the MATLAB path.



Now you can test TensorNet using the command

``` matlab

vl_test_ttlayers

```



To test GPU support (if you have compiled MatConvNet in GPU mode) use:

``` matlab

vl_test_ttlayers(1)

```



### Theano+Lasagne version

Install fresh version of [Theano](http://deeplearning.net/software/theano/) and [Lasagne](https://lasagne.readthedocs.org/en/latest/).



Copy this repository and add the `src/python` folder into the Python path.



# Pretrained models

### MNIST shapes

In this experiment we compared how shapes and ranks influence the performance of the TT-layer using the MNIST dataset (see figure 1 and section 6.1 of the original paper for the details). Download [models in the MatConvNet format](https://www.dropbox.com/s/zk3fqnj2pyxek5c/mnist_shapes.mat?dl=1) (.mat file, 2.9 Mb) and [preprocessed MNIST dataset](https://www.dropbox.com/s/annpg39hbmdxrig/imdb.mat?dl=1) (.mat file, 132 Mb).



You will find a cell array of models with metadata, the first and the last epochs of training included for each model. Example of usage (computing the validation error):

``` matlab

imdb = load('imdb.mat');

load('mnist_shapes.mat');

% Choose (for example) the 5-th model whose shape equal 4 x 8 x 8 x 4.

net = models{5}.lastEpoch.net;

% Remove the softmax layer (unnecessary during the validation).

net.layers(end) = [];

valIdx = find(imdb.images.set == 3);

res = vl_simplenn(net, imdb.images.data(:, :, :, valIdx));

scores = squeeze(res(end).x);

[bestScore, best] = max(scores);

acc = mean(best == imdb.images.labels(valIdx));

fprintf('Accuracy is %f\n', acc);

```



# Reproducing experiments

Right now just one basic example on the MNIST dataset is available (more experiments from the paper are coming soon). To try it out, navigate to the `experiments/mnist` folder and type the following command in the MATLAB prompt:

``` matlab

[net_tt, info_tt] = cnn_mnist_tt('expDir', 'data/mnist-tt');

```