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StrassenNets

Code to reproduce the experiments in the paper StrassenNets: Deep learning with a multiplication budget, M. Tschannen, A. Khanna, A. Anandkumar, 2018. In a nutshell, the proposed approach approximates the (generalized) matrix multiplications in deep neural network (DNN) layers by 2-layer sum-product networks (SPNs) and learns the SPNs end-to-end. The CIFAR10 code (Jupyter Notebook) gives an overview of the method.

Requirements

The code was tested using Python 3.5 and MXNet 1.0. The language model code only runs on GPU.

CIFAR-10

To train Strassen-ResNet-20 on CIFAR-10, use StrassenNetsCIFAR10.ipynb with the Jupyter Notebook. For the VGG-inspired 7-layer architecture, run

python3 StrassenNetworksCIFAR10_VGG.py --nbr_mul 1 --out_patch 1 --gpu_index 0

The multiplication budget can be adjusted using --nbr_mul and --out_patch.

Penn Tree Bank (PTB)

1. Download the preprocessed PTB training, testing, and validation files e.g. from here (the default location used by the training script is langmod/data/ptb/, adapt it using the option --data_dir in the command below)
2. Use

python3 train.py --quant_mode 5 --max_epochs 40 --decay_when 0.5 --learning_rate 2

(in langmod/) to train the full precision model, and

python3 train.py --quant_mode 2 --max_epochs 20 --decay_when 0.5 --nbr_mul 1.0 --out_nbr_mul 2000 --learning_rate 0.2 --epochs_pre 20 --learning_rate_pre 2

to train a Strassen language model. To change the multiplication budget, adapt --nbr_mul (budget of all but the last layer, relative to the number of hidden units) and --out_nbr_mul (budget of the last layer). The log files and trained models are stored in the folder langmod/logs/. To train with a full precision teacher model, use the option --teacher_model <path to teacher model>.